NIJ Funded Research on Firearms 

Violence in Urban Cities: Advancing 

Scientific Evidence to Inform 

November 8, 2021 | 1:00 PM ET 

The webinar will begin shortly 
 

 

 

Important Notification 

The studies presented here were supported by Award Numbers 

2016-IJ-CX-0008, 2017-IJ-CX-002, and 2017-IJ-CX-0020 from 

the National Institute of Justice of the U.S. Department of Justice. 

The opinions, findings, and conclusions or recommendations 

expressed here are those of the authors and do not necessarily 

reflect the positions or policies of the Department of Justice. 
 

 

‘GOTTA MAKE 

YOUR OWN 

HEAVEN’: 

GUNS, SAFETY, AND 

THE EDGE OF 

ADULTHOOD 

IN NEW YORK CITY 

RACHEL SWANER, PHD, 

MPA 

RESEARCH DIRECTOR 

NOVEMBER 8, 2021 
 

 

STUDY OVERVIEW 

 Goal to understand why young people in NYC are 

carrying guns 

 Interviewed 330 people: 

 16-24 years old 

 Carried/owned a gun (87%) 

 Shot or shot at (81%) 

 Seed recruitment 

 Cure Violence programs 

 Outdoor NYCHA “hot spots” 

 Indoor gang spaces 
 

 

 

PARTICIPATORY METHODS 

AND TRUST BUILDING 

 Field researchers that reflected study population 

 Consistently present in places important to youth 

 Respect for gang politics/engagement of gang 

leaders 

 Long hours in the community 

 Different approaches across and within 

neighborhoods 
 

 

 

WHO DID WE TALK TO? 

 330 youth 

 Gender: 79% men, 21% women 

 Race/Ethnicity: 71% Black, 14% Latinx, 9% 

Multiracial 

 Public Housing Resident: 78% 

 Have Children: 37% 

 Currently in School: 40% 

 Criminal Justice System Involvement: 

 91% stopped by police in last 2 years (average 

of 10 times) 

 88% ever arrested (average of 7 times) 

 63% every incarcerated 
 

 

 

 

Violence was a near universal experience 

67% 

70% 

81% 

8 

CONTEXTUAL FACTORS 



Shot/Shot At 

Attacked,... 

 Alternative economy as survival 

At least ten people probably got their OSHA card, right. I bet if we 

went across the street, at this new site, probably couldn’t get us a job. 

[...] Why can’t we do it? Why can’t we work? 
 

 

 

 

 

 

 

 

GUN CARRYING TO INCREASE 

SAFETY 

 Generalized fear: pervasive sense of neighborhood distrust 

and a feeling that they could be victimized at any time 

I’m a paranoid person ever since I got stabbed. I come out of work 

and nobody’s after me [...] I still be lookin’ behind me. I don’t trust 

nobody. 

 Localized fear: protection from retaliation 

I got beef. I got shot and I been shot at. Niggas I done robbed, niggas 

I done took niggas’ chain. I done embarrassed grown-ass men. So I 

need one. I’m a female. Niggas want to shoot at you. 

 Fear of the police 

They’re picking off people for no reason. [...] So Imma carry mine 

because I have to protect myself from you now. It’s not a regular 

person I gotta worry about. 
 

 

 

 

 

 

CAUTION AROUND 

GUN USE 

Many talked about exercising restraint 

I’m not gonna just pull it out, tryna shoot you for no reason. 

Our number one rule was just: Don’t bust unless you need to 

bust. 

 67% reported using marijuana daily, and talked about its 

calming effect 

I’ve been in positions where I’m hot and wanna go pick up my 

gun [...] and then I smoke and I’m thinking completely different. 

Like, “Yo, I’m wilding.” Like, “That shit wasn’t even worth it.” 
 

 

EXTREME DISTRUST OF POLICE 

100% 

50% 35% 19% 15% 

0% Try to protect public from Want to understand Have good reasons for 

violent crime community needs arrests 

They think we out here shooting people for fun. 

They’ll gun you down for anything. 

At this point, being Black, it’s like you expect to be 

arrested or for them to bother you. 
 

 

 

 

 

 

 

 

 

 

 

KEY TAKEAWAYS 

 Youth mostly carrying to increase feelings of safety 

 Unprotected by the police, punished for protecting 

themselves 

I worry about getting in trouble with the police because I know it’s 

against the law to have any type of weapon without a license, but at 

the same time, I just feel like I need to carry it to make sure generally 

I’m safe. 

 Institutional abandonment and lack of any pathway to methods of 

survival (economic, physical, emotional) 

It's like we're already in the trap, with the system and with the 

law. Everybody's going to jail, there's no jobs, minimum amount 

of services provided for us, health-wise. [...] So you can't go 

nowhere, you're just around. You do violent things or robberies 

when you got no other way out. 
RECOMMENDATIONS 

 Bring services to spaces important to youth 

 Hire more credible messengers 

 Invest in community safety strategies outside 

of the police 

 Create jobs for youth and those with criminal 

records 

 Want to access these youth? Engage gang 

leadership 
 

FOR MORE INFORMATION 

Email: rswaner@nycourts.gov 

Read/listen: 

https://www.courtinnovation.org/publications/gunviolence-NYC 

Other topics covered: gangs and social networks, 

criminal justice involvement, access to guns, 

neighborhood perceptions 
 

 

Effects of Building Demolitions on Firearm Violence 

in Detroit, Michigan 

Rose Kagawa, PhD, MPH 

University of California, Davis 


 

Life Expectancy at Birth 

Wayne County 

Designed by B Tejada-Vera, B Bastian, E Arias, L Escobedo and B Salant: National Center for Health Statistics. 
VPRP 

VPRP 

-

-

 

 

Randomized Controlled Trial, Lot Clean -Up 

Charles C. Branas et al. PNAS 2018;115:12:2946 2951 

©2018 by National Academy of Sciences 

Moyer R, MacDonald JM, Ridgeway G, Branas CC. Effect of Remediating Blighted Vacant Land on Shootings: A Citywide Cluster Randomized Trial. Am J Public 

Health. 2019;109(1):140 144. doi:10.2105/AJPH.2018.304752 

What was the effect of building demolition in 

Detroit, Michigan in 2017 on the prevalence of 

violent and firearm-related crime? 

VPRP 
Demolitions in Detroit 

VPRP http://www.detroitmi.gov/ 
 

Outcomes 

1. Homicide, rape, robbery, and 

aggravated assaults 

2. Homicide, robbery, and 

aggravated assaults where a 

firearm was used 

• Unit of analysis = block 
Actual Crime Prevalence in Detroit, 2017 

Q1 Q2 Q3 Q4 

VPRP 
Actual Crime Prevalence in Detroit, 2017 

Q1 Q2 Q3 Q4 

VPRP 
Actual Crime Prevalence in Detroit, 2017 

Q1 Q2 Q3 Q4 

VPRP 
Counterfactual Crime Prevalence in Detroit, 2017 

? 

? 

? 

Q1 Q2 Q3 Q4 

VPRP 
Targeted Minimum Loss-based Estimation (TMLE) 

VPRP 
 

 

 

High Dimensional Data + Super Learner 

• 100+ variables 

• Home type and quality 

• Sales values 

• Foreclosure rates 

• Vacancy 

• Owner/renter occupancy 

• Demographics 

• Socioeconomic indicators 

• ... 

• Demolitions 2009-2017 

• Spatial lags Polley, Eric. “Chapter 3: Superlearning.” Targeted Learning: Causal 

Inference for Observational and Experimental Data, by M. J. van der. 

Laan and Sherri Rose, Springer, 2011. 
The Sample 

Gold and Blue = Higher SES 

Pink and Green = Lower SES 

VPRP 
Demolition in the Study Period 

VPRP 
Demolition in the Study Period 

VPRP 
Firearm Violence in the Study Period 

VPRP 
 

Estimated Effects not Distinguishable from Zero* 

Total Violent Crimes 

Difference in Proportions 

(95% Confidence Intervals) 

Total Violent Crimes w/ Firearms 

Difference in Proportions 

(95% Confidence Intervals) 

Q2 

-0.0003 

(-0.005, 0.005) 

-0.0005 

(-0.003, 0.002) 

Q3 

0.0007 

(-0.005, 0.006) 

0.0004 

(-0.004, 0.003) 

Q4 

-0.0009 

(-0.006, 0.004) 

-0.0006 

(-0.003, 0.002) 

*Preliminary results, subject to change 

VPRP 
 

 

 

Conclusions and Limitations 

• Models do not allow effect to travel outside of block 

• Limited data availability 

• 2017 demolitions unique 

• Higher ”dose” (Jay et. al. 2019) 

• >5 demolitions in Detroit associated with reductions in 

firearm assaults 

• Next steps 

• Drug crimes, lower level crimes (arson, theft from a 

building, vandalism, prostitution, disorderly conduct, 

drunkenness, and trespassing) 

• Cleveland 

Image Source: Kahari King on Unsplash and Google Street View 
Thanks to funding from the National Institute of Justice 

Award No. 2017-IJ-CX-002 

Research Team 

Benjamin Calnin Francisca Richter 

Magdalena Cerdá Kara Rudolph 

Tsui Chan Michael Schramm 

Claudia Coulton Colette Smirniotis 

Alaina de Biasi Garen Wintemute 

Michael Henderson 
 

 

Latent 

environmental 

determinants of 

firearm violence: An 

exploratory factor 

analysis 

Max Griswold, Priscillia Hunt, 

Rosanna Smart, Sean McKenna 

Nov 8th, 2021 
 

 

 

 

 

Background and Motivation 

• Lots of theories on the relationship

between built environment & 

incidence of crime. 

• Previous studies typically focused on a

single built environment feature’s

effect on crime. 

• However, we might expect clusters of

built environment features within a 

given neighborhood. 

• Knowing whether these types of built 

environment features cluster together

and are associated with firearm 

violence can help researchers design

better empirical models & urban 

planners design cities. 
 

 

Research Questions 

1. Are there latent built environment factors associated with 

firearm violence (i.e., zoning, neighborhood characteristics, etc.)? 

2. Do latent built environment factors correlate with firearm 

violence separately from built environment features? 

3. Generally: How suitable is current open-source administrative 

data for NIJ-funded academic research on the built environment? 
Related Research 
 

 

 

 

 

 

Methods 

1. Extract publicly-available data and clean datasets. 

2. For each set of built environment and socio-economic status 

variables: 

1. Determine inclusion & number of factor variables. 

2. Conduct exploratory factor analysis. 

3. Then, use regression models to estimate association between built

environment and firearm crimes, adjusting for neighborhood &

socio-economic variables. 

4. Rank models on out-of-sample goodness-of-fit 

5. For best performing models: Assess relationship between built

environment variables & firearm crime 
 

 

 

 

Data 

All publicly available, at the tract-level: 

• Reported crimes with a firearm (homicide, robbery, aggravated 

assault) 

• Extracted from cities’ police department’s open databases 

on incident reports. 

• Years of coverage: 2015 to 2018 

• N = (2k, 4.5, 20k, 30k) for Pittsburgh, New Orleans, Detroit, 

Los Angeles, respectively. 

• Built environment features 

• Office of Assessor – Property assessment datasets 

• N = 100k – 1 million observations property types 

• Years of coverage: 2015-2018, 

• Socioeconomic status and population demographics. 

• American Community Survey 5-Year series, U.S. Census 

Bureau 

• Averaged over 2013-2018 
Firearm crimes, 2015-2018 

New Orleans Pittsburgh Detroit Los Angeles 
Identified factors 

typically have 

intuitive 

interpretations 

Sport Field 

Tennis Courts 

Public Pool 

Public Park 

Playground 
Identified factors 

typically have 

intuitive 

interpretations 

Sport Field 

Tennis Courts 

Public Pool 

Public Park 

Playground 
 

UPS 

Universities/Colleges 

Transit Station 

Tobacco Retailer 

State Government Buildings 

Federal Government Building 

FedEx 

Fast Food Restaurants 

Credit Unions 

Convenience Stores 

Child Care Providers 

Banks 

Some factors are 

more mysterious 

without additional 

investigation. 
UPS 

Universities/Colleges 

Transit Station 

Tobacco Retailer 

State Government Buildings 

Federal Government Building 

FedEx 

Fast Food Restaurants 

Credit Unions 

Convenience Stores 

Child Care Providers 

Banks 

But additional analysis revealed their 

interpretation. 

Golden Triangle) 

Score for Built Environment Factor 

Pittsburg 

downtown 

(known as 
Factor loadings 

In Los Angeles 
 BE variables 

& SES 

Predicted 

count of 

firearm 

crimes 

Number of buildings BE variables BE factors 

Model: BE factors & 

in tract BE variables & BE variables & BE factors & factors SES factors 

SES factors SES factors SES variables 
Observed Predicted 

Goodness-of-Fit 

R2 = 0.77 

RMSE = 15.8 
 

 

 

 

 

Limitations 

• Like all observational studies, 

results are correlative unless 

we captured all relevant 

adjusters (which is true of 

quasi-experimental approaches 

as well). 

• Constructed factors have been 

robust to alternative methods 

and optimization routines but 

can be difficult to interpret 

concretely. 

• Underlying datasets have 

significant limitations 

themselves. 
 

 

 

 

Conclusions 

• Administrative data held 

useful information but 

could be improved for 

research purposes. 

• Built environment factors 

(e.g. zoning) greatly 

improve predictions of 

firearm crimes at the 

census tract-level. 

• It is unlikely previous 

studies uncovered robust 

causal estimates of the 

relationship between built 

environment variables & 

crime. 
 

 

 

 

Quantifying Attractive and 

Repellent Influences of 

Firearms Violence in the 

Built Environment 

Luke Muggy, Ph.D. 

Co-authors: Tyna Eloundou Nekoul, Max Griswold, Sean McKenna, Rosanna Smart, and Priscillia Hunt 

Funding acknowledgement: National Institute of Justice 

NIJ Funded Research on Firearms Violence in Urban Cities: Advancing Scientific Evidence to Inform Practice 

November 8th, 2021 
Agenda 

• Background and Motivation 

• Method 

• Data 

• Results 

• Discussion 
 

 

 

 

 

 

 

 

 

Firearms violence is a serious problem in the U.S. 

• In 2018, there were 57,000+ incidents of reported firearm violence1 

• Resulted in over 28,000 injuries1 and 14,789 willful or accidental deaths2 

• Compared to countries like Canada (2.05), United Kingdom (0.23), and 

Japan (0.06), the U.S. experiences significantly more firearm-related 

homicides per 100,000 citizens (12.21)2 

• That said, rates in Honduras (60) and Venezuela (49.22) are much worse2 

• The costs to U.S. society are enormous: Social costs of a homicide are an 

estimated $8.6 million per life lost by homicide4 

1. USDOJ (2019) 

2. Gun Violence Archive (2020) 

3. World Population Review (2020) 

4. Heaton (2011) 
 

 

 

 

 

 

 

 

Firearm violence is not necessarily widespread 

• Long history of literature on place-based crime1,2 

• More recently, “hotspot” research shows shootings tend to be

heavily clustered in specific geographical areas3 

• Where and Why? One area of research examines built environment

features 

• Knowing this could help: 

• Law enforcement focus patrols in areas that attract crime 

• City planners focus on managing land use, physical development, and

supporting infrastructure 

• Both may leverage this information to strategize the locations of new built

environment (ex. Substations, Schools, etc.) that deters crime 

1. Jacobs (1961) 

2. Aufrichtig et al. 

(2017) 
 

 

 

 

 

 

 

 

What does previous research find, and 

what is our contribution? 

• Previous studies, largely in New England states using risk-terrain modeling, find: 

• Risk Factors: Retail businesses1, “At-risk” housing, bars, liquor stores, dance halls2, grocery stores, bus stops, and residential foreclosures risk factors7 

• Not risk factors: Gas stations, fast food restaurants, laundry/dry-cleaning services, and 

schools3 

• This study seeks to: Better understand influence of built environment features 

on incidence of firearm violence in multiple U.S. cities. 

• Our contributions to literature: 

1. Examine four new city-landscapes across the United States: West (Los Angeles), South

(New Orleans), Midwest (Detroit) and Northeast (Pittsburgh) 

2. Test new features 

3. Use actual street networks (rather than blocks or “crow flies” distances) 

4. Take into account socio-economic status. 

1. Kennedy and Caplan (2009) 

2. Kennedy et al. (2011) 

3. Xu and Griffiths (2017) 
Agenda 

• Background and Motivation 

• Method 

• Data 

• Results 

• Discussion 
We test the Spatial Randomness Hypothesis to determine 

if influence on firearm violence

• In practice, what are we doing?

Cumulative edge length in network

Number of features (eg. Libraries) Number of Instances of Firearms Violence

ppiiii

tt

= � 1 if building ii is within distance tt of shot jj

0 Otherwise

Network Cross-K Function for Stochastic Spatial Events

KKaaaa tt =

llEE

nnaann �

ii=1

nnaa

�

=1

nnbb

ppiiii

tt

1. Compute observed Cross-K values on the observed data

2a. Randomly distribute firearms violence locations over many iterations of Monte Carlo simulation

2b. After each iteration, compute simulated Cross-K values 

3. Compare observed with simulated

A. If observed greater than simulated: Attractive influence 

B. If observed less than simulated: Repellent influence
1. Compute Shot Density per Facility values using the observed 

data for each feature 

2. Perform Auto-changepoint detection using Pruned Exact Linear 

Time (Pelt) algorithm

3. Apply piecewise linear regression to each segment

If influence is detected, 

we evaluate “Risk” at varying distances

ppiiii

tt

= � 1 if building ii is within distance tt of shot jj

0 Otherwise

SS tt =

∑ii=1

nnaa ∑ =1

nnbb ppiiii

nnaatt

Number of 

features Distance value

Example. Given two restaurants, suppose one firearm incident occurred 

within 100ft of one of the restaurants.

SS 100 = 1

2(100) = 0.005
Agenda 

• Background and Motivation 

• Method 

• Data 

• Results 

• Discussion 
 

 

 

 

Our analysis uses open source, geocoded data 

• Firearm violence data 

• Total reported crime (homicide, robberies, & aggravated assault) w/ firearm 

• Uniform Crime Report defined, local police department data 

• Built environment feature data 

• Feature types differ across cities due to data sources 

• Many features including: Restaurants, grocery stores, libraries, and so on... 

City Years Types of 

Features 

Number of 

Features 

Number of 

Firearms Incidents 

Pittsburgh 2015 - 2017 36 5,315 11,535 

New Orleans 2015 - 2017 27 3,116 7,656 

Detroit 2014 - 2016 25 6,277 14,141 

Los Angeles 2013 - 2017 25 17,160 29,348 
 

Geospatial distribution of total reported crimes

with a firearm by city 

Pittsburgh 

New Orleans Los Angeles 

Detroit 
Agenda 

• Background and Motivation 

• Method 

• Data 

• Results 

• Discussion 
Detailed results for attractive features Pittsburgh, PA New Orleans, LA Los Angeles, CA 

Simulated (Blue) 

and 

Observed (Black) 

Cross-K Values 

Tobacco Retailer (427) Lodging (316) Transit Stations (57) 

Shot 

Density 

Values 

(with 5% 

error band) 

Low SES in black, 

High SES in gray. 
 

Detailed results for attractive features 

Los Angeles, CA – Convenient Stores and Supermarkets 

Simulated (Blue) 

and 

Observed (Black) 

Cross-K Values 

Low-SES areas High-SES areas 

Shot 

Density 

Values 

(with 5% 

error band) 

Low SES in black, 

High SES in gray. 
Detailed results for repellent features 

Pittsburgh, PA New Orleans, LA Detroit Los Angeles, CA 

Simulated (Blue) 

and 

Observed (Black) 

Cross-K Values 

Veterans Affairs Public Park (65) School (155) Sport Venue (2) Centers (14) 

Shot 

Density 

Values 

(with 5% 

error band) 
Agenda 

• Background and Motivation 

• Method 

• Data 

• Results 

• Discussion 
 

 

 

 

 

 

 

 

 

We estimate ‘average’ results within a city 

• Majority of features exhibit repellent influence 

• Common features with repellent influence include: Schools, Sport Venues, 

Nursing Homes, Veteran’s Affairs Centers 

• RAND colleagues doing qualitative work heard a lot about sport venues as well. 

• Firearm violence occurs near some built environment features 

• Lodging in New Orleans attract up to 1,000ft. (Novel result) 

• Alcohol outlets in New Orleans attract up to 250ft. (also found for Newark, NJ1 

and Irvington, NJ2) 

• Transit Stations in Los Angeles attract up to 360ft. (also found for Irvington, NJ5) 

• No significant influence for fast food restaurants. (also found for Newark, NJ3) 

1. Kennedy et al. (2011) 

2. Kennedy and Caplan (2009) 

3. Xu and Griffiths (2017) 
 

 

 

But this is not the end of the story... 

• Still need to understand why we might be obtaining these results 

• Likely some important heterogeneity to figure out: 

• Firearms incidents along network borders 

• Number of features 

• Socio-economic status (We have presented an approach for quantitative 

study) 

• Ambient population 

• Physical size of features (e.g. campus universities) 
Thank you!