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Short Courses


Advanced Hotspot Analysis

Predictive Crime Mapping

Hypothesis Testing Analysis

Crime Analysis

Understanding Hotspots

Strategic Intelligence Assessments

Dates to be confirmed

Geographic Profiling Analysis

26th June - 7th July 2017

Department of Security and Crime Science

ICIAC 2012 Seminar stream 5B

Abstracts and slides

Risky and emerging places

Identifying risky places for street robberies: A GIS based analysis of the time geographical vulnerability to street robbery emergence

Yasemin Gaziarifoglu, Leslie W. Kennedy and Joel M. Caplan, Rutgers Center on Public Security, USA

Key words: Street Robbery, GIS, Risk, Spatial Influence, Vulnerability
Slides: Yet to be supplied by presenter

In their study titled "How well do criminologists explain crime"  Weisburd and Piquero (2008) reached three important conclusions. First, criminological work typically leaves 80-90% of the variance unexplained. Second, individual based models do not have a strong explanatory power. And last but not least, the ability of researchers to explain crime patterns has not improved over time. These three daunting conclusions led to the main conclusion that there is a disconnection between theory building and data analysis. As a reflection of this disconnection, recently criminology scholars started using the emergence framework to further our understanding of crime.

According to the emergence framework, new crimes emerge based on the interaction between the individual and the situation. As advocates of the emergence framework Kennedy and Van Brunschot (2009) argued that to understand crime patterns, one should think about the process whereby the crime occurs. They further suggested focusing on the common elements of crime risk and using risk as a metric to tie different components of the crime problem. Looking at the past studies of emergence, although not termed as emergence, the relationship between situation and crime has been extensively studied by social ecology, human ecology and social area analysis starting with Chicago School and going until 1980's. But with the relatively underdeveloped analytics at the time, although these studies acknowledged the interaction between individual and the situation they couldn't go beyond being macro level depictions of geographical descriptives. The spatial crime analysis as we know it today became possible and popular in the late 1990's with the increased use of GIS among the criminal justice society. Researchers first and foremost addressed the spatial concentration of crimes at micro geographies — namely hot spots. Although hot spot analysis has been successful in identifying where crime clusters, it has not addressed why crime clusters at certain geographies. To close this gap most recent contextual studies of crime (Caplan, 2011; Kennedy et al., 2011) incorporated the features of landscape into crime  analysis and found that places that are most vulnerable to the influence of these features crime, are also places where crimes occur the most.

Despite the variety and extensiveness of the aforementioned research, current studies have left one issue unexplored. Studies of the link between features of micro places and crime have assumed a temporarily uniform criminogenic influence, omitting the dynamic nature of places. As Miller (2004) has stated, human activities occur at specific locations for a limited duration. Situations are deeply dynamic in the sense that "no space retains its social relevancy permanently" (Kinney, 2010, p.485). Therefore, a risk inducing feature of a landscape can lose this characteristic (or vice versa) depending on the social relevancy of places at different times.  Keeping these points in mind, the goal of this research is to study why certain places are more vulnerable to street robbery risk in Newark, NJ, U.S.A in 2010 by modeling robbery risk at different times as a function of vulnerability to the spatial influence of risk factors at micro geographies. 

Detecting emerging incident/crime patterns in Central London

Tao Cheng and Monsuru Adepeju, Department of Civil, Environmental and Geomatic Engineering, University College London

Key words: Prospective Crime Surveillance, Spatio-Temporal Scan Statistics, Emerging Patterns, Crime prevention
Slides: Cheng

Certain crime types, notably crimes with ‘repeat’ attributes, exhibit infectious characteristics of diseases. These crimes contribute significantly to the overall clustering of crimes in both space and time. Understanding the dynamics of these crimes as well as their relationships with other social economic factors within an area could provide clues to understanding how they emerge over a period of time within a geographical region. However, developing techniques to adequately explore this phenomenon remains an important area of research in space-time crime analysis.

Here the Space Time Scan Statistical (STSS) model, which stem on strong statistical theory, was used on the police (Computer Aided Dispatch - CAD) crime/incident datasets of Camden borough of London, in both retrospective and prospective manner to detect ‘past’ and ‘emerging’ space-time clusters respectively. The outcome of the analysis shows the possibility of detecting crime clusters as they emerge simultaneously in both space and time before becoming statistically significant. The effectiveness of each prospective cluster’s surveillance was evaluated by comparing its ‘emergence’ date with its respective retrospective cluster ‘start date’. The overall significance of the results obtained in this analysis however, lies in the fact that various security agents can now systematically monitor an entire geographical region, on a daily basis, using this technique to proactively identify areas of potential crime outburst so as to facilitate immediate prevention strategies in such areas.    

Page last modified on 30 jan 13 16:51