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Development and Spatial Validation of a Random Forest Prediction Model for Firearm-Related Injury Risk in Chicago Census Tracts

Wed, 01 Oct 2025 00:00:00 +0000

Presentation at Women in Statistics and Data Science Conference

Our presentation, Modeling Firearm Injury Risk Using Spatial Random Forests, was delivered by Dr. Gia Barboza-Salerno at the Women in Statistics and Data Science on November 12, 2025 in Cincinnati, OH.

Abstract

This research develops and validates a spatial random forest prediction model for firearm-related injury risk at the census tract level in Chicago. By incorporating spatial autocorrelation and neighborhood-level predictors, the model achieves high predictive accuracy in identifying areas at elevated risk for gun violence.

Methodology

Spatial Random Forest Approach

The spatial random forest model extends traditional random forest methods by:

Capturing Non-Linear Relationships: Modeling complex interactions between neighborhood characteristics and shooting risk
Handling Spatial Autocorrelation: Incorporating spatial structure through lag variables
Variable Importance: Identifying which neighborhood features are most predictive
Robust Predictions: Providing out-of-sample risk estimates

Data Sources

Shooting incident data from Chicago
Census tract-level socioeconomic indicators
Built environment features
Spatial lag variables to capture spillover effects

Model Validation

Cross-validation for predictive performance assessment
Spatial autocorrelation diagnostics
Comparison with traditional regression approaches

Key Findings

The predicted shooting risk map (shown above) demonstrates:

High Spatial Variation: Risk scores range from 0 to over 80 predicted incidents
Concentrated Risk: Certain neighborhoods show elevated firearm violence risk
Model Accuracy: Spatial random forest successfully captures fine-grained risk patterns
Spatial Clustering: Violence risk exhibits significant spatial autocorrelation

Applications

This spatial prediction model has important applications for:

Resource Allocation: Targeting violence prevention resources to high-risk areas
Prevention Planning: Understanding neighborhood-level drivers of gun violence
Policy Evaluation: Assessing how neighborhood changes affect violence risk
Early Warning: Detecting emerging hotspots before violence escalates

Implications

The research demonstrates that spatial random forest models can:

Improve prediction of firearm violence risk beyond traditional methods
Identify actionable neighborhood-level intervention targets
Account for complex spatial dependencies in violence patterns
Support evidence-based violence prevention strategies

Conference: Women in Statistics and Data Science Conference
Location: Cincinnati, Ohio
Date: October 1-3, 2025
Authors: Gia Barboza-Salerno, Hexin Yang, Taylor Harrington

A PostgreSQL Tutorial for Analyzing Racial and Geographic Disparities in HMDA Applications

Wed, 16 Jul 2025 00:00:00 +0000

My lab recently published a paper examining the association between redlining, reinvestment, racial segregation, and firearm-related injury in Chicago, Illinois. You can read the paper here: Redlining, reinvestment, and racial segregation: a Bayesian spatial analysis of mortgage lending trajectories and firearm-related violence.

To develop reverse redlining measures, we relied on data from the Home Mortgage Disclosure Act (HMDA).

If you’re interested in patterns of lending discrimination, the HMDA dataset is an invaluable resource. It contains millions of individual loan application records submitted annually by financial institutions, making it one of the most extensive and detailed public data sources on mortgage lending in the United States. The 2024 nationwide Modified Loan/Application Register (LAR) alone contains over 12 million records and 85 fields that detail applicant and co-applicant demographics, loan terms, and property characteristics — including race, income, loan amount, interest rate, property value, and loan outcomes.

Each record includes the census tract of the loan origination, which allows researchers to situate applicants within their geographic context. This spatial identifier can be linked to other data sources (e.g., Census or ACS data), enabling robust analyses of racial disparities in lending, neighborhood-level disinvestment, and broader patterns of residential inequality.

I highly recommend The Color of Law by Richard Rothstein for anyone interested in the deep-rooted history of discriminatory housing practices in the United States. The book compellingly explains how federal, state, and local policies intentionally created and reinforced de jure segregation. Chicago, one of the most highly segregated urban areas in the country, is prominently featured.

Due to its sheer volume, the HMDA data is not well-suited for analysis using spreadsheets like Excel — particularly when analyzing multiple years or conducting joins. Instead, its relational structure lends itself to PostgreSQL, an open-source relational database management system (RDBMS) that enables efficient querying, filtering, and aggregation of large datasets. PostgreSQL also supports the PostGIS geospatial extension, enabling direct spatial querying and linking to geographic units such as census tracts or ZIP codes. Download PostgreSQL if you don’t already have it.

Step 1: Download the Dataset

The dataset is freely available to the public.

Navigate to https://ffiec.cfpb.gov/data-publication/modified-lar/2024
Choose Year: 2024 → Nationwide: Combined file
Check Include File Header, then click Download Combined Modified LAR with Header

Save or extract the file to: C:\Users\YOUR_USER_NAME\Downloads\2024_combined_mlar_header\

Step 2: Confirm the File Structure in PowerShell

The file is a pipe-delimited .txt file — it can be tricky to read in spreadsheet tools. Use Windows PowerShell to inspect the structure first:

cd "C:\Users\YOUR_USER_NAME\Downloads\2024_combined_mlar_header"

# View the header (column names)
Get-Content .\2024_combined_mlar_header.txt -TotalCount 1

# Count the number of columns (should be 85)
((Get-Content .\2024_combined_mlar_header.txt -TotalCount 1) -split '\|').Count

Step 3: Create the PostgreSQL Table

Launch your SQL interface (pgAdmin Query Tool or psql):

psql -U your_username -d your_database_name

Then define the 85-column table structure:

DROP TABLE IF EXISTS lar2024;
CREATE TABLE lar2024 (
 activity_year INTEGER,
 lei TEXT,
 loan_type INTEGER,
 loan_purpose INTEGER,
 preapproval INTEGER,
 construction_method INTEGER,
 occupancy_type INTEGER,
 loan_amount BIGINT,
 action_taken INTEGER,
 state_code TEXT,
 county_code TEXT,
 census_tract TEXT,
 applicant_ethnicity_1 INTEGER,
 applicant_ethnicity_2 TEXT,
 applicant_ethnicity_3 TEXT,
 applicant_ethnicity_4 TEXT,
 applicant_ethnicity_5 TEXT,
 co_applicant_ethnicity_1 INTEGER,
 co_applicant_ethnicity_2 TEXT,
 co_applicant_ethnicity_3 TEXT,
 co_applicant_ethnicity_4 TEXT,
 co_applicant_ethnicity_5 TEXT,
 applicant_ethnicity_observed INTEGER,
 co_applicant_ethnicity_observed INTEGER,
 applicant_race_1 INTEGER,
 applicant_race_2 TEXT,
 applicant_race_3 TEXT,
 applicant_race_4 TEXT,
 applicant_race_5 TEXT,
 co_applicant_race_1 INTEGER,
 co_applicant_race_2 TEXT,
 co_applicant_race_3 TEXT,
 co_applicant_race_4 TEXT,
 co_applicant_race_5 TEXT,
 applicant_race_observed INTEGER,
 co_applicant_race_observed INTEGER,
 applicant_sex INTEGER,
 co_applicant_sex INTEGER,
 applicant_sex_observed INTEGER,
 co_applicant_sex_observed INTEGER,
 applicant_age TEXT,
 applicant_age_above_62 TEXT,
 co_applicant_age TEXT,
 co_applicant_age_above_62 TEXT,
 income TEXT,
 purchaser_type INTEGER,
 rate_spread TEXT,
 hoepa_status INTEGER,
 lien_status INTEGER,
 applicant_credit_scoring_model INTEGER,
 co_applicant_credit_scoring_model INTEGER,
 denial_reason_1 TEXT,
 denial_reason_2 TEXT,
 denial_reason_3 TEXT,
 denial_reason_4 TEXT,
 total_loan_costs TEXT,
 total_points_and_fees TEXT,
 origination_charges TEXT,
 discount_points TEXT,
 lender_credits TEXT,
 interest_rate TEXT,
 prepayment_penalty_term TEXT,
 debt_to_income_ratio TEXT,
 combined_loan_to_value_ratio TEXT,
 loan_term TEXT,
 intro_rate_period TEXT,
 balloon_payment INTEGER,
 interest_only_payment INTEGER,
 negative_amortization INTEGER,
 other_non_amortizing_features INTEGER,
 property_value TEXT,
 manufactured_home_secured_property_type INTEGER,
 manufactured_home_land_property_interest INTEGER,
 total_units TEXT,
 multifamily_affordable_units TEXT,
 submission_of_application INTEGER,
 initially_payable_to_institution INTEGER,
 aus_1 INTEGER,
 aus_2 TEXT,
 aus_3 TEXT,
 aus_4 TEXT,
 aus_5 TEXT,
 reverse_mortgage INTEGER,
 open_end_line_of_credit INTEGER,
 business_or_commercial_purpose INTEGER
);

Step 4: Import the Data

In psql, use the following single-line import command:

\copy lar2024 FROM 'C:/Users/YOUR-USER-NAME/Downloads/2024_combined_mlar_header/2024_combined_mlar_header.txt' WITH (FORMAT csv, DELIMITER '|', HEADER true, NULL '');

This tells PostgreSQL to:

Read a pipe-delimited file
Use the header row to match columns
Treat empty strings as NULL

Note: This must be entered as a single line. You will get a confirmation message when the data import succeeds.

Step 5: Verify the Import

The official CFPB download page indicates that the 2024 nationwide file contains approximately 12 million rows. After importing, verify with:

SELECT COUNT(*) FROM lar2024;
SELECT * FROM lar2024 LIMIT 5;

If the row count is substantially less than 12 million, the import did not complete successfully.

Create the Chicago, Illinois Subset

With the full dataset loaded, you can create a focused subset with a CREATE TABLE AS SELECT statement. The query below selects conventional, owner-occupied, site-built single-family loans in Cook County (FIPS 17031), excluding missing or exempt values:

DROP TABLE IF EXISTS public.il2024f1;
CREATE TABLE public.il2024f1 AS
SELECT
 l.activity_year,
 l.state_code,
 l.county_code,
 l.census_tract,
 l.debt_to_income_ratio,
 l.combined_loan_to_value_ratio,
 l.income,
 l.property_value,
 l.loan_amount,
 l.interest_rate,
 l.applicant_ethnicity_1,
 l.applicant_race_1,
 l.denial_reason_1,
 l.rate_spread,
 l.hoepa_status,
 l.lien_status
FROM public.lar2024 l
WHERE
 l.loan_purpose = '1' -- Home purchase
 AND l.action_taken = '1' -- Loan originated
 AND l.loan_type = '1' -- Conventional
 AND l.occupancy_type = '1' -- Owner-occupied
 AND l.state_code = 'IL'
 AND l.county_code = '17031' -- Cook County
 AND l.property_value ~ '^[0-9]+$'
 AND l.income ~ '^[0-9]+$'
 AND l.loan_amount IS NOT NULL
 AND l.loan_amount <> 99999
 AND l.construction_method = '1' -- Site-built
 AND l.total_units IN ('1','2','3','4','');

Prepare for Additional Analysis

Once exported to CSV, I divided the number of Black applicants in each census tract by the total number of applicants in that tract to get the share of Black applicants. I also downloaded Median Household Income data using the tidycensus package in R (ACS 5-year estimates 2018–2023).

The maps below compare neighborhood income levels with the geographic concentration of Black mortgage applicants receiving loans in 2024. Do you observe any patterns?

The spatial overlap — or rather, the striking lack of it — between high-income areas and Black loan recipients is consistent with decades of research documenting structural barriers to homeownership for Black families in Chicago.

Conclusion

The HMDA dataset offers an unparalleled opportunity to examine lending patterns, racial disparities, and neighborhood-level investment across the United States. By importing the 2024 Modified LAR into PostgreSQL, researchers can efficiently manage, filter, and analyze millions of records while linking them to geographic and demographic data.

Next steps may include:

Join with Census shapefiles to visualize patterns by tract
Summarize by loan purpose, race, or geography
Export to PostGIS for spatial queries
Link to neighborhood typologies or redlining indices

Happy Coding!

Development and Spatial Validation of a Random Forest Prediction Model for Firearm-Related Injury Risk in Chicago Census Tracts

Wed, 01 Jan 2025 00:00:00 +0000

Plain-Language Summary

This study describes the development and validation of a machine learning model (random forest) to predict the risk of firearm-related injuries across Chicago neighborhoods. By combining multi-source data, including local crime reports, socioeconomic vulnerabilities, and environmental exposures, the model maps out areas with the highest predicted risk.

The model’s predictions were validated against actual administrative records to verify its spatial accuracy. This predictive modeling framework shows how machine learning can help public health officials and community organizations anticipate high-risk spatial hot spots and deploy community-based interventions more effectively.

The Land Cover Paradox

Wed, 01 Jan 2025 00:00:00 +0000

Plain-Language Summary

This study explores the associations between developed open space, tree canopy, blue space, and land cover diversity and suicide risk across Chicago neighborhoods. Using Bayesian spatial Poisson models at the Census Block Group level, the research reveals a land cover paradox: while developed open spaces and blue spaces are associated with lower suicide rates overall, the protective effects are context-dependent and are not uniformly experienced across urban neighborhoods. The findings emphasize that suicide prevention efforts should incorporate neighborhood-level environmental factors and structural disadvantages rather than focusing solely on individual risk profiles.

Spatial accessibility to gun violence exposure on walkable routes to and from school

Mon, 01 Jul 2013 00:00:00 +0000

Plain-Language Summary

This study evaluates the exposure of school children to gun violence along their walkable routes to and from school in Englewood, Chicago. Using acoustic sensor detection technology (ShotSpotter) alongside realistic network routing models, the research quantifies both direct and indirect gunfire exposure during daily school commutes.

The findings show that children walking to school face frequent and geographically concentrated gunfire exposure, which varies significantly depending on specific walking routes and neighborhood social vulnerability. This work demonstrates the power of integrating spatial routing algorithms with acoustic gunshot data to guide school safety policies, active transit planning, and targeted community policing.