Spatial Analysis Of Crime Research Articles

Personalized crime location prediction

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.

Identifying Venues for Female Commercial Sex Work Using Spatial Analysis of Geocoded Advertisements

Despite being widely visible on the web, Internet-promoted commercial sex work has so far attracted limited attention from the side of researchers. Current studies outline the issues that new forms of sex work are associated with, however, very little is known to date about their spatial manifestation. In this research we follow the environmental perspective in spatial analysis of crime and deviance with the assumption that the location of venues for provision of commercial sex work can be modeled via the algorithms trained on the distribution of possible correlates in the proximity to the existing venues. Visualization of the acquired results is presented herein along with the errors and score metrics for evaluation of the applicability of specific methods of machine learning. The paper is concluded with the estimation of potential extensions and peculiarities of data used in the research.

Techniki GIS – w poszukiwaniu hot spotów przestępczości

Techniki GIS – w poszukiwaniu hot spotów przestępczości

Spatial Information Divergence: Using Global and Local Indices to Compare Geographical Masks Applied to Crime Data

AbstractAdvances in Geographic Information Science (GISc) and the increasing availability of location data have facilitated the dissemination of crime data and the abundance of crime mapping websites. However, data holders acknowledge that when releasing sensitive crime data there is a risk of compromising the victims' privacy. Hence, protection methodologies are primarily applied to the data to ensure that individual privacy is not violated. This article addresses one group of location protection methodologies, namely geographical masks that are applicable for crime data representations. The purpose is to identify which mask is the most appropriate for crime incident visualizations. A global divergence index (GDi) and a local divergence index (LDi) are developed to compare the effects that these masks have on the original crime point pattern. The indices calculate how dissimilar the spatial information of the masked data is from the spatial information of the original data in regards to the information obtained via spatial crime analysis. The results of the analysis show that the variable radius mask and the donut geomask should be primarily used for crime representations as they produce less spatial information divergence of the original crime point pattern than the alternative local random rotation mask and circular mask.

Why does geoprivacy matter? The scientific publication of confidential data presented on maps.

We examined published maps containing sensitive data, and the protection methods, if any, that were used. We investigated whether the many published warnings about disclosure risk have been effective in reducing privacy risk. During an 8-year period (2005-2012), 19 journals related to GIScience, geography, spatial crime analysis, and health geography were examined. We identified 41 articles that display actual confidential information and 16 articles where confidential information is protected by the use of a geographical mask. During the investigated time frame, the numbers of articles with unmasked confidential data increased, and in total more than 68,000 home addresses were disclosed. One of the more significant findings of this study is that efforts to instill sensitivity to location privacy and disclosure risk have been relatively unsuccessful.

A Metric Comparison of Predictive Hot Spot Techniques and RTM

There are numerous hot spot mapping techniques that can be used in research and in practice for predicting future crime locations. Due to differences in the varying techniques, metrics were developed to compare the accuracy and precision of these techniques. The predictive accuracy index (PAI) and recapture rate index (RRI) were used to assess six different hot spot techniques. Spatial and Temporal Analysis of Crime, Nearest Neighbor Hierarchical, Kernel Density Estimation, and Risk Terrain Modeling were the general techniques compared in relation to their PAI and RRI values for short-term and long-term prediction of robberies. The results of the study were discussed with an emphasis on the utility of using multiple techniques jointly for analysis.

Addressing issues of spatial spillover effects and non-stationarity in analysis of residential burglary crime

The aim of this paper is to address two critical but largely neglected issues in the spatial analysis of urban crime which are spatial spillover effects of crime penetrating neighborhood boundaries and non-stationarity regarding the relationships between contextual factors and neighborhood crime. We use a GIS-based spatial approach to normalize the estimate of burglary crime at block group level and use the geographically weighted regression (GWR) to investigate the correlates of neighborhood crime. Results suggest that the use of normalized measure of neighborhood crime helps better reveal the spatial patterns of burglary crime and the use of GWR accounts for the spatial variations of relationships between contextual factors and crime. In particular, the normalized measure of crime has implications for improving the measurement accuracy of the risk of crime across urban neighborhoods and can be applied to the spatial analysis of other socioeconomic issues such as housing foreclosures and environmental hazards which are also plagued by the spatial spillover issue when geographically contiguous data are analyzed.

Issues in the aggregation and spatial analysis of neighborhood crime

Prior studies of neighborhood crime have largely overlooked a border issue in the aggregation of criminal incidents to small areal units and a denominator problem arising from the use of population to normalize crime counts and have failed to discern racial disparities in crime victimization due to the use of non-race-specific crime data. This article explores the relationships between neighborhood characteristics and robbery victimization by applying an improved method for neighborhood crime aggregation and normalization to race-disaggregated incident data. Results suggest that controlling for above issues helps better explain the spatial variations of robbery victimization across urban neighborhoods. The methods and findings have implications for improving the measurement accuracy of neighborhood crime and disentangling the effects of race and other socioeconomic factors on urban crime.

Proactive Policing Effects on Repeat and Near-Repeat Shootings in Houston

The spatial analysis of crime and community problems can inform police operations by revealing where resources can be most effectively deployed. Advances in understanding the spatial concentrations of crime show that some locations are repeatedly victimized and that some nearby locations are at an elevated risk for a subsequent crime during a relatively short period of time. These are known as repeat and near-repeat phenomena. Police may be able to have a strong preventive impact on crime if these risk patterns can be identified and disrupted. This analysis reports on whether a specialized, proactive patrol unit deployed to high-crime areas was effective in disrupting repeat and near-repeat patterns of shootings. Results suggest the proactive unit did not disrupt concentrations of shootings in a meaningful way. To improve effectiveness, police practitioners and researchers should seek to understand the factors driving these patterns and then design specific interventions to address them.

Street‐level Spatial Scan Statistic and STAC for Analysing Street Crime Concentrations

AbstractThis study develops new types of hotspot detection methods to describe the micro‐space variation of the locations of crime incidents at the street level. It expands on two of the most widely used hotspot detection methods, Spatial and Temporal Analysis of Crime and Spatial Scan Statistic, and applies them to the analysis of the network space. The study first describes the conceptual and the methodological framework of the new methods followed by analyses using: (1) a simulated distribution of points along the street network; and (2) real street‐crime incident data. The simulation study using simulated point distributions confirms that the proposed methods is more accurate, stable and sensitive in detecting street‐level hotspots than their conventional counterparts are. The empirical analysis with real crime data focuses on the distribution of the drug markets and robberies in downtown Buffalo, NY in 1995 and 1996. The drug markets are found to form hotspots that are dense, compact and stable whereas hotspots of the robberies are observed more thinly across a wider area. The study also reveals that the location of the highest risk remains on the same spot over time for both types of crimes, indicating the presence of hotbeds which requires further attention.

Introduction: Progress in Research on the Geography of Crime

The research presented in this article provides an accurate narrative of the historical evolution of the geography of crime, including its major ideological controversies. It further gives credit to geographers whose works have gained considerable respect in other academic communities. Additionally, it aims to convince geographers that they have potentially very valuable contributions to make to the study of crime. The final and most important point that this narrative is trying to make is that geography's intellectual territory has become attractive to other disciplines and fields embracing the geography and spatial analysis of crime.

The Ambient Population and Crime Analysis

This article uses an alternative measure of the population at risk, the ambient population (provided by Oak Ridge National Laboratory), in crime rate calculations. It is shown through a variety of statistical analyses at two different scales of aggregation that this alternatively calculated crime rate is not always related to the conventionally calculated crime rate. The implications of this finding are that past theoretical testing and policy formation might have been based on spurious results, showing the importance of remaining current with the developments of geographic information science technologies and data availability when undertaking a spatial analysis of crime.

Risk Clusters, Hotspots, and Spatial Intelligence: Risk Terrain Modeling as an Algorithm for Police Resource Allocation Strategies

The study reported here follows the suggestion by Caplan et al. (Justice Q, 2010) that risk terrain modeling (RTM) be developed by doing more work to elaborate, operationalize, and test variables that would provide added value to its application in police operations. Building on the ideas presented by Caplan et al., we address three important issues related to RTM that sets it apart from current approaches to spatial crime analysis. First, we address the selection criteria used in determining which risk layers to include in risk terrain models. Second, we compare the “best model” risk terrain derived from our analysis to the traditional hotspot density mapping technique by considering both the statistical power and overall usefulness of each approach. Third, we test for “risk clusters” in risk terrain maps to determine how they can be used to target police resources in a way that improves upon the current practice of using density maps of past crime in determining future locations of crime occurrence. This paper concludes with an in depth exploration of how one might develop strategies for incorporating risk terrains into police decision-making. RTM can be developed to the point where it may be more readily adopted by police crime analysts and enable police to be more effectively proactive and identify areas with the greatest probability of becoming locations for crime in the future. The targeting of police interventions that emerges would be based on a sound understanding of geographic attributes and qualities of space that connect to crime outcomes and would not be the result of identifying individuals from specific groups or characteristics of people as likely candidates for crime, a tactic that has led police agencies to be accused of profiling. In addition, place-based interventions may offer a more efficient method of impacting crime than efforts focused on individuals.

A Geodemographic Profiler for High Offender Propensity Areas in the City of Tshwane, South Africa

Postapartheid South Africa has been plagued by an increase in crime across all categories. While a significant amount of criminological research has been undertaken in the country, the spatial analysis of crime and offenders, a basic prerequisite for a functional crime management strategy, has not been adequately addressed at a sufficiently fine scale of aggregation. This paper reports on the geodemographic development of offender risk profiles for neighbourhoods in the City of Tshwane Metropolitan Municipality in South Africa. Geodemographics is a relatively new geo-analytical technique that is being increasingly used in policing applications to complement law enforcement techniques and provide further insight into offenders and their offences. Findings of the study indicate that neighbourhoods at a high risk for offender development are amongst the most socially and economically deprived in the municipality and are disproportionately occupied by black Africans. The results highlight a need to reassess the current law enforcement approach to crime reduction in the country and return to the crime prevention initiatives that were part of the National Crime Prevention Strategy of the 1990s.

Análise espacial da criminalidade no Rio Grande do Sul

Este artigo faz uma análise espacial da criminalidade no Estado doRio Grande do Sul. Para este fim, é apresentado um modelo econométricoespacial para os determinantes da criminalidade. O modelo segue as contribuiçõesda economia do crime, porém acrescenta as contribuições da abordagemecológica e as teorias do aprendizado social. No modelo, a criminalidadeem cidades pode ser explicada por características locais em que o ambiente, avizinhança e o histórico do indivíduo afetam a criminalidade. São utilizadosdados municipais agregados para homicídios, roubos e furtos no ano de 2000.Os testes realizados mostram a existência de dependência espacial em roubosfurtos e independência espacial em homicídios. No artigo ficam destacadosos papéis da desigualdade de renda e das aglomerações urbanas como fatoresque potencializam a criminalidade em cidades. No artigo são discutidas tambémas importâncias da família e da escola na explicação da criminalidade.Os resultados obtidos mostram que problemas na estrutura familiar e a ineficiênciado ensino no Estado afetam positivamente a criminalidade. Nesteartigo, os benefícios do crime e os custos de oportunidade são divididos, o quepermite concluir que o crescimento econômico não implica diretamente oaumento da criminalidade. Isto porque se houver um aumento da renda dosmais pobres a criminalidade diminui.

Location Quotients, Ambient Populations, and the Spatial Analysis of Crime in Vancouver, Canada

This paper uses the location quotient, a common measurement from economic geography and regional economics, to capture the specialization of criminal activity in Vancouver, Canada. Location quotients have barely been introduced into criminological research, yet they provide additional insight into crime analysis not available using crime counts and crime rates. The location quotients for automotive theft, break and enter, and violent crimes are mapped for Vancouver, Canada, and tested using social disorganization and routine activity theory as a theoretical framework. Strong support is found for these theories to predict specialization in criminal activity by interpreting their expectations in the context of crime-specific attractors.

Geoforensic Analysis Revisited—The Application of Newton’s Geographic Profiling Method to Serial Burglaries in London, UK

The geoforensic analysis method is one of the earliest criminal geographic profiling models introduced by Newton in the late 1980s. The literature on geographic profiling acknowledges its existence, however, its methodological approach remains mostly unknown. The two main contributions of this research are first, to fill the gap in the spatial crime analysis and geographic profiling literature and second, to evaluate the applicability of the geoforensic analysis method to a larger data‐set with multiple serial offenses, compared to a single serial offense analyzed in the original study.

Software Review: Spatial Data Analysis of Crime

CrimeStat III is a spatial statistics program for the analysis of crime incident locations. The program has three main components: (1) the Spatial Description component allows the user to conduct analyses regarding spatial distribution of data, (2) the Spatial Modeling component allows the user to analyze the spatial behavior of data, and (3) the Crime Travel Demand Modeling component allows the user to analyze the behavior of potential serial offenders. Overall, CrimeStat III is fairly easy to use once an initial familiarity has been established with the software. This can be accomplished through the manual or the intuitive GUI. Currently, the only significant restriction to the utility of the software is its reliance on the Windows operating system. This review provides a discussion of technical utilization of the software, and a look at how this software is useful in a practical manner for the spatial data analysis of crime.

A spatial analysis of crime in Vancouver, British Columbia: a synthesis of social disorganization and routine activity theory

This article investigates the spatial dimension of automotive theft, break and enter, and violent crime in Vancouver, British Columbia in 1996. The article uses and synthesizes social disorganization theory and routine activity theory as a theoretical backcloth and employs a spatial autoregressive regression procedure that accounts for spatial autocorrelation between crime rates and socio‐economic characteristics at the census tract level. Strong support is found for synthesizing these two most common spatial theories of crime. In particular, high unemployment (social disorganization theory) and the presence of young populations (routine activity theory) are the strongest predictors of criminal activity.

Analyse spatiale et criminologie

This article addresses the spatial analysis of crime. Following a brief historical overview, the author discusses major conceptual and methodological issues relating to the study of space and crime. Emphasis is placed on the description of spatial statistical tools (i.e. spatial mean, standard distance, turbulence...) and on the problem of spatial autocorrection. Two maps are presented and discussed.