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.     

Gaussian Process Regression and Bayesian Model Averaging: An Alternative Approach to Modeling Spatial Phenomena

Gaussian Process Regression (GPR) is a nonparametric technique that is capable of yielding reliable out‐of‐sample predictions in the presence of highly nonlinear unknown relationships between dependent and explanatory variables. But in terms of identifying relevant explanatory variables, this method is far less explicit about questions of statistical significance. In contrast, more traditional spatial econometric models, such as spatial autoregressive models or spatial error models, place rather strong prior restrictions on the functional form of relationships, but allow direct inference with respect to explanatory variables. In this article, we attempt to combine the best of both techniques by augmenting GPR with a Bayesian Model Averaging (BMA) component that allows for the identification of statistically relevant explanatory variables while retaining the predictive performance of GPR. In particular, GPR‐BMA yields a posterior probability interpretation of model‐inclusion frequencies that provides a natural measure of the statistical relevance of each variable. Moreover, while such frequencies offer no direct information about the signs of local marginal effects, it is shown that partial derivatives based on the mean GPR predictions do provide such information. We illustrate the additional insights made possible by this approach by applying GPR‐BMA to a benchmark BMA data set involving potential determinants of cross‐country economic growth. It is shown that localized marginal effects based on partial derivatives of mean GPR predictions yield additional insights into comparative growth effects across countries.     

A Spatiotemporal Bayesian Hierarchical Approach to Investigating Patterns of Confidence in the Police at the Neighborhood Level

Public confidence in the police is crucial to effective policing. Improving understanding of public confidence at the local level will better enable the police to conduct proactive confidence interventions to meet the concerns of local communities. Conventional approaches do not consider that public confidence varies across geographic space as well as in time. Neighborhood level approaches to modeling public confidence in the police are hampered by the small number problem and the resulting instability in the estimates and uncertainty in the results. This research illustrates a spatiotemporal Bayesian approach for estimating and forecasting public confidence at the neighborhood level and we use it to examine trends in public confidence in the police in London, UK, for Q2 2006 to Q3 2013. Our approach overcomes the limitations of the small number problem and specifically, we investigate the effect of the spatiotemporal representation structure chosen on the estimates of public confidence produced. We then investigate the use of the model for forecasting by producing one‐step ahead forecasts of the final third of the time series. The results are compared with the forecasts from traditional time‐series forecasting methods like naïve, exponential smoothing, ARIMA, STARIMA, and others. A model with spatially structured and unstructured random effects as well as a normally distributed spatiotemporal interaction term was the most parsimonious and produced the most realistic estimates. It also provided the best forecasts at the London‐wide, Borough, and neighborhood level.     

Using Simulated Data to Examine the Determinants of Acute Hospital Demand at the Small Area Level

The aim of this article is to establish whether spatial variation exists in acute hospital utilization in Ireland and, if it does, to identify the microlevel factors influencing this variation. First, an alignment process is used to calibrate the acute inpatient attendance and nights spent in hospital variables produced by a spatial microsimulation model at both the national and the subnational levels. Comparing the results of the national and subnational alignment allows us to examine whether spatial variation exists. Second, after establishing that hospital utilization displays a significant spatial pattern, we use a nationally representative survey to determine which individual‐level factors significantly affect inpatient attendance and the number of nights spent in hospitals. Using the calibrated data from the aforementioned spatial microsimulation model, we examine whether the spatial patterns of those variables found to influence hospital utilization match the spatial pattern of actual hospital utilization rates at the small area, electoral division level. That is, are the individuals/areas with the highest demand for acute hospital services utilizing acute hospital services? Finally, the results of this research are discussed in relation to both the national and international literature. El objetivo de este artículo es determinar si existe variación espacial en la utilización aguda y urgente (acute) de hospitales en Irlanda, y de ser así, identificar los factores microeconómicos que influyen en esta variación. En primer lugar, se utiliza un proceso de alineación para calibrar las variables de concurrencia (asistencia) de pacientes hospitalizados con casos agudos y de noches de estancia en el hospital, producidas por un modelo microsimulación espacial, tanto para niveles nacionales como subnacionales. Comparar los resultados de alineación de los niveles nacionales y subnacionales nos permite examinar si existe variación espacial. En segundo lugar, después de establecer que la utilización de hospitales muestra un patrón espacial significativo, empleamos una encuesta (un censo) nacional representativa para determinar qué factores a nivel individual afectan significativamente la concurrencia de pacientes hospitalizados y el número de noches de estancia en los hospitales. Al usar los datos calibrados del ya mencionado modelo de microsimulación espacial, analizamos si los patrones espaciales de esas variables que influyen en la utilización de hospitales concuerdan con el patrón espacial de las tasas reales (existentes) de utilización hospitalaria al nivel de las áreas pequeñas de división electoral (DE). En otras palabras, ¿Son las/los individuos/áreas con la mayor demanda de casos agudos de servicios de hospitalización las/los que están utilizados los servicios de hospitalización aguda? Finalmente, los resultados de esta investigación se analizan (discuten) en relación a la literatura especializada nacional e internacional. 本文的研究目的在于通过建立模型以检验爱尔兰急症医院的利用是否存在空间分异。如果存在空间分异,则识别影响其空间分异的微观尺度的驱动因子。首先,在国家和地方两个尺度,通过空间微观模型对所要构建模型的求诊人数及他们花费的夜间数进行校正和调整,并通过对比国家与地方两个尺度的校准结果,来检验其是否存在空间分异。其次,通过建立的模型检验出急诊医院的利用具有显著的空间模式,利用一份具有全国代表性的调查以确定哪些个体层面因素显著影响着求诊人数与在医院花费的夜间数。利用上述空间微观模拟模型检测影响医院利用的那些变量的空间模式,与实际的医院利用率的空间模式在被划分的小区域尺度上是否匹配。也就是说,这些对急症医院服务具有最大需求的个体或区域是否真正实际在使用着这些服务。最后,将本文结果与国内和国际文献进行了相关探讨。.     

A Bayesian Approach to Prediction Using the Gravity Model,with an Application to Patient Flow Modeling

This paper investigates the potential for estimation and prediction by Bayesian methods of hospitalization flows classified by place of residence and hospital site. The focus is especially with respect to emergency (unplanned) admissions to hospitals. The need for strategic modeling and forecasting arises since the structure of U.K. emergency service provision is subject to changes involving site closures or changes in bed numbers. The gravity model, reflecting patient demand, hospital supply, and distance effects has been applied to patient flows, but generally in a situation of unchanged destination states. It may be modified, however, in accordance with major changes in hospital service structure, to include access effects (the interplay of supply and distance) and temporal variation in its parameters. Therefore, prediction may be applied to a “new” situation defined, for example, by closures of entire hospital sites. The modeling approach used may be adapted to other flow models where destinations may be added or eliminated (for example, trade‐area models). A case study involves a sector of London subject to such a restructuring following the U.K. government's 1997–98 review of London's emergency services.     

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.     

Modeling the Impact of Technological Innovation on Environmental Efficiency: A Spatial Panel Data Approach

Technological progress has widely been acknowledged as a key driver in the achievement of environmental sustainability, and a new concern has recently arisen for assessing the role of differences in regional innovation performances with respect to the pursuit of pollution targets. We investigate the impact of innovative activities on environmental efficiency at the regional level, adopting a spatial panel data approach to account for dependence between geographical observations and to get deeper insights into spatial knowledge spillovers arising from agglomeration economies. The spatial panel analysis covers 103 Italian provinces (equivalent to NUTS3 regions) on four major air pollutants (i.e., CO₂, CH₄, NMVOC, CO) over the 1995–2010 period. Our findings show that the state of local technology development, proxied by patents, has a positive impact on GDP emission efficiency and that its magnitude is higher for more localized pollutants as a result of larger spillover effects. Finally, to analyze the Italian North-South dualism of emission levels of pollutants, we offer a spillover effect study to emphasize the remarkable difference among effects in the territory. This calls for closer attention to the local relevance of the pollution damage when environmental impact of innovation strategies is to be evaluated.     

Policy Coalitions in the U.S. Congress: A Spatial Duration Modeling Approach

This paper applies spatial duration models to the analysis of cosponsorship coalitions in the U.S. House of Representatives. This approach provides a unique and simultaneous statistical analysis of ideological space (specifically, coalition formation) and geographical space. Typically, duration models are associated with temporal longitudinal data, but recently have been adapted to the spatial domain (Pellegrini and Reader 1996). In this paper, spatial duration models are further adapted to examine ideological space including a consideration of unobserved sources of spatial variation (or omitted variable bias). We examine two features of cosponsorship coalitions, breadth and clustering. Breadth is defined as the ideological distance between the two most extreme members of the coalition which is an important “signal” to the rest of Congress regarding the scope and broad appeal of the proposed legislation. In contrast, clustering refers to the distance between individual members of a coalition and reveals the tendency, or not, of ideologically similar members of Congress to support various bills. To examine breadth and clustering, we employ spatial duration models of cosponsorship that permit a multivariate analysis incorporating both the characteristics of members of Congress and the geographical regions they represent. Results indicate that cosponsorship coalition patterns are primarily determined by the content of the legislation, not the actions of the coalition leadership. While the leadership characteristics of sponsors have a limited effect on cosponsorship breadth, the size of the coalition is the primary determinent. Leadership characteristics also have little effect on cosponsorship clustering. Rather, clustering is due to members' policy preferences, as measured by distance to the coalition leader. In addition, the duration analysis results suggest that geographical proximity between members of Congress “overcomes” ideological distance. Finally, the spatial duration approach is noted as a fruitful methodology for examining explicitly spatial patterns in both ideological or geographical space.     

周公摄政与周初政教关系初探

周公摄政导致了周初二王并存的局面,而二王并存的实质是国家政权和教权的分离。周公是周代初期的巫史领袖,并于武王后期开始执掌教权,由于不能和政权明确分开,引起王族的猜疑。周公为此迁居东都洛邑,并致力于制礼作乐,目的是使政教分权更加明晰化,造成了一次意义深远的社会制度和思想化的革命。     

A Statistical Method for Analyzing the Spatial Relationship between the Distribution of Activity Points and the Distribution of Activity Continuously Distributed over a Region

This paper shows a statistical method for analyzing the spatial relationship between the distributions of two different kinds of activity in a region. One kind of activity is discretely distributed as points in a region (such as the distribution of retail stores), and the other kind of activity is continuously distributed over the region (such as the distribution of population). First, three models representing the relationship between the above two distributions are formulated. Second, statistical methods for fitting these models to data are developed and the measures of fitness are proposed. Third, using these measures, the relationship between the distributions of thirty-seven kinds of retail stores and the distribution of population is examined in a suburb of Osaka in Japan.     

Between Memory and Materiality: An Archaeological Approach to Studying the Nazi Concentration Camps

Abstract

By its very nature, historical narrative that is rooted exclusively in textual sources is destined to be more linear, more univocal, and less equipped to deal with the problematic. On the other hand, due to its unique abilities and approaches, historical archaeology thrives on the tensions inherent to any attempt to understand past and present experience. In this article I negotiate between two approaches to studying the concentration camps of the Third Reich—one canonical the other experimental. It is suggested that when studying the camps, we are faced with a series of tensions: between past and present, between remembering and forgetting, and between live human actors and the material record. This article explores two research paradigms: first, the traditional text-centric historical approach, and second, an approach that might be called 'historical archaeological'. I embrace the inherent tensions between the two approaches, and put forward some innovative ways for coming to terms with these places of internment.     

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.     

A Bayesian Approach to Ageing Perinatal Skeletal Material from Archaeological Sites: Implications for the Evidence for Infanticide in Roman-Britain

The skeletal remains of substantial numbers of perinatal human infants have been excavated from within a variety of archaeological contexts dating to the Romano-British period. It has been argued that the distribution of ages at death of these infants, which appears to exhibit a pronounced neonatal peak, provides evidence for infanticide. This study re-evaluates the osteological evidence for infanticide in Roman Britain by first identifying biases in traditional techniques for estimating the age of perinatal skeletons and then using a Bayesian procedure to reassess the ages at deaths of almost 400 infants from a number of Roman sites throughout England. We conclude that the apparent peak in neonatal mortality shown by earlier investigations is an artefact of regression-based age estimation. The distribution of ages at death in Romano-British infants is similar to a natural mortality profile.     

试论博物馆与公众关系的四种研究路径

近年来,公众之于博物馆的重要性持续攀升,甚至开始居于一种主导性的优先地位.博物馆学界相应地出现了 一系列暗含"公众转向"的话语.基于此,博物馆与公众关系成为一个重要的研究议题.博物馆与公众关系有四种路径:公众研究路径聚焦存在于博物馆内外的公众群体,以此分别发现公众之于博物馆和博物馆之于公众的价值;沟通关系路径侧重博物馆...     

从鱼鸟相战到鱼鸟相融──仰韶文化鱼鸟彩陶图试析

在仰韶文化彩陶图中,有一些是鱼鸟纹同绘于一件器物之上。本文通过综合分析,认为其中反映了仰韶文化不同支系之间社会集团的相互关系。即“鱼”集团与“鸟”集团之间的战争与融合。     

Social Spaces of Daily Life: A Reflexive Approach to the Analysis of Chemical Residues by Multivariate Spatial Analysis

Studying human activities requires an examination of the inherent epistemological problems in building arguments about the past based on chemical residues and modern observations. A reflexive approach to the analysis of chemical residues at the San Lucas archaeological site, a Classic Hohokam settlement located in Marana, Arizona, represents a unique opportunity to evaluate current techniques and paradigms for the interpretation of daily life activities. By incorporating an innovative program rooted in satellite remote sensing image analysis and spatial statistics, including new techniques, such as bulk density, loss on ignition, electrical conductivity, and salinity, results suggest that soil chemical analysis will benefit more from learning about structure and agency than from one single activity.     

A Multi-Method Approach to Reconstructing Occupational History and Activity Areas: A Case Study at the Formative Site of Tlalancaleca,Central Mexico

While surface collection constitutes an important component of field research at the site and regional levels, the association between surface and sub-surface materials needs to be examined through independent lines of evidence. This study employed manual bucket auger probing and soil geochemistry at the Formative site of Tlalancaleca, Central Mexico (800 b.c.–a.d. 250), to evaluate the results of surface collection and to understand the formation process of the anthropogenic landscape. The combined results at one of the largest architectural complexes at Tlalancaleca indicate that it was built during the Late Formative period (500–100 b.c.) and rebuilt during the subsequent Terminal Formative period (100 b.c.–a.d. 250), which is corroborated by radiocarbon dates. Moreover, the results suggest segregated uses of space such as cooking/storage, waste disposal, and craft production areas within the complex. Overall results demonstrate the effectiveness of this multi-method approach for reconstructing occupational history and activity areas.     

试析夏商西周文化和冀西北、京津唐地区北方青铜文化的关系

夏商西周文化对冀西北、京津唐地区北方青铜文化格局的变迁具有重要影响.夏文化与这个地区的北方青铜文化存在间接的联系;商文化向北的扩张与收缩极大地冲击了这个地区原有的文化格局;西周文化对这个地区的文化渗透最终使得这个地区纳入了中原青铜文化区.     

Navigating the Social Safety Net: A State‐Level Analysis of the Relationship between Medicaid and Consumer Bankruptcy

The social safety net in the United States consists of a variety of programs administered by both federal and state governments. Although the populations served by social programs such as Medicaid and consumer bankruptcy likely overlap, policymakers tend to look at individual programs unilaterally and often fail to communicate appropriately with legislators and political leaders at differing levels of government. The result of such an incremental approach to policy reform is a fragmented social safety net system in the United States. In this study, the relationship between two social programs belonging to the larger social safety net is examined, and recommendations for policymakers are offered.