This is the code for the project "Coloring Inside the Lines: The Jagged Legacy of the HOLC Neighborhood Risk Maps". Below you will find information on how to build the data product and reproduce the findings of the study.
- Anaconda
- Python (>= 3.6)
- Lots of RAM (if you're rebuilding all data sources, > 16GB is preferred)
See environment.yml for more details on the package requirements.
- Clone this repository.
- Build the anaconda environment using the following command:
conda env create -f environment.yml. - Download and unzip the data package from here. (Or, if you're feeling particularly adventurous, build the data product yourself by getting the data from IPUMS and using
redlining-maps-crosswalks-2010.ipynb) - Pick a city and run the chains on it with the following command:
python measure-parallel.py <CITY> <STATE> <FIPS>.
usage: measure-parallel.py [-h] [-s STEPS]
[-w WORKERS]
city state fips
positional arguments:
city city name, i.e. Atlanta
state state code, i.e. GA
fips state FIPS code (zero-
padded on the end), i.e.
130
optional arguments:
-h, --help show this help message
and exit
-s STEPS, --steps STEPS
number of steps for each
markov chain
-w WORKERS, --workers WORKERS
total # of worker
processes across both
proposals
NOTE: If you wish to simply try out the code, use the flag -w 2 to minimize the number of threads you'll have to manually kill if things go wrong.
If you are running to reproduce, use -s 1000000 and make sure the random seed is set to 2020 (line 306 in measure-parallel.py). Note that this will take a VERY long amount of time, and thus it's only recommended to be run on a cloud instance or a very powerful machine.
Author: Arunav Gupta (arunavg (at) ucsd (dot) edu)
Advisor: Dr. Isaac Martin, Urban Studies and Planning Department, UC San Diego
Project was funded in part by the 2020 Halicioglu Data Science Undergraduate Research Scholarship.
This project was heavily inspired by the work of Dr. Moon Duchin and Metric Geography and Gerrymandering Group, as well as the Racial Dot Map from the University of Virginia.
Data Sources:
IPUMS NHGIS: Steven Manson, Jonathan Schroeder, David Van Riper, and Steven Ruggles. IPUMS National Historical Geographic Information System: Version 14.0 [Database]. Minneapolis, MN: IPUMS. 2019. http://doi.org/10.18128/D050.V14.0.
Mapping Inequality: Robert K. Nelson, LaDale Winling, Richard Marciano, Nathan Connolly, et al., “Mapping Inequality,” American Panorama, ed. Robert K. Nelson and Edward L. Ayers, accessed February 23, 2021, https://dsl.richmond.edu/panorama/redlining/.