Crime Hot Spots Research Articles

Crime concentration in Perth CBD: a comparison of officer predicted hot spots, data derived hot spots and officer GPS patrol data

ABSTRACTIn an applied criminology context, recent meta-analyses and randomized control trials have demonstrated the benefits of targeting police patrols at hot spots or concentrations of street level crime and disorder. This study asked a group of 79 police officers from Perth to make a prediction, based on their experience, of where hot spots of crime would occur in the near future. Officer-defined hot spots were then compared with hot spots derived from police crime data over the preceding 24 month period. Finally, officer patrol time was tracked using a GPS-enabled smart phone and overlayed against both types of hot spot. This analysis indicates that police officers should be supported with hot spot mapping tools which identify data derived micro-places with persistent issues. Analysis also reveals officers patrol both their own and data-derived hot spots regularly; however, they only stay for a matter of a few minutes. These short stays are contrary to best evidence, which dictates officer patrols in hot spots should last for approximately 15 minutes in order to create both initial and residual deterrence.

Examining the impact of the Freddie Gray unrest on perceptions of the police

Criminology & Public PolicyVolume 18, Issue 1 p. 207-207 CORRIGENDUMFree Access Examining the impact of the Freddie Gray unrest on perceptions of the police First published: 24 March 2019 https://doi.org/10.1111/1745-9133.12439AboutSectionsPDF ToolsExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL In White, Weisburd, and Wire (2018), the authors wish to correct the footnote 4 to: The initial threshold for violent and drug crime was 18 drug calls and 19 violence-related calls, respectively (approximately the top 2.5% of segments in the city for each category). Although, this was the final threshold for the combined violent and drug crime hot spots, to meet sampling goals for streets that were hot spots of violence or hot spots of drug crime the threshold was reduced to 17 violent calls and 16 drug crime calls respectively (approximately the top 3% of all city street segments in that category). We also required that streets evidence drug or violent crime throughout the year by setting a criterion that calls be spread across at least 6 months. In our sampling frame of residential streets (4,630), 284 were classified at violent crime hot spots, 248 as drug crime hot spots, 98 as combined drug and violent hot spots, and 4,000 were comparison street segments. We apologize for this error. REFERENCE White, C., Weisburd, D., & Wire, S. (2018). Examining the impact of the Freddie Gray unrest on perceptions of the police. Criminology & Public Policy, 17, 829– 858. https://doi.org/10.1111/1745-9133.12404 Volume18, Issue1February 2019Pages 207-207 ReferencesRelatedInformation

The Spatial and Social Patterning of Property and Violent Crime in Toronto Neighbourhoods: A Spatial-Quantitative Approach

Criminal activities are often unevenly distributed over space. The literature shows that the occurrence of crime is frequently concentrated in particular neighbourhoods and is related to a variety of socioeconomic and crime opportunity factors. This study explores the broad patterning of property and violent crime among different socio-economic stratums and across space by examining the neighbourhood socioeconomic conditions and individual characteristics of offenders associated with crime in the city of Toronto, which consists of 140 neighbourhoods. Despite being the largest urban centre in Canada, with a fast-growing population, Toronto is under-studied in crime analysis from a spatial perspective. In this study, both property and violent crime data sets from the years 2014 to 2016 and census-based Ontario-Marginalisation index are analysed using spatial and quantitative methods. Spatial techniques such as Local Moran’s I are applied to analyse the spatial distribution of criminal activity while accounting for spatial autocorrelation. Distance-to-crime is measured to explore the spatial behaviour of criminal activity. Ordinary Least Squares (OLS) linear regression is conducted to explore the ways in which individual and neighbourhood demographic characteristics relate to crime rates at the neighbourhood level. Geographically Weighted Regression (GWR) is used to further our understanding of the spatially varying relationships between crime and the independent variables included in the OLS model. Property and violent crime across the three years of the study show a similar distribution of significant crime hot spots in the core, northwest, and east end of the city. The OLS model indicates offender-related demographics (i.e., age, marital status) to be a significant predictor of both types of crime, but in different ways. Neighbourhood contextual variables are measured by the four dimensions of the Ontario-Marginalisation Index. They are significantly associated with violent and property crime in different ways. The GWR is a more suitable model to explain the variations in observed property crime rates across different neighbourhoods. It also identifies spatial non-stationarity in relationships. The study provides implications for crime prevention and security through an enhanced understanding of crime patterns and factors. It points to the need for safe neighbourhoods, to be built not only by the law enforcement sector but by a wide range of social and economic sectors and services.

Geospatial Assessment of Crime and Security in Ido Local Government Area, Oyo State, SouthWest Nigeria

Utilization of Information Technology is required in public safety management in the cities and their suburb such as the present Ido area, Ido local government Ibadan. Geographic Information System (GIS) is an important tool that can be used to locate crime area, and thus allow police to proactively respond to the situations before constituting a problem. This research investigates crime and security in Ido Local Government Area of Oyo State, Southwest Nigeria with the aim of mapping and assessing the area prone to crime, leading causes. The methodology adopted in this research was field surveying methods with the use of Hi-target differential GPS to acquire the spatial coordinate (x, y, z) of crime hotspots/locations and police stations in the study area. The local government consists of five areas namely; Omi-Adio with seven (7) communities, Ologuneru with eight (8) communities, Ido with thirteen (13) communities, Apete with fifteen (15) communities and Apata division with eleven (11) communities. The data acquired through field surveying measurement with the Hi-target differential GPS was processed with Hi-target Geomatics Office (HGO) and Global Navigation Satellite System (GNSS) application software in order to acquire the spatial location x, y, z data of the crime hotspots and police stations. Proximity of police stations to crime hotspots was calculated to know how each of the crime hotspot was distant to the police station. Further processing was done with the use of Quick Bird satellite imagery, ArcGIS 10.2.1 (Arcmap 10.2.1) GIS application software in analyzing the location of crime and also identification of crime patterns. The result from the crime data obtained from the field measurement and police was presented in form of charts and map/plan. Generally from the research, it can be concluded that the major crime types common in the study area. Therefore, the security issues in Ido local government has been dealt with but still requires more attentions by the government and the law enforcement agencies.

Assist Crime Prevention Using Machine Learning

Crime rate is increasing significantly over the years. Crime prevention is the attempt to reduce and deter the crimes and the criminals. The government must go beyond law enforcement and criminal justice to tackle the risk factors that cause crime because it is more cost effective and leads to greater social benefits. The data driven method is used which is based on the broken windows theory, having an enormous impact on the working of the police department. The theory links disorder and incivility within a community to subsequent occurrences of serious crimes. Predictive policing is used by the law enforcement stakeholders for taking proactive measures against crimes. This will help the police departments to efficiently focus their resources on the potential crime hotspots. The model is built to predict the crime rate based on demographic and economic information of particular localities using decision trees, linear classification, regression and spatial analysis.

Crime Risk Maps: A Multivariate Spatial Analysis of Crime Data

In crime analyses, maps showing the degree of risk help police departments to make decisions on operational matters, such as where to patrol or how to deploy police officers. This study statistically models spatial crime data for multiple crime types in order to produce joint crime risk maps. To effectively model and map the spatial crime data, we consider two important characteristics of crime occurrences: the spatial dependence between sites, and the dependence between multiple crime types. We reflect both characteristics in the model simultaneously using a generalized multivariate conditional autoregressive model. As a real‐data application, we examine the number of incidents of vehicle theft, larceny, and burglary in 83 census tracts of San Francisco in 2010. Then, we employ a Bayesian approach using a Markov chain Monte Carlo method to estimate the model parameters. Based on the results, we detect the crime hotspots, thus demonstrating the advantage of using a multivariate spatial analysis for crime data.

Community Crime Prevention in High–Crime Areas: The Seattle Neighborhood Group Hot Spots Project

Hot spots policing, in which police resources are directed toward small geographic areas with high crime levels, has been widely implemented and evaluated, but less is known about the effectiveness of nonpolice efforts to address high–crime locations. Here, we examine the effectiveness of two hot spot interventions led by a community–based nonprofit organization in Seattle, Washington. We use interrupted time series analysis to assess changes in total calls, as well as drug and disorder events at each site and in catchment areas surrounding each site. We find evidence of significant postintervention declines in calls for one treatment site and a decline in disorder in the second site. Overall, the results provide initial evidence that community–led crime prevention efforts can have a positive impact on calls in crime and disorder hot spots without significant spatial displacement of crime and disorder. Furthermore, these approaches may be an optimal response to residential hot spots in particular, given current concerns about community reactions to intensive police approaches focused primarily on enforcement.

Spatiotemporal Modeling of Correlated Small‐Area Outcomes: Analyzing the Shared and Type‐Specific Patterns of Crime and Disorder

This research applies a Bayesian multivariate modeling approach to analyze the spatiotemporal patterns of physical disorder, social disorder, property crime, and violent crime at the small‐area scale. Despite crime and disorder exhibiting similar spatiotemporal patterns, as hypothesized by broken windows and collective efficacy theories, past studies often analyze a single outcome and overlook the correlation structures between multiple crime and disorder types. Accounting for five covariates, the best‐fitting model partitions the residual risk of each crime and disorder type into one spatial shared component, one temporal shared component, and type‐specific spatial, temporal, and space–time components. The shared components capture the underlying spatial pattern and time trend common to all types of crime and disorder. Results show that population size, residential mobility, and the central business district are positively associated with all outcomes. The spatial shared component is found to explain the largest proportion of residual variability for all types of crime and disorder. Spatiotemporal hotspots of crime and disorder are examined to contextualize broken windows theory. Applications of multivariate spatiotemporal modeling with shared components to ecological crime theories and crime prevention policy are discussed.

Registered crime hot spots in Ostrava – location, development and influencing factors

Registered crime hot spots in Ostrava – location, development and influencing factors

Crime modeling with truncated Lévy flights for residential burglary models

Statistical agent-based models for crime have shown that repeat victimization can lead to predictable crime hotspots (see e.g. M. B. Short, M. R. D’Orsogna, V. B. Pasour, G. E. Tita, P. J. Brantingham, A. L. Bertozzi and L. B. Chayes, A statistical model of criminal behavior, Math. Models Methods Appl. Sci. 18 (2008) 1249–1267.), then a recent study in one-space dimension (S. Chaturapruek, J. Breslau, D. Yazdi, T. Kolokolnikov and S. G. McCalla, Crime modeling with Lévy flights, SIAM J. Appl. Math. 73 (2013) 1703–1720.) shows that the hotspot dynamics changes when movement patterns of the criminals involve long-tailed Lévy distributions for the jump length as opposed to classical random walks. In reality, criminals move in confined areas with a maximum jump length. In this paper, we develop a mean-field continuum model with truncated Lévy flights (TLFs) for residential burglary in one-space dimension. The continuum model yields local Laplace diffusion, rather than fractional diffusion. We present an asymptotic theory to derive the continuum equations and show excellent agreement between the continuum model and the agent-based simulations. This suggests that local diffusion models are universal for continuum limits of this problem, the important quantity being the diffusion coefficient. Law enforcement agents are also incorporated into the model, and the relative effectiveness of their deployment strategies are compared quantitatively.

ANALYSING THE TEMPORAL PATTERN OF CRIME HOTSPOTS IN SOUTH YORKSHIRE, UNITED KINGDOM

Visualizing the temporal pattern of crime hot spots supports the police authority in allocating resources, tactical planning, better management and decision making processes. This paper aimed at identifying temporal pattern of selected crime types (Antisocial behaviour (ASB), Vehicle crime and violent crime) in South Yorkshire, United Kingdom (UK).The study analysed 2011 South Yorkshire Police recorded crime data using statistical based method (graphical display) in Microsoft excel to identify monthly, seasonal pattern and trends of each crime type. Visual inspection from the temporal pattern of each crime type hot spot (figure 1 to 6) shows relationship between each crime type and months or seasons of the year. Result of the study reveals that autumn 2011 recorded the highest rates of violent crime and Spring 2011 had the lowest rates (figure 6).Finding of the research depicts that vehicle crime were higher in autumn and lower in spring 2011 (figure 4). July 2011 had the highest rates of ASB rates while December 2011 displayed lowest rates in figure 1.Monthly trend of crime in 2011 indicates (figure 5) that March 2011 had the highest rates of violent crime while September had the lowest rates. An interesting pattern in monthly violent crime was shown in figure 5, as R2 value of 0.67 which reveals strong positive correlation between of the months of the year while R2 value of 0.3967 indicates weak correlation in seasonal trend of violent crime (figure 6). Rates of vehicle crime were higher in January 2011 and lower in April 2011 (figure 3). Monthly pattern of vehicle shows R2 value of 0.01048 in figure 3 shows no correlation between vehicle crime and the months of the year while figure 4 displays a remarkable pattern with R2 value of 0.75 that indicates strong positive correlation between the seasons and vehicle crime. Interestingly, an examination from the R 2 of ASB temporal hot spots shows(figure 1 to 2), R2 of 0.00112 shows that no correlation exists between ASB hot spots and the months while strong positive exist between the seasons with R 2 value of 0.7392.

K-Means Clustering and Firefly Algorithm for Shortest Route Solution based on Crime Hotspots

K-Means Clustering and Firefly Algorithm for Shortest Route Solution based on Crime Hotspots

Mapping Crime Hotspots in Akure, Nigeria: A GIS Perspective

<p><em>African countries in recent times have witnessed an unprecedented level of insecurity. This has made national security threat to be a major issue for the government and has prompted huge allocation of the national budget to security. The objective of this paper is to mark out crime hotspot areas in the central area of Akure metropolis; and examine the rate at which crimes are committed, the surveillance method used and its effectiveness. The research made use of Google Earth imagery and Geographical Information System (GIS) technology to delineate crime hotspots in Akure. A 100m buffer was created around the identified crime hotspots to select houses that are susceptible to the impacts of crime activities in the study area. A structured questionnaire was also used to elicit information on crime, surveillance method, and effect of crime on the people and level of occurrence in the city among others. Findings identified six major crimes in eight hotspot areas; noise, loss of property and threat to life were some of the effects of hotspots on residents. The paper recommends construction/repair of roads in the core residential areas for effective surveillance of hotspots. The use of satellite images to combat crime in the city is also canvassed.</em><em></em></p>

The Impact of Hot Spots Policing on Collective Efficacy: Findings from a Randomized Field Trial

In disadvantaged neighborhoods, prior research has found reduced social cohesion and less willingness among residents to address disruptive behaviors and violations of social norms. This deficiency is commonly associated with higher levels of disorder and crime. Therefore, recent scholarship has begun to consider whether police can help foster collective efficacy, especially in places struggling with serious crime problems. Early results are somewhat mixed. Yet the cooperation hypothesis asserts that when neighborhood residents see police as a more viable and reliable resource, residents will be emboldened to exert informal social control to address problems. Over the last two decades, hot spots policing has been recognized as an effective method to reduce crime. At the same time, there have been few rigorous studies of whether this approach impacts collective efficacy at hot spots. To investigate this question, we conducted an experiment in 71 crime hot spots, comparing a collaborative problem solving versus a directed patrol (police presence) approach versus standard policing practices. Over time, a substantial increase in police presence did appear to promote modest improvements in collective efficacy. We attribute this finding to the cooperation hypothesis.

Examining the extent to which repeat and near repeat patterns can prevent crime

PurposeThe purpose of this paper is to examine the extent and variation in the estimates to which crime can be prevented using patterns of repeats and near repeats, and whether hotspot analysis complements these patterns.Design/methodology/approachCrime data for four study areas in New Zealand are used to examine differences in the extent of burglary repeat and near repeat victimisation. Hotspots of burglary are also created to determine the extent to which burglary repeats and near repeats spatially intersect hotspots.FindingsThe extent of repeats and near repeats varies, meaning there is variation in the estimated prevention benefits that repeat and near repeat patterns offer. In addition, at least half of the burglaries repeats and near repeats were not located within hotspots.Research limitations/implicationsThe use of other techniques for examining crime concentration could be used to improve the research observations.Practical implicationsBy showing that levels of repeats and near repeats vary, the extent to which these observations coincide in hotspots offers practitioners a better means of determining whether repeat and near repeat patterns are reliable for informing crime prediction and crime prevention activities.Originality/valueThe paper is the first known research study that explicitly measures the variation in the extent of repeats and near repeats and the spatial intersection of these patterns within crime hotspots. The results suggest that rather than considering the use of repeat and near repeat patterns as a superior method for predicting and preventing crime, value remains in using hotspot analysis for determining where crime is likely to occur, particularly when hotspot analysis emphasises other locations for resource targeting.

Collaborative problem-solving at youth crime hot spots: a pilot study

PurposeThe purpose of this paper is to describe a case study of a pilot program in which a collaborative problem-solving approach was implemented at hot spots of juvenile and youth crime in downtown Seattle, Washington.Design/methodology/approachTwo matched pairs of youth crime hot spots were allocated at random to treatment (“non-enforcement problem-solving”) or comparison (“policing-as-usual”) conditions within matched pairs. In the treatment condition, police collaborated with community and local government partners to develop problem-solving strategies that deemphasized arrests and other traditional law enforcement approaches. Impacts on crime incidents, calls for service, and police activity were assessed using difference-in-differences Poisson regression with robust standard errors.FindingsNo significant impact on crime or calls for service was observed at one site, where several problem-solving approaches were successfully implemented. However, crime and calls for service were significantly lower at the other site, where some enforcement activity took place but non-enforcement problem-solving was limited.Research limitations/implicationsThe authors find mixed support for non-enforcement problem-solving at hot spots. The enforcement may be necessary for stabilization, and must be balanced with the risks of justice system involvement for youth. Political support at the city level is necessary for collaboration. Limitations include the small number of sites in this pilot study and key differences between treatment and comparison locations.Originality/valueThis study is one of the first to assess the impact of primarily non-enforcement problem-solving specifically at youth crime hot spots.

Mean Streets and Mental Health: Depression and Post-Traumatic Stress Disorder at Crime Hot Spots.

This study explores the relationship between mental health and place at microgeographic units of analysis. We examine self-reported symptomology for depression and PTSD for 2,724 survey respondents interviewed in three types of randomly selected street segments: violent crime hot spots, cool spots, and cold spots. We find that the mean symptomology score is 61% higher for depression in violent crime hot spots than cold spots, and 85% higher for PTSD. Overall, we estimate that 14.8% of residents of violent crime hot spots meet thresholds for moderate depression or a diagnosis of PTSD. This can be compared to only 6.5% of residents at the cold spots. Using PSM and weighted negative binomial regression approaches we show that observable selection factors are not responsible for the relationships identified. Examining geographic influences, we find an important area effect of violent crime for both mental health measures, and an additional impact of the specific street of residence for PTSD.

Testing Indicators of Risk Populations for Theft from the Person across Space and Time: The Significance of Mobility and Outdoor Activity

In recent years, it has increasingly been recognized that due to the uncertain geographic context problem caused by daily human mobility, the residential population is too static to serve as a valid measure of the population at risk for criminal victimization. Various alternative measures have been suggested instead. Guided by the routine activity approach, this study furthers the concept of crime risk population and its measurement across space and time. Using exceptionally comprehensive data sets on population mobility and on theft from the person in a large city in China, we select the best indicator of the risk population from the following four candidates: residential population, subway ridership, taxi ridership, and mobile phone users. Controlling for the potentially confounding effects of offender and guardian presence, we show that on both weekdays and weekends, the best indicators of risk population vary over the course of the day. In the morning, residential population outperforms other measures. In the afternoon and evening, taxi ridership and phone users are better indicators. Although the mobile phone user base forms an arguably more representative measure of ambient population, during some periods taxi ridership is superior because it provides a better indicator of outdoor (as opposed to indoor) activities. In terms of practical applications to security policy and law enforcement, these findings can help identify crime hot spots by calculating accurate crime risks.

Crime Mapping in GIS By Using Hotspot

Police forces use hotspot mapping to provide a single out towards to resource allocation, ensuring police officers are post to areas of high crime where their existence will have the most smash. Hotspot intelligence products are reliant on crime data sourced from police databases, and positional mistake in this data will have an impact on the accuracy of the hotspot maps make The postion of crime hotspots varies across both space and time. Crime mapping and analysis plays an integral role in essentially advanced form of crime representation, visualization and respond satisfactorily to the problem of criminality. It also let the analysts to figure out how crimes are spread evenly over the zone. GIS plays an effective role in mapping of crime. This paper puts on the diverse utilities of hotspot in GIS to recognize the crime in addition to encourage the advancement of investigation inclination strategy for policing. The functional approach in the present investigation for crime mapping can be successfully applied for improvement of user-interfaces stage for the advancement of safe city strategies.

Asynchronous Instabilities of Crime Hotspots for a 1-D Reaction-Diffusion Model of Urban Crime with Focused Police Patrol

We analyze the existence and linear stability of steady-state localized hotspot patterns for a 1-D three-component singularly perturbed reaction-diffusion (RD) system modeling urban crime in the pr...