This repo contains all the data and code powering the Spatial Equity Data Tool. It also contains the files needed to create, and deploy the Spatial Equity Data Tool and all of it's required infrastructure on AWS COdestar (a CI/CD service). If you are just interested in the tool methodology/calculations and/or don't want to setup your own AWS infrastructure for the tool, you can skip most of the files in this repo and head straight to scripts/lambda/equity_calculations.py.

If you are not planning on deploying to AWS, or using AWS services, you can skip this section

• template.yml: A template that defines all the AWS resources needed for the operation of the Spatial Equity Data Tool. This includes an S3 bucket, 2 lambda functions, an API Gateway, and appropriate permissions for all of them. Note that we make use of some predefined roles, like equity-assessment-tool-role that we have manually created and is only available in Urban's AWS account. For reproducibility we have included a copy of the single IAM policy that is attached to the equity-assessment-tool-role in the docs/ folder so you can recreate this role if you want. Note that you might need to edit the IAM role to reflect your S3 bucket path more directly. There are also several placeholder values in this template that start with INSERT_ that you will need to provide yourself.

• buildspec.yml: This file is used by AWS CodeBuild to package your application for deployment to AWS Lambda. It is a collection of build commands to build your Cloudformation template and run your build. We have modified this to also manually update the equity tool lambda function using aws lambda update-function-code as we have noticed Codestar often has problems updating lambda functions that rely on deployment packages stored in S3.

• template-configuration.json - this file contains the project ARN with placeholders used for tagging resources with the project ID

• equity_tool_deployment_package.zip - the deployment package with the compiled dependencies and the code (equity_calculations.py) that powers the workhorse lambda function. For instructions on how to create this deployment package from scratch, please see docs/creating_deployment_pkg_with_geopandas.md,

This is the code powering the lambda functions defined in the Codestar template

• equity_calculations.py: This is the workhorse function that does the geographic and demographic disparity calculations. It reads in user uploaded data, determines the dataset's source city (by performing a spatial join on a small sample of the data), reads in the city's demographic and geographic data, and calculates our disparity scores. It then writes out the outputs into S3 to be returned to the enduser by the API. Because this is a lambda function, the main code logic is contained in the hander function. This code is only meant to work in conjunction with the other AWS infrastructure setup by template.yml. If you do not want to use our AWS infrastructure to run the equity calculations, you will need to modify this script a good deal. To start with, you'd need to rewrite the data readin/writeout functions to read/write data locally instead of from S3,and remove most of the update_status_json calls. If you want to create a version of this script to locally calculate disparity scores and need help modifying this script to meet your needs, please reach out to us!

• getstatus_and_getfile.py: The lambda function powering the API for data retrieval and checking the status of submitted jobs. There is a separate API that handles submissions of new jobs (and data). This function assumes that the data retrieval API has two endpoints defined in template.yml.

These are scripts to generate the sample data used by the tool.

• generate_sample_bike_data: This generates the Minneapolis Nice Ride bike station location dataset used as a sample dataset in our tool. The Nice Ride system data only allows download of thier system information as a JSON< so we transfrom that into a CSV for use with the tool. In the comments of this script, we explain how we downloaded the New Orleans 311 data (another sample dataset in our tool) using a custom Socrata API URL. For more information on all the sample datasets we use in the tool and how we downloaded/cleaed them, please see our Data Catalog entry

These scripts download and update the ACS data powering the Spatial Equity Data Tool

• 01_download_ref_data.R: Generates list of census places in US with populations over 50k. For each census place, it generates two data files:

  • {place_geoid}.geojson: A geojson of all tracts in the census place (as determined by a spatial join). This geojson also contains fields for all the baseline ACS variables the tool uses. Note that place_geoid will be the full 7 digit GEOID (2 digit state geoid + 5 digit place geoid).
  • {place_geoid}.csv: A CSV of citywide statistics and standard errors for the selected demographic ACS variables the tool uses. It uses the Census formulas for standard errors of user derived proportions/percentages to generate SEs. These two output files are placed in the reference-data/{year}/ folder on your local Github repo. This script also places a file called big_places_us_manual_join_ip_cutoff.geojson inside the reference-data/{year}/ directory, which is a listing of all census places in the US with populations over 50,000 and the corresponding tracts within that place. See the technical appendix for more information on how the tool defines the boundaries of a city using census tracts.

• 02_upload_ref_data_to_s3.py: Uploads all the CSV's and geojsons (including the Census place geojson) located in the reference-data/{year} folder to S3 as pickles. We convert to pickles as we saw noteicable speed gains when the data were read in by the tool as pickles instead of geojsons/csvs. You can change the output_bucket and output_region in this script as needed. Note this step is optional and not necessary if you aren't planning on using the AWS infrastructure.

• 03_check_problematic_na_tracts.R: This checks that for all the cities covered by our tool, there are no tracts with NA values for any of our demographic variables. We found that across all 73,000+ tracts, only 143 tracts contained NA values for our demographic variables of interest. Of those 143 tracts, none of them were located in Census places with a population greater than 50,000. As a result, these problematic tracts do not affect our tools calculations.

This folder contains the data written out locally by update-data scripts.

Contains some documentation around setup of the lambda functions and sample datasets.

• sample datasets/: Contains all the sample datasets used in the tool, with the exception of the New Orleans data as it is bigger than Github's 100 MB file size limit. This folder also contains the form jsons that encodes the filters, and weights used by the tool for each sample dataset.
• creating_deployment_pkg_with_geopandas: A how-to guide on creating a deployment packages with geopandas for use in AWS lambda. By creating this deployment package, you can import and use geopandas functions in your lambda code. We have found this to be cost effective, and efficient way to do spatial operations in AWS. This was last updated 2020-05-15.
• creating_deployment_pkg.sh: A helper script that performs the steps laid out in the guide.