Understanding the Predictors of Street Robbery Hot Spots: A Matched Pairs Analysis and Systematic Social Observation

Abstract

This study examines the environmental predictors that classify street robbery hot spots and control street segments in Indianapolis. Empirical controls were generated by matching each hot spot to a corresponding set of zero-crime control and low-crime control units. Then, units were evaluated based on the presence of crime generators and attractors, which were downloaded from open data sources and spatially joined to the street segments, and disorder indicators obtained via systematic social observation using Google Street View. The findings provide information about the influence environmental predictors have on the presence of street robbery hot spots, and whether the composition of hot spots significantly differs from that of similar places that experienced no crime or low counts of crime.

The effect of the Seattle Police‐Free CHOP zone on crime: A microsynthetic control evaluation

“…In total, 10 covariates were incorporated in the microsynth matching algorithm as informed by prior crime‐and‐place research (Braga et al., 2012; Connealy et al., 2020; Connealy, 2021; Piza et al., 2020): Preintervention crime counts: 6‐day crime count totals were matched on for the identified target zone from 6/1/2019 to 6/7/2020 (62 separate 6‐day matching blocks). High‐crime units: street segments with a pre‐CHOP crime total in the 80th percentile or above were dichotomously identified and operationalized as preintervention high‐crime units. High‐crime location quotient: using values ranging from –1 to 1, this measure accounts for the clustering of identified high‐crime units within the same block group as a measure of crime density (see Connealy et al., 2020; Piza et al., 2020). High call for service beat: SPD beats were dichotomously identified if their total volume of calls for service was in the 80th percentile as a potential proxy for police‐related activity and presence. Business total: the total count of commercial business establishments on each unit (aggregated in the matching model as the target total) serves as a measure of land usage and activity. Consumer‐facing establishment total: the total count of consumer‐facing establishments on each unit (aggregated in the matching model as the target total) designed to draw in foot traffic. Principal or arterial roadway: units were dichotomously identified if the street segment unit was a principal or arterial roadway or not. Street length: the total length of all segments was summed in the specified target zone. Concentrated disadvantage index: the standardized percentages of the percent residents receiving public assistance, percent families living below poverty line, percent female headed households, percent unemployed, and percent of population under 18 were summed and dichotomously operationalized as the total number of units above/below the mean level of disadvantage. Demographic index: the standardized percentage of percent non‐White, percent of residents aged 15–29, percent of vacancies, and percent of owned/rented homes at the block group level were summed and operationalized dichotomously as the total number of units above/below the mean. …”

“…In total, 10 covariates were incorporated in the microsynth matching algorithm as informed by prior crime-and-place research (Braga et al, 2012;Connealy et al, 2020;Connealy, 2021;:…”

When police pull back: Neighborhood-level effects of de-policing on violent and property crime

“…The scientific evidence is so clear at this point that some refer to this phenomenon as the "law of crime concentration" (Weisburd, 2015). In any given city there will be neighborhoods with higher crime rates, and within those neighborhoods there will be street segments that account for the vast majority of crime in the area (Connealy, 2021;Connealy & Piza, 2019;Sherman et al, 1989). This is one of the reasons that police activity is not evenly distributed throughout a jurisdiction.…”

Measuring the Influence of Multiscale Geographic Space on the Heterogeneity of Crime Distribution

“…With the availability of mobile phone data or trajectory data, the influence of human activity (or mobility) on crime also is widely investigated [33,34]. Recently, with the development of deep learning and street view image, the influence of visual perception (e.g., living, boring, and disorder) of the environment on crime can also be measured [15,[35][36][37]. It should be noted that environment-related and human-related factors are not independent but correlated with each other, especially with the underlying geographic space.…”