├── .gitignore ├── data ├── fig1.png ├── table2.png ├── table3.png ├── README.md └── Variables_PCF.csv ├── code ├── install_gds_stack.yml ├── PCA_Liverpool.do ├── README.md └── check_gds_stack.ipynb ├── .travis.yml ├── appveyor.yml └── README.md /.gitignore: -------------------------------------------------------------------------------- 1 | .DS_Store 2 | .ipynb_checkpoints/ 3 | */.ipynb_checkpoints/ 4 | -------------------------------------------------------------------------------- /data/fig1.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/darribas/satellite_led_liverpool/HEAD/data/fig1.png -------------------------------------------------------------------------------- /data/table2.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/darribas/satellite_led_liverpool/HEAD/data/table2.png -------------------------------------------------------------------------------- /data/table3.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/darribas/satellite_led_liverpool/HEAD/data/table3.png -------------------------------------------------------------------------------- /code/install_gds_stack.yml: -------------------------------------------------------------------------------- 1 | # Run `conda-env create -f install_gds_stack.yml` 2 | name: gds 3 | channels: 4 | - conda-forge 5 | - ioos 6 | - defaults 7 | dependencies: 8 | - python=3.5 9 | - pip 10 | - bokeh 11 | - cartopy 12 | - dask 13 | - geopandas 14 | - ipython 15 | - ipywidgets 16 | - jupyter 17 | - mplleaflet 18 | - networkx 19 | - palettable 20 | - pillow 21 | - pip: 22 | - contextily 23 | - qgrid 24 | - rasterio 25 | - scikit-learn 26 | - seaborn 27 | - statsmodels 28 | - xlrd 29 | - xlsxwriter 30 | -------------------------------------------------------------------------------- /.travis.yml: -------------------------------------------------------------------------------- 1 | language: c 2 | sudo: false 3 | branches: 4 | only: 5 | - master 6 | os: 7 | - linux 8 | before_install: 9 | - wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -O miniconda.sh 10 | - chmod +x miniconda.sh 11 | - ./miniconda.sh -b -p ./miniconda 12 | - export PATH=`pwd`/miniconda/bin:$PATH 13 | - conda update --yes conda 14 | - conda install --yes psutil yaml pyyaml 15 | - conda-env create -f code/install_gds_stack.yml 16 | - source activate gds 17 | 18 | script: 19 | - jupyter nbconvert --execute --ExecutePreprocessor.timeout=480 code/check_gds_stack.ipynb 20 | 21 | -------------------------------------------------------------------------------- /code/PCA_Liverpool.do: -------------------------------------------------------------------------------- 1 | *****PRINCIPAL COMPONENTS FACTORING (PCF)***** 2 | *****Paper Liverpool***** 3 | 4 | 5 | *capture clear 6 | *capture log close 7 | set more off 8 | cd "/Users/juanduque/satellite_led_liverpool/" 9 | log using "PCF.log", replace 10 | 11 | *Lectura de variables 12 | insheet using "data/Variables.csv", clear case 13 | 14 | *COMPONENTES PRINCIPALES 15 | factor iMEAN1 iDEVST1 iMEAN2 iDEVST2 iMEAN3 iDEVST3 iMEAN_EDG iDEVST_EDG iUNIFOR iENTROP iCONTRAS iIDM iCOVAR iVARIAN iCORRELAC iSKEWNESS iKURTOSIS iRVF iRSF iFDO iSDT iMFM iVFM iDMF iRMM iSDF iAFM, pcf 16 | 17 | factor iMEAN1 iDEVST1 iMEAN2 iDEVST2 iMEAN3 iDEVST3 iMEAN_EDG iDEVST_EDG iUNIFOR iENTROP iCONTRAS iIDM iCOVAR iVARIAN iCORRELAC iSKEWNESS iKURTOSIS iRVF iRSF iFDO iSDT iMFM iVFM iDMF iRMM iSDF iAFM, pcf factors(4) 18 | 19 | rotate 20 | predict f1 f2 f3 f4 21 | 22 | outsheet using "/Users/juanduque/satellite_led_liverpool/data/Variables_PCF.csv", comma replace 23 | 24 | -------------------------------------------------------------------------------- /appveyor.yml: -------------------------------------------------------------------------------- 1 | # From: http://tjelvarolsson.com/blog/how-to-continuously-test-your-python-code-on-windows-using-appveyor/ 2 | build: false 3 | 4 | environment: 5 | matrix: 6 | - PYTHON: "C:\\Miniconda-x64" 7 | PYTHON_VERSION: "3.5" 8 | PYTHON_ARCH: "64" 9 | 10 | init: 11 | - "ECHO %PYTHON_VERSION% %MINICONDA%" 12 | 13 | install: 14 | - "set PATH=%PYTHON%;%PYTHON%\\Scripts;%PATH%" 15 | - conda config --set always_yes yes --set changeps1 no 16 | - conda install conda-build psutil 17 | - conda update -q conda 18 | - conda info -a 19 | - "conda-env create -f code/install_gds_stack.yml" 20 | #- "conda create --name gds --file content/infrastructure/gds_requirements_win-%PYTHON_ARCH%.txt" 21 | - activate gds 22 | 23 | test_script: 24 | - jupyter nbconvert --to markdown --execute --ExecutePreprocessor.timeout=120 code/check_gds_stack.ipynb 25 | #- cd content/labs 26 | #- jupyter nbconvert --execute --ExecutePreprocessor.timeout=120 *.ipynb 27 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # `satellite_led_liverpool` 2 | 3 | This repository contains the data and code used in the paper ["Remote Sensing-Based Measurement of Living Environment Deprivation - *Improving Classical Approaches with Machine Learning*"](https://doi.org/10.1371/journal.pone.0176684), by Dani Arribas-Bel, Jorge Patiño and Juanca Duque, published in PLoS ONE in May 2017. 4 | 5 | ## Citation 6 | 7 | ```tex 8 | @article{10.1371/journal.pone.0176684, 9 | author = {Arribas-Bel, Daniel AND Patino, Jorge E. AND Duque, Juan C.}, 10 | journal = {PLOS ONE}, 11 | publisher = {Public Library of Science}, 12 | title = {Remote sensing-based measurement of Living Environment Deprivation: Improving classical approaches with machine learning}, 13 | year = {2017}, 14 | month = {05}, 15 | volume = {12}, 16 | url = {https://doi.org/10.1371/journal.pone.0176684}, 17 | pages = {1-25}, 18 | abstract = {This paper provides evidence on the usefulness of very high spatial resolution (VHR) imagery in gathering socioeconomic information in urban settlements. We use land cover, spectral, structure and texture features extracted from a Google Earth image of Liverpool (UK) to evaluate their potential to predict Living Environment Deprivation at a small statistical area level. We also contribute to the methodological literature on the estimation of socioeconomic indices with remote-sensing data by introducing elements from modern machine learning. In addition to classical approaches such as Ordinary Least Squares (OLS) regression and a spatial lag model, we explore the potential of the Gradient Boost Regressor and Random Forests to improve predictive performance and accuracy. In addition to novel predicting methods, we also introduce tools for model interpretation and evaluation such as feature importance and partial dependence plots, or cross-validation. Our results show that Random Forest proved to be the best model with an R2 of around 0.54, followed by Gradient Boost Regressor with 0.5. Both the spatial lag model and the OLS fall behind with significantly lower performances of 0.43 and 0.3, respectively.}, 19 | number = {5}, 20 | doi = {10.1371/journal.pone.0176684} 21 | } 22 | ``` 23 | 24 | ## License 25 | 26 | Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License. 27 | 28 | ## Computational environment 29 | 30 | | OS | Status | 31 | | ------- | -----------------| 32 | | Linux | [![Build Status](https://travis-ci.org/darribas/satellite_led_liverpool.svg?branch=master)](https://travis-ci.org/darribas/satellite_led_liverpool) | 33 | | Windows | [![Build status](https://ci.appveyor.com/api/projects/status/71lh6a7knkukl8j6?svg=true)](https://ci.appveyor.com/project/darribas/satellite-led-liverpool) | 34 | 35 | 36 | -------------------------------------------------------------------------------- /code/README.md: -------------------------------------------------------------------------------- 1 | # Code 2 | 3 | | OS | Status | 4 | | ------- | -----------------| 5 | | Linux | [![Build Status](https://travis-ci.org/darribas/satellite_led_liverpool.svg?branch=master)](https://travis-ci.org/darribas/satellite_led_liverpool) | 6 | | Windows | [![Build status](https://ci.appveyor.com/api/projects/status/71lh6a7knkukl8j6?svg=true)](https://ci.appveyor.com/project/darribas/satellite-led-liverpool) | 7 | 8 | 9 | ## Structure 10 | 11 | This folder contains the code required to reproduce the results presented in 12 | the paper as well as a file to setup a `conda` environment with all the 13 | Python-3 libraries required. In particular, the following files may be found: 14 | 15 | * `install_gds_stack.yml`: setup file to create a `conda` environment loaded 16 | with all the libraries needed (and a few more!). See instructions below to 17 | set it up on your own. 18 | * `paper_figs.ipynb`: Jupyter notebook to reproduce figures and tables in the 19 | paper. Note it assumes the file is placed within the repository with the 20 | data files under the relative path `../data/`. 21 | 22 | ## Setup 23 | 24 | We rely on Python 3 and a series of additional libraries for this 25 | analysis. To create the environment, you may follow these instructions (mostly 26 | taken from [here](http://darribas.org/gds16/software.html)). 27 | 28 | The easiest way to install locally and natively the software stack required is to install a full scientific Python distribution. Although other good alternatives are also available (e.g. [Canopy](https://www.enthought.com/products/canopy/), [Sage](http://www.sagemath.org)), we recommend to install [Anaconda](https://store.continuum.io/cshop/anaconda/). Aim to install a **Python 3.5** version (although the environment will set it up otherwise for yourself). Please follow the instructions provided in the link for installation. 29 | 30 | Once you have a fully working Anaconda distribution installed in your computer, you can setup an isolated environment that contains all the required libraries by running the install script provided with this guide. Exact instructions vary depending on the platform you are on. 31 | 32 | ### macOS/Linux 33 | 34 | Open up a terminal ("Applications --> Utilities --> Terminal" in macOS and "ctr+alt+T" in Linux) and run the following commands: 35 | 36 | * Navigate to the folder where this file is (e.g. Downloads): 37 | 38 | ``` 39 | cd /path/to/folder/ 40 | ``` 41 | 42 | * Execute the following command: 43 | 44 | ``` 45 | conda-env create -f install_gds_stack.yml 46 | ``` 47 | 48 | * Once this has run, you should be able to activate the environment: 49 | 50 | ``` 51 | source activate gds 52 | ``` 53 | 54 | ### Windows 55 | 56 | Open up the Anaconda Command Prompt (search for it on the Startup Menu) and run the following commands: 57 | 58 | * Navigate to the folder where this file is (e.g. Downloads): 59 | 60 | ``` 61 | cd /path/to/folder/ 62 | ``` 63 | 64 | * Execute the following command: 65 | 66 | ``` 67 | conda-env create -f install_gds_stack.yml 68 | ``` 69 | 70 | * Once this has run, you should be able to activate the environment: 71 | 72 | ``` 73 | activate gds 74 | ``` 75 | 76 | ## Check 77 | 78 | To check things have installed correctly, an additional file is included, `check_gds_stack.ipynb`. To run the check, open a terminal (macOS/Linux) or the Anaconda Command Prompt (Windows), navigate to the folder as showed above and activate the environment: 79 | 80 | * macOS/Linux: 81 | 82 | ``` 83 | source activate gds 84 | ``` 85 | 86 | * Windows: 87 | 88 | ``` 89 | activate gds 90 | ``` 91 | 92 | You should now see `(gds)` on the beginning of the line at the terminal/command prompt. You can now run the test as: 93 | 94 | `jupyter nbconvert --execute check_gds_stack.ipynb` 95 | 96 | This will run and, when finished, produce an HTML file in the same folder. Open it and check there are no errors reported. If that is the case, you are good to go! 97 | 98 | 99 | -------------------------------------------------------------------------------- /code/check_gds_stack.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# Check `GDS` stack\n", 8 | "\n", 9 | "This notebook checks all software requirements for the course Geographic Data Science are correctly installed. \n", 10 | "\n", 11 | "A successful run of the notebook implies no errors returned in any cell *and* every cell beyond the first one returning a printout of `True`. This ensures a correct environment installed." 12 | ] 13 | }, 14 | { 15 | "cell_type": "markdown", 16 | "metadata": {}, 17 | "source": [ 18 | "---" 19 | ] 20 | }, 21 | { 22 | "cell_type": "code", 23 | "execution_count": null, 24 | "metadata": { 25 | "collapsed": true 26 | }, 27 | "outputs": [], 28 | "source": [ 29 | "import bokeh as bk\n", 30 | "float(bk.__version__[:4]) >= 0.12" 31 | ] 32 | }, 33 | { 34 | "cell_type": "code", 35 | "execution_count": null, 36 | "metadata": { 37 | "collapsed": true 38 | }, 39 | "outputs": [], 40 | "source": [ 41 | "import matplotlib as mpl\n", 42 | "float(mpl.__version__[:3]) >= 1.5" 43 | ] 44 | }, 45 | { 46 | "cell_type": "code", 47 | "execution_count": null, 48 | "metadata": { 49 | "collapsed": true 50 | }, 51 | "outputs": [], 52 | "source": [ 53 | "import mplleaflet as mpll" 54 | ] 55 | }, 56 | { 57 | "cell_type": "code", 58 | "execution_count": null, 59 | "metadata": { 60 | "collapsed": true 61 | }, 62 | "outputs": [], 63 | "source": [ 64 | "import seaborn as sns\n", 65 | "float(sns.__version__[:3]) >= 0.6" 66 | ] 67 | }, 68 | { 69 | "cell_type": "markdown", 70 | "metadata": {}, 71 | "source": [ 72 | "---" 73 | ] 74 | }, 75 | { 76 | "cell_type": "code", 77 | "execution_count": null, 78 | "metadata": { 79 | "collapsed": true 80 | }, 81 | "outputs": [], 82 | "source": [ 83 | "import qgrid" 84 | ] 85 | }, 86 | { 87 | "cell_type": "code", 88 | "execution_count": null, 89 | "metadata": { 90 | "collapsed": true 91 | }, 92 | "outputs": [], 93 | "source": [ 94 | "import pandas as pd\n", 95 | "float(pd.__version__[:4]) >= 0.18" 96 | ] 97 | }, 98 | { 99 | "cell_type": "code", 100 | "execution_count": null, 101 | "metadata": { 102 | "collapsed": true 103 | }, 104 | "outputs": [], 105 | "source": [ 106 | "import sklearn\n", 107 | "float(sklearn.__version__[:4]) >= 0.17" 108 | ] 109 | }, 110 | { 111 | "cell_type": "code", 112 | "execution_count": null, 113 | "metadata": { 114 | "collapsed": true 115 | }, 116 | "outputs": [], 117 | "source": [ 118 | "import statsmodels.api as sm\n", 119 | "float(sm.version.version[:3]) >= 0.6" 120 | ] 121 | }, 122 | { 123 | "cell_type": "code", 124 | "execution_count": null, 125 | "metadata": { 126 | "collapsed": true 127 | }, 128 | "outputs": [], 129 | "source": [ 130 | "import xlrd" 131 | ] 132 | }, 133 | { 134 | "cell_type": "code", 135 | "execution_count": null, 136 | "metadata": { 137 | "collapsed": true 138 | }, 139 | "outputs": [], 140 | "source": [ 141 | "import xlsxwriter" 142 | ] 143 | }, 144 | { 145 | "cell_type": "markdown", 146 | "metadata": {}, 147 | "source": [ 148 | "---" 149 | ] 150 | }, 151 | { 152 | "cell_type": "code", 153 | "execution_count": null, 154 | "metadata": { 155 | "collapsed": true 156 | }, 157 | "outputs": [], 158 | "source": [ 159 | "import fiona\n", 160 | "float(fiona.__version__[:3]) >= 1.7" 161 | ] 162 | }, 163 | { 164 | "cell_type": "code", 165 | "execution_count": null, 166 | "metadata": { 167 | "collapsed": true 168 | }, 169 | "outputs": [], 170 | "source": [ 171 | "import geopandas as gpd\n", 172 | "float(gpd.__version__[:3]) >= 0.2" 173 | ] 174 | }, 175 | { 176 | "cell_type": "code", 177 | "execution_count": null, 178 | "metadata": { 179 | "collapsed": true, 180 | "scrolled": true 181 | }, 182 | "outputs": [], 183 | "source": [ 184 | "import pysal as ps\n", 185 | "float(ps.version[:4]) >= 1.11" 186 | ] 187 | }, 188 | { 189 | "cell_type": "code", 190 | "execution_count": null, 191 | "metadata": { 192 | "collapsed": true 193 | }, 194 | "outputs": [], 195 | "source": [ 196 | "import rasterio as rio" 197 | ] 198 | }, 199 | { 200 | "cell_type": "markdown", 201 | "metadata": {}, 202 | "source": [ 203 | "# Test" 204 | ] 205 | }, 206 | { 207 | "cell_type": "code", 208 | "execution_count": null, 209 | "metadata": { 210 | "collapsed": true 211 | }, 212 | "outputs": [], 213 | "source": [ 214 | "shp = ps.examples.get_path('columbus.shp')\n", 215 | "db = gpd.read_file(shp)\n", 216 | "db.head()" 217 | ] 218 | }, 219 | { 220 | "cell_type": "code", 221 | "execution_count": null, 222 | "metadata": { 223 | "collapsed": true 224 | }, 225 | "outputs": [], 226 | "source": [ 227 | "import matplotlib.pyplot as plt\n", 228 | "%matplotlib inline\n", 229 | "f, ax = plt.subplots(1)\n", 230 | "for poly in db['geometry']:\n", 231 | " gpd.plotting.plot_multipolygon(ax, poly, facecolor='yellow', edgecolor='grey', linewidth=0.1)\n", 232 | "ax.set_axis_off()\n", 233 | "plt.show()" 234 | ] 235 | }, 236 | { 237 | "cell_type": "code", 238 | "execution_count": null, 239 | "metadata": { 240 | "collapsed": true 241 | }, 242 | "outputs": [], 243 | "source": [ 244 | "db.crs['init'] = 'epsg:26918'" 245 | ] 246 | }, 247 | { 248 | "cell_type": "code", 249 | "execution_count": null, 250 | "metadata": { 251 | "collapsed": true 252 | }, 253 | "outputs": [], 254 | "source": [ 255 | "db_wgs84 = db.to_crs(epsg=4326)\n", 256 | "db_wgs84.plot()\n", 257 | "plt.show()" 258 | ] 259 | }, 260 | { 261 | "cell_type": "code", 262 | "execution_count": null, 263 | "metadata": { 264 | "collapsed": true, 265 | "scrolled": true 266 | }, 267 | "outputs": [], 268 | "source": [ 269 | "db.plot(column='INC', scheme='fisher_jenks', cmap=plt.matplotlib.cm.Blues)\n", 270 | "plt.show()" 271 | ] 272 | }, 273 | { 274 | "cell_type": "code", 275 | "execution_count": null, 276 | "metadata": { 277 | "collapsed": true 278 | }, 279 | "outputs": [], 280 | "source": [ 281 | "import numpy as np\n", 282 | "import contextily as ctx\n", 283 | "tl = ctx.tile_providers.ST_TONER_LITE\n", 284 | "\n", 285 | "db = gpd.read_file(ps.examples.get_path('us48.shp'))\n", 286 | "db.crs = {'init': 'epsg:4326'}\n", 287 | "w, s, e, n = db.to_crs(epsg=3857).unary_union.bounds\n", 288 | "dbp = db.to_crs(epsg=3857)\n", 289 | "# Download raster\n", 290 | "_ = ctx.bounds2raster(w, s, e, n, 4, 'us.tif', url=tl)\n", 291 | "# Load up and plot\n", 292 | "source = rio.open('us.tif', 'r')\n", 293 | "red = source.read(1)\n", 294 | "green = source.read(2)\n", 295 | "blue = source.read(3)\n", 296 | "pix = np.dstack((red, green, blue))\n", 297 | "bounds = (source.bounds.left, source.bounds.right, \\\n", 298 | " source.bounds.bottom, source.bounds.top)\n", 299 | "f = plt.figure(figsize=(6, 6))\n", 300 | "ax = plt.imshow(pix, extent=bounds)" 301 | ] 302 | } 303 | ], 304 | "metadata": { 305 | "kernelspec": { 306 | "display_name": "Python 3", 307 | "language": "python", 308 | "name": "python3" 309 | }, 310 | "language_info": { 311 | "codemirror_mode": { 312 | "name": "ipython", 313 | "version": 3 314 | }, 315 | "file_extension": ".py", 316 | "mimetype": "text/x-python", 317 | "name": "python", 318 | "nbconvert_exporter": "python", 319 | "pygments_lexer": "ipython3", 320 | "version": "3.5.3" 321 | } 322 | }, 323 | "nbformat": 4, 324 | "nbformat_minor": 1 325 | } 326 | -------------------------------------------------------------------------------- /data/README.md: -------------------------------------------------------------------------------- 1 | # Description of Data 2 | 3 | ## General information 4 | 5 | * **Location**: Liverpool local authority, England. 6 | * **Format**: Spatial database, GIS vector layer. 7 | * **Spatial Unit of Analysis**: Lower Layer Super Output Area (LSOA). 8 | * **Coordinate System**: EPSG 27700 OSGB 1936 British National Grid. 9 | * **Format**: GeoJson vector file. 10 | * **Geometry**: Polygon. 11 | * **Number of features**: 298. 12 | 13 | Data used in this research corresponds to a polygon GeoJson file 14 | composed of 298 contiguous Lower Layer Super Output Areas (LSOA) of 15 | Liverpool, England (Figure 1). 16 | 17 | ![](fig1.png) 18 | 19 | Figure 1. Living 20 | deprivation index (for 2015) at LSOA level in Liverpool. 21 | 22 | ## Attributes 23 | 24 | ### The English Indices of Deprivation 25 | 26 | The Living Environment Deprivation index is one of the seven dimensions 27 | that compose the English Indices of Deprivation, which includes income; 28 | employment; health and disability; education, skills and training; 29 | crime; barriers to housing and services; and living environment. 30 | According to (Smith et al., 2015), the model of multiple deprivation is 31 | based on the idea that (1) the separate dimensions of deprivation can be 32 | recognized and measured separately, (2) these dimensions of deprivation 33 | are experienced by individuals living in an area, and (3) an area-level 34 | measure of deprivation for each of the dimensions can be calculated 35 | (Smith et al., 2015). 36 | 37 | Although the paper uses only one index (LED index), the GIS layer 38 | contains all the indices of (Smith et al., 2015). For a full description 39 | of these indices, see 40 | 41 | > [https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/464485/English_Indices_of_Deprivation_2015_-_Technical-Report.pdf](https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/464485/English_Indices_of_Deprivation_2015_-_Technical-Report.pdf) 42 | 43 | Table 1. English indices of deprivation variables. 44 | 45 | **Variable name** | **Description** 46 | ----------------- | ------------------------------------------------------------------------- 47 | `imd\_rank` | Rank according the Index of Multiple Deprivation 48 | `imd\_score` | Score of the Index of Multiple Deprivation 49 | `income` | Score of the Income Deprivation domain of the IMD 50 | `employment` | Score of the Employment Deprivation domain of the IMD 51 | `education` | Score of the Education, Skills & Training Deprivation domain of the IMD 52 | `health` | Score of the Health Deprivation & Disability domain of the IMD 53 | `crime` | Score of the Crime domain of the IMD 54 | `housing` | Score of the Barriers to Housing & Services domain of the IMD 55 | `living-env` | Score of the Living Environment Deprivation domain of the IMD 56 | `idaci` | Score of the Income Deprivation Affecting Children Index 57 | `idaopi` | Score of the Income Deprivation Affecting Older People Index 58 | 59 | ### Remote sensing derived variables 60 | 61 | We processed a satellite image of Liverpool city downloaded in February 62 | 2016 from the Google Satellite Map web service with enough zoom level to 63 | be similar to VHR imagery with sub-meter pixel size using a tool for 64 | automatic downloading of image tiles (); 65 | we then combined them into a single mosaic in geographic coordinates. 66 | According to the data provider information, displayed in Google Earth, 67 | and the Google Earth blog, these Liverpool VHR imagery were collected by 68 | Google itself in 4th April 2015. We projected the image mosaic onto the 69 | same coordinate system of the spatial database of deprivation indices of 70 | Liverpool and obtained a georeferenced image with a pixel size of 70 cm. 71 | We used that image as an input to calculate image-derived features or 72 | variables for the same spatial units for which the LED index is reported 73 | (LSOA). 74 | 75 | A per-pixel classification of the scene was performed to calculate a set of land-cover variables within LSOA polygons. Next, we used an automatic tool for image feature extraction at the object level called FETEX 2.0 (Ruiz, Recio, Fernández-Sarría, & Hermosilla, 2011) to extract spectral, texture and structure features at the LSOA level (Table 3). FETEX 2.0 is available at the Geo-Environmental Cartography and Remote Sensing Group website (at Universitat Politècnica de València): 76 | 77 | > [http://cgat.webs.upv.es/software/](http://cgat.webs.upv.es/software/) 78 | 79 | 80 | #### Land cover variables 81 | 82 | We used the red, green and blue bands of the GE image as inputs to a supervised per-pixel classification in ENVI software using a maximum likelihood classifier. The classification accuracy was assessed using a point-based technique with a reference dataset of randomly selected points. We collected a sample of on-screen ground truth points and divided it randomly into two datasets: 80% as the classification training set and 20% as the validation set. Table 2 shows the confusion matrix as well as the overall accuracy and the kappa coefficient of the classification results. The classified image at the pixel level was used to calculate the following aggregated land cover variables at the LSOA level: the percentage of impervious surfaces (gray and orange), the percentage of orange impervious surfaces, the percentage of impervious surfaces without orange surfaces, the fraction of orange surfaces over the impervious surfaces, the percentage of vegetation, the percentage of soil, the percentage of shadows, and the percentage of water. 83 | 84 | Table 2. Accuracy assessment of GE image classification results. 85 | 86 | ![](table2.png) 87 | 88 | For a full description of the image processing workflow, see: 89 | 90 | > [http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0176684#sec002](http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0176684#sec002) 91 | 92 | #### Spectral variables 93 | 94 | The spectral features are the summary statistics of pixel values inside objects; i.e., LSOA boundaries in this study case. These features complement the information provided by the land cover features, as they give a general measure of the color of the areas. We selected the mean and standard deviation in each RGB band for this exercise (R=1, G=2, B=3, variables MEAN1, DEVST1, MEAN2, DEVST2, MEAN3, and DEVST3 in Table 3). 95 | 96 | 97 | #### Texture variables 98 | 99 | Texture variables describe the spatial distribution of intensity values in an image and provide information on contrast, uniformity, roughness, etc. (Baraldi & Parmiggiani, 1995). The histogram of pixel values and the Grey Level Co-occurrence Matrix (GLCM) inside each polygon were used for texture feature extraction in FETEX 2.0. The histogram is used to compute the kurtosis and skewness features. The GLCM describes the co-occurrences of the pixel values that are separated at a distance of one pixel inside the polygon (Baraldi & Parmiggiani, 1995), and it is used to calculate a set of texture variables proposed by Haralick, Shanmugam, & Dinstein (1973) that are widely used in image processing: uniformity, entropy, contrast, inverse difference moment, covariance, variance, and correlation. The edgeness factor is a feature that represents the density of edges present in a neighborhood (Sutton & Hall, 1972), and FETEX also computes the mean and standard deviation of the edgeness factor within this set of texture features. These variables were calculated in FETEX with the LSOA polygons using the red band (1) of the GE image. 100 | 101 | 102 | #### Structure variables 103 | 104 | Structural variables characterize the spatial arrangement of elements within the polygons in terms of randomness or regularity of the distribution of the elements, and these data were computed in FETEX using the experimental semivariogram approach (Balaguer, Ruiz, Hermosilla, & Recio, 2010; Balaguer-Beser, Ruiz, Hermosilla, & Recio, 2013). The semivariogram is a suitable tool for the quantitative characterization of spatial patterns because it quantifies the spatial associations of the pixel intensity values and measures the degree of spatial correlation between different pixels in an image (Ruiz et al., 2011). Structural features are derived from a zonal analysis defined by a set of singular points on the semivariogram: the first maximum, first minimum, and second maximum. We used the red band (1) of the GE image with the LSOA polygons to calculate these variables in FETEX (for a full description of these features see Balaguer et al., 2010; Ruiz et al., 2011). 105 | 106 | Table 3. Remote sensing-derived variables at LSOA level. 107 | 108 | ![](table3.png) 109 | 110 | **References:** 111 | 112 | Balaguer, A., Ruiz, L. A., Hermosilla, T., & Recio, J. A. (2010). Definition of a comprehensive set of texture semivariogram features and their evaluation for object-oriented image classification. Computers & Geosciences, 36(2), 231–240. doi:10.1016/j.cageo.2009.05.003 113 | 114 | Balaguer-Beser, A., Ruiz, L. A., Hermosilla, T., & Recio, J. A. (2013). Using semivariogram indices to analyse heterogeneity in spatial patterns in remotely sensed images. Computers & Geosciences, 50, 115–127. doi:10.1016/j.cageo.2012.08.001 115 | 116 | Baraldi, A., & Parmiggiani, F. (1995). An investigation of the textural characteristics associated with gray level cooccurrence matrix statistical parameters. IEEE Transactions on Geoscience and Remote Sensing, 33(2), 293–304. doi:10.1109/36.377929 117 | 118 | Haralick, R. M., Shanmugam, K., & Dinstein, I. (1973). Textural features for image classification. IEEE Transactions on Systems, Man, and Cybernetics, SMC-3(6), 610–621. 119 | 120 | Ruiz, L. A., Recio, J. A., Fernández-Sarría, A., & Hermosilla, T. (2011). A feature extraction software tool for agricultural object-based image analysis. Computers and Electronics in Agriculture, 76(2), 284–296. https://doi.org/10.1016/j.compag.2011.02.007 121 | 122 | Smith, T., Noble, M., Noble, S., Wright, G., McLennan, D., & Plunkett, E. (2015). The English Indices of Deprivation 2015: Technical Report. London: Department of Communities and Local Government. Retrieved from https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/464485/English_Indices_of_Deprivation_2015_-_Technical-Report.pdf 123 | 124 | Sutton, R. N., & Hall, E. L. (1972). Texture Measures for Automatic Classification of Pulmonary Disease. 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