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Code Repository

Introduction

Code Repository of "Socio-geographic Disparities in ...." [Full title to be revealed after acceptance]. Preprint. 2023. Please note that all dataset needed for this study will be available upon request.

How to replicate results in Conda Environment

  • Install Anaconda. Enable pip dependency.
  • Create a conda environment named fp_env.
conda create -n fp_env python=3.8
pip install -r requirements.txt
conda install -c conda-forge googlemaps
  • After creating the virtual environment fp_env, run the following command to activate the environment:
conda activate fp_env
  • Place the following six data files from the attached Google Drive to the data folder in the repository (Google Drive Link to Data):
    • fp_bg_pairs.pkl (37.1M pairs of pantry and BGs in the U.S.)
    • bg_pantry_travel_time_updated.csv (~238K BG and pantry pairs with transit time information)
    • bg_fp_regression.csv (ALL 239K BGs' distance, ADI, travel time, rurality)
    • ruralurbancodes2013.csv (US county and rural/urban codes)
    • US_2020_ADI_Census Block Group_v3.2.csv (2020 ADI percentiles)
    • us-state-fips.csv (All US states and their FIPS code)
  • Run main.ipynb to replicate the results in main body texts.
  • Run accessibility_regression.ipynb to replicate the results in regression analysis. (Supplementary Table 2)
  • Run supp_material.ipynb to replicate the results in supplementary Information.

Please note that prepare_data.ipynb and compute_travel_time.ipynb are to show how we were able to prepare our dataset for the main study. This files will take considerable amount of time and require your own API key, so please skip to main.ipynb to observe the results and understand insights about our study.

For Figure 1(b), Supp. Figures 4 and 5, they were created by Tableau Desktop and require you to have your own API key to access the visuals. Please contact me if you further would like to see the software.