Block-Level Analysis of the Attractors of Robbery in a Downtown Area

Abstract

This article examines the predictions of crime pattern theory in a unique neighborhood type. It tested potential crime attracting facilities against street robbery data from 2009 to 2013 in the Police Districts I & II in Downtown Houston. The analysis modeled the four daily human routine periods described in the American Time Use Survey (ATUS). Generalized linear simultaneous negative binomial regression model was used to determine the size of the influence of the variables (beta coefficients) and their significance for each model outcome. The findings show some distinct patterns of street robbery due to the immediate and lagged effects of the variables relatable to the study environment’s unique setting. Two variables, geographic mobility, and barbershops were particularly significant across three of the outcome models. The results suggest that the physical and social structure of neighborhoods determined by land-use regulations would enhance understanding of the time-based influence on robbery patterns due to crime-attracting facilities.

Risk Terrain and Multilevel Modeling of Street Robbery Distribution in Baltimore City

“…Based on prior literature on city-wide crime distribution (Ejiogu, 2020; Haberman & Ratcliffe, 2015; Ratcliffe, 2012; Thomas et al, 2020), 19 potential crime-attracting and crime-generating facilities and contextual factors, and one criminal offender anchor type (Table 1) identified as crime influencing variables within the study area. The list of the addresses of the physical facilities was scrapped from the Baltimore City Yellow Pages using an accessible online instant data-scraper google chrome extension tool.…”

“…Borgess and Hipp (2010) argue that the racial/ethnic context of crime may affect residential mobility. The residential mobility variable is a proxy for the proportion of renters compared to homeowners, which measures the stability of neighborhood social efficacy (Ejiogu, 2020; Haberman & Ratcliffe, 2015). Residential stability positively affects crime rates (Kasarda & Janowitz, 1974; Sampson & Groves, 1989).…”

“…Residential stability positively affects crime rates (Kasarda & Janowitz, 1974; Sampson & Groves, 1989). Racial heterogeneity is another critical sociodemographic risk variable influencing crime concentration across space (Ejiogu, 2020; Haberman & Ratcliffe, 2015). As observed by numerous studies (Bernasco et al, 2017; Ejiogu, 2020; Haberman & Ratcliffe, 2015), neighborhoods with a high proportion of minority residents being vulnerable to crime points to a downward trend in residential stability (Borgess & Hipp, 2010).…”

“…Sociodemographic Predictors Variables. Based on literature related to the operative sociodemographic variables in major US Cities, the variables, racial heterogeneity, percentage under 18, percentage of female-headed households, percent renter, and total population were also included (Bernasco et al, 2017;Braga et al, 2009;Ejiogu, 2020;Haberman & Ratcliffe, 2015;Ratcliffe, 2012;Tseloni, 2006). First, the sociodemographic variables include a constructed factor-racial heterogeneity.…”

Understanding the Spatiotemporal Pattern of Crimes in Changchun, China: A Bayesian Modeling Approach

“…During recent decades, most cities in China have been undergoing extraordinary changes in many aspects, such as demographic characteristics, socioeconomic status, land use and building environments, which are often proven to be associated with the spatial pattern of crimes in the urban environment in the literature on environmental criminology [1][2][3]. Meanwhile, the crime control effort by law enforcement agencies, which provide official guardianship to deter crime, has also greatly intensified in recent years.…”