├── ds ├── models │ └── .gitkeep ├── data │ ├── aux │ │ ├── .gitkeep │ │ ├── stem_gtr.txt │ │ ├── equivalents_regions.csv │ │ ├── stem_hesa.txt │ │ ├── gtr_stem_disciplines.txt │ │ ├── cordis_parse_opts.json │ │ ├── cb_tech_groups.txt │ │ ├── nice_class_category_lookup.json │ │ ├── stem_ref.txt │ │ ├── scottish_lads.txt │ │ ├── shapefile_urls.json │ │ ├── framework.json │ │ ├── cordis_url_suffixes.json │ │ ├── terminated_to_active_lad_lookup.json │ │ ├── eurostat_data_queries.txt │ │ └── priority_aggregation.json │ ├── raw │ │ └── .gitkeep │ ├── interim │ │ └── .gitkeep │ ├── processed │ │ ├── .gitkeep │ │ ├── hesa │ │ │ ├── total_university_buildings.lep.yaml │ │ │ ├── gbp_research_income.lep.yaml │ │ │ ├── area_university_site.lep.yaml │ │ │ ├── total_postgraduates.lep.yaml │ │ │ ├── fte_research_students.lep.yaml │ │ │ ├── total_university_buildings.nuts2.yaml │ │ │ ├── total_university_buildings.nuts3.yaml │ │ │ ├── gbp_research_income.nuts3.yaml │ │ │ ├── area_university_site.nuts3.yaml │ │ │ ├── gbp_research_income.nuts2.yaml │ │ │ ├── area_university_site.nuts2.yaml │ │ │ ├── total_postgraduates.nuts2.yaml │ │ │ ├── total_postgraduates.nuts3.yaml │ │ │ ├── fte_research_students.nuts2.yaml │ │ │ ├── fte_research_students.nuts3.yaml │ │ │ ├── total_stem_students.lep.yaml │ │ │ ├── total_stem_postgraduates.lep.yaml │ │ │ ├── total_stem_students.nuts2.yaml │ │ │ ├── total_stem_students.nuts3.yaml │ │ │ ├── total_stem_postgraduates.nuts2.yaml │ │ │ └── total_stem_postgraduates.nuts3.yaml │ │ ├── ref │ │ │ ├── mean_ref_stem.csv │ │ │ ├── mean_ref.csv │ │ │ ├── total_4_fte.csv │ │ │ ├── mean_ref.yaml │ │ │ ├── mean_ref_stem.yaml │ │ │ └── total_4_fte.yaml │ │ ├── hebci │ │ │ ├── graduate_startups.lep.yaml │ │ │ ├── graduate_startups.nuts2.yaml │ │ │ ├── graduate_startups.nuts3.yaml │ │ │ ├── ce_cpd_learner_days.lep.yaml │ │ │ ├── ce_cpd_income.lep.yaml │ │ │ ├── ce_cpd_learner_days.nuts2.yaml │ │ │ ├── ce_cpd_learner_days.nuts3.yaml │ │ │ ├── ip_revenue.lep.yaml │ │ │ ├── ce_cpd_income.nuts2.yaml │ │ │ ├── ce_cpd_income.nuts3.yaml │ │ │ ├── ip_revenue.nuts2.yaml │ │ │ ├── ip_revenue.nuts3.yaml │ │ │ ├── contract_research_sme.lep.yaml │ │ │ ├── contract_research_sme.nuts2.yaml │ │ │ ├── contract_research_sme.nuts3.yaml │ │ │ ├── gbp_turnover_per_active_spinoff.yaml │ │ │ ├── collaborative_research_cash.lep.yaml │ │ │ ├── contract_research_non_sme.lep.yaml │ │ │ ├── consultancy_facilities_sme.lep.yaml │ │ │ ├── collaborative_research_cash.nuts2.yaml │ │ │ ├── collaborative_research_cash.nuts3.yaml │ │ │ ├── consultancy_facilities_sme.nuts2.yaml │ │ │ ├── consultancy_facilities_sme.nuts3.yaml │ │ │ ├── spinoff_investment.lep.yaml │ │ │ ├── contract_research_non_sme.nuts2.yaml │ │ │ ├── contract_research_non_sme.nuts3.yaml │ │ │ ├── consultancy_facilities_non_sme.lep.yaml │ │ │ ├── contract_research_public_third.lep.yaml │ │ │ ├── spinoff_investment.nuts2.yaml │ │ │ ├── spinoff_investment.nuts3.yaml │ │ │ ├── spinoff_revenue.lep.yaml │ │ │ ├── consultancy_facilities_non_sme.nuts2.yaml │ │ │ ├── consultancy_facilities_non_sme.nuts3.yaml │ │ │ ├── contract_research_public_third.nuts2.yaml │ │ │ ├── contract_research_public_third.nuts3.yaml │ │ │ ├── spinoff_revenue.nuts2.yaml │ │ │ ├── spinoff_revenue.nuts3.yaml │ │ │ ├── consultancy_facilities_public_third.lep.yaml │ │ │ ├── consultancy_facilities_public_third.nuts2.yaml │ │ │ ├── consultancy_facilities_public_third.nuts3.yaml │ │ │ ├── regeneration_development.lep.yaml │ │ │ ├── regeneration_development.nuts2.yaml │ │ │ └── regeneration_development.nuts3.yaml │ │ ├── eurostat │ │ │ ├── eurostat_gdp_per_capita.nuts2.yaml │ │ │ ├── eurostat_berd_data.nuts2.yaml │ │ │ ├── eurostat_gov_rd_workforce_data.nuts2.yaml │ │ │ ├── eurostat_private_rd_headcount_workforce_data.nuts2.yaml │ │ │ ├── eu_trademark_applications.nuts2.yaml │ │ │ ├── eu_trademark_applications.nuts3.yaml │ │ │ ├── eurostat_higher_ed_rd_workforce_data.nuts2.yaml │ │ │ ├── eurostat_private_non_profit_rd_workforce_data.nuts2.yaml │ │ │ ├── eurostat_private_rd_fte_workforce_data.nuts2.yaml │ │ │ ├── eurostat_private_households_income.nuts2.yaml │ │ │ ├── epo_patent_applications.nuts2.yaml │ │ │ └── epo_patent_applications.nuts3.yaml │ │ ├── ashe_mean_salary │ │ │ └── ashe_mean_salary.nuts2.csv │ │ ├── housing │ │ │ └── house_price_normalised.nuts2.csv │ │ ├── travel │ │ │ ├── travel_time_to_airport.nuts2.yaml │ │ │ ├── travel_time_to_airport.nuts3.yaml │ │ │ ├── travel_time_to_rail.nuts2.yaml │ │ │ ├── travel_time_to_rail.nuts3.yaml │ │ │ ├── travel_time_to_road_junctions.nuts2.yaml │ │ │ ├── travel_time_to_road_junctions.nuts3.yaml │ │ │ ├── travel_time_to_airport.lep.yaml │ │ │ ├── travel_time_to_rail.lep.yaml │ │ │ └── travel_time_to_road_junctions.lep.yaml │ │ ├── industry │ │ │ ├── economic_complexity_index.nuts2.yaml │ │ │ ├── economic_complexity_index.lep.yaml │ │ │ ├── employment_culture_entertainment_recreation.lep.yaml │ │ │ ├── economic_complexity_index.nuts3.yaml │ │ │ └── employment_culture_entertainment_recreation.nuts3.yaml │ │ ├── gtr │ │ │ ├── total_gtr_projects_stem.yaml │ │ │ ├── total_ukri_funding.lep.yaml │ │ │ ├── total_gtr_projects_all_disciplines.yaml │ │ │ ├── total_ukri_funding.nuts2.yaml │ │ │ └── total_ukri_funding.nuts3.yaml │ │ ├── aps │ │ │ ├── aps_econ_active_stem_profs_data.lep.yaml │ │ │ ├── aps_econ_active_stem_associate_profs_data.lep.yaml │ │ │ ├── aps_econ_active_stem_density_data.lep.yaml │ │ │ ├── aps_pro_occupations_data.lep.yaml │ │ │ └── aps_nvq4_education_data.lep.yaml │ │ ├── innovate_uk │ │ │ ├── gbp_innovate_uk_funding.nuts2.yaml │ │ │ └── gbp_innovate_uk_funding.nuts3.yaml │ │ ├── cordis │ │ │ ├── cordis_funding.lep.yaml │ │ │ ├── cordis_funding.nuts2.yaml │ │ │ └── cordis_funding.nuts3.yaml │ │ └── crunchbase │ │ │ └── gbp_venture_capital_received.yaml │ ├── metadata │ │ └── uni_nuts.json │ ├── README.md │ └── schema │ │ └── types_schema.yaml ├── notebooks │ ├── .gitkeep │ ├── dev │ │ └── .gitkeep │ └── lab_notes.md ├── references │ └── .gitkeep ├── reports │ ├── .gitkeep │ └── figures │ │ └── .gitkeep ├── beis_indicators │ ├── .gitkeep │ ├── data │ │ ├── .gitkeep │ │ ├── __init__.py │ │ ├── defra │ │ │ └── __init__.py │ │ ├── make_dataset.py │ │ └── process_patstat.py │ ├── estimators │ │ ├── .gitkeep │ │ ├── __init__.py │ │ └── build_estimators.py │ ├── features │ │ ├── .gitkeep │ │ ├── __init__.py │ │ ├── build_features.py │ │ └── process_text.py │ ├── gtr │ │ ├── __init__.py │ │ └── make_gtr_funding.py │ ├── hesa │ │ └── __init__.py │ ├── models │ │ ├── .gitkeep │ │ ├── __init__.py │ │ ├── predict_model.py │ │ ├── train_model.py │ │ └── build_queries.py │ ├── ref │ │ └── __init__.py │ ├── crunchbase │ │ ├── __init__.py │ │ └── make_crunchbase_tech_companies.py │ ├── industry │ │ └── __init__.py │ ├── trademarks │ │ └── __init__.py │ ├── transformers │ │ └── .gitkeep │ ├── visualization │ │ ├── .gitkeep │ │ ├── __init__.py │ │ └── visualize.py │ ├── geo │ │ ├── __init__.py │ │ ├── make_reverse_geocode_universities.py │ │ └── lep.py │ ├── nomis │ │ ├── __init__.py │ │ └── nomis_aps_run_all.py │ ├── defra │ │ ├── __init__.py │ │ └── defra_processing.py │ ├── utils │ │ ├── __init__.py │ │ ├── nesta_utils.py │ │ ├── geo_utils.py │ │ └── pandas.py │ ├── __init__.py │ ├── hebci │ │ └── make_hebci.py │ ├── cordis │ │ └── make_cordis.py │ └── travel │ │ ├── make_travel_work.py │ │ └── make_travel.py ├── tox.ini ├── requirements.txt ├── setup.py ├── docs │ ├── getting-started.rst │ ├── commands.rst │ └── index.rst ├── test_environment.py ├── .gitattributes ├── conda_environment.yaml └── logging.yaml ├── ui ├── README.md ├── test │ ├── data │ │ └── README.md │ ├── pa11y │ │ └── validate.js │ └── README.md ├── src │ ├── node_modules │ │ └── app │ │ │ ├── utils │ │ │ ├── version.js │ │ │ ├── env.js │ │ │ ├── assets.js │ │ │ └── domain.js │ │ │ ├── data │ │ │ ├── types.js │ │ │ └── groups.js │ │ │ ├── theme.js │ │ │ ├── components │ │ │ ├── glyphs │ │ │ │ ├── FormatClear.svelte │ │ │ │ └── ColorClear.svelte │ │ │ ├── NoScript.svelte │ │ │ └── content │ │ │ │ └── info │ │ │ │ └── Beta.svelte │ │ │ └── stores │ │ │ ├── selection.js │ │ │ └── data.js │ ├── client.js │ ├── routes │ │ ├── info │ │ │ ├── index.svelte │ │ │ ├── beta.svelte │ │ │ ├── privacy.svelte │ │ │ └── feedback.svelte │ │ ├── guides │ │ │ ├── index.svelte │ │ │ ├── app.svelte │ │ │ └── indicators.svelte │ │ ├── indicators │ │ │ ├── index.svelte │ │ │ ├── _layout.svelte │ │ │ └── [id] │ │ │ │ ├── index.svelte │ │ │ │ └── [year].svelte │ │ ├── feedback.svelte │ │ └── _error.svelte │ ├── bin │ │ └── utils.js │ ├── server.js │ └── template.html ├── static │ ├── images │ │ ├── favicon.png │ │ ├── logo-192.png │ │ ├── logo-512.png │ │ └── OpenGraphPollution.png │ ├── font │ │ ├── OpenDyslexic │ │ │ ├── Bold.otf │ │ │ ├── Italic.otf │ │ │ ├── Regular.otf │ │ │ └── BoldItalic.otf │ │ ├── AvenirNext │ │ │ └── Variable.ttf │ │ ├── NobotoFlex │ │ │ └── Variable.woff2 │ │ └── Archivo │ │ │ ├── VariableFont_wdth,wght.ttf │ │ │ └── Italic-VariableFont_wdth,wght.ttf │ └── manifest.json └── .gitignore ├── .gitignore ├── netlify.toml ├── package.json └── LICENSE /ds/models/.gitkeep: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /ds/data/aux/.gitkeep: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /ds/data/raw/.gitkeep: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /ds/notebooks/.gitkeep: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /ds/references/.gitkeep: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /ds/reports/.gitkeep: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /ds/beis_indicators/.gitkeep: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /ds/data/interim/.gitkeep: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /ds/data/processed/.gitkeep: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /ds/notebooks/dev/.gitkeep: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /ds/reports/figures/.gitkeep: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /ds/beis_indicators/data/.gitkeep: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /ds/data/metadata/uni_nuts.json: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /ds/beis_indicators/data/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /ds/beis_indicators/estimators/.gitkeep: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /ds/beis_indicators/features/.gitkeep: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /ds/beis_indicators/gtr/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /ds/beis_indicators/hesa/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /ds/beis_indicators/models/.gitkeep: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /ds/beis_indicators/models/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /ds/beis_indicators/ref/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /ui/README.md: -------------------------------------------------------------------------------- 1 | # BEIS indicators UI 2 | -------------------------------------------------------------------------------- /ds/beis_indicators/crunchbase/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /ds/beis_indicators/estimators/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /ds/beis_indicators/features/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /ds/beis_indicators/industry/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /ds/beis_indicators/models/predict_model.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /ds/beis_indicators/models/train_model.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /ds/beis_indicators/trademarks/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /ds/beis_indicators/transformers/.gitkeep: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /ds/beis_indicators/visualization/.gitkeep: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /ds/beis_indicators/features/build_features.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /ds/beis_indicators/visualization/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /ds/beis_indicators/visualization/visualize.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /ds/beis_indicators/estimators/build_estimators.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /ds/tox.ini: -------------------------------------------------------------------------------- 1 | [flake8] 2 | max-line-length = 79 3 | max-complexity = 10 4 | -------------------------------------------------------------------------------- /ui/test/data/README.md: -------------------------------------------------------------------------------- 1 | This directory will store Browserstack test results. 2 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # Mac OS-specific storage files 2 | .DS_Store 3 | 4 | # vim 5 | *.swp 6 | *.swo 7 | -------------------------------------------------------------------------------- /ds/beis_indicators/geo/__init__.py: -------------------------------------------------------------------------------- 1 | from beis_indicators.geo.coders import NutsCoder, LepCoder 2 | -------------------------------------------------------------------------------- /ui/src/node_modules/app/utils/version.js: -------------------------------------------------------------------------------- 1 | export {version} from '../../../../../package.json'; 2 | -------------------------------------------------------------------------------- /ds/data/aux/stem_gtr.txt: -------------------------------------------------------------------------------- 1 | disc_biological 2 | disc_eng_tech 3 | disc_env 4 | disc_maths_comp 5 | disc_physics -------------------------------------------------------------------------------- /ui/src/node_modules/app/data/types.js: -------------------------------------------------------------------------------- 1 | export {default} from '../../../../../ds/data/schema/types.yaml'; 2 | -------------------------------------------------------------------------------- /ds/beis_indicators/nomis/__init__.py: -------------------------------------------------------------------------------- 1 | from . import nomis 2 | from .nomis import * 3 | from .complexity import * 4 | -------------------------------------------------------------------------------- /ds/beis_indicators/defra/__init__.py: -------------------------------------------------------------------------------- 1 | from beis_indicators.defra.make_defra_indicators import make_air_pollution_nuts 2 | -------------------------------------------------------------------------------- /ds/beis_indicators/data/defra/__init__.py: -------------------------------------------------------------------------------- 1 | from beis_indicators.data.defra.air_pollution import get_uk_nuts2_air_pollution 2 | -------------------------------------------------------------------------------- /ui/static/images/favicon.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mindrones/nestauk-beis-indicators/dev/ui/static/images/favicon.png -------------------------------------------------------------------------------- /ui/static/images/logo-192.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mindrones/nestauk-beis-indicators/dev/ui/static/images/logo-192.png -------------------------------------------------------------------------------- /ui/static/images/logo-512.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mindrones/nestauk-beis-indicators/dev/ui/static/images/logo-512.png -------------------------------------------------------------------------------- /ds/data/aux/equivalents_regions.csv: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mindrones/nestauk-beis-indicators/dev/ds/data/aux/equivalents_regions.csv -------------------------------------------------------------------------------- /ui/src/client.js: -------------------------------------------------------------------------------- 1 | import * as sapper from '@sapper/app'; 2 | 3 | sapper.start({ 4 | target: document.querySelector('#app') 5 | }); 6 | -------------------------------------------------------------------------------- /ui/static/font/OpenDyslexic/Bold.otf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mindrones/nestauk-beis-indicators/dev/ui/static/font/OpenDyslexic/Bold.otf -------------------------------------------------------------------------------- /ui/static/font/AvenirNext/Variable.ttf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mindrones/nestauk-beis-indicators/dev/ui/static/font/AvenirNext/Variable.ttf -------------------------------------------------------------------------------- /ui/static/font/NobotoFlex/Variable.woff2: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mindrones/nestauk-beis-indicators/dev/ui/static/font/NobotoFlex/Variable.woff2 -------------------------------------------------------------------------------- /ui/static/font/OpenDyslexic/Italic.otf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mindrones/nestauk-beis-indicators/dev/ui/static/font/OpenDyslexic/Italic.otf -------------------------------------------------------------------------------- /ui/static/font/OpenDyslexic/Regular.otf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mindrones/nestauk-beis-indicators/dev/ui/static/font/OpenDyslexic/Regular.otf -------------------------------------------------------------------------------- /ui/static/images/OpenGraphPollution.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mindrones/nestauk-beis-indicators/dev/ui/static/images/OpenGraphPollution.png -------------------------------------------------------------------------------- /ui/.gitignore: -------------------------------------------------------------------------------- 1 | /__sapper__/ 2 | /cypress/screenshots/ 3 | /node_modules/ 4 | /src/node_modules/@sapper/ 5 | /static/data/ 6 | /static/font/unused/ 7 | -------------------------------------------------------------------------------- /ui/src/node_modules/app/utils/env.js: -------------------------------------------------------------------------------- 1 | export const isServerSide = typeof window === 'undefined'; 2 | 3 | export const isClientSide = !isServerSide; 4 | -------------------------------------------------------------------------------- /ui/static/font/OpenDyslexic/BoldItalic.otf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mindrones/nestauk-beis-indicators/dev/ui/static/font/OpenDyslexic/BoldItalic.otf -------------------------------------------------------------------------------- /netlify.toml: -------------------------------------------------------------------------------- 1 | [build] 2 | base = "/" 3 | publish = "ui/__sapper__/export/" 4 | command = "npm run deploy" 5 | environment = { NODE_VERSION = "16.3.0" } 6 | -------------------------------------------------------------------------------- /ds/data/aux/stem_hesa.txt: -------------------------------------------------------------------------------- 1 | (3) Biological sciences 2 | (6) Physical sciences 3 | (7) Mathematical sciences 4 | (8) Computer science 5 | (9) Engineering and technology -------------------------------------------------------------------------------- /ui/static/font/Archivo/VariableFont_wdth,wght.ttf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mindrones/nestauk-beis-indicators/dev/ui/static/font/Archivo/VariableFont_wdth,wght.ttf -------------------------------------------------------------------------------- /ds/data/aux/gtr_stem_disciplines.txt: -------------------------------------------------------------------------------- 1 | disc_biological_project_n 2 | disc_eng_tech_project_n 3 | disc_env_project_n 4 | disc_maths_comp_project_n 5 | disc_physics_project_n -------------------------------------------------------------------------------- /ui/src/routes/info/index.svelte: -------------------------------------------------------------------------------- 1 | 6 | -------------------------------------------------------------------------------- /ui/static/font/Archivo/Italic-VariableFont_wdth,wght.ttf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mindrones/nestauk-beis-indicators/dev/ui/static/font/Archivo/Italic-VariableFont_wdth,wght.ttf -------------------------------------------------------------------------------- /ui/src/routes/guides/index.svelte: -------------------------------------------------------------------------------- 1 | 6 | -------------------------------------------------------------------------------- /ui/src/node_modules/app/theme.js: -------------------------------------------------------------------------------- 1 | export default { 2 | colorLink: '#0658aa', // --color-link 3 | colorMain: '#075aa2', // --color-main 4 | colorMainLighter: '#555', 5 | colorBlackish: '#333', 6 | } 7 | -------------------------------------------------------------------------------- /ds/requirements.txt: -------------------------------------------------------------------------------- 1 | ipython 2 | setuptools 3 | 4 | # local package 5 | -e . 6 | 7 | # external requirements 8 | dvc 9 | jq 10 | click 11 | Sphinx 12 | coverage 13 | awscli 14 | flake8 15 | python-dotenv>=0.5.1 16 | -------------------------------------------------------------------------------- /ds/data/aux/cordis_parse_opts.json: -------------------------------------------------------------------------------- 1 | {"projects": {"list_sep": ";", "list_cols": ["participants", "participantCountries", "programme"], "drop_cols": ["subjects"]}, "organizations": {"list_sep": ";", "list_cols": ["programme"], "drop_cols": []}, "sdgs": {}} 2 | -------------------------------------------------------------------------------- /ds/setup.py: -------------------------------------------------------------------------------- 1 | from setuptools import find_packages, setup 2 | 3 | setup( 4 | name='beis_indicators', 5 | packages=find_packages(), 6 | version='0.1.0', 7 | description='Regional indicators for BEIS.', 8 | author='Nesta Innovation Mapping', 9 | license='MIT', 10 | ) 11 | -------------------------------------------------------------------------------- /ds/docs/getting-started.rst: -------------------------------------------------------------------------------- 1 | Getting started 2 | =============== 3 | 4 | This is where you describe how to get set up on a clean install, including the 5 | commands necessary to get the raw data (using the `sync_data_from_s3` command, 6 | for example), and then how to make the cleaned, final data sets. 7 | -------------------------------------------------------------------------------- /package.json: -------------------------------------------------------------------------------- 1 | { 2 | "description": "BEIS regional indicators", 3 | "license": "MIT", 4 | "name": "beis_nesta_research_and_development_spatial_data", 5 | "scripts": { 6 | "deploy": "cd ui && npm install && npm run makedata && npm run export" 7 | }, 8 | "version": "0.1.4" 9 | } 10 | -------------------------------------------------------------------------------- /ds/data/aux/cb_tech_groups.txt: -------------------------------------------------------------------------------- 1 | Apps 2 | Artificial Intelligence 3 | Biotechnology 4 | Consumer Electronics 5 | Data and Analytics 6 | Energy 7 | Gaming 8 | Hardware 9 | Information Technology 10 | Internet Services 11 | Manufacturing 12 | Mobile 13 | Platforms 14 | Science and Engineering 15 | Software -------------------------------------------------------------------------------- /ui/src/node_modules/app/components/glyphs/FormatClear.svelte: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | -------------------------------------------------------------------------------- /ui/src/node_modules/app/utils/assets.js: -------------------------------------------------------------------------------- 1 | import {version} from './version'; 2 | 3 | export const availableDownloadIds = ['LEP', 'NUTS2', 'NUTS3']; 4 | export const basename = `beis_indicators_${version.replace(/\./ug, '_')}`; 5 | export const allNUTS2IndicatorsCsvName = `${basename}.NUTS2.csv`; 6 | export const zipUrl = `/data/${basename}.zip`; 7 | -------------------------------------------------------------------------------- /ui/src/routes/info/beta.svelte: -------------------------------------------------------------------------------- 1 | 5 | 6 | 7 | Beta release - {toolName} 8 | 12 | 13 | 14 | 15 | -------------------------------------------------------------------------------- /ds/beis_indicators/geo/make_reverse_geocode_universities.py: -------------------------------------------------------------------------------- 1 | import pandas as pd 2 | 3 | from beis_indicators import project_dir 4 | from beis_indicators.geo.university_reverse_geocode import get_uni_metadata, reverse_geocode_unis 5 | 6 | get_uni_metadata() 7 | uni_meta = pd.read_csv(f'{project_dir}/data/raw/universities/uni_metadata.csv') 8 | reverse_geocode_unis(uni_meta) 9 | -------------------------------------------------------------------------------- /ui/src/routes/info/privacy.svelte: -------------------------------------------------------------------------------- 1 | 5 | 6 | 7 | Privacy - {toolName} 8 | 12 | 13 | 14 | 15 | -------------------------------------------------------------------------------- /ui/src/node_modules/app/stores/selection.js: -------------------------------------------------------------------------------- 1 | import {writable} from 'svelte/store'; 2 | 3 | export const _selectedYear = writable(); 4 | export const resetSelectedYear = () => { 5 | _selectedYear.set(); 6 | } 7 | 8 | export const _availableYears = writable([]); 9 | 10 | export const resetSelection = () => { 11 | _availableYears.set([]); 12 | resetSelectedYear(); 13 | }; 14 | -------------------------------------------------------------------------------- /ui/src/bin/utils.js: -------------------------------------------------------------------------------- 1 | import path from 'path'; 2 | 3 | /* NUTS */ 4 | export const isNotNuts3File = name => !path.parse(name).name.endsWith('.nuts3'); 5 | export const isNuts3File = name => path.parse(name).name.endsWith('.nuts3'); 6 | 7 | /* LEP */ 8 | export const isNotLepFile = name => !path.parse(name).name.endsWith('.lep'); 9 | export const isLepFile = name => path.parse(name).name.endsWith('.lep'); 10 | -------------------------------------------------------------------------------- /ds/data/aux/nice_class_category_lookup.json: -------------------------------------------------------------------------------- 1 | { 2 | 'industrial':[1,2,3,4,5,6,7,8,11,12,13,14,16,17], 3 | 'housing_construction':[19,20,21,27,37], 4 | 'consumer_goods':[15,28], 5 | 'textiles':[24,25,26,27,18], 6 | 'food_beverages':[30,31,32,33,34], 7 | 'scientific':[9,10,42], 8 | 'telecommunications:[38], 9 | 'professional_services_education':[35,36,45], 10 | 'health':[41], 11 | 'education':[44] 12 | } -------------------------------------------------------------------------------- /ds/data/aux/stem_ref.txt: -------------------------------------------------------------------------------- 1 | Aeronautical, Mechanical, Chemical and Manufacturing Engineering 2 | Agriculture, Veterinary and Food Science 3 | Biological Sciences 4 | Chemistry 5 | Civil and Construction Engineering 6 | Computer Science and Informatics 7 | Earth Systems and Environmental Sciences 8 | Electrical and Electronic Engineering, Metallurgy and Materials 9 | General Engineering 10 | Mathematical Sciences 11 | Physics -------------------------------------------------------------------------------- /ds/beis_indicators/nomis/nomis_aps_run_all.py: -------------------------------------------------------------------------------- 1 | """ 2 | """ 3 | 4 | import os 5 | from beis_indicators import project_dir 6 | 7 | print(f"{project_dir}") 8 | 9 | print('Collecting raw APS files') 10 | 11 | os.system (f"python '{project_dir}/beis_indicators/nomis/aps/nomis_aps.py'") 12 | 13 | print('Creating indicator') 14 | 15 | os.system (f"python '{project_dir}/beis_indicators/nomis/aps/make_aps_indicators.py'") 16 | -------------------------------------------------------------------------------- /ui/src/node_modules/app/data/groups.js: -------------------------------------------------------------------------------- 1 | import { 2 | getYearExtent, 3 | makeIndicatorsLookup 4 | } from '@svizzle/time_region_value/src/node_modules/utils/data'; 5 | import {inclusiveRange} from '@svizzle/utils'; 6 | 7 | import groups from './indicatorsGroups.json'; 8 | 9 | export const lookup = makeIndicatorsLookup(groups); 10 | export const yearExtent = getYearExtent(groups); 11 | export const yearRange = inclusiveRange(yearExtent); 12 | -------------------------------------------------------------------------------- /ds/docs/commands.rst: -------------------------------------------------------------------------------- 1 | Commands 2 | ======== 3 | 4 | The Makefile contains the central entry points for common tasks related to this project. 5 | 6 | Syncing data to S3 7 | ^^^^^^^^^^^^^^^^^^ 8 | 9 | * `make sync_data_to_s3` will use `aws s3 sync` to recursively sync files in `data/` up to `s3://beis-indicators/data/`. 10 | * `make sync_data_from_s3` will use `aws s3 sync` to recursively sync files from `s3://beis-indicators/data/` to `data/`. 11 | -------------------------------------------------------------------------------- /ds/test_environment.py: -------------------------------------------------------------------------------- 1 | import sys 2 | 3 | def main(): 4 | system_major = sys.version_info.major 5 | required_major = 3 6 | 7 | if system_major != required_major: 8 | raise TypeError( 9 | "This project requires Python {}. Found: Python {}".format( 10 | required_major, sys.version)) 11 | else: 12 | print(">>> Development environment passes all tests!") 13 | 14 | 15 | if __name__ == '__main__': 16 | main() 17 | -------------------------------------------------------------------------------- /ui/src/node_modules/app/utils/domain.js: -------------------------------------------------------------------------------- 1 | import * as _ from 'lamb'; 2 | import {csvParse} from 'd3-dsv'; 3 | import {transformValues} from '@svizzle/utils'; 4 | 5 | export const getNutsId = _.getKey('nuts_id'); 6 | 7 | export const sortAscByYear = _.sortWith([_.sorter(_.getKey('year'))]); 8 | 9 | const sanitizeValue = id => transformValues({ 10 | [id]: Number, 11 | // year: Number 12 | }); 13 | export const parseCSV = id => 14 | t => csvParse(t, sanitizeValue(id)); 15 | -------------------------------------------------------------------------------- /ui/static/manifest.json: -------------------------------------------------------------------------------- 1 | { 2 | "background_color": "#ffffff", 3 | "theme_color": "#333333", 4 | "name": "BEIS_indicators", 5 | "short_name": "BEIS_indicators", 6 | "display": "minimal-ui", 7 | "start_url": "/", 8 | "icons": [ 9 | { 10 | "src": "images/logo-192.png", 11 | "sizes": "192x192", 12 | "type": "image/png" 13 | }, 14 | { 15 | "src": "images/logo-512.png", 16 | "sizes": "512x512", 17 | "type": "image/png" 18 | } 19 | ] 20 | } 21 | -------------------------------------------------------------------------------- /ui/src/server.js: -------------------------------------------------------------------------------- 1 | import sirv from 'sirv'; 2 | import polka from 'polka'; 3 | import compression from 'compression'; 4 | import * as sapper from '@sapper/server'; 5 | 6 | import {isDev} from 'app/config'; 7 | 8 | const {PORT} = process.env; 9 | 10 | polka() 11 | .use( 12 | compression({threshold: 0}), 13 | sirv('static', {dev: isDev}), 14 | sapper.middleware() 15 | ) 16 | .listen(PORT, err => { 17 | if (err) { 18 | console.log('error', err) 19 | } 20 | }); 21 | -------------------------------------------------------------------------------- /ds/data/aux/scottish_lads.txt: -------------------------------------------------------------------------------- 1 | Orkney Islands, Fife, East Renfrewshire, West Lothian, Perth and Kinross, North Lanarkshire, City of Edinburgh, Dumfries and Galloway, South Ayrshire, Glasgow City, Midlothian, East Ayrshire, Highland, Moray, Scottish Borders, North Ayrshire, Shetland Islands, Aberdeenshire, Renfrewshire, East Dunbartonshire, Dundee City, Aberdeen City, Inverclyde, East Lothian, Clackmannanshire, West Dunbartonshire, South Lanarkshire, Falkirk, Na h-Eileanan Siar, Angus, Argyll and Bute, Stirling -------------------------------------------------------------------------------- /ds/beis_indicators/geo/lep.py: -------------------------------------------------------------------------------- 1 | import geopandas as gpd 2 | 3 | 4 | def get_lep_shape(year): 5 | """get_lep_shape 6 | 7 | Args: 8 | year (int): LEP version year. Options are 2020, 2017 and 2014 9 | 10 | """ 11 | with open(f'{project_dir}/data/aux/shapefile_urls.json','r') as infile: 12 | shape_lookup = json.load(infile) 13 | 14 | url = shape_lookup[f'leps_{year}'] 15 | fname = 'lep_{year}_shp.zip' 16 | fout = f'{shapefile_dir}/{fname}' 17 | urlretrieve(url, fout) 18 | -------------------------------------------------------------------------------- /ui/src/routes/indicators/index.svelte: -------------------------------------------------------------------------------- 1 | 7 | 8 | 9 | Indicators - {toolName} 10 | 14 | 15 | 16 | 20 | -------------------------------------------------------------------------------- /ds/data/README.md: -------------------------------------------------------------------------------- 1 | # BEIS Indicators Data 2 | 3 | ## Indicator Data 4 | 5 | Indicators are stored in `processed`. 6 | 7 | Each individual indicator is stored as a single csv with a consistent format and column order: 8 | 9 | ``` 10 | year, nuts_id, nuts_year_spec, 11 | ``` 12 | 13 | ## Schemas and Metadata 14 | 15 | Each indicator requires an individual schema to describe the dataset fields as well as the provenance of the data. An indicator schema template can be found in `.schema/schema_template.yaml`. 16 | 17 | -------------------------------------------------------------------------------- /ui/src/routes/indicators/_layout.svelte: -------------------------------------------------------------------------------- 1 | 11 | 12 | 21 | 22 | 23 | -------------------------------------------------------------------------------- /ds/docs/index.rst: -------------------------------------------------------------------------------- 1 | .. beis_indicators documentation master file, created by 2 | sphinx-quickstart. 3 | You can adapt this file completely to your liking, but it should at least 4 | contain the root `toctree` directive. 5 | 6 | beis_indicators documentation! 7 | ============================================== 8 | 9 | Contents: 10 | 11 | .. toctree:: 12 | :maxdepth: 2 13 | 14 | getting-started 15 | commands 16 | 17 | 18 | 19 | Indices and tables 20 | ================== 21 | 22 | * :ref:`genindex` 23 | * :ref:`modindex` 24 | * :ref:`search` 25 | -------------------------------------------------------------------------------- /ui/src/template.html: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | %sapper.base% 8 | 9 | 10 | 11 | %sapper.styles% 12 | %sapper.head% 13 | 14 | 15 |
%sapper.html%
16 | %sapper.scripts% 17 | 18 | 19 | -------------------------------------------------------------------------------- /ds/beis_indicators/utils/__init__.py: -------------------------------------------------------------------------------- 1 | from . import pandas 2 | import re 3 | 4 | 5 | def camel_to_snake(name): 6 | '''Convert lowerCamelCase or upperCamelCase to snake_case 7 | Args: 8 | name (str): lowerCamelCase or upperCamelCase word. 9 | Returns: 10 | _name (str): snake_case word. 11 | ''' 12 | s1 = re.sub('(.)([A-Z][a-z]+)', r'\1_\2', name) 13 | return re.sub('([a-z0-9])([A-Z])', r'\1_\2', s1).lower() 14 | 15 | 16 | def chunks(lst, n): 17 | """Yield successive n-sized chunks from lst.""" 18 | for i in range(0, len(lst), n): 19 | yield lst[i:i + n] 20 | -------------------------------------------------------------------------------- /ds/beis_indicators/features/process_text.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | 3 | import mi_scotland 4 | from mi_scotland.features.nlproc import tokenize_document 5 | from mi_scotland.utils.data_transformation import flatten_lists 6 | 7 | np.random.seed(mi_scotland.config['seed']) 8 | 9 | 10 | def process_abstracts(text): 11 | """Process text data. 12 | 13 | Args: 14 | text (:obj:`str`): Text data. 15 | 16 | Return: 17 | (:obj:`list` of :obj:`str`): 18 | 19 | """ 20 | if isinstance(text, str): 21 | return flatten_lists(tokenize_document(text)) 22 | else: 23 | np.nan 24 | -------------------------------------------------------------------------------- /ui/src/routes/guides/app.svelte: -------------------------------------------------------------------------------- 1 | 10 | 11 | 12 | App guide - {toolName} 13 | 17 | 18 | 19 | {#if $_screen?.sizes.medium} 20 | 21 | {:else} 22 | 23 | {/if} 24 | -------------------------------------------------------------------------------- /ds/beis_indicators/models/build_queries.py: -------------------------------------------------------------------------------- 1 | import gensim 2 | import numpy as np 3 | import mi_scotland 4 | 5 | np.random.seed(mi_scotland.config['seed']) 6 | 7 | 8 | def sim_words(w2v, token, topn=50): 9 | """Query a word2vec model with words and return a list of similar terms. 10 | 11 | Args: 12 | w2v: Pre-trained word vectors model. 13 | token (:obj:`str`): Token to query word2vec with. 14 | topn (:obj:`int`): Number of most similar tokens to return. 15 | 16 | Return 17 | (:obj:`list` of :obj:`str`) 18 | 19 | """ 20 | return [tup[0] for tup in w2v.wv.most_similar([token], topn=topn)] 21 | -------------------------------------------------------------------------------- /ui/src/routes/info/feedback.svelte: -------------------------------------------------------------------------------- 1 | 4 | 5 | 6 | Feedback - {toolName} 7 | 11 | 12 | 13 |
14 | 23 |
24 | 25 | 31 | -------------------------------------------------------------------------------- /ds/notebooks/lab_notes.md: -------------------------------------------------------------------------------- 1 | # Lab notes 2 | 3 | ## HESA + HE-BCI 4 | 5 | We access the HESA data by requesting tables from their website. The tables are generally long which makes them relatively easy to analyse, although we need to be careful when selecting categories to avoid double counting. The `filter_data` function takes care of that. 6 | 7 | We aggregate institutions into NUTS using a learning provider metadata file and the `nuts-finder` package developed by Joel. 8 | 9 | We could easily change the output indicators by modifying the code in the notebooks. 10 | 11 | There is some repetition and overlaps between the HESA and HE-BCI notebooks because both of them are extracting information from the same website. There is much scope for refactoring. 12 | 13 | 14 | 15 | -------------------------------------------------------------------------------- /ui/src/routes/_error.svelte: -------------------------------------------------------------------------------- 1 | 7 | 8 | 9 | {toolName}: {status} 10 | 14 | 15 | 16 |

{status}

17 | 18 |

{error.message}

19 | 20 | {#if isDev && error.stack} 21 | 
{error.stack}
22 | {/if} 23 | 24 | 45 | -------------------------------------------------------------------------------- /ui/src/node_modules/app/components/NoScript.svelte: -------------------------------------------------------------------------------- 1 | 12 | 13 | 39 | -------------------------------------------------------------------------------- /ds/conda_environment.yaml: -------------------------------------------------------------------------------- 1 | name: beis_indicators 2 | channels: 3 | - conda-forge 4 | - defaults 5 | dependencies: 6 | - pip 7 | - python=3.6.5 8 | - numpy 9 | - scipy 10 | - pandas 11 | - matplotlib 12 | - jupyter 13 | - ipython 14 | - jupyterlab 15 | - requests 16 | - rtree 17 | - scikit-learn 18 | - seaborn 19 | - xlrd 20 | - pip: 21 | - geopandas 22 | - shapely 23 | - descartes 24 | - mapclassify 25 | - ratelim 26 | - eurostat 27 | 28 | # Tooling requirements 29 | - tqdm 30 | - pyyaml 31 | - tables 32 | - dvc 33 | - click 34 | - Sphinx 35 | - sphinxcontrib.napoleon 36 | - coverage 37 | - awscli 38 | - flake8 39 | - python-dotenv>=0.5.1 40 | - jupyter_contrib_nbextensions 41 | - jupyter_nbextensions_configurator 42 | - jq 43 | - pytest 44 | -------------------------------------------------------------------------------- /ds/beis_indicators/crunchbase/make_crunchbase_tech_companies.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import logging 3 | 4 | from beis_indicators import project_dir 5 | from beis_indicators.geo import NutsCoder, LepCoder 6 | from beis_indicators.indicators import points_to_indicator, save_indicator 7 | from beis_indicators.crunchbase.process_cb import load_org_founded_counts 8 | 9 | 10 | coders = { 11 | 'nuts2': NutsCoder(level=2), 12 | 'nuts3': NutsCoder(level=3), 13 | 'lep': LepCoder() 14 | } 15 | 16 | founded = load_org_founded_counts() 17 | 18 | for geo, coder in coders.items(): 19 | total_companies = points_to_indicator(founded, value_col='companies_founded', 20 | coder=coder, aggfunc=np.sum, projection='EPSG:4326', 21 | x_col='longitude', y_col='latitude', astype=int) 22 | save_indicator(total_companies, 'crunchbase', geo) 23 | 24 | -------------------------------------------------------------------------------- /ds/data/aux/shapefile_urls.json: -------------------------------------------------------------------------------- 1 | {"nuts2_2016":"https://ec.europa.eu/eurostat/cache/GISCO/distribution/v2/nuts/download/ref-nuts-2016-01m.shp.zip", 2 | "nuts2_2013":"https://ec.europa.eu/eurostat/cache/GISCO/distribution/v2/nuts/download/ref-nuts-2013-01m.shp.zip", 3 | "nuts2_2010":"https://ec.europa.eu/eurostat/cache/GISCO/distribution/v2/nuts/download/ref-nuts-2010-01m.shp.zip", 4 | "nuts2_2006":"https://ec.europa.eu/eurostat/cache/GISCO/distribution/v2/nuts/download/ref-nuts-2006-01m.shp.zip", 5 | "nuts2_2003":"https://ec.europa.eu/eurostat/cache/GISCO/distribution/v2/nuts/download/ref-nuts-2003-01m.shp.zip", 6 | "leps_2014":"https://opendata.arcgis.com/datasets/17c92615a55f4dbf945e8eaf642eaa87_0.zip", 7 | "leps_2017":"https://opendata.arcgis.com/datasets/9da81147e3114dc2849dc7ab2d586e5a_0.zip", 8 | "leps_2020":"https://opendata.arcgis.com/datasets/0af0f6e04abf44f48868b441afd67e0e_0.zip"} 9 | 10 | -------------------------------------------------------------------------------- /ds/data/aux/framework.json: -------------------------------------------------------------------------------- 1 | [{ 2 | "description": "Existing capability to perform public R&D", 3 | "id": "public_rnd", 4 | "label": "Public R&D", 5 | "order": 1 6 | }, 7 | { 8 | "description": "Knowledge exchange and university commercialisation", 9 | "id": "knowledge_exchange", 10 | "label": "Knowledge exchange and university commercialisation", 11 | "order": 2 12 | }, 13 | { 14 | "description": "Business absorptive capacity and private R&D investment", 15 | "id": "private_rnd", 16 | "label": "Private R&D", 17 | "order": 3 18 | }, 19 | { 20 | "description": "Place potential to attract researchers and innovators", 21 | "id": "place_potential", 22 | "label": "Places attracting R&D firms and workers", 23 | "order": 4 24 | }, 25 | { 26 | "description": "Challenge oriented activity", 27 | "id": "challenge_activity", 28 | "label": "Challenge oriented activity", 29 | "order": 5 30 | }] 31 | -------------------------------------------------------------------------------- /ds/beis_indicators/gtr/make_gtr_funding.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import logging 3 | 4 | from beis_indicators import project_dir 5 | from beis_indicators.geo import NutsCoder, LepCoder 6 | from beis_indicators.indicators import points_to_indicator, save_indicator 7 | from beis_indicators.gtr.gtr_processing import load_gtr_funding_by_loc 8 | 9 | 10 | var_name = 'total_ukri_funding' 11 | 12 | coders = { 13 | 'nuts2': NutsCoder(level=2), 14 | 'nuts3': NutsCoder(level=3), 15 | 'lep': LepCoder() 16 | } 17 | 18 | funding = load_gtr_funding_by_loc(min_year=2006, max_year=2019) 19 | 20 | for geo, coder in coders.items(): 21 | total_funding = points_to_indicator(funding, value_col='amount', coder=coder, 22 | aggfunc=np.mean, value_rename=var_name, 23 | projection='EPSG:4326', x_col='longitude', y_col='latitude') 24 | save_indicator(total_funding, 'gtr', geo) 25 | -------------------------------------------------------------------------------- /ds/beis_indicators/hebci/make_hebci.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import logging 3 | 4 | from beis_indicators import project_dir 5 | from beis_indicators.geo import NutsCoder, LepCoder 6 | from beis_indicators.indicators import points_to_indicator, save_indicator 7 | from beis_indicators.hebci.process_hebci import load_hebci 8 | 9 | 10 | hebci_data = load_hebci() 11 | 12 | coders = { 13 | 'nuts2': NutsCoder(level=2), 14 | 'nuts3': NutsCoder(level=3), 15 | 'lep': LepCoder() 16 | } 17 | 18 | for name, data in hebci_data.items(): 19 | for geo, coder in coders.items(): 20 | indicator = points_to_indicator(data, value_col='Value', coder=coder, 21 | aggfunc=np.sum, value_rename=name, dp=0, 22 | projection='EPSG:4326', x_col='longitude', y_col='latitude') 23 | indicator[name] = indicator[name].astype(int) 24 | save_indicator(indicator, 'hebci', geo) 25 | 26 | -------------------------------------------------------------------------------- /ds/data/processed/hesa/total_university_buildings.lep.yaml: -------------------------------------------------------------------------------- 1 | api_doc_url: https://www.hesa.ac.uk/data-and-analysis/estates/table-1 2 | api_type: FETCH 3 | title: Total number of university buildings 4 | subtitle: Number of university buildings in a LEP region 5 | endpoint_url: https://www.hesa.ac.uk/data-and-analysis/estates/data.csv 6 | framework_group: public_rnd 7 | is_experimental: False 8 | order: [year, lep_id, lep_year_spec, value.id] 9 | region: 10 | type: LepRegion 11 | source_url: https://geoportal.statistics.gov.uk/search?collection=Dataset&sort=name&tags=all(BDY_LEP) 12 | schema: 13 | lep_id: 14 | type: LepRegion.id 15 | lep_year_spec: 16 | type: LepRegion.year_spec 17 | value: 18 | data_type: int 19 | id: total_university_buildings 20 | label: Total number of buildings 21 | year: 22 | data_type: int 23 | label: Academic year (start) 24 | source_name: HESA (Higher Education Statistical Agency) 25 | source_url: https://www.hesa.ac.uk/ 26 | -------------------------------------------------------------------------------- /ds/data/processed/hesa/gbp_research_income.lep.yaml: -------------------------------------------------------------------------------- 1 | api_doc_url: https://www.hesa.ac.uk/data-and-analysis/estates/table-1 2 | api_type: FETCH 3 | title: Research income (GBP) 4 | subtitle: Research income received by universities in the LEP region 5 | endpoint_url: https://www.hesa.ac.uk/data-and-analysis/estates/data.csv 6 | framework_group: public_rnd 7 | is_experimental: False 8 | order: [year, lep_id, lep_year_spec, value.id] 9 | region: 10 | type: LepRegion 11 | source_url: https://geoportal.statistics.gov.uk/search?collection=Dataset&sort=name&tags=all(BDY_LEP) 12 | schema: 13 | lep_id: 14 | type: LepRegion.id 15 | lep_year_spec: 16 | type: LepRegion.year_spec 17 | value: 18 | data_type: int 19 | format: ',' 20 | id: gbp_research_income 21 | label: Research income 22 | type: GBP 23 | year: 24 | data_type: int 25 | label: Academic year (start) 26 | source_name: HESA (Higher Education Statistical Agency) 27 | source_url: https://www.hesa.ac.uk/ 28 | -------------------------------------------------------------------------------- /ds/data/processed/ref/mean_ref_stem.csv: -------------------------------------------------------------------------------- 1 | year,nuts_id,nuts_year_spec,mean_ref_stem 2 | 2014,UKH1,2013,3.35 3 | 2014,UKJ1,2013,3.26 4 | 2014,UKI3,2013,3.25 5 | 2014,UKL2,2013,3.15 6 | 2014,UKK1,2013,3.15 7 | 2014,UKM2,2013,3.14 8 | 2014,UKJ3,2013,3.13 9 | 2014,UKE3,2013,3.12 10 | 2014,UKE2,2013,3.12 11 | 2014,UKC1,2013,3.1 12 | 2014,UKD3,2013,3.09 13 | 2014,UKG3,2013,3.07 14 | 2014,UKD7,2013,3.04 15 | 2014,UKM3,2013,3.03 16 | 2014,UKF1,2013,3.01 17 | 2014,UKE4,2013,3.0 18 | 2014,UKK4,2013,3.0 19 | 2014,UKD4,2013,2.98 20 | 2014,UKJ2,2013,2.98 21 | 2014,UKM5,2013,2.97 22 | 2014,UKC2,2013,2.95 23 | 2014,UKN0,2013,2.95 24 | 2014,UKL1,2013,2.92 25 | 2014,UKI4,2013,2.92 26 | 2014,UKM6,2013,2.91 27 | 2014,UKF2,2013,2.85 28 | 2014,UKJ4,2013,2.84 29 | 2014,UKE1,2013,2.83 30 | 2014,UKH2,2013,2.83 31 | 2014,UKG2,2013,2.67 32 | 2014,UKI7,2013,2.59 33 | 2014,UKF3,2013,2.55 34 | 2014,UKH3,2013,2.51 35 | 2014,UKI5,2013,2.47 36 | 2014,UKI6,2013,2.44 37 | 2014,UKK2,2013,2.31 38 | 2014,UKG1,2013,2.24 39 | 2014,UKD6,2013,1.76 40 | -------------------------------------------------------------------------------- /ds/data/aux/cordis_url_suffixes.json: -------------------------------------------------------------------------------- 1 | {"h2020": {"organizations": "cordis-h2020projectDeliverables.csv", "project_deliverables": "cordis-h2020projectDeliverables.csv", "project_publications": "cordis-h2020projectPublications.csv", "projects": "cordis-h2020projects.csv", "reports": "cordis-h2020reports.csv"}, "fp7": {"organizations": "cordis-fp7organizations.csv", "projects": "cordis-fp7projects.csv", "reports": "cordis-fp7reports.csv"}, "fp6": {"organizations": "FP6/cordis-fp6organizations.csv", "projects": "FP6/cordis-fp6projects.csv"}, "fp5": {"organizations": "FP5/cordis-fp5organizations.csv", "projects": "FP5/cordis-fp5projects.csv"}, "fp4": {"organizations": "FP4/cordis-fp4organizations.csv", "projects": "FP4/cordis-fp4projects.csv"}, "fp3": {"organizations": "FP3/cordis-fp3organizations.csv", "projects": "FP3/cordis-fp3projects.csv"}, "fp2": {"organizations": "FP2/cordis-fp2organizations.csv", "projects": "FP2/cordis-fp2projects.csv"}, "fp1": {"organizations": "FP1/cordis-fp1organizations.csv", "projects": "FP1/cordis-fp1projects.csv"}} -------------------------------------------------------------------------------- /ds/data/processed/hesa/area_university_site.lep.yaml: -------------------------------------------------------------------------------- 1 | api_doc_url: https://www.hesa.ac.uk/data-and-analysis/estates/table-1 2 | api_type: FETCH 3 | data_date: 20200820 4 | title: Area of university site 5 | subtitle: Area of university sites for universities in the LEP region 6 | endpoint_url: https://www.hesa.ac.uk/data-and-analysis/estates/data.csv 7 | framework_group: public_rnd 8 | is_experimental: False 9 | order: [year, lep_id, lep_year_spec, value.id] 10 | region: 11 | type: LepRegion 12 | source_url: https://geoportal.statistics.gov.uk/search?collection=Dataset&sort=name&tags=all(BDY_LEP) 13 | schema: 14 | lep_id: 15 | type: LepRegion.id 16 | lep_year_spec: 17 | type: LepRegion.year_spec 18 | value: 19 | format: .2f 20 | id: area_university_site 21 | label: Total area of university sites 22 | type: Area_hectare 23 | year: 24 | data_type: int 25 | label: Academic year (start) 26 | source_name: HESA (Higher Education Statistical Agency) 27 | source_url: https://www.hesa.ac.uk/ 28 | -------------------------------------------------------------------------------- /ds/data/processed/ref/mean_ref.csv: -------------------------------------------------------------------------------- 1 | year,nuts_id,nuts_year_spec,mean_ref 2 | 2014,UKH1,2013,3.29 3 | 2014,UKI3,2013,3.2 4 | 2014,UKL2,2013,3.2 5 | 2014,UKJ1,2013,3.18 6 | 2014,UKE3,2013,3.1 7 | 2014,UKM2,2013,3.08 8 | 2014,UKC1,2013,3.08 9 | 2014,UKE2,2013,3.06 10 | 2014,UKK1,2013,3.05 11 | 2014,UKG3,2013,3.02 12 | 2014,UKD3,2013,3.0 13 | 2014,UKJ3,2013,3.0 14 | 2014,UKM3,2013,2.99 15 | 2014,UKJ2,2013,2.98 16 | 2014,UKI4,2013,2.97 17 | 2014,UKK4,2013,2.97 18 | 2014,UKF1,2013,2.96 19 | 2014,UKN0,2013,2.95 20 | 2014,UKE4,2013,2.93 21 | 2014,UKD7,2013,2.92 22 | 2014,UKL1,2013,2.91 23 | 2014,UKC2,2013,2.89 24 | 2014,UKM5,2013,2.88 25 | 2014,UKF2,2013,2.85 26 | 2014,UKD4,2013,2.85 27 | 2014,UKJ4,2013,2.84 28 | 2014,UKH3,2013,2.79 29 | 2014,UKH2,2013,2.77 30 | 2014,UKM6,2013,2.76 31 | 2014,UKG2,2013,2.72 32 | 2014,UKI6,2013,2.7 33 | 2014,UKE1,2013,2.7 34 | 2014,UKK2,2013,2.69 35 | 2014,UKI7,2013,2.58 36 | 2014,UKF3,2013,2.51 37 | 2014,UKI5,2013,2.35 38 | 2014,UKD1,2013,2.13 39 | 2014,UKD6,2013,2.08 40 | 2014,UKG1,2013,2.08 41 | 2014,UKK3,2013,2.04 42 | -------------------------------------------------------------------------------- /ds/data/processed/hesa/total_postgraduates.lep.yaml: -------------------------------------------------------------------------------- 1 | api_doc_url: https://www.hesa.ac.uk/data-and-analysis/students 2 | api_type: FETCH 3 | title: Total number of postgraduates 4 | subtitle: Number of postgraduate (research) students enrolled full-time in universities in a LEP region in the starting academic year. 5 | endpoint_url: https://www.hesa.ac.uk/data-and-analysis/students/table-13.csv 6 | framework_group: public_rnd 7 | is_experimental: False 8 | order: [year, lep_id, lep_year_spec, value.id] 9 | region: 10 | type: LepRegion 11 | source_url: https://geoportal.statistics.gov.uk/search?collection=Dataset&sort=name&tags=all(BDY_LEP) 12 | schema: 13 | lep_id: 14 | type: LepRegion.id 15 | lep_year_spec: 16 | type: LepRegion.year_spec 17 | value: 18 | data_type: int 19 | id: total_postgraduates 20 | label: Total number of postgraduate students 21 | year: 22 | data_type: int 23 | label: Academic year (start) 24 | source_name: HESA (Higher Education Statistical Agency) 25 | source_url: https://www.hesa.ac.uk/ 26 | -------------------------------------------------------------------------------- /ds/data/processed/hesa/fte_research_students.lep.yaml: -------------------------------------------------------------------------------- 1 | api_doc_url: https://www.hesa.ac.uk/data-and-analysis/estates/table-1 2 | api_type: FETCH 3 | title: Number of research students (FTE) 4 | subtitle: Aggregate of Full Time Equivalent (FTE) of research students enrolled in universities in the LEP region 5 | endpoint_url: https://www.hesa.ac.uk/data-and-analysis/estates/data.csv 6 | framework_group: public_rnd 7 | is_experimental: False 8 | order: [year, lep_id, lep_year_spec, value.id] 9 | region: 10 | type: LepRegion 11 | source_url: https://geoportal.statistics.gov.uk/search?collection=Dataset&sort=name&tags=all(BDY_LEP) 12 | schema: 13 | lep_id: 14 | type: LepRegion.id 15 | lep_year_spec: 16 | type: LepRegion.year_spec 17 | value: 18 | id: fte_research_students 19 | label: Research students (Full Time Equivalent) 20 | type: FTE 21 | data_type: int 22 | year: 23 | data_type: int 24 | label: Academic year (start) 25 | source_name: HESA (Higher Education Statistical Agency) 26 | source_url: https://www.hesa.ac.uk/ 27 | -------------------------------------------------------------------------------- /ds/data/processed/hesa/total_university_buildings.nuts2.yaml: -------------------------------------------------------------------------------- 1 | api_doc_url: https://www.hesa.ac.uk/data-and-analysis/estates/table-1 2 | api_type: FETCH 3 | title: Total number of university buildings 4 | subtitle: Number of university buildings in a NUTS2 region 5 | endpoint_url: https://www.hesa.ac.uk/data-and-analysis/estates/data.csv 6 | framework_group: public_rnd 7 | is_experimental: False 8 | order: [year, nuts_id, nuts_year_spec, value.id] 9 | region: 10 | type: NutsRegion 11 | level: 2 12 | source_url: http://geoportal1-ons.opendata.arcgis.com/datasets/48b6b85bb7ea43699ee85f4ecd12fd36_0.zip?outSR=%7B%22latestWkid%22:27700,%22wkid%22:27700%7D 13 | schema: 14 | nuts_id: 15 | type: NutsRegion.id 16 | nuts_year_spec: 17 | type: NutsRegion.year_spec 18 | value: 19 | data_type: int 20 | id: total_university_buildings 21 | label: Total number of buildings 22 | year: 23 | data_type: int 24 | label: Academic year (start) 25 | source_name: HESA (Higher Education Statistical Agency) 26 | source_url: https://www.hesa.ac.uk/ 27 | -------------------------------------------------------------------------------- /ds/data/processed/hesa/total_university_buildings.nuts3.yaml: -------------------------------------------------------------------------------- 1 | api_doc_url: https://www.hesa.ac.uk/data-and-analysis/estates/table-1 2 | api_type: FETCH 3 | title: Total number of university buildings 4 | subtitle: Number of university buildings in a NUTS3 region 5 | endpoint_url: https://www.hesa.ac.uk/data-and-analysis/estates/data.csv 6 | framework_group: public_rnd 7 | is_experimental: False 8 | order: [year, nuts_id, nuts_year_spec, value.id] 9 | region: 10 | type: NutsRegion 11 | level: 3 12 | source_url: http://geoportal1-ons.opendata.arcgis.com/datasets/48b6b85bb7ea43699ee85f4ecd12fd36_0.zip?outSR=%7B%22latestWkid%22:27700,%22wkid%22:27700%7D 13 | schema: 14 | nuts_id: 15 | type: NutsRegion.id 16 | nuts_year_spec: 17 | type: NutsRegion.year_spec 18 | value: 19 | data_type: int 20 | id: total_university_buildings 21 | label: Total number of buildings 22 | year: 23 | data_type: int 24 | label: Academic year (start) 25 | source_name: HESA (Higher Education Statistical Agency) 26 | source_url: https://www.hesa.ac.uk/ 27 | -------------------------------------------------------------------------------- /ds/data/processed/hesa/gbp_research_income.nuts3.yaml: -------------------------------------------------------------------------------- 1 | api_doc_url: https://www.hesa.ac.uk/data-and-analysis/estates/table-1 2 | api_type: FETCH 3 | title: Research income (GBP) 4 | subtitle: Research income received by universities in the NUTS3 region 5 | endpoint_url: https://www.hesa.ac.uk/data-and-analysis/estates/data.csv 6 | framework_group: public_rnd 7 | is_experimental: False 8 | order: [year, nuts_id, nuts_year_spec, value.id] 9 | region: 10 | type: NutsRegion 11 | level: 3 12 | source_url: http://geoportal1-ons.opendata.arcgis.com/datasets/48b6b85bb7ea43699ee85f4ecd12fd36_0.zip?outSR=%7B%22latestWkid%22:27700,%22wkid%22:27700%7D 13 | schema: 14 | nuts_id: 15 | type: NutsRegion.id 16 | nuts_year_spec: 17 | type: NutsRegion.year_spec 18 | value: 19 | data_type: int 20 | format: ',' 21 | id: gbp_research_income 22 | label: Research income 23 | type: GBP 24 | year: 25 | data_type: int 26 | label: Academic year (start) 27 | source_name: HESA (Higher Education Statistical Agency) 28 | source_url: https://www.hesa.ac.uk/ 29 | -------------------------------------------------------------------------------- /ds/data/processed/hesa/area_university_site.nuts3.yaml: -------------------------------------------------------------------------------- 1 | api_doc_url: https://www.hesa.ac.uk/data-and-analysis/estates/table-1 2 | api_type: FETCH 3 | title: Area of university site 4 | subtitle: Area of university sites for universities in the NUTS3 region 5 | endpoint_url: https://www.hesa.ac.uk/data-and-analysis/estates/data.csv 6 | framework_group: public_rnd 7 | is_experimental: False 8 | order: [year, nuts_id, nuts_year_spec, value.id] 9 | region: 10 | type: NutsRegion 11 | level: 3 12 | source_url: http://geoportal1-ons.opendata.arcgis.com/datasets/48b6b85bb7ea43699ee85f4ecd12fd36_0.zip?outSR=%7B%22latestWkid%22:27700,%22wkid%22:27700%7D 13 | schema: 14 | nuts_id: 15 | type: NutsRegion.id 16 | nuts_year_spec: 17 | type: NutsRegion.year_spec 18 | value: 19 | format: .2f 20 | id: area_university_site 21 | label: Total area of university sites 22 | type: Area_hectare 23 | year: 24 | data_type: int 25 | label: Academic year (start) 26 | source_name: HESA (Higher Education Statistical Agency) 27 | source_url: https://www.hesa.ac.uk/ 28 | -------------------------------------------------------------------------------- /ds/data/processed/hebci/graduate_startups.lep.yaml: -------------------------------------------------------------------------------- 1 | api_doc_url: https://www.hesa.ac.uk/data-and-analysis/providers/business-community/ 2 | api_type: FETCH 3 | data_date: 20200928 4 | description: Number of active startups founded by higher education graduates. 5 | title: Active graduate startups 6 | subtitle: Number of active startups founded by higher education graduates. 7 | endpoint_url: https://www.hesa.ac.uk/data-and-analysis/providers/business-community/table-4e.csv 8 | framework_group: knowledge_exchange 9 | is_experimental: False 10 | is_public: True 11 | order: [year, lep_id, lep_year_spec, value.id] 12 | region: 13 | type: LepRegion 14 | source_url: LepRegion.source_url 15 | source: LepRegion.source 16 | schema: 17 | lep_id: 18 | type: LepRegion.lep_id 19 | lep_year_spec: 20 | type: LepRegion.lep_year_spec 21 | value: 22 | data_type: int 23 | id: graduate_startups 24 | label: Startups 25 | year: 26 | data_type: int 27 | label: Year 28 | source_name: HESA (Higher Education Statistical Agency) 29 | source_url: https://www.hesa.ac.uk/ 30 | -------------------------------------------------------------------------------- /ds/data/processed/hesa/gbp_research_income.nuts2.yaml: -------------------------------------------------------------------------------- 1 | api_doc_url: https://www.hesa.ac.uk/data-and-analysis/estates/table-1 2 | api_type: FETCH 3 | title: Research income (GBP) 4 | subtitle: Research income received by universities in the NUTS2 region 5 | endpoint_url: https://www.hesa.ac.uk/data-and-analysis/estates/data.csv 6 | framework_group: public_rnd 7 | is_experimental: False 8 | nuts_level: 2 9 | order: [year, nuts_id, nuts_year_spec, value.id] 10 | region: 11 | type: NutsRegion 12 | level: 2 13 | source_url: http://geoportal1-ons.opendata.arcgis.com/datasets/48b6b85bb7ea43699ee85f4ecd12fd36_0.zip?outSR=%7B%22latestWkid%22:27700,%22wkid%22:27700%7D 14 | schema: 15 | nuts_id: 16 | type: NutsRegion.id 17 | nuts_year_spec: 18 | type: NutsRegion.year_spec 19 | value: 20 | data_type: int 21 | format: ',' 22 | id: gbp_research_income 23 | label: Research income 24 | type: GBP 25 | year: 26 | data_type: int 27 | label: Academic year (start) 28 | source_name: HESA (Higher Education Statistical Agency) 29 | source_url: https://www.hesa.ac.uk/ 30 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | The MIT License (MIT) 2 | 3 | Copyright (c) 2021, Nesta 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: 6 | 7 | The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. 8 | 9 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. 10 | -------------------------------------------------------------------------------- /ds/data/processed/eurostat/eurostat_gdp_per_capita.nuts2.yaml: -------------------------------------------------------------------------------- 1 | api_doc_url: https://ec.europa.eu/eurostat/cache/metadata/en/rd_esms.htm 2 | api_type: FETCH 3 | title: GDP per capita 4 | subtitle: Regional gross domestic product (GDP) expressed in purchasing power standards by NUTS 2 regions. 5 | endpoint_url: http://ec.europa.eu/eurostat/wdds/rest/data/v2.1/json/en/tgs00004 6 | framework_group: private_rnd 7 | is_experimental: False 8 | order: [year, nuts_id, nuts_year_spec, value.id] # do not change; specifies the order of the exported indicator fields 9 | region: 10 | type: NutsRegion 11 | level: 2 12 | source: PROVIDED 13 | schema: 14 | nuts_id: 15 | type: NutsRegion.nuts_id 16 | nuts_year_spec: 17 | type: NutsRegion.nuts_year_spec 18 | value: 19 | data_type: int 20 | format: ',' 21 | id: eurostat_gdp_per_capita 22 | label: Expenditure (to nearest 10000) 23 | type: EUR 24 | year: 25 | data_type: int 26 | label: Year 27 | source_name: Eurostat (European Statistical Office) 28 | source_url: https://ec.europa.eu/eurostat/ 29 | -------------------------------------------------------------------------------- /ui/src/node_modules/app/components/glyphs/ColorClear.svelte: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 14 | -------------------------------------------------------------------------------- /ds/data/processed/hesa/area_university_site.nuts2.yaml: -------------------------------------------------------------------------------- 1 | api_doc_url: https://www.hesa.ac.uk/data-and-analysis/estates/table-1 2 | api_type: FETCH 3 | data_date: 20200820 4 | title: Area of university site 5 | subtitle: Area of university sites for universities in the NUTS2 region 6 | endpoint_url: https://www.hesa.ac.uk/data-and-analysis/estates/data.csv 7 | framework_group: public_rnd 8 | is_experimental: False 9 | order: [year, nuts_id, nuts_year_spec, value.id] 10 | region: 11 | type: NutsRegion 12 | level: 2 13 | source_url: http://geoportal1-ons.opendata.arcgis.com/datasets/48b6b85bb7ea43699ee85f4ecd12fd36_0.zip?outSR=%7B%22latestWkid%22:27700,%22wkid%22:27700%7D 14 | schema: 15 | nuts_id: 16 | type: NutsRegion.id 17 | nuts_year_spec: 18 | type: NutsRegion.year_spec 19 | value: 20 | format: .2f 21 | id: area_university_site 22 | label: Total area of university sites 23 | type: Area_hectare 24 | year: 25 | data_type: int 26 | label: Academic year (start) 27 | source_name: HESA (Higher Education Statistical Agency) 28 | source_url: https://www.hesa.ac.uk/ 29 | -------------------------------------------------------------------------------- /ds/data/processed/hebci/graduate_startups.nuts2.yaml: -------------------------------------------------------------------------------- 1 | api_doc_url: https://www.hesa.ac.uk/data-and-analysis/providers/business-community/ 2 | api_type: FETCH 3 | data_date: 20200928 4 | description: Number of active startups founded by higher education graduates. 5 | title: Active graduate startups 6 | subtitle: Number of active startups founded by higher education graduates. 7 | endpoint_url: https://www.hesa.ac.uk/data-and-analysis/providers/business-community/table-4e.csv 8 | framework_group: knowledge_exchange 9 | is_experimental: False 10 | is_public: True 11 | order: [year, nuts_id, nuts_year_spec, value.id] 12 | region: 13 | level: 2 14 | type: NutsRegion 15 | source_url: NutsRegion.source_url 16 | source: NutsRegion.source 17 | schema: 18 | nuts_id: 19 | type: NutsRegion.nuts_id 20 | nuts_year_spec: 21 | type: NutsRegion.nuts_year_spec 22 | value: 23 | data_type: int 24 | id: graduate_startups 25 | label: Startups 26 | year: 27 | data_type: int 28 | label: Year 29 | source_name: HESA (Higher Education Statistical Agency) 30 | source_url: https://www.hesa.ac.uk/ 31 | -------------------------------------------------------------------------------- /ds/data/processed/hebci/graduate_startups.nuts3.yaml: -------------------------------------------------------------------------------- 1 | api_doc_url: https://www.hesa.ac.uk/data-and-analysis/providers/business-community/ 2 | api_type: FETCH 3 | data_date: 20200928 4 | description: Number of active startups founded by higher education graduates. 5 | title: Active graduate startups 6 | subtitle: Number of active startups founded by higher education graduates. 7 | endpoint_url: https://www.hesa.ac.uk/data-and-analysis/providers/business-community/table-4e.csv 8 | framework_group: knowledge_exchange 9 | is_experimental: False 10 | is_public: True 11 | order: [year, nuts_id, nuts_year_spec, value.id] 12 | region: 13 | level: 3 14 | type: NutsRegion 15 | source_url: NutsRegion.source_url 16 | source: NutsRegion.source 17 | schema: 18 | nuts_id: 19 | type: NutsRegion.nuts_id 20 | nuts_year_spec: 21 | type: NutsRegion.nuts_year_spec 22 | value: 23 | data_type: int 24 | id: graduate_startups 25 | label: Startups 26 | year: 27 | data_type: int 28 | label: Year 29 | source_name: HESA (Higher Education Statistical Agency) 30 | source_url: https://www.hesa.ac.uk/ 31 | -------------------------------------------------------------------------------- /ds/data/processed/ref/total_4_fte.csv: -------------------------------------------------------------------------------- 1 | year,nuts_id,nuts_year_spec,total_4_fte 2 | 2014,UKI3,2013,3099.78 3 | 2014,UKJ1,2013,1438.36 4 | 2014,UKM2,2013,1163.48 5 | 2014,UKH1,2013,1116.53 6 | 2014,UKG3,2013,757.93 7 | 2014,UKD3,2013,629.42 8 | 2014,UKK1,2013,620.86 9 | 2014,UKM3,2013,537.79 10 | 2014,UKF1,2013,485.0 11 | 2014,UKE4,2013,453.53 12 | 2014,UKJ3,2013,416.84 13 | 2014,UKI4,2013,410.57 14 | 2014,UKJ2,2013,410.38 15 | 2014,UKE3,2013,387.85 16 | 2014,UKC2,2013,344.19 17 | 2014,UKF2,2013,333.84 18 | 2014,UKN0,2013,322.85 19 | 2014,UKL2,2013,311.32 20 | 2014,UKK4,2013,267.39 21 | 2014,UKL1,2013,262.55 22 | 2014,UKD7,2013,256.35 23 | 2014,UKC1,2013,249.22 24 | 2014,UKD4,2013,232.56 25 | 2014,UKE2,2013,231.48 26 | 2014,UKJ4,2013,158.39 27 | 2014,UKM5,2013,150.54 28 | 2014,UKI7,2013,122.68 29 | 2014,UKH3,2013,112.29 30 | 2014,UKH2,2013,102.15 31 | 2014,UKG2,2013,62.55 32 | 2014,UKE1,2013,50.53 33 | 2014,UKK2,2013,27.53 34 | 2014,UKF3,2013,23.74 35 | 2014,UKI6,2013,22.62 36 | 2014,UKI5,2013,19.69 37 | 2014,UKM6,2013,8.7 38 | 2014,UKD6,2013,7.62 39 | 2014,UKG1,2013,4.9 40 | 2014,UKK3,2013,3.62 41 | 2014,UKD1,2013,1.73 42 | -------------------------------------------------------------------------------- /ds/data/processed/ashe_mean_salary/ashe_mean_salary.nuts2.csv: -------------------------------------------------------------------------------- 1 | year,nuts_id,nuts_year_spec,ashe_mean_salary 2 | 2019,UKC1,2016,31976 3 | 2019,UKC2,2016,32795 4 | 2019,UKD1,2016,34006 5 | 2019,UKD3,2016,32861 6 | 2019,UKD4,2016,32471 7 | 2019,UKD6,2016,38717 8 | 2019,UKD7,2016,32582 9 | 2019,UKE1,2016,32072 10 | 2019,UKE2,2016,37332 11 | 2019,UKE3,2016,31799 12 | 2019,UKE4,2016,32522 13 | 2019,UKF1,2016,33446 14 | 2019,UKF2,2016,33875 15 | 2019,UKF3,2016,31386 16 | 2019,UKG1,2016,32073 17 | 2019,UKG2,2016,33224 18 | 2019,UKG3,2016,33040 19 | 2019,UKH1,2016,35815 20 | 2019,UKH2,2016,42462 21 | 2019,UKH3,2016,41541 22 | 2019,UKI3,2016,55072 23 | 2019,UKI4,2016,50838 24 | 2019,UKI5,2016,42570 25 | 2019,UKI6,2016,48140 26 | 2019,UKI7,2016,44523 27 | 2019,UKJ1,2016,39196 28 | 2019,UKJ2,2016,42960 29 | 2019,UKJ3,2016,38634 30 | 2019,UKJ4,2016,40130 31 | 2019,UKK1,2016,36953 32 | 2019,UKK2,2016,34667 33 | 2019,UKK3,2016,28809 34 | 2019,UKK4,2016,30047 35 | 2019,UKL1,2016,30387 36 | 2019,UKL2,2016,33972 37 | 2019,UKM5,2016,39261 38 | 2019,UKM6,2016,32843 39 | 2019,UKM7,2016,34306 40 | 2019,UKM8,2016,34841 41 | 2019,UKM9,2016,33673 42 | -------------------------------------------------------------------------------- /ds/data/processed/hebci/ce_cpd_learner_days.lep.yaml: -------------------------------------------------------------------------------- 1 | api_doc_url: https://www.hesa.ac.uk/data-and-analysis/providers/business-community/ 2 | api_type: FETCH 3 | data_date: 20200928 4 | description: Number of continuing professional development and continuing education learner days 5 | title: CPD learner days 6 | subtitle: Number of continuing professional development (CPD) and continuing education learner days 7 | endpoint_url: https://www.hesa.ac.uk/data-and-analysis/providers/business-community/table-2b.csv 8 | framework_group: knowledge_exchange 9 | is_experimental: False 10 | is_public: True 11 | order: [year, lep_id, lep_year_spec, value.id] 12 | region: 13 | type: LepRegion 14 | source_url: LepRegion.source_url 15 | source: LepRegion.source 16 | schema: 17 | lep_id: 18 | type: LepRegion.lep_id 19 | lep_year_spec: 20 | type: LepRegion.lep_year_spec 21 | value: 22 | data_type: int 23 | id: ce_cpd_learner_days 24 | label: Days 25 | year: 26 | data_type: int 27 | label: Year 28 | source_name: HESA (Higher Education Statistical Agency) 29 | source_url: https://www.hesa.ac.uk/ 30 | -------------------------------------------------------------------------------- /ds/data/processed/housing/house_price_normalised.nuts2.csv: -------------------------------------------------------------------------------- 1 | year,nuts_id,nuts_year_spec,house_price_normalised 2 | 2018,UKC1,2016,3.684 3 | 2018,UKC2,2016,4.473 4 | 2018,UKD1,2016,4.86 5 | 2018,UKD3,2016,5.274 6 | 2018,UKD4,2016,4.514 7 | 2018,UKD6,2016,5.684 8 | 2018,UKD7,2016,4.536 9 | 2018,UKE1,2016,7.623 10 | 2018,UKE2,2016,7.695 11 | 2018,UKE3,2016,4.555 12 | 2018,UKE4,2016,4.86 13 | 2018,UKF1,2016,5.422 14 | 2018,UKF2,2016,6.899 15 | 2018,UKF3,2016,6.024 16 | 2018,UKG1,2016,6.805 17 | 2018,UKG2,2016,5.6 18 | 2018,UKG3,2016,5.717 19 | 2018,UKH1,2016,7.191 20 | 2018,UKH2,2016,9.339 21 | 2018,UKH3,2016,7.607 22 | 2018,UKI3,2016,11.684 23 | 2018,UKI4,2016,9.764 24 | 2018,UKI5,2016,9.426 25 | 2018,UKI6,2016,9.127 26 | 2018,UKI7,2016,11.485 27 | 2018,UKJ1,2016,8.615 28 | 2018,UKJ2,2016,8.846 29 | 2018,UKJ3,2016,8.746 30 | 2018,UKJ4,2016,7.701 31 | 2018,UKK1,2016,7.963 32 | 2018,UKK2,2016,7.918 33 | 2018,UKK3,2016,8.078 34 | 2018,UKK4,2016,7.71 35 | 2018,UKL1,2016,4.722 36 | 2018,UKL2,2016,5.821 37 | 2018,UKM5,2016,4.418 38 | 2018,UKM6,2016,5.001 39 | 2018,UKM7,2016,5.061 40 | 2018,UKM8,2016,4.17 41 | 2018,UKM9,2016,4.274 42 | -------------------------------------------------------------------------------- /ds/data/processed/hesa/total_postgraduates.nuts2.yaml: -------------------------------------------------------------------------------- 1 | api_doc_url: https://www.hesa.ac.uk/data-and-analysis/students 2 | api_type: FETCH 3 | title: Total number of postgraduates 4 | subtitle: Number of postgraduate (research) students enrolled full-time in universities in a NUTS2 region in the starting academic year. 5 | endpoint_url: https://www.hesa.ac.uk/data-and-analysis/students/table-13.csv 6 | framework_group: public_rnd 7 | is_experimental: False 8 | order: [year, nuts_id, nuts_year_spec, value.id] 9 | region: 10 | type: NutsRegion 11 | level: 2 12 | source_url: http://geoportal1-ons.opendata.arcgis.com/datasets/48b6b85bb7ea43699ee85f4ecd12fd36_0.zip?outSR=%7B%22latestWkid%22:27700,%22wkid%22:27700%7D 13 | schema: 14 | nuts_id: 15 | type: NutsRegion.id 16 | nuts_year_spec: 17 | type: NutsRegion.year_spec 18 | value: 19 | data_type: int 20 | id: total_postgraduates 21 | label: Total number of postgraduate students 22 | year: 23 | data_type: int 24 | label: Academic year (start) 25 | source_name: HESA (Higher Education Statistical Agency) 26 | source_url: https://www.hesa.ac.uk/ 27 | -------------------------------------------------------------------------------- /ds/data/processed/hesa/total_postgraduates.nuts3.yaml: -------------------------------------------------------------------------------- 1 | api_doc_url: https://www.hesa.ac.uk/data-and-analysis/students 2 | api_type: FETCH 3 | title: Total number of postgraduates 4 | subtitle: Number of postgraduate (research) students enrolled full-time in universities in a NUTS3 region in the starting academic year. 5 | endpoint_url: https://www.hesa.ac.uk/data-and-analysis/students/table-13.csv 6 | framework_group: public_rnd 7 | is_experimental: False 8 | order: [year, nuts_id, nuts_year_spec, value.id] 9 | region: 10 | type: NutsRegion 11 | level: 3 12 | source_url: http://geoportal1-ons.opendata.arcgis.com/datasets/48b6b85bb7ea43699ee85f4ecd12fd36_0.zip?outSR=%7B%22latestWkid%22:27700,%22wkid%22:27700%7D 13 | schema: 14 | nuts_id: 15 | type: NutsRegion.id 16 | nuts_year_spec: 17 | type: NutsRegion.year_spec 18 | value: 19 | data_type: int 20 | id: total_postgraduates 21 | label: Total number of postgraduate students 22 | year: 23 | data_type: int 24 | label: Academic year (start) 25 | source_name: HESA (Higher Education Statistical Agency) 26 | source_url: https://www.hesa.ac.uk/ 27 | -------------------------------------------------------------------------------- /ds/beis_indicators/cordis/make_cordis.py: -------------------------------------------------------------------------------- 1 | import logging 2 | import numpy as np 3 | 4 | from beis_indicators.geo import NutsCoder, LepCoder 5 | from beis_indicators.indicators import points_to_indicator, save_indicator 6 | from beis_indicators.cordis.cordis_processing import prep_funding_data 7 | 8 | import pandas as pd 9 | 10 | 11 | logger = logging.getLogger(__name__) 12 | 13 | var_name = f'cordis_funding' 14 | aggfunc = np.sum 15 | out_dir = 'cordis' 16 | min_year = 2014 17 | max_year = 2020 18 | 19 | coders = { 20 | 'nuts2': NutsCoder(level=2), 21 | 'nuts3': NutsCoder(level=3), 22 | 'lep': LepCoder() 23 | } 24 | 25 | 26 | funding = prep_funding_data(fps=['h2020']) 27 | funding = funding[(funding['year'] >= min_year) & (funding['year'] <= max_year)] 28 | funding = funding.dropna() 29 | 30 | for geo, coder in coders.items(): 31 | funding_agg = points_to_indicator(funding, value_col='ecContribution', coder=coder, 32 | aggfunc=aggfunc, value_rename=var_name, 33 | projection='EPSG:4326', x_col='lon', y_col='lat') 34 | save_indicator(funding_agg, 'cordis', geo) 35 | 36 | -------------------------------------------------------------------------------- /ds/data/processed/hesa/fte_research_students.nuts2.yaml: -------------------------------------------------------------------------------- 1 | api_doc_url: https://www.hesa.ac.uk/data-and-analysis/estates/table-1 2 | api_type: FETCH 3 | title: Number of research students (FTE) 4 | subtitle: Aggregate of Full Time Equivalent (FTE) of research students enrolled in universities in the NUTS2 region 5 | endpoint_url: https://www.hesa.ac.uk/data-and-analysis/estates/data.csv 6 | framework_group: public_rnd 7 | is_experimental: False 8 | order: [year, nuts_id, nuts_year_spec, value.id] 9 | region: 10 | type: NutsRegion 11 | level: 2 12 | source_url: http://geoportal1-ons.opendata.arcgis.com/datasets/48b6b85bb7ea43699ee85f4ecd12fd36_0.zip?outSR=%7B%22latestWkid%22:27700,%22wkid%22:27700%7D 13 | schema: 14 | nuts_id: 15 | type: NutsRegion.id 16 | nuts_year_spec: 17 | type: NutsRegion.year_spec 18 | value: 19 | data_type: int 20 | id: fte_research_students 21 | label: Research students (Full Time Equivalent) 22 | type: FTE 23 | year: 24 | data_type: int 25 | label: Academic year (start) 26 | source_name: HESA (Higher Education Statistical Agency) 27 | source_url: https://www.hesa.ac.uk/ 28 | -------------------------------------------------------------------------------- /ds/data/processed/hesa/fte_research_students.nuts3.yaml: -------------------------------------------------------------------------------- 1 | api_doc_url: https://www.hesa.ac.uk/data-and-analysis/estates/table-1 2 | api_type: FETCH 3 | title: Number of research students (FTE) 4 | subtitle: Aggregate of Full Time Equivalent (FTE) of research students enrolled in universities in the NUTS3 region 5 | endpoint_url: https://www.hesa.ac.uk/data-and-analysis/estates/data.csv 6 | framework_group: public_rnd 7 | is_experimental: False 8 | order: [year, nuts_id, nuts_year_spec, value.id] 9 | region: 10 | type: NutsRegion 11 | level: 3 12 | source_url: http://geoportal1-ons.opendata.arcgis.com/datasets/48b6b85bb7ea43699ee85f4ecd12fd36_0.zip?outSR=%7B%22latestWkid%22:27700,%22wkid%22:27700%7D 13 | schema: 14 | nuts_id: 15 | type: NutsRegion.id 16 | nuts_year_spec: 17 | type: NutsRegion.year_spec 18 | value: 19 | data_type: int 20 | id: fte_research_students 21 | label: Research students (Full Time Equivalent) 22 | type: FTE 23 | year: 24 | data_type: int 25 | label: Academic year (start) 26 | source_name: HESA (Higher Education Statistical Agency) 27 | source_url: https://www.hesa.ac.uk/ 28 | -------------------------------------------------------------------------------- /ds/data/processed/hebci/ce_cpd_income.lep.yaml: -------------------------------------------------------------------------------- 1 | api_doc_url: https://www.hesa.ac.uk/data-and-analysis/providers/business-community/ 2 | api_type: FETCH 3 | data_date: 20200928 4 | description: Income from continuing professional development and continuing education. 5 | title: Continuing professional development and education income 6 | subtitle: Income from continuing professional development and continuing education. 7 | endpoint_url: https://www.hesa.ac.uk/data-and-analysis/providers/business-community/table-2b.csv 8 | framework_group: knowledge_exchange 9 | is_experimental: False 10 | is_public: True 11 | order: [year, lep_id, lep_year_spec, value.id] 12 | region: 13 | type: LepRegion 14 | source_url: LepRegion.source_url 15 | source: LepRegion.source 16 | schema: 17 | lep_id: 18 | type: LepRegion.lep_id 19 | lep_year_spec: 20 | type: LepRegion.lep_year_spec 21 | value: 22 | data_type: int 23 | id: ce_cpd_income 24 | label: Income 25 | type: GBP 26 | year: 27 | data_type: int 28 | label: Year 29 | source_name: HESA (Higher Education Statistical Agency) 30 | source_url: https://www.hesa.ac.uk/ 31 | -------------------------------------------------------------------------------- /ds/data/processed/travel/travel_time_to_airport.nuts2.yaml: -------------------------------------------------------------------------------- 1 | api_type: FETCH 2 | title: Travel time to airport 3 | subtitle: Minimum travel time to nearest airport from a NUTS 2 regions for England (2011 & 2013) and Wales (2011 only) 4 | endpoint_url: 5 | 2011: http://data.dft.gov.uk.s3.amazonaws.com/connectivity-data/Airports-travel-times.zip 6 | 2013: http://data.dft.gov.uk.s3.amazonaws.com/connectivity-data/2013-travel-times.zip 7 | # year: endpoint_url 8 | framework_group: place_potential 9 | is_experimental: False 10 | order: [year, nuts_id, nuts_year_spec, value.id] # do not change; specifies the order of the exported indicator fields 11 | region: 12 | type: NutsRegion # the region type, e.g. NutsRegion or LepRegion 13 | level: 2 14 | source: PROVIDED 15 | schema: 16 | nuts_id: 17 | type: NutsRegion.nuts_id 18 | nuts_year_spec: 19 | type: NutsRegion.nuts_year_spec 20 | value: 21 | format: .2f 22 | id: travel_time_to_airport 23 | label: Time (Minutes) 24 | data_type: float 25 | year: 26 | data_type: int 27 | label: Year 28 | source_name: Department for Transport 29 | -------------------------------------------------------------------------------- /ds/data/processed/travel/travel_time_to_airport.nuts3.yaml: -------------------------------------------------------------------------------- 1 | api_type: FETCH 2 | title: Travel time to airport 3 | subtitle: Minimum travel time to nearest airport from a NUTS 3 regions for England (2011 & 2013) and Wales (2011 only) 4 | endpoint_url: 5 | 2011: http://data.dft.gov.uk.s3.amazonaws.com/connectivity-data/Airports-travel-times.zip 6 | 2013: http://data.dft.gov.uk.s3.amazonaws.com/connectivity-data/2013-travel-times.zip 7 | # year: endpoint_url 8 | framework_group: place_potential 9 | is_experimental: False 10 | order: [year, nuts_id, nuts_year_spec, value.id] # do not change; specifies the order of the exported indicator fields 11 | region: 12 | type: NutsRegion # the region type, e.g. NutsRegion or LepRegion 13 | level: 3 14 | source: PROVIDED 15 | schema: 16 | nuts_id: 17 | type: NutsRegion.nuts_id 18 | nuts_year_spec: 19 | type: NutsRegion.nuts_year_spec 20 | value: 21 | format: .2f 22 | id: travel_time_to_airport 23 | label: Time (Minutes) 24 | data_type: float 25 | year: 26 | data_type: int 27 | label: Year 28 | source_name: Department for Transport 29 | -------------------------------------------------------------------------------- /ds/data/processed/hebci/ce_cpd_learner_days.nuts2.yaml: -------------------------------------------------------------------------------- 1 | api_doc_url: https://www.hesa.ac.uk/data-and-analysis/providers/business-community/ 2 | api_type: FETCH 3 | data_date: 20200928 4 | description: Number of continuing professional development and continuing education learner days 5 | title: CPD learner days 6 | subtitle: Number of continuing professional development (CPD) and continuing education learner days 7 | endpoint_url: https://www.hesa.ac.uk/data-and-analysis/providers/business-community/table-2b.csv 8 | framework_group: knowledge_exchange 9 | is_experimental: False 10 | is_public: True 11 | order: [year, nuts_id, nuts_year_spec, value.id] 12 | region: 13 | level: 2 14 | type: NutsRegion 15 | source_url: NutsRegion.source_url 16 | source: NutsRegion.source 17 | schema: 18 | nuts_id: 19 | type: NutsRegion.nuts_id 20 | nuts_year_spec: 21 | type: NutsRegion.nuts_year_spec 22 | value: 23 | data_type: int 24 | id: ce_cpd_learner_days 25 | label: Days 26 | year: 27 | data_type: int 28 | label: Year 29 | source_name: HESA (Higher Education Statistical Agency) 30 | source_url: https://www.hesa.ac.uk/ 31 | -------------------------------------------------------------------------------- /ds/data/processed/hebci/ce_cpd_learner_days.nuts3.yaml: -------------------------------------------------------------------------------- 1 | api_doc_url: https://www.hesa.ac.uk/data-and-analysis/providers/business-community/ 2 | api_type: FETCH 3 | data_date: 20200928 4 | description: Number of continuing professional development and continuing education learner days 5 | title: CPD learner days 6 | subtitle: Number of continuing professional development (CPD) and continuing education learner days 7 | endpoint_url: https://www.hesa.ac.uk/data-and-analysis/providers/business-community/table-2b.csv 8 | framework_group: knowledge_exchange 9 | is_experimental: False 10 | is_public: True 11 | order: [year, nuts_id, nuts_year_spec, value.id] 12 | region: 13 | level: 3 14 | type: NutsRegion 15 | source_url: NutsRegion.source_url 16 | source: NutsRegion.source 17 | schema: 18 | nuts_id: 19 | type: NutsRegion.nuts_id 20 | nuts_year_spec: 21 | type: NutsRegion.nuts_year_spec 22 | value: 23 | data_type: int 24 | id: ce_cpd_learner_days 25 | label: Days 26 | year: 27 | data_type: int 28 | label: Year 29 | source_name: HESA (Higher Education Statistical Agency) 30 | source_url: https://www.hesa.ac.uk/ 31 | -------------------------------------------------------------------------------- /ds/data/processed/hesa/total_stem_students.lep.yaml: -------------------------------------------------------------------------------- 1 | api_doc_url: https://www.hesa.ac.uk/data-and-analysis/students 2 | api_type: FETCH 3 | title: Total number of STEM students 4 | subtitle: Number of students enrolled full-time in STEM subjects 5 | description: Number of students enrolled full-time in STEM subjects in a NUTS3 region (definition of STEM subjects in aux folder) in the starting academic year. 6 | endpoint_url: https://www.hesa.ac.uk/data-and-analysis/students/table-13.csv 7 | framework_group: public_rnd 8 | is_experimental: False 9 | order: [year, lep_id, lep_year_spec, value.id] 10 | region: 11 | type: LepRegion 12 | source_url: https://geoportal.statistics.gov.uk/search?collection=Dataset&sort=name&tags=all(BDY_LEP) 13 | schema: 14 | lep_id: 15 | type: LepRegion.id 16 | lep_year_spec: 17 | type: LepRegion.year_spec 18 | value: 19 | data_type: int 20 | id: total_stem_students 21 | label: Total number of students in STEM subjects 22 | year: 23 | data_type: int 24 | label: Academic year (start) 25 | source_name: HESA (Higher Education Statistical Agency) 26 | source_url: https://www.hesa.ac.uk/ 27 | -------------------------------------------------------------------------------- /ds/data/processed/travel/travel_time_to_rail.nuts2.yaml: -------------------------------------------------------------------------------- 1 | api_type: FETCH 2 | title: Travel time to rail station 3 | subtitle: Minimum travel time to nearest rail station from a NUTS 2 regions for England (2011 & 2013) and Wales (2011 only) 4 | endpoint_url: 5 | 2011: http://data.dft.gov.uk.s3.amazonaws.com/connectivity-data/Rail-stations-travel-times.zip 6 | 2013: http://data.dft.gov.uk.s3.amazonaws.com/connectivity-data/2013-travel-times.zip 7 | # year: endpoint_url 8 | framework_group: place_potential 9 | is_experimental: False 10 | order: [year, nuts_id, nuts_year_spec, value.id] # do not change; specifies the order of the exported indicator fields 11 | region: 12 | type: NutsRegion # the region type, e.g. NutsRegion or LepRegion 13 | level: 2 14 | source: PROVIDED 15 | schema: 16 | nuts_id: 17 | type: NutsRegion.nuts_id 18 | nuts_year_spec: 19 | type: NutsRegion.nuts_year_spec 20 | value: 21 | format: .2f 22 | id: travel_time_to_rail 23 | label: Time (Minutes) 24 | data_type: float 25 | year: 26 | data_type: int 27 | label: Year 28 | source_name: Department for Transport 29 | -------------------------------------------------------------------------------- /ds/data/processed/travel/travel_time_to_rail.nuts3.yaml: -------------------------------------------------------------------------------- 1 | api_type: FETCH 2 | title: Travel time to rail station 3 | subtitle: Minimum travel time to nearest rail station from a NUTS 3 regions for England (2011 & 2013) and Wales (2011 only) 4 | endpoint_url: 5 | 2011: http://data.dft.gov.uk.s3.amazonaws.com/connectivity-data/Rail-stations-travel-times.zip 6 | 2013: http://data.dft.gov.uk.s3.amazonaws.com/connectivity-data/2013-travel-times.zip 7 | # year: endpoint_url 8 | framework_group: place_potential 9 | is_experimental: False 10 | order: [year, nuts_id, nuts_year_spec, value.id] # do not change; specifies the order of the exported indicator fields 11 | region: 12 | type: NutsRegion # the region type, e.g. NutsRegion or LepRegion 13 | level: 3 14 | source: PROVIDED 15 | schema: 16 | nuts_id: 17 | type: NutsRegion.nuts_id 18 | nuts_year_spec: 19 | type: NutsRegion.nuts_year_spec 20 | value: 21 | format: .2f 22 | id: travel_time_to_rail 23 | label: Time (Minutes) 24 | data_type: float 25 | year: 26 | data_type: int 27 | label: Year 28 | source_name: Department for Transport 29 | -------------------------------------------------------------------------------- /ds/data/processed/eurostat/eurostat_berd_data.nuts2.yaml: -------------------------------------------------------------------------------- 1 | api_doc_url: https://ec.europa.eu/eurostat/cache/metadata/en/rd_esms.htm 2 | api_type: FETCH 3 | title: Business enterprise R&D 4 | subtitle: Business enterprise research & development (R&D) expenditure by NUTS 2 regions. 5 | endpoint_url: http://ec.europa.eu/eurostat/wdds/rest/data/v2.1/json/en/rd_e_gerdreg?sinceTimePeriod=2012&geoLevel=nuts2&precision=6§perf=BES&unit=MIO_EUR 6 | framework_group: private_rnd 7 | is_experimental: False 8 | order: [year, nuts_id, nuts_year_spec, value.id] # do not change; specifies the order of the exported indicator fields 9 | region: 10 | type: NutsRegion 11 | level: 2 12 | source: PROVIDED 13 | schema: 14 | nuts_id: 15 | type: NutsRegion.nuts_id 16 | nuts_year_spec: 17 | type: NutsRegion.nuts_year_spec 18 | value: 19 | data_type: int 20 | format: ',' 21 | id: eurostat_berd_data 22 | label: Expenditure (to nearest 1000) 23 | type: EUR 24 | year: 25 | data_type: int 26 | label: Year 27 | source_name: Eurostat (European Statistical Office) 28 | source_url: https://ec.europa.eu/eurostat/ 29 | -------------------------------------------------------------------------------- /ds/data/processed/hebci/ip_revenue.lep.yaml: -------------------------------------------------------------------------------- 1 | api_doc_url: https://www.hesa.ac.uk/data-and-analysis/providers/business-community/ 2 | api_type: FETCH 3 | data_date: 20200924 4 | description: Income from intellectual property (including patents, copyright, design, registration and trade marks) by higher education providers. 5 | title: HE intellectual property income 6 | subtitle: Income from intellectual property by higher education providers. 7 | endpoint_url: https://www.hesa.ac.uk/data-and-analysis/providers/business-community/table-4d.csv 8 | framework_group: knowledge_exchange 9 | is_experimental: False 10 | is_public: True 11 | order: [year, lep_id, lep_year_spec, value.id] 12 | region: 13 | type: LepRegion 14 | source_url: LepRegion.source_url 15 | source: LepRegion.source 16 | schema: 17 | lep_id: 18 | type: LepRegion.lep_id 19 | lep_year_spec: 20 | type: LepRegion.lep_year_spec 21 | value: 22 | data_type: int 23 | id: ip_revenue 24 | label: Income 25 | type: GBP 26 | year: 27 | data_type: int 28 | label: Year 29 | source_name: HESA (Higher Education Statistical Agency) 30 | source_url: https://www.hesa.ac.uk/ 31 | -------------------------------------------------------------------------------- /ds/logging.yaml: -------------------------------------------------------------------------------- 1 | version: 1 2 | disable_existing_loggers: False 3 | formatters: 4 | simple: 5 | format: "%(asctime)s - %(name)s - %(levelname)s - %(message)s" 6 | 7 | handlers: 8 | console: 9 | class: logging.StreamHandler 10 | level: DEBUG 11 | formatter: simple 12 | stream: ext://sys.stdout 13 | 14 | info_file_handler: 15 | class: logging.handlers.RotatingFileHandler 16 | level: INFO 17 | formatter: simple 18 | filename: ext://beis_indicators.info_out 19 | maxBytes: 10485760 # 10MB 20 | backupCount: 20 21 | encoding: utf8 22 | 23 | error_file_handler: 24 | class: logging.handlers.RotatingFileHandler 25 | level: ERROR 26 | formatter: simple 27 | filename: ext://beis_indicators.error_out 28 | maxBytes: 10485760 # 10MB 29 | backupCount: 20 30 | encoding: utf8 31 | 32 | loggers: 33 | beis_indicators: 34 | level: INFO 35 | handlers: [console, info_file_handler, error_file_handler] 36 | propagate: no 37 | 38 | root: 39 | level: INFO 40 | handlers: [console] 41 | -------------------------------------------------------------------------------- /ds/data/processed/eurostat/eurostat_gov_rd_workforce_data.nuts2.yaml: -------------------------------------------------------------------------------- 1 | api_doc_url: https://ec.europa.eu/eurostat/cache/metadata/en/rd_esms.htm 2 | api_type: FETCH 3 | title: Government R&D 4 | subtitle: Government performed research & development (R&D) expenditure by NUTS 2 regions. 5 | endpoint_url: http://ec.europa.eu/eurostat/wdds/rest/data/v2.1/json/en/rd_e_gerdreg?sinceTimePeriod=2007&geoLevel=nuts2&precision=2§perf=GOV&unit=MIO_EUR 6 | framework_group: public_rnd 7 | is_experimental: False 8 | order: [year, nuts_id, nuts_year_spec, value.id] # do not change; specifies the order of the exported indicator fields 9 | region: 10 | type: NutsRegion 11 | level: 2 12 | source: PROVIDED 13 | schema: 14 | nuts_id: 15 | type: NutsRegion.nuts_id 16 | nuts_year_spec: 17 | type: NutsRegion.nuts_year_spec 18 | value: 19 | data_type: int 20 | format: ',' 21 | id: eurostat_gov_rd_workforce_data 22 | label: Expenditure (to nearest 1000) 23 | type: EUR 24 | year: 25 | data_type: int 26 | label: Year 27 | source_name: Eurostat (European Statistical Office) 28 | source_url: https://ec.europa.eu/eurostat/ 29 | -------------------------------------------------------------------------------- /ds/data/processed/eurostat/eurostat_private_rd_headcount_workforce_data.nuts2.yaml: -------------------------------------------------------------------------------- 1 | api_doc_url: https://ec.europa.eu/eurostat/cache/metadata/en/rd_esms.htm 2 | api_type: FETCH 3 | title: HC of R&D workforce 4 | subtitle: Head count (HC) OF private sector research & development (R&D) workforce by NUTS 2 regions. 5 | endpoint_url: http://ec.europa.eu/eurostat/wdds/rest/data/v2.1/json/en/rd_p_persreg?sinceTimePeriod=2007&geoLevel=nuts2&precision=1&sex=T§perf=BES&prof_pos=TOTAL&unit=HC 6 | framework_group: private_rnd 7 | is_experimental: False 8 | order: [year, nuts_id, nuts_year_spec, value.id] # do not change; specifies the order of the exported indicator fields 9 | region: 10 | type: NutsRegion 11 | level: 2 12 | source: PROVIDED 13 | schema: 14 | nuts_id: 15 | type: NutsRegion.nuts_id 16 | nuts_year_spec: 17 | type: NutsRegion.nuts_year_spec 18 | value: 19 | id: eurostat_private_rd_headcount_workforce_data 20 | label: Head count 21 | data_type: int 22 | year: 23 | data_type: int 24 | label: Year 25 | source_name: Eurostat (European Statistical Office) 26 | source_url: https://ec.europa.eu/eurostat/ 27 | -------------------------------------------------------------------------------- /ds/data/processed/hebci/ce_cpd_income.nuts2.yaml: -------------------------------------------------------------------------------- 1 | api_doc_url: https://www.hesa.ac.uk/data-and-analysis/providers/business-community/ 2 | api_type: FETCH 3 | data_date: 20200928 4 | description: Income from continuing professional development and continuing education. 5 | title: Continuing professional development and education income 6 | subtitle: Income from continuing professional development and continuing education. 7 | endpoint_url: https://www.hesa.ac.uk/data-and-analysis/providers/business-community/table-2b.csv 8 | framework_group: knowledge_exchange 9 | is_experimental: False 10 | is_public: True 11 | order: [year, nuts_id, nuts_year_spec, value.id] 12 | region: 13 | level: 2 14 | type: NutsRegion 15 | source_url: NutsRegion.source_url 16 | source: NutsRegion.source 17 | schema: 18 | nuts_id: 19 | type: NutsRegion.nuts_id 20 | nuts_year_spec: 21 | type: NutsRegion.nuts_year_spec 22 | value: 23 | data_type: int 24 | id: ce_cpd_income 25 | label: Income 26 | type: GBP 27 | year: 28 | data_type: int 29 | label: Year 30 | source_name: HESA (Higher Education Statistical Agency) 31 | source_url: https://www.hesa.ac.uk/ 32 | -------------------------------------------------------------------------------- /ds/data/processed/hebci/ce_cpd_income.nuts3.yaml: -------------------------------------------------------------------------------- 1 | api_doc_url: https://www.hesa.ac.uk/data-and-analysis/providers/business-community/ 2 | api_type: FETCH 3 | data_date: 20200928 4 | description: Income from continuing professional development and continuing education. 5 | title: Continuing professional development and education income 6 | subtitle: Income from continuing professional development and continuing education. 7 | endpoint_url: https://www.hesa.ac.uk/data-and-analysis/providers/business-community/table-2b.csv 8 | framework_group: knowledge_exchange 9 | is_experimental: False 10 | is_public: True 11 | order: [year, nuts_id, nuts_year_spec, value.id] 12 | region: 13 | level: 3 14 | type: NutsRegion 15 | source_url: NutsRegion.source_url 16 | source: NutsRegion.source 17 | schema: 18 | nuts_id: 19 | type: NutsRegion.nuts_id 20 | nuts_year_spec: 21 | type: NutsRegion.nuts_year_spec 22 | value: 23 | data_type: int 24 | id: ce_cpd_income 25 | label: Income 26 | type: GBP 27 | year: 28 | data_type: int 29 | label: Year 30 | source_name: HESA (Higher Education Statistical Agency) 31 | source_url: https://www.hesa.ac.uk/ 32 | -------------------------------------------------------------------------------- /ds/data/processed/eurostat/eu_trademark_applications.nuts2.yaml: -------------------------------------------------------------------------------- 1 | api_doc_url: https://ec.europa.eu/eurostat/cache/metadata/en/ipr_t_esms.htm 2 | api_type: FETCH 3 | data_date: 20200818 4 | description: Trademark applications are a proxy for commercialisation. This indiator captures trademark registrations in the EU. 5 | title: Trademark 6 | subtitle: Count of trademark applications 7 | endpoint_url: https://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=ipr_ta_reg&lang=en 8 | framework_group: knowledge_exchange 9 | is_experimental: False 10 | is_public: True 11 | order: [year, nuts_id, nuts_year_spec, value.id] 12 | region: 13 | type: NutsRegion 14 | level: 2 15 | schema: 16 | nuts_id: 17 | type: NutsRegion.nuts_id 18 | nuts_year_spec: 19 | type: NutsRegion.nuts_year_spec 20 | value: 21 | data_type: int 22 | id: eu_trademark_applications 23 | label: Trademark applications 24 | year: 25 | data_type: int 26 | label: Year 27 | source_name: Eurostat 28 | source_url: https://ec.europa.eu/eurostat 29 | warning: The regional breakdown of the EU Member States is based on the Nomenclature of Territorial Units for Statistics – NUTS 2010 level 3 30 | -------------------------------------------------------------------------------- /ds/data/processed/eurostat/eu_trademark_applications.nuts3.yaml: -------------------------------------------------------------------------------- 1 | api_doc_url: https://ec.europa.eu/eurostat/cache/metadata/en/ipr_t_esms.htm 2 | api_type: FETCH 3 | data_date: 20200818 4 | description: Trademark applications are a proxy for commercialisation. This indiator captures trademark registrations in the EU. 5 | title: Trademark 6 | subtitle: Count of trademark applications 7 | endpoint_url: https://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=ipr_ta_reg&lang=en 8 | framework_group: knowledge_exchange 9 | is_experimental: False 10 | is_public: True 11 | order: [year, nuts_id, nuts_year_spec, value.id] 12 | region: 13 | type: NutsRegion 14 | level: 3 15 | schema: 16 | nuts_id: 17 | type: NutsRegion.nuts_id 18 | nuts_year_spec: 19 | type: NutsRegion.nuts_year_spec 20 | value: 21 | data_type: int 22 | id: eu_trademark_applications 23 | label: Trademark applications 24 | year: 25 | data_type: int 26 | label: Year 27 | source_name: Eurostat 28 | source_url: https://ec.europa.eu/eurostat 29 | warning: The regional breakdown of the EU Member States is based on the Nomenclature of Territorial Units for Statistics – NUTS 2010 level 3 30 | -------------------------------------------------------------------------------- /ds/data/processed/hesa/total_stem_postgraduates.lep.yaml: -------------------------------------------------------------------------------- 1 | api_doc_url: https://www.hesa.ac.uk/data-and-analysis/students 2 | api_type: FETCH 3 | title: Total number of STEM postgraduates 4 | subtitle: Number of postgraduate students enrolled full-time in STEM subjects. 5 | description: Number of postgraduate students enrolled full-time in STEM subjects in the starting academic year. Definition of STEM subjects in aux folder. 6 | endpoint_url: https://www.hesa.ac.uk/data-and-analysis/students/table-13.csv 7 | framework_group: public_rnd 8 | is_experimental: False 9 | order: [year, lep_id, lep_year_spec, value.id] 10 | region: 11 | type: LepRegion 12 | source_url: https://geoportal.statistics.gov.uk/search?collection=Dataset&sort=name&tags=all(BDY_LEP) 13 | schema: 14 | lep_id: 15 | type: LepRegion.id 16 | lep_year_spec: 17 | type: LepRegion.year_spec 18 | value: 19 | data_type: int 20 | id: total_stem_postgraduates 21 | label: Total number of postgraduate students in STEM subjects 22 | year: 23 | data_type: int 24 | label: Academic year (start) 25 | source_name: HESA (Higher Education Statistical Agency) 26 | source_url: https://www.hesa.ac.uk/ 27 | -------------------------------------------------------------------------------- /ds/data/processed/hebci/ip_revenue.nuts2.yaml: -------------------------------------------------------------------------------- 1 | api_doc_url: https://www.hesa.ac.uk/data-and-analysis/providers/business-community/ 2 | api_type: FETCH 3 | data_date: 20200924 4 | description: Income from intellectual property (including patents, copyright, design, registration and trade marks) by higher education providers. 5 | title: HE intellectual property income 6 | subtitle: Income from intellectual property by higher education providers. 7 | endpoint_url: https://www.hesa.ac.uk/data-and-analysis/providers/business-community/table-4d.csv 8 | framework_group: knowledge_exchange 9 | is_experimental: False 10 | is_public: True 11 | order: [year, nuts_id, nuts_year_spec, value.id] 12 | region: 13 | type: NutsRegion 14 | level: 2 15 | source_url: NutsRegion.source_url 16 | source: NutsRegion.source 17 | schema: 18 | nuts_id: 19 | type: NutsRegion.nuts_id 20 | nuts_year_spec: 21 | type: NutsRegion.nuts_year_spec 22 | value: 23 | data_type: int 24 | id: ip_revenue 25 | label: Income 26 | type: GBP 27 | year: 28 | data_type: int 29 | label: Year 30 | source_name: HESA (Higher Education Statistical Agency) 31 | source_url: https://www.hesa.ac.uk/ 32 | -------------------------------------------------------------------------------- /ds/data/processed/hebci/ip_revenue.nuts3.yaml: -------------------------------------------------------------------------------- 1 | api_doc_url: https://www.hesa.ac.uk/data-and-analysis/providers/business-community/ 2 | api_type: FETCH 3 | data_date: 20200924 4 | description: Income from intellectual property (including patents, copyright, design, registration and trade marks) by higher education providers. 5 | title: HE intellectual property income 6 | subtitle: Income from intellectual property by higher education providers. 7 | endpoint_url: https://www.hesa.ac.uk/data-and-analysis/providers/business-community/table-4d.csv 8 | framework_group: knowledge_exchange 9 | is_experimental: False 10 | is_public: True 11 | order: [year, nuts_id, nuts_year_spec, value.id] 12 | region: 13 | type: NutsRegion 14 | level: 3 15 | source_url: NutsRegion.source_url 16 | source: NutsRegion.source 17 | schema: 18 | nuts_id: 19 | type: NutsRegion.nuts_id 20 | nuts_year_spec: 21 | type: NutsRegion.nuts_year_spec 22 | value: 23 | data_type: int 24 | id: ip_revenue 25 | label: Income 26 | type: GBP 27 | year: 28 | data_type: int 29 | label: Year 30 | source_name: HESA (Higher Education Statistical Agency) 31 | source_url: https://www.hesa.ac.uk/ 32 | -------------------------------------------------------------------------------- /ui/src/node_modules/app/components/content/info/Beta.svelte: -------------------------------------------------------------------------------- 1 | 11 | 12 |
13 |

Beta release

14 |

15 | Welcome to the {toolLongName}. 16 |

17 |

18 | The tool is currently in a Beta release. 19 |

20 |

21 | We accept feedback from live users: please use the provided 22 | feedback form 26 | or email your observations to 27 | 31 | {contactEmail}. 32 | 33 |

34 | 35 |

Thanks!

36 |
37 | 38 | 54 | -------------------------------------------------------------------------------- /ds/data/processed/hebci/contract_research_sme.lep.yaml: -------------------------------------------------------------------------------- 1 | api_doc_url: https://www.hesa.ac.uk/data-and-analysis/providers/business-community/ 2 | api_type: FETCH 3 | data_date: 20200924 4 | description: Income for higher education institutions from providing contract research services to SMEs. 5 | title: HE contract research income from SMEs 6 | subtitle: Income for higher education institutions from providing contract research services to SMEs. 7 | endpoint_url: https://www.hesa.ac.uk/data-and-analysis/providers/business-community/table-2a.csv 8 | framework_group: knowledge_exchange 9 | is_experimental: False 10 | is_public: True 11 | order: [year, lep_id, lep_year_spec, value.id] 12 | region: 13 | type: LepRegion 14 | source_url: LepRegion.source_url 15 | source: LepRegion.source 16 | schema: 17 | lep_id: 18 | type: LepRegion.lep_id 19 | lep_year_spec: 20 | type: LepRegion.lep_year_spec 21 | value: 22 | data_type: int 23 | id: contract_research_sme 24 | label: Income from contract research 25 | type: GBP 26 | year: 27 | data_type: int 28 | label: Year 29 | source_name: HESA (Higher Education Statistical Agency) 30 | source_url: https://www.hesa.ac.uk/ 31 | -------------------------------------------------------------------------------- /ds/data/processed/eurostat/eurostat_higher_ed_rd_workforce_data.nuts2.yaml: -------------------------------------------------------------------------------- 1 | api_doc_url: https://ec.europa.eu/eurostat/cache/metadata/en/rd_esms.htm 2 | api_type: FETCH 3 | title: Higher Edu R&D 4 | subtitle: Higher education sector enterprise research & development (R&D) expenditure by NUTS 2 regions. 5 | endpoint_url: http://ec.europa.eu/eurostat/wdds/rest/data/v2.1/json/en/rd_e_gerdreg?sinceTimePeriod=2007&geoLevel=nuts2&precision=6§perf=HES&unit=MIO_EUR' 6 | framework_group: public_rnd 7 | is_experimental: False 8 | order: [year, nuts_id, nuts_year_spec, value.id] # do not change; specifies the order of the exported indicator fields 9 | region: 10 | type: NutsRegion 11 | level: 2 12 | source: PROVIDED 13 | schema: 14 | nuts_id: 15 | type: NutsRegion.nuts_id 16 | nuts_year_spec: 17 | type: NutsRegion.nuts_year_spec 18 | value: 19 | data_type: int 20 | format: ',' 21 | id: eurostat_higher_ed_rd_workforce_data 22 | label: Expenditure (to nearest 1000) 23 | type: EUR 24 | year: 25 | data_type: int 26 | label: Year 27 | source_name: Eurostat (European Statistical Office) 28 | source_url: https://ec.europa.eu/eurostat/ 29 | -------------------------------------------------------------------------------- /ds/data/processed/travel/travel_time_to_road_junctions.nuts2.yaml: -------------------------------------------------------------------------------- 1 | api_type: FETCH 2 | title: Travel time to nearest five road junctions 3 | subtitle: Average minimum travel time to nearest five road junctions from a NUTS 2 regions for England (2011 & 2013) and Wales (2011 only) 4 | endpoint_url: 5 | 2011: http://data.dft.gov.uk.s3.amazonaws.com/connectivity-data/Road-junctions-travel-times.zip 6 | 2013: http://data.dft.gov.uk.s3.amazonaws.com/connectivity-data/2013-travel-times.zip 7 | # year: endpoint_url 8 | framework_group: place_potential 9 | is_experimental: False 10 | order: [year, nuts_id, nuts_year_spec, value.id] # do not change; specifies the order of the exported indicator fields 11 | region: 12 | type: NutsRegion # the region type, e.g. NutsRegion or LepRegion 13 | level: 2 14 | source: PROVIDED 15 | schema: 16 | nuts_id: 17 | type: NutsRegion.nuts_id 18 | nuts_year_spec: 19 | type: NutsRegion.nuts_year_spec 20 | value: 21 | format: .2f 22 | id: travel_time_to_road_junctions 23 | label: Time (Minutes) 24 | data_type: float 25 | year: 26 | data_type: int 27 | label: Year 28 | source_name: Department for Transport 29 | -------------------------------------------------------------------------------- /ds/data/processed/travel/travel_time_to_road_junctions.nuts3.yaml: -------------------------------------------------------------------------------- 1 | api_type: FETCH 2 | title: Travel time to nearest five road junctions 3 | subtitle: Average minimum travel time to nearest five road junctions from a NUTS 3 regions for England (2011 & 2013) and Wales (2011 only) 4 | endpoint_url: 5 | 2011: http://data.dft.gov.uk.s3.amazonaws.com/connectivity-data/Road-junctions-travel-times.zip 6 | 2013: http://data.dft.gov.uk.s3.amazonaws.com/connectivity-data/2013-travel-times.zip 7 | # year: endpoint_url 8 | framework_group: place_potential 9 | is_experimental: False 10 | order: [year, nuts_id, nuts_year_spec, value.id] # do not change; specifies the order of the exported indicator fields 11 | region: 12 | type: NutsRegion # the region type, e.g. NutsRegion or LepRegion 13 | level: 3 14 | source: PROVIDED 15 | schema: 16 | nuts_id: 17 | type: NutsRegion.nuts_id 18 | nuts_year_spec: 19 | type: NutsRegion.nuts_year_spec 20 | value: 21 | format: .2f 22 | id: travel_time_to_road_junctions 23 | label: Time (Minutes) 24 | data_type: float 25 | year: 26 | data_type: int 27 | label: Year 28 | source_name: Department for Transport 29 | -------------------------------------------------------------------------------- /ds/beis_indicators/utils/geo_utils.py: -------------------------------------------------------------------------------- 1 | import geopandas as gpd 2 | import os 3 | from urllib.request import urlretrieve 4 | from zipfile import ZipFile 5 | from beis_indicators.utils.nuts_utils import NUTS_INTRODUCED, NUTS_ENFORCED 6 | 7 | 8 | def get_shape(file_name, path): 9 | ''' 10 | Utility function to extract and the shapefile 11 | 12 | Arguments: 13 | url: url for the shapefile zip 14 | file_name: name of the file where we want to extract the data 15 | 16 | ''' 17 | 18 | #Do we need to get the data or is it already there? 19 | 20 | shape_names = os.listdir(f'{project_dir}/data/raw/shapefiles') 21 | 22 | if file_name not in shape_names: 23 | 24 | #Get the data 25 | print(f'getting {file_name}...') 26 | 27 | #Get url 28 | url = shape_lookup[file_name] 29 | 30 | #Request data 31 | req = requests.get(url) 32 | 33 | #Parse the content 34 | z = ZipFile(BytesIO(req.content)) 35 | 36 | #Save 37 | print(f'saving {file_name}...') 38 | z.extractall(f'{path}{file_name}') 39 | 40 | else: 41 | print(f'{file_name} already collected') 42 | 43 | 44 | -------------------------------------------------------------------------------- /ds/data/aux/terminated_to_active_lad_lookup.json: -------------------------------------------------------------------------------- 1 | {"W06000007": "W06000023", "W06000017": "W06000024", "E07000002": "E06000055", "E07000001": "E06000056", "E07000003": "E06000056", "E07000013": "E06000050", "E07000016": "E06000050", "E07000018": "E06000050", "E07000014": "E06000049", "E07000015": "E06000049", "E07000017": "E06000049", "E07000019": "E06000052", "E07000020": "E06000052", "E07000021": "E06000052", "E07000022": "E06000052", "E07000023": "E06000052", "E07000024": "E06000052", "E07000025": "E06000053", "E07000054": "E06000047", "E07000055": "E06000047", "E07000056": "E06000047", "E07000057": "E06000047", "E07000058": "E06000047", "E07000059": "E06000047", "E07000060": "E06000047", "E07000157": "E06000048", "E07000158": "E06000048", "E07000159": "E06000048", "E07000160": "E06000048", "E07000161": "E06000048", "E07000162": "E06000048", "E07000182": "E06000051", "E07000183": "E06000051", "E07000184": "E06000051", "E07000185": "E06000051", "E07000186": "E06000051", "E07000230": "E06000054", "E07000231": "E06000054", "E07000232": "E06000054", "E07000233": "E06000054", "E07000100": "E07000240", "E07000104": "E07000241", "E08000020": "E08000037", "E06000048": "E06000057", "E07000097": "E07000242", "E07000101": "E07000243"} -------------------------------------------------------------------------------- /ds/data/processed/industry/economic_complexity_index.nuts2.yaml: -------------------------------------------------------------------------------- 1 | api_doc_url: https://www.nomisweb.co.uk/api/v01/help 2 | api_type: POST 3 | data_date: 20200218 4 | title: Economic Complexity Index 5 | subtitle: The Economic Complexity Index (ECI) captures the sophistication of economic activities in a location 6 | description: This indicator measures the economic complexity of NUTS 3 locations based on an analysis of its industrial composition ((based on Nesta sectoral definition, which clusters 4-digit SIC codes based on their similarity)) 7 | endpoint_url: http://www.nomisweb.co.uk/api/v01/dataset/ 8 | framework_group: private_rnd 9 | is_experimental: True 10 | order: [year, nuts_id, nuts_year_spec, value.id] 11 | region: 12 | type: NutsRegion 13 | level: 2 14 | source: PROVIDED 15 | schema: 16 | region_id: 17 | type: NutsRegion.nuts_id 18 | region_year_spec: 19 | type: NutsRegion.nuts_year_spec 20 | value: 21 | format: .4f 22 | id: economic_complexity_index 23 | label: Economic complexity Index 24 | data_type: float 25 | year: 26 | data_type: int 27 | label: Year 28 | source_name: NOMIS (official labour market statistics) 29 | source_url: https://www.nomisweb.co.uk 30 | -------------------------------------------------------------------------------- /ds/data/processed/eurostat/eurostat_private_non_profit_rd_workforce_data.nuts2.yaml: -------------------------------------------------------------------------------- 1 | api_doc_url: https://ec.europa.eu/eurostat/cache/metadata/en/rd_esms.htm 2 | api_type: FETCH 3 | title: Private non-profit R&D 4 | subtitle: Private non-profit sector enterprise research & development (R&D) expenditure by NUTS 2 regions. 5 | endpoint_url: http://ec.europa.eu/eurostat/wdds/rest/data/v2.1/json/en/rd_e_gerdreg?sinceTimePeriod=2007&geoLevel=nuts2&precision=6§perf=PNP&unit=MIO_EUR' 6 | framework_group: private_rnd 7 | is_experimental: False 8 | order: [year, nuts_id, nuts_year_spec, value.id] # do not change; specifies the order of the exported indicator fields 9 | region: 10 | type: NutsRegion 11 | level: 2 12 | source: PROVIDED 13 | schema: 14 | nuts_id: 15 | type: NutsRegion.nuts_id 16 | nuts_year_spec: 17 | type: NutsRegion.nuts_year_spec 18 | value: 19 | data_type: int 20 | format: ',' 21 | id: eurostat_private_non_profit_rd_workforce_data 22 | label: Expenditure (to nearest 1000) 23 | type: EUR 24 | year: 25 | data_type: int 26 | label: Year 27 | source_name: Eurostat (European Statistical Office) 28 | source_url: https://ec.europa.eu/eurostat/ 29 | -------------------------------------------------------------------------------- /ds/data/processed/eurostat/eurostat_private_rd_fte_workforce_data.nuts2.yaml: -------------------------------------------------------------------------------- 1 | api_doc_url: https://ec.europa.eu/eurostat/cache/metadata/en/rd_esms.htm 2 | api_type: FETCH 3 | title: FTE of R&D workforce 4 | subtitle: Full time equivalent (FTE) of private sector research & development (R&D) workforce by NUTS 2 regions. 5 | endpoint_url: http://ec.europa.eu/eurostat/wdds/rest/data/v2.1/json/en/rd_p_persreg?sinceTimePeriod=2007&geoLevel=nuts2&precision=1&sex=T§perf=BES&prof_pos=TOTAL&unit=FTE 6 | framework_group: private_rnd 7 | is_experimental: False 8 | order: [year, nuts_id, nuts_year_spec, value.id] # do not change; specifies the order of the exported indicator fields 9 | region: 10 | type: NutsRegion 11 | level: 2 12 | source: PROVIDED 13 | schema: 14 | nuts_id: 15 | type: NutsRegion.nuts_id 16 | nuts_year_spec: 17 | type: NutsRegion.nuts_year_spec 18 | value: 19 | format: .2f 20 | id: eurostat_private_rd_fte_workforce_data 21 | label: Full Time Equivalent 22 | type: FTE 23 | data_type: float 24 | year: 25 | data_type: int 26 | label: Year 27 | source_name: Eurostat (European Statistical Office) 28 | source_url: https://ec.europa.eu/eurostat/ 29 | -------------------------------------------------------------------------------- /ds/data/processed/hebci/contract_research_sme.nuts2.yaml: -------------------------------------------------------------------------------- 1 | api_doc_url: https://www.hesa.ac.uk/data-and-analysis/providers/business-community/ 2 | api_type: FETCH 3 | data_date: 20200924 4 | description: Income for higher education institutions from providing contract research services to SMEs. 5 | title: HE contract research income from SMEs 6 | subtitle: Income for higher education institutions from providing contract research services to SMEs. 7 | endpoint_url: https://www.hesa.ac.uk/data-and-analysis/providers/business-community/table-2a.csv 8 | framework_group: knowledge_exchange 9 | is_experimental: False 10 | is_public: True 11 | order: [year, nuts_id, nuts_year_spec, value.id] 12 | region: 13 | type: NutsRegion 14 | level: 2 15 | source_url: NutsRegion.source_url 16 | source: NutsRegion.source 17 | schema: 18 | nuts_id: 19 | type: NutsRegion.nuts_id 20 | nuts_year_spec: 21 | type: NutsRegion.nuts_year_spec 22 | value: 23 | data_type: int 24 | id: contract_research_sme 25 | label: Income from contract research 26 | type: GBP 27 | year: 28 | data_type: int 29 | label: Year 30 | source_name: HESA (Higher Education Statistical Agency) 31 | source_url: https://www.hesa.ac.uk/ 32 | -------------------------------------------------------------------------------- /ds/data/processed/hebci/contract_research_sme.nuts3.yaml: -------------------------------------------------------------------------------- 1 | api_doc_url: https://www.hesa.ac.uk/data-and-analysis/providers/business-community/ 2 | api_type: FETCH 3 | data_date: 20200924 4 | description: Income for higher education institutions from providing contract research services to SMEs. 5 | title: HE contract research income from SMEs 6 | subtitle: Income for higher education institutions from providing contract research services to SMEs. 7 | endpoint_url: https://www.hesa.ac.uk/data-and-analysis/providers/business-community/table-2a.csv 8 | framework_group: knowledge_exchange 9 | is_experimental: False 10 | is_public: True 11 | order: [year, nuts_id, nuts_year_spec, value.id] 12 | region: 13 | type: NutsRegion 14 | level: 3 15 | source_url: NutsRegion.source_url 16 | source: NutsRegion.source 17 | schema: 18 | nuts_id: 19 | type: NutsRegion.nuts_id 20 | nuts_year_spec: 21 | type: NutsRegion.nuts_year_spec 22 | value: 23 | data_type: int 24 | id: contract_research_sme 25 | label: Income from contract research 26 | type: GBP 27 | year: 28 | data_type: int 29 | label: Year 30 | source_name: HESA (Higher Education Statistical Agency) 31 | source_url: https://www.hesa.ac.uk/ 32 | -------------------------------------------------------------------------------- /ds/data/processed/hebci/gbp_turnover_per_active_spinoff.yaml: -------------------------------------------------------------------------------- 1 | api_doc_url: https://www.hesa.ac.uk/support/definitions/hebci 2 | api_type: FETCH 3 | data_date: 20202002 4 | title: Current turnover of active university spinoffs 5 | subtitle: Total current turnover of active spinoffs involving local universities in academic year starting in year 6 | endpoint_url: https://www.hesa.ac.uk/data-and-analysis/providers/business-community/table-4e.csv 7 | framework_group: knowledge_exchange 8 | is_experimental: False 9 | order: [year, nuts_id, nuts_year_spec, value.id] 10 | region: 11 | type: NutsRegion 12 | level: 2 13 | source_url: https://opendata.arcgis.com/datasets/48b6b85bb7ea43699ee85f4ecd12fd36_0.zip?outSR=%7B%22latestWkid%22%3A27700%2C%22wkid%22%3A27700%7D 14 | schema: 15 | nuts_id: 16 | type: NutsRegion.nuts_id 17 | nuts_year_spec: 18 | type: NutsRegion.nuts_year_spec 19 | value: 20 | data_type: int 21 | format: ',' 22 | id: gbp_turnover_per_active_spinoff 23 | label: Current turnover of active university spinoffs 24 | type: GBP 25 | year: 26 | data_type: int 27 | label: Year 28 | source_name: HESA (Higher Education Statistical Agency) 29 | source_url: https://www.hesa.ac.uk/ 30 | -------------------------------------------------------------------------------- /ds/data/processed/hesa/total_stem_students.nuts2.yaml: -------------------------------------------------------------------------------- 1 | api_doc_url: https://www.hesa.ac.uk/data-and-analysis/students 2 | api_type: FETCH 3 | title: Total number of STEM students 4 | subtitle: Number of students enrolled full-time in STEM subjects 5 | description: Number of students enrolled full-time in STEM subjects in a NUTS3 region (definition of STEM subjects in aux folder) in the starting academic year. 6 | endpoint_url: https://www.hesa.ac.uk/data-and-analysis/students/table-13.csv 7 | framework_group: public_rnd 8 | is_experimental: False 9 | order: [year, nuts_id, nuts_year_spec, value.id] 10 | region: 11 | type: NutsRegion 12 | level: 2 13 | source_url: http://geoportal1-ons.opendata.arcgis.com/datasets/48b6b85bb7ea43699ee85f4ecd12fd36_0.zip?outSR=%7B%22latestWkid%22:27700,%22wkid%22:27700%7D 14 | schema: 15 | nuts_id: 16 | type: NutsRegion.id 17 | nuts_year_spec: 18 | type: NutsRegion.year_spec 19 | value: 20 | data_type: int 21 | id: total_stem_students 22 | label: Total number of students in STEM subjects 23 | year: 24 | data_type: int 25 | label: Academic year (start) 26 | source_name: HESA (Higher Education Statistical Agency) 27 | source_url: https://www.hesa.ac.uk/ 28 | -------------------------------------------------------------------------------- /ds/data/processed/hesa/total_stem_students.nuts3.yaml: -------------------------------------------------------------------------------- 1 | api_doc_url: https://www.hesa.ac.uk/data-and-analysis/students 2 | api_type: FETCH 3 | title: Total number of STEM students 4 | subtitle: Number of students enrolled full-time in STEM subjects 5 | description: Number of students enrolled full-time in STEM subjects in a NUTS3 region (definition of STEM subjects in aux folder) in the starting academic year. 6 | endpoint_url: https://www.hesa.ac.uk/data-and-analysis/students/table-13.csv 7 | framework_group: public_rnd 8 | is_experimental: False 9 | order: [year, nuts_id, nuts_year_spec, value.id] 10 | region: 11 | type: NutsRegion 12 | level: 3 13 | source_url: http://geoportal1-ons.opendata.arcgis.com/datasets/48b6b85bb7ea43699ee85f4ecd12fd36_0.zip?outSR=%7B%22latestWkid%22:27700,%22wkid%22:27700%7D 14 | schema: 15 | nuts_id: 16 | type: NutsRegion.id 17 | nuts_year_spec: 18 | type: NutsRegion.year_spec 19 | value: 20 | data_type: int 21 | id: total_stem_students 22 | label: Total number of students in STEM subjects 23 | year: 24 | data_type: int 25 | label: Academic year (start) 26 | source_name: HESA (Higher Education Statistical Agency) 27 | source_url: https://www.hesa.ac.uk/ 28 | -------------------------------------------------------------------------------- /ds/data/schema/types_schema.yaml: -------------------------------------------------------------------------------- 1 | # GEOGRAPHY 2 | 3 | GeoRegion: 4 | _id: string # the region id e.g. UKF2 (here the key can be `nuts_id`, `lep_id`, etc) 5 | _year_enforced: int # OPTIONAL - year of spec being enforced (here the key can be `nuts_year_enforced`, `lep_year_enforced`, etc) 6 | _year_spec: int # year of specification release (here the key can be `nuts_year_spec`, `lep_year_spec`, etc) 7 | level: int # OPTIONAL – used for NUTS, e.g. 2 8 | name: string # name of the region, e.g. Cornwall, France 9 | region_type: string # the region type (e.g. `nuts`, `lep`, etc) 10 | source_url: URL # OPTIONAL - url of the boundaries used for reverse geocoding, if known 11 | source: string # OPTIONAL - use `PROVIDED` in case `source_url` is unknown because the data have already been reverse geocoded 12 | 13 | # UNITS 14 | 15 | Unit: 16 | kind: string # e.g. density, currency 17 | label: string # short text 18 | data_type: string 19 | unit_string: string # e.g. mg m^-3 20 | unit_latex: string # e.g. \SI{32}{mg.m^{-3}} 21 | description: string # OPTIONAL 22 | 23 | Datestring: 24 | data_type: string 25 | format: string # e.g. YYYYMMDD 26 | kind: string # date 27 | -------------------------------------------------------------------------------- /ds/data/processed/hebci/collaborative_research_cash.lep.yaml: -------------------------------------------------------------------------------- 1 | api_doc_url: https://www.hesa.ac.uk/data-and-analysis/providers/business-community/ 2 | api_type: FETCH 3 | data_date: 20200924 4 | description: Value of collaborations with non-academic partners. This indicator only considers the value of collaborations in cash and excludes the value of in-kind collaboration. 5 | title: Collaborative research funding 6 | subtitle: Cash value of collaborations with non-academic parterns. 7 | endpoint_url: https://www.hesa.ac.uk/data-and-analysis/providers/business-community/table-1.csv 8 | framework_group: knowledge_exchange 9 | is_experimental: False 10 | is_public: True 11 | order: [year, lep_id, lep_year_spec, value.id] 12 | region: 13 | type: LepRegion 14 | source_url: LepRegion.source_url 15 | source: LepRegion.source 16 | schema: 17 | lep_id: 18 | type: LepRegion.lep_id 19 | lep_year_spec: 20 | type: LepRegion.lep_year_spec 21 | value: 22 | data_type: int 23 | id: collaborative_research_cash 24 | label: Collaborative research income 25 | type: GBP 26 | year: 27 | data_type: int 28 | label: Year 29 | source_name: HESA (Higher Education Statistical Agency) 30 | source_url: https://www.hesa.ac.uk/ 31 | -------------------------------------------------------------------------------- /ds/data/processed/gtr/total_gtr_projects_stem.yaml: -------------------------------------------------------------------------------- 1 | api_doc_url: https://gtr.ukri.org/resources/GtR-2-API-v1.7.4.pdf 2 | api_type: FETCH 3 | auth_provider: Nesta 4 | data_date: 20200222 5 | title: STEM projects led by organisations in the region 6 | subtitle: Total number of projects led by organisations in the NUTS area in STEM subjects 7 | endpoint_url: https://s3.console.aws.amazon.com/s3/object/nesta-data-getters/17_9_2019_gtr_orgs.csv 8 | framework_group: public_rnd 9 | is_experimental: True 10 | is_public: False 11 | nuts_level: 2 12 | order: [year, nuts_id, nuts_year_spec, value.id] 13 | region: 14 | type: NutsRegion 15 | level: 2 16 | source_url: http://geoportal1-ons.opendata.arcgis.com/datasets/48b6b85bb7ea43699ee85f4ecd12fd36_0.zip?outSR=%7B%22latestWkid%22:27700,%22wkid%22:27700%7D 17 | schema: 18 | nuts_id: 19 | type: NutsRegion.id 20 | nuts_year_spec: 21 | type: NutsRegion.year_spec 22 | value: 23 | format: ',' 24 | id: total_gtr_projects_stem 25 | label: Number of projects in STEM disciplines led by organisations in the region 26 | data_type: int 27 | year: 28 | data_type: int 29 | label: Year 30 | source_name: UKRI (UK Research and Innovation) 31 | source_url: https://gtr.ukri.org 32 | -------------------------------------------------------------------------------- /ds/data/processed/hebci/contract_research_non_sme.lep.yaml: -------------------------------------------------------------------------------- 1 | api_doc_url: https://www.hesa.ac.uk/data-and-analysis/providers/business-community/ 2 | api_type: FETCH 3 | data_date: 20200924 4 | description: Income for higher education institutions from providing contract research services to non-SME companies. 5 | title: HE contract research income from non-SME companies 6 | subtitle: Income for higher education institutions from providing contract research services to non-SME companies. 7 | endpoint_url: https://www.hesa.ac.uk/data-and-analysis/providers/business-community/table-2a.csv 8 | framework_group: knowledge_exchange 9 | is_experimental: False 10 | is_public: True 11 | order: [year, lep_id, lep_year_spec, value.id] 12 | region: 13 | type: LepRegion 14 | source_url: LepRegion.source_url 15 | source: LepRegion.source 16 | schema: 17 | lep_id: 18 | type: LepRegion.lep_id 19 | lep_year_spec: 20 | type: LepRegion.lep_year_spec 21 | value: 22 | data_type: int 23 | id: contract_research_non_sme 24 | label: Income from contract research 25 | type: GBP 26 | year: 27 | data_type: int 28 | label: Year 29 | source_name: HESA (Higher Education Statistical Agency) 30 | source_url: https://www.hesa.ac.uk/ 31 | -------------------------------------------------------------------------------- /ds/beis_indicators/utils/pandas.py: -------------------------------------------------------------------------------- 1 | """ 2 | """ 3 | import logging 4 | from IPython.display import display 5 | import pandas as pd 6 | import beis_indicators 7 | 8 | logger = logging.getLogger(__name__) 9 | 10 | 11 | def _clean_strings(x): 12 | """ Simple column name cleaning 13 | 14 | Remove leading/trailing whitespace, convert to lowercase, 15 | and replace spaces with underscores. 16 | 17 | Args: 18 | x (pandas.DataFrame): DataFrame 19 | 20 | Returns: 21 | pandas.DataFrame 22 | """ 23 | 24 | return x.str.lstrip().str.rstrip().str.lower().str.replace(" ", "_") 25 | 26 | 27 | def preview(x, nrows=5, T=False): 28 | """ Print a preview of DataFrame and return input 29 | 30 | Args: 31 | x (pandas.DataFrame): DataFrame to preview 32 | nrows (int, optional): 33 | T (bool, optional): Whether to print transposed 34 | 35 | Returns: 36 | pandas.DataFrame 37 | Original input 38 | """ 39 | 40 | if nrows >= x.shape[0]: 41 | out = x 42 | else: 43 | out = pd.concat([x.head(nrows), x.tail(nrows)]) 44 | 45 | if T: 46 | out = out.T 47 | 48 | display(out) 49 | print(f"Shape: {x.shape}") 50 | 51 | return x 52 | -------------------------------------------------------------------------------- /ds/data/processed/gtr/total_ukri_funding.lep.yaml: -------------------------------------------------------------------------------- 1 | api_doc_url: https://gtr.ukri.org/resources/GtR-2-API-v1.7.4.pdf 2 | api_type: MySQL 3 | auth_provider: Nesta 4 | data_date: 20200910 5 | description: Total amount of funding (GBP) awarded by UK Research and Innovation (UKRI) for research and innovation projects. 6 | title: Total funding (GBP) for research awarded by UKRI 7 | subtitle: Sum of all research funding (GBP) awarded by UK Research and Innovation (UKRI) 8 | framework_group: public_rnd 9 | is_experimental: False 10 | is_public: False 11 | order: [year, lep_id, lep_year_spec, value.id] 12 | region: 13 | type: LepRegion 14 | schema: 15 | lep_id: 16 | type: LepRegion.id 17 | lep_year_spec: 18 | type: LepRegion.year_spec 19 | value: 20 | data_type: int 21 | format: ',' 22 | id: total_ukri_funding 23 | label: Total Funding 24 | type: GBP 25 | year: 26 | data_type: int 27 | label: Year 28 | source_name: UKRI (UK Research and Innovation) 29 | source_url: https://gtr.ukri.org 30 | warning: Some projects have multiple organisations who may have been awarded funding, however due to limitations with the data provided by Gateway to Research we have attributed the total funding for a project to the lead organisation. -------------------------------------------------------------------------------- /ds/data/processed/hebci/consultancy_facilities_sme.lep.yaml: -------------------------------------------------------------------------------- 1 | api_doc_url: https://www.hesa.ac.uk/data-and-analysis/providers/business-community/ 2 | api_type: FETCH 3 | data_date: 20200924 4 | description: Income for higher education institutions from providing consultancy and facilities services to SMEs. 5 | title: HE consultancy and facilities income from SMEs 6 | subtitle: Income for higher education institutions from providing consultancy and facilities services to SMEs 7 | endpoint_url: https://www.hesa.ac.uk/data-and-analysis/providers/business-community/table-2a.csv 8 | framework_group: knowledge_exchange 9 | is_experimental: False 10 | is_public: True 11 | order: [year, lep_id, lep_year_spec, value.id] 12 | region: 13 | type: LepRegion 14 | level: 2 15 | source_url: LepRegion.source_url 16 | source: LepRegion.source 17 | schema: 18 | lep_id: 19 | type: LepRegion.lep_id 20 | lep_year_spec: 21 | type: LepRegion.lep_year_spec 22 | value: 23 | data_type: int 24 | id: consultancy_facilities_sme 25 | label: Income from consultancy and facilities 26 | type: GBP 27 | year: 28 | data_type: int 29 | label: Year 30 | source_name: HESA (Higher Education Statistical Agency) 31 | source_url: https://www.hesa.ac.uk/ 32 | -------------------------------------------------------------------------------- /ds/data/processed/hesa/total_stem_postgraduates.nuts2.yaml: -------------------------------------------------------------------------------- 1 | api_doc_url: https://www.hesa.ac.uk/data-and-analysis/students 2 | api_type: FETCH 3 | title: Total number of STEM postgraduates 4 | subtitle: Number of postgraduate students enrolled full-time in STEM subjects. 5 | description: Number of postgraduate students enrolled full-time in STEM subjects in the starting academic year. Definition of STEM subjects in aux folder. 6 | endpoint_url: https://www.hesa.ac.uk/data-and-analysis/students/table-13.csv 7 | framework_group: public_rnd 8 | is_experimental: False 9 | order: [year, nuts_id, nuts_year_spec, value.id] 10 | region: 11 | type: NutsRegion 12 | level: 2 13 | source_url: http://geoportal1-ons.opendata.arcgis.com/datasets/48b6b85bb7ea43699ee85f4ecd12fd36_0.zip?outSR=%7B%22latestWkid%22:27700,%22wkid%22:27700%7D 14 | schema: 15 | nuts_id: 16 | type: NutsRegion.id 17 | nuts_year_spec: 18 | type: NutsRegion.year_spec 19 | value: 20 | data_type: int 21 | id: total_stem_postgraduates 22 | label: Total number of postgraduate students in STEM subjects 23 | year: 24 | data_type: int 25 | label: Academic year (start) 26 | source_name: HESA (Higher Education Statistical Agency) 27 | source_url: https://www.hesa.ac.uk/ 28 | -------------------------------------------------------------------------------- /ds/data/processed/hesa/total_stem_postgraduates.nuts3.yaml: -------------------------------------------------------------------------------- 1 | api_doc_url: https://www.hesa.ac.uk/data-and-analysis/students 2 | api_type: FETCH 3 | title: Total number of STEM postgraduates 4 | subtitle: Number of postgraduate students enrolled full-time in STEM subjects. 5 | description: Number of postgraduate students enrolled full-time in STEM subjects in the starting academic year. Definition of STEM subjects in aux folder. 6 | endpoint_url: https://www.hesa.ac.uk/data-and-analysis/students/table-13.csv 7 | framework_group: public_rnd 8 | is_experimental: False 9 | order: [year, nuts_id, nuts_year_spec, value.id] 10 | region: 11 | type: NutsRegion 12 | level: 3 13 | source_url: http://geoportal1-ons.opendata.arcgis.com/datasets/48b6b85bb7ea43699ee85f4ecd12fd36_0.zip?outSR=%7B%22latestWkid%22:27700,%22wkid%22:27700%7D 14 | schema: 15 | nuts_id: 16 | type: NutsRegion.id 17 | nuts_year_spec: 18 | type: NutsRegion.year_spec 19 | value: 20 | data_type: int 21 | id: total_stem_postgraduates 22 | label: Total number of postgraduate students in STEM subjects 23 | year: 24 | data_type: int 25 | label: Academic year (start) 26 | source_name: HESA (Higher Education Statistical Agency) 27 | source_url: https://www.hesa.ac.uk/ 28 | -------------------------------------------------------------------------------- /ds/data/processed/aps/aps_econ_active_stem_profs_data.lep.yaml: -------------------------------------------------------------------------------- 1 | api_doc_url: https://www.nomisweb.co.uk/api/v01/help 2 | api_type: FETCH 3 | title: STEM professionals 4 | subtitle: Number of Science, Research, Engineering and Technology professionals by LEP regions. 5 | endpoint_url: http://www.nomisweb.co.uk/api/v01/dataset/NM_17_1.data.csv?geography=TYPE459&date=2019-12,2018-12,2017-12,2016-12,2015-12,2014-12,2013-12,2012-12,2011-12,2010-12&cell=404882177&measures=20100,20701&select=date,date_name,date_code,geography_type,geography_code,measures_name,variable,cell_name,obs_value,obs_status_name,obs_status,record_count 6 | framework_group: private_rnd 7 | is_experimental: False 8 | order: [year, lep_id, lep_year_spec, value.id] # do not change; specifies the order of the exported indicator fields 9 | region: 10 | type: LepRegion 11 | source: PROVIDED 12 | schema: 13 | nuts_id: 14 | type: LepRegion.lep_id 15 | nuts_year_spec: 16 | type: LepRegion.lep_year_spec 17 | value: 18 | id: aps_econ_active_stem_profs_data 19 | label: Frequency 20 | data_type: int 21 | year: 22 | data_type: int 23 | label: Year 24 | source_name: NOMIS (official labour market statistics) 25 | source_url: https://www.nomisweb.co.uk 26 | -------------------------------------------------------------------------------- /ds/data/processed/gtr/total_gtr_projects_all_disciplines.yaml: -------------------------------------------------------------------------------- 1 | api_doc_url: https://gtr.ukri.org/resources/GtR-2-API-v1.7.4.pdf 2 | api_type: FETCH 3 | auth_provider: Nesta 4 | data_date: 20210617 5 | title: Number of projects led by organisations in the region 6 | subtitle: Total number of projects led by organisations in the NUTS area in all disciplines 7 | endpoint_url: https://s3.console.aws.amazon.com/s3/object/nesta-data-getters/17_9_2019_gtr_orgs.csv 8 | framework_group: public_rnd 9 | is_experimental: True 10 | is_public: False 11 | nuts_level: 2 12 | order: [year, nuts_id, nuts_year_spec, value.id] 13 | region: 14 | type: NutsRegion 15 | level: 2 16 | source_url: http://geoportal1-ons.opendata.arcgis.com/datasets/48b6b85bb7ea43699ee85f4ecd12fd36_0.zip?outSR=%7B%22latestWkid%22:27700,%22wkid%22:27700%7D 17 | schema: 18 | nuts_id: 19 | type: NutsRegion.id 20 | nuts_year_spec: 21 | type: NutsRegion.year_spec 22 | value: 23 | format: ',' 24 | id: total_gtr_projects_all_disciplines 25 | label: Number of projects in all disciplines led by organisations in the region 26 | data_type: int 27 | year: 28 | data_type: int 29 | label: Year 30 | source_name: UKRI (UK Research and Innovation) 31 | source_url: https://gtr.ukri.org 32 | -------------------------------------------------------------------------------- /ds/data/processed/hebci/collaborative_research_cash.nuts2.yaml: -------------------------------------------------------------------------------- 1 | api_doc_url: https://www.hesa.ac.uk/data-and-analysis/providers/business-community/ 2 | api_type: FETCH 3 | data_date: 20200924 4 | description: Value of collaborations with non-academic partners. This indicator only considers the value of collaborations in cash and excludes the value of in-kind collaboration. 5 | title: Collaborative research funding 6 | subtitle: Cash value of collaborations with non-academic parterns. 7 | endpoint_url: https://www.hesa.ac.uk/data-and-analysis/providers/business-community/table-1.csv 8 | framework_group: knowledge_exchange 9 | is_experimental: False 10 | is_public: True 11 | order: [year, nuts_id, nuts_year_spec, value.id] 12 | region: 13 | level: 2 14 | type: NutsRegion 15 | source_url: NutsRegion.source_url 16 | source: NutsRegion.source 17 | schema: 18 | nuts_id: 19 | type: NutsRegion.nuts_id 20 | nuts_year_spec: 21 | type: NutsRegion.nuts_year_spec 22 | value: 23 | data_type: int 24 | id: collaborative_research_cash 25 | label: Collaborative research income 26 | type: GBP 27 | year: 28 | data_type: int 29 | label: Year 30 | source_name: HESA (Higher Education Statistical Agency) 31 | source_url: https://www.hesa.ac.uk/ 32 | -------------------------------------------------------------------------------- /ds/data/processed/hebci/collaborative_research_cash.nuts3.yaml: -------------------------------------------------------------------------------- 1 | api_doc_url: https://www.hesa.ac.uk/data-and-analysis/providers/business-community/ 2 | api_type: FETCH 3 | data_date: 20200924 4 | description: Value of collaborations with non-academic partners. This indicator only considers the value of collaborations in cash and excludes the value of in-kind collaboration. 5 | title: Collaborative research funding 6 | subtitle: Cash value of collaborations with non-academic parterns. 7 | endpoint_url: https://www.hesa.ac.uk/data-and-analysis/providers/business-community/table-1.csv 8 | framework_group: knowledge_exchange 9 | is_experimental: False 10 | is_public: True 11 | order: [year, nuts_id, nuts_year_spec, value.id] 12 | region: 13 | level: 3 14 | type: NutsRegion 15 | source_url: NutsRegion.source_url 16 | source: NutsRegion.source 17 | schema: 18 | nuts_id: 19 | type: NutsRegion.nuts_id 20 | nuts_year_spec: 21 | type: NutsRegion.nuts_year_spec 22 | value: 23 | data_type: int 24 | id: collaborative_research_cash 25 | label: Collaborative research income 26 | type: GBP 27 | year: 28 | data_type: int 29 | label: Year 30 | source_name: HESA (Higher Education Statistical Agency) 31 | source_url: https://www.hesa.ac.uk/ 32 | -------------------------------------------------------------------------------- /ds/data/processed/hebci/consultancy_facilities_sme.nuts2.yaml: -------------------------------------------------------------------------------- 1 | api_doc_url: https://www.hesa.ac.uk/data-and-analysis/providers/business-community/ 2 | api_type: FETCH 3 | data_date: 20200924 4 | description: Income for higher education institutions from providing consultancy and facilities services to SMEs 5 | title: HE consultancy and facilities income from SMEs 6 | subtitle: Income for higher education institutions from providing consultancy and facilities services to SMEs 7 | endpoint_url: https://www.hesa.ac.uk/data-and-analysis/providers/business-community/table-2a.csv 8 | framework_group: knowledge_exchange 9 | is_experimental: False 10 | is_public: True 11 | order: [year, nuts_id, nuts_year_spec, value.id] 12 | region: 13 | type: NutsRegion 14 | level: 2 15 | source_url: NutsRegion.source_url 16 | source: NutsRegion.source 17 | schema: 18 | nuts_id: 19 | type: NutsRegion.nuts_id 20 | nuts_year_spec: 21 | type: NutsRegion.nuts_year_spec 22 | value: 23 | data_type: int 24 | id: consultancy_facilities_sme 25 | label: Income from consultancy and facilities 26 | type: GBP 27 | year: 28 | data_type: int 29 | label: Year 30 | source_name: HESA (Higher Education Statistical Agency) 31 | source_url: https://www.hesa.ac.uk/ 32 | -------------------------------------------------------------------------------- /ds/data/processed/hebci/consultancy_facilities_sme.nuts3.yaml: -------------------------------------------------------------------------------- 1 | api_doc_url: https://www.hesa.ac.uk/data-and-analysis/providers/business-community/ 2 | api_type: FETCH 3 | data_date: 20200924 4 | description: Income for higher education institutions from providing consultancy and facilities services to SMEs 5 | title: HE consultancy and facilities income from SMEs 6 | subtitle: Income for higher education institutions from providing consultancy and facilities services to SMEs 7 | endpoint_url: https://www.hesa.ac.uk/data-and-analysis/providers/business-community/table-2a.csv 8 | framework_group: knowledge_exchange 9 | is_experimental: False 10 | is_public: True 11 | order: [year, nuts_id, nuts_year_spec, value.id] 12 | region: 13 | type: NutsRegion 14 | level: 3 15 | source_url: NutsRegion.source_url 16 | source: NutsRegion.source 17 | schema: 18 | nuts_id: 19 | type: NutsRegion.nuts_id 20 | nuts_year_spec: 21 | type: NutsRegion.nuts_year_spec 22 | value: 23 | data_type: int 24 | id: consultancy_facilities_sme 25 | label: Income from consultancy and facilities 26 | type: GBP 27 | year: 28 | data_type: int 29 | label: Year 30 | source_name: HESA (Higher Education Statistical Agency) 31 | source_url: https://www.hesa.ac.uk/ 32 | -------------------------------------------------------------------------------- /ds/data/processed/hebci/spinoff_investment.lep.yaml: -------------------------------------------------------------------------------- 1 | api_doc_url: https://www.hesa.ac.uk/data-and-analysis/providers/business-community/ 2 | api_type: FETCH 3 | data_date: 20200928 4 | description: Total external investment in all spin-off activities associated with higher education institutions (spin-offs with some HEP ownership, formal spin-offs not HEP owned, staff start-ups, graduate start-ups and social enterprises). 5 | title: External investment in higher education spin-offs 6 | subtitle: Investment in higher education spin-offs 7 | endpoint_url: https://www.hesa.ac.uk/data-and-analysis/providers/business-community/table-4e.csv 8 | framework_group: knowledge_exchange 9 | is_experimental: False 10 | is_public: True 11 | order: [year, lep_id, lep_year_spec, value.id] 12 | region: 13 | type: LepRegion 14 | source_url: LepRegion.source_url 15 | source: LepRegion.source 16 | schema: 17 | lep_id: 18 | type: LepRegion.lep_id 19 | lep_year_spec: 20 | type: LepRegion.lep_year_spec 21 | value: 22 | data_type: int 23 | id: spinoff_investment 24 | label: Investment 25 | type: GBP 26 | year: 27 | data_type: int 28 | label: Year 29 | source_name: HESA (Higher Education Statistical Agency) 30 | source_url: https://www.hesa.ac.uk/ 31 | -------------------------------------------------------------------------------- /ds/data/processed/industry/economic_complexity_index.lep.yaml: -------------------------------------------------------------------------------- 1 | api_doc_url: https://www.nomisweb.co.uk/api/v01/help 2 | api_type: POST 3 | data_date: 20200218 4 | title: Economic Complexity Index 5 | subtitle: The Economic Complexity Index (ECI) captures the sophistication of economic activities in a location 6 | description: This indicator measures the economic complexity of NUTS 3 locations based on an analysis of its industrial composition ((based on Nesta sectoral definition, which clusters 4-digit SIC codes based on their similarity)) 7 | endpoint_url: http://www.nomisweb.co.uk/api/v01/dataset/ 8 | framework_group: private_rnd 9 | is_experimental: True 10 | order: [year, lep_id, lep_year_spec, value.id] 11 | region: 12 | type: LepRegion 13 | source: PROVIDED 14 | schema: 15 | region_id: 16 | type: LepRegion.lep_id 17 | region_year_spec: 18 | type: LepRegion.lep_year_spec 19 | value: 20 | description: Economic complexity Index 21 | format: .4f 22 | id: economic_complexity_index 23 | label: Economic complexity Index 24 | data_type: float 25 | year: 26 | data_type: int 27 | label: Year 28 | source_name: NOMIS (official labour market statistics) 29 | source_url: https://www.nomisweb.co.uk 30 | year_range: [2016, 2019] 31 | -------------------------------------------------------------------------------- /ds/data/aux/eurostat_data_queries.txt: -------------------------------------------------------------------------------- 1 | [ 2 | {'indicator': 'eurostat_private_rd_headcount_workforce_data', 3 | 'query': 'rd_p_persreg?sinceTimePeriod=2007&geoLevel=nuts2&precision=1&sex=T§perf=BES&prof_pos=TOTAL&unit=HC' }, 4 | 5 | {'indicator': 'eurostat_private_rd_fte_workforce_data', 6 | 'query': 'rd_p_persreg?sinceTimePeriod=2007&geoLevel=nuts2&precision=6&sex=T§perf=BES&prof_pos=TOTAL&unit=FTE'}, 7 | 8 | {'indicator': 'eurostat_berd_data', 9 | 'query': 'rd_e_gerdreg?sinceTimePeriod=2007&geoLevel=nuts2&precision=6§perf=BES&unit=MIO_EUR'}, 10 | 11 | {'indicator': 'eurostat_private_non_profit_rd_workforce_data', 12 | 'query': 'rd_e_gerdreg?sinceTimePeriod=2007&geoLevel=nuts2&precision=6§perf=PNP&unit=MIO_EUR'}, 13 | 14 | {'indicator': 'eurostat_higher_ed_rd_workforce_data', 15 | 'query': 'rd_e_gerdreg?sinceTimePeriod=2007&geoLevel=nuts2&precision=6§perf=HES&unit=MIO_EUR'}, 16 | 17 | {'indicator': 'eurostat_gov_rd_workforce_data', 18 | 'query': 'rd_e_gerdreg?sinceTimePeriod=2007&geoLevel=nuts2&precision=2§perf=GOV&unit=MIO_EUR'}, 19 | 20 | {'indicator': 'eurostat_private_households_income', 21 | 'query': 'tgs00036?sinceTimePeriod=2007&precision=2&unit=PPS_HAB'} 22 | ] 23 | -------------------------------------------------------------------------------- /ds/data/processed/hebci/contract_research_non_sme.nuts2.yaml: -------------------------------------------------------------------------------- 1 | api_doc_url: https://www.hesa.ac.uk/data-and-analysis/providers/business-community/ 2 | api_type: FETCH 3 | data_date: 20200924 4 | description: Income for higher education institutions from providing contract research services to non-SME companies. 5 | title: HE contract research income from non-SME companies 6 | subtitle: Income for higher education institutions from providing contract research services to non-SME companies. 7 | endpoint_url: https://www.hesa.ac.uk/data-and-analysis/providers/business-community/table-2a.csv 8 | framework_group: knowledge_exchange 9 | is_experimental: False 10 | is_public: True 11 | order: [year, nuts_id, nuts_year_spec, value.id] 12 | region: 13 | type: NutsRegion 14 | level: 2 15 | source_url: NutsRegion.source_url 16 | source: NutsRegion.source 17 | schema: 18 | nuts_id: 19 | type: NutsRegion.nuts_id 20 | nuts_year_spec: 21 | type: NutsRegion.nuts_year_spec 22 | value: 23 | data_type: int 24 | id: contract_research_non_sme 25 | label: Income from contract research 26 | type: GBP 27 | year: 28 | data_type: int 29 | label: Year 30 | source_name: HESA (Higher Education Statistical Agency) 31 | source_url: https://www.hesa.ac.uk/ 32 | -------------------------------------------------------------------------------- /ds/data/processed/hebci/contract_research_non_sme.nuts3.yaml: -------------------------------------------------------------------------------- 1 | api_doc_url: https://www.hesa.ac.uk/data-and-analysis/providers/business-community/ 2 | api_type: FETCH 3 | data_date: 20200924 4 | description: Income for higher education institutions from providing contract research services to non-SME companies. 5 | title: HE contract research income from non-SME companies 6 | subtitle: Income for higher education institutions from providing contract research services to non-SME companies. 7 | endpoint_url: https://www.hesa.ac.uk/data-and-analysis/providers/business-community/table-2a.csv 8 | framework_group: knowledge_exchange 9 | is_experimental: False 10 | is_public: True 11 | order: [year, nuts_id, nuts_year_spec, value.id] 12 | region: 13 | type: NutsRegion 14 | level: 3 15 | source_url: NutsRegion.source_url 16 | source: NutsRegion.source 17 | schema: 18 | nuts_id: 19 | type: NutsRegion.nuts_id 20 | nuts_year_spec: 21 | type: NutsRegion.nuts_year_spec 22 | value: 23 | data_type: int 24 | id: contract_research_non_sme 25 | label: Income from contract research 26 | type: GBP 27 | year: 28 | data_type: int 29 | label: Year 30 | source_name: HESA (Higher Education Statistical Agency) 31 | source_url: https://www.hesa.ac.uk/ 32 | -------------------------------------------------------------------------------- /ui/test/pa11y/validate.js: -------------------------------------------------------------------------------- 1 | import fs from 'fs'; 2 | import * as _ from 'lamb'; 3 | import pa11y from 'pa11y'; 4 | import htmlReporter from 'pa11y-reporter-html'; 5 | import Queue from 'queue-promise'; 6 | 7 | import {lighthouseUrls, urlBases} from '../../src/node_modules/app/config'; 8 | 9 | const queue = new Queue({ 10 | concurrent: 1 11 | }); 12 | 13 | queue.on('end', () => { 14 | console.log('Done!'); 15 | }); 16 | 17 | const auditURL = async (id, url) => { 18 | const options = { 19 | standard: 'WCAG2AAA' 20 | }; 21 | const runnerResult = await pa11y( 22 | urlBases.development + url, 23 | options 24 | ); 25 | const reportHtml = await htmlReporter.results(runnerResult, url); 26 | 27 | // eslint-disable-next-line no-sync 28 | fs.writeFileSync(`static/audits/pa11y/${id}.html`, reportHtml); 29 | 30 | // `.lhr` is the Lighthouse Result as a JS object 31 | console.log( 32 | 'Report is done for', 33 | runnerResult.pageUrl 34 | ); 35 | console.log( 36 | 'Issues found:', 37 | runnerResult.issues.length 38 | ); 39 | } 40 | 41 | const enqueueTask = ([id, url]) => 42 | queue.enqueue(async () => await auditURL(id, url)); 43 | 44 | const auditUrls = _.pipe([ 45 | _.pairs, 46 | _.mapWith(enqueueTask) 47 | ]); 48 | 49 | auditUrls(lighthouseUrls); 50 | -------------------------------------------------------------------------------- /ds/data/processed/gtr/total_ukri_funding.nuts2.yaml: -------------------------------------------------------------------------------- 1 | api_doc_url: https://gtr.ukri.org/resources/GtR-2-API-v1.7.4.pdf 2 | api_type: MySQL 3 | auth_provider: Nesta 4 | data_date: 20200910 5 | description: Total amount of funding (GBP) awarded by UK Research and Innovation (UKRI) for research and innovation projects. 6 | title: Total funding (GBP) for research awarded by UKRI 7 | subtitle: Sum of all research funding (GBP) awarded by UK Research and Innovation (UKRI) 8 | framework_group: public_rnd 9 | is_experimental: False 10 | is_public: False 11 | order: [year, nuts_id, nuts_year_spec, value.id] 12 | region: 13 | type: NutsRegion 14 | level: 2 15 | schema: 16 | nuts_id: 17 | type: NutsRegion.id 18 | nuts_year_spec: 19 | type: NutsRegion.year_spec 20 | value: 21 | data_type: int 22 | format: ',' 23 | id: total_ukri_funding 24 | label: Total Funding 25 | type: GBP 26 | year: 27 | data_type: int 28 | label: Year 29 | source_name: UKRI (UK Research and Innovation) 30 | source_url: https://gtr.ukri.org 31 | warning: Some projects have multiple organisations who may have been awarded funding, however due to limitations with the data provided by Gateway to Research we have attributed the total funding for a project to the lead organisation. -------------------------------------------------------------------------------- /ds/data/processed/gtr/total_ukri_funding.nuts3.yaml: -------------------------------------------------------------------------------- 1 | api_doc_url: https://gtr.ukri.org/resources/GtR-2-API-v1.7.4.pdf 2 | api_type: MySQL 3 | auth_provider: Nesta 4 | data_date: 20200910 5 | description: Total amount of funding (GBP) awarded by UK Research and Innovation (UKRI) for research and innovation projects. 6 | title: Total funding (GBP) for research awarded by UKRI 7 | subtitle: Sum of all research funding (GBP) awarded by UK Research and Innovation (UKRI) 8 | framework_group: public_rnd 9 | is_experimental: False 10 | is_public: False 11 | order: [year, nuts_id, nuts_year_spec, value.id] 12 | region: 13 | type: NutsRegion 14 | level: 3 15 | schema: 16 | nuts_id: 17 | type: NutsRegion.id 18 | nuts_year_spec: 19 | type: NutsRegion.year_spec 20 | value: 21 | data_type: int 22 | format: ',' 23 | id: total_ukri_funding 24 | label: Total Funding 25 | type: GBP 26 | year: 27 | data_type: int 28 | label: Year 29 | source_name: UKRI (UK Research and Innovation) 30 | source_url: https://gtr.ukri.org 31 | warning: Some projects have multiple organisations who may have been awarded funding, however due to limitations with the data provided by Gateway to Research we have attributed the total funding for a project to the lead organisation. -------------------------------------------------------------------------------- /ds/data/processed/industry/employment_culture_entertainment_recreation.lep.yaml: -------------------------------------------------------------------------------- 1 | api_doc_url: https://www.nomisweb.co.uk/api/v01/help 2 | api_type: POST 3 | data_date: 20200218 4 | title: Employment in cultural, entertainment and leisure industries 5 | subtitle: This indicator measures level of employment in cultural, entertainment and leisure industries in LEP regions based on the Business Register Employment Survey. Those sectors are identified as clusters of SIC-4 (industry) codes 6 | endpoint_url: http://www.nomisweb.co.uk/api/v01/dataset/ 7 | framework_group: place_potential 8 | is_experimental: True 9 | order: [year, lep_id, lep_year_spec, value.id] 10 | region: 11 | type: LepRegion 12 | source: PROVIDED 13 | schema: 14 | region_id: 15 | type: LepRegion.lep_id 16 | region_year_spec: 17 | type: LepRegion.lep_year_spec 18 | value: 19 | description: Total employment in cultural, entertainment and leisure industries 20 | id: employment_culture_entertainment_recreation 21 | label: Employment in cultural, entertainment and leisure industries 22 | data_type: int 23 | year: 24 | data_type: int 25 | label: Year 26 | source_name: NOMIS (official labour market statistics) 27 | source_url: https://www.nomisweb.co.uk 28 | year_range: [2016, 2018] 29 | -------------------------------------------------------------------------------- /ds/data/processed/innovate_uk/gbp_innovate_uk_funding.nuts2.yaml: -------------------------------------------------------------------------------- 1 | api_doc_url: https://www.gov.uk/government/publications/innovate-uk-funded-projects 2 | api_type: FETCH 3 | data_date: 20200821 4 | description: Level of funding awarded by Innovate UK to organisations in a region. Only includes awards that have not been withdrawn are not on hold. 5 | title: Funding received from Innovate UK 6 | subtitle: Funding awarded by Innovate UK to organisations in a location 7 | endpoint_url: https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/909140/20200801_Innovate_UK_Funded_Projects.xlsx 8 | framework_group: public_rnd 9 | is_experimental: False 10 | is_public: True 11 | order: [year, nuts_id, nuts_year_spec, value.id] 12 | region: 13 | type: NutsRegion 14 | level: 2 15 | schema: 16 | nuts_id: 17 | type: NutsRegion.nuts_id 18 | nuts_year_spec: # change this key to `nuts_year_spec` or `lep_year_spec` 19 | type: NutsRegion.nuts_year_spec 20 | value: 21 | data_type: int 22 | format: ',' 23 | id: gbp_innovate_uk_funding 24 | label: Funding received from Innovate UK 25 | type: GBP 26 | year: 27 | data_type: int 28 | label: Year 29 | source_name: Innovate UK (via gov.uk) 30 | source_url: https://www.gov.uk/ 31 | -------------------------------------------------------------------------------- /ds/beis_indicators/data/make_dataset.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | import logging 3 | import os 4 | 5 | import click 6 | import requests 7 | from dotenv import find_dotenv, load_dotenv 8 | 9 | import beis_indicators 10 | from beis_indicators import nomis 11 | 12 | logger = logging.getLogger("beis_indicators") 13 | 14 | 15 | def main(): 16 | """ Runs data processing scripts. 17 | 18 | Turn raw data into cleaned data ready to be analyzed. 19 | 20 | Usage: `python make_dataset.py -e True` 21 | 22 | Args: 23 | external (bool, optional): If True, force download of external files 24 | """ 25 | 26 | config = beis_indicators.config["data"] 27 | project_dir = beis_indicators.project_dir 28 | 29 | logger.info("Building nomis data") 30 | nomis_years = config["nomis"]["years"] 31 | nomis_geog = config["nomis"]["geography"].upper() 32 | nomis.make_nomis(geo_type=nomis_geog, year_l=nomis_years) 33 | nomis.make_nomis_complexity() 34 | 35 | 36 | if __name__ == "__main__": 37 | load_dotenv(find_dotenv()) 38 | 39 | try: 40 | msg = f"Making datasets" 41 | logger.info(msg) 42 | main() 43 | except (Exception, KeyboardInterrupt) as e: 44 | logger.exception(e, stack_info=True) 45 | raise e 46 | -------------------------------------------------------------------------------- /ds/data/processed/hebci/consultancy_facilities_non_sme.lep.yaml: -------------------------------------------------------------------------------- 1 | api_doc_url: https://www.hesa.ac.uk/data-and-analysis/providers/business-community/ 2 | api_type: FETCH 3 | data_date: 20200924 4 | description: Income for higher education institutions from providing consultancy and facilities services to non-SME companies. 5 | title: HE consultancy and facilities income from non-SME companies 6 | subtitle: Income for higher education institutions from providing consultancy and facilities services to non-SME companies. 7 | endpoint_url: https://www.hesa.ac.uk/data-and-analysis/providers/business-community/table-2a.csv 8 | framework_group: knowledge_exchange 9 | is_experimental: False 10 | is_public: True 11 | order: [year, lep_id, lep_year_spec, value.id] 12 | region: 13 | type: LepRegion 14 | source_url: LepRegion.source_url 15 | source: LepRegion.source 16 | schema: 17 | lep_id: 18 | type: LepRegion.lep_id 19 | lep_year_spec: 20 | type: LepRegion.lep_year_spec 21 | value: 22 | data_type: int 23 | id: consultancy_facilities_non_sme 24 | label: Income from consultancy and facilities 25 | type: GBP 26 | year: 27 | data_type: int 28 | label: Year 29 | source_name: HESA (Higher Education Statistical Agency) 30 | source_url: https://www.hesa.ac.uk/ 31 | -------------------------------------------------------------------------------- /ds/data/processed/hebci/contract_research_public_third.lep.yaml: -------------------------------------------------------------------------------- 1 | api_doc_url: https://www.hesa.ac.uk/data-and-analysis/providers/business-community/ 2 | api_type: FETCH 3 | data_date: 20200924 4 | description: Income for higher education institutions from providing contract research services to public and third sector organisations. 5 | title: Contract research income from government and NGOs 6 | subtitle: Income for higher education institutions from providing contract research services to public and third sector organisations 7 | endpoint_url: https://www.hesa.ac.uk/data-and-analysis/providers/business-community/table-2a.csv 8 | framework_group: knowledge_exchange 9 | is_experimental: False 10 | is_public: True 11 | order: [year, lep_id, lep_year_spec, value.id] 12 | region: 13 | type: LepRegion 14 | source_url: LepRegion.source_url 15 | source: LepRegion.source 16 | schema: 17 | lep_id: 18 | type: LepRegion.lep_id 19 | lep_year_spec: 20 | type: LepRegion.lep_year_spec 21 | value: 22 | data_type: int 23 | id: contract_research_public_third 24 | label: Income from contract research 25 | type: GBP 26 | year: 27 | data_type: int 28 | label: Year 29 | source_name: HESA (Higher Education Statistical Agency) 30 | source_url: https://www.hesa.ac.uk/ 31 | -------------------------------------------------------------------------------- /ds/data/processed/industry/economic_complexity_index.nuts3.yaml: -------------------------------------------------------------------------------- 1 | api_doc_url: https://www.nomisweb.co.uk/api/v01/help 2 | api_type: POST 3 | data_date: 20200218 4 | title: Economic Complexity Index 5 | subtitle: The Economic Complexity Index (ECI) captures the sophistication of economic activities in a location 6 | description: This indicator measures the economic complexity of NUTS 3 locations based on an analysis of its industrial composition ((based on Nesta sectoral definition, which clusters 4-digit SIC codes based on their similarity)) 7 | endpoint_url: http://www.nomisweb.co.uk/api/v01/dataset/ 8 | framework_group: private_rnd 9 | is_experimental: True 10 | order: [year, nuts_id, nuts_year_spec, value.id] 11 | region: 12 | type: NutsRegion 13 | level: 3 14 | source: PROVIDED 15 | schema: 16 | region_id: 17 | type: NutsRegion.nuts_id 18 | region_year_spec: 19 | type: NutsRegion.nuts_year_spec 20 | value: 21 | description: Economic complexity Index 22 | format: .4f 23 | id: economic_complexity_index 24 | label: Economic complexity Index 25 | data_type: float 26 | year: 27 | data_type: int 28 | label: Year 29 | source_name: NOMIS (official labour market statistics) 30 | source_url: https://www.nomisweb.co.uk 31 | year_range: [2016, 2019] 32 | -------------------------------------------------------------------------------- /ds/data/processed/hebci/spinoff_investment.nuts2.yaml: -------------------------------------------------------------------------------- 1 | api_doc_url: https://www.hesa.ac.uk/data-and-analysis/providers/business-community/ 2 | api_type: FETCH 3 | data_date: 20200928 4 | description: Total external investment in all spin-off activities associated with higher education institutions (spin-offs with some HEP ownership, formal spin-offs not HEP owned, staff start-ups, graduate start-ups and social enterprises). 5 | title: External investment in higher education spin-offs 6 | subtitle: Investment in higher education spin-offs 7 | endpoint_url: https://www.hesa.ac.uk/data-and-analysis/providers/business-community/table-4e.csv 8 | framework_group: knowledge_exchange 9 | is_experimental: False 10 | is_public: True 11 | order: [year, nuts_id, nuts_year_spec, value.id] 12 | region: 13 | level: 2 14 | type: NutsRegion 15 | source_url: NutsRegion.source_url 16 | source: NutsRegion.source 17 | schema: 18 | nuts_id: 19 | type: NutsRegion.nuts_id 20 | nuts_year_spec: 21 | type: NutsRegion.nuts_year_spec 22 | value: 23 | data_type: int 24 | id: spinoff_investment 25 | label: Investment 26 | type: GBP 27 | year: 28 | data_type: int 29 | label: Year 30 | source_name: HESA (Higher Education Statistical Agency) 31 | source_url: https://www.hesa.ac.uk/ 32 | -------------------------------------------------------------------------------- /ds/data/processed/hebci/spinoff_investment.nuts3.yaml: -------------------------------------------------------------------------------- 1 | api_doc_url: https://www.hesa.ac.uk/data-and-analysis/providers/business-community/ 2 | api_type: FETCH 3 | data_date: 20200928 4 | description: Total external investment in all spin-off activities associated with higher education institutions (spin-offs with some HEP ownership, formal spin-offs not HEP owned, staff start-ups, graduate start-ups and social enterprises). 5 | title: External investment in higher education spin-offs 6 | subtitle: Investment in higher education spin-offs 7 | endpoint_url: https://www.hesa.ac.uk/data-and-analysis/providers/business-community/table-4e.csv 8 | framework_group: knowledge_exchange 9 | is_experimental: False 10 | is_public: True 11 | order: [year, nuts_id, nuts_year_spec, value.id] 12 | region: 13 | level: 3 14 | type: NutsRegion 15 | source_url: NutsRegion.source_url 16 | source: NutsRegion.source 17 | schema: 18 | nuts_id: 19 | type: NutsRegion.nuts_id 20 | nuts_year_spec: 21 | type: NutsRegion.nuts_year_spec 22 | value: 23 | data_type: int 24 | id: spinoff_investment 25 | label: Investment 26 | type: GBP 27 | year: 28 | data_type: int 29 | label: Year 30 | source_name: HESA (Higher Education Statistical Agency) 31 | source_url: https://www.hesa.ac.uk/ 32 | -------------------------------------------------------------------------------- /ds/data/processed/hebci/spinoff_revenue.lep.yaml: -------------------------------------------------------------------------------- 1 | api_doc_url: https://www.hesa.ac.uk/data-and-analysis/providers/business-community/ 2 | api_type: FETCH 3 | data_date: 20200928 4 | description: Total turnover of all active spin-off activities associated with higher education institutions (spin-offs with some HEP ownership, formal spin-offs not HEP owned, staff start-ups, graduate start-ups and social enterprises). 5 | title: Total turnover of higher education spin-offs 6 | subtitle: Total turnover of all spin-off activities associated with higher education institutions. 7 | endpoint_url: https://www.hesa.ac.uk/data-and-analysis/providers/business-community/table-4e.csv 8 | framework_group: knowledge_exchange 9 | is_experimental: False 10 | is_public: True 11 | order: [year, lep_id, lep_year_spec, value.id] 12 | region: 13 | type: LepRegion 14 | source_url: LepRegion.source_url 15 | source: LepRegion.source 16 | schema: 17 | lep_id: 18 | type: LepRegion.lep_id 19 | lep_year_spec: 20 | type: LepRegion.lep_year_spec 21 | value: 22 | data_type: int 23 | id: spinoff_revenue 24 | label: Turnover 25 | type: GBP 26 | year: 27 | data_type: int 28 | label: Year 29 | source_name: HESA (Higher Education Statistical Agency) 30 | source_url: https://www.hesa.ac.uk/ 31 | -------------------------------------------------------------------------------- /ds/data/processed/aps/aps_econ_active_stem_associate_profs_data.lep.yaml: -------------------------------------------------------------------------------- 1 | api_doc_url: https://www.nomisweb.co.uk/api/v01/help 2 | api_type: FETCH 3 | title: STEM associate professionals 4 | subtitle: Number of Science, Research, Engineering and Technology associated professionals by LEP regions 5 | endpoint_url: http://www.nomisweb.co.uk/api/v01/dataset/NM_17_1.data.csv?geography=TYPE459&date=2019-12,2018-12,2017-12,2016-12,2015-12,2014-12,2013-12,2012-12,2011-12,2010-12&cell=404883201&measures=20100,20701&select=date,date_name,date_code,geography_type,geography_code,measures_name,variable,cell_name,obs_value,obs_status_name,obs_status,record_count 6 | framework_group: private_rnd 7 | is_experimental: False 8 | order: [year, lep_id, lep_year_spec, value.id] # do not change; specifies the order of the exported indicator fields 9 | region: 10 | type: LepRegion 11 | source: PROVIDED 12 | schema: 13 | nuts_id: 14 | type: LepRegion.lep_id 15 | nuts_year_spec: 16 | type: LepRegion.lep_year_spec 17 | value: 18 | id: aps_econ_active_stem_associate_profs_data 19 | label: Frequency 20 | data_type: int 21 | year: 22 | data_type: int 23 | label: Year 24 | source_name: NOMIS (official labour market statistics) 25 | source_url: https://www.nomisweb.co.uk 26 | -------------------------------------------------------------------------------- /ds/data/processed/industry/employment_culture_entertainment_recreation.nuts3.yaml: -------------------------------------------------------------------------------- 1 | api_doc_url: https://www.nomisweb.co.uk/api/v01/help 2 | api_type: POST 3 | data_date: 20200218 4 | title: Employment in cultural, entertainment and leisure industries 5 | subtitle: This indicator measures level of employment in cultural, entertainment and leisure industries in NUTS 3 regions based on the Business Register Employment Survey. Those sectors are identified as clusters of SIC-4 (industry) codes 6 | endpoint_url: http://www.nomisweb.co.uk/api/v01/dataset/ 7 | framework_group: place_potential 8 | is_experimental: True 9 | order: [year, nuts_id, nuts_year_spec, value.id] 10 | region: 11 | type: NutsRegion 12 | level: 3 13 | source: PROVIDED 14 | schema: 15 | region_id: 16 | type: NutsRegion.nuts_id 17 | region_year_spec: 18 | type: NutsRegion.nuts_year_spec 19 | value: 20 | description: Total employment in cultural, entertainment and leisure industries 21 | id: employment_culture_entertainment_recreation 22 | label: Employment in cultural, entertainment and leisure industries 23 | data_type: int 24 | year: 25 | data_type: int 26 | label: Year 27 | source_name: NOMIS (official labour market statistics) 28 | source_url: https://www.nomisweb.co.uk 29 | year_range: [2016, 2018] 30 | -------------------------------------------------------------------------------- /ds/data/processed/hebci/consultancy_facilities_non_sme.nuts2.yaml: -------------------------------------------------------------------------------- 1 | api_doc_url: https://www.hesa.ac.uk/data-and-analysis/providers/business-community/ 2 | api_type: FETCH 3 | data_date: 20200924 4 | description: Income for higher education institutions from providing consultancy and facilities services to non-SME companies. 5 | title: HE consultancy and facilities income from non-SME companies 6 | subtitle: Income for higher education institutions from providing consultancy and facilities services to non-SME companies. 7 | endpoint_url: https://www.hesa.ac.uk/data-and-analysis/providers/business-community/table-2a.csv 8 | framework_group: knowledge_exchange 9 | is_experimental: False 10 | is_public: True 11 | order: [year, nuts_id, nuts_year_spec, value.id] 12 | region: 13 | type: NutsRegion 14 | level: 2 15 | source_url: NutsRegion.source_url 16 | source: NutsRegion.source 17 | schema: 18 | nuts_id: 19 | type: NutsRegion.nuts_id 20 | nuts_year_spec: 21 | type: NutsRegion.nuts_year_spec 22 | value: 23 | data_type: int 24 | id: consultancy_facilities_non_sme 25 | label: Income from consultancy and facilities 26 | type: GBP 27 | year: 28 | data_type: int 29 | label: Year 30 | source_name: HESA (Higher Education Statistical Agency) 31 | source_url: https://www.hesa.ac.uk/ 32 | -------------------------------------------------------------------------------- /ds/data/processed/hebci/consultancy_facilities_non_sme.nuts3.yaml: -------------------------------------------------------------------------------- 1 | api_doc_url: https://www.hesa.ac.uk/data-and-analysis/providers/business-community/ 2 | api_type: FETCH 3 | data_date: 20200924 4 | description: Income for higher education institutions from providing consultancy and facilities services to non-SME companies. 5 | title: HE consultancy and facilities income from non-SME companies 6 | subtitle: Income for higher education institutions from providing consultancy and facilities services to non-SME companies. 7 | endpoint_url: https://www.hesa.ac.uk/data-and-analysis/providers/business-community/table-2a.csv 8 | framework_group: knowledge_exchange 9 | is_experimental: False 10 | is_public: True 11 | order: [year, nuts_id, nuts_year_spec, value.id] 12 | region: 13 | type: NutsRegion 14 | level: 3 15 | source_url: NutsRegion.source_url 16 | source: NutsRegion.source 17 | schema: 18 | nuts_id: 19 | type: NutsRegion.nuts_id 20 | nuts_year_spec: 21 | type: NutsRegion.nuts_year_spec 22 | value: 23 | data_type: int 24 | id: consultancy_facilities_non_sme 25 | label: Income from consultancy and facilities 26 | type: GBP 27 | year: 28 | data_type: int 29 | label: Year 30 | source_name: HESA (Higher Education Statistical Agency) 31 | source_url: https://www.hesa.ac.uk/ 32 | -------------------------------------------------------------------------------- /ds/data/processed/hebci/contract_research_public_third.nuts2.yaml: -------------------------------------------------------------------------------- 1 | api_doc_url: https://www.hesa.ac.uk/data-and-analysis/providers/business-community/ 2 | api_type: FETCH 3 | data_date: 20200924 4 | description: Income for higher education institutions from providing contract research services to public and third sector organisations. 5 | title: Contract research income from government and NGOs 6 | subtitle: Income for higher education institutions from providing contract research services to public and third sector organisations 7 | endpoint_url: https://www.hesa.ac.uk/data-and-analysis/providers/business-community/table-2a.csv 8 | framework_group: knowledge_exchange 9 | is_experimental: False 10 | is_public: True 11 | order: [year, nuts_id, nuts_year_spec, value.id] 12 | region: 13 | type: NutsRegion 14 | level: 2 15 | source_url: NutsRegion.source_url 16 | source: NutsRegion.source 17 | schema: 18 | nuts_id: 19 | type: NutsRegion.nuts_id 20 | nuts_year_spec: 21 | type: NutsRegion.nuts_year_spec 22 | value: 23 | data_type: int 24 | id: contract_research_public_third 25 | label: Income from contract research 26 | type: GBP 27 | year: 28 | data_type: int 29 | label: Year 30 | source_name: HESA (Higher Education Statistical Agency) 31 | source_url: https://www.hesa.ac.uk/ 32 | -------------------------------------------------------------------------------- /ds/data/processed/hebci/contract_research_public_third.nuts3.yaml: -------------------------------------------------------------------------------- 1 | api_doc_url: https://www.hesa.ac.uk/data-and-analysis/providers/business-community/ 2 | api_type: FETCH 3 | data_date: 20200924 4 | description: Income for higher education institutions from providing contract research services to public and third sector organisations. 5 | title: Contract research income from government and NGOs 6 | subtitle: Income for higher education institutions from providing contract research services to public and third sector organisations 7 | endpoint_url: https://www.hesa.ac.uk/data-and-analysis/providers/business-community/table-2a.csv 8 | framework_group: knowledge_exchange 9 | is_experimental: False 10 | is_public: True 11 | order: [year, nuts_id, nuts_year_spec, value.id] 12 | region: 13 | type: NutsRegion 14 | level: 3 15 | source_url: NutsRegion.source_url 16 | source: NutsRegion.source 17 | schema: 18 | nuts_id: 19 | type: NutsRegion.nuts_id 20 | nuts_year_spec: 21 | type: NutsRegion.nuts_year_spec 22 | value: 23 | data_type: int 24 | id: contract_research_public_third 25 | label: Income from contract research 26 | type: GBP 27 | year: 28 | data_type: int 29 | label: Year 30 | source_name: HESA (Higher Education Statistical Agency) 31 | source_url: https://www.hesa.ac.uk/ 32 | -------------------------------------------------------------------------------- /ds/data/processed/hebci/spinoff_revenue.nuts2.yaml: -------------------------------------------------------------------------------- 1 | api_doc_url: https://www.hesa.ac.uk/data-and-analysis/providers/business-community/ 2 | api_type: FETCH 3 | data_date: 20200928 4 | description: Total turnover of all active spin-off activities associated with higher education institutions (spin-offs with some HEP ownership, formal spin-offs not HEP owned, staff start-ups, graduate start-ups and social enterprises). 5 | title: Total turnover of higher education spin-offs 6 | subtitle: Total turnover of all spin-off activities associated with higher education institutions. 7 | endpoint_url: https://www.hesa.ac.uk/data-and-analysis/providers/business-community/table-4e.csv 8 | framework_group: knowledge_exchange 9 | is_experimental: False 10 | is_public: True 11 | order: [year, nuts_id, nuts_year_spec, value.id] 12 | region: 13 | level: 2 14 | type: NutsRegion 15 | source_url: NutsRegion.source_url 16 | source: NutsRegion.source 17 | schema: 18 | nuts_id: 19 | type: NutsRegion.nuts_id 20 | nuts_year_spec: 21 | type: NutsRegion.nuts_year_spec 22 | value: 23 | data_type: int 24 | id: spinoff_revenue 25 | label: Turnover 26 | type: GBP 27 | year: 28 | data_type: int 29 | label: Year 30 | source_name: HESA (Higher Education Statistical Agency) 31 | source_url: https://www.hesa.ac.uk/ 32 | -------------------------------------------------------------------------------- /ds/data/processed/hebci/spinoff_revenue.nuts3.yaml: -------------------------------------------------------------------------------- 1 | api_doc_url: https://www.hesa.ac.uk/data-and-analysis/providers/business-community/ 2 | api_type: FETCH 3 | data_date: 20200928 4 | description: Total turnover of all active spin-off activities associated with higher education institutions (spin-offs with some HEP ownership, formal spin-offs not HEP owned, staff start-ups, graduate start-ups and social enterprises). 5 | title: Total turnover of higher education spin-offs 6 | subtitle: Total turnover of all spin-off activities associated with higher education institutions. 7 | endpoint_url: https://www.hesa.ac.uk/data-and-analysis/providers/business-community/table-4e.csv 8 | framework_group: knowledge_exchange 9 | is_experimental: False 10 | is_public: True 11 | order: [year, nuts_id, nuts_year_spec, value.id] 12 | region: 13 | level: 3 14 | type: NutsRegion 15 | source_url: NutsRegion.source_url 16 | source: NutsRegion.source 17 | schema: 18 | nuts_id: 19 | type: NutsRegion.nuts_id 20 | nuts_year_spec: 21 | type: NutsRegion.nuts_year_spec 22 | value: 23 | data_type: int 24 | id: spinoff_revenue 25 | label: Turnover 26 | type: GBP 27 | year: 28 | data_type: int 29 | label: Year 30 | source_name: HESA (Higher Education Statistical Agency) 31 | source_url: https://www.hesa.ac.uk/ 32 | -------------------------------------------------------------------------------- /ds/data/processed/hebci/consultancy_facilities_public_third.lep.yaml: -------------------------------------------------------------------------------- 1 | api_doc_url: https://www.hesa.ac.uk/data-and-analysis/providers/business-community/ 2 | api_type: FETCH 3 | data_date: 20200924 4 | description: Income for higher education institutions from providing consultancy and facilities services to public and third sector organisations. 5 | title: Consultancy and facilities income from governent and NGOs 6 | subtitle: Income for higher education institutions from providing consultancy and facilities services to public and third sector organisations 7 | endpoint_url: https://www.hesa.ac.uk/data-and-analysis/providers/business-community/table-2a.csv 8 | framework_group: knowledge_exchange 9 | is_experimental: False 10 | is_public: True 11 | order: [year, lep_id, lep_year_spec, value.id] 12 | region: 13 | type: LepRegion 14 | source_url: LepRegion.source_url 15 | source: LepRegion.source 16 | schema: 17 | lep_id: 18 | type: LepRegion.lep_id 19 | lep_year_spec: 20 | type: LepRegion.lep_year_spec 21 | value: 22 | data_type: int 23 | id: consultancy_facilities_public_third 24 | label: Income from consultancy and facilities 25 | type: GBP 26 | year: 27 | data_type: int 28 | label: Year 29 | source_name: HESA (Higher Education Statistical Agency) 30 | source_url: https://www.hesa.ac.uk/ 31 | -------------------------------------------------------------------------------- /ds/data/processed/ref/mean_ref.yaml: -------------------------------------------------------------------------------- 1 | api_doc_url: https://www.ref.ac.uk/2014/pubs/201401/ 2 | api_type: FETCH 3 | data_date: 20200204 4 | title: Mean REF score 5 | description: These are the results of the Research Excellence Framework, where university departments in various disciplines are assessed on the quality of their research. The latest REF was conducted in 2014 so data is available only for one year 6 | subtitle: Mean Research Excellence Framework score for higher education institutions in the region 7 | endpoint_url: https://results.ref.ac.uk/(S(hlvnuqzwkag44jp3df3d4q14))/DownloadFile/AllResults/xlsx 8 | framework_group: public_rnd 9 | is_experimental: False 10 | nuts_level: 2 11 | order: [year, nuts_id, nuts_year_spec, value.id] 12 | region: 13 | type: NutsRegion 14 | level: 2 15 | source_url: http://geoportal1-ons.opendata.arcgis.com/datasets/48b6b85bb7ea43699ee85f4ecd12fd36_0.zip?outSR=%7B%22latestWkid%22:27700,%22wkid%22:27700%7D 16 | schema: 17 | nuts_id: 18 | type: NutsRegion.id 19 | nuts_year_spec: 20 | type: NutsRegion.year_spec 21 | value: 22 | format: .2f 23 | id: mean_ref 24 | label: Mean Research Excellence Framework Score 25 | type: REF 26 | year: 27 | data_type: int 28 | label: Year 29 | source_name: REF (Research Excellence Framework) 30 | source_url: https://results.ref.ac.uk/ 31 | -------------------------------------------------------------------------------- /ds/data/processed/travel/travel_time_to_airport.lep.yaml: -------------------------------------------------------------------------------- 1 | api_type: FETCH 2 | title: Travel time to nearest airport 3 | subtitle: Minimum travel time to nearest airport from a LEP region for England (2011 & 2013) and Wales (2011 only) 4 | description: Minimum travel time to nearest airport from a LEP region for England (2011 & 2013) and Wales (2011 only). Overlapping LEP regions are used therefore some calculations may be under-estimated or over-estimated. 5 | endpoint_url: 6 | 2011: http://data.dft.gov.uk.s3.amazonaws.com/connectivity-data/Airports-travel-times.zip 7 | 2013: http://data.dft.gov.uk.s3.amazonaws.com/connectivity-data/2013-travel-times.zip 8 | # year: endpoint_url 9 | framework_group: place_potential 10 | is_experimental: False 11 | order: [year, lep_id, lep_year_spec, value.id] # do not change; specifies the order of the exported indicator fields 12 | region: 13 | type: LepRegion # the region type, e.g. NutsRegion or LepRegion 14 | source: PROVIDED 15 | schema: 16 | nuts_id: 17 | type: LepRegion.lep_id 18 | nuts_year_spec: 19 | type: LepRegion.lep_year_spec 20 | value: 21 | format: .2f 22 | id: travel_time_to_airport 23 | label: Time (Minutes) 24 | data_type: float 25 | year: 26 | data_type: int 27 | label: Year 28 | source_name: Department for Transport 29 | -------------------------------------------------------------------------------- /ds/data/processed/travel/travel_time_to_rail.lep.yaml: -------------------------------------------------------------------------------- 1 | api_type: FETCH 2 | title: Travel time to nearest rail station 3 | subtitle: Minimum travel time to nearest rail station from a LEP region for England (2011 & 2013) and Wales (2011 only) 4 | description: Minimum travel time to nearest rail station from a LEP region for England (2011 & 2013) and Wales (2011 only). Overlapping LEP regions are used therefore some calculations may be under-estimated or over-estimated. 5 | endpoint_url: 6 | 2011: http://data.dft.gov.uk.s3.amazonaws.com/connectivity-data/Rail-stations-travel-times.zip 7 | 2013: http://data.dft.gov.uk.s3.amazonaws.com/connectivity-data/2013-travel-times.zip 8 | # year: endpoint_url 9 | framework_group: place_potential 10 | is_experimental: False 11 | order: [year, lep_id, lep_year_spec, value.id] # do not change; specifies the order of the exported indicator fields 12 | region: 13 | type: LepRegion # the region type, e.g. NutsRegion or LepRegion 14 | source: PROVIDED 15 | schema: 16 | nuts_id: 17 | type: LepRegion.lep_id 18 | nuts_year_spec: 19 | type: LepRegion.lep_year_spec 20 | value: 21 | format: .2f 22 | id: travel_time_to_rail 23 | label: Time (Minutes) 24 | data_type: float 25 | year: 26 | data_type: int 27 | label: Year 28 | source_name: Department for Transport 29 | -------------------------------------------------------------------------------- /ds/data/processed/hebci/consultancy_facilities_public_third.nuts2.yaml: -------------------------------------------------------------------------------- 1 | api_doc_url: https://www.hesa.ac.uk/data-and-analysis/providers/business-community/ 2 | api_type: FETCH 3 | data_date: 20200924 4 | description: Income for higher education institutions from providing consultancy and facilities services to public and third sector organisations. 5 | title: Consultancy and facilities income from governent and NGOs 6 | subtitle: Income for higher education institutions from providing consultancy and facilities services to public and third sector organisations 7 | endpoint_url: https://www.hesa.ac.uk/data-and-analysis/providers/business-community/table-2a.csv 8 | framework_group: knowledge_exchange 9 | is_experimental: False 10 | is_public: True 11 | order: [year, nuts_id, nuts_year_spec, value.id] 12 | region: 13 | type: NutsRegion 14 | level: 2 15 | source_url: NutsRegion.source_url 16 | source: NutsRegion.source 17 | schema: 18 | nuts_id: 19 | type: NutsRegion.nuts_id 20 | nuts_year_spec: 21 | type: NutsRegion.nuts_year_spec 22 | value: 23 | data_type: int 24 | id: consultancy_facilities_public_third 25 | label: Income from consultancy and facilities 26 | type: GBP 27 | year: 28 | data_type: int 29 | label: Year 30 | source_name: HESA (Higher Education Statistical Agency) 31 | source_url: https://www.hesa.ac.uk/ 32 | -------------------------------------------------------------------------------- /ds/data/processed/hebci/consultancy_facilities_public_third.nuts3.yaml: -------------------------------------------------------------------------------- 1 | api_doc_url: https://www.hesa.ac.uk/data-and-analysis/providers/business-community/ 2 | api_type: FETCH 3 | data_date: 20200924 4 | description: Income for higher education institutions from providing consultancy and facilities services to public and third sector organisations. 5 | title: Consultancy and facilities income from governent and NGOs 6 | subtitle: Income for higher education institutions from providing consultancy and facilities services to public and third sector organisations 7 | endpoint_url: https://www.hesa.ac.uk/data-and-analysis/providers/business-community/table-2a.csv 8 | framework_group: knowledge_exchange 9 | is_experimental: False 10 | is_public: True 11 | order: [year, nuts_id, nuts_year_spec, value.id] 12 | region: 13 | type: NutsRegion 14 | level: 3 15 | source_url: NutsRegion.source_url 16 | source: NutsRegion.source 17 | schema: 18 | nuts_id: 19 | type: NutsRegion.nuts_id 20 | nuts_year_spec: 21 | type: NutsRegion.nuts_year_spec 22 | value: 23 | data_type: int 24 | id: consultancy_facilities_public_third 25 | label: Income from consultancy and facilities 26 | type: GBP 27 | year: 28 | data_type: int 29 | label: Year 30 | source_name: HESA (Higher Education Statistical Agency) 31 | source_url: https://www.hesa.ac.uk/ 32 | -------------------------------------------------------------------------------- /ds/beis_indicators/data/process_patstat.py: -------------------------------------------------------------------------------- 1 | import os 2 | import pandas as pd 3 | from dotenv import find_dotenv, load_dotenv 4 | 5 | from data_getters.patstat import select_patstat 6 | 7 | load_dotenv(find_dotenv()) 8 | CONFIG = os.getenv("CONFIG") 9 | 10 | 11 | def get_patstat( 12 | person_ctry_code="GB", earliest_filing_year=2010, database="patstat_2019_05_13" 13 | ): 14 | """Data getter wrapper to get patent data from our database. 15 | 16 | Args: 17 | person_ctry_code (:obj:`str`): Country code of the patent applicant. 18 | earliest_filing_year (:obj:`int`): Year of the earliest patent to retrieve. 19 | database (:obj:`str`): Database name. 20 | 21 | Returns: 22 | (:obj:`dict` of :obj:`pd.DataFrame`) PATSTAT tables. 23 | 24 | """ 25 | return select_patstat( 26 | CONFIG, 27 | person_ctry_code=person_ctry_code, 28 | earliest_filing_year=earliest_filing_year, 29 | database=database, 30 | ) 31 | 32 | 33 | def prepare_patstat(dfs): 34 | """Get patent application IDs and patent descriptions that are written in English 35 | 36 | Args: 37 | dfs (:obj:`dict` of :obj:`pd.DataFrame`) PATSTAT tables. 38 | 39 | Return: 40 | (:obj:`pd.DataFrame`) 41 | 42 | """ 43 | return dfs["appln_abstr"][dfs["appln_abstr"].appln_abstract_lg == "en"] 44 | -------------------------------------------------------------------------------- /ds/data/processed/aps/aps_econ_active_stem_density_data.lep.yaml: -------------------------------------------------------------------------------- 1 | api_doc_url: https://www.nomisweb.co.uk/api/v01/help 2 | api_type: FETCH 3 | title: STEM employee density 4 | subtitle: Percentage of population employed in science, research, engineering and technology professional by LEP regions. 5 | endpoint_url: http://www.nomisweb.co.uk/api/v01/dataset/NM_17_5.data.csv?geography=TYPE459&date=2019-12,2018-12,2017-12,2016-12,2015-12,2014-12,2013-12,2012-12,2011-12,2010-12&variable=1543&measures=20599,21001,21002,21003&select=date,date_name,date_code,geography_type,geography_code,measures_name,variable,variable_name,obs_value,obs_status_name,obs_status,record_count 6 | framework_group: private_rnd 7 | is_experimental: False 8 | order: [year, lep_id, lep_year_spec, value.id] # do not change; specifies the order of the exported indicator fields 9 | region: 10 | type: LepRegion 11 | source: PROVIDED 12 | schema: 13 | nuts_id: 14 | type: LepRegion.lep_id 15 | nuts_year_spec: 16 | type: LepRegion.lep_year_spec 17 | value: 18 | format: .1f 19 | id: aps_econ_active_stem_density_data 20 | label: Percentage 21 | data_type: float 22 | unit_string: '%' 23 | year: 24 | data_type: int 25 | label: Year 26 | source_name: NOMIS (official labour market statistics) 27 | source_url: https://www.nomisweb.co.uk 28 | -------------------------------------------------------------------------------- /ds/data/processed/ref/mean_ref_stem.yaml: -------------------------------------------------------------------------------- 1 | api_doc_url: https://www.ref.ac.uk/2014/pubs/201401/ 2 | api_type: FETCH 3 | data_date: 20200204 4 | title: Mean REF Score in STEM disciplines 5 | description: These are the results of the Research Excellence Framework, where university departments in various disciplines are assessed on the quality of their research. The latest REF was conducted in 2014 so data is available only for one year 6 | subtitle: Mean REF score in STEM subjects for higher education institutions in the region 7 | endpoint_url: https://results.ref.ac.uk/(S(hlvnuqzwkag44jp3df3d4q14))/DownloadFile/AllResults/xlsx 8 | framework_group: public_rnd 9 | is_experimental: False 10 | nuts_level: 2 11 | order: [year, nuts_id, nuts_year_spec, value.id] 12 | region: 13 | type: NutsRegion 14 | level: 2 15 | source_url: http://geoportal1-ons.opendata.arcgis.com/datasets/48b6b85bb7ea43699ee85f4ecd12fd36_0.zip?outSR=%7B%22latestWkid%22:27700,%22wkid%22:27700%7D 16 | schema: 17 | nuts_id: 18 | type: NutsRegion.id 19 | nuts_year_spec: 20 | type: NutsRegion.year_spec 21 | value: 22 | format: .2f 23 | id: mean_ref_stem 24 | label: Mean Research Excellence Framework Score 25 | type: REF 26 | year: 27 | data_type: int 28 | label: Year 29 | source_name: REF (Research Excellence Framework) 30 | source_url: https://results.ref.ac.uk/ 31 | -------------------------------------------------------------------------------- /ds/data/processed/aps/aps_pro_occupations_data.lep.yaml: -------------------------------------------------------------------------------- 1 | api_doc_url: https://www.nomisweb.co.uk/api/v01/help 2 | api_type: FETCH 3 | title: Professional occupations employment 4 | subtitle: Percentage of population employed in professional occupations by LEP regions. 5 | endpoint_url: http://www.nomisweb.co.uk/api/v01/dataset/NM_17_5.data.csv?geography=TYPE459&date=2019-12,2018-12,2017-12,2016-12,2015-12,2014-12,2013-12,2012-12,2011-12,2010-12&variable=1533&measures=20599,21001,21002,21003&select=date,date_name,date_code,geography_type,geography_code,measures_name,variable,variable_name,obs_value,obs_status_name,obs_status,record_count 6 | framework_group: public_rnd 7 | is_experimental: False 8 | order: [year, lep_id, lep_year_spec, value.id] # do not change; specifies the order of the exported indicator fields 9 | region: 10 | type: LepRegion # the region type, e.g. NutsRegion or LepRegion 11 | source: PROVIDED 12 | schema: 13 | nuts_id: 14 | type: LepRegion.lep_id 15 | nuts_year_spec: 16 | type: LepRegion.lep_year_spec 17 | value: 18 | data_type: float 19 | format: .1f 20 | id: aps_pro_occupations_data 21 | label: Percentage 22 | unit_string: '%' 23 | year: 24 | data_type: int 25 | label: Year 26 | source_name: NOMIS (official labour market statistics) 27 | source_url: https://www.nomisweb.co.uk 28 | -------------------------------------------------------------------------------- /ds/data/processed/aps/aps_nvq4_education_data.lep.yaml: -------------------------------------------------------------------------------- 1 | api_doc_url: https://www.nomisweb.co.uk/api/v01/help 2 | api_type: FETCH 3 | title: Economically active professionals with NVQ4 or above 4 | subtitle: Percentage of economically active persons in professional occupations with NVQ level 4 or above by LEP regions. 5 | endpoint_url: http://www.nomisweb.co.uk/api/v01/dataset/NM_17_5.data.csv?geography=TYPE459&date=2019-12,2018-12,2017-12,2016-12,2015-12,2014-12,2013-12,2012-12,2011-12,2010-12&variable=546&measures=20599,21001,21002,21003&select=date,date_name,date_code,geography_type,geography_code,measures_name,variable,variable_name,obs_value,obs_status_name,obs_status,record_count 6 | framework_group: place_potential 7 | is_experimental: False 8 | order: [year, lep_id, lep_year_spec, value.id] # do not change; specifies the order of the exported indicator fields 9 | region: 10 | type: LepRegion 11 | source: PROVIDED 12 | schema: 13 | nuts_id: 14 | type: LepRegion.lep_id 15 | nuts_year_spec: 16 | type: LepRegion.lep_year_spec 17 | value: 18 | data_type: float 19 | format: .1f 20 | id: aps_nvq4_education_data 21 | label: Percentage 22 | unit_string: '%' 23 | year: 24 | data_type: int 25 | label: Year 26 | source_name: NOMIS (official labour market statistics) 27 | source_url: https://www.nomisweb.co.uk 28 | -------------------------------------------------------------------------------- /ds/data/processed/eurostat/eurostat_private_households_income.nuts2.yaml: -------------------------------------------------------------------------------- 1 | api_doc_url: https://ec.europa.eu/eurostat/cache/metadata/en/rd_esms.htm 2 | api_type: FETCH 3 | title: Private household income 4 | subtitle: Primary income of private households by NUTS 2 regions. 5 | description: Income of private households including income from labour, income of assets (interest, dividends and rents) and income from net operating surplus and self-employment. Negative values for households are interest and rents payable. 6 | endpoint_url: http://ec.europa.eu/eurostat/wdds/rest/data/v2.1/json/en/tgs00036?sinceTimePeriod=2007&precision=2&unit=PPS_HAB' 7 | framework_group: place_potential 8 | is_experimental: False 9 | order: [year, nuts_id, nuts_year_spec, value.id] # do not change; specifies the order of the exported indicator fields 10 | region: 11 | type: NutsRegion 12 | level: 2 13 | source: PROVIDED 14 | schema: 15 | nuts_id: 16 | type: NutsRegion.nuts_id 17 | nuts_year_spec: 18 | type: NutsRegion.nuts_year_spec 19 | value: 20 | format: ',' 21 | id: eurostat_private_households_income 22 | label: Income (to nearest 100) 23 | type: EUR 24 | data_type: int 25 | year: 26 | data_type: int 27 | label: Year 28 | source_name: Eurostat (European Statistical Office) 29 | source_url: https://ec.europa.eu/eurostat/ 30 | -------------------------------------------------------------------------------- /ds/data/processed/innovate_uk/gbp_innovate_uk_funding.nuts3.yaml: -------------------------------------------------------------------------------- 1 | api_doc_url: https://www.gov.uk/government/publications/innovate-uk-funded-projects 2 | api_type: FETCH 3 | data_date: 20200821 4 | description: Level of funding awarded by Innovate UK to organisations in a region. Only includes awards that have not been withdrawn are not on hold. It was not possible to match a small fraction of the awards due to issues with the postcodes. 5 | title: Funding received from Innovate UK 6 | subtitle: Funding awarded by Innovate UK to organisations in a location 7 | endpoint_url: https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/909140/20200801_Innovate_UK_Funded_Projects.xlsx 8 | framework_group: public_rnd 9 | is_experimental: False 10 | is_public: True 11 | order: [year, nuts_id, nuts_year_spec, value.id] 12 | region: 13 | type: NutsRegion 14 | level: 3 15 | schema: 16 | nuts_id: 17 | type: NutsRegion.nuts_id 18 | nuts_year_spec: # change this key to `nuts_year_spec` or `lep_year_spec` 19 | type: NutsRegion.nuts_year_spec 20 | value: 21 | data_type: int 22 | format: ',' 23 | id: gbp_innovate_uk_funding 24 | label: Funding received from Innovate UK 25 | type: GBP 26 | year: 27 | data_type: int 28 | label: Year 29 | source_name: Innovate UK (via gov.uk) 30 | source_url: https://www.gov.uk/ 31 | -------------------------------------------------------------------------------- /ds/data/aux/priority_aggregation.json: -------------------------------------------------------------------------------- 1 | {"AI":"data_analytics_ai", 2 | "AR/VR":"digital_applications", 3 | "Advanced manufacturing":"industrial_technologies", 4 | "Advanced materials":"scientific_biomedical", 5 | "Advanced sensing":"industrial_technologies", 6 | "Autonomous vehicles":"transport_aerospace", 7 | "Big data":"data_analytics_ai", 8 | "Blockchain":"digital_applications", 9 | "Carbon capture and storage":"environmental_technologies", 10 | "Circular economy":"environmental_technologies", 11 | "Cloud computing":"ict", 12 | "Cyber security":"ict", 13 | "Digital health":"digital_applications", 14 | "Digital twins":"data_analytics_ai", 15 | "Distributed generation":"environmental_technologies", 16 | "Drones":"transport_aerospace", 17 | "Education technologies":"digital_applications", 18 | "Electric vehicles":"transport_aerospace", 19 | "Gene therapy":"scientific_biomedical", 20 | "Grid parity & Renewables":"environmental_technologies", 21 | "Intelligent transport systems":"transport_aerospace", 22 | "IoT":"industrial_technologies", 23 | "NBIC Cluster":"scientific_biomedical", 24 | "Photonics":"scientific_biomedical", 25 | "Quantum tech":"scientific_biomedical", 26 | "Robotics":"industrial_technologies", 27 | "Satellite tech":"transport_aerospace", 28 | "Smart cities":"transport_aerospace", 29 | "Smart grids":"environmental_technologies", 30 | "Wearables":"digital_applications"} -------------------------------------------------------------------------------- /ui/src/routes/indicators/[id]/index.svelte: -------------------------------------------------------------------------------- 1 | 12 | 13 | 43 | 44 | 45 | {title} - {toolName} 46 | 50 | 51 | 52 | 58 | -------------------------------------------------------------------------------- /ds/data/processed/ref/total_4_fte.yaml: -------------------------------------------------------------------------------- 1 | api_doc_url: https://www.ref.ac.uk/2014/pubs/201401/ 2 | api_type: FETCH 3 | data_date: 20200204 4 | title: Total number of researchers assessed as excellent 5 | description: These are the results of the Research Excellence Framework, where university departments in various disciplines are assessed on the quality of their research. The latest REF was conducted in 2014 so data is available only for one year 6 | subtitle: Full-time equivalent researchers assessed as excellent in the Research Excellence Framework 7 | endpoint_url: https://results.ref.ac.uk/(S(hlvnuqzwkag44jp3df3d4q14))/DownloadFile/AllResults/xlsx 8 | framework_group: public_rnd 9 | is_experimental: False 10 | nuts_level: 2 11 | order: [year, nuts_id, nuts_year_spec, value.id] 12 | region: 13 | type: NutsRegion 14 | level: 2 15 | source_url: http://geoportal1-ons.opendata.arcgis.com/datasets/48b6b85bb7ea43699ee85f4ecd12fd36_0.zip?outSR=%7B%22latestWkid%22:27700,%22wkid%22:27700%7D 16 | schema: 17 | nuts_id: 18 | type: NutsRegion.id 19 | nuts_year_spec: 20 | type: NutsRegion.year_spec 21 | value: 22 | format: .2f 23 | id: total_4_fte 24 | label: Total number of excellent researchers in REF (Full Time Equivalent) 25 | type: FTE 26 | year: 27 | data_type: int 28 | label: Year 29 | source_name: REF (Research Excellence Framework) 30 | source_url: https://results.ref.ac.uk/ 31 | -------------------------------------------------------------------------------- /ds/data/processed/travel/travel_time_to_road_junctions.lep.yaml: -------------------------------------------------------------------------------- 1 | api_type: FETCH 2 | title: Travel time to nearest five road junctions 3 | subtitle: Average minimum travel time to nearest five road junctions from a LEP region for England (2011 & 2013) and Wales (2011 only) 4 | description: Average minimum travel time to nearest five road junctions from a LEP region for England (2011 & 2013) and Wales (2011 only). Overlapping LEP regions are used therefore some calculations may be under-estimated or over-estimated. 5 | endpoint_url: 6 | 2011: http://data.dft.gov.uk.s3.amazonaws.com/connectivity-data/Road-junctions-travel-times.zip 7 | 2013: http://data.dft.gov.uk.s3.amazonaws.com/connectivity-data/2013-travel-times.zip 8 | # year: endpoint_url 9 | framework_group: place_potential 10 | is_experimental: False 11 | order: [year, lep_id, lep_year_spec, value.id] # do not change; specifies the order of the exported indicator fields 12 | region: 13 | type: LepRegion # the region type, e.g. NutsRegion or LepRegion 14 | source: PROVIDED 15 | schema: 16 | nuts_id: 17 | type: LepRegion.lep_id 18 | nuts_year_spec: 19 | type: LepRegion.lep_year_spec 20 | value: 21 | format: .2f 22 | id: travel_time_to_road_junctions 23 | label: Time (Minutes) 24 | data_type: float 25 | year: 26 | data_type: int 27 | label: Year 28 | source_name: Department for Transport 29 | -------------------------------------------------------------------------------- /ds/beis_indicators/travel/make_travel_work.py: -------------------------------------------------------------------------------- 1 | import logging 2 | import numpy as np 3 | import glob 4 | 5 | from beis_indicators import project_dir 6 | 7 | from beis_indicators.geo import NutsCoder, LepCoder 8 | from beis_indicators.indicators import points_to_indicator, save_indicator 9 | from beis_indicators.travel.travel_work_processing import get_travel_work_data 10 | 11 | import pandas as pd 12 | 13 | logger = logging.getLogger(__name__) 14 | 15 | 16 | 17 | coders = { 18 | 'nuts2': NutsCoder(level=2), 19 | 'nuts3': NutsCoder(level=3), 20 | 'lep': LepCoder() 21 | } 22 | 23 | get_travel_work_data() 24 | destination_df = pd.read_csv(f'{project_dir}/data/interim/travel_to_work_all_years.csv') 25 | for geo, coder in coders.items(): 26 | time_mean = points_to_indicator(destination_df, value_col='Mean', coder=coder, 27 | aggfunc=np.mean, value_rename= 'Mean', 28 | projection='EPSG:4326', x_col='long', y_col='lat') 29 | if geo == 'lep': 30 | time_mean = time_mean.rename(columns = {'Mean': 'travel_time_to_work'}).sort_values(['lep_id', 'year']).reset_index(drop=True) 31 | else: 32 | time_mean = time_mean.rename(columns = {'Mean': 'travel_time_to_work'}).sort_values(['nuts_id', 'year']).reset_index(drop=True) 33 | 34 | 35 | # print(time_mean.head()) 36 | save_indicator(time_mean, 'travel', geo) 37 | -------------------------------------------------------------------------------- /ui/test/README.md: -------------------------------------------------------------------------------- 1 | # Testing on the browser 2 | 3 | `npm run selenium` will launch all unit tests cointained in 4 | `test/browserstack/scripts/automate`. 5 | 6 | A Github action is configured to run the Selenium tests on pull requests to the 7 | `dev` branch. As a consequence, it's not recommended that commits be pushed 8 | directly to the `dev` branch so that the tests can be run before merging. The 9 | action can be found at `.github/workflows/browsersupport.yml` and may be 10 | triggered to run in a Github action runner by adding `RUN_BROWSERSTACK` 11 | anywhere in a commit message. 12 | 13 | Test results for the original repository can be found here: 14 | https://gist.github.com/NestaTestUser/9b517e016820851429322515f0a5aa29 . 15 | 16 | Forks of the repository should be configured with the following action secrets 17 | in `org/repo/settings/secrets/actions` in Github: 18 | 19 | * `BROWSERSTACK_USERNAME`: The account name to use in Browserstack. 20 | * `BROWSERSTACK_ACCESS_KEY`: The access key provided by Browserstack. 21 | * `BROWSERSTACK_GIST_TOKEN`: A Github token authorized to create Gists. 22 | 23 | TODO: 24 | 25 | * Rewrite test runner in functional style, separating test-list generation from 26 | test running. 27 | * Add Browserstack session & queue management. 28 | * Describe testing setup procedure on Browserstack and Github. 29 | * Run on all os/browser/version configurations available on Browserstack. 30 | -------------------------------------------------------------------------------- /ds/data/processed/hebci/regeneration_development.lep.yaml: -------------------------------------------------------------------------------- 1 | api_doc_url: https://www.hesa.ac.uk/data-and-analysis/business-community/regeneration 2 | api_type: FETCH 3 | data_date: 20200924 4 | description: Income for higher education institutions from public regeneration and development programmes. This funding is described by HESA as \"a way for HE providers to invest intellectual assets in economic, physical and socially beneficial projects.\" The indicator values represent the sum of income from all public bodies. 5 | title: HE regeneration and development income 6 | subtitle: Income for higher education institutions from public regeneration and development programmes. 7 | endpoint_url: https://www.hesa.ac.uk/data-and-analysis/providers/business-community/table-3.csv 8 | framework_group: knowledge_exchange 9 | is_experimental: False 10 | is_public: True 11 | order: [year, lep_id, lep_year_spec, value.id] 12 | region: 13 | type: LepRegion 14 | source_url: LepRegion.source_url 15 | source: LepRegion.source 16 | schema: 17 | lep_id: 18 | type: LepRegion.lep_id 19 | lep_year_spec: 20 | type: LepRegion.lep_year_spec 21 | value: 22 | data_type: int 23 | id: regeneration_development 24 | label: Income from regeneration and development 25 | type: GBP 26 | year: 27 | data_type: int 28 | label: Year 29 | source_name: HESA (Higher Education Statistical Agency) 30 | source_url: https://www.hesa.ac.uk/ 31 | -------------------------------------------------------------------------------- /ui/src/routes/indicators/[id]/[year].svelte: -------------------------------------------------------------------------------- 1 | 12 | 13 | 41 | 42 | 43 | {title} ({year}) - {toolName} 44 | 48 | 49 | 50 | 57 | -------------------------------------------------------------------------------- /ds/data/processed/hebci/regeneration_development.nuts2.yaml: -------------------------------------------------------------------------------- 1 | api_doc_url: https://www.hesa.ac.uk/data-and-analysis/business-community/regeneration 2 | api_type: FETCH 3 | data_date: 20200924 4 | description: Income for higher education institutions from public regeneration and development programmes. This funding is described by HESA as \"a way for HE providers to invest intellectual assets in economic, physical and socially beneficial projects.\" The indicator values represent the sum of income from all public bodies. 5 | title: HE regeneration and development income 6 | subtitle: Income for higher education institutions from public regeneration and development programmes. 7 | endpoint_url: https://www.hesa.ac.uk/data-and-analysis/providers/business-community/table-3.csv 8 | framework_group: knowledge_exchange 9 | is_experimental: False 10 | is_public: True 11 | order: [year, nuts_id, nuts_year_spec, value.id] 12 | region: 13 | type: NutsRegion 14 | level: 2 15 | source_url: NutsRegion.source_url 16 | source: NutsRegion.source 17 | schema: 18 | nuts_id: 19 | type: NutsRegion.nuts_id 20 | nuts_year_spec: 21 | type: NutsRegion.nuts_year_spec 22 | value: 23 | data_type: int 24 | id: regeneration_development 25 | label: Income from regeneration and development 26 | type: GBP 27 | year: 28 | data_type: int 29 | label: Year 30 | source_name: HESA (Higher Education Statistical Agency) 31 | source_url: https://www.hesa.ac.uk/ 32 | -------------------------------------------------------------------------------- /ds/data/processed/hebci/regeneration_development.nuts3.yaml: -------------------------------------------------------------------------------- 1 | api_doc_url: https://www.hesa.ac.uk/data-and-analysis/business-community/regeneration 2 | api_type: FETCH 3 | data_date: 20200924 4 | description: Income for higher education institutions from public regeneration and development programmes. This funding is described by HESA as \"a way for HE providers to invest intellectual assets in economic, physical and socially beneficial projects.\" The indicator values represent the sum of income from all public bodies. 5 | title: HE regeneration and development income 6 | subtitle: Income for higher education institutions from public regeneration and development programmes. 7 | endpoint_url: https://www.hesa.ac.uk/data-and-analysis/providers/business-community/table-3.csv 8 | framework_group: knowledge_exchange 9 | is_experimental: False 10 | is_public: True 11 | order: [year, nuts_id, nuts_year_spec, value.id] 12 | region: 13 | type: NutsRegion 14 | level: 3 15 | source_url: NutsRegion.source_url 16 | source: NutsRegion.source 17 | schema: 18 | nuts_id: 19 | type: NutsRegion.nuts_id 20 | nuts_year_spec: 21 | type: NutsRegion.nuts_year_spec 22 | value: 23 | data_type: int 24 | id: regeneration_development 25 | label: Income from regeneration and development 26 | type: GBP 27 | year: 28 | data_type: int 29 | label: Year 30 | source_name: HESA (Higher Education Statistical Agency) 31 | source_url: https://www.hesa.ac.uk/ 32 | -------------------------------------------------------------------------------- /ds/beis_indicators/travel/make_travel.py: -------------------------------------------------------------------------------- 1 | import logging 2 | import numpy as np 3 | import glob 4 | 5 | from beis_indicators import project_dir 6 | 7 | from beis_indicators.geo import NutsCoder, LepCoder 8 | from beis_indicators.indicators import points_to_indicator, save_indicator 9 | from beis_indicators.travel.travel_processing import get_travel_data 10 | 11 | import pandas as pd 12 | 13 | logger = logging.getLogger(__name__) 14 | 15 | 16 | 17 | coders = { 18 | 'nuts2': NutsCoder(level=2), 19 | 'nuts3': NutsCoder(level=3), 20 | 'lep': LepCoder() 21 | } 22 | 23 | 24 | 25 | destinations = {'road_junctions': 'travel_time_to_road_junctions', 26 | 'rail_stations': 'travel_time_to_rail', 27 | 'airports': 'travel_time_to_airport'} 28 | 29 | for destination,val_name in destinations.items(): 30 | get_travel_data(destination) 31 | destination_df = pd.read_csv(f'{project_dir}/data/interim/{destination}_df.csv') 32 | for geo, coder in coders.items(): 33 | time_mean = points_to_indicator(destination_df, value_col='RepTime', coder=coder, 34 | aggfunc=np.mean, value_rename= val_name, 35 | projection='EPSG:4326', x_col='lon', y_col='lat') 36 | # save_indicator(funding_agg, 'cordis', geo) 37 | time_mean = time_mean.sort_values(by=time_mean.columns[1]) 38 | save_indicator(time_mean, 'travel', geo) 39 | -------------------------------------------------------------------------------- /ds/beis_indicators/defra/defra_processing.py: -------------------------------------------------------------------------------- 1 | import geopandas as gpd 2 | import os 3 | import pyproj 4 | 5 | 6 | def coordinates_to_points(df, x_coord_name, y_coord_name): 7 | '''coordinates_to_points 8 | Take a DataFrame with coordinate columns and returns a GeoDataFrame with 9 | a single Point geometry column. 10 | 11 | Args: 12 | df (pandas.DataFrame): A DataFrame with spatial coordinate data. 13 | x_coord_name (str): Name of the horizontal coordinate column. 14 | y_coord_name (str): Name of the vertical coordinate column. 15 | 16 | Returns: 17 | (geopandas.GeoDataFrame): GeoDataFrame with Point objects in `geometry` column. 18 | ''' 19 | return gpd.GeoDataFrame(df, geometry=gpd.points_from_xy(df[x_coord_name], df[y_coord_name])) 20 | 21 | 22 | def translate_coordinates(x, y, pin, pout): 23 | '''translate_coordinates 24 | Translates vectors of spatial coordinates from one projection to another. 25 | 26 | Args: 27 | x (array-like): Vector of horizontal spatial coordinates. 28 | y (array-like): Vector of vertical spatial coortinates. 29 | pin (str): Projection of input vectors. 30 | pout (str): Output projection. 31 | 32 | Returns: 33 | (tuple of array-like): Translated coordinate vectors. 34 | ''' 35 | proj_in = pyproj.Proj(pin) 36 | proj_out = pyproj.Proj(pout) 37 | return pyproj.transform(proj_in, proj_out, x, y) 38 | -------------------------------------------------------------------------------- /ds/data/processed/cordis/cordis_funding.lep.yaml: -------------------------------------------------------------------------------- 1 | api_doc_url: https://data.europa.eu/euodp/en/data/dataset/cordisH2020projects 2 | api_type: FETCH 3 | data_date: 20200909 4 | description: Total amount of funding (in Euros) awarded by the European Commission for R&D projects in the Horizon 2020 funding programme (2014 - 2020). The funding is grouped by the start year of the project. 5 | title: Horizon 2020 funding 6 | subtitle: Sum of all EC funding awarded through the Horizon 2020 programme (2014-2020) 7 | endpoint_url: 8 | H2020 projects: https://cordis.europa.eu/data/cordis-h2020projects-xml.zip 9 | H2020 project descriptions: https://cordis.europa.eu/data/cordis-h2020projects.csv 10 | framework_group: public_rnd 11 | is_experimental: False 12 | is_public: True 13 | order: [year, lep_id, lep_year_spec, value.id] 14 | region: 15 | type: LepRegion 16 | schema: 17 | lep_id: # change this key to `lep_id` or `lep_id` 18 | type: LepRegion.lep_id # change this key to `LepRegion.lep_id` or `LepRegion.lep_id` 19 | lep_year_spec: # change this key to `lep_year_spec` or `lep_year_spec` 20 | type: LepRegion.lep_year_spec # change this key to `LepRegion.lep_year_spec` or `LepRegion.lep_year_spec` 21 | value: 22 | data_type: int 23 | format: ',' 24 | id: cordis_funding 25 | label: Total Funding 26 | type: EUR 27 | year: 28 | data_type: int 29 | label: Year 30 | source_name: EU Open Data Portal 31 | source_url: https://data.europa.eu/ 32 | -------------------------------------------------------------------------------- /ds/data/processed/crunchbase/gbp_venture_capital_received.yaml: -------------------------------------------------------------------------------- 1 | api_doc_url: https://github.com/nestauk/data_getters#crunchbase (schema here- https://docs.google.com/presentation/d/1e1aWpQ7M3klHc4iJXJhgpCD77uWFKaFTjVkyvdiNEXw/edit) 2 | api_type: FETCH 3 | auth_provider: Nesta 4 | data_date: 20200218 5 | title: Venture capital investment 6 | subtitle: Venture capital investment in ventures based on a region. 7 | description: Level of venture capital investment in ventures based on a region based on data from CrunchBase. A small number of deals have been converted to GBP at the date when they were announced. 8 | endpoint_url: https://crunchbase-export.s3.eu-west-2.amazonaws.com/organizations.csv 9 | framework_group: private_rnd 10 | is_experimental: True 11 | is_public: False 12 | order: [year, nuts_id, nuts_year_spec, value.id] 13 | region: 14 | type: NutsRegion 15 | level: 2 16 | source_url: https://opendata.arcgis.com/datasets/48b6b85bb7ea43699ee85f4ecd12fd36_0.zip?outSR=%7B%22latestWkid%22%3A27700%2C%22wkid%22%3A27700%7D 17 | schema: 18 | region_id: 19 | type: NutsRegion.id 20 | region_year_spec: 21 | type: NutsRegion.year_spec 22 | value: 23 | data_type: int 24 | format: ',' 25 | id: gbp_venture_capital_received 26 | label: Venture capital investment received 27 | type: GBP 28 | year: 29 | data_type: int 30 | label: Year 31 | source_name: Crunchbase (business information about companies) 32 | source_url: https://www.crunchbase.com/ 33 | -------------------------------------------------------------------------------- /ds/data/processed/eurostat/epo_patent_applications.nuts2.yaml: -------------------------------------------------------------------------------- 1 | api_doc_url: https://ec.europa.eu/eurostat/cache/metadata/en/pat_esms.htm 2 | api_type: FETCH 3 | data_date: 20200818 4 | description: Patent applications are a proxy for inventions. We focus on applications to the European Patent Office (EPO). The year is the priority year for the patent i.e. the first year that the patent was filed with any patenting authority. There are significant lags between patent applications and publication, which explains the low timeliness of the data. 5 | title: Patent applications (EPO) 6 | subtitle: Count of patent applications normalised by the number of applicants in a patent if there are more than one 7 | endpoint_url: https://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=pat_ep_rtot&lang=en 8 | framework_group: knowledge_exchange 9 | is_experimental: False 10 | is_public: True 11 | order: [year, nuts_id, nuts_year_spec, value.id] 12 | region: 13 | type: NutsRegion 14 | level: 2 15 | schema: 16 | nuts_id: 17 | type: NutsRegion.nuts_id 18 | nuts_year_spec: 19 | type: NutsRegion.nuts_year_spec 20 | value: 21 | data_type: int 22 | id: epo_patent_applications 23 | label: Patent applications 24 | year: 25 | data_type: int 26 | label: Year 27 | source_name: Eurostat 28 | source_url: https://ec.europa.eu/eurostat 29 | warning: The regional breakdown of the EU Member States is based on the Nomenclature of Territorial Units for Statistics – NUTS 2010 level 3 30 | -------------------------------------------------------------------------------- /ds/data/processed/eurostat/epo_patent_applications.nuts3.yaml: -------------------------------------------------------------------------------- 1 | api_doc_url: https://ec.europa.eu/eurostat/cache/metadata/en/pat_esms.htm 2 | api_type: FETCH 3 | data_date: 20200818 4 | description: Patent applications are a proxy for inventions. We focus on applications to the European Patent Office (EPO). The year is the priority year for the patent i.e. the first year that the patent was filed with any patenting authority. There are significant lags between patent applications and publication, which explains the low timeliness of the data. 5 | title: Patent applications (EPO) 6 | subtitle: Count of patent applications normalised by the number of applicants in a patent if there are more than one 7 | endpoint_url: https://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=pat_ep_rtot&lang=en 8 | framework_group: knowledge_exchange 9 | is_experimental: False 10 | is_public: True 11 | order: [year, nuts_id, nuts_year_spec, value.id] 12 | region: 13 | type: NutsRegion 14 | level: 3 15 | schema: 16 | nuts_id: 17 | type: NutsRegion.nuts_id 18 | nuts_year_spec: 19 | type: NutsRegion.nuts_year_spec 20 | value: 21 | data_type: int 22 | id: epo_patent_applications 23 | label: Patent applications 24 | year: 25 | data_type: int 26 | label: Year 27 | source_name: Eurostat 28 | source_url: https://ec.europa.eu/eurostat 29 | warning: The regional breakdown of the EU Member States is based on the Nomenclature of Territorial Units for Statistics – NUTS 2010 level 3 30 | -------------------------------------------------------------------------------- /ds/data/processed/cordis/cordis_funding.nuts2.yaml: -------------------------------------------------------------------------------- 1 | api_doc_url: https://data.europa.eu/euodp/en/data/dataset/cordisH2020projects 2 | api_type: FETCH 3 | data_date: 20200909 4 | description: Total amount of funding (in Euros) awarded by the European Commission for R&D projects in the Horizon 2020 funding programme (2014 - 2020). The funding is grouped by the start year of the project. 5 | title: Horizon 2020 funding 6 | subtitle: Sum of all EC funding awarded through the Horizon 2020 programme (2014-2020) 7 | endpoint_url: 8 | H2020 projects: https://cordis.europa.eu/data/cordis-h2020projects-xml.zip 9 | H2020 project descriptions: https://cordis.europa.eu/data/cordis-h2020projects.csv 10 | framework_group: public_rnd 11 | is_experimental: False 12 | is_public: True 13 | order: [year, nuts_id, nuts_year_spec, value.id] 14 | region: 15 | type: NutsRegion 16 | level: 2 17 | schema: 18 | nuts_id: # change this key to `nuts_id` or `lep_id` 19 | type: NutsRegion.nuts_id # change this key to `NutsRegion.nuts_id` or `LepRegion.lep_id` 20 | nuts_year_spec: # change this key to `nuts_year_spec` or `lep_year_spec` 21 | type: NutsRegion.nuts_year_spec # change this key to `NutsRegion.nuts_year_spec` or `LepRegion.lep_year_spec` 22 | value: 23 | data_type: int 24 | format: ',' 25 | id: cordis_funding 26 | label: Total Funding 27 | type: EUR 28 | year: 29 | data_type: int 30 | label: Year 31 | source_name: EU Open Data Portal 32 | source_url: https://data.europa.eu/ 33 | -------------------------------------------------------------------------------- /ds/data/processed/cordis/cordis_funding.nuts3.yaml: -------------------------------------------------------------------------------- 1 | api_doc_url: https://data.europa.eu/euodp/en/data/dataset/cordisH2020projects 2 | api_type: FETCH 3 | data_date: 20200909 4 | description: Total amount of funding (in Euros) awarded by the European Commission for R&D projects in the Horizon 2020 funding programme (2014 - 2020). The funding is grouped by the start year of the project. 5 | title: Horizon 2020 funding 6 | subtitle: Sum of all EC funding awarded through the Horizon 2020 programme (2014-2020) 7 | endpoint_url: 8 | H2020 project: https://cordis.europa.eu/data/cordis-h2020projects-xml.zip 9 | H2020 project descriptions: https://cordis.europa.eu/data/cordis-h2020projects.csv 10 | framework_group: public_rnd 11 | is_experimental: False 12 | is_public: True 13 | order: [year, nuts_id, nuts_year_spec, value.id] 14 | region: 15 | type: NutsRegion 16 | level: 3 17 | schema: 18 | nuts_id: # change this key to `nuts_id` or `lep_id` 19 | type: NutsRegion.nuts_id # change this key to `NutsRegion.nuts_id` or `LepRegion.lep_id` 20 | nuts_year_spec: # change this key to `nuts_year_spec` or `lep_year_spec` 21 | type: NutsRegion.nuts_year_spec # change this key to `NutsRegion.nuts_year_spec` or `LepRegion.lep_year_spec` 22 | value: 23 | data_type: int 24 | format: ',' 25 | id: cordis_funding 26 | label: Total Funding 27 | type: EUR 28 | year: 29 | data_type: int 30 | label: Year 31 | source_name: EU Open Data Portal 32 | source_url: https://data.europa.eu/ 33 | --------------------------------------------------------------------------------