Applying Crime Pattern Theory and Risk Terrain Modeling to Examine Environmental Crime in Cambodia

2020 ◽  
Vol 36 (3) ◽  
pp. 327-350
Author(s):  
Devin Cowan ◽  
William D. Moreto ◽  
Christina Burton ◽  
Matt R. Nobles ◽  
Rohit Singh
Keyword(s):  
Temporal Analysis ◽  
Environmental Crime ◽  
Terrain Modeling ◽  
Pattern Theory ◽  
Crime Pattern Theory ◽  
Risk Terrain Modeling ◽  
Units Of Analysis ◽  
Illegal Activities ◽  
Spatial Temporal Analysis ◽  
Crime Pattern

The spatial-temporal analysis of crime has significantly evolved. One innovative technique recently developed is risk terrain modeling (RTM). RTM, however, has yet to be used for environmental crime. This research applies RTM and draws from crime pattern theory to examine illegal activities in two protected areas in Cambodia. Findings suggest that pathways, edges, areas with suitable targets, conservation posts, landcover, and prior incidents are related to fauna- and flora-related illegal activities, though this relationship varies by season, units of analysis, and study area (i.e., patrol-based compared with official designation). Implications for theory and policy are outlined.

Personalized crime location prediction

2016 ◽  
Vol 27 (3) ◽  
pp. 422-450 ◽  
Author(s):  
MOHAMMAD A. TAYEBI ◽  
UWE GLÄSSER ◽  
MARTIN ESTER ◽  
PATRICIA L. BRANTINGHAM
Keyword(s):  
Law Enforcement ◽  
Urban Crime ◽  
Crime Analysis ◽  
Urban Landscapes ◽  
Location Prediction ◽  
Prevention Strategies ◽  
Pattern Theory ◽  
Activity Spaces ◽  
Crime Pattern Theory ◽  
Crime Pattern

Crime reduction and prevention strategies are vital for policymakers and law enforcement to face inevitable increases in urban crime rates as a side effect of the projected growth of urban population by the year 2030. Studies conclude that crime does not occur uniformly across urban landscapes but concentrates in certain areas. This phenomenon has drawn attention to spatial crime analysis, primarily focusing on crime hotspots, areas with disproportionally higher crime density. In this paper, we present CrimeTracer1, a personalized random walk-based approach to spatial crime analysis and crime location prediction outside of hotspots. We propose a probabilistic model of spatial behaviour of known offenders within their activity spaces. Crime Pattern Theory concludes that offenders, rather than venture into unknown territory, frequently select targets in or near places they are most familiar with as part of their activity space. Our experiments on a large real-world crime dataset show that CrimeTracer outperforms all other methods used for location recommendation we evaluate here.

Geoprofiling Terrorism

2018 ◽  
Author(s):  
Kim Rossmo
Keyword(s):  
Spatial Relationship ◽  
Environmental Criminology ◽  
Improvised Explosive Devices ◽  
Geographic Profiling ◽  
Pattern Theory ◽  
Crime Pattern Theory ◽  
Improvised Explosive ◽  
Explosive Devices ◽  
Crime Pattern

A number of recent research projects have explored applications of geographic profiling to counterterrorism and counterinsurgency. These efforts analyzed geospatial patterns of terrorist cells (e.g., the spatial relationship between safe houses and weapon storage sites), tested the ability of these techniques to locate terrorist bases from minor crimes and seditious graffiti, and examined the utility of geoprofiling for locating preparation sites used by insurgents for improvised explosive devices and rocket attacks. In appropriate cases, geoprofiling models have utility for prioritizing geo-intelligence and identifying logistic bases of terrorist operations. This chapter first discusses environmental criminology and the geography of crime. It then covers the basics of geographic profiling, its various applications, and the role of geospatial intelligence and crime pattern theory in counterterrorism. Finally, it examines the geospatial and temporal patterns of terrorism to show how geoprofiling can be used to analyze seditious graffiti, insurgency attacks, cyberterrorism, and bioterrorism.

A Socioeconomic Edge Effect: Rational Crime in Small Areas of Sharp Socioeconomic Contrast

2020 ◽  
pp. 002242782094500
Author(s):  
Robert Drew Heinzeroth
Keyword(s):  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Economic Incentive ◽  
Future Research ◽  
Census Block ◽  
Pattern Theory ◽  
Crime Pattern Theory ◽  
Dependent Variables ◽  
Block Groups ◽  
Crime Pattern

Objectives: To determine whether criminogenic “edges,” as defined by crime pattern theory, exist at points of sharp contrast of socioeconomic status (SES). Methods: The study uses a quasi-experimental design with pattern matching logic. A series of negative binomial regression models separately examine five different crimes with an economic incentive as dependent variables, and five crimes without an economic incentive as nonequivalent dependent variables, to determine whether census block groups of predominantly and comparatively higher SES than the wider surrounding area experience greater reported rational crime than would otherwise be expected. Results: The census block groups of comparatively higher SES located within and/or near areas of predominantly lower SES experienced one of the five crimes with an economic incentive, robberies by firearm, 40 percent more frequently than would otherwise be expected. Conclusions: The study’s findings are partially consistent with its hypothesis, which is grounded in crime pattern, rational choice, routine activities, and social disorganization theories. The findings encourage future research that may extend the definition of an “edge” under crime pattern theory as well as research at the intersection of criminological theories.

scholarly journals A framework for estimating crime location choice based on awareness space

Crime Science ◽  
2020 ◽  
Vol 9 (1) ◽  
Author(s):  
Sophie Curtis-Ham ◽  
Wim Bernasco ◽  
Oleg N. Medvedev ◽  
Devon Polaschek
Keyword(s):  
Location Choice ◽  
Empirical Work ◽  
Routine Activity ◽  
Pattern Theory ◽  
Individual Level ◽  
Crime Pattern Theory ◽  
Reliable Knowledge ◽  
Crime Pattern ◽  
Behaviour Type ◽  
Selection Of

Abstract This paper extends Crime Pattern Theory, proposing a theoretical framework which aims to explain how offenders’ previous routine activity locations influence their future offence locations. The framework draws on studies of individual level crime location choice and location choice in non-criminal contexts, to identify attributes of prior activities associated with the selection of the location for future crime. We group these attributes into two proposed mechanisms: reliability and relevance. Offenders are more likely to commit crime where they have reliable knowledge that is relevant to the particular crime. The perceived reliability of offenders’ knowledge about a potential crime location is affected by the frequency, recency and duration of their prior activities in that location. Relevance reflects knowledge of a potential crime location’s crime opportunities and is affected by the type of behaviour, type of location and timing of prior activities in that location. We apply the framework to generate testable hypotheses to guide future studies of crime location choice and suggest directions for further theoretical and empirical work. Understanding crime location choice using this framework could also help inform policing investigations and crime prevention strategies.

scholarly journals Visualising property crime in Gauteng: Applying GIS to crime pattern theory

2016 ◽  
Author(s):  
Alexandra Hiropoulos ◽  
Jeremy Porter
Keyword(s):  
South African ◽  
Property Crime ◽  
High Rate ◽  
Motor Vehicles ◽  
Pattern Theory ◽  
Crime Pattern Theory ◽  
South African Police Service ◽  
Practical Tool ◽  
South African Police ◽  
Crime Pattern

While the high rate of crime in South Africa has received much international attention, mainly focused on violent crime, the vast majority of offences reported to the South African Police Service concern property and other non-violent offences. The present study explores the relationship between one of the most frequently reported property crimes (thefts out of motor vehicles) and the environment in which they occur, using Geographic Information Systems (GIS). Utilising the framework of crime pattern theory, crime generators and attractors are visually examined in order to determine whether they can explain concentrations of crime. We argue that when used in conjunction with relevant social theory aimed at the examination of the determinants of crime and criminality, GIS can be a powerful practical tool in the presentation of crime data.

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