A case study of bicycle theft on the Washington DC Metrorail system using a Routine Activities and Crime Pattern theory framework

2017 ◽  
Vol 31 (1) ◽  
pp. 226-246 ◽  
Author(s):  
Jeremy M. Levy ◽  
Yasemin Irvin-Erickson ◽  
Nancy La Vigne
Keyword(s):  
Routine Activities ◽  
Washington Dc ◽  
Pattern Theory ◽  
Crime Pattern Theory ◽  
Crime Pattern ◽  
Theory Framework

Tactical Social Network Analysis: Using Affiliation Networks to Aid Serial Homicide Investigation

2016 ◽  
Vol 21 (2) ◽  
pp. 133-158 ◽  
Author(s):  
Gisela Bichler ◽  
Steven Lim ◽  
Edgar Larin
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Green River ◽  
Pattern Theory ◽  
Crime Pattern Theory ◽  
Affiliation Networks ◽  
Applied Criminology ◽  
Crime Pattern

Integrating crime pattern theory with tenets of social network theory, we argue that linking people who frequent the same places reveals intersecting behavioral patterns illustrative of case connectivity. Using the Green River serial murder investigation as a case study, we demonstrate that structural statistics may be useful in focusing investigative efforts. Significant shifts in the centrality of suspects emerge when we track the evolution of this case at 6-month increments, suggesting that the initial working case hypothesis misled investigators. Continued exploration into the utility of social network analysis (SNA) for tactical purposes will help advance applied criminology.

scholarly journals Right place, right time? Making crime pattern theory time-specific

Crime Science ◽  
2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Sabine E. M. van Sleeuwen ◽  
Stijn Ruiter ◽  
Wouter Steenbeek
Keyword(s):  
Online Survey ◽  
Choice Model ◽  
Time Of Day ◽  
Routine Activities ◽  
Spatial Knowledge ◽  
Small Sample ◽  
Daily Routine ◽  
Pattern Theory ◽  
Crime Pattern Theory ◽  
Crime Pattern

Abstract Objectives Crime pattern theory and the related empirical research have remained rather a-temporal, as if the timing of routine activities and crime plays no role. Building on previous geography of crime research, we extend crime pattern theory and propose that an offender’s spatial knowledge acquired during daily routine activities is not equally applicable to all times of day. Methods We put this extended theory to a first empirical test by applying a discrete spatial choice model to detailed information from the Netherlands on 71 offences committed by 30 offenders collected through a unique online survey instrument. The offenders reported on their most important activity nodes and offence locations over the past year, as well as the specific times they regularly visited these locations. Results The results show that almost 40% of the offences are committed within the neighbourhoods of offenders’ activity nodes, increasing to 85% when including first-, second- and third-order neighbourhoods. Though not statistically significant in our small sample, the results further suggest that offenders are more likely to commit crime in neighbourhoods they have regularly visited at the same time of day than in neighbourhoods they have regularly visited at different times of day. Conclusion Our extension of crime pattern theory is only tentatively supported. We argue for replication research with larger samples before any firm conclusions are warranted.

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.

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