Assessing the Differential Impact of Vacancy on Criminal Violence in the City of St. Louis, MO

Fox, Branson; Trolard, Anne; Simmons, Mason; Meyers, Jessica; Vogel, Matt

doi:10.1177/0734016821996795

Cited by 10 publications

(3 citation statements)

References 49 publications

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“…Underscoring these differences, those from high tax delinquency neighborhoods are also more likely to die at an earlier age (Zuberi & Teixeira, 2021). Similarly, a recent study found that the number of tax delinquent and vacant properties in a neighborhood also predict greater rates of violent crime (Fox et al, 2021). Despite this evidence, no known research has examined the potential for divergent sources of neighborhood instability to differentially predict violence at the neighborhood level.…”

Section: Neighborhood Residential Stability and Violencementioning

confidence: 99%

Comparing Forms of Neighborhood Instability as Predictors of Violence in Richmond, VA

West

Bishop²,

Chapman³

et al. 2022

Preprint

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Violence events tend to cluster together geospatially. Various features of communities and their residents have been highlighted as explanations for such clustering in the literature. One reliable correlate of violence is neighborhood instability. Research on neighborhood instability indicates that such instability can be measured as property tax delinquency, yet no known work has contrasted external and internal sources of instability in predicting neighborhood violence. To this end we collected data on violence events, company and personal property tax delinquency, population density, race, income, food stamps, and alcohol outlets for each of Richmond, Virginia’s 148 neighborhoods. We constructed and compared ordinary least-squares (OLS) to geographically weighted regression (GWR) models before constructing a final algorithm-selected GWR model. Our results indicated that the tax delinquency of company-owned properties (e.g., rental homes, apartments) was the only variable in our model that predicted violence in all 148 neighborhoods, accounting for 62% of variation in violence events. We replicated this analysis using violence data from a later point in time which yielded largely identical results. These findings indicate that external sources of neighborhood instability may be more important to predicting violence than internal sources. Our results further provide support for social disorganization theory and point to opportunities to expand this framework.

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Section: Neighborhood Residential Stability and Violencementioning

confidence: 99%

Comparing Forms of Neighborhood Instability as Predictors of Violence in Richmond, VA

West

Bishop²,

Chapman³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Fourth, the set of urban features that we included in the spatiotemporal analysis can be extended to include additional features. For example, Fox et al [75] found vacancy as the key driver of violent crime across the north and south sides of St. Louis, MO. Lastly, the sample is limited to Chicago, IL, which significantly curbs the generalizability of this study to other cities.…”

Section: Limitationsmentioning

confidence: 99%

Crime Risk Stations: Examining Spatiotemporal Influence of Urban Features through Distance-Aware Risk Signal Functions

Hakyemez

Badur

2021

IJGI

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Static indicators may fail to capture spatiotemporal differences in the spatial influence of urban features on different crime types. In this study, with a base station analogy, we introduced crime risk stations that conceptualize the spatial influence of urban features as crime risk signals broadcasted throughout a coverage area. We operationalized these risk signals with two novel risk scores, risk strength and risk intensity, obtained from novel distance-aware risk signal functions. With a crime-specific spatiotemporal approach, through a spatiotemporal influence analysis we examined and compared these risk scores for different crime types across various spatiotemporal models. Using a correlation analysis, we examined their relationships with concentrated disadvantage. The results showed that bus stops had relatively lower risk intensity, but higher risk strength, while fast-food restaurants had a higher risk intensity, but a lower risk strength. The correlation analysis identified elevated risk intensity and strength around gas stations in disadvantaged areas during late-night hours and weekends. The results provided empirical evidence for a dynamic spatial influence that changes across space, time, and crime type. The proposed risk functions and risk scores could help in the creation of spatiotemporal crime hotspot maps across cities by accurately quantifying crime risk around urban features.

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“…Underscoring these differences, those from high tax delinquency neighborhoods are also more likely to die at an earlier age [ 22 ]. Similarly, a recent study found that the number of tax delinquent and vacant properties in a neighborhood also predict greater rates of violent crime [ 23 ]. Despite this evidence, no known research has examined the potential for divergent sources of neighborhood instability to differentially predict violence at the neighborhood level.…”

Section: Introductionmentioning

confidence: 99%

Comparing forms of neighborhood instability as predictors of violence in Richmond, VA

et al. 2022

View full text Add to dashboard Cite

Violence events tend to cluster together geospatially. Various features of communities and their residents have been highlighted as explanations for such clustering in the literature. One reliable correlate of violence is neighborhood instability. Research on neighborhood instability indicates that such instability can be measured as property tax delinquency, yet no known work has contrasted external and internal sources of instability in predicting neighborhood violence. To this end we collected data on violence events, company and personal property tax delinquency, population density, race, income, food stamps, and alcohol outlets for each of Richmond, Virginia’s 148 neighborhoods. We constructed and compared ordinary least-squares (OLS) to geographically weighted regression (GWR) models before constructing a final algorithm-selected GWR model. Our results indicated that the tax delinquency of company-owned properties (e.g., rental homes, apartments) was the only variable in our model (R2 = 0.62) that was associated with violence in all but four Richmond neighborhoods. We replicated this analysis using violence data from a later point in time which yielded largely identical results. These findings indicate that external sources of neighborhood instability may be more important to predicting violence than internal sources. Our results further provide support for social disorganization theory and point to opportunities to expand this framework.

show abstract

Assessing the Differential Impact of Vacancy on Criminal Violence in the City of St. Louis, MO

Cited by 10 publications

References 49 publications

Comparing Forms of Neighborhood Instability as Predictors of Violence in Richmond, VA

Comparing Forms of Neighborhood Instability as Predictors of Violence in Richmond, VA

Crime Risk Stations: Examining Spatiotemporal Influence of Urban Features through Distance-Aware Risk Signal Functions

Comparing forms of neighborhood instability as predictors of violence in Richmond, VA

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