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.     

Crime Exposure Along My Way Home: Estimating Crime Risk Along Personal Trajectory by Visual Analytics

Crime has been one of the notorious public threats in cities. Fortunately, the increasing digital crime data provide great opportunities to analyze and control crime incidents. However, studies that predict the risk of crime exposure for an individual’s spatiotemporal paths based on historical crime big data are still limited. In this study, we have proposed the crime risk index (CRI) for spatiotemporal trajectory and built a model to estimate the CRI. Furthermore, an online crime risk analysis platform has been developed based on the model. First, we proposed a multi-scale tile system and a novel algorithm to estimate trajectory-based CRI using big historical crime data and entropy-based weighting. Second, we created a web-based platform that allows users to provide a spatiotemporal trajectory and estimate the crime risk for such trajectory. We conducted several experiments based on the crime data in Detroit. Results demonstrate the practicability and generalizability of our platform. The proposed model and platform can be applied to multiple cities, providing useful references for crime information and public safety.     

The sociospatial ecology of deaths during police interactions in the United States, 2016–2020

Who dies in police custody? Where? To answer these questions, we use spatially disaggregated georeferenced data that measure 9098 deaths occurring by multiple causes during interactions with police throughout the U.S. from 2016 to 2020. We use a Sociospatial Ecology framework and Bayesian statistics over U.S. counties that establishes the relationship between social contexts – regional poverty, White/non-White population, violent crime rates, and political identity – and the risk of dying during police interactions. In addition, we evaluate the effects of police force Whiteness on deaths during police interactions. Controlling for alternative explanations, we show heterogeneous distributions of fatality risk, with large clusters in the Southwest and isolated high-probability pockets in other states. Risk maps allowing for visualization of these patterns are provided. We arrive at five main results. 1) There is a general trend of higher death during police interaction in areas of high poverty, fewer White people, higher violent crime rates, and higher populations with conservative values. 2) A great risk of deadly encounters for Black people exists throughout most of the U.S., while regional patterns of high risk exist for all other people of color. 3) White deaths during police interactions are most sensitive to ecological factors. 4) The risk of Blacks getting killed by police increases in White areas regardless of violent crime rates. 5) Higher proportions of White police within U.S. counties leads to higher interactive death risk for all races/ethnicities except Asian/Pacific Islander. Ultimately, our findings identify widespread racial/ethnic biases in situations of power and control.     

Applying Geostatistical Analysis to Crime Data: Car-Related Thefts in the Baltic States

Geostatistical methods have rarely been applied to area-level offense data. This article demonstrates their potential for improving the interpretation and understanding of crime patterns using previously analyzed data about car-related thefts for Estonia, Latvia, and Lithuania in 2000. The variogram is used to inform about the scales of variation in offense, social, and economic data. Area-to-area and area-to-point Poisson kriging are used to filter the noise caused by the small number problem. The latter is also used to produce continuous maps of the estimated crime risk (expected number of crimes per 10,000 habitants), thereby reducing the visual bias of large spatial units. In seeking to detect the most likely crime clusters, the uncertainty attached to crime risk estimates is handled through a local cluster analysis using stochastic simulation. Factorial kriging analysis is used to estimate the local- and regional-scale spatial components of the crime risk and explanatory variables. Then regression modeling is used to determine which factors are associated with the risk of car-related theft at different scales.     

A Bayesian Approach to Modeling Binary Data: The Case of High‐Intensity Crime Areas

This paper reports the fitting of a number of Bayesian logistic models with spatially structured or/and unstructured random effects to binary data with the purpose of explaining the distribution of high‐intensity crime areas (HIAs) in the city of Sheffield, England. Bayesian approaches to spatial modeling are attracting considerable interest at the present time. This is because of the availability of rigorously tested software for fitting a certain class of spatial models. This paper considers issues associated with the specification, estimation, and validation, including sensitivity analysis, of spatial models using the WinBUGS software. It pays particular attention to the visualization of results. We discuss a map decomposition strategy and an approach that examines properties of the full posterior distribution. The Bayesian spatial model reported provides some interesting insights into the different factors underlying the existence of the three police‐defined HIAs in Sheffield.     

A Structural Equation Approach to Models with Spatial Dependence

We introduce the class of structural equation models (SEMs) and corresponding estimation procedures into a spatial dependence framework. SEM allows both latent and observed variables within one and the same (causal) model. Compared with models with observed variables only, this feature makes it possible to obtain a closer correspondence between theory and empirics, to explicitly account for measurement errors, and to reduce multicollinearity. We extend the standard SEM maximum likelihood estimator to allow for spatial dependence and propose easily accessible SEM software like LISREL 8 and Mx. We present an illustration based on Anselin's Columbus, OH, crime data set. Furthermore, we combine the spatial lag model with the latent multiple-indicators–multiple-causes model and discuss estimation of this latent spatial lag model. We present an illustration based on the Anselin crime data set again.     

Property and Personal Crime in Istanbul

In recent studies on urban safety, close relationships between physical and demographic characteristics have been found in crime levels in cities. In many countries social, political and economic turmoil have been the main reasons for the increase in urban crime and violence in the last 50 years. In physically deprived environments, the most important factors that increase urban crime are socially isolated communities, economic discrimination and lack of equality in political citizenship rights. In developing countries, it is difficult to obtain data about crime and safety. For this reason, there are very few studies on crime compared to developed countries. In the research in this paper, the similarities and differences of crime ratios against property and persons in Istanbul are compared with those in other countries. For this purpose, the spatial distribution of crimes committed were analysed on a comparative basis between 1998–2002 in 32 districts displaying different characteristics in terms of distance to the centre, use of land, value of land, physical and demographic features. The research revealed that the crime rates in Istanbul against property and persons were in parallel with developed countries. The districts which have mixed use (residential and commercial, residential and industrial), high population increase, high number of households, high density and high land value, property and personal crime levels are high; when date of becoming a district is recent and the size of the district is large, property and personal crime levels are low.     

Nonlinear Multivariate Spatial Modeling Using NLPCA and Pair‐Copulas

A novel geostatistical modeling approach is developed to model nonlinear multivariate spatial dependence using nonlinear principal component analysis (NLPCA) and pair‐copulas. In spatial studies, multivariate measurements are frequently collected at each location. The dependence between such measurements can be complex. In this article, a multivariate geostatistical model is developed that can capture both nonlinear spatial dependence across locations and nonlinear dependence between measurements at a particular location. Nonlinear multivariate dependence between spatial variables is removed using NLPCA. Subsequently, a pair‐copula based model is fitted to each transformed variable to model the univariate nonlinear spatial dependencies. NLPCA and pair‐copulas, within the proposed model, are compared with stepwise conditional transformation (SCT) and conventional kriging. The results show that, for the two case studies presented, the proposed model that utilizes NLPCA and pair‐copulas reproduces nonlinear multivariate structures and univariate distributions better than existing methods based on SCT and kriging.     

Geopolicing Race,Gender, and Class: How the Police Immobilise Urban Allochthones

City landscapes are ever-changing stages for the protagonists that pass through it. For police officers they serve as canvasses to positively and negatively code subjects. As such, geography matters to the body. Rather than taking geographic locations, crime statistics, predictive maps and human bodies as objective truths, I focus on the work of police officers, not in terms of an instrumental-rational “meeting of policy targets” or attempts to reduce crime, but the work required to make raced, gendered and classed geographical differentiations. This process culminates in geopolicing: the spatial imaginations and practices of police officers as to who, what and where to police and, of course, why. Geopolicing includes the aesthetic re-ordering and cleansing of urban “matter out of place”. Police officers perceive exclusionary territories in which landscapes racialised as white and identified as affluent are threatened by urban allochthones identified by class, race, gender, age and residential status. The findings are based on my ethnography among police officers in the city of Amsterdam, The Netherlands, between 2007 and 2011.     

不同类型道路密度对公共空间盗窃犯罪率的影响——基于ZG市的实证研究

道路密度对犯罪分布存在影响已得到大多数学者的证实,但忽略了不同类型道路属性的差异对犯罪的影响。不同类型道路在社会-建成环境等各种属性方面存在较大的差异,因此明确不同类型道路密度对公共空间盗窃犯罪率存在的影响有助于犯罪的防控。基于此,本文以派出所为单元构建多元线性回归模型进行研究。研究发现,不同类型道路密度对公共空间盗窃犯罪率影响不同：城市次干道、城市支路和其他可通车道路密度对公共空间盗窃犯罪率有正向影响;不可通车道路密度对公共空间盗窃犯罪率有负向影响;城市主干道密度对公共空间盗窃犯罪率影响不显著。不同类型道路社会-建成环境的差异是公共空间盗窃犯罪率不同的原因。研究结果可为犯罪精准防控提供指导。     

城市犯罪风险区位因子与风险地形分析——以武汉市主城区为例

从犯罪背景空间、场所空间、联接空间和聚集空间四个层次,构建城市犯罪风险区位因子体系。以武汉市主城区为研究区域,以立案判决的“两抢一盗”犯罪为数据源,综合运用空间句法、犯罪近重复分析和地理加权回归模型方法进行犯罪风险地形建模实证研究。结果表明,犯罪风险区位因子对犯罪空间分布的影响具有显著的空间异质性;依据多层次的犯罪风险区位因子体系及其对犯罪行为的影响机制,城市犯罪高风险区域可分为城市商业中心、火车站交通枢纽、城中村和城乡结合部等几大类型。基于犯罪地理学理论构建的犯罪风险区位因子体系模拟的犯罪风险地形对实际犯罪空间分布有良好的解释度,可为城市犯罪分布环境形成机制的研究提供相关借鉴。     

CRIME IN A MULTI‐JURISDICTIONAL MODEL WITH PRIVATE AND PUBLIC PREVENTION*

ABSTRACT Criminals move between jurisdictions in response to differences in the net returns to crime that depend on the opportunity for crime and the effort to prevent crime. An increase in police protection of a jurisdiction diverts crime to other jurisdictions when only public crime prevention such as police protection is available. However, residents also invest in private prevention (private security, burglar alarms, etc.), and the value of these measures depends on the level of local public protection. In a spatial context, an increase in public prevention of a jurisdiction not only alters the incentives of individuals of the jurisdiction, but also of other jurisdictions as well, and such a change in private crime prevention may end up attracting crime to the jurisdiction. An increase in public prevention of a jurisdiction thus may divert or attract crime. This ambiguous effect stands in contrast with the literature and may appear counterintuitive, but is logical under plausible conditions.     

Broken Windows Policing and Constructions of Space and Crime: Flatbush,Brooklyn

While broken windows policing has triggered explosive debates about law enforcement and racism across US cities, it has maintained considerable support by racialized urbanites. Focusing on Flatbush, Brooklyn, this paper seeks to understand the striking resilience of broken windows in inner‐city contexts. It uses Laclau and Mouffe's discourse theory to analyze dialogue at Precinct Community Council meetings and interviews with attendees. The paper makes the case that the New York Police Department normalizes broken windows through discursive constructions of social space and crime that naturalize the precinct scale, produce spatial meanings, and cast social difference in the mold of broken windows theory. The article illustrates beyond the politics of racialized fearmongering, the normalization of broken windows also occurs through this meticulous production of geographic knowledge. It also emphasizes that deconstructing the way the police portray space and crime provides signposts for substantive reform to broken windows.     

THE IMPACT OF CRIME ON APARTMENT PRICES: EVIDENCE FROM STOCKHOLM,SWEDEN

This study uses data of about 9,000 apartment sales in Stockholm, Sweden, to assess the impact of crime on property prices. The study employs hedonic pricing modelling to estimate the impact of crime controlling for other factors (property and neighbourhood characteristics). Geographic Information System (GIS) is used to combine apartment sales by coordinates with offences, land use characteristics and demographic data of the population. The novelty of this research is threefold. First, it explores a set of land use attributes created by spatial techniques in GIS in combination with detailed geographical data in hedonic pricing modelling. Second, the effect of crime in neighbouring zones at one place can be measured by incorporating spatial lagged variables of offence rates into the model. Third, the study provides evidence of the impact of crime on housing prices in a capital city of a traditional welfare state, information otherwise lacking in the international literature. Our results indicate that apartment prices in a specific area are strongly affected by crime in its neighbouring zones, regardless of crime type. When offences were broken down by types, residential burglary, theft, vandalism, assault and robbery individually had a significant negative effect on property values. However, for residential burglary such an effect is not homogenous across space, and apartment prices in central areas are often less discounted by being exposed to crime than those in the city's outskirts.     

基于因子分析的长春市犯罪分区研究

选取了2008年长春市主城区各警区(共74个)内的8种常见犯罪,并对其进行了基于主成分的因子分析。基于日常活动理论(routine activities theory)的理论框架,通过分析不同主因子得分的空间分异状况,并结合各地段人口集聚、社会经济、土地利用等方面的特征,探究各主因子的内涵、关联和空间格局的成因。结果表明长春市2008年8种常见犯罪可以归为3个主因子上。其中主因子1在伤害、诈骗和偶发性暴力犯罪上载荷最大,可以归结为经济活动主导(或就业主导型)型因子;主因子2载荷最大的犯罪包括抢劫、抢夺和扒窃,可以归结为商贸、餐饮、文娱活动主导型因子;主因子3载荷最大的犯罪包括入室盗窃和机动车盗窃,可以归结为居住活动主导型因子。     

Crime and the City Solution: Crime Fiction, Urban Knowledge, and Radical Geography

Radical urban geography has recently begun a critique of crime fiction, seeing its ideological shortcomings as politically instructive. This paper argues that this critique is theoretically naive and suggests that a concentration on the epistemological claims of both fiction and urban geography is more fruitful. The paper turns the critique back on radical geography and celebrates the critical possibilities of some forms of crime fiction. Specifically, the police procedurals of British author John Harvey are used to illustrate the genre's ability to articulate alternative epistemologies, ways of knowing the city that track the structure of everyday life and thus offer a critical, realistic, and reflexive approach to the city and itsproblems.     

国外旅游犯罪研究综述

旅游犯罪作为一个严重的社会问题,近年来引起了社会各界的广泛关注,并成为国外旅游研究的重要领域之一。本文在梳理国外相关文献的基础上,对旅游与犯罪的关系、旅游犯罪的形成原因、时空分布规律、分类专项研究、旅游者与居民对犯罪的感知、相关社会影响和防范治理等多个方面进行了较详细的分析,以期能为国内旅游犯罪的防治和相关研究提供有益的思路和借鉴。     

Analyzing Hotspots of Crime Using a Bayesian Spatiotemporal Modeling Approach: A Case Study of Violent Crime in the Greater Toronto Area

Conventional methods used to identify crime hotspots at the small‐area scale are frequentist and employ data for one time period. Methodologically, these approaches are limited by an inability to overcome the small number problem, which occurs in spatiotemporal analysis at the small‐area level when crime and population counts for areas are low. The small number problem may lead to unstable risk estimates and unreliable results. Also, conventional approaches use only one data observation per area, providing limited information about the temporal processes influencing hotspots and how law enforcement resources should be allocated to manage crime change. Examining violent crime in the Regional Municipality of York, Ontario, for 2006 and 2007, this research illustrates a Bayesian spatiotemporal modeling approach that analyzes crime trend and identifies hotspots while addressing the small number problem and overcoming limitations of conventional frequentist methods. Specifically, this research tests for an overall trend of violent crime for the study region, determines area‐specific violent crime trends for small‐area units, and identifies hotspots based on crime trend from 2006 to 2007. Overall violent crime trend was found to be insignificant despite increasing area‐specific trends in the north and decreasing area‐specific trends in the southeast. Posterior probabilities of area‐specific trends greater than zero were mapped to identify hotspots, highlighting hotspots in the north of the study region. We discuss the conceptual differences between this Bayesian spatiotemporal method and conventional frequentist approaches as well as the effectiveness of this Bayesian spatiotemporal approach for identifying hotspots from a law enforcement perspective.     

Exploring scale-dependent correlations between cancer mortality rates using factorial kriging and population-weighted semivariograms

This article presents a geostatistical methodology that accounts for spatially varying population size in the processing of cancer mortality data. The approach proceeds in two steps: (1) spatial patterns are first described and modeled using population-weighted semivariogram estimators, (2) spatial components corresponding to nested structures identified on semivariograms are then estimated and mapped using a variant of factorial kriging. The main benefit over traditional spatial smoothers is that the pattern of spatial variability (i.e., direction-dependent variability, range of correlation, presence of nested scales of variability) is directly incorporated into the computation of weights assigned to surrounding observations. Moreover, besides filtering the noise in the data, the procedure allows the decomposition of the structured component into several spatial components (i.e., local versus regional variability) on the basis of semivariogram models. A simulation study demonstrates that maps of spatial components are closer to the underlying risk maps in terms of prediction errors and provide a better visualization of regional patterns than the original maps of mortality rates or the maps smoothed using weighted linear averages. The proposed approach also attenuates the underestimation of the magnitude of the correlation between various cancer rates resulting from noise attached to the data. This methodology has great potential to explore scale-dependent correlation between risks of developing cancers and to detect clusters at various spatial scales, which should lead to a more accurate representation of geographic variation in cancer risk, and ultimately to a better understanding of causative relationships.     

Crime and disorder, and house sales and prices around the casino sites in Windsor, Ontario, Canada

This study is an analysis of the impact of the opening or closure of a new urban casino on crime and disorder, and house sales and prices in its neighbourhood. The two sets of time-series data were the calls to the police about three types of offences and the house sales through the Multiple Listing Service in two neighbourhoods near the casinos in Windsor, Ontario, Canada. Temporally, the weekly numbers of offences and the mean monthly house prices were oscillating almost randomly around their respective declining or stationary long-term averages with no impulse from when a casino either opened or closed. Geographically, only two types of offences increased in number nearer to a casino site, but their respective distance decays neither moderated nor steepened after a casino had closed or opened. Even so, the conclusion is premature that the open or closed casinos have had benign effects on offences and house sales in the neighbourhoods.