ISSUES Lab | ISSUES | The Ohio State University

Modeling Firearm Injury Risk Using Spatial Random Forests

Thu, 23 Oct 2025 00:00:00 +0000

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

This study introduces a spatial machine learning framework for predicting firearm injury risk across Chicago census tracts. Using Spatial Random Forest Modeling (SRFM), we integrate social, built, and natural environment indicators, such as social vulnerability, housing burden, green space, traffic volume, and tree canopy, to identify localized structural determinants of firearm violence.

Results demonstrate that racial composition, economic hardship, and environmental stressors jointly predict spatial clustering of firearm injury, while local variable importance reveals that built environment features exert context-specific effects across neighborhoods. These findings highlight the value of spatially aware, data-driven approaches for informing equitable, place-based violence prevention and policy.

Abstract: Gun violence remains a critical public health crisis, particularly in urban centers like Chicago, where firearm-related injuries are among the highest in the nation. These injuries are not randomly distributed; they are spatially concentrated in neighborhoods shaped by structural inequality, including racial segregation, economic disinvestment, and environmental neglect. While random forest models have shown promise in predicting firearm violence and identifying neighborhood-level risk factors, conventional approaches often ignore spatial dependence, leading to biased estimates, inflated variable importance, and reduced geographic generalizability. This study applies spatially explicit machine learning to model non-fatal shootings across Chicago census tracts from 2020 to 2024. A curated set of predictors was compiled, including measures of social and environmental vulnerability, the built environment, and housing inequality. Three research questions guided the analysis: (1) Does incorporating spatial structure via Moran’s Eigenvector Maps (MEMs) in spatial random forests and spatially structured random effects in a Bayesian spatial model improve predictive accuracy compared to non-spatial models? (2) Which neighborhoods exhibit elevated firearm violence risk due to unmeasured spatial processes, as captured by spatial random effects? (3) Does prediction error vary across geographic space, revealing limits to model transferability? Findings suggest that the spatial random forest model outperformed the non-spatial model (pseudo R² = 0.98 vs. test R² = 0.50). Spatial cross-validation revealed geographic variation in accuracy (median nRMSE = 0.76), while spatial random effects from a Bayesian Poisson model identified unexplained latent risk. The enhanced predictive accuracy reveals hidden geographic risk patterns, suggesting that social, structural, and environmental factors can efficiently inform targeted violence prevention strategies.

Download the presentation here .

No Bright Line: How AI-driven Technology Blurs Legal Boundaries Between Prevention and Punishment in Family Regulation

Wed, 22 Oct 2025 00:00:00 +0000

This new article by Dr. Gia E. Barboza-Salerno argues that Predictive Risk Models (PRMs) can be effective when focused on prevention. The argument is grounded in two core principles. The first is a rights-based balancing approach that extends beyond privacy concerns to encompass due process protections tied to legally recognized status.
The second emphasizes that PRMs, rather than serving solely as detection or risk assessment tools, can help identify structural risk factors, guide equitable public investment, and promote accountability, without expanding surveillance or punitive measures that compound systemic harm.

Together, these principles challenge the false dichotomy between prevention and punishment that underlies much of AI’s deployment in family regulation. By integrating a legal and ethical framework rooted in rights protection and structural accountability, the article redefines what responsible and justice-oriented AI looks like in the context of family welfare systems.

Read the full article here: “Barboza-Salerno, G. E. (2025). No bright line: How AI-driven technology blurs legal boundaries between prevention and punishment in family regulation. Family Advocate, 47(2), 00–00. American Bar Association.”

Beyond Urban–Rural Binaries: Remote Sensing Indicators of Built Environment Risk for Firearm Injury in Cook County, Illinois

Wed, 22 Oct 2025 00:00:00 +0000

Presented at the 2025 National Research Conference for the Prevention of Firearm-Related Harms, this study develops a remote-sensing–based framework to identify built environment risk factors for firearm injury in Cook County, Illinois. Using satellite-derived indicators of nighttime light intensity, impervious surface coverage, vegetation (NDVI), and built-up density merged with firearm injury data from 2020–2024, we fit spatial random forest models and Bayesian spatial Poisson models to quantify environmental predictors of firearm harm.

Findings show that built environment characteristics—rather than traditional urban–rural categories—more accurately distinguish high-risk census tracts. High built-up intensity, fragmented greenness, and asymmetric nighttime activity remain strong predictors even after accounting for area-level deprivation. These results demonstrate the utility of remote sensing for identifying structural vulnerability and guiding place-based firearm injury prevention.

📄 Presentation

Download the presentation here .

GIS Day 2025

Wed, 22 Oct 2025 00:00:00 +0000

Congratulations to our undergraduates Oliva McLucas and Malcolm McCarthy for their abstracts accepted to the 2025 GIS Day at The Ohio State University on November 19, 2025.

Olivia McLucas: Introducing a Spatial Adverse Childhood Environments Index using Geographical Information Systems We extend the cumulative Adverse Childhood Experiences (ACEs) framework to the neighborhood scale with spACEs (Spatial Adverse Childhood Environments), an index of structural disadvantage in childhood. The spACES index is constructed from county-level measures in the 1990 Census, including poverty, educational attainment, employment structure, housing burden, single-parent prevalence, and racial residential segregation, which reflect the environments in which the Opportunity Atlas birth cohorts (children born 1978 to 1985) were raised. The Opportunity Atlas links childhood places of residence to adult outcomes, capturing household income rank, educational attainment, incarceration, and family formation (2010-2015), as well as credit outcomes (2020). Using GIS, we visualize geographic variation in spACEs and assess its association with long-term adult outcomes. We propose spACEs as a revised measure of childhood structural adversity that captures the enduring impact of neighborhood disadvantage across the life course.

McCarthy, M.: Mapping Traumatic Brain Injury using Geographic Information Systems Traumatic brain injury (TBI) is a critical public health problem linked to numerous mechanisms, including unintentional injuries such as falls and motor vehicle crashes and intentional injuries such as assaults. Despite the magnitude of this burden, no research has systematically examined the geographic distribution of TBI hospitalizations and their county-level correlates. We utilize county-level hospitalization data from the Washington State Department of Health, filtered to include all TBI-related admissions by year. These records are linked to the Area Deprivation Index (ADI) for the period 2016–2023, encompassing domains of economic hardship and inequality, educational attainment, and financial strength. The merged dataset enables the analysis of spatial and temporal patterns of TBI hospitalization risk and tests whether county-level social and economic disadvantage is associated with higher rates of TBI hospitalization. Using GIS visualization and regression models, we demonstrate how community structural conditions shape the distribution of TBI and inform strategies for prevention.

The presentations will be made available after the conference.

Enhancing Brain Injury Surveillance Using Large Language Models and Topic Modeling

Sun, 19 Oct 2025 00:00:00 +0000

Our presentation, Enhancing Brain Injury Surveillance Using Large Language Models and Topic Modeling, was delivered by Dr. Shockley McCarthy at the AI for BI Conference at The Ohio State University on October 17, 2025.

This work introduces an AI-driven framework for improving the detection and contextual understanding of brain injury cases using unstructured medical and administrative text. By applying large language models (LLMs) and structured topic modeling, we extract interpretable patterns from injury narratives and ICD-coded data to identify mechanisms of injury, intent, and underreported cases of intimate partner violence (IPV)–related brain injury.

Integrating methods from public health, law, and computational linguistics, our approach aims to enhance the precision of injury surveillance systems and inform equitable prevention and policy strategies addressing violence-related traumatic brain injury (TBI).

Download the presentation here .

Dr. Barboza-Salerno Addresses Grand Challenge to Prevent Gun Violence

Tue, 06 Jan 2026 00:00:00 +0000

Dr. Gia Barboza-Salerno addresses the Grand Challenge to Prevent Gun Violence.

Comparative Politics and the Comparative Method

Tue, 01 Jun 2021 00:00:00 +0000

Content

In module 1.1, we will be discussing an overview of comparative politics. First, we will talk about some of the most important theoretical perspectives in the discipline, emphasizing the role of institutions in politics. Second, we will discuss the important elements of an explanation in political science: independent variables, dependent variables and causal mechanisms. You will complete a causal diagram exercise with a partner to practice this concept.Third, we’ll talk about the difference between correlation and causation, or in other words how variables are sometimes related to one another through a causal relationship whereas at other times the relationship between two variables is purely spurious.

In module 1.2, we will be discussing the comparative method. In this module we’ll be looking at the role of hypotheses in comparative politics and how they can be tested using a handful of simple methodological tools that harness variation in independent and dependent variables across country or regional cases.

In both modules you will be reading relevant articles and textbook chapters, as well as watch lecture and supporting videos that relay important information about the concepts listed above. You will then take a quiz to ensure you have understood what you read. These quizzes will help prepare you for the final exam.

Objectives

By the end of unit, you should be able to:

Explain the difference between an independent and dependent variable. (Course objective 1)

Demonstrate how “causal mechanisms” link independent and dependent variables. (Course objectives 1, 2, & 4)

Devise an appropriate test for whether a causal relationship between a dependent and independent variable exists. (Course objective 6)

Lecture Slides

Module 1.1 Slides

Module 1.2 Slides

About the ISSUES Lab

Mon, 01 Jan 0001 00:00:00 +0000

What’s in a Name?

The name ISSUES (Investigating Spatial Structures in Urban Environments) is a deliberate play on words. In the legal field an issue is the specific point of law or fact that is being disputed in a case and is the central question that determines the outcome. Just as a legal issue requires rigorous examination to reach a just verdict so do these spatial structures require rigorous spatial and statistical analysis to drive effective policy reform.

We are a multidisciplinary research group comprised of underrepresented scholars in the academy. Our work focuses on using geographic data science in social work policy and practice at the Colleges of Social Work and Public Health at The Ohio State University (OSU), a leading global university based in Columbus, Ohio.

Our mission is to do applied work that impacts social policy by leveraging data and recent advances in spatial and statistical analysis. We are particularly interested in the intersection of the social and physical environment and its impact on child and adolescent well-being broadly defined. This includes adverse childhood experiences, child abuse and neglect, and other experiences of trauma.

Language is important. We no longer use terms that dehumanize individuals, families, and communities. This means that we do not refer to people as delinquents, we do not refer to households as dysfunctional, and we do not refer to communities as disorganized.

Our group follows the 4Cs philosophy that emphasizes culture, community, collaboration, and communication. Briefly, this means that we engage in thoughtful communication and collaboration that emphasizes community as a critical source for social change. Click here to read more about our philosophy and practice.

Dr. Gia Elise Barboza-Salerno created the research group in 2023. Motivated primarily by a desire to effectuate systemic reform, particularly in child welfare, criminal justice and juvenile justice. You can read more about our research agenda here . We are particularly interested in bringing urban analytics and geospatial analysis to examine the spatial distribution of victimization in urban areas.

Below is a map we created for one of our most recent manuscripts that uses computational network analysis to understand firearm-related harm in Compton, California. View our github site to see how this map was made using the prettymaps package in python.

We are involved in several community-based and academic collaborations such as the Ohio Policy Evaluation Network (OPEN) and the Franklin County Public Defender.

In the News

Mon, 01 Jan 0001 00:00:00 +0000

Recent Highlights

Dr. Barboza-Salerno Addresses Grand Challenge to Prevent Gun Violence

Dr. Gia Barboza-Salerno was featured by the College of Social Work for her work addressing the Grand Challenge to Prevent Gun Violence. Her research applies geospatial analysis and machine learning to understand the spatial distribution of firearm-related harm and inform prevention strategies.

Read the full story →

Amy Watson-Grace Awarded the Prestigious Presidential Fellowship

Congratulations to Amy Watson-Grace for receiving the Presidential Fellowship, one of the highest honors for graduate students at The Ohio State University. This fellowship recognizes outstanding academic achievement and research excellence.

Olivia McLucas Receives Scholarship for Outstanding Statistics Student

Olivia McLucas has been recognized with a scholarship for outstanding achievement in statistics. Her work in the ISSUES Lab focuses on applying advanced statistical methods to public health research.

Upcoming Presentations & Talks

IPR Seminar Series: Abortion Accessibility & Maternal Health

March 10, 2026 | 12:30-1:30 PM | Townshend Hall & Zoom

Dr. Gia Barboza-Salerno will present on “Accessibility to Abortion Services, Maternal Vulnerability, and Birth Outcomes” at the Institute for Population Research Seminar Series. The talk examines how spatial constraints on abortion access align with community-level maternal vulnerability and adverse birth outcomes using a novel Maternal Vulnerability Index and GIS-based transportation network modeling.

Event details →

Ohio Recovery Housing Conference 2026

April 15, 2026 | 1:00-2:15 PM | Breakout Session D

Dr. Gia Barboza-Salerno and Taylor Harrington will present research on recovery housing at the 2026 Ohio Recovery Housing Conference. The presentation will share findings on the role of recovery housing in supporting individuals with substance use disorder, particularly in underserved and high-risk communities.

Event details →

SDSS 2026: Detecting IPV Using Large Language Models

April 29, 2026 | 3:45-5:15 PM CT | Milwaukee, WI

Dr. Gia Barboza-Salerno, Olivia McLucas, and Dr. Karla Shockley will present “Detecting Intimate Partner Violence Injury Mechanisms from Emergency Department Narratives Using Large Language Models and Latent Dirichlet Allocation” at the 2026 Symposium on Data Science & Statistics. This 25-minute refereed presentation will be part of the AI and LLM Applications I session.

Event details →

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A PostgreSQL Tutorial for Analyzing Racial and Geographic Disparities in Home Mortgage Disclosure Act (HMDA) Data

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 most 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 (effectively a neighborhood) 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 book titled “The Color of Law” by Richard Rothstein for anyone interested in understanding the deep-rooted history of discriminatory housing practices in the United States. The book compellingly explains how federal, state, and local policies — across multiple domains — intentionally created and reinforced de jure segregation. Chicago, which remains one of the most highly segregated urban areas in the country, is prominently featured.

Why PostgreSQL?

Due to its sheer volume in both size and number of fields, 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 the use of a tool like PostgreSQL, an open-source relational database management system (RDBMS) that enables efficient querying, filtering, and aggregation of large datasets.

PostgreSQL is especially well-suited for the HMDA dataset because it can:

Handle millions of rows
Perform complex joins and filters
Scale to support both interactive and batch analysis
Support geospatial extensions like PostGIS for direct spatial querying and linking to geographic units such as census tracts or ZIP codes

If you don’t already have it, download PostgreSQL now.

Motivation

My motivation for this analysis stems from a broader interest in how housing insecurity shapes health outcomes, consistent with a large body of research indicating that where you live significantly impacts your health. In my current work, I used HMDA data to create neighborhood typologies based on the race and income of applicants, and to generate measures of neighborhood change. While the process isn’t particularly difficult, it also isn’t obvious — hence this tutorial.

This walkthrough covers the complete process of downloading, inspecting, and importing the 2024 HMDA Modified LAR dataset into PostgreSQL using PowerShell and psql. It assumes you’re using a Windows machine.

Step 1: Download the Dataset

The dataset is freely available to the public. To download it follow these steps:

Navigate to https://ffiec.cfpb.gov/data-publication/modified-lar/2024
Choose:
- Year: 2024
- Nationwide: Combined file
Download the file: 2024_combined_mlar_header.txt
Save or extract the file to: C:\Users\YOUR_USER_NAME\Downloads\2024_combined_mlar_header\

Multiple years are available on the FFIEC website. You can download historical data to examine lending trends over time.

Step 2: Confirm the File Structure in PowerShell

The file structure of the HMDA dataset is not typical for most users or applications. It is a pipe-delimited text file (.txt), which can make it difficult to read or interpret using standard spreadsheet tools like Excel. The data appears in a long, unformatted stream, separated by vertical bars (|), and without intuitive formatting.

To extract the necessary information and prepare the data for import into PostgreSQL, I recommend using Windows PowerShell to inspect the file structure first. This allows you to confirm key details such as the number of fields (columns), the presence of a header row, and the overall formatting of the data.

Open PowerShell and run the following:

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

To create a table called lar2024 in PostgreSQL using SQL, begin by launching your SQL interface. If you’re using a graphical tool like pgAdmin, open the Query Tool by right-clicking on your database and selecting “Query Tool.” If you’re using the psql command-line interface, connect to your target database like this:

psql -U your_username -d your_database_name

Replace the placeholders with your actual credentials.

Once you’re connected to your PostgreSQL database, you’ll use a CREATE TABLE SQL statement to define the structure of the lar2024 table:

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

Now that the table structure is created, you can import the data. I prefer using the command-line interface, which is called psql in PostgreSQL, to 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

This must be entered as a single line. You will get a notification that the data were successfully imported.

Step 5: Verify the Import

I always like to verify that what I am doing is correct. To verify the number of rows expected after importing the 2024 HMDA Modified Loan/Application Register (LAR) data into PostgreSQL, I consulted the official HMDA data publication website maintained by the Consumer Financial Protection Bureau (CFPB).

After importing the file using the \copy command in psql, run a simple SQL query to verify the total number of records successfully loaded:

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

Expected row count for the whole nation: ~12 million.

Create the Chicago, Illinois, Subset

I want my analysis to focus on a specific subset of the data (e.g., applicant race, loan originals that are single-family homes, etc., for Chicago). I can now create a sub-query containing a smaller subset of the data, export it to a .csv file, and then import it into R or QGIS.

The simple query I used to select the columns and filter the data to Cook County (which contains Chicago) is shown below:

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'
 AND l.action_taken = '1'
 AND l.loan_type = '1'
 AND l.occupancy_type = '1'
 AND l.state_code = 'IL'
 AND l.county_code = '17031'
 -- Only accept property values that are purely numeric
 AND l.property_value ~ '^[0-9]+$'
 AND l.income ~ '^[0-9]+$'
 AND l.loan_amount IS NOT NULL
 AND l.loan_amount <> 99999
 -- Equivalent to derived_dwelling_category = 'Single Family'
 AND l.construction_method = '1'
 AND l.total_units IN ('1', '2', '3', '4', '')
;

This query selects conventional, owner-occupied, site-built single-family loans in Cook County, Illinois, excluding missing or exempt values.

Prepare File for Additional Analysis

However, we first need to prepare the file for further analysis. I will leave that to you since you can now do this easily in Excel, etc. Below, 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 Income data using the tidycensus package in R. This allows us to compare the average median household income 5-year estimates with the share of Black applicants receiving a loan in each neighborhood.

Do you observe any patterns? I do.

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 data into PostgreSQL, researchers can efficiently manage, filter, and analyze millions of loan records while linking them to geographic and demographic data.

Next Steps

Join with Census shapefiles to visualize by tract
Summarize by loan purpose, race, or geography
Export to PostGIS for spatial queries

Happy Coding!

States, State Capacity and State Fragility

Tue, 01 Jun 2021 00:00:00 +0000

Content

In module 2.1, we will be discussing state capacity and state building. We will begin our discussion by addressing some basic definitional questions about states. First of all, what is a state? How is this concept used in the field of comparative politics and how should we define it? What does it mean to say that a state has more or less capacity? What aspects of state capacity are we most concerned with in our analysis of modern nation-states? Finally, what does it mean to say that a state is ‘fragile’ or that a state has ‘failed’? What are some examples of failed states and what are some the implications of state failure and fragility?

In module 2.2 we will continue with our discussion of state capacity and asking why some states are stronger than others. We will start with the rise of the modern state in Europe, where we will focus on Charles Tilly’s analysis of Europe. We will then move on to state building in Africa, where we broaden our scope beyond the western European experience. You will complete a Two-by-Two diagram to help you compare and analyze two states.

Objectives

By the end of unit, you should be able to:

Identify the basic functions of a state. (Course objectives 1, 3, & 4) Identify measures of state capacity. (Course objectives 1, 3, & 4) Analyze the relationship between a state and its citizens. (Course objectives 1, 3, & 4) Explain why some states fail while others succeed. (Course objectives 3, 4, & 6)

Lecture Slides

Module 2.1 Slides

Module 2.2 Slides

How to Analyze Caselaw Using CourtListener's API in R

Fri, 01 Aug 2025 00:00:00 +0000

I previously showed how to pull ‘Indian’ Child Welfare Act (ICWA) cases using the Caselaw Access Project (Harvard Law). That API has since changed. This post updates the workflow using the Free Law Project’s CourtListener, which now partners with Harvard to make this data freely available.

Finding the Cases

Start at the CourtListener website and search for “Indian Child Welfare Act” (or “icwa”).

You’ll see a page of opinion results.

Filtering by Jurisdiction

Click Select Jurisdiction → State. Clear all, then select:

Ohio Supreme Court
Ohio Court of Appeals
Ohio Court of Claims

This gives you a quick check on the expected volume so you can validate the code’s output below.

The site reported 36 cases from Ohio when this tutorial was written. Note that the exact number may change over time as new opinions are added or metadata is corrected.

Getting the Data into R

The Search API returns clean JSON results by querying for opinions that contain the exact phrase “indian child welfare act”, restricting to published opinions, ordering by relevance, and (optionally) limiting the results to three Ohio courts.

Required Libraries

library(httr2)
library(purrr)
library(dplyr)
library(tibble)
library(tidyr)
library(stringr)

API Configuration

SEARCH_BASE <- "https://www.courtlistener.com/api/rest/v4/search/"
UA <- "R/httr2 (your-email-address) – ICWA metadata collection"

query_params <- list(
 q = '"indian child welfare act"',
 type = "o",
 order_by = "score desc",
 stat_Published = "on",
 court = "ohio ohioctapp ohioctcl" # optional: specify the court name
)

Helper Functions

pull_chr <- function(x, ...) {
 v <- purrr::pluck(x, ..., .default = NA)
 if (is.null(v) || length(v) == 0) return(NA_character_)
 as.character(v)
}

pull_num <- function(x, ...) {
 v <- purrr::pluck(x, ..., .default = NA_real_)
 suppressWarnings(as.numeric(v))
}

get_json <- function(url) {
 resp <- request(url) |>
 req_user_agent(UA) |>
 req_url_query() |>
 req_perform()
 resp_check_status(resp)
 resp_body_json(resp, simplifyVector = FALSE)
}

Fetching Search Results

fetch_search_pages <- function() {
 req <- request(SEARCH_BASE) |>
 req_user_agent(UA) |>
 req_url_query(!!!query_params)

 dat <- req |>
 req_perform() |>
 (\(r){
 resp_check_status(r)
 resp_body_json(r, simplifyVector=FALSE)
 })()

 pages <- list(dat)

 while (!is.null(dat$`next`)) {
 dat <- request(dat$`next`) |>
 req_user_agent(UA) |>
 req_perform() |>
 (\(r){
 resp_check_status(r)
 resp_body_json(r, simplifyVector=FALSE)
 })()
 pages <- append(pages, list(dat))
 }

 pages
}

pages <- fetch_search_pages()
results <- pages |> map("results") |> flatten()

results is a list of per-case records from the API. Each element includes:

Case name
Filing date
Court identifiers
Citation strings
Public URL where the caselaw text is available
Other relevant metadata

Creating a Dataframe

For Ohio, this produced ~35 unique case names versus 36 displayed on the site. In comparing the case_name variable to the search results, you can reconcile the discrepancy.

Minor discrepancies happen because search hits can include multiple opinions or siblings per matter, and because the index is updated over time.

Downloading Case Text

Each opinion has an API endpoint with plain text and HTML fields. A small function can loop over the URL field, request the text, and save one .txt file per opinion. A short pause between requests is included and retried if necessary.

# Example function to download case text
download_case_text <- function(case_url, output_dir) {
 # Add rate limiting
 Sys.sleep(1)

 # Request the case data
 case_data <- get_json(case_url)

 # Extract plain text
 case_text <- pull_chr(case_data, "plain_text")

 # Save to file
 file_name <- paste0(output_dir, "/",
 gsub("[^A-Za-z0-9]", "_", pull_chr(case_data, "case_name")),
 ".txt")
 writeLines(case_text, file_name)
}

Remember to be considerate of rate limits and to include a User Agent string with your contact information.

Visualizing the Results

You can use ggplot2 to visualize the results, for example:

Filings by Year

library(ggplot2)

# Assuming you have a dataframe with filing dates
cases_df %>%
 mutate(year = lubridate::year(date_filed)) %>%
 count(year) %>%
 ggplot(aes(x = year, y = n)) +
 geom_col(fill = "#bb0000") +
 labs(
 title = "ICWA Cases Over Time",
 x = "Year",
 y = "Number of Cases"
 ) +
 theme_minimal()

Volume by Court

# Top 25 jurisdictions
cases_df %>%
 count(court, sort = TRUE) %>%
 head(25) %>%
 ggplot(aes(x = reorder(court, n), y = n)) +
 geom_col(fill = "#bb0000") +
 coord_flip() +
 labs(
 title = "Top 25 Jurisdictions for ICWA Cases",
 x = "Court",
 y = "Number of Cases"
 ) +
 theme_minimal()

Next Steps

After downloading all data, including caselaw text and metadata, you can:

Use Natural Language Processing to examine sentiment or common words
Perform topic modeling to explore latent themes in ICWA cases
Map filings by jurisdiction
Integrate the data with other sources (e.g., demographic data, child welfare statistics)

Code Repository

The whole workflow is available on our GitHub repository.

Happy Coding!

States, State Capacity and State Fragility

Tue, 01 Jun 2021 00:00:00 +0000

Content

In module 3.1, we will be covering the concepts of “ethnicity” and “nationalism” and asking how these two concepts impact political and economic outcomes

In module 3.2 we will focus on institutional solutions to conflict in divided societies. First, we’ll expand upon our discussion in the last lecture about how ethnicity and exclusion give rise to civil conflict. Then, we will talk about constitutional design and how the right set of political institutions might help to alleviate conflict. We will introduce two contrasting theories of constitutional design for divided societies–consociationalism and centripetalism. You will complete a Most Similar Systems Design to analyze these concepts in regards to analyzing if political institutions can be used to manage ethnic diversity.

In both modules, you will be reading relevant articles and textbook chapters, as well as watching lectures and supporting videos that relay important information about the concepts listed above. You will then take a quiz to ensure you have understood what you read. These quizzes will help prepare you for the final exam.

Objectives

By the end of unit, you should be able to:

Define the concept of “ethnicity”. (Course objectives 1 & 3)
Define the concept of “nationalism”. (Course objectives 1 & 3)
Identify the root causes of ethnic conflict in developing countries. (Course objectives 1, 3, & 4)
Discuss potential policy solutions to reduce tensions between ethnic groups. (Course objectives 1, 3, 4, & 6)
Explain the link between ethnic diversity and public goods provisioning. (Course objectives 1, 3, & 4)

Lecture Slides

Module 3.1 Slides

Module 3.2 Slides

States, State Capacity and State Fragility

Tue, 01 Jun 2021 00:00:00 +0000

Content

In module 4.1 we are going to be talking about regime type, trends in democracy, and the “third wave” of democratization that brought free and fair elections, as well as civil liberties, to many countries in the developing world.

In module 4.2 we’ll talk about authoritarian and hybrid regimes and how they differ from democracies. We’ll look closely at the case of Russia and countries in the Middle East to better understand how nondemocratic regimes work in practice. You will develop a Most Different Design System to analyze the causes of democracy, and specifically why some countries are democracies while others remain authoritarian or hybrid regimes

Objectives

By the end of unit, you should be able to:

Identify the difference between democratic, authoritarian, and hybrid regimes. (Course objectives 1, 3, & 5)
Analyze the stability of democratic and authoritarian regimes. (Course objectives 1, 3, 4, & 5)
Define the concept of “democratic consolidation”. (Course objectives 1 & 3)
Analyze the causes of democratic backsliding. (Course objectives (1, 3, & 4)

Lecture Slides

Module 4.1 Slides

Module 4.2 Slides

Women and Politics

Mon, 14 Jun 2021 00:00:00 +0000

Content

In module 5.1 we will begin our discussion of equal representation and women’s role in politics. We’ll discuss trends in female representation over time and talk a little bit about what accounts for these trends. In our discussion, we’ll pay special attention to the role of gender quotas in boosting female representation. You will do this by completing a Scatter Plot. The purpose of this exercise is to encourage you to think about some of the economic factors related to unequal representation and to teach you how to illustrate relationships between variables using a scatter plot.

In module 5.2 we are going to focus on descriptive representation as it pertains to gender. First we are going to ask how descriptive representation is achieved through gender quotas. Then we will look at how descriptive representation influences a leader’s policy preferences. Finally, we will look at how descriptive representation influences public policy. In both modules you will be reading relevant articles and textbook chapters, as well as watch lecture and supporting videos that relay important information about the concepts listed above. You will then take a quiz to ensure you have understood what you read. These quizzes will prepare you for the final exam.

Objectives

By the end of unit, you should be able to:

Analyze why women are more represented in some countries relative to others. (Course objectives 1, 3, & 4)
Discuss changes in women’s representation over time. (Course objectives 1, 3, 4, & 6)
Identify the effects of women’s representation on policy. (Course objectives 1, 3, & 4)
Analyze the cultural effects of women’s leadership. (Course objectives 1, 3, & 4)

Lecture Slides

Module 5.1 Slides

Module 5.2 Slides

Political Economy

Mon, 14 Jun 2021 00:00:00 +0000

Content

In module 6.1 we are going to be looking at the political economy of the welfare state and, in the next module, we will talk about institutions and economic development.

In module 6.2 we will consider the causes of economic growth and development and how a country’s regime type (whether it is authoritarian or democratic) relates to development outcomes. Here you will complete a Data Mapping exercise. The purpose of this exercise is to encourage you to think about economic trends in the BRICS nations and to illustrate these trends using the IMF Data Mapper tool.

Objectives

By the end of unit, you should be able to:

Define “social democracy”. (Course objective 1) Identify the ways that social democracies differ from other wealthy democracies. (Course objectives 1, 3, & 4)
Define and compare “economic growth” and “human development”. (Course objectives 1, 3, & 4)
Identify the drivers of growth and development. (Course objectives 1 & 4)
Analyze the effects of globalization on domestic politics. (Course objectives 1, 3, & 4)

Lecture Slides

Module 6.1 Slides

Module 6.2 Slides

An assessment of opioid overdose mortality risk across the urban–rural continuum: Integrating satellite-derived and socioeconomic indicators

Wed, 04 Nov 2026 10:45:00 +0000

Session: Built environment and community-based approaches to advancing health equity
Program: Public Health Education and Health Promotion
Date: Wednesday, November 4, 2026 | 10:45 AM – 11:00 AM

Disability-related depression inequities are driven by accessibility deficits: Evidence from crowdsourced sidewalk data and machine learning

Tue, 03 Nov 2026 14:45:00 +0000

Session: Mobility, Transit, and Built Environments for Disabled People
Program: Disability
Date: Monday, November 2, 2026 | 2:45 PM – 3:00 PM

Extreme heat and suicide risk among older adults: A longitudinal time-series analysis in an urban U.S. setting

Mon, 02 Nov 2026 12:30:00 +0000

Session: Climate, Culture, and Life-Course Contexts in Mental Health
Program: Mental Health
Date: Monday, November 2, 2026 | 12:30 PM – 1:30 PM

Improving surveillance of IPV-related head and neck injuries using AI-enhanced ED narratives

Mon, 02 Nov 2026 10:30:00 +0000

Session: Injury Epidemiology, Trauma Systems, and Health Care Utilization Posters
Program: Injury Control and Emergency Health Services
Date: Monday, November 2, 2026 | 10:30 AM – 11:30 AM

Identifying place-specific mortality mechanisms by integrating cause-of-death narratives and neighborhood typologies using machine learning

Mon, 02 Nov 2026 09:15:00 +0000

Session: Epidemiology Section: Conceptualization and Measurement
Program: Epidemiology
Date: Monday, November 2, 2026 | 9:15 AM – 9:30 AM

Predicting harsh school discipline in Albuquerque Public Schools: A machine learning analysis with temporal validation

Mon, 02 Nov 2026 08:30:00 +0000

Session: Public Health Social Work Perspectives on Health and Well-Being Across the Lifespan
Program: Public Health Social Work
Date: Monday, November 2, 2026 | 8:30 AM – 10:00 AM

New Publication: Profiles of Stressful Life Experiences in Pregnancy

Wed, 02 Sep 2026 00:00:00 +0000

We are excited to announce a new publication from the ISSUES Lab!

Profiles of stressful life experiences in pregnancy: a latent class approach linking early childhood adversity, intimate partner coercive control, and material hardship to adverse birth outcomes

Gia Elise Barboza-Salerno & Taylor Harrington

Published August 31, 2026 in BMC Pregnancy and Childbirth (Open Access)

Abstract

Acute antenatal stressful life events (SLEs) during pregnancy are well-established risk factors for adverse birth outcomes. However, little is known about how distinct constellations of antenatal SLEs, shaped by early adversity, material hardship, and intimate partner coercive control, combine to influence perinatal health.

Data were drawn from 5,280 participants in the Kansas Pregnancy Risk Assessment Monitoring System. Using latent class analysis, five distinct stress profiles were identified:

Family Member Illness and Death (16.3%)
Multiple Overlapping Stressors (5.2%)
Financial Insecurity (11.2%)
Residential Instability and Family Conflict (8.7%)
Low Stress (58.6%)

Compared with the Low Stress class, individuals in all high-risk classes had significantly higher odds of reporting ACEs, IPV, material hardship, and cigarette use before pregnancy. The Multiple Overlapping Stressors class showed the greatest risk of low birth weight, small for gestational age, and preterm birth.

Key Takeaway

Tailoring perinatal interventions to specific SLE profiles, particularly for individuals with histories of ACEs and IPV, may reduce poor birth outcomes.

Download PDF | View Publication

Gia's Journey through Oceania and the 2026 ISPCAN Conference in Melbourne, Australia

Mon, 31 Aug 2026 00:00:00 +0000

Dr. Gia Barboza-Salerno recently traveled through Oceania and presented at the 2026 ISPCAN World Congress on Child Abuse and Neglect in Melbourne, Australia (August 24–26, 2026).

Explore their full journey, including stops along the way, conference highlights, and reflections on the trip in the interactive feature below.

View Gia’s Journey through Oceania

Profiles of stressful life experiences in pregnancy: a latent class approach linking early childhood adversity, intimate partner coercive control, and material hardship to adverse birth outcomes

Mon, 31 Aug 2026 00:00:00 +0000

Detecting Hidden Mechanisms of Abuse-Related Injury in Children: Structured Topic Modeling of Clinical Death Reviews and Legal Opinions

Mon, 24 Aug 2026 00:00:00 +0000

Presented at the ISPCAN World Congress on Child Abuse and Neglect, Melbourne, Australia, August 2426, 2026.

Full title: Detecting Hidden Mechanisms of Abuse-Related Injury in Children: Structured Topic Modeling of Clinical Death Reviews and Legal Opinions Triangulating Multidisciplinary Child Fatality Reviews with Factual and Procedural Narratives from Case Law

Abstract

Existing child maltreatment surveillance systems miss or misclassify between 8090% of maltreatment deaths, and CPS records undercount fatalities by 5576%. Unstructured narrative text clinical death reviews, court opinions, and social work records accounts for 8090% of all organizational data produced, yet it is rarely leveraged for injury surveillance.

This presentation introduces a two-study AI + Structured Topic Modeling (STM) pipeline that integrates two distinct narrative corpora to recover latent, abuse-related injury mechanisms that structured administrative codes routinely obscure.

Study Design

	Study 1: CFRT Death Reviews	Study 2: CourtListener Case Law
Source	Colorado Child Fatality Review Team reports	Appellate opinions retrieved from CourtListener
Documents	223 child-level narrative records	751 opinions → 335 confirmed cases from Ohio
Text	Two free-text fields per report (clinical + administrative)	Facts + procedural history
LLM Extraction	GPT-4o-mini: caregiver behavior, injury mechanism, setting, diagnosis	GPT-4o-mini: 9 structured fields including perpetrator, victim type, legal outcome
STM Models	Risk & Contributing Factors; Circumstances	Injury Mechanism; Long-Term Consequences
Covariate	Incident year (temporal prevalence)	Judicial ruling (affirmed / reversed)

Research Questions

Can large language models transform unstructured child fatality narratives into standardized semantic features caregiver behavior, injury mechanism, setting, diagnosis?
What latent themes emerge, and what typology of risk and incident circumstances do they suggest?
Do the recovered themes reveal recurring patterns of head and neck trauma consistent with abuse-related injury?
How does the prevalence of these themes shift across incident years (20202025)?

Key Findings

Both corpora converge on craniofacial injury as the dominant mechanism routinely undercounted by ICD codes.
Strangulation emerged as a critical lethality predictor; soft-tissue damage can surface 23 days post-injury, requiring clinician and court training on delayed sequelae.
Records document long-term neurological harm (cerebral palsy, pituitary damage, blindness) in children often years before clinical follow-up.
Children represent a structurally distinct injury profile in both datasets, warranting specialized clinical and legal protocols.
“Accidental” injuries (falls, environmental contact) are frequently embedded in an abuse context screening for mechanism, not just diagnosis, is essential.
Appellate opinions and death reviews function as injury-surveillance sources beyond structured administrative fields.

Methodology

The pipeline applies:

PDF parsing (pdftools) and regex isolation of narrative fields
GPT-4o-mini for LLM-based structured field extraction
Structured Topic Modeling (STM) with incident year or judicial ruling as a prevalence covariate
BART zero-shot classification as an independent supervised-style validation check
FREX word scoring (frequency � exclusivity) for topic labeling

Lab Members

This presentation was developed by the ISSUES Lab team at The Ohio State University:

Dr. Gia E. Barboza-Salerno (Founding Director), Dr. Henriikka Weir, Malcolm McCarthy, Dr. Karla Shockley McCarthy, Dr. Amy Watson-Grace, Dr. Keith Warren, Esenia Cassidy, and Olivia McLucas with collaborators Scottye Cash and Suzanne Perkins.

📄 Presentation

Download the presentation slides here .

Congratulations to Keith Warren and Gia Barboza-Salerno for Completing the Agent-Based Modeling Certificate

Sun, 09 Aug 2026 00:00:00 +0000

We are proud to announce that Keith Warren and Gia Barboza-Salerno have successfully completed the Santa Fe Institute’s Introduction to Agent-Based Modeling certificate course through abm.complexityexplorer.org! 🎓

Agent-Based Modeling is a powerful computational method for simulating the actions and interactions of autonomous agents including individuals, households, and organizations in order to understand how macro-level patterns emerge from micro-level behaviors. ABM is particularly well-suited for studying complex social systems such as neighborhood dynamics, health behavior spread, child welfare intervention cascades, and community-level resilience.

The addition of ABM to the ISSUES Lab’s methodological toolkit opens exciting new avenues for our research on structural inequality, environmental justice, and social determinants of health.

Congratulations to Keith and Gia on this outstanding achievement! We look forward to seeing how these new computational skills enrich the lab’s work.

Congratulations to Malcolm McCarthy for Presenting at the American Psychological Association 2026 Annual Convention

Fri, 07 Aug 2026 00:00:00 +0000

We are thrilled to congratulate Malcolm McCarthy, an undergraduate psychology student in the ISSUES Lab, for presenting his first-authored research poster at the American Psychological Association (APA) 2026 Annual Convention, held August 6-8, 2026 at the Walter E. Washington Convention Center in Washington, DC!

Malcolm’s poster, titled:

“How Multiple Environmental Factors Influence Mental Health Outcomes”

explores how neighborhood-level environmental conditions including greenspace exposure and suicide death concentrations shape mental health and suicide risk across census tracts in Cook County, Illinois. The study integrates spatial data and statistical modeling to disentangle the complex relationships between depression burden, environmental predictors, and suicide mortality.

Key Findings

Neighborhoods with higher depression burden were not the same neighborhoods with higher suicide mortality, highlighting that these are distinct spatial phenomena.
Higher greenspace predicted greater depression while also being associated with reduced suicide incidence.
Walkability and greenspace together influenced the co-location of depression and suicidality.
Suicide locations were closer to greenspace boundaries than random control points, and suicide risk increased within 1 km of nearby greenspace.

Policy Implications

The research points to actionable environmental design strategies such as accessible greenspace, improved walkability, and targeted community support services as levers for reducing mental health disparities in urban areas.

Co-authored with Dr. Gia Barboza-Salerno and Dr. Karla Shockley-McCarthy, Malcolm’s presentation was part of the Society for the Psychological Study of Social Issues (SPSSI) poster session (Division 9).

📄 Download the Poster

This is an outstanding achievement for an undergraduate researcher. Malcolm’s presentation reflects the caliber of work being done in the ISSUES Lab and his dedication to evidence-based science in service of community wellbeing. We are incredibly proud of you, Malcolm!

Our Work

Sun, 12 Jul 2026 00:00:00 +0000

Below is an overview of the key research themes at the ISSUES Lab:

Violence Across the Lifespan

Our research examines the manifestation of violence from childhood to old age, employing a 'Big Data for Social Justice' framework to investigate elder abuse, firearm injury, and ecological determinants.

Adverse Childhood Experiences (ACEs)

Our research investigates the complex patterns of Adverse Childhood Experiences (ACEs), utilizing latent class analysis and growth mixture modeling to understand cumulative trauma and its developmental consequences.

Drug Use, Overdose, and Policy

Our research investigates how neighborhood conditions and social policies, such as Medicaid expansion and recovery housing, shape patterns of opioid overdose risk using Bayesian spatiotemporal analysis.

Education and Youth Safety

Our research examines the impact of gun violence, neighborhood safety, and school environments on youth educational outcomes and well-being, utilizing acoustic detection data and spatial modeling.

Maternal and Child Health

Our research investigates the intersection of maternal and child health with structural inequities, focusing on the impact of intimate partner violence during pregnancy, child maltreatment, and reproductive access.

What 25 Years of Jury Selection Data Reveal About the Questions Raised by the Karmelo Anthony Trial

Fri, 10 Jul 2026 00:00:00 +0000

In Batson v. Kentucky (1986), the Supreme Court held that prosecutors may not exclude potential jurors solely because of their race. Nearly four decades later, questions about racial disparities in jury selection continue to arise. They resurfaced during the 2026 murder trial of Karmelo Anthony, where the defense raised a Batson challenge after all eligible Black jurors were removed from the pool and an all-White jury was ultimately seated.

The Anthony case cannot be evaluated using the Mississippi data analyzed here. It occurred in a different state, a different county, and under different circumstances, and nothing below should be read as a claim about it. However, the case does bring up an important question that the criminal legal system should confront: if race-based juror exclusion is unconstitutional, do measurable racial disparities in jury selection still persist? To examine that question, I reanalyzed a publicly available dataset assembled by APM Reports containing jury selection records from 305 criminal trials in Mississippi’s Fifth Circuit Court District spanning roughly 1991–2017.

Five Key Findings

Racial disparity in prosecution strikes persisted after Batson. Black jurors remained roughly 37–38 percentage points more likely than non-Black jurors to be struck by prosecutors, even as overall strike rates declined.
All-White juries occurred far more often than a race-neutral process would predict. Across 218 trials with complete jury-composition data, 14 juries contained no Black members. Under race-neutral selection, the model predicts roughly 1.3.
The disparity was robust across statistical approaches. The odds of a Black eligible juror being struck are nearly eight times those of non-Black jurors, and the gap persisted after adjusting for county, defendant race, and trial-level clustering.
The disparity persisted even when defendants were White. The pattern cannot be explained solely as an effort to match juror demographics to defendant demographics.
Every county examined showed a statistically significant disparity. The magnitude varied; no county showed the gap disappearing.

These are descriptions of patterns in a particular, non-random sample of trials. They are not, and should not be read as, determinations of intent in any individual decision, nor as population estimates for trials in general.

Overview

In 2018, reporters working on the podcast In the Dark published a detailed statistical investigation of jury selection in Mississippi’s Fifth Circuit Court District. Their analysis documented that, across hundreds of criminal trials in seven counties, prosecutors used their discretionary “peremptory” challenges to remove Black prospective jurors at rates far exceeding those for white jurors. That investigation is available at: features.apmreports.org/in-the-dark/mississippi-district-attorney-striking-black-jurors/

This article independently replicates the findings reported by APM Reports using publicly available jury selection data and extends the analysis through multilevel modeling, county-level decomposition, time-trend analyses, and probability-based estimates of how often the observed outcomes would be expected under race-neutral jury selection. The article concludes by placing the 2026 murder trial of Karmelo Anthony in a broader historical context. While the Mississippi data cannot determine what occurred in that specific case, the questions it raised about Batson challenges, race-neutral explanations for juror strikes, and the seating of an all-White jury are consistent with these historical patterns.

Throughout, the analysis is descriptive and statistical. Racial disparities in aggregate data are documented as patterns; they are not, and should not, be read as determinations of intent in any individual decision.

About the Dataset

The APM Reports jury dataset and analysis are available at github.com/APM-Reports/jury-data. It covers 305 criminal trials across seven Mississippi counties that are within the Fifth Circuit Court District. The trial coverage is roughly 1991 to 2017, although not all data are complete. All cases were ultimately appealed, creating selection bias that makes the sample non-random and may have analytic implications. Nevertheless, anyone can easily replicate the analysis below using the code shown in each code block.

For each trial, the dataset records the defendant’s race, charges, judge, and prosecuting attorneys. For each of the 14,874 individual prospective jurors, it records race, gender, what happened during selection (struck for cause, struck by state, struck by defense, or seated), and a strike_eligibility field indicating which side retained peremptory power at that juror’s position in the process.

jurors_clean <- jurors |>
 mutate(
 race_clean = str_to_lower(str_trim(race)),
 struck_by_clean = str_to_lower(str_trim(struck_by)),
 race_group = case_when(
 race_clean == "black" ~ "Black",
 race_clean %in% c("white", "asian", "latino") ~ "Non-Black",
 TRUE ~ NA_character_
 ),
 state_struck = if_else(struck_by_clean == "struck by the state", 1L, 0L)
 )

jury_df <- jurors_clean |>
 left_join(
 trials |> select(id, county, defendant_race, judge,
 prosecutor_1, prosecutor_2, prosecutor_3,
 verdict, cause_number, batson_claim_by_defense),
 by = c("trial_id" = "id")
 )

article_universe <- jurors_clean |>
 filter(
 race_clean %in% c("black", "white"),
 !struck_by %in% c("Juror not struck", "Struck without notation",
 "Juror excused/absent", "Unknown")
 )

eligible_df <- jury_df |>
 filter(
 !is.na(race_group),
 strike_eligibility %in% c("Both State and Defense", "State")
 ) |>
 mutate(
 race_group = factor(race_group, levels = c("Non-Black", "Black")),
 defendant_race = as.factor(defendant_race),
 county = as.factor(county)
 )

Below, “Non-Black” combines white, Asian, and Latino jurors, who together are overwhelmingly white in these counties; I use “non-Black” when the comparison is the analytic contrast, and “white” only when the underlying group is effectively all White (for example, when describing all-White juries).

Replication of Headline Statistics

APM Reports described a pool of 6,763 prospective jurors. I identify the equivalent group as all jurors recorded as Black or White who received a definitive outcome, excluding ambiguous categories (“Juror not struck,” “Struck without notation,” and similar).

sfc <- article_universe |> filter(struck_by == "Struck for cause")
after_cause <- article_universe |> filter(struck_by != "Struck for cause")
prose_str <- article_universe |> filter(struck_by == "Struck by the state")
def_str <- article_universe |> filter(struck_by == "Struck by the defense")
served <- article_universe |>
 filter(struck_by %in% c("Juror chosen to serve on jury", "Juror chosen as alternate"))
after_pros <- article_universe |>
 filter(struck_by %in% c("Struck by the defense",
 "Juror chosen to serve on jury",
 "Juror chosen as alternate"))

tibble(
 Stage = c("Total prospective pool", "Struck for cause",
 "Pool entering peremptory", "Prosecution strikes",
 "Pool after prosecution", "Defense strikes",
 "Seated (jurors + alternates)"),
 `My N` = c(nrow(article_universe), nrow(sfc), nrow(after_cause),
 nrow(prose_str), nrow(after_pros), nrow(def_str), nrow(served)),
 `Article N` = c("6,763","1,342","~5,421","1,275","-","1,458","2,688"),
 `My % Black` = c(
 percent(mean(article_universe$race_clean == "black"), 0.1),
 percent(mean(sfc$race_clean == "black"), 0.1),
 percent(mean(after_cause$race_clean == "black"), 0.1),
 percent(mean(prose_str$race_clean == "black"), 0.1),
 percent(mean(after_pros$race_clean == "black"), 0.1),
 percent(mean(def_str$race_clean == "black"), 0.1),
 percent(mean(served$race_clean == "black"), 0.1)
 ),
 `Article % Black` = c("39%","57%","35%","71%","21%","9%","32%")
)

# A tibble: 7 × 5
 Stage `My N` `Article N` `My % Black` `Article % Black`
 <chr> <int> <chr> <chr> <chr>
1 Total prospective pool 6795 6,763 39.1% 39%
2 Struck for cause 1342 1,342 56.6% 57%
3 Pool entering peremptory 5453 ~5,421 34.8% 35%
4 Prosecution strikes 1285 1,275 70.9% 71%
5 Pool after prosecution 4168 - 23.7% 21%
6 Defense strikes 1466 1,458 8.7% 9%
7 Seated (jurors + alternates) 2702 2,688 31.9% 32%

All percentage figures replicate to within rounding error. The point of reproducing the previous analysis is to verify before building on it.

The Flow of Jurors

The prosecution strike flow shows that 70.9% of state strikes removed Black jurors. The defense flow runs the other way, 91.3% white, but only because, by that stage, the pool was already heavily white. Together, they show how Black jurors entered the process at roughly their expected share (39.1%) yet were “funneled” away from selection at specific stages of voir dire.

Statistical Analysis

Overall Strike Rates

Among jurors eligible for peremptory challenges, 49.8% of Black jurors were struck by the prosecution, compared with 11.2% of non-Black jurors. The unadjusted odds ratio is 7.84 (95% CI: 6.81–9.04). Adjusting for defendant race and county yields an OR of 8.25, indicating that the disparity persists after accounting for these factors.

# A tibble: 2 × 5
 race_group n struck_n strike_rate pct
 <fct> <int> <int> <dbl> <chr>
1 Non-Black 3320 373 0.112 11.2%
2 Black 1811 902 0.498 49.8%

County-level Variation

The disparity appears across all counties, with odds ratios ranging from 4.6 (Choctaw) to 17.3 (Carroll). In Carroll County, an eligible Black juror was struck at roughly 17 times the odds of a non-Black juror. All county-level estimates are statistically significant (p < 0.001).

# A tibble: 7 × 5
# Groups: county [7]
 county OR lower upper p.value
 <fct> <dbl> <dbl> <dbl> <dbl>
1 Carroll 17.3 8.09 40.8 3.19e-12
2 Montgomery 12.6 8.62 18.7 9.82e-38
3 Webster 9.97 3.94 26.9 2.21e- 6
4 Grenada 9.21 7.10 12.0 5.93e-61
5 Winston 8.14 5.35 12.6 7.18e-22
6 Attala 6.14 4.64 8.17 2.94e-36
7 Choctaw 4.60 2.92 7.26 4.40e-11

Does Defendant’s Race Matter?

When the defendant is Black, 52.6% of Black jurors are struck versus 9.7% of non-Black jurors. In White-defendant cases, the absolute rates differ (Black: 40.0%, non-Black: 16.2%), but the gap persists. The interaction term (OR = 0.34, p < 0.001) indicates that the disparity is statistically smaller in White-defendant cases but not eliminated.

# A tibble: 4 × 5
 term estimate conf.low conf.high p.value
 <chr> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 0.108 0.0946 0.123 4.59e-244
2 race_groupBlack 10.3 8.69 12.2 4.63e-162
3 defendant_raceWhite 1.79 1.40 2.27 2.10e- 6
4 race_groupBlack:defendant_raceWhite 0.336 0.242 0.468 9.48e- 11

Time Trend, 1992–2012

Over the span with reasonably complete data, both strike rates drifted downward together, and the gap between them barely moved. As shown by the smoothed (LOESS) trends, the Black-juror strike rate declined modestly while the non-Black rate stayed roughly flat, leaving a gap that began near 38 percentage points and ended near 32, averaging about 37 points across the period. The stability of the gap is the primary takeaway.

County-level Poverty

Six of the seven counties have poverty rates between roughly 22% and 26%, near the Mississippi median (~23%). Carroll County has the lowest poverty rate (~14%) yet the highest odds ratio (17.3). With only seven counties, this cannot support an inferential claim, but there is no visible tendency for poorer counties to show larger disparities, so local economic conditions are not an obvious explanation for the pattern.

Multilevel Model: Accounting for Clustering

Standard logistic regression treats each juror as independent. In reality, jurors in the same trial share a common local context, such as the same prosecutor, judge, defendant, and case facts, creating within-trial correlation. Ignoring this correlation produces artificially narrow confidence intervals. A mixed-effects (multilevel) logistic model accounts for this by estimating a random intercept for each trial.

The multilevel model yields an OR of 8.55 (95% CI: 7.34–9.95), compared with 7.84 from the simple logistic model. The ICC of 4.5% indicates that approximately 4.5% of the variation in strike decisions reflects between-trial differences, although the majority of the variation is within trials.

# A tibble: 3 × 4
 Model OR `95% CI` Notes
 <chr> <dbl> <chr> <chr>
1 Simple logistic (no clustering) 7.84 6.81–9.04 Assumes jurors …
2 Multilevel logistic (jurors nested in trials) 8.55 7.34–9.95 ICC = 4.5%
3 Multilevel logistic (+ defendant race) 8.54 7.33–9.95 Adjusts for def…

The Mathematics of Exclusion

How Likely are All-White Juries?

After strikes for cause, 34.8% of the eligible pool in this district was Black. The question is how often an all-White twelve-person jury should appear under race-neutral selection, and how it changes after prosecution strikes are applied.

Under a reasonable approximation, the chance of seating a twelve-person jury with no Black members from a pool that is 34.8% Black is about 0.6%, roughly 1 in 170. After applying the observed prosecution strike rates, the effective share of Black jurors available at seating falls to about 23.7%, and the probability of an all-White jury rises to about 3.9%, roughly 1 in 26.

All else equal, the observed strike behavior is associated with an all-White jury becoming about 6.6 times more likely than the race-neutral benchmark.

Across Many Trials

Small per-trial differences compound across many trials. The table below shows the probability of observing at least one all-White jury under the race-neutral benchmark and under the observed strike rates.

Number of Trials	Race-Neutral Selection	Observed Strike Rates
10	5.7%	32.7%
25	13.7%	62.8%
50	25.4%	86.1%
100	44.4%	98.1%
500	94.7%	100.0%
1,000	99.7%	100.0%

Observed Versus Expected

Across 218 trials with complete jury-composition data, 14 juries contained no Black members. Under the race-neutral benchmark, the expected count is about 1.3. Therefore, the observed number is roughly 11 times the benchmark. An exact binomial test rejects a race-neutral process at p < .001.

What These Data Can—and Cannot—Tell Us

Every trial here was appealed, and appeal is not random, so the rates (the 49.8% strike rate, the “1 in 26” probability, the per-trial figures) describe this non-random set of trials, not jury selection in the district at large or elsewhere. But selection bias cannot easily explain the disparity’s direction, consistency, and magnitude: an eightfold odds ratio that survives adjustment for county and defendant race, appears in every county at p < 0.001, and holds even when the defendant is White is not what a selection artifact looks like.

The data still cannot establish intent by any prosecutor or why any particular juror was struck; those turn on voir dire responses and case-specific reasons absent here. What it shows is an aggregate disparity: prosecutors struck Black eligible jurors at far higher rates than non-Black jurors, and 14 of 218 trials produced all-White juries, where a binomial model based on each county’s Black share predicts only about 1.3. That does not prove discrimination, but it sharpens the question the Anthony trial revived: if race-based exclusion is unconstitutional, why do disparities this large persist decades after Batson?

Transparency, Data, and the Future of Jury Selection Analysis

Debates about jury selection tend to run on speculation and competing narratives precisely because the underlying records are rarely available for independent evaluation. When data do exist, analyzing them is not straightforward, and the results must be read within an appropriate legal and statistical frame, including an unbiased account of how the data were chosen, cleaned, and analyzed.

Expanding access to court records, voir dire transcripts, and structured jury-selection datasets would let researchers, journalists, attorneys, and the public assess claims with evidence rather than assumption, and would let the kind of selection problem flagged above be measured rather than merely conceded.

Winner of the Urban Science Best Paper Award: Uncovering Spatial Patterns of Residential Settlements, Segregation, and Vulnerability of Urban Seniors

Fri, 10 Jul 2026 00:00:00 +0000

We are pleased to announce that our paper, “Uncovering Spatial Patterns of Residential Settlements, Segregation, and Vulnerability of Urban Seniors Using Geospatial Analytics and Modeling Techniques”, published in Urban Science (July 2024), has been selected as the winner of the Urban Science Best Paper Award.

The award was determined by the committee based on an overall evaluation of the originality and significance of the papers, considering citations and downloads. Congratulations to the author team!

Racial and Ethnic Disparities in Non-Fatal Self-Inflicted Firearm Injuries Among Adults in the United States, 2000-2021

Wed, 17 Jun 2026 00:00:00 +0000

Plain-Language Summary

This study analyzes nonfatal, self-inflicted firearm injuries in the United States over a 22-year period (2000-2021) using the National Electronic Injury Surveillance System (NEISS-FISS). Because structured database codes often fail to capture the context or intent behind a self-inflicted discharge, the research team used text mining and a machine learning (LASSO) classification model to analyze the unstructured clinical narratives written by emergency department staff.

The results show that while 58% of visits were likely unintentional discharges (accidents), 10% were probable suicide attempts. The narrative text mining revealed distinct, racially disparate patterns of injury: Non-Hispanic Black patients suffered the highest proportion of lower-extremity injuries (51%), while Hispanic patients had the highest rates of craniofacial injuries (26%) and a significant association with alcohol involvement (increasing from 12% in coded data to 20% in narrative data). This highlights the value of using natural language processing to analyze emergency department notes for better injury prevention and public health surveillance.

New Publication: Racial and Ethnic Disparities in Non-Fatal Self-Inflicted Firearm Injuries

Mon, 15 Jun 2026 00:00:00 +0000

Abstract

Nonfatal self-inflicted firearm injuries represent an important and understudied public health concern in the United States. We analyzed nationally representative data from the National Electronic Injury Surveillance System–Firearm Injury Surveillance Study (NEISS-FISS), 2000–2021, to characterize nonfatal self-inflicted firearm injuries among U.S. adults aged 18–64 years. Emergency department (ED) narratives were combined using text mining and an elastic net (LASSO) classification model to augment structured surveillance data. Among 7,362 unweighted cases (representing approximately 260,602 ED visits, 58% were classified as probable unintentional discharges, whereas approximately 10% were probable suicide attempts. Among cases with complete race and ethnicity data, Non-Hispanic White (NHW; 69.2%), Non-Hispanic Black (NHB; 23.2%), and Hispanic (7.6%) individuals comprised the primary analytic groups. Narrative analysis substantially increased detection of contextual factors, particularly alcohol involvement among Hispanic patients (12.0% to 20.1%). NHB patients had the highest proportion of lower-extremity injuries (51%), while Hispanic patients had the highest prevalence of craniofacial injuries (26%). The LASSO model effectively distinguished terms associated with probable suicide attempts from unintentional firearm discharges based on narrative language. Findings highlight limitations of structured intent coding within NEISS-FISS and demonstrate the value of narrative for improving characterization of firearm injury circumstances, revealing distinct racial and ethnic group patterns, to inform more tailored prevention strategies.

Plain-Language Summary

Congratulations to Dr. Amy Watson-Grace for receiving the prestigious Presidential Fellowship

Fri, 15 May 2026 00:00:00 +0000

Congratulations to Dr. Amy Watson-Grace for receiving the prestigious Presidential Fellowship! This fellowship recognizes her outstanding academic achievement and promising research directions.

Congratulations to Olivia McLucas for being awarded a scholarship to study statistics from the Colleges of Arts and Sciences

Sun, 10 May 2026 00:00:00 +0000

Congratulations to Olivia McLucas for being awarded a scholarship to study statistics from the Colleges of Arts and Sciences! This award supports her continued studies and contributions to spatial statistics.

Congratulations to Hexin Yang and Taylor Harrington who have recently become PhD candidates

Fri, 01 May 2026 00:00:00 +0000

Congratulations to Hexin Yang and Taylor Harrington who have recently become PhD candidates! We look forward to their continued research contributions.

Detecting Intimate Partner Violence Injury Mechanisms Using LLMs and LDA

Wed, 29 Apr 2026 15:45:00 -0500

Dr. Gia Barboza-Salerno, Olivia McLucas, and Dr. Karla Shockley will present their research at the 2026 Symposium on Data Science & Statistics (SDSS) in Milwaukee, Wisconsin.

Presentation Details

Title: Automated Detection of Intimate Partner Violence Injury Mechanisms from Emergency Department Narratives Using Large Language Models and Latent Dirichlet Allocation

Session: AI and LLM Applications I
Date: Tuesday, April 29, 2026
Time: 3:45 - 5:15 PM CT
Format: 25-minute refereed presentation
Location: Milwaukee, Wisconsin

Registration: Register for SDSS 2026
Registration Deadline: March 25, 2026

Abstract

We developed an AI pipeline designed to overcome limitations in existing surveillance systems by distinguishing the underlying mechanisms of Traumatic Brain Injury (TBI) in intimate partner violence (IPV). The primary goal was to apply a Large Language Model (LLM) to extract and classify behavioral and contextual information from unstructured free-text narratives within the National Electronic Injury Surveillance System (NEISS).

The pipeline first cleans the narratives and uses the LLM to convert the text into six targeted structured semantic representations (e.g., action, cause, diagnosis), which are then quantified using Latent Dirichlet Allocation (LDA) topic modeling. Our results can be used to enhance injury surveillance to identify IPV in emergency department settings.

Introduction

Head, face, and neck injuries are common IPV-related injuries and frequently involve TBI, yet NEISS diagnostic codes lack the behavioral detail needed to differentiate mechanisms of harm. Although NEISS narratives contain brief descriptions of arguments, aggression, object use, and environmental context, they have been underutilized in surveillance research and rarely analyzed at scale.

As a result, diagnostic codes collapse distinct injury pathways—including accidental falls, object impacts, impulsive actions during conflict, and direct assaults—into the same categories, obscuring meaningful variation in risk and limiting clinical screening and prevention efforts.

Our method fills this gap by applying AI-based extraction and topic modeling to detect behavioral and situational information embedded in NEISS narratives, producing a more nuanced understanding of IPV-related injury mechanisms.

Methods

Analytical Framework

We developed an AI pipeline to extract and classify behavioral and contextual information from unstructured NEISS narratives related to IPV:

Text Preprocessing: Used regular expressions to flag domestic-violence–related incidents, expand abbreviations, clean shorthand, and isolate IPV cases
Custom Prompting: Generated prompts by merging cleaned narratives with structured NEISS variables
LLM Extraction: Transformed narratives into six semantic fields (action, cause, diagnosis, mechanism, object, location)
Topic Modeling: Applied LDA to identify latent themes within each semantic field
Typology Classification: Integrated regex-based typology and LDA clusters to produce quantitative injury pathways

Text Processing and Prompting

We used vectorized regular expressions to:

Flag violence-related narratives
Expand abbreviations and clean shorthand
Isolate IPV cases by identifying relationship and conflict terms
Exclude clearly non-IPV incidents

After preprocessing, we generated custom prompts by merging each cleaned narrative with structured NEISS variables to produce context-rich descriptions of the event.

LLM Extraction and Topic Modeling

The LLM transformed cleaned narratives into six structured semantic representations:

Action: What happened (e.g., verbal disputes, punching, striking objects)
Cause: Why it happened (e.g., intoxication, falls, collisions)
Diagnosis: Medical outcome (e.g., fractures, contusions, lacerations)
How: Mechanism of injury (e.g., physical struggle, impulsive acts)
What: Objects involved (e.g., walls, windows, doors, fists)
Where: Location (e.g., home, public place, bathroom)

LDA topic modeling systematically discovered and quantified overarching themes across all responses, resulting in quantifiable dimensions like interpersonal conflict or blunt impact injuries.

Data & Results

Quantification of Contextual Dimensions (LDA Topics)

Topic modeling produced interpretable themes across all semantic dimensions:

Action: verbal disputes, play fighting, punching, striking objects
Cause: intoxication, falls, collisions with walls, windows, or furniture
Diagnosis: fractures, contusions, lacerations, closed head injury
How: physical struggle, impulsive acts, heated altercations
What: walls, windows, doors, bricks, glass, fists
Where: home, public place, outside environment, bathrooms

Identified Head/Neck Injury Typologies

Narrative- and topic-derived clusters aligned with six major typologies:

Typology	%	Representative Narrative
Accidental fall or slip	38.6%	“Slipped and fell hitting head on carpet.”
Self-inflicted harm	19.9%	“Banging head against wall during argument.”
Physical assault	25.1%	“Stabbed in neck with kitchen knife.”
Blunt object impact	9.9%	“Head-butted wall; forehead hematoma.”
Environmental	2.3%	“Pepper spray during fight.”
Vehicle-related impact	1.7%	“Swerved and crashed; head/neck pain.”

Word Cloud Visualization

The figure above shows word-cloud representations of dominant latent topics extracted from LLM-generated semantic dimensions (Cause, Diagnosis, How). Each cloud visualizes high-frequency terms within a topic cluster, highlighting recurrent mechanisms of injury across IPV-related emergency department visits.

Key patterns identified:

Heavy concentration of head trauma in incidents involving walls, floors, or doors
High rates of head contact in intoxication-related falls
Frequent head or neck injury during impulsive self-directed actions

Discussion & Conclusions

Combining regex-based classification with LLM extraction and LDA provides a detailed mapping of how, where, and why brain injuries occur during IPV-related incidents.

Key Findings

Blurred Boundaries: Many injuries categorized as “accidental” or “environmental” occur in the context of relationship conflict, revealing blurred boundaries between unintentional and interpersonal harm
Enhanced Surveillance: These methods improve surveillance and detection of IPV-related injuries in emergency departments by recovering behavioral information that structured diagnostic codes cannot capture
Mechanism Identification: The narrative-based approach identifies specific mechanisms that contribute to injury, whereas structured codes often make it difficult to distinguish between distinct behavioral pathways

Future Work

Expanding prompts to capture richer contextual cues
Validating typologies with larger datasets
Examining variation across sociodemographic and clinical subgroups

About SDSS

The Symposium on Data Science & Statistics is a premier conference bringing together researchers, practitioners, and students working at the intersection of statistics, data science, and computational methods. The 2026 symposium takes place April 28 - May 1 in Milwaukee, Wisconsin.

Keywords: Traumatic brain injury, intimate partner violence, surveillance, Natural Language Processing, topic modeling, emergency medicine, LLM, LDA

Welcome to our new Graduate Research Assistants joining the ISSUES Lab!

Mon, 20 Apr 2026 00:00:00 +0000

The ISSUES Lab is excited to welcome our new Graduate Research Assistants joining us for the upcoming academic term. We look forward to working together on advanced spatial modeling projects!

Ohio Recovery Housing Conference 2026

Wed, 15 Apr 2026 13:00:00 +0000

Dr. Keith Warren, Dr. Gia Barboza-Salerno, and Taylor Harrington will present at the 2026 Ohio Recovery Housing Conference during Breakout Session D (1:00 - 2:15 PM) on Wednesday, April 15, 2026.

This presentation will share findings from the lab’s ongoing research on recovery housing, examining the role of recovery housing in supporting individuals with substance use disorder, particularly in underserved and high-risk communities.

Conference Details:

Date: April 15, 2026
Time: 1:00 - 2:15 PM
Session: Breakout Session D
Registration: Register here
Full Schedule: View conference agenda

AERA 2026: Harsher Punishments, Lesser Infractions - Disciplinary Injustice for Native American Students

Sat, 11 Apr 2026 00:00:00 +0000

Presentation at the American Educational Research Association (AERA) Annual Meeting

Date: Saturday, April 11, 2026
Time: 9:45 AM to 11:15 AM PDT (12:45 PM to 2:15 PM EDT)
Location: JW Marriott Los Angeles L.A. LIVE, 4th Floor, Diamond 2

Abstract

Exclusionary discipline disproportionately affects youth of color, but Indigenous student experiences remain underexplored. Using 2019-2020 New Mexico K-12 data, we applied random intercept multilevel modeling to examine the impact of race and infraction severity on odds of receiving harsh discipline (out-of-school suspension, justice system referral) versus in-school suspension.

Key Findings:

White students had 20% lower odds (p = .003) of receiving harsh discipline compared to Native American students
While odds of harsh discipline increased for all students as infraction severity increased, odds of receiving harsh discipline declined more sharply for White, Black, and Latine students compared to Native American students as infraction severity decreased
Native American students faced harsher consequences for less serious infractions, underscoring the need for equity-focused discipline reform

This research highlights critical disparities in school discipline practices and calls for systemic reform to ensure equitable treatment of Indigenous students in New Mexico public schools.

Authors: Allie Watson-Grace, Hannah Yang, and Dr. Gia Barboza-Salerno

This manuscript is currently under review at AERA Open.

IPR Seminar Series: Accessibility to Abortion Services, Maternal Vulnerability, and Birth Outcomes

Tue, 10 Mar 2026 12:30:00 +0000

Dr. Gia Barboza-Salerno will present at the Institute for Population Research (IPR) Seminar Series on Tuesday, March 10, 2026.

Presentation Details

Title: Accessibility to Abortion Services, Maternal Vulnerability, and Birth Outcomes

Date: Tuesday, March 10, 2026
Time: 12:30 - 1:30 PM
Location: Townshend Hall, room 038, 1885 Neil Ave Mall and Zoom

Registration: Register for Zoom access

Abstract

Does spatial accessibility to abortion care intersect with maternal vulnerability to shape adverse birth outcomes? In the post-Dobbs landscape, access to abortion services has become increasingly uneven, with substantial geographic barriers emerging across large portions of the state. This study examines whether spatial constraints on abortion access align with community-level maternal vulnerability and how these structural conditions are associated with adverse birth outcomes.

The study uses a novel Maternal Vulnerability Index (MVI), a validated, census-tract-level measure that captures multidimensional drivers of poor maternal health, including:

Healthcare access
Reproductive health resources
Physical and mental health
Environmental conditions
Socioeconomic disadvantage

Geographic information systems (GIS) and multimodal transportation network modeling are used to quantify travel burden, service deserts, and spatial accessibility to abortion. These spatial access measures are then linked to adverse birth outcomes (e.g., preterm birth), to assess whether communities facing greater abortion access constraints experience elevated perinatal risk.

By situating abortion accessibility within a comprehensive measure of maternal vulnerability, this study highlights how geographic and structural inequities in reproductive healthcare access may contribute to persistent disparities in maternal and infant health.

How to Apply

Wed, 04 Feb 2026 00:00:00 +0000

Sorry, there are no openings for Spring 2026.

Living Here Predicts How You Will Die: Neighborhood Context and Mortality Patterns

Wed, 04 Feb 2026 00:00:00 +0000

Abstract

Understanding how neighborhood context shapes patterns of death is critical to identifying the spatial distribution of health inequities. Medical examiner data contain rich narrative descriptions of causes and circumstances of death that are often absent from clinical records and can offer novel insights for public health prevention.

This study integrates Geographic Information Systems (GIS), machine learning (ML), and natural language processing (NLP) frameworks to demonstrate how neighborhood clustering and predictive text analytics can jointly reveal spatially stratified mortality patterns to examine how structural neighborhood conditions predict not only whether but how people die, thereby advancing geographically tailored public health insights.

Methods

Tract-level mortgage lending and demographic data were integrated with narrative cause-of-death records from the Cook County Medical Examiner. A GIS-informed machine learning framework was used to classify neighborhood context and link it to mortality patterns in Cook County, Illinois.

Using tract geometries from the American Community Survey (ACS) 2020 and integrating:

2018 racial/ethnic composition
Median household income
Shifts in mortgage lending by borrower race/ethnicity and income from HMDA
CDC Social Vulnerability Index

A standardized feature set capturing racial/ethnic composition, economic resources, and structural change in lending markets was constructed. Unsupervised machine learning methods with gap statistics were used to derive five interpretable neighborhood clusters.

Text descriptions from cause-of-death fields were cleaned, lemmatized, and harmonized into medical concepts, then summarized by cluster using frequency and tf–idf contrasts.

Key Findings

The five neighborhood clusters showed distinct mortality profiles that mirrored structural conditions:

Disinvested Black neighborhoods: Marked by violence and street-level drug toxicity
Affluent White enclaves: Reflected prescription/synthetic drug deaths and self-harm
Latino working-class areas: Exhibited mixed patterns of chronic disease, injury, and substance involvement
White-Latino transition neighborhoods: Dominated by chronic illness and degenerative diseases
Lower-income Black neighborhoods: Burdened by chronic illness, degenerative diseases, infectious causes, and substance-related mortality

Predictive Modeling Results

Penalized logistic regression models (glmnet) were used to predict cluster membership from:

Text alone
Covariates (income, rent, poverty, age, gender)
Both combined

Key Results:

Text features exhibited high sensitivity across clusters
Socioeconomic covariates improved specificity, particularly in affluent White enclaves
Combined models (text + covariates) achieved the highest overall accuracy
Performance varied by cluster: affluent White enclaves were well distinguished by socioeconomic covariates, while text features more easily identified disinvested Black and lower-income Black neighborhoods

Research Questions

RQ1: Can neighborhood structural typologies derived from mortgage lending and demographic patterns be meaningfully linked to cause-of-death narratives?

RQ2: Does narrative text from Medical Examiner death records improve prediction of neighborhood typology?

Conclusions

These results highlight how integrating structured tract-level data with unstructured death narratives uncovers not only place-based disparities in mortality risk, but also systematic differences in how death is recorded across structurally distinct neighborhoods.

Five neighborhood clusters were identified using k-means clustering on tract-level indicators. Penalized logistic regression models demonstrated that:

Combined text and covariate models achieved the highest accuracy across most neighborhood types
Narrative language varied systematically by neighborhood type, reflecting place-based inequality in death documentation

Topics: Mortality patterns, neighborhood effects, health inequities, machine learning, natural language processing, GIS, medical examiner data, Cook County

Openings

Wed, 04 Feb 2026 00:00:00 +0000

Sorry, there are no openings for Spring 2026.

Dr. Barboza-Salerno Addresses Grand Challenge to Prevent Gun Violence

Tue, 06 Jan 2026 00:00:00 +0000

Dr. Gia Barboza-Salerno addresses the Grand Challenge to Prevent Gun Violence in a feature by the College of Social Work. Read more about the initiatives to address gun violence on the College of Social Work blog.

A Geospatial Analysis of Built and Social Environmental Factors Associated with Student Crash Risk Near Schools in Boston, Massachusetts

Sun, 04 Jan 2026 09:00:00 +0000

Emotional and Physical Symptoms Following Intimate Partner Violence Victimization in the United States: Implications for Law and Public Health Policy

Mon, 01 Dec 2025 00:00:00 +0000

Living Here Predicts How You’ll Die: Linking Death Narratives to Neighborhood Economic Disinvestment with Unsupervised Machine Learning

Wed, 19 Nov 2025 12:00:00 +0000

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

SDOH Example

Wed, 10 Sep 2025 00:00:00 +0000

This is the SDOH example page.

Mapping the SDoH

Goal: Build a tract-level map of food insecurity and housing insecurity in Franklin County, Ohio using the CDC PLACES dataset, then export an sf/GeoJSON layer for reuse on the ISSUES Lab site.

Pipeline overview

Query PLACES (Socrata API) for two indicators filtered to Franklin County via FIPS.
Coerce indicators to numeric.
Download census tract polygons (TIGER/Line via tigris) and left_join on tract GEOID.
Mean-impute missing values for food insecurity (for quick visualization only).
Plot a fast choropleth and write a GeoJSON file.

ℹ️ The PLACES “GIS-friendly” table is attributes only. Socrata “location” endpoints typically expose centroids, not tract polygons—so we fetch attributes and join to TIGER/Line shapes.

Requirements (install once)

install.packages(c("RSocrata", "dplyr", "tigris", "sf"))

## Requirements (install once)

```r
install.packages(c("RSocrata", "dplyr", "tigris", "sf"))

## Load each session

- **RSocrata** — read data from data.cdc.gov (Socrata)
- **dplyr** — data manipulation
- **tigris** — tract geometries (TIGER/Line)
- **sf** — spatial classes, I/O (GeoJSON)
- **utils** — base R utilities

### Optional nicety

 options(tigris_use_cache = TRUE) # cache shapefiles locally between runs

#########################################
# Downloading PLACES data to #
# map the social determinants of health #
# for food and housing insecurity for #
# the ISSUES lab next paper :) #
# September 9, 2025 #
# Example #
#########################################

# install.packages("RSocrata") etc once
library(RSocrata) # read online data
library(dplyr) # manipulate dataframes
library(utils) # utilities

base <- "https://data.cdc.gov/resource/yjkw-uj5s.json"

# Note there is a geojson endpoint however the geometry is the centroid
# so it's better to download the non-spatial data and then get the census tract,
# and then merge the data to the census tract polygons.
fields <- c(
 "stateabbr","countyname","countyfips","tractfips",
 "foodinsecu_crudeprev","housinsecu_crudeprev"
)

select_q <- paste(fields, collapse = ",")

# Filter to Franklin County, Ohio (prefer FIPS for precision)
where_q <- "stateabbr = 'OH' AND countyfips = '39049'"

(url <- paste0(
 base, "?$select=", select_q,
 "&$where=", URLencode(where_q, reserved = TRUE)
))

df_franklin <- read.socrata(url) %>%
 mutate(
 foodinsecu_crudeprev = as.numeric(foodinsecu_crudeprev),
 housinsecu_crudeprev = as.numeric(housinsecu_crudeprev)
 )

glimpse(df_franklin)

library(tigris) # connect to census mapping files
options(tigris_use_cache = TRUE)

# We have census tract level data for Ohio (state FIPS = 39),
# Franklin County (county FIPS = 049). Using 2022 TIGER (2020 vintage).
oh_ct <- tracts(state = "Ohio", county = "Franklin", year = 2022, class = "sf")

df_franklin <- df_franklin %>% dplyr::rename(GEOID = tractfips)
oh_ct <- oh_ct %>% dplyr::left_join(df_franklin, by = "GEOID")

# Quick map (before imputation)
plot(oh_ct["foodinsecu_crudeprev"], main = "Food Insecurity (%) — Franklin County, OH")

# Mean-impute missing (visualization only)
m_food <- mean(oh_ct$foodinsecu_crudeprev, na.rm = TRUE)
oh_ct$foodinsecu_crudeprev[is.na(oh_ct$foodinsecu_crudeprev)] <- m_food

# Plot (after imputation)
plot(oh_ct["foodinsecu_crudeprev"], main = "Food Insecurity (%) — Franklin County, OH")

library(sf)
st_write(
 oh_ct,
 "C:/Users/barboza-salerno.1/Documents/lab/content/resources/datasets/ohio-franklin-sdoh.geojson",
 delete_dsn = TRUE
)

Tips & adaptations

Change geography: Edit where_q to your target stateabbr and countyfips (or drop the filter for entire states/US).
More indicators: Add PLACES columns to fields (e.g., lacktrpt_crudeprev, emotionspt_crudeprev).
Prettier maps: Use ggplot2 + geom_sf() with scale_fill_viridis_c() for publication graphics.
Missing data: Mean-imputation here is for a quick map; use a different imputation method.
Export path: Update st_write() to download the geojson file to your hard drive and map it with QGIS.

Downloads

R script is here: Download the R script
Mapping file is here: Download the geojson file for mapping

Craniofacial Injuries in IPV-Related Physical Abuse

Wed, 27 Aug 2025 00:00:00 +0000

Overview

Intimate Partner Violence (IPV) is a widespread public health crisis, and craniofacial injuries—including trauma to the head, face, and neck—are among the most common physical consequences. Many of these injuries involve brain trauma such as concussion, traumatic brain injury (TBI), or hypoxic-ischemic brain injury (HIBI).

National emergency department data indicate that nearly 900,000 IPV-related visits occurred during the study period, with almost three in five involving craniofacial trauma. Female survivors experienced injury rates nearly five times higher than males, with the starkest disparities in early adulthood (ages 20–34).

Policy Relevance

Legal Context: Recognition of IPV-related craniofacial injury in statutes and clinical guidelines can improve survivor protections and treatment access.
Health Systems: Emergency medicine protocols should include screening for IPV in patients with head, face, and neck injuries.
Civil Rights: Survivors face barriers in healthcare and justice systems; data-driven policy is needed to reduce disparities in detection, treatment, and support.

Recommendations

Mandate IPV screening protocols in emergency departments, especially for craniofacial injuries.
Expand Medicaid and social safety net coverage for long-term treatment of IPV-related traumatic brain injuries.
Increase funding for prevention and survivor support programs that integrate health and legal services.

Download

Download the full brief (PDF)

Historical Discipline in Colorado School Districts (2011–2017)

Wed, 27 Aug 2025 00:00:00 +0000

Overview

School discipline practices are a critical measure of equity in education. Suspensions and expulsions are strongly linked to negative academic and developmental outcomes, including higher dropout risk, lower graduation rates, and greater involvement with the juvenile justice system.

Analysis of Colorado Civil Rights Data Collection (CRDC) records from 2011 to 2017 shows that exclusionary discipline is not distributed randomly. Districts serving larger proportions of children in poverty face disproportionately high rates of in-school and out-of-school suspension. Across all years studied, there is a strong poverty gradient: each 1 percentage-point increase in child poverty predicts about +0.20 OSS and +0.18 ISS per 100 students. These disparities highlight the structural nature of discipline outcomes and the urgent need for systemic policy responses.

Policy Relevance

Legal Context: Colorado law requires that districts implement fair, clearly communicated conduct policies and ensure that discipline allows for discretion and supportive alternatives. Yet this legal mandate is often a neglected component of discipline outcomes and processes, leaving inequities in enforcement unchecked.
Educational Equity: Disproportionately high suspension rates in high-poverty districts fuel achievement gaps, dropout risk, and long-term justice involvement.
Civil Rights: Unequal enforcement of discipline undermines the legal promise of equal educational opportunity. Stronger accountability mechanisms are needed to ensure fair application of state and federal mandates across districts.

Recommendations

Address the Poverty Gradient: Direct funding and resources to high-poverty districts, where suspension rates are consistently highest.
Support Alternatives to Suspension: Expand restorative practices, mental health supports, and culturally responsive interventions to reduce reliance on exclusionary discipline.
Enforce Legal Mandates Equally: Strengthen oversight to ensure that fair, clearly communicated conduct codes and supportive alternatives are applied consistently across all districts.
Improve Data Transparency: Require disaggregated reporting (by race, sex, disability) to monitor compounding disparities and support equity-focused interventions.

Download

Download the full brief (PDF)

Intimate Partner Violence & Brain Injury in U.S. Adults: Findings from the Medical Expenditure Panel Survey, 2021

Wed, 27 Aug 2025 00:00:00 +0000

Overview

National survey data show that adults who screen positive for intimate partner violence (IPV) experience more injuries and face greater medical costs than those who do not. Nine million screened IPV-positive using the HITS screening tool, and survivors were significantly more likely to utilize emergency rooms and hospitals for treating their injuries. Head and brain injuries were much more frequent among IPV survivors, and the average cost of treating these injuries was nearly twice as high compared to adults who did not screen positive for IPV.

Key Messages

Approximately 9 million adults screened positive for IPV in 2021.
IPV-positive adults had 3–4× higher ER and inpatient injury rates than IPV-negative adults.
Adults who screened positive for IPV experienced more than twice the rate of brain-injury healthcare events compared to those who screened negative.
Average per-capita spending on brain-injury care was about twice as high among IPV-positive adults.
Inpatient TBI events are less common than ER events but far more costly; counts and spending are higher among IPV-positive adults.
Poverty, social stressors, depression, and chronic conditions are significantly associated with IPV screen positivity.

Policy Relevance & Recommendations

Standardized protocols: Implement routine protocols so that IPV-positive patients with head injury are screened for brain injury, and patients with head injury are also screened for IPV.
Integrated referral pathways: Establish clear referral systems across emergency, primary, and specialty care so that when screening protocols are triggered, patients are automatically connected to appropriate follow-up services.
Follow-up for delayed symptoms: Incorporate temporal follow-up in care plans, recognizing that brain injury symptoms may appear or worsen over time.
Accessibility and appropriateness: Ensure screening and follow-up services are accessible, widely available, and tailored to the needs of IPV survivors.
Holistic screening: Extend screening beyond identifying head injury to include social and health needs that may magnify injury effects and increase vulnerability to future IPV.
Care coordination: Utilize medical social workers and interdisciplinary teams to integrate medical, psychosocial, and safety needs into a comprehensive care response.

Methodology

This brief analyzes nationally representative data from the Medical Expenditure Panel Survey—Household Component (MEPS-HC) 2021. Adults (age ≥18) were classified as IPV screen-positive if they scored ≥10 on the Hurt, Insult, Threaten, Scream (HITS) scale, a validated four-item screening tool for intimate partner violence.

Injury outcomes were identified using ICD-10-CM codes and Clinical Classifications Software Refined (CCSR) groupings from the MEPS Conditions file, with condition–event linkages used to assign emergency department (ER), inpatient, outpatient, office-based, home health, and prescription care to specific injuries. Brain injuries were defined as traumatic brain injury (ICD-10 S06) and other head injuries (S00–S09).

We estimated:

Injury and brain-injury events per 100 adults,
Per-capita spending (in 2021 USD), and
Weighted counts of injury-linked encounters.

Analyses applied MEPS survey weights (PERWT21F) and accounted for the complex design (VARSTR, VARPSU). We used survey-weighted generalized linear models (svyglm) to generate adjusted comparisons standardized over age, sex, and race/ethnicity. Risk ratios were estimated for sociodemographic factors, health-related social needs, and chronic health conditions associated with IPV screen positivity.

Suggested Citation

Barboza-Salerno, G. E., Shockley McCarthy, K., Watson-Grace, A., Harrington, T., McCarthy, M. A., Peremen, N., & McLucas, O. (2025). Intimate Partner Violence & Brain Injury in U.S. Adults: Findings from the Medical Expenditure Panel Survey, 2021. Policy brief. The Ohio State University, ISSUE Lab (Investigating Spatial Structures in Urban Environments).

Download

Download the full brief for more information

How to Analyze Caselaw Using CourtListener's API in R

Fri, 01 Aug 2025 00:00:00 +0000

I previously showed how to pull Indian Child Welfare Act (ICWA) cases using the Caselaw Access Project (Harvard Law). That API has since changed. This post updates the workflow using the Free Law Project’s CourtListener, which now partners with Harvard to make this data freely available.

Finding the Cases

Start at the CourtListener website and search for “Indian Child Welfare Act” (or "icwa"). You’ll see a page of opinion results.

Click Select Jurisdiction → State. Clear all, then select Ohio Supreme Court, Ohio Court of Appeals, and Ohio Court of Claims. This gives you a quick check on the expected volume to validate the code’s output below.

The site reported 36 cases from Ohio. Note that the exact number may change over time as new opinions are added or metadata is corrected.

Getting the Data into R

The Search API returns clean JSON results by querying for opinions that contain the exact phrase "indian child welfare act", restricting to published opinions, ordering by relevance, and (optionally) limiting results to three Ohio courts.

library(httr2)
library(purrr)
library(dplyr)
library(tibble)
library(tidyr)
library(stringr)

SEARCH_BASE <- "https://www.courtlistener.com/api/rest/v4/search/"
UA <- "R/httr2 (your-email-address) – ICWA metadata collection"

query_params <- list(
 q = '"indian child welfare act"',
 type = "o",
 order_by = "score desc",
 stat_Published = "on",
 court = "ohio ohioctapp ohioctcl" # optional: specify court
)

A few helper functions keep the parsing tidy:

pull_chr <- function(x, ...) {
 v <- purrr::pluck(x, ..., .default = NA)
 if (is.null(v) || length(v) == 0) return(NA_character_)
 as.character(v)
}

pull_num <- function(x, ...) {
 v <- purrr::pluck(x, ..., .default = NA_real_)
 suppressWarnings(as.numeric(v))
}

get_json <- function(url) {
 resp <- request(url) |> req_user_agent(UA) |> req_url_query() |> req_perform()
 resp_check_status(resp)
 resp_body_json(resp, simplifyVector = FALSE)
}

This function handles pagination automatically, following the next cursor until all pages are exhausted:

fetch_search_pages <- function() {
 req <- request(SEARCH_BASE) |>
 req_user_agent(UA) |>
 req_url_query(!!!query_params)

 dat <- req |> req_perform() |>
 (\(r){ resp_check_status(r); resp_body_json(r, simplifyVector = FALSE) })()
 pages <- list(dat)

 while (!is.null(dat$`next`)) {
 dat <- request(dat$`next`) |> req_user_agent(UA) |> req_perform() |>
 (\(r){ resp_check_status(r); resp_body_json(r, simplifyVector = FALSE) })()
 pages <- append(pages, list(dat))
 }
 pages
}

pages <- fetch_search_pages()
results <- pages |> map("results") |> flatten()

results is a list of per-case records from the API. Each element includes the case name, filing date, court identifiers, citation strings, a public URL where the caselaw text is available, and other metadata.

For Ohio, this produced ~35 unique case names versus 36 displayed on the site. Minor discrepancies happen because search hits can include multiple opinions or sibling opinions per matter, and because the index is updated over time.

Downloading the Full Text

Each opinion has an API endpoint with plain-text and HTML fields. A small loop can request the text for each case URL, with a short pause and retry between requests, and save one .txt file per opinion:

download_opinion_text <- function(url, out_dir = "opinions") {
 dir.create(out_dir, showWarnings = FALSE)
 slug <- basename(url)
 path <- file.path(out_dir, paste0(slug, ".txt"))

 resp <- request(url) |> req_user_agent(UA) |> req_perform()
 resp_check_status(resp)
 body <- resp_body_json(resp, simplifyVector = FALSE)

 text <- body$plain_text %||% body$html_with_citations %||% ""
 writeLines(text, path)
 Sys.sleep(0.5) # be considerate of rate limits
 invisible(path)
}

walk(map_chr(results, "absolute_url"), download_opinion_text)

Here are the Ohio ICWA cases downloaded to a local folder:

Visualizing the Results

You can use ggplot2 to visualize the results. Below are two examples: filings by year and volume by court.

What You Can Do Next

After downloading all data — including caselaw text and metadata — you can:

Natural Language Processing — examine sentiment or common words across opinions
Topic Modeling — explore latent themes in ICWA cases using LDA or STM
Map filings by jurisdiction — visualize geographic patterns of ICWA litigation
Integrate with other sources — link to child welfare administrative data or Census indicators

Remember to be considerate of rate limits and to include a User Agent string with your contact information in every request.

The complete workflow is available on my GitHub.

Happy Coding!

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

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

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!

Applying Social Geography to Structural Mitigation in Capital Defense

Wed, 02 Jul 2025 00:00:00 +0000

Summary:
This project applies social geography and spatial data analysis to strengthen structural mitigation arguments in capital defense. By documenting how neighborhood conditions—including poverty, segregation, environmental stressors, and disinvestment—shape an individual’s developmental context and legal outcomes, we provide place-based, legally admissible evidence aligned with Ohio law and ABA mitigation guidelines.

Research Questions:

RQ1: How can spatial analysis of public data reveal community-level risk factors—such as poverty, segregation, and housing instability—that contribute to life trajectories relevant to culpability and sentencing?
RQ2: In what ways can social geography and structural disadvantage be systematically incorporated into mitigation investigations to support fair, contextualized legal outcomes in death penalty cases?


Short description:	This project integrates spatial data and social geography to produce legally admissible, objective evidence of how systemic community-level factors influence life outcomes relevant to culpability and sentencing, advancing structural mitigation in capital defense.
Lead developer:	ProfGEBS
Further reading:	Review the presentation slides from the Ohio Bar Association’s Death Penalty Defense Seminar
Download:	Github repo
Main data source(s):	U.S. Census, Area Deprivation Index, Urban Institute data, Cook County parcel data, historical redlining maps
Coverage:	Ohio and Cook County, Illinois
Citation:	Barboza-Salerno, G. (2025). Applying Social Geography to Structural Mitigation in Capital Defense. Presented at the Ohio Bar Association Death Penalty Defense Seminar, approved by the Supreme Court of Ohio for capital case CLE requirements.

Check out this environmental risk mapping tutorial for a deeper look at spatial methods used in this work.

Evaluating Child Fatality Reports using Structural Topic Models

Wed, 02 Jul 2025 00:00:00 +0000

Adverse Childhood Experiences (ACEs)

Fri, 25 Apr 2025 00:00:00 +0000

Our lab takes a nuanced, data-driven approach to understanding Adverse Childhood Experiences (ACEs). Moving beyond simple cumulative scores, we examine the configuration and context of adversity. Our pioneering work on "Latent Classes and Cumulative Impacts of Adverse Childhood Experiences" identifies distinct patterns of abuse, neglect, and household dysfunction. We investigate the developmental cascade of trauma to understand how early life adversity differentially impacts long-term outcomes, including suicide risk among gender-diverse youth.

Core Questions We Address

How do specific latent classes of ACEs (e.g., household dysfunction vs. physical abuse) relate to differing risks for delinquency and mental health outcomes?
What is the cumulative impact of ACEs on long-term health, and how does this vary by race, gender, and sexual orientation?
How do neighborhood structural factors interact with individual-level adversity to compound risk for system involvement?

Methods and Approaches

We employ advanced statistical techniques to uncover these complex relationships using large-scale datasets:

Latent Class Analysis (LCA): Identifying subgroups of individuals with similar profiles of ACE exposure to tailor interventions.
Growth Mixture Models: Examining the longitudinal trajectories of trauma and resilience from early childhood through adulthood.
Parallel Process Latent Growth Curve Models: analyzing the co-development of post-traumatic stress and externalizing behaviors over time.

Select Publications

ACEs & Justice System Involvement

Duhaney, S., Barboza-Salerno, G., & Sasser, G. (2025). Differences in adolescent sexual risk-taking profiles and sexually transmitted infections in adulthood by justice systems involvement: A multigroup latent class analysis. Discover Public Health, 22, 416. https://doi.org/10.1186/s12982-025-00838-6
Barboza-Salerno, G., Chang, Y., Stanek, C. & Thurston, H. (2024). An examination of the effect of area-level characteristics on juvenile justice and child welfare referrals using multivariate Bayesian spatial modeling. International Journal of Child Abuse and Neglect, Vol. 160. https://doi.org/10.1016/j.chiabu.2024.107203

ACEs & Mental Health

Barboza-Salerno, G., Meshelemiah, J.C.A. (2023). Associations between Early Child Adversity and Lifetime Suicide Attempts among Gender Diverse Individuals: A Moderated Mediation. International Journal of Child Abuse & Neglect, Vol. 149. https://doi.org/10.1016/j.chiabu.2024.106705
Barboza, G. E., & Valentine, R. (2022). A Network Analysis of Post-Traumatic Stress Among Youth Aging Out of the Foster Care System. Children and Youth Services Review, Vol. 140. https://doi.org/10.1016/j.childyouth.2022.106589

Foundational ACEs Research

Barboza, G. (2017). Latent Classes and Cumulative Impacts of Adverse Childhood Experiences. Child Maltreatment, Vol. 23, Issue 2. https://doi.org/10.1177/1077559517736628

Manuscripts Under Review
Zhang, Y., Johnson-Motoyama, M., Yoon, S., Barboza-Salerno, G. Patterns of family harsh parenting and the effects of cumulative risks. Submitted to Child & Family Social Work.

Related Resources
View all publications
GitHub Repository

Drug Use, Overdose, and Policy

Fri, 25 Apr 2025 00:00:00 +0000

Our lab studies the intersection of substance use, overdose risk, and the built environment. We investigate how neighborhood conditions—including poverty, housing instability, and access to services—interact with social policies to shape the opioid crisis. Our work on the "Medicaid Expansion Project" highlights the role of policy environments in mitigating risk and supporting recovery. We examine the spatial variability in opioid-related drug overdoses to pinpoint communities in greatest need of intervention.

Core Questions We Address

How do neighborhood conditions and built environment features influence local patterns of drug use and overdose?
What is the impact of state and local policy environments (e.g., recovery housing initiatives) on treatment access and overdose mortality?
How can Bayesian spatial models help target harm reduction resources to the communities most in need?

Methods and Approaches

We apply rigorous spatial and policy analysis to address the overdose crisis:

Bayesian Spatiotemporal Analysis: Modeling the variability in opioid-related drug overdoses across space and time to identify emerging hotspots.
Policy Evaluation: Assessing the effectiveness of legislative interventions, such as those impacting recovery housing, on long-term treatment outcomes.
Community-Level Modeling: Understanding the dynamic relationship between neighborhood change and substance use trends.

Select Publications

Opioid Overdose & Spatial Analysis

Srinivasan, S. Shrestha, S., Harris, D., Lewis, O., Rock, P., Silwal, A., Pustz, J., Oh, S., Barboza-Salerno, G., & Stopka, T. (2024). Employment industry and opioid overdose risk: A pre- and post-COVID-19 comparison in Kentucky and Massachusetts 2018-2021. Spatial and Spatiotemporal Epidemiology, Vol. 52. https://doi.org/10.1016/j.sste.2024.100701
Harris, D., Shikar, S., Rock, P., Silwal, A., Barboza-Salerno, G., Lewis, O., Srinivasan, S., Stopka, T. (2024). The Impact of Buprenorphine Prescriber Data on Geospatial Access to Treatment in HEALing Communities Study Communities. Journal of Substance Use and Addiction Treatment, Vol. 171. https://doi.org/10.1016/j.josat.2025.209625
Barboza, G. E., & Angulski, K., Hines, L. & Brown, P. (2022). Variability in opioid-related drug overdoses, social distancing, and area-level deprivation during the COVID-19 pandemic: A Bayesian spatiotemporal analysis. Journal of Urban Health, Vol. 99, Issue 5. https://doi.org/10.1007/s11524-022-00675-x

Manuscripts Under Review
Lewis, O., Srinivasan, S., Shrestha, S., Harris, D., Barboza-Salerno, G., Rock, P., Walley, A., Samet, J., and Stopka, T. Enhancing access to methadone treatment in three HEALing Communities Study sites: A spatial and geostatistical study. Revise and Resubmit from Substance Use & Misuse.
Warren, K., Barboza-Salerno, G., Duhaney, S., Harrington, T., & Helms, E. Beyond the Neighborhood: Recovery House Resident Employment Prospects in Three Midwestern Cities. Submitted to the International Journal of Drug Policy.
Warren, K., Barboza-Salerno, G. & Bunger, A. Connecting the Community: Brief Social Work Interventions for Therapeutic Communities. Revise and Resubmit from the International Journal of Therapeutic Communities.

Current Funded Projects

Medicaid Expansion Project

Funding: Government Resources Center, $136,000 (2025-26)

Role: Principal Investigator

Description: Examining the impact of Medicaid expansion on substance use treatment access and overdose mortality across multiple states.

Related Resources

Education and Youth Safety

Fri, 25 Apr 2025 00:00:00 +0000

Our lab explores the profound connection between neighborhood safety and educational opportunity. A major focus of our work is the impact of gun violence on youth and how exposure to community violence affects student attendance, achievement, and mental health. We also examine disciplinary injustice in schools, particularly for Native American students and other marginalized groups, investigating how exclusionary discipline policies perpetuate the school-to-prison pipeline.

Core Questions We Address

How does exposure to neighborhood gun violence on routes to school impact academic performance and disciplinary outcomes?
What are the spatial patterns of violence near schools, and how do they respond to policy changes like Safe Passage programs?
How do structural inequities like exclusionary discipline and racial segregation interact with safe neighborhood conditions?
What role does geospatial analysis play in understanding school safety and identifying high-risk corridors for students?

Methods and Approaches

We apply rigorous spatial and statistical techniques to understand the educational context:

Acoustic Detection Sensors: Utilizing ShotSpotter data to capture precise, real-time incidents of gunfire that are often underreported in administrative crime data.
Spatial Accessibility Analysis: Modeling the density of gun violence along student travel routes to identify high-risk corridors.
Geospatial Machine Learning: Using AI to analyze the urban built environment and its relationship to school safety and student outcomes.
Policy Evaluation: Assessing the effectiveness of Safe Passages interventions and reforms aimed at dismantling the school-to-prison pipeline.

Select Publications

Gun Violence & School Safety

Barboza-Salerno, G., Duhaney, S., & Yang, H. (2024). Spatial accessibility to gun violence exposure on walkable routes to and from school. SSM Population Health, Vol. 28. https://doi.org/10.1016/j.ssmph.2024.101730
Barboza-Salerno, G., Meshelemiah, J.C.A (2023). Gun Violence on Walkable Routes to and from School: Recommendations for Policy and Practice. Journal of Urban Health, Vol. 100, Issue 6. https://doi.org/10.1007/s11524-023-00802-2

Featured: National Criminal Justice Association write-up on acoustic sensors detecting gunfire near schools

Manuscripts Under Review
Watson-Grace, A., Yang, H., & Barboza-Salerno, G. Harsher punishments, lesser infractions: Disciplinary injustice for Native American students in New Mexico public schools. Submitted to AERA Open.

Policy Briefs & Reports
Barboza-Salerno, G. (2025). Historical Discipline in Colorado School Districts. ISSUESLAB.

Upcoming Presentations
Hawaii International Conference on Education (HICE), January 2026: Geospatial Analysis of School Safety and Student Outcomes

Related Resources
View all publications
CourtListener Tutorial for Analyzing Caselaw
GitHub Repository

Maternal and Child Health

Fri, 25 Apr 2025 00:00:00 +0000

As part of the Investigating Spatial Structures in Urban Environments (ISSUES) lab, our work on Maternal and Child Health (MCH) is grounded in a transdisciplinary approach that links law, public health, and spatial data science. We investigate how structural inequities—such as housing instability, neighborhood disinvestment, and restricted reproductive access—shape health trajectories from pregnancy through childhood.

Core Questions We Address

How does spatial accessibility to abortion care intersect with maternal vulnerability to shape adverse birth outcomes in the post-Dobbs landscape?
How does intimate partner violence (IPV) during pregnancy influence maternal morbidity and infant health outcomes like low birth weight?
What are the spatial patterns of maternal stress and how do psychosocial risk factors cluster geographically?
How do reproductive health policies and abortion restrictions interact with social determinants to exacerbate health disparities?

Methods and Approaches

We utilize advanced quantitative methods to rigorously analyze these complex issues:

GIS & Transportation Network Modeling: Quantifying travel burden, service deserts, and spatial accessibility to abortion and reproductive healthcare using multimodal transportation networks.
Maternal Vulnerability Index (MVI): A novel, validated census-tract-level measure capturing multidimensional drivers of poor maternal health including healthcare access, reproductive resources, physical/mental health, environmental conditions, and socioeconomic disadvantage.
Latent Class Analysis: Identifying distinct risk profiles among expecting mothers using PRAMS (Pregnancy Risk Assessment Monitoring System) data.
Mediation Analysis: Examining pathways through which IPV affects birth outcomes via psychosocial stressors, health problems, and substance use.

Select Publications

Maternal Health & Birth Outcomes

Barboza-Salerno, G., Ramesh, B., Freisthler, B., Ye, Y., Kieninger, K., & Thurston, H. (2025). Escaping Stress: Relationship Between Time Spent In Outdoor Recreational Areas And Self-Reported Stress Among Parents During The Early COVID-19 Lockdown - A gEMA Study. Spatial and Spatiotemporal Epidemiology.
Zhang, Y., Johnson-Motoyama, M., Yoon, S., & Barboza-Salerno, G. (2025). From material hardship to harsh parenting: Testing the Family Stress Model in the dyadic family context. Public Health. https://doi.org/10.1016/j.puhe.2025.105917

Child Health & Well-Being

Barboza-Salerno, G., Liebhard, B., Duhaney, S. & Shockley-McCarthy, K. (2025). Pushing the boundary of child well-being: A spatial examination of child death rates in transition zones of extreme economic inequality and material hardship. PLOS ONE. https://doi.org/10.1371/journal.pone.0330449
Stanek, C., Barboza-Salerno, G. & Lizzappi, A. (2025). Child Domestic Servitude and Haitian Girls’ Mental Health. Journal of Interpersonal Violence.

Manuscripts Under Review
Booth, R., Nurre, M., Ramesh, B., Purie, B., Secor, J., Alhajjaj, Z., & Barboza-Salerno, G. Linking Intimate Partner Violence During Pregnancy to Infant Birthweight: Examining Mediating Pathways of Psychosocial Stressors, Health Problems, and Cigarette Use. Submitted to Public Health Nursing.
Barboza-Salerno, G., Ramesh, B., & Harrington, T. A Latent Class Analysis of Maternal Stress During Pregnancy: Associations with Adverse Maternal and Infant Birth Outcomes and Psychosocial Risk Factors in K-PRAMS Participants. Submitted to BMC Pregnancy and Childbirth.
Zhang, Y., Johnson-Motoyama, M., Yoon, S., Barboza-Salerno, G. Rethinking parental risk factors of harsh parenting: The perspective of psychometric networks. Revise and Resubmit from Child & Family Social Work.

Current Funded Projects

Spatial Accessibility to Abortion Services & Maternal Vulnerability

Funding: The Ohio State University, Institute for Population Research, $25,000 (2023-24)

Project: Spatial Accessibility of Reproductive Health Care Services Following Dobbs v. Jackson Women’s Health Organization

Description: This study examines whether spatial constraints on abortion access align with community-level maternal vulnerability and how these structural conditions are associated with adverse birth outcomes. Using the novel Maternal Vulnerability Index (MVI) and GIS-based transportation network modeling, we quantify travel burden, service deserts, and spatial accessibility to abortion care in the post-Dobbs landscape.

Presentation: IPR Seminar Series, March 10, 2026

Policy Briefs & Reports

Barboza-Salerno, G. (2025). Intimate Partner Violence & Brain Injury in U.S. Adults: Findings from the Medical Expenditure Panel Survey, 2021. ISSUESLAB.
Barboza-Salerno, G. (2025). Craniofacial Injuries in IPV-Related Physical Abuse. ISSUESLAB.

Related Resources
View all publications
PRAMS Data Resources
GitHub Repository

An examination of the effect of area-level characteristics on juvenile justice and child welfare referrals using multivariate Bayesian spatial modeling

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

Plain-Language Summary

This study examined the spatial overlap of child welfare and juvenile justice involvement using multivariate Bayesian spatial modeling. Results show a significant positive association between juvenile justice and child maltreatment referrals across neighborhoods. After adjusting for residential instability, immigrant concentration, and residential segregation, children in the least deprived 20% of neighborhoods were 95.2% and 55.5% less likely to be referred to child welfare or justice systems, respectively, compared to those in the most deprived 20%.

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.

Street-View Measures of Urban Greenness, Area-Level Deprivation, and the Risk of Firearm-Related Homicide: A Matched Case-Control Study

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

Plain-Language Summary

This study examined how pedestrian-level green space relates to firearm-related and natural deaths in Cook County, Illinois, using a matched case-control design. Various greenness measures, including street-view green imagery, tree canopy coverage, and green space along walkable streets, were inversely associated with firearm homicide risk. Protective effects of tree canopy and localized greenery were stronger in less deprived neighborhoods, while neighborhood racial composition influenced the relationship between park proximity and homicide risk. Overall, street-level green space is linked to lower homicide odds and may offer greater protection in vulnerable communities.

Structured Topic Modeling of Child Fatal Injury Narratives Using Machine Learning with County-Level Demographic Indicators to Strengthen Prevention Strategies

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

Plain-Language Summary

This study applies machine learning and natural language processing to analyze child fatality reports in Pennsylvania from 2016 to 2023. Unstructured text narratives from child death investigations were combined with census data to identify recurring themes and environmental factors surrounding child neglect and traumatic injuries.

Using structured topic modeling, the researchers identified 11 distinct circumstances of death, including substance misuse, sleep-related deaths, firearms, and supervisory neglect. The analysis showed that counties with higher poverty levels and larger non-White populations experienced a higher prevalence of firearm-related and severe traumatic injuries. The findings demonstrate how combining clinical narratives with machine learning can strengthen fatality surveillance and help construct more effective, targeted prevention programs.

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.

Utilizing Artificial Intelligence and Geospatial Analysis to Examine the Urban Built Environment, Social Vulnerability, and Child Neglect in Los Angeles

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

Plain-Language Summary

This study uses artificial intelligence and computer vision to analyze street-level imagery from Google Street View across 141 census block groups in Los Angeles, California. By measuring features of the built and natural environment, the research team constructed neighborhood indices for natural surveillance, lighting, land-use mix, and greenness.

The findings show that higher quality built environments, specifically better lighting, visibility (natural surveillance), and green space, are associated with lower rates of neighborhood child neglect, even when controlling for area-level deprivation and poverty. This research demonstrates how AI street-level audits can provide actionable data for city planners, social workers, and policymakers to design community-based prevention strategies that protect vulnerable children.

Working with Home Mortgage Disclosure Act Data

Thu, 19 Dec 2024 00:00:00 +0000

Introduction

Home Mortgage Act data is

Key Findings

Finding 1
Finding 2

Learn more about our work here.

Examining the Interplay Between the Natural and Socio-physical Environment Using Machine Learning Algorithms

Thu, 01 Aug 2024 00:00:00 +0000

Plain-Language Summary

This research presentation examines the interplay between the built, natural, and social environments in predicting the risk of child abuse and neglect (CAN). By utilizing machine learning algorithms, the study categorizes environmental determinants of child well-being into key dimensions including urban design, environmental contaminants, and neighborhood socioeconomic conditions.

The study demonstrates that characteristics of the physical and natural environment, such as neighborhood walkability, lighting, and greenness, interact with social vulnerabilities to shape family stress and safety. This work highlights the value of using computational models to translate physical streetscape characteristics into policy-relevant indicators for child maltreatment prevention.

A Multilevel Analysis of Individual and Community Factors Associated With Case Dispositions Following Child Maltreatment Investigations

Tue, 18 Jun 2024 00:00:00 +0000

Plain-Language Summary

This study investigates how individual case characteristics and county-level social vulnerabilities influence the decisions made by child protective services (CPS) investigators when recommending services for families. Analyzing over 12,000 cases across New Mexico, the study looks at factors such as child age, abuse type, prior history, and county-level deprivation (e.g., housing and transportation deficits).

The results show that county-level characteristics significantly influence whether a family receives support services. Even when families present with similar risk levels, those living in counties with higher structural vulnerabilities are subject to different service disposition outcomes. The findings highlight the need for standardized decision-making tools to ensure equitable treatment and service distribution across different geographic regions.

Tour

Mon, 24 Oct 2022 00:00:00 +0000

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.

Variability and stability in child maltreatment risk across time and space and its association with neighborhood social & housing vulnerability in New Mexico: A bayesian space-time model

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

Plain-Language Summary

This study examines the geographic and temporal patterns of child abuse and neglect referrals across New Mexico census tracts over a 9-year period. By utilizing Bayesian spatial models, the researchers identified areas with persistently high risk and examined how social and housing vulnerabilities affect maltreatment rates.

The results demonstrate that neighborhood deprivation, housing insecurity, and residential instability are strong predictors of substantiated child abuse and neglect. The study highlights the utility of using space-time models to pinpoint communities that require enhanced support services, enabling child welfare agencies to allocate resources more equitably and proactively.

Mon, 01 Jan 0001 00:00:00 +0000

ISSUES Lab Member Page Template

Please share this template with new or existing lab members to gather the information needed for their website profiles.

1. Information to Email to the Lab Director

Copy and paste the section below into an email to members:

Hi everyone,

We are updating the ISSUES Lab website member directory. Please reply to this email with the following details so I can create or update your profile page:

1. Full Name: (e.g., Jemima Grace)
2. Preferred Display Name: (e.g., Jemima Grace)
3. Title/Role in Lab: (e.g., PhD Student, Graduate Research Assistant)
4. OSU College/Department: (e.g., College of Social Work)
5. Short Bio (1-2 sentences): (A brief summary of your background or research motivation)
6. Research Interests (3 to 5 bullet points):
 - Interest 1
 - Interest 2
 - Interest 3
7. Education (List your degrees):
 - Degree (e.g., MSW, BS), Field/Subject, Institution, Year
8. Contact Info & Social Links (Only provide what you want public on the site):
 - Email:
 - LinkedIn Profile Link:
 - Google Scholar Link:
 - GitHub Profile Link:
 - X (Twitter) Profile Link:
9. Profile Photo: Please attach a high-quality headshot image.
 - Rename the file to: firstname-lastname.jpg (or .png)
 - Close-cropped square photos work best!

2. Markdown File Structure (For Website Upload)

When a member sends back their details, create a file named _index.md inside a new folder content/authors/firstname-lastname/ using this format:

---
name: "First Last"
title: "First Last"
weight: 30 # 1=PI, 10=Co-Director, 20-39=Graduate Students, 40+=Undergraduates
authors:
- firstname-lastname
superuser: false
role: "Role Name"
organizations:
- name: "The Ohio State University"
 url: "https://www.osu.edu/"
bio: "Short 1-2 sentence bio goes here."
interests:
- Interest 1
- Interest 2
- Interest 3
education:
 courses:
 - course: "Degree Name"
 institution: "Institution Name"
 year: 2026
social:
- icon: envelope
 icon_pack: fas
 link: "mailto:email@buckeyemail.osu.edu"
- icon: linkedin
 icon_pack: fab
 link: "https://www.linkedin.com/in/username"
# Email for Gravatar (optional)
email: "email@buckeyemail.osu.edu"
user_groups:
- Graduate Students  # Use "Faculty", "Graduate Students", or "Undergraduates"
---

Detailed biography paragraphs go here...

Save their headshot photo as avatar.jpg or avatar.png in the same folder.

Mon, 01 Jan 0001 00:00:00 +0000

Bibliography

IRT / Rasch

Schneider, Lennart; Strobl, Carolin; Zeileis, Achim; Debelak, Rudolf (2021). An R toolbox for score-based measurement invariance tests in IRT models. Behavior Research Methods 54(5), 2101-2113. doi:10.3758/s13428-021-01689-0

GIS

Mon, 01 Jan 0001 00:00:00 +0000

Lab Software and Data Science Resources

Below are links to download and install essential software for data analysis, visualization, and research management. All tools are free and open-source unless otherwise noted.

Core Tools

1. QGIS

Purpose: Open-source geographic information system (GIS) for spatial analysis and mapping.
Download: https://qgis.org/en/site/forusers/download.html

2. R and RStudio

Purpose: Statistical computing, data science, and reproducible research.
R Download: https://cran.r-project.org
RStudio Download: https://posit.co/download/rstudio-desktop/

3. Jamovi

Purpose: User-friendly statistics software built on R, ideal for applied data analysis.
Download: https://www.jamovi.org/download.html

4. Zotero

Purpose: Reference manager for organizing citations, generating bibliographies, and collaborative research.
Download: https://www.zotero.org/download/

Additional Recommended Resources

Git and GitHub

Purpose: Version control and collaborative coding/documentation.
Git Download: https://git-scm.com/downloads
GitHub (student benefits): https://education.github.com/pack

Anaconda / Miniconda

Purpose: Python and data science environment management.
Anaconda: https://www.anaconda.com/download
Miniconda (lightweight): https://docs.conda.io/en/latest/miniconda.html

VS Code

Purpose: General-purpose code editor with strong R, Python, and Markdown support.
Download: https://code.visualstudio.com/download

Quarto

Purpose: Next-generation tool for reproducible documents, presentations, and websites (supports R, Python, Julia).
Download: https://quarto.org/docs/get-started/

Notes for Students

Always install R before RStudio.
Use Zotero with the browser connector for easy reference saving.
If new to GitHub, check the GitHub Student Developer Pack for free private repos and tools.
For spatial analysis, QGIS is sufficient, but you may also want to try GeoDa (https://geodacenter.github.io/) for exploratory spatial data analysis.

Mon, 01 Jan 0001 00:00:00 +0000

ISSUES Lab Undergraduate Research Experience – Spring 2026

Lab Directors: Drs. Gia Elise Barboza-Salerno & Karla Shockley
Meeting Time: Wednesdays, 1:00 – 2:00 PM (Zoom)
Semester Dates: January 13, 2026 – May 2, 2026
Evaluation: Pass/Fail (based on participation, assignments, and project engagement)

Lab Description

The ISSUES Lab (Investigating Spatial Structures in Urban Environments) translates environmental exposure into law, policy, and practice. We use geospatial data science, machine learning, and advanced statistical methods to study environmental exposures, trauma, and violence-related harms. Our research bridges social work, public health, law, and statistics, and emphasizes justice-centered approaches that inform prevention and policy.

The lab is structured around a three-tiered mentoring model. Faculty provide direct mentorship and professional development to graduate students and senior researchers (Tier 1); graduate students and advanced lab members mentor undergraduates through project guidance, methods training, and peer collaboration (Tier 2); and undergraduates extend mentorship to newer students or interns, reinforcing their own skills while cultivating leadership (Tier 3). This cascade structure ensures that every member both receives and provides mentorship, building a supportive pipeline for scholarly growth and community impact.

Undergraduate research assistants complete a structured 16-week curriculum that begins with a competency assessment and personal profile, transitions to project involvement, and culminates in research products and presentations. Students also engage with professional meetings where the lab is presenting (e.g., APHA, WSDS, SSWR, HICE) to connect their work to the broader research community.

Requirements for Pass

Attend weekly Wednesday meetings.
Complete weekly deliverables.
Participate consistently in lab project work.
Deliver a final presentation and written product.

Weekly Schedule and Deliverables

Week 1 – Jan 13

Topic: Competency Quiz, Lab Introduction, & Personal Profile
Deliverables:
- Complete baseline quiz (spatial, law/policy, statistics)
- 1-page reflection on quiz results and goals for the semester
- Fill out personal profile template for the lab website

Week 2 – Jan 20

Topic: Lab Orientation & Project Familiarization
Deliverable: One-page overview of the project you are interested in joining, including the research questions it addresses and how it connects to law, policy, or public health.

Week 3 – Jan 27

Topic: Project Selection
Deliverable: 1-page project interest statement; confirm project assignment

Week 4 – Feb 3

Topic: Research Tools Setup
Deliverable: Screenshot of Zotero, RStudio, GitHub, QGIS installs; run lab R script

Week 5 – Feb 10

Topic: Literature Review Skills + Policy Engagement
Deliverables:
- Annotated bibliography of 3 articles in Zotero + PDF copy
- Brief reflection (1–2 paragraphs) connecting your project interests to the upcoming policy briefing
Special Event: Policy briefing with Rep. Dingell’s Office on domestic violence and changes to Medicaid/social safety net

Week 6 – Feb 17

Topic: Data Familiarization
Deliverable: Write-up of a dataset of interest (1–2 pages) describing the source, variables, and potential applications for research.

Week 7 – Feb 24

Topic: Coding and Reproducibility + Medicaid & Opioid Policy Project
Deliverables:
- Reproducible R script with comments
- Policy brief/memo on how changes in Medicaid policy may impact opioid use and treatment access, drawing on the CareLink Georgia project context

Week 8 – Mar 3

Topic: Research Writing Skills
Deliverable: Draft 1–2 page project summary (background, methods, findings)

Week 9 – Mar 10

Topic: Policy & Practice Connections
Deliverable: 1-page reflection linking project to law, policy, or practice
Special Event: IPR Seminar Series - Dr. Barboza-Salerno presents on Abortion Accessibility & Maternal Health (Mar 10, 12:30-1:30 PM)

Week 10 – Mar 17

Topic: Spring Break - No Meeting

Week 11 – Mar 24

Topic: Data Visualization
Deliverable: One figure, map, or table with caption

Week 12 – Mar 31

Topic: Mid-Semester Lightning Talks
Deliverables:
- 5-minute oral presentation (2–3 slides)

Week 13 – Apr 7

Topic: Advanced Skill Development
Deliverable: Evidence of advanced task (e.g., advanced map, regression, coded law data)
Special Event: Ohio Recovery Housing Conference (Apr 15, 1:00-2:15 PM)

Week 14 – Apr 14

Topic: Draft Final Product
Deliverable: Draft poster, policy brief, or paper section (review with project lead)
Special Event: SDSS 2026 - AI & LLM Applications (Apr 29, 3:45-5:15 PM CT, Milwaukee, WI)

Week 15 – Apr 21

Topic: Mentorship & Peer Feedback
Deliverable: 1-page feedback on peer draft

Week 16 – Apr 28

Topic: Final Presentations
Deliverable: 10-minute final presentation (poster, slides, or brief)

Week 17 – May 2

Topic: Reflection & Wrap-Up
Deliverables:
- Final written product (poster, policy brief, or manuscript section)
- 1–2 page reflective essay on skills learned and future goals

At-a-Glance Weekly Checklist

Week	Date	Topic	Deliverable(s)
1	Jan 13	Competency Quiz & Profile	Baseline quiz + 1-page reflection + personal profile template
2	Jan 20	Lab Orientation & Project Familiarization	One-page overview of the project you are interested in joining
3	Jan 27	Project Selection	Project interest statement; confirm project assignment
4	Feb 3	Research Tools Setup	Proof of installs; run lab R script
5	Feb 10	Literature Review + Policy Engagement	Annotated bibliography (3 articles); reflection on project interests Policy briefing with Rep. Dingell’s Office (domestic violence & Medicaid/social safety net)
6	Feb 17	Data Familiarization	Write-up of a dataset of interest (1–2 pages)
7	Feb 24	Coding & Reproducibility + Medicaid & Opioid Policy	Reproducible R script with comments; policy brief/memo on Medicaid changes and opioid treatment (CareLink Georgia)
8	Mar 3	Research Writing	Draft 1–2 page project summary
9	Mar 10	Policy & Practice Links	1-page reflection IPR Seminar Series - Dr. Barboza-Salerno (Mar 10, 12:30-1:30 PM)
10	Mar 17	Spring Break	No meeting
11	Mar 24	Data Visualization	One figure/map/table with caption
12	Mar 31	Mid-Semester Lightning Talks	5-min talk with slides
13	Apr 7	Advanced Skills	Evidence of advanced task Ohio Recovery Housing Conference (Apr 15, 1:00-2:15 PM)
14	Apr 14	Draft Final Product	Draft poster, policy brief, or paper section SDSS 2026 - AI & LLM Applications (Apr 29, 3:45-5:15 PM CT, Milwaukee, WI)
15	Apr 21	Peer Feedback & Mentorship	1-page feedback on peer draft
16	Apr 28	Final Presentations	10-min presentation
17	May 2	Reflection & Wrap-Up	Final written product + 1–2 page reflection essay

Professional Engagements

IPR Seminar Series: March 10, 2026 (Columbus, OH)
Ohio Recovery Housing Conference: April 15, 2026 (Ohio)
SDSS (Symposium on Data Science & Statistics): April 28 - May 1, 2026 (Milwaukee, WI)

Current Projects

Abortion/Reproductive Health:
This project examines the impacts of abortion restrictions and reproductive health policies on maternal and child well-being. We use spatial and policy analysis to study how access to reproductive care varies across neighborhoods and populations, with a focus on structural inequities and social safety net protections.
Treatment/Prevention for Substance Use Disorder:
In collaboration with community partners such as CareLink Georgia, this project evaluates how Medicaid and other policy changes affect access to treatment and prevention for opioid and other substance use disorders. Analyses integrate health service data, policy environments, and neighborhood-level conditions to identify structural barriers and opportunities for equitable care.
Recovery Housing:
This study investigates the role of recovery housing in supporting individuals with substance use disorder, particularly in underserved and high-risk communities. We assess housing availability, policy frameworks, and long-term outcomes to inform prevention and treatment policy.
Violence-Related Harm:
This project applies spatial epidemiology, machine learning, and Bayesian modeling to study the distribution and determinants of firearm injury, child maltreatment, intimate partner violence, and suicide risk. Our work emphasizes structural inequities, environmental exposures, and legal contexts as drivers of violence-related harm across neighborhoods.
School Discipline:
We study patterns of school exclusion, including suspensions, expulsions, and referrals, and their intersections with race, disability, and neighborhood disadvantage. Using administrative data and spatial analysis, this project evaluates how discipline practices shape student outcomes and contribute to broader inequities.
Housing and Houselessness:
This project investigates how housing instability and houselessness are shaped by lending practices, neighborhood disinvestment, and policy responses. By combining mortgage lending data, census indicators, and policy analysis, we aim to identify structural pathways that contribute to housing precarity and its public health consequences.

A Community-Based Approach to the Opioid Crisis

Mon, 01 Jan 0001 00:00:00 +0000

In progress

Real Help–Real Hope: Where Do We Go From Here?

A Community-Based Approach to the Opioid Crisis

Summary:
This keynote presentation, delivered at the Opioid Treatment Providers of Georgia conference, highlights the need for a comprehensive, public health–centered response to the opioid crisis. It emphasizes how social, environmental, and structural factors—such as poverty, racial inequities, and limited healthcare access—shape opioid-related harms, and critiques punitive policies in favor of community-driven, evidence-based solutions.

Key Details:


Short description:	This presentation critiques the criminalization-focused “War on Drugs” and outlines a public health framework for addressing the opioid crisis, emphasizing prevention, harm reduction, structural analysis, and equity-focused solutions for Georgia and beyond.
Lead developer:	Dr. Gia Elise Barboza-Salerno
Further reading:	Download the presentation slides for full details.
Main data source(s):	Opioid-related mortality, overdose surveillance, Naloxone distribution, treatment access data, regulatory frameworks, and structural vulnerability indicators for Georgia and the United States.
Coverage:	Georgia, with comparisons to national opioid trends.
Citation:	Barboza-Salerno, G.E. (2024). Real Help–Real Hope: Where Do We Go From Here? A Community-Based Approach to the Opioid Crisis. Presented at the Opioid Treatment Providers of Georgia Annual Conference, November 11, 2024.

Check out this accessibility and spatial equity tutorial for more details.

Data and code

Mon, 01 Jan 0001 00:00:00 +0000

We publish most of our code and data openly through our Github account.

Spatial Accessibility


Short description:	Spatial Accessibility to Abortion Facilities in Ohio, Kentucky, and West Virginia.
Lead developer:	Barboza
Download:	Github repo

Check out our tutorial on spatial accessibility for more details.

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

Mon, 01 Jan 0001 00:00:00 +0000

The land cover paradox

This paper explores associations between blue- and green- space and suicide mortality in Cook County, Illinois


Short description:	The land cover paradox: Characteristics of blue- and green spaces within and beyond high-risk suicide clusters - ScienceDirect
Lead developer:	Barboza-Salerno
Further reading:	h
Download:	Github repo
Main data source(s):	MLCD land cover, tree- and park- equity
Coverage:	Cook County, IL
Citation:	@article{BARBOZA2025101820, title = {The Land Cover Paradox: {{Characteristics}} of Blue- and Green Spaces within and beyond High-Risk Suicide Clusters}, author = {Barboza, Gia E. and {Watson-Grace}, Amy and {Shockley-McCarthy}, Karla and Harrington, Taylor and Warren, Keith and Steelsmith, Danielle}, year = {2025}, journal = {SSM - Population Health}, pages = {101820}, issn = {2352-8273}, doi = {10.1016/j.ssmph.2025.101820}}

Gun violence remains a critical public health crisis, particularly in urban centers like Chicago, where firearm-related injuries are among the highest in the nation. These injuries are not randomly distributed; they are spatially concentrated in neighborhoods shaped by structural inequality, including racial segregation, economic disinvestment, and environmental neglect. While random forest models have shown promise in predicting firearm violence and identifying neighborhood-level risk factors, conventional approaches often ignore spatial dependence, leading to biased estimates, inflated variable importance, and reduced geographic generalizability. This study applies spatially explicit machine learning to model non-fatal shootings across Chicago census tracts from 2020 to 2024. A curated set of predictors was compiled, including measures of social and environmental vulnerability, the built environment, and housing inequality. Three research questions guided the analysis: (1) Does incorporating spatial structure via Moran’s Eigenvector Maps (MEMs) in spatial random forests and spatially structured random effects in a Bayesian spatial model improve predictive accuracy compared to non-spatial models? (2) Which neighborhoods exhibit elevated firearm violence risk due to unmeasured spatial processes, as captured by spatial random effects? (3) Does prediction error vary across geographic space, revealing limits to model transferability? Findings suggest that the spatial random forest model outperformed the non-spatial model (pseudo R² = 0.98 vs. test R² = 0.50). Spatial cross-validation revealed geographic variation in accuracy (median nRMSE = 0.76), while spatial random effects from a Bayesian Poisson model identified unexplained latent risk. The enhanced predictive accuracy reveals hidden geographic risk patterns, suggesting that social, structural, and environmental factors can efficiently inform targeted violence prevention strategies. Check out this Access our tutorial on na here for more details.

Examining the Interplay Between the Natural and Socio-physical Environment Using Machine Learning Algorithms

Mon, 01 Jan 0001 00:00:00 +0000

Our work on the socio-physical and natural enviornment was presented at the ISPCAN 2024 international conference.

No previous research has comprehensively examined how different aspects of the sociophysical and built environments increase child abuse and neglect (CAN) risk. Characteristics of the built and natural environment are underexplored in research on CAN. Five categories of environmental determinants of child well-being include: (i) urban design, (ii) contaminants, (iii) the parenting environment, (iv) socio-economic conditions, and (v) climate change1

Download the presentation here .

Integrating AI, Deep Learning, and Spatial Analytics to Investigate Environmental Correlates of Child Neglect

Mon, 01 Jan 0001 00:00:00 +0000

In progress

Integrating AI, Deep Learning, and Spatial Analytics to Investigate Environmental Correlates of Child Neglect

Summary:
The presentation discusses how to use computer vision to create indices of the built and perceptual environment and examine their association with child neglect.

Key Details:


Short description:	The presentation discusses how to use computer vision to create indices of the built and perceptual environment and examine their association with child neglect.
Lead developer:	Dr. Gia Elise Barboza-Salerno
Further reading:	Download the [presentation slides](/Lightning talk- nationwide.pdf) for full details.
Main data source(s):	ZenSVI, Mapillary, Area Deprivation Index, Tree Equity Scores.
Coverage:	Los Angeles, California.
Citation:	Barboza-Salerno, G.E. (2025). Integrating AI, Deep Learning, and Spatial Analytics to Investigate Environmental Correlates of Child Neglect. Presented at the 3rd Annual Family Violence Research-Practice Networking Event, Aug 13, 2025.

Check out the code using ZenSVI on our github page.

Pregnancy Justice

Mon, 01 Jan 0001 00:00:00 +0000

We have work here

In progress

Travel Times Post-Dobbs


Short description:	Spatial Accessibility to Abortion Facilities in Ohio, Kentucky, and West Virginia.
Lead developer:	barboza-salerno

Check out this Access our tutorial on spatial accessibility here for more details.

Pregnancy Justice

Mon, 01 Jan 0001 00:00:00 +0000

In progress

Travel Times Post-Dobbs


Short description:	Spatial Accessibility to Abortion Facilities in Ohio, Kentucky, and West Virginia.
Lead developer:	Barboza
Further reading:	Please read the paper published in the ISPRS Journal of Photogrammetry and Remote Sensing
Download:	Github repo

Check out our tutorial on spatial accessibility here for more details.

Reproductive Coercive Control

Mon, 01 Jan 0001 00:00:00 +0000

We have work here

Read my op-ed in Ms. Magazine here

Resources

Mon, 01 Jan 0001 00:00:00 +0000

R script is here: Download the R script
Mapping file is here: Download the geojson file for mapping

Resources for Learning

Mon, 01 Jan 0001 00:00:00 +0000

Everyone should have a LinkedIn profile and connect with other ISSUES Lab members.
LinkedIn is a way to connect with people outside of the research/academic space (policy makers, practitioners, advocates, etc.) and to disseminate research.
Also a great way to recruit participants for primary research.

LinkedIn Learning

OSU offers free access to all OSU employees and students.
LinkedIn Learning at OSU

ResearchGate

Everyone should have a ResearchGate profile and follow other ISSUES Lab members.
ResearchGate is a means of disseminating research and connecting with other researchers.
ResearchGate

Google Scholar

Everyone should have a Google Scholar profile and add ISSUES Lab members as co-authors.
Google Scholar is the most accessible database of researchers and their publications — it is widely used by the general public.
Be sure to check your profile to make sure publications and conference presentations are included and accurate.

R Resources

HealthyR: Free courses and resources for health data science with R.
HarvardX: Data Science: R Basics | edX

Statistics

Introductory guide: Statistics for Non-Statisticians (email Karla for the PDF).

Learn Many Things

edX | Online Courses, Certificates & Degrees from Leading Institutions

Resources for Learning

Mon, 01 Jan 0001 00:00:00 +0000

Everyone should have a LinkedIn profile and connect with other ISSUES Lab members.
LinkedIn is a way to connect with people outside of the research/academic space (policy makers, practitioners, advocates, etc.) and to disseminate research.
Also a great way to recruit participants for primary research.

LinkedIn Learning

OSU offers free access to all OSU employees and students.
LinkedIn Learning at OSU

ResearchGate

Everyone should have a ResearchGate profile and follow other ISSUES Lab members.
ResearchGate is a means of disseminating research and connecting with other researchers.
ResearchGate

Google Scholar

Everyone should have a Google Scholar profile and add ISSUES Lab members as co-authors.
Google Scholar is the most accessible database of researchers and their publications — it is widely used by the general public.
Be sure to check your profile to make sure publications and conference presentations are included and accurate.

R Resources

HealthyR: Free courses and resources for health data science with R.
HarvardX: Data Science: R Basics | edX

Statistics

Introductory guide: Statistics for Non-Statisticians (email Karla for the PDF).

Learn Many Things

edX | Online Courses, Certificates & Degrees from Leading Institutions

Social Geography of Child Adversity

Mon, 01 Jan 0001 00:00:00 +0000

We are collaborating with the public defender’s office to integrate geospatial and socio-legal research into legal defense strategies. Our work aims to contextualize clients’ life histories using evidence-based indicators of childhood adversity, systemic deprivation, and community-level disinvestment.

In progress

Geographies of Risk and Resilience


Short description:	Mapping the spatial distribution of child adversity to inform contextual defense.
Lead developer:	Barboza
Partners:	Office of the Public Defender, Legal Aid, Community Organizations
Data sources:	ACS, Opportunity Atlas, Child Opportunity Index, CRDT fatality reviews
Methods:	Bayesian spatial modeling, risk typologies, qualitative interviews
Geographic focus:	Urban and rural counties across the Midwest
Objectives:	Illuminate how systemic inequality shapes pathways to legal system involvement

We are also exploring how these data can be presented during mitigation to underscore the role of systemic harms in shaping client trajectories.

Conference Presentation

I will be speaking at the upcoming Death Penalty Defense seminar, hosted by the Ohio State Bar Association, taking place as part of their 2025 CLE Live Seminars.

Enhance your expertise in capital defense at our annual program, designed exclusively for criminal defense attorneys. Over two dynamic days, engage in in-depth discussions and gain valuable insights into key considerations when handling capital cases. Don’t miss this opportunity to elevate your practice with expert-led presentations and interactive sessions.

This seminar offers:

12.0 hours of training on the defense of capital cases, fulfilling requirements set by the Commission on Appointment of Counsel in Capital Cases
6.0 hours of training on the appeal of capital cases (for those who attend both days)

My presentation will focus on integrating social science into criminal legal defense—specifically, how geospatial models of child adversity can be mobilized to inform capital mitigation, challenge systemic bias, and support holistic defense strategies.

For related work, visit our Pregnancy Justice page or explore our Spatial Accessibility tutorial.

Spatial Access Analysis

Mon, 01 Jan 0001 00:00:00 +0000

Utilizing AI-Driven Technology and Geospatial Analysis to Explore Landscape Morphology, Social Vulnerability, and Child Neglect in Los Angeles

Mon, 01 Jan 0001 00:00:00 +0000

Our work on Utilizing AI-Driven Technology and Geospatial Analysis to Explore Landscape Morphology, Social Vulnerability, and Child Neglect in Los Angeles will be presented at ISUF 2025 in Torino, Italy.

No previous research has comprehensively examined how different aspects of the sociophysical and built environments increase child abuse and neglect (CAN) risk. Characteristics of the built and natural environment are underexplored in research on CAN. Five categories of environmental determinants of child well-being include: (i) urban design, (ii) contaminants, (iii) the parenting environment, (iv) socio-economic conditions, and (v) climate change.