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

• Published in: PloS one Vol. 17; no. 9; p. e0273718
• Main Authors: West, Samuel J, Bishop, Diane, Chapman, Derek A, Thomson, Nicholas D
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 06.09.2022; Public Library of Science (PLoS)
• Subjects: Aggression; Algorithms; Antisocial personality disorder; Biology and Life Sciences; Chaos theory; Clustering; Companies; Crime; Data collection; Delinquency; Domestic violence; Earth Sciences; Epidemiology; Evaluation; Evictions; Evidence; Income; Instability; Least-Squares Analysis; Low income groups; Management; Medicine and Health Sciences; Murders & murder attempts; Neighborhoods; Physical Sciences; Population density; Prevention; Property taxes; Public health administration; Regression analysis; Renovation & restoration; Residence Characteristics; Science Policy; Sex crimes; Social disorganization; Social Sciences; Stability; Tenants; Violence; United States; United States > US
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0273718

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.