Special Issue "Geographic Crime Analysis"

Special Issue Editors

Dr. Spencer Chainey
E-Mail Website
Chief Guest Editor
University College London Jill Dando Institute of Security and Crime Science, 35 Tavistock Square, London WC1H 9EZ, England
Interests: crime analysis; problem-oriented policing; hot spot policing; intelligence-led policing
Special Issues and Collections in MDPI journals
Dr. Matt Ashby
E-Mail Website
Co-Guest Editor
University College London Jill Dando Institute of Security and Crime Science, 35 Tavistock Square, London WC1H 9EZ, England
Interests: crime analysis; crime concentration; crime prevention; crime on public transport
Dr. Patricio Estevez-Soto
E-Mail Website
Co-Guest Editor
University College London Jill Dando Institute of Security and Crime Science, 35 Tavistock Square, London WC1H 9EZ, England
Interests: hot spots policing; crime in Latin America and the Caribbean; Problem Oriented Policing; situational prevention of organised crime
Ms. Sophie Curtis-Ham
E-Mail Website
Assistant Guest Editor
Faculty of Arts and Social Sciences & NZ Institute of Security and Crime Science, University of Waikato, Knighton Road, Hamilton 3240, New Zealand
Interests: geographic crime analysis; geographic offender profiling; behavioural offender profiling; environmental criminology; investigative psychology; evidence based policing; crime harm
Mr. José Luis Hernandez
E-Mail Website
Assistant Guest Editor
University College London Jill Dando Institute of Security and Crime Science, 35 Tavistock Square, London WC1H 9EZ, England
Interests: Hot spots policing; Spatio-temporal analysis; crime scripts of criminal groups; Networks of criminal groups; Geographic Intelligence; Situational Prevention

Special Issue Information

Dear Colleagues,

Crime has an inherent geographic quality. For a crime to occur, it has to happen at some place, at some time. Analyzing the geography of crime is vital for developing our understanding of crime.

This Special Issue will provide contemporary research on geographic crime analysis. We are seeking contributions that advance existing techniques or introduces new techniques for better understanding the geography of crime. Papers should be original research manuscripts that meet with the journal's research articles requirements. Topics the Special Issue on Geographic Crime Analysis we anticipate will include are:

  • Crime concentration and hot spot analysis
  • Spatial-temporal analysis
  • Repeat and near-repeat victimization
  • Risky facilities
  • Persistent, emerging and dispersed spatial patterns of crime
  • Geographic offender profiling (for criminal investigations)
  • Spatial regression analysis
  • Mapping and analyzing risk (including forecasting and prediction)
  • Crime harm mapping
  • Impact evaluation techniques
  • Simulation of crime patterns (and testing “what if“ scenarios)

Papers submitted for consideration must identify which of these topics the paper addresses by listing one (or more) of these topics in the key words associated with the manuscript

 

Guest Editors

Dr. Spencer Chainey

Dr. Matt Ashby

Dr. Patricio Estevez-Soto

Sophie Curtis-Ham

José Luis Hernandez


 

Keywords

  • geographic crime analysis
  • spatial and Spatio-temporal analysis techniques
  • geographic offender profiling
  • spatial and Spatio-temporal patterns of crime

Published Papers (3 papers)

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Research

Article
Do Mobile Phone Data Provide a Better Denominator in Crime Rates and Improve Spatiotemporal Predictions of Crime?
ISPRS Int. J. Geo-Inf. 2021, 10(6), 369; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10060369 - 31 May 2021
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Abstract
This article assesses whether ambient population is a more suitable population-at-risk measure for crime types with mobile targets than residential population for the purpose of intelligence-led policing applications. Specifically, the potential use of ambient population as a crime rate denominator and predictor for [...] Read more.
This article assesses whether ambient population is a more suitable population-at-risk measure for crime types with mobile targets than residential population for the purpose of intelligence-led policing applications. Specifically, the potential use of ambient population as a crime rate denominator and predictor for predictive policing models is evaluated, using mobile phone data (with a total of 9,397,473 data points) as a proxy. The results show that ambient population correlates more strongly with crime than residential population. Crime rates based on ambient population designate different problem areas than crime rates based on residential population. The prediction performance of predictive policing models can be improved by using ambient population instead of residential population. These findings support that ambient population is a more suitable population-at-risk measure, as it better reflects the underlying dynamics in spatiotemporal crime trends. Its use has therefore much as-of-yet unused potential not only for criminal research and theory testing, but also for intelligence-led policy and practice. Full article
(This article belongs to the Special Issue Geographic Crime Analysis)
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Article
Improving Victimization Risk Estimation: A Geographically Weighted Regression Approach
ISPRS Int. J. Geo-Inf. 2021, 10(6), 364; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10060364 - 28 May 2021
Viewed by 511
Abstract
Standardized crime rates (e.g., “homicides per 100,000 people”) are commonly used in crime analysis as indicators of victimization risk but are prone to several issues that can lead to bias and error. In this study, a more robust approach (GWRisk) is proposed for [...] Read more.
Standardized crime rates (e.g., “homicides per 100,000 people”) are commonly used in crime analysis as indicators of victimization risk but are prone to several issues that can lead to bias and error. In this study, a more robust approach (GWRisk) is proposed for tackling the problem of estimating victimization risk. After formally defining victimization risk and modeling its sources of uncertainty, a new method is presented: GWRisk uses geographically weighted regression to model the relation between crime counts and population size, and the geographically varying coefficient generated can be interpreted as the victimization risk. A simulation study shows how GWRisk outperforms naïve standardization and Empirical Bayesian Estimators in estimating risk. In addition, to illustrate its use, GWRisk is applied to the case of residential burglaries in Belo Horizonte, Brazil. This new approach allows more robust estimates of victimization risk than other traditional methods. Spurious spikes of victimization risk, commonly found in areas with small populations when other methods are used, are filtered out by GWRisk. Finally, GWRisk allows separating a reference population into segments (e.g., houses, apartments), estimating the risk for each segment even if crime counts were not provided per segment. Full article
(This article belongs to the Special Issue Geographic Crime Analysis)
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Article
A National Examination of the Spatial Extent and Similarity of Offenders’ Activity Spaces Using Police Data
ISPRS Int. J. Geo-Inf. 2021, 10(2), 47; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10020047 - 23 Jan 2021
Viewed by 1335
Abstract
It is well established that offenders’ routine activity locations (nodes) shape their crime locations, but research examining the geography of offenders’ routine activity spaces has to date largely been limited to a few core nodes such as homes and prior offense locations, and [...] Read more.
It is well established that offenders’ routine activity locations (nodes) shape their crime locations, but research examining the geography of offenders’ routine activity spaces has to date largely been limited to a few core nodes such as homes and prior offense locations, and to small study areas. This paper explores the utility of police data to provide novel insights into the spatial extent of, and overlap between, individual offenders’ activity spaces. It includes a wider set of activity nodes (including relatives’ homes, schools, and non-crime incidents) and broadens the geographical scale to a national level, by comparison to previous studies. Using a police dataset including n = 60,229 burglary, robbery, and extra-familial sex offenders in New Zealand, a wide range of activity nodes were present for most burglary and robbery offenders, but fewer for sex offenders, reflecting sparser histories of police contact. In a novel test of the criminal profiling assumptions of homology and differentiation in a spatial context, we find that those who offend in nearby locations tend to share more activity space than those who offend further apart. However, in finding many offenders’ activity spaces span wide geographic distances, we highlight challenges for crime location choice research and geographic profiling practice. Full article
(This article belongs to the Special Issue Geographic Crime Analysis)
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