Modeling Spatial Interactions between Areas to Assess the Burglary Risk

Abstract

It is generally acknowledged that the urban environment presents different types of risk factors, but how the structural effects of areas influence the risk levels in neighboring areas has been less widely investigated. This research assesses the local effects of burglary contributory factors on burglary over small areas in a large metropolitan region. A comparative framework is developed for analyzing the effects of geographic dependence on burglary rates, and for assessing how such dependence conditions the community context and the urban land use. A local indicators spatial autocorrelation analysis assesses burglaries over five years (2011)(2012)(2013)(2014)(2015) to identify risk clusters. Thereafter, effects of different variables (e.g., unemployment, building density) on burglary frequency are estimated in a series of regression models while controlling for changes in the risk levels of nearby surrounding areas. Results uncover strong evidence that the configuration of the surroundings influences risk. After controlling for area-based interaction, patterns are identified that contrast with the previous literature, such as lower burglary frequency in areas with higher tenancy in social housing units. Together the findings demonstrate that the spatial arrangement of areas is as crucial as contextual crime factors, particularly when assessing the risk for small areas.

Environmental Risk Factors influencing Bicycle Theft: A Spatial Analysis in London, UK

“…The second key strength of this risk analysis is that it explores a rich set of area-based urban characteristics, including both the community context and the distinct features of land use in targeted areas. The community context has been highlighted in many studies (e.g., [ 25 , 61 , 68 ]). However, urban land use turned out to have more influence on the risk of bicycle theft, in line with similar observations that propose criminogenic places to be more finely-distributed than the broader area-based socioeconomic risk factors [ 30 , 48 ].…”

“…The first ten correspond to the presence of the public amenities that have been described above. Given that d ij is the distance from an amenity to the midpoint of a buffer area and d max is the maximum distance value, an inverse distance weighting scheme was used to quantify amenity presence within the road network buffers as follows [ 25 ]: …”

“…Routine Activity Theory is often used to attribute urban crime to the simultaneous coexistence of three elements: 1) motivated offenders, 2) suitable targets, and 3) insufficient guardianship [ 22 , 24 ]. Daily routine activities set the scene for interaction of these elements, and this is why the places that attract multitudes might also present opportunities for crimes such as bicycle theft [ 23 , 25 ]. For instance, field surveys in urban areas have shown that bicycles are commonly stolen from on-street locations [ 2 , 16 , 20 ] and around facilities such as railway stations and bicycle stands [ 2 , 14 , 19 ].…”

Predicting Safe Parking Spaces: A Machine Learning Approach to Geospatial Urban and Crime Data

“…Boldt and Borg [25] proposed a novel method to approximate offense times for residential burglaries using a dataset of all Swedish residential burglaries committed from 2010 to 2014 (a total of 103,029). Having access to burglary data over five years (2011-2015), Mburu and Bakillah [26] developed a series of regression models to identify local indicators of the urban environment (e.g. unemployment, building density, and type) that increase the risk of burglaries.…”

Frontiers in Spatial and Spatiotemporal Crime Analytics—An Editorial

“…The paper by Glasner and Leitner [15] was dedicated to repeat victimization through street robberies between 2009 and 2013 in Vienna, Austria, utilizing geovisualization coupled with spatial and temporal analyses. Promoting a rich set of spatial econometrics models, Mburu and Bakillah [16] assessed the burglary risk for Greater London, UK. Complementing their global regression, Du and Law [17] explored the spatially heterogeneous associations between vegetation and transportation networks on crime in Kitchener-Waterloo, Canada, by means of geographically weighted regressions.…”