Place-based correlates of Motor Vehicle Theft and Recovery: Measuring spatial influence across neighbourhood context

Abstract

Social scientists have long shown great interest in the spatial correlates of crime patterns. A subset of the literature has focused on how micro-level spatial factors influence the formation of crime hot spots. At the same time, tangential research has highlighted how neighbourhood disadvantage influences crime occurrence. The current study focuses on the intersection of these perspectives through a spatial analysis of Motor Vehicle Theft (MVT) and Motor Vehicle Recovery (MVR) in Colorado Springs, CO. We begin by conducting a Risk Terrain Modelling analysis to identify spatial risk factors significantly related to MVT and MVR occurrence. We then test whether the spatial influences of the criminogenic risk factors differ across traditional measures of neighbourhood disadvantage. Findings suggest that while a citywide effect is evident for multiple risk factors, their spatial influence on crime significantly varies across neighbourhood contexts.

Bus Stops and Violence, Are Risky Places Really Risky?

“…Piza et al (2016) for instance noted that while there are city wide risk factors there is also significant variation between different types of neighborhoods in their impact. For instance it was noted that the association of a park with motor vehicle theft was seven times greater in neighborhoods with a high share of young males in the population ).…”

“…In addition it builds on the findings of Piza et al (2016) and Ceccato et al (2013) that suggested potential benefits from including the neighborhood level variable of collective efficacy, by employing mixed effects models of bus stops nested in neighborhoods with concentrated disadvantage and collective efficacy as neighborhood level variables. As the bus stop data was only available for a full year it was however not possible to include near repeat patterning or similarly dynamic models into the present study.…”

“…The risk factors employed in the present study resemble the risk factors used in prior studies employing RTM (See Piza et al 2016 for an overview). Schools, ATMs, bars, and bus stops were all included in a RTM for aggravated assaults in Chicago , and the present study additionally looks at restaurants and high schools as separate categories combined with data from a systematic social observation (SSO) of bus stops to yield data over visually identifiable markers of the terrain and its land use.…”

Grid-Based Crime Prediction Using Geographical Features

“…Vehicle Theft data for Taoyuan City was sourced from an open data platform (Open Data Platform of Taiwan: https://data.gov.tw). We chose the Vehicle Theft to be our prediction target because it is obviously affected by environment factors [35]. We used a data period from January 2015 to April 2018, with about 220 criminal incidents occurring each month.…”

Space–Time Analysis of Vehicle Theft Patterns in Shanghai, China

“…The costs of vehicle theft include the direct uninsured financial losses, the opportunity cost of the time taken to deal with the crime, the opportunity cost of the temporary unavailability of a vehicle, and the psychological costs of victimization [3][4][5]. Although the amount of vehicle theft and other crimes greatly decreased in the past few decades in many countries [6,7], vehicle theft still substantially contributes to crime statistics [8][9][10][11][12]. Most studies on vehicle theft were conducted in developed countries, though many developing countries suffer from growing rates of such crime.…”