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.     

Conceptions of Space and Crime in the Punitive Neoliberal City

The entrenchment of neoliberalism in the United States has coincided with an unprecedented expansion of punishment practices that intensify social divisions rooted in class and race. We explore the political culture of this hyperpunitiveness through a discussion of two popularized explanations for urban crime: broken windows and situational crime prevention. These popular criminological theories help legitimate the deepening of social and spatial divisions. They also rest their precepts upon the foundation of a particular geographic imagination. We use this paper to reveal and critique the core assumptions about space upon which each of these theories critically relies. We suggest that each theory understands society–space interactions too simplistically to provide comprehensive insight into the dynamics of landscape construction and interpretation. We argue further that the logics and practices of broken windows and situational crime prevention possess significant elective affinities with social dynamics characteristic of neoliberalism. For these reasons, these popularized criminologies both reflect and reinforce the processes through which neoliberalism exacerbates social differences.     

Integration Processes in European Research and Development: A Comparative Spatial Interaction Approach Using Project Based Research and Development Networks,Co‐Patent Networks and Co‐Publication Networks

This study focuses on integration processes in European Research and Development (R&D) by analyzing the spatiotemporal dimension of three different R&D collaboration networks across Europe. The studied networks cover different types of knowledge creation, namely project‐based R&D networks within the European Union (EU) Framework Programmes (FPs), co‐patent networks, and co‐publication networks. Integration in European R&D—one of the main pillars of the EU Science Technology and Innovation policy—refers to the harmonization of fragmented national research systems across Europe and to the free movement of knowledge and researchers. The objective is to describe and compare spatiotemporal patterns at a regional level and to estimate the evolution of separation effects over the time period 1999–2006 that influence the probability of cross‐region collaborations in the distinct networks under consideration. The study adopts a spatial interaction modeling perspective, econometrically specifying a panel generalized linear model relationship, taking into account spatial autocorrelation among flows using eigenfunction spatial filtering methods. The results show that geographical factors are a lower hurdle for R&D collaborations in the FP networks than in co‐patent networks and co‐publication networks. Furthermore, it is shown that the geographical integration is higher in the FP network.     

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.     

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.     

Persistence of Crime Hot Spots: An Ordered Probit Analysis

The temporal persistence of crime hot spots is recognized as a valuable indicator of consistent problem areas. The current literature has not adequately addressed the mechanisms that perpetuate or interrupt persistent crime hot spots. Investigating the persistence of violent crime hot spots in Columbus, Ohio, from 1994 to 2002, this study fills a gap in the literature by identifying neighborhood structural correlates that drive the persistence of hot spots. Specifically, this study identifies yearly crime hot spots, and estimates an ordered probit model to explore the neighborhood structural determinants. The results indicate that socio‐economic factors, identified from a synthesis of social disorganization theory and routine activity theory, significantly correlate with persistent patterns of violent crime hot spots. This gives evidence that a combination of the two ruling spatial theories of crime provides an applicable framework for understanding the temporal dimension of violent crime hot spots. By identifying the factors that contribute to the persistence of hot spots of crime, insights gained from the results can help to inform focused crime prevention efforts.     

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.     

On Geocomputational Determinants of Entropic Variations for Urban Dynamics Studies

Monitoring population characteristics and their patterns of spatial evolution are fundamental components for urban management and policy decision‐making. Societal issues such as health, transport, or crime are often explored using a range of models describing the urban dynamics of population attributes at specific scales that can be seen as complementary. Using and simulating data at different scales of aggregation asks for the need to analyze and compare spatiotemporal variations in order to better understand the model behaviors and emerging properties of the geosimulation. This article analyzes the uses of the entropy measure in the literature and constraining factors needed for its potential extension to explore the variations in geographic and time scales. In particular, the article discusses the need for a truly spatial entropy that takes into account the spatial contiguities of the observations usually aggregated within a zoning system of areal units. Two generic solutions are exposed for the various geometries and attribute structures used for census‐related analyses; they are based on existing measures for point data using (i) co‐occurrences of observations and (ii) discriminant ratios of distances between groups of observations. Their extensions to areal compositional data are articulated around their conceptual changes and geocomputational challenges. A revisited and new version of the entropy decomposition theorem, encompassing a spatiality concept semantically related to correlation, is also presented as efficiently reusing the constrained hierarchical zoning system of administrative units to enable discovery of emerging spatial pattern features from the geosimulation. A comparison of the results between the classical use of entropy and the spatial entropy framework devised shows the flexibility and added capabilities of the approach for new types of analyses, thus allowing new insight into studies of population dynamics.     

ART‐P‐MAP Neural Networks Modeling of Land‐Use Change: Accounting for Spatial Heterogeneity and Uncertainty

Spatial land‐use models over large geographic areas and at fine spatial resolutions face the challenges of spatial heterogeneity, model predictability, data quality, and of the ensuing uncertainty. We propose an improved neural network model, ART‐Probability‐Map (ART‐P‐MAP), tailored to address these issues in the context of spatial modeling of land‐use change. First, it adaptively forms its own network structure to account for spatial heterogeneity. Second, it explicitly infers posterior probabilities of land conversion that facilitates the quantification of prediction uncertainty. Extensive calibration under various test settings is conducted on the proposed model to optimize its utility in seeking useful information within a spatially heterogeneous environment. The calibration strategy involves building a bagging ensemble for training and stratified sampling with varying category proportions for experimentation. Through a temporal validation approach, we examine models’ performance within a systematic assessment framework consisting of global metrics and cell‐level uncertainty measurement. Compared with two baselines, ART‐P‐MAP achieves consistently good and stable performance across experiments and exhibits superior capability to handle the spatial heterogeneity and uncertainty involved in the land‐use change problem. Finally, we conclude that, as a general probabilistic regression model, ART‐P‐MAP is applicable to a broad range of land‐use change modeling approaches, which deserves future research.     

Geographical and Temporal Weighted Regression (GTWR)

Both space and time are fundamental in human activities as well as in various physical processes. Spatiotemporal analysis and modeling has long been a major concern of geographical information science (GIScience), environmental science, hydrology, epidemiology, and other research areas. Although the importance of incorporating the temporal dimension into spatial analysis and modeling has been well recognized, challenges still exist given the complexity of spatiotemporal models. Of particular interest in this article is the spatiotemporal modeling of local nonstationary processes. Specifically, an extension of geographically weighted regression (GWR), geographical and temporal weighted regression (GTWR), is developed in order to account for local effects in both space and time. An efficient model calibration approach is proposed for this statistical technique. Using a 19‐year set of house price data in London from 1980 to 1998, empirical results from the application of GTWR to hedonic house price modeling demonstrate the effectiveness of the proposed method and its superiority to the traditional GWR approach, highlighting the importance of temporally explicit spatial modeling.     

Analyzing Space–Time Crime Incidents Using Eigenvector Spatial Filtering: An Application to Vehicle Burglary

Poisson models generally are utilized in analyzing spatial patterns of crime count data. When spatial autocorrelation is present, these models are extended to account for it. Among various methods, eigenvector spatial filtering (ESF) furnishes an efficient means of analysis. However, because space–time crime data have temporal components as well as spatial components, Poisson models need to be further adjusted to reflect the two types of components simultaneously. This article discusses how the ESF method can be utilized to model space–time crime data, extending the generalized linear mixed model specification for it. This approach is illustrated with an application to space–time vehicle burglary incidents in the city of Plano, Texas, during 2004–2009. Los modelos de Poisson generalmente se utilizan en el análisis de los patrones espaciales de los datos de recuento de crimen. Cuando hay autocorrelación espacial, estos modelos son modificados para dar cuenta de ello. Entre los diversos métodos existentes, el método Eigenvector (autovector, vector propio) de filtrado espacial (Eigenvector Spatial Filtering‐ESF) proporciona un medio eficaz para dicho análisis. Sin embargo, dado que los datos de criminalidad espacio‐temporales tienen tanto componentes temporales como espaciales, los modelos tipo Poisson requieren de un ajuste adicional para reflejar ambos tipos de componentes de manera simultánea. El artículo presente expone cómo el método ESF puede ser utilizado para modelar datos espacio‐temporales sobre delitos mediante la modificación del modelo mixto lineal generalizado (Generalized Linear Mixed Model‐GLMM). El procedimiento propuesto se ilustra con el caso de incidentes espacio‐temporales de robos de vehículos en la ciudad de Plano, Texas, durante 2004–2009. 泊松模型一般用于犯罪计数数据的空间模式分析中,当空间自相关关系呈现时,这类模型可扩展以解释潜在的分布特征。在各种模型中,特征向量空间滤波（ESF）提供了一种有效的分析方法。然而,由于时空犯罪数据包含时间和空间组分,因此泊松模型需要进一步调整以同时反映这两种不同类型的数据。本文讨论了如何利用特征向量空间滤波(ESF)模型对时空犯罪数据进行建模,并采用扩展广义线性混合模型(GLMM)进行规范。最后,以德克萨斯州普莱诺市2004‐2009年的车辆盗窃案数据进行了实证验证。     

A SPATIAL AND TEMPORAL AUTOREGRESSIVE LOCAL ESTIMATION FOR THE PARIS HOUSING MARKET

ABSTRACT This original study examines the potential of a spatiotemporal autoregressive Local (LSTAR) approach in modeling transaction prices for the housing market in inner Paris. We use a data set from the Paris Region notary office (Chambre des notaires d’Île‐de‐France) which consists of approximately 250,000 transactions units between the first quarter of 1990 and the end of 2005. We use the exact X–Y coordinates and transaction date to spatially and temporally sort each transaction. We first choose to use the STAR approach proposed by Pace et al., 1998 . This method incorporates a spatiotemporal filtering process into the conventional hedonic function and attempts to correct for spatial and temporal correlative effects. We find significant estimates of spatial dependence effects. Moreover, using an original methodology, we find evidence of a strong presence of both spatial and temporal heterogeneity in the model. It suggests that spatial and temporal drifts in households socio‐economic profiles and local housing market structure effects are certainly major determinants of the price level for the Paris Housing Market.     

A SPATIO‐TEMPORAL KERNEL METHOD FOR MAPPING CHANGES IN PREHISTORIC LAND‐USE PATTERNS

Archaeologists are accustomed to considering both the spatial distributions of sites and the temporal distributions of dates as means of analysing the dynamics of prehistoric societies. However, spatial and temporal approaches have thus far remained largely separate, rather than being combined within a single, unified framework. A formal methodology is outlined that combines univariate kernel density estimation based on radiocarbon dates with bivariate kernel density estimation based on spatial site coordinates; the approach allows archaeologists to arrive at reconstructed land‐use distributions through time that not only correct for the problematic issue of site contemporaneity, but also reflect the continuous nature of the archaeological record. The model is implemented using as a data set a series of sites from the Mesolithic of Atlantic Iberia; the results demonstrate that it is capable of providing key insights into changing patterns of land use that are not apparent from either the temporal or the spatial perspective alone.     

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。     

Comparison of Moran's I and Geary's c in Multivariate Spatial Pattern Analysis

This article compares multivariate spatial analysis methods that include not only multivariate covariance, but also spatial dependence of the data explicitly and simultaneously in model design by extending two univariate autocorrelation measures, namely Moran's I and Geary's c. The results derived from the simulation datasets indicate that the standard Moran component analysis is preferable to Geary component analysis as a tool for summarizing multivariate spatial structures. However, the generalized Geary principal component analysis developed in this study by adding variance into the optimization criterion and solved as a trace ratio optimization problem performs as well as, if not better than its counterpart the Moran principal component analysis does. With respect to the sensitivity in detecting subtle spatial structures, the choice of the appropriate tool is dependent on the correlation and variance of the spatial multivariate data. Finally, the four techniques are applied to the Social Determinants of Health dataset to analyze its multivariate spatial pattern. The two generalized methods detect more urban areas and higher autocorrelation structures than the other two standard methods, and provide more obvious contrast between urban and rural areas due to the large variance of the spatial component.     

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 Multivariate Model for Spatio‐temporal Health Outcomes with an Application to Suicide Mortality

This article considers models for multivariate mortality outcomes (e.g., bivariate, trivariate, or higher dimensional) observed over a set of areas and through time. The model outlined here allows for spatially structured and white noise errors and for their intercorrelation. It also includes possible temporal continuity in such types of error via structured temporal effects. An extension to spatially varying regression effects is considered, as well as the option of nonparametric specification of priors for spatial residuals and regression effects. Allowing for spatially correlated intercepts or regression effects may alter inferences regarding the changing impact on mortality of socioeconomic or environmental predictors. The modeling framework is illustrated by an application to male and female suicide mortality in London, focusing on the impact on suicide of deprivation and social fragmentation (“anomie”) in the 33 London boroughs during three periods: 1979–83, 1984–88 and 1989–93. Suicide trends by age group are also considered and show considerable differences in the trends in impacts of deprivation and social fragmentation.     

Nonparametric Significance Test for Discovery of Network‐Constrained Spatial Co‐Location Patterns

Spatial co‐location patterns are useful for understanding positive spatial interactions among different geographical phenomena. Existing methods for detecting spatial co‐location patterns are mostly developed based on planar space assumption; however, geographical phenomena related to human activities are strongly constrained by road networks. Although these methods can be simply modified to consider the constraints of networks by using the network distance or network partitioning scheme, user‐specified parameters or priori assumptions for determining prevalent co‐location patterns are still subjective. As a result, some co‐location patterns may be wrongly reported or omitted. Therefore, a nonparametric significance test without priori assumptions about the distributions of the spatial features is proposed in this article. Both point‐dependent and location‐dependent network‐constrained summary statistics are first utilized to model the distribution characteristics of the spatial features. Then, by using these summary statistics, a network‐constrained pattern reconstruction method is developed to construct the null model of the test, and the prevalence degree of co‐location patterns is modeled as the significance level. The significance test is evaluated using the facility points‐of‐interest data sets. Experiments and comparisons show that the significance test can effectively detect network‐constrained spatial co‐location patterns with less priori knowledge and outperforms two state‐of‐the‐art methods in excluding spurious patterns.     

BAYSIAN INFERENCE FOR ORDERED RESPONSE DATA WITH A DYNAMIC SPATIAL‐ORDERED PROBIT MODEL

ABSTRACT Many databases involve ordered discrete responses in a temporal and spatial context, including, for example, land development intensity levels, vehicle ownership, and pavement conditions. An appreciation of such behaviors requires rigorous statistical methods, recognizing spatial effects and dynamic processes. This study develops a dynamic spatial‐ordered probit (DSOP) model in order to capture patterns of spatial and temporal autocorrelation in ordered categorical response data. This model is estimated in a Bayesian framework using Gibbs sampling and data augmentation, in order to generate all autocorrelated latent variables. It incorporates spatial effects in an ordered probit model by allowing for interregional spatial interactions and heteroskedasticity, along with random effects across regions or any clusters of observational units. The model assumes an autoregressive, AR(1), process across latent response values, thereby recognizing time‐series dynamics in panel data sets. The model code and estimation approach is tested on simulated data sets, in order to reproduce known parameter values and provide insights into estimation performance, yielding much more accurate estimates than standard, nonspatial techniques. The proposed and tested DSOP model is felt to be a significant contribution to the field of spatial econometrics, where binary applications (for discrete response data) have been seen as the cutting edge. The Bayesian framework and Gibbs sampling techniques used here permit such complexity, in world of two‐dimensional autocorrelation.     

Pore‐scale multiphase flow experiments in bead packs of variable wettability

The aim of this experimental study is to investigate the impact of wetting characteristics on multiphase flow, sweep efficiency, and residual fluid distribution in unconsolidated porous media. A sequence of oil and water injections was performed on bead packs with uniform porosity and permeability, but different wettability characteristics. Uniform and mixed‐wet bead packs with varying degree of wettability were fabricated to analyze how the residual saturation profiles and the distribution of fluid phases at the pore scale respond to changes in wettability. X‐ray microtomography was used to visualize and analyze the fluid distribution in each bead pack at the end of oil and brine injection. It was found that sweep efficiency was high for the uniform, strongly wetting glass bead pack. For the intermediate‐wet plastic bead pack, we observed evidence of viscous fingering resulting in degenerating sweep efficiency after water injection. In media with mixed wetting surfaces, the spatial distribution of wettability influenced the topology of the saturation profiles and resulted in larger quantities of disconnected fluid blobs. Results also showed that the average blob size was independent of the average residual saturation. In addition, the difference in saturation conditions preceding each injection affected sweep efficiency. The residual saturation after the 1st displacement was higher than the residual saturation after the 2nd displacement.