Spatial Patterns Of Crime Research Articles

Estimating the Spatial Distribution of Crime Events around a Football Stadium from Georeferenced Tweets

Crowd-based events, such as football matches, are considered generators of crime. Criminological research on the influence of football matches has consistently uncovered differences in spatial crime patterns, particularly in the areas around stadia. At the same time, social media data mining research on football matches shows a high volume of data created during football events. This study seeks to build on these two research streams by exploring the spatial relationship between crime events and nearby Twitter activity around a football stadium, and estimating the possible influence of tweets for explaining the presence or absence of crime in the area around a football stadium on match days. Aggregated hourly crime data and geotagged tweets for the same area around the stadium are analysed using exploratory and inferential methods. Spatial clustering, spatial statistics, text mining as well as a hurdle negative binomial logistic regression for spatiotemporal explanations are utilized in our analysis. Findings indicate a statistically significant spatial relationship between three crime types (criminal damage, theft and handling, and violence against the person) and tweet patterns, and that such a relationship can be used to explain future incidents of crime.

Football pollution: an investigation of spatial and temporal patterns of crime in and around stadia in England

Inspired by theories of environmental criminology, this paper is concerned with the criminogenic potential of football matches. Do matches generate patterns of crime within grounds and beyond them? If so, over what period and over what distance are these effects produced? Police-recorded data for five football stadia for a 6-year timeframe (2005–2010) are examined using non-parametric permutation tests. The spatial extent of any patterns are quantified and used to further examine differences in the temporal distribution of crime and incidents. Change in the spatial distribution of crime and incidents occurred around all five stadia and did so during those periods when the ambient population was elevated on match days. The results provide further support for theories of environmental criminology, suggesting that there is a higher risk of crime and incidents in the areas immediately around stadia during the hours when matches take place.

The Local Colocation Patterns of Crime and Land-Use Features in Wuhan, China

Most studies of spatial colocation patterns of crime and land-use features in geographical information science and environmental criminology employ global measures, potentially obscuring spatial inhomogeneity. This study investigated the relationships of three types of crime with 22 types of land-use in Wuhan, China. First, global colocation patterns were examined. Then, local colocation patterns were examined based on the recently-proposed local colocation quotient, followed by a detailed comparison of the results. Different types of crimes were encouraged or discouraged by different types of land-use features with varying intensity, and the local colocation patterns demonstrated spatial inhomogeneity.

Spatial/Temporal Variations of Crime: A Routine Activity Theory Perspective.

Temporal and spatial patterns of crime in Campinas, Brazil, are analyzed considering the relevance of routine activity theory in a Latin American context. We use geo-referenced criminal event data, 2010-2013, analyzing spatial patterns using census tracts and temporal patterns considering seasons, months, days, and hours. Our analyses include difference in means tests, count-based regression models, and Kulldorff's scan test. We find that crime in Campinas, Brazil, exhibits both temporal and spatial-temporal patterns. However, the presence of these patterns at the different temporal scales varies by crime type. Specifically, not all crime types have statistically significant temporal patterns at all scales of analysis. As such, routine activity theory works well to explain temporal and spatial-temporal patterns of crime in Campinas, Brazil. However, local knowledge of Brazilian culture is necessary for understanding a portion of these crime patterns.

A spatio-temporal analysis of urban crime in Beijing: Based on data for property crime

In recent years, Chinese crime information has become more transparent and open than ever, thus providing an excellent opportunity for urban crime study by academics. To obtain a better understanding of the spatial pattern of urban crime, the city of Beijing is chosen as a study area and GIS software is employed to collect spatial data. The authors try to establish the quantitative representation of geographical characteristics of crime associated with urban space in order to create a geographical model of urban crime and space. The authors find that the overall spatial distribution of urban crime in Beijing displays a picture of polycentric structure and distance decay, and that the spatial distribution of urban crime has a reference to traffic centres, concentration of urban commerce and population migration. The numbers of suspects and locations where different types of crime happen have inter-annual variation, while both the total number of crime sites and their criminal density within each district are relatively stable. The authors point out that the spatio-temporal characteristics of sites act on both the participants in property crimes and the criminal factors, which will decide whether or not an offender can commit a crime successfully. The hot spots and periods of time of urban crime in Beijing have a close relation to the fact that success in committing a property crime is based on certain conditions of sites and times which appear in Beijing’s environment. In this sense, socio-spatial dialectic provides a better understanding of the dynamics of China’s crime space. Finally, the spatial anti-crime strategies for urban crime and the insufficiency of research are discussed.

INVESTIGATING THE INFLUENCE OF TREE COVERAGE ON PROPERTY CRIME: A CASE STUDY IN THE CITY OF VANCOUVER, BRITISH COLUMBIA, CANADA

Abstract. With the development of Geographic Information Systems (GIS), crime mapping becomes an effective approach to investigate the spatial pattern of crime in a defined area. Understanding the relationship between crime and its surrounding environment can reveal possible strategies that can reduce crime in a neighbourhood. The relationship between vegetation density and crime has been under debate for a long time. This research is conducted to investigate the impacts of tree coverage on property crime in the City of Vancouver. High spatial resolution airborne LiDAR data collected in 2013 was used for the extraction of tree covered area for cross-sectional analysis. The independent variables were put into Ordinary Least-Squares (OLS) regression, Spatial Lag regression, and Geographically Weighted Regression (GWR) models to examine their influences on property crime rates. According to the results, the cross-sectional analysis demonstrated statistical evidences that property crime rates had negative correlations with tree coverage, with greater influences occurred around Downtown Vancouver.

INVESTIGATING THE INFLUENCE OF TREE COVERAGE ON PROPERTY CRIME: A CASE STUDY IN THE CITY OF VANCOUVER, BRITISH COLUMBIA, CANADA

With the development of Geographic Information Systems (GIS), crime mapping becomes an effective approach to investigate the spatial pattern of crime in a defined area. Understanding the relationship between crime and its surrounding environment can reveal possible strategies that can reduce crime in a neighbourhood. The relationship between vegetation density and crime has been under debate for a long time. This research is conducted to investigate the impacts of tree coverage on property crime in the City of Vancouver. High spatial resolution airborne LiDAR data collected in 2013 was used for the extraction of tree covered area for cross-sectional analysis. The independent variables were put into Ordinary Least-Squares (OLS) regression, Spatial Lag regression, and Geographically Weighted Regression (GWR) models to examine their influences on property crime rates. According to the results, the cross-sectional analysis demonstrated statistical evidences that property crime rates had negative correlations with tree coverage, with greater influences occurred around Downtown Vancouver.

Street profile analysis: A new method for mapping crime on major roadways

Street profile analysis is a new method for analyzing temporal and spatial crime patterns along major roadways in metropolitan areas. This crime mapping technique allows for the identification of crime patterns along these street segments. These are linear spaces where aggregate crime patterns merge with crime attractors/generators and human movement to demonstrate how directionality is embedded in city infrastructures. Visually presenting the interplay between these criminological concepts and land use can improve police crime management strategies. This research presents how this crime mapping technique can be applied to a major roadway in Burnaby, Canada. This technique is contrasted with other crime mapping methods to demonstrate the utility of this approach when analyzing the rate and velocity of crime patterns overtime and in space.

Intra-week spatial-temporal patterns of crime

Since its original publication, routine activity theory has proven most instructive for understanding temporal patterns in crime. The most prominent of the temporal crime patterns investigated is seasonality: crime (most often assault) increases during the summer months and decreases once routine activities are less often outside. Despite the rather widespread literature on the seasonality of crime, there is very little research investigating temporal patterns of crime at shorter time intervals such as within the week or even within the day. This paper contributes to this literature through a spatial-temporal analysis of crime patterns for different days of the week. It is found that temporal patterns are present for different days of the week (more crime on weekends, as would be expected) and there is a spatial component to that temporal change. Specifically, aside from robbery and sexual assault at the micro-spatial unit of analysis (street segments) the spatial patterns of crime changed. With regard to the spatial pattern changes, we found that assaults and theft from vehicle had their spatial patterns change in predictable ways on Saturdays: assaults increased in the bar district and theft from vehicles increased in the downtown and recreational car park areas.

Crime seasonality and the micro-spatial patterns of property crime in Vancouver, BC and Ottawa, ON

PurposeEarly criminological research has found that the frequency of crime is not consistent throughout the year and that crime often peaks in the summer months. What has gone largely understudied, however, is how the respective spatial patterns are impacted over time. This study investigates whether seasonal variation exists temporally across different property crime types and whether these same offences possess micro-spatial patterns that vary substantially over the calendar year. MethodsA series of Andresen’s (2009) spatial point pattern tests were employed using police report data to examine the seasonal spatial patterns of crime in two Canadian cities with differing climates, namely Vancouver, BC and Ottawa, ON. ResultsOverall, results suggest that: (a) property crimes exhibit distinct temporal peaks in humid continental climates (i.e. Ottawa) and not in temperate ones (i.e. Vancouver); and (b) regardless of climate, micro-spatial patterns of property crime remain relatively constant throughout the year. ConclusionsThe findings indicate that both the temporal and spatial components of crime seasonality should be considered whenever possible in order to better understand when and where to implement crime prevention programs.

Spatial Patterns of Crime in Ottawa: The Role of Universities

This article explores the spatial distribution of crime in Ottawa in 2006. Social disorganization theory and routine activity theory provide the theoretical framework for examining the relationship between the rates of burglary, robbery, and motor vehicle theft, and the two universities, University of Ottawa and Carleton University. A spatial regression procedure that accounts for spatial autocorrelation is used in the analyses. We find support for the use of social disorganization theory and routine activity theory, with the expected relationships between the socio-demographic and socio-economic variables and crime. We also find that universities are the strongest predictors of the rates of burglary and motor vehicle theft.

Identifying changes in spatial patterns from police interventions: the importance of multiple methods of analysis

The policing initiative of foot patrol was implemented to reduce crime and disorder as well as benefit the relationship between the community and the police in the community of Lower Lonsdale, North Vancouver, British Columbia. In this paper, police incident data are analyzed to evaluate the impact of police foot patrol on the hot spots of crime in this community. Specifically, two spatial analysis techniques (kernel density estimation and local Moran’s I) are used to show how the nuances of changes in spatial crime patterns can emerge when multiple methods of analysis are used.

Marked point process hotspot maps for homicide and gun crime prediction in Chicago

Crime hotspot maps are a widely used and successful method of displaying spatial crime patterns and allocating police resources. However, hotspot maps are often created over a single timescale using only one crime type. In the case of short-term hotspot maps that utilize several weeks of crime data, risk estimates suffer from a high variance, especially for low frequency crimes such as homicide. Long-term hotspot maps that utilize several years of data fail to take into account near-repeat effects and emerging hotspot trends. In this paper we show how point process models of crime can be extended to include leading indicator crime types, while capturing both short-term and long-term patterns of risk, through a marked point process approach. Several years of data and many different crime types are systematically combined to yield accurate hotspot maps that can be used for the purpose of predictive policing of gun-related crime. We apply the methodology to a large, open source data set which has been made available to the general public online by the Chicago Police Department.

Collective Efficacy, Deprivation and Violence in London

This paper examines the importance of neighbourhood context in explaining violence in London. Exploring in a new context Sampson’s work on the relationship between interdependent spatial patterns of concentrated disadvantage and crime, we assess whether collective efficacy (i.e. shared expectations about norms, values and goals, as well as the ability of members of the community to realize these goals) mediates the potential impact on violence of neighbourhood deprivation, residential stability and population heterogeneity. Reporting findings from a dataset based on face-to-face interviews with 60,000 individuals living in 4,700 London neighbourhoods, we find that collective efficacy is negatively related to police-recorded violence. But, unlike previous research, we find that collective efficacy does not mediate the statistical relationship between structural characteristics of the neighbourhood and violence. After finding that collective efficacy is unrelated to an alternative measure of neighbourhood violence, we discuss limitations and possible explanations for our results, before setting out plans for further research.

Exploring Spatial Patterns of Property Crime Risks in Changchun, China

Urban crime has increasingly become a major issue for Chinese cities. Using crime data collected at police precincts in 2008, the main aim of this research is to examine the spatial distribution of property crime which accounted for almost 82% of all crimes in the city of Changchun, and analyze the relationship between the spatial patterns of property crime and neighborhood characteristics. Standardized property crime rates (SCR) were applied to assess the relative risk of property crime across the city. Statistically significant clusters of high-risk areas or hot-spots were detected. A global ordinary least squares (OLS) regression model and a geographically weighted regression (GWR) model were calibrated to explore the risk of property crime as a function of contextual neighborhood characteristics. The analytical results show that significant local variations exist in the relationship between the risk of property crime and several neighborhood socioeconomic variables.

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.

Spatial Analysis of Residents' Fear and Feeling of Insecurity in Ile-Ife, Nigeria

This study examined spatial pattern of crime and residents’ fear and feeling of insecurity in Ile-Ife, Nigeria. To obtain the primary data, Ile-Ife was stratified into four residential zones namely traditional town centre, middle income, high income and post-crisis residential areas. Sample was selected using systematic sampling from each zone. Level of insecurity perceived by residents in the night and day time showed that post crisis residential areas of the town recorded relative insecurity index (RII) of 4.71 in the night. The RII of traditional town centre in the night was put at 3.74. RII of 2.43 and 2.14 were computed for middle income and low income residential areas of the town in the day time. Using stepwise regression analysis, it was discovered that 86.5% of the total variance in residents’ fear and feeling of insecurity could be explained by independent variable observed. The study concluded that significant variation exist in the residents’ level of insecurity feeling in different residential zones of the study area as rate of occurrence of criminal activities in different residential areas varied significantly. Key words: Crime, Fear, Insecurity, Spatial, Risk, Victimisation.

Offenses around Stadiums

Objectives: Inspired by ecological theories of crime, the aim of this study was to make use of a natural experiment to see if a U.K. soccer stadium generates or attracts crime in the area that surrounds it. Method: Data for theft and violent crime around Wembley stadium are analyzed to see if the rate (per-unit time and ambient population) of crime differ for days on which the stadium is used and those it is not. In addition, differences in the spatial and temporal distribution of crime are examined for these two types of days. Results: Analyses indicate that on days when the stadium is used, the rate of crime per-unit time is elevated, but that the rate per ambient population at risk is not. The spatial and temporal pattern of crime also clearly differs for the two types of days. For example, the level of crime is elevated in the surrounding area when the stadium is used relative to when it is not. Conclusions. The case study suggests that the facility studied contributes to levels of crime in the area that surrounds it. The research provides further support for ecological theories of crime and their utility in informing criminological understanding and policy-related questions.

The (in)appropriateness of aggregating across crime types

Researchers in the spatial analysis literature commonly aggregate crime types: property, violent, and total crime, for example. This practice is done for a number of reasons that range from low crime counts to confidentiality to the form of data provided to researchers. But is this practice appropriate, particularly in the context of studying spatial patterns? In this paper, we analyze the appropriateness of such crime type aggregations. We find that in almost all cases, aggregating crime types is inappropriate for polygon-based analyses. Only in the cases of micro-spatial units of analysis (street segments) may these aggregations be appropriate. Therefore, aggregating crime types in a study that implicitly or explicitly considers the spatial patterns of crime is inappropriate.

Crime in rural Sweden

The objective of this article is to assess the levels and spatial patterns of crime in rural Sweden. This involves a summary of the changing levels and composition of a selected group of offences from 1996 to 2007 in two groups of rural areas (remote and accessible) in relation to urban areas. Crime rates are modelled cross-sectionally as a function of the municipalities’ structural indicators. Geographical information systems (GIS) and spatial statistics techniques are used to assess shifting patterns of concentration of thefts and violence as well as for modelling crime rates. Findings show that rural areas have become more criminogenic than they were a decade ago. Changes in the rates and geography were found using cluster techniques for both violence and theft. Although models of rural crime do not show any special ‘rural dimension’, the predicting variables in models containing both urban and rural areas are not exactly the same as in models with rural areas only. Crime is often linked to the presence of alcohol-selling premises, characteristics of family structure and proportion of young male population.