├── Data_Description ├── Air_Pollution │ ├── Readme.md │ └── air_pollution1.png ├── Basic_Geographic_Info │ ├── Readme.md │ ├── basic1.png │ ├── basic2.png │ └── basic3.png ├── Carbon │ ├── Carbon1.png │ └── Readme.md ├── Data.png ├── Mobility │ ├── Readme.md │ ├── mobility1.png │ └── mobility2.png ├── Population │ ├── Population1.png │ ├── Population10.png │ ├── Population11.png │ ├── Population3.png │ ├── Population4.png │ └── Readme.md └── Visual_Semantics │ ├── Readme.md │ └── Visual1.png ├── README.md ├── Submission_Format.pptx └── src └── Exemplar_Ideas.md /Data_Description/Air_Pollution/Readme.md: -------------------------------------------------------------------------------- 1 | # Air Pollution 2 | - **Air_Pollution/daily_{pollutant}_{year}.csv** 3 | - Include the daily air pollution information measured by monitors across the United States between 2018 to 2021. It is collected from the Environmental Protection Agency of the United States. 4 | - Each row corresponds to the air pollution information at a certain (latitude, longitude) pair on a certain day. 5 | - The columns record [Latitude, Longitude, Parameter Name, Sample Duration, Date Local, Units of Measure, Observation Count, Arithmetic Mean, State Name, County Name, City Name, CBSA Name], respectively. 6 | - Observation Count represents the number of observations (samples) taken during the day, and Arithmetic Mean is the average (arithmetic mean) value for the day. 7 | - State/County/City/CBSA Name(s) are the name(s) of the state/county/city/core bases statistical area (metropolitan area) where the monitoring site is located. 8 | 9 | 10 | **Data Preview:** 11 | 12 | ![Image text](air_pollution1.png) 13 | -------------------------------------------------------------------------------- /Data_Description/Air_Pollution/air_pollution1.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/tsinghua-fib-lab/Urban-Cup-2023/d6946352c1cd0946ee9112668333445effc1ad34/Data_Description/Air_Pollution/air_pollution1.png -------------------------------------------------------------------------------- /Data_Description/Basic_Geographic_Info/Readme.md: -------------------------------------------------------------------------------- 1 | # Basic Geographic Info 2 | - **This folder contains three files:** 3 | - Basic_Geographic_Statistics_CBG.csv 4 | - Basic_Geographic_Statistics_City.csv 5 | - Geographic_Lookup_Table_Between_City_CBG.csv 6 | 7 | - **Basic_Geographic_Statistics_CBG.csv** 8 | - Contains the information of each CBG used in this dataset, such as City Name (which city they belong to), CBG Code, Year, Population, Area, Geographic Centroid, Geographical Boundary. 9 | 10 | 11 | **Data Preview:** 12 | 13 | ![Image text](basic1.png) 14 | 15 | - **Basic_Geographic_Statistics_City.csv** 16 | - Contains the information of each city used in this dataset, such as City Name, City GeoID, Year, Population, Area, Geographic Centroid, Geographical Boundary. 17 | 18 | 19 | **Data Preview:** 20 | 21 | ![Image text](basic2.png) 22 | 23 | - **Geographic_Lookup_Table_Between_City_CBG.csv** 24 | - Contains the spatial correspondance between the city and CBG. The CBG is annotated by the CBG code and the city shows City Name and City GeoID. 25 | 26 | 27 | **Data Preview:** 28 | 29 | ![Image text](basic3.png) 30 | 31 | -------------------------------------------------------------------------------- /Data_Description/Basic_Geographic_Info/basic1.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/tsinghua-fib-lab/Urban-Cup-2023/d6946352c1cd0946ee9112668333445effc1ad34/Data_Description/Basic_Geographic_Info/basic1.png -------------------------------------------------------------------------------- /Data_Description/Basic_Geographic_Info/basic2.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/tsinghua-fib-lab/Urban-Cup-2023/d6946352c1cd0946ee9112668333445effc1ad34/Data_Description/Basic_Geographic_Info/basic2.png -------------------------------------------------------------------------------- /Data_Description/Basic_Geographic_Info/basic3.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/tsinghua-fib-lab/Urban-Cup-2023/d6946352c1cd0946ee9112668333445effc1ad34/Data_Description/Basic_Geographic_Info/basic3.png -------------------------------------------------------------------------------- /Data_Description/Carbon/Carbon1.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/tsinghua-fib-lab/Urban-Cup-2023/d6946352c1cd0946ee9112668333445effc1ad34/Data_Description/Carbon/Carbon1.png -------------------------------------------------------------------------------- /Data_Description/Carbon/Readme.md: -------------------------------------------------------------------------------- 1 | # Carbon 2 | - **Carbon/odiac_carbon_mainland_us.csv** 3 | - Record the 1kmx1km monthly CO2 emission statistics from the ODIAC database (https://odiac.org/data-product.html) in a CSV file. ODIAC is an open-data inventory that pioneered the combined use of nighttime lights data and point source data to achieve its global 1x1km resolution emission field. 4 | - You can use following codes to load the data: 5 | ``` 6 | import pandas as pd 7 | odiac_values=pd.read_csv('odiac_carbon_mainland_us.csv') 8 | ``` 9 | where each row contains the location information (latitude, longitude), the temporal information (year, month) of the point, and the monthly carbon emission values in units of Megatonne. 10 | 11 | 12 | **Data Preview:** 13 | 14 | ![Image text](Carbon1.png) 15 | -------------------------------------------------------------------------------- /Data_Description/Data.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/tsinghua-fib-lab/Urban-Cup-2023/d6946352c1cd0946ee9112668333445effc1ad34/Data_Description/Data.png -------------------------------------------------------------------------------- /Data_Description/Mobility/Readme.md: -------------------------------------------------------------------------------- 1 | # Mobility 2 | - **Mobility/cbg_visit_{month}_{city_id}.npy** 3 | - Include the monthly frequency matrix of CBG-to-CBG population movements within each city from January 2018 to March 2022. 4 | - Within an NPY file, both the rows and columns represent a Census Block Group (CBG, a geographical region in the city), which is a square matrix with the number of people moving from the row CBG to the column CBG. 5 | - city_id mapping: 1 New York, NY 2 Los Angeles, CA 3 Chicago, IL 4 Houston, TX 5 Phoenix, AZ 6 Philadelphia, PA 7 San Antonio, TX 8 San Diego, CA 9 Dallas, TX 10 San Jose, CA 6 | - The CBG ID represented by each column or row can be found in ID dicts. 7 | - You can use following codes to load a frequency matrix:​ 8 | ``` 9 | import numpy as np 10 | cbg_visit_matrix = np.load('cbg_visit_2018-01_1.npy') 11 | ``` 12 | ​ 13 | **Data Preview:** 14 | ![Image text](mobility1.png) 15 | 16 | 17 | - **Mobility/id_dict_{city_id}.pkl** 18 | - Contains a dict mapping the row and column number in the previous matrices to the corresponding CBG ID. 19 | - The CBG ID can be associated with the population attributes of each region in the Census dataset. 20 | - You can use following codes to load an ID dict: 21 | ``` 22 | import pickle 23 | with open('id_dict_1.pkl', 'rb') as f: 24 | id_dict = pickle.load(f) 25 | ``` 26 | 27 | **Data Preview:** 28 | ![Image text](mobility2.png) 29 | -------------------------------------------------------------------------------- /Data_Description/Mobility/mobility1.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/tsinghua-fib-lab/Urban-Cup-2023/d6946352c1cd0946ee9112668333445effc1ad34/Data_Description/Mobility/mobility1.png -------------------------------------------------------------------------------- /Data_Description/Mobility/mobility2.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/tsinghua-fib-lab/Urban-Cup-2023/d6946352c1cd0946ee9112668333445effc1ad34/Data_Description/Mobility/mobility2.png -------------------------------------------------------------------------------- /Data_Description/Population/Population1.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/tsinghua-fib-lab/Urban-Cup-2023/d6946352c1cd0946ee9112668333445effc1ad34/Data_Description/Population/Population1.png -------------------------------------------------------------------------------- /Data_Description/Population/Population10.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/tsinghua-fib-lab/Urban-Cup-2023/d6946352c1cd0946ee9112668333445effc1ad34/Data_Description/Population/Population10.png -------------------------------------------------------------------------------- /Data_Description/Population/Population11.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/tsinghua-fib-lab/Urban-Cup-2023/d6946352c1cd0946ee9112668333445effc1ad34/Data_Description/Population/Population11.png -------------------------------------------------------------------------------- /Data_Description/Population/Population3.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/tsinghua-fib-lab/Urban-Cup-2023/d6946352c1cd0946ee9112668333445effc1ad34/Data_Description/Population/Population3.png -------------------------------------------------------------------------------- /Data_Description/Population/Population4.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/tsinghua-fib-lab/Urban-Cup-2023/d6946352c1cd0946ee9112668333445effc1ad34/Data_Description/Population/Population4.png -------------------------------------------------------------------------------- /Data_Description/Population/Readme.md: -------------------------------------------------------------------------------- 1 | # Population 2 | - **This folder contains ten files:** 3 | - 01_Poverty_CBG_level.csv 4 | - 01_Poverty_city_level.csv 5 | - 03_Health_Insurance_CBG_level.csv 6 | - 03_Health_Insurance_city_level.csv 7 | - 04_Education_CBG_level.csv 8 | - 04_Education_city_level.csv 9 | - 10_Gini_CBG_level.csv 10 | - 10_Gini_city_level.csv 11 | - 11_Built_Environment_CBG_level.csv 12 | - 11_Built_Environment_city_level.csv 13 | 14 | 15 | - **01_Poverty_CBG_level.csv (01_Poverty_City_level.csv)** 16 | - Contains the median household income, population above/below poverty, population with a ratio of income to poverty level under 0.5, and population with a ratio of income to poverty level between 0.5 to 0.99 at the CBG/city level. 17 | 18 | 19 | **Data Preview:** 20 | 21 | ![Image text](Population1.png) 22 | 23 | - **03_ Health_Insurance_CBG_level.csv (03_ Health_Insurance_City_level.csv)** 24 | - Contains civilian noninstitutionalized population, population with no health insurance under 18, population with no health insurance between 18 to 34, population with no health insurance between 35 to 64, and population with no health insurance over 65 years old at the CBG/city level. 25 | 26 | 27 | **Data Preview:** 28 | 29 | ![Image text](Population3.png) 30 | 31 | - **04_Education_CBG_level.csv (04_Education_City_level.csv)** 32 | - Contains population enrolled in college, population that graduated from high school, population with a bachelor’s degree, a master’s degree, and a doctorate at the CBG/city level. 33 | 34 | 35 | **Data Preview:** 36 | 37 | ![Image text](Population4.png) 38 | 39 | - **10_Gini_CBG_level.csv (10_Gini_City_level.csv)** 40 | - Contains light Gini at the CBG level and income Gini and light Gini at the city level. 41 | 42 | 43 | **Data Preview:** 44 | 45 | ![Image text](Population10.png) 46 | 47 | - **11_Built_Environment_CBG_level.csv (11_Built_Environment_City_level.csv)** 48 | - Contains building density, driving/cycling/walking road density, POI density, land use information (commercial, industrial, construction, and residential), and residential segregation (index of dissimilarity and entropy index) at the CBG/city level. 49 | 50 | 51 | **Data Preview:** 52 | 53 | ![Image text](Population11.png) 54 | -------------------------------------------------------------------------------- /Data_Description/Visual_Semantics/Readme.md: -------------------------------------------------------------------------------- 1 | # Visual Semantics 2 | - **This folder contains two files:** 3 | - Visual_attributes_from_satellite_imagery_CBG_Level.csv 4 | - Visual_attributes_from_satellite_imagery_City_Level.csv 5 | 6 | Each file (CBG/city level) contains the object numbers and land cover semantic attributes processed from satellite imagery of the years 2014 to 2023. The object categories include planes, airports, passenger vehicles, trucks, railway vehicles, ships, engineering vehicles, bridges, roundabouts, vehicle lots, swimming pools, soccer fields, basketball courts, ground track fields, baseball diamonds, tennis courts, and buildings (number of buildings). The land cover semantic attributes contain background, building (pixel percentage), road, water, barren, forest, and agriculture. There are altogether 24 visual attributes obtained from satellite imagery. 7 | 8 | 9 | **Data Preview:** 10 | 11 | ![Image text](Visual1.png) 12 | -------------------------------------------------------------------------------- /Data_Description/Visual_Semantics/Visual1.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/tsinghua-fib-lab/Urban-Cup-2023/d6946352c1cd0946ee9112668333445effc1ad34/Data_Description/Visual_Semantics/Visual1.png -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Urban Cup 2023 2 | 3 | ## Dear Participants, 4 | Welcome to Urban Cup 2023 of the first Research Summit on Urban Science and Human Dynamics! We sincerely invite you and your team to join the fascinating journey towards data-driven urban science for sustainability and social good! With fine-grained data on urban mobility, local infrastructure, land use, gas emission as well as poverty, health insurance, education attainment, etc., we welcome you to submit an original and publicly understandable impression of urban phenomena, patterns, or connections concerning urban sustainability and social good. The impression should be aesthetically, scientifically, and accurately visualized based on your data analysis. **Two or more** forms of the provided data should be used, and additional information (e.g., Internet activities) may but need not be linked to. Please also make sure to submit the source codes that guide you to your amazing discoveries. 5 | 6 | Please summarize the impression in a **one-page slide** and format your submission as [this template](Submission_Format.pptx). Each team should consist of **no more than 3 people**, with **at least one** team member present at the conference venue to **present the impression**. Please email all files to **rsushd2023@163.com by August 5th @ 8am (Beijing time, UTC/GMT+08:00)** to complete the submission. Late submissions will not be considered. 7 | 8 | Exemplar ideas of the impressions can be found [here](src/Exemplar_Ideas.md). 9 | 10 | Here are downloadable [U.S. datasets](https://cloud.tsinghua.edu.cn/d/98a3d3ceb3e34333bf7f/) and [England datasets](https://github.com/0oshowero0/HealthyCitiesDataset). 11 | 12 | ## Datasets 13 | We provide **6 city-scale datasets** collected from the largest U.S. cities and **1 sub-city scale multisourced dataset** collected from 1039 middle layer super output areas (MSOAs) of 29 England cities. The vast majority of them originate from [A satellite imagery dataset for long-term sustainable development in United States cities](https://arxiv.org/abs/2308.00465) and [Healthy Cities, A comprehensive dataset for environmental determinants of health in England cities](https://www.nature.com/articles/s41597-023-02060-y) published/accepted in Nature Scientific Data (IF 9.8). The datasets are briefly described as follows: 14 | 15 | ### 1. U.S. mobility dataset 16 | This dataset is extracted from SafeGraph [1]. It details the monthly population movement between CBGs within the 10 largest cities in the United States from 2018.1 to 2022.4. The movements were aggregated from anonymized mobile devices. Please click [here](Data_Description/Mobility/Readme.md) for more details about the dataset. 17 | 18 | ### 2. U.S. carbon emission dataset 19 | This dataset is collected from Open-source Data Inventory for Anthropogenic CO2 (ODIAC) [2]. It records the CO2 emission within the 10 largest cities in the United States between 2018 and 2021. The data provides monthly CO2 emissions from fossil fuel combustion, cement production and gas flaring on a 1x1 km spatial resolution. Please click [here](Data_Description/Carbon/Readme.md) for more details about the dataset. 20 | 21 | ### 3. U.S. air pollution dataset 22 | This dataset is derived from the Environmental Protection Agency of the United States [3]. It is collected from air quality monitors and consists of the daily O3, SO2, NO2, CO, PM2.5, and PM10 information across the United States between 2018 and 2021. Please click [here](Data_Description/Air_Pollution/Readme.md) for more details about the dataset. 23 | 24 | ### 4. U.S. population characteristic dataset 25 | This dataset is part of [4] and is obtained from American Community Survey (ACS), Earth Observation Group, and OpenStreetMap. It provides both CBG-level and city-level poverty, health insurance, education, income Gini & light Gini, and built environment & racial segregation data from 2014 to 2023 across the 10 largest cities in the United States. Please click [here](Data_Description/Population/Readme.md) for more details about the dataset. 26 | 27 | ### 5. U.S. visual semantics dataset 28 | This dataset is part of [4] and provides semantic attributes processed from satellite remote sensing data. It depicts both CBG-level and city-level urban infrastructural information from 2014 to 2023 across the 10 largest cities in the United States. Please click [here](Data_Description/Visual_Semantics/Readme.md) for more details about the dataset. 29 | 30 | ### 6. U.S. basic geographical information dataset 31 | This dataset is part of [4] and consists of basic geographical information of CBGs across the 10 largest cities in the United States between 2014 and 2021. It records the city, area, population, centroid, and boundary of the CBGs within each year. Please click [here](Data_Description/Basic_Geographic_Info/Readme.md) for more details about the dataset. 32 | 33 | The data entries of the above mentioned datasets are summarized as below: 34 | 35 | ![Image text](Data_Description/Data.png) 36 | 37 | ### 7. England environment and health datasets 38 | 39 | This is a fine-grained and multi-sourced environment and health dataset collected from cities in England, which is published in our [Nature Scientific Data paper](https://www.nature.com/articles/s41597-023-02060-y) (IF 9.8) [5]. The corresponding data repo is available at [here](https://github.com/0oshowero0/HealthyCitiesDataset). It records the health outcomes of citizens covering physical health (COVID-19 cases, asthma medication expenditure, etc.), mental health (psychological medication expenditure), and life expectancy estimations. It presents the corresponding environmental determinants from four perspectives, including basic statistics (population, area, etc.), behavioural environment (availability of tobacco, health-care services, etc.), built environment (road density, street view features, etc.), and natural environment (air quality, temperature, etc.). To reveal regional differences, this dataset extracts and integrates massive environment and health indicators from heterogeneous sources into two unified spatial scales, i.e., at the middle layer super output area (MSOA) and the city level, via big data processing and deep learning techniques. 40 | 41 | A comprehensive data table that contains all the subsections is also provided, which is organized into a long table with columns as follows: 42 | | Column Name | Description | Example | 43 | | ------ | -----------|-----------| 44 | | TopCategory| The top category of the dataset.| HealthOutcome, EnvironmentalDeterminants| 45 | | SecondCategory |The second category of the dataset.| PhysicalHealth, MentalHealth, LifeExpectancy, NaturalEnvironment, BehaviourEnvironment, BuiltEnvironment, BasicStatistics| 46 | | ThirdCategory | The third category of the dataset.| DementiaExpenditure, Weather, TobaccoAvailability, RoadDensity, Population | 47 | | CityCode | Exclusive ID for each city. | J01000007| 48 | | CityName | Name of the city. | Birmingham| 49 | | MSOACode | Exclusive ID for each MSOA.| E02001834| 50 | | MSOAName | Name of the MSOA. |Birmingham 008 | 51 | | Time | Time of the data record. For those without timestamps, `None` is filled to this column.| 2019-01-01 52 | | Key | Specifications of the record, such as `Mean` for `HousePrice` and `Female` for `LifeExpectancy` | Mean, Female, PM2.5 | 53 | | Value | Value of the data record.| | 54 | 55 | ### References 56 | [1] SafeGraph. Patterns. https://docs.safegraph.com/docs/monthly-patterns (2022). 57 | 58 | [2] Oda, Tomohiro and Maksyutov, Shamil. ODIAC Fossil Fuel CO2 Emissions Dataset (Version ODIAC2022), Center for Global Environmental Research, National Institute for Environmental Studies (2015). 59 | 60 | [3] United States Environmental Protection Agency. Pre-Generated Data Files. https://aqs.epa.gov/aqsweb/airdata/download_files.html (2022). 61 | 62 | [4] Xi, Yanxin, et al. "A satellite imagery dataset for long-term sustainable development in united states cities." Scientific data (2023). https://arxiv.org/abs/2308.00465. 63 | 64 | [5] Han, Zhenyu, et al. "Healthy Cities, A comprehensive dataset for environmental determinants of health in England cities." Scientific data 10.1 (2023): 165. https://www.nature.com/articles/s41597-023-02060-y. 65 | -------------------------------------------------------------------------------- /Submission_Format.pptx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/tsinghua-fib-lab/Urban-Cup-2023/d6946352c1cd0946ee9112668333445effc1ad34/Submission_Format.pptx -------------------------------------------------------------------------------- /src/Exemplar_Ideas.md: -------------------------------------------------------------------------------- 1 | # Exemplar Ideas 2 | 3 | ### 1. How air pollution affects experienced segregation 4 | Experienced segregation is defined as how likely urban dwellers can encounter others from different social classes [1], which is an important metric for urban inclusiveness. Air pollution will likely reduce urban mobility [2]. Therefore, it is interesting to evaluate the potential link between air pollution and urban experienced segregation. 5 | 6 | **Datasets involved**: Mobility dataset, Population characteristic dataset, Air pollution dataset 7 | 8 | **References**: 9 | 10 | [1] Moro, Esteban, et al. "Mobility patterns are associated with experienced income segregation in large US cities." Nature Communications 12.1 (2021): 4633. 11 | 12 | [2] Chu, Junhong, Haoming Liu, and Alberto Salvo. "Air pollution as a determinant of food delivery and related plastic waste." Nature Human Behaviour 5.2 (2021): 212-220. 13 | 14 | 15 | ### 2. How urban infrastructure affects carbon emission 16 | Cities are major contributors to carbon emissions. Urban infrastructure, including transportation systems, buildings, energy supply, and waste management, plays a critical role in shaping the environmental sustainability of cities. It is interesting to investigate how infrastructures in multiple cities affect carbon emission, for example, what infrastructures contribute most to emission, which can provide valuable insights for policymakers and urban planners to develop sustainable and low-carbon cities. 17 | 18 | **Datasets involved**: Visual Semantics, Carbon 19 | 20 | **References:** 21 | 22 | [1] Lin, Yatang, et al. "Impact of high-speed rail on road traffic and greenhouse gas emissions." Nature Climate Change 11.11 (2021): 952-957. 23 | 24 | [2] Böhm, Matteo, Mirco Nanni, and Luca Pappalardo. "Gross polluters and vehicle emissions reduction." Nature Sustainability 5.8 (2022): 699-707. 25 | 26 | 27 | ### 3. Does COVID hindered sustainable development? 28 | Previous studies have witnessed reduction in human activity during the COVID [1, 2], which raise the concern that COVID may disrupt the efforts toward sustainable development. It is interesting to use datasets including mobility, visual semantics, built environments to assess the speed of sustainable transition before and after COVID, for instance, the expansion of greenlands, sports fields, and the increasing communications between neighbourhoods of diverse backgrounds. 29 | 30 | **Datasets involved**: Mobility dataset, Population, Visual Semantics, (+COVID-19 dataset) 31 | 32 | **References:** 33 | 34 | [1] Chang, Serina, et al. "Mobility network models of COVID-19 explain inequities and inform reopening." Nature 589.7840 (2021): 82-87. 35 | 36 | [2] Weill, Joakim A., et al. "Social distancing responses to COVID-19 emergency declarations strongly differentiated by income." Proceedings of the national academy of sciences 117.33 (2020): 19658-19660. 37 | 38 | 39 | ### 4. Urban facility and inequality 40 | Retrieve the number of sports fields and investigate whether certain marginalized groups are more disadvantaged in the provision of sports fields. Explore its correlation with health insurance and health attainment. Determine the influence of the construction of new sports fields on local health and education. 41 | 42 | **Datasets involved**: Population, Visual Semantics 43 | 44 | 45 | ### 5. Road density and urban vitality 46 | Examine the changes in the desity of driving/cycling/walking roads and explore its correlation with urban vitality characterized by visitations. 47 | 48 | **Datasets involved**: Mobility dataset, Population 49 | 50 | 51 | ### 6. Environmental inequality 52 | **Representative Papers:** 53 | 54 | [1] Chambliss, Sarah E., et al. "Local-and regional-scale racial and ethnic disparities in air pollution determined by long-term mobile monitoring." Proceedings of the National Academy of Sciences 118, no. 37 (2021): e2109249118. 55 | 56 | [2] Brazil, Noli. "Environmental inequality in the neighborhood networks of urban mobility in US cities." Proceedings of the National Academy of Sciences 119.17 (2022): e2117776119. 57 | 58 | --------------------------------------------------------------------------------