Urban census tract and HOLC polygon grade summaries and scripts from Chakraborty et al. 2023

Published: 17 April 2023| Version 1 | DOI: 10.17632/jgv5hmzc44.1
TC Chakraborty,
Andrew Newman,
Yun Qian,
Angel Hsu,
Glenn Sheriff


All data and code required to generate census tract and HOLC polygon level heat stress indices, as well as the urban-scale segregation indices can be found here. Urbanized_CT_summer_env.csv includes the 2014-2018 average maximum summer skin temperature (TSKINmax), air temperature (ATmax), and minimum and maximum specific humidity (QTmax and QTmin) from the model simulations, as well as the corresponding derived land surface temperature from the MODIS Aqua daytime overpass (MODIS_LST_day), for each urban census tract in the continental US. Urbanized_CT_sociodemo.csv includes socioeconomic information from the 5-year 2017 American Community Survey, including population (Pop), income (Income), and number of people who are white (White), black (Black), American Indian (AmInNat), asian (Asian), hawaiian (Hawaiin), or of another race (Other) for each urban census tract in the continental US. Urban_census_tracts.geojson includes the spatial extent of all urban census tracts in the continental US. The three datasets above can be linked using the 'Census_geoid' column. Redlining_summer_env.csv is similar to Urbanized_CT_summer_env.csv, but corresponding to the Home Owners’ Loan Corporation grades, the polygons for which were digitized by University of Richmond’s Digital Scholarship Lab. Within the Scripts folder, there are two scripts: Calculate heat indices.ipynb is a Jupyter Notebook for estimating the lower and upper bound for average maximum summer relative humidity (RH_min and RH_max) and heat index (HI_min and HI_max), as well as the lower bound for Humidex (HUMIDEX). Segregation_calculate.R is an R script for calculating various inequality indices, including entropy index, dissimilarity index, gini coefficient, and concentration indices against race and income for the heat index.



Remote Sensing, Climate, Census Data, Modelling of Weathering