A discrete spatial choice model of burglary target selection at the house-level

Abstract

This article studies how burglars select a house to burglarize. We draw on the rational choice perspective to investigate how burglars select a target by relying on house-related attributes to optimize a combination of perceived rewards, efforts and risk. It extends current applications of the discrete spatial choice framework to burglary target selection by adopting the house as the spatial unit of analysis and studies burglars' target selection process in a larger and more diverse study area than that of earlier studies. Using data on 650 residential burglaries and on approximately 500,000 residential properties in the Belgian province East Flanders, we consider a discrete spatial choice model of burglary target selection to establish which house-related attributes influence burglars' target selection process. Our findings demonstrate that terraced houses, houses without a garage, houses that have not been outfitted with a central heating and/or air-conditioning system and houses nearby burglars' residences are more likely to be selected. Overall, our analysis suggests that burglars rely on effort-related attributes to distinguish between targets while higher perceived rewards actually decrease the odds of a house being burglarized. Risk-related attributes are unimportant for burglars' target choice. Highlights We study the burglary target selection process at the house-level  We apply the discrete choice model with a choice set of over 500,000 houses  Higher perceived rewards reduce the odds of burglary  Lower perceived efforts increase the odds of burglary  Burglars do not rely on risk-related information to distinguish between targets

Modeling Spatial Interactions between Areas to Assess the Burglary Risk

“…Areas with high unemployment, high population density, and/or which lacked a motor-vehicle and central heating system had higher likelihood for burglary. This is consistent with previous studies that attributed high burglary risk with populous areas [29,30] and high vulnerability of households with insufficient controls [13,17,45]. It also agrees with the hypothesis related to increased risk within unstable institutions [16,17].…”

“…A number of socioeconomic indicators were drawn from the ONS census data for 2011 as guided by the previous literature [1,12,35,36,45]. The first four variables correspond to conditions of areas where social problems are likely to arise and affect informal controls over crime [14,17].…”

Modeling Spatial Effect in Residential Burglary: A Case Study from ZG City, China

“…According to the social disorganization and routine activity theories, various environmental factors are found to be related to burglary, including road configurations [1,2], residential instability [3,4], demographics [5], income [6,7], unemployment rate [8], land use mix [9], housing characteristics [10][11][12][13], guardianship [14,15], accessibility [16], and the physical environment [17].…”

Understanding site selection of illegal border crossings into a fenced protected area: a rational choice approach

“…In the case of target selection, offenders first select an area within which to offend and then select a specific target (Bernasco and Nieuwbeerta 2005;Cornish and Clarke 1986). RCP has been applied to explain crime events in urban areas (Bernasco and Nieuwbeerta 2005;Gottschalk 2016;Vandeviver et al 2015), but can also be used to explain crime in nature reserves, including wildlife poaching (Hill et al 2014).…”