Geography of crime in a Brazilian context: an application of social disorganization theory

Geography of crime research dates back to the early 1800s, most of which in English and in the context of the United States and Europe, but with a growing and significant literature studying the developing world. We contribute to this literature through an application of social disorganization theory in a Latin American context: Campinas, Brazil. We consider a number of property and violent crime types using census tracts as the spatial unit of analysis. Implementing a spatial regression method, we find support for social disorganization theory, but not as strong as similar studies in Europe and North America. However, because of the context of Campinas, Brazil, a different understanding of the local conditions proves to be important for understanding the geography of crime in this context. The implications of these results are discussed in the context of theoretical developments as well as crime prevention initiatives.     

Voronoi Diagrams and Spatial Analysis of Crime

A range of spatial analyses are used in the field of crime mapping, such as kernel density estimation, Ripley's K-function, and spatial autocorrelation, but there is limited use of Voronoi diagrams (VDs). The goal of this article is to contribute to the spatial analysis of crime through the use of VDs. We use four years of commercial robbery data from Campinas, Brazil, and employ several VD techniques: (1) We analyze crime concentrations through the properties of VDs—area and number of vertices—and coverage curve; (2) we introduce a new crime geovisualization with VD in three dimensions; and (3) we apply a network VD technique to crime analysis. The results demonstrate associations between these VD techniques and the ability of the researcher to recognize crime patterns associated with crime concentration, crime along pathways, and the highly regularized distribution of crime in limited areas spatially.     

犯罪共生空间的类型识别及其特征分析

以ZG市公共空间盗窃、入室盗窃、寻衅滋事、接触诈骗、抢劫抢夺犯罪为研究对象,采用K均值聚类法识别不同类型的犯罪共生空间;并通过决策树模型分析了不同犯罪共生空间的特征。结果表明,ZG市犯罪共生空间可划分为4种类型：① 无犯罪类型共生区;② 公共空间盗窃和接触诈骗犯罪共生区;③ 所有类型犯罪共生区;④ 入室盗窃、寻衅滋事和抢劫抢夺犯罪共生区。城市中各异的社会环境和建成环境产生了不同类型的犯罪机会,而且各类社会环境和建成环境之间存在着条件交互性作用。研究结果为制定犯罪的联合防控策略和实现有限警力的合理布控并且提高执法效率提供了理论基础。     

The spatial stability of alcohol outlets and crime in post‐disaster Christchurch,New Zealand

The devastating Canterbury Earthquakes of 2010 and 2011 left an indelible mark on the city of Christchurch. The social and economic upheaval that immediately followed the Earthquakes has, in time, been replaced with a period of rebuild and transformation. In this study we investigate the effects that the Canterbury Earthquakes had on two important and inter‐related phenomena in the city: alcohol availability and crime. More specifically, we investigate how alcohol outlets and crime across six different categories changed in magnitude and spatial distribution pre‐ (end‐2009) and post‐ (end‐2014) earthquake. We do this using a variety of geospatial techniques including a relatively new method: the spatial point pattern test which allows for the identification of changes in spatial patterns at the local level. Results indicate that both alcohol outlets and crime have decreased in magnitude since the Canterbury Earthquakes. Using the spatial point pattern test we found statistically significant differences in spatial point patterns for both alcohol outlets and all crime types pre‐ to post‐earthquake. The similarity in the differences of the spatial distributions of alcohol outlets and crime provides a first empirical clue of their potential association in the city post‐earthquake.     

Crime seasonality and its variations across space

Investigations into the seasonal patterns of crime date back 180 years to the beginnings of spatial criminology. This original research by Adolphe Quetelet, and much subsequent work, has shown that various crimes have a seasonal component, but the strength and timing of the respective seasonalities vary by crime type. In this paper, we first investigate the existence of seasonality for a number of different crime types, but also the variations of seasonality across space. We find that not only do the various crime types exhibit seasonal patterns, but those seasonal patterns have relatively distinct spatial patterns. This has implications for theory and policy.     

职业足球比赛期间球场周边扒窃犯罪的空间分布特征研究

现阶段,关于足球比赛等体育活动影响周边区域犯罪风险的研究主要关注于一般性验证,而缺少对比赛期间不同时间范围内犯罪风险的态势分析及相关空间影响因素的研究。本文以英国伯明翰市的维拉公园球场为例开展实证研究,对2005—2009年赛季的104场职业足球比赛在比赛日和对照日、开赛前和开赛后等不同时间范围内球场周边3 km区域内发生的扒窃类案件的空间分布特征进行了分析,并利用负二项回归模型研究了相应案件的空间影响要素。结果表明：维拉公园球场周边区域内比赛日期间的扒窃案件发案量要明显高于对照日,而在具体的时间分布上,比赛日开赛时间前2小时到开赛后1小时内发生的扒窃案件显著偏多,并且案件在比赛日和对照日、比赛日的开赛前和开赛后期间的空间分布特征明显不同;回归分析结果显示,比赛日期间餐饮消费类场所的扒窃案件风险较为突出,且高于对照日,而对照日期间的扒窃案件风险则主要集中于休闲娱乐等常规性犯罪热点场所;在比赛日的开赛前,扒窃案件风险主要分布于一些人群聚集的公共场所,而在开赛后则主要集中于餐饮类场所,且随着比赛的开始扒窃案件的风险有转移的趋势。本研究对开展基于大数据分析的大型体育活动的犯罪风险精准防控具有一定的指导意义。     

微观尺度下毒品犯罪时空分布及成因分析——以SZ市NH、DM街道为例

毒品犯罪是全球共同关注的犯罪问题,许多学者从不同的专业领域开展了大量研究。已有文献对毒品犯罪的成因进行分析,由于数据的局限性,较少从微观尺度对毒品犯罪热点的时空分布进行研究。论文以SZ市NH、DM街道内社区为例,基于毒品犯罪案件数据,利用探索性数据分析和时空扫描识别毒品犯罪时空热点分布,结合用地类型、动态人流量等数据定量分析毒品犯罪案件的时空分布影响因素。研究结果如下：① 毒品犯罪主要分布在商业发达地区和城中村地区,且城中村的毒品犯罪时空热点分布的起始时间较商业发达地区更早,影响范围也更大;② 毒品犯罪在不同用地类型分布是不均匀的,其中“住宿旅游娱乐”“商业百货批发零售”“餐饮经营服务品牌”3类用地类型与毒品犯罪具有高度相关性;③ 人流量高热区的面积占比与毒品犯罪的发生有一定的相关性,高热区面积占比大于5%或为0时,能够抑制毒品犯罪的发生;高热区面积占比在0~5%之间,能够促进毒品犯罪的发生。     

接触型与非接触型犯罪时空稳定性对比及其联合防控——以盗窃和电信网络诈骗为例

近年来,以盗窃为代表的接触型犯罪和以电信网络诈骗为代表的非接触型犯罪均呈多发态势,严重影响社会治安稳定。已有研究对不同类型犯罪分布模式的时空稳定性关注仍不够,且未能提出不同类型犯罪的空间联合防控策略。本文以ZG市HT区为例,以社区为分析单元,运用核密度估计、时空跃迁测度法等方法,对比分析2017年盗窃犯罪和电信网络诈骗犯罪的时空分布特征及其空间分布模式的月度稳定性,并从犯罪防控角度改进时空跃迁测度法,结合二阶聚类法识别两类犯罪联合防控空间类型。研究发现：① 两类犯罪时空稳定性差异大,盗窃犯罪的空间分布模式稳定,月度变化小;而电信网络诈骗犯罪空间稳定性整体波动起伏大,月度变化较大;② 识别出两类犯罪的四种联合防控空间类型,分别是“两类犯罪无需防控社区”“两类犯罪邻域防控社区”“盗窃犯罪热点防控、电信网络诈骗犯罪无需防控社区”“盗窃犯罪连片防控、电信网络诈骗综合防控社区”。该研究有助于了解接触型犯罪和非接触型犯罪时空特征的共性和差异性,给警务联合防控提供决策支持。     

Minimum geocoding match rates: an international study of the impact of data and areal unit sizes

ABSTRACT

The analysis of geographically referenced data, specifically point data, is predicated on the accurate geocoding of those data. Geocoding refers to the process in which geographically referenced data (addresses, for example) are placed on a map. This process may lead to issues with positional accuracy or the inability to geocode an address. In this paper, we conduct an international investigation into the impact of the (in)ability to geocode an address on the resulting spatial pattern. We use a variety of point data sets of crime events (varying numbers of events and types of crime), a variety of areal units of analysis (varying the number and size of areal units), from a variety of countries (varying underlying administrative systems), and a locally-based spatial point pattern test to find the levels of geocoding match rates to maintain the spatial patterns of the original data when addresses are missing at random. We find that the level of geocoding success depends on the number of points and the number of areal units under analysis, but generally show that the necessary levels of geocoding success are lower than found in previous research. This finding is consistent across different national contexts.     

Integrating multi-source remotely sensed datasets to examine the impact of tree height and pattern information on crimes in Milwaukee,Wisconsin

There have been a great number of debates about the impacts of trees on crimes: some researchers believed that trees are a crime facilitator because of the concealment provision for potential criminals, while others argued that they are a crime deterrent because of the increased surveillance possibility and the therapeutic effect on psychological fatigue. To better answer this question, this study incorporated detailed tree features by using multi-source remotely sensed data at a very high resolution into environmental criminology analysis across the entire City of Milwaukee. Trees were extracted from aerial photographs, and broken down into two categories based on their heights to consider the effects of tree height on view obstruction. By controlling for confounding socioeconomic variables, the relationship between crimes and a series of composition and configuration indicators of trees with different height were investigated by using global and local spatial regressions. Results from classic and spatial statistical techniques finds complicated relationship between crimes and trees, which can be summarized in two aspects. First, the mixed effects of trees can be observed among different crime types. Second, the trends of spatial nonstationarity of the composition and configuration of trees with different heights were observed across the entire study area. The study outcomes could provide reasonable implications for making appropriate policies for crime prevention through environmental design to strengthen neighborhoods and communities in a city.     

Hotspots vs. harmspots: Shifting the focus from counts to harm in the criminology of place

The prevailing pattern in much of the social sciences, including geography and criminology, relies on count data. “Hotspots” — geospatial areas with disproportionally more crime than the rest of the city — are usually identified by the number of events in these areas. Yet no attention is given to their severity, or any other weighting system of harm, despite the common-sense view that not all crimes are created equal. To illustrate the value of focusing on harm in addition to count data, we turn to a spatial analysis of crime by observing crime concentrations (hotspots) against harm concentrations (harmspots), across fifteen councils in the United Kingdom. The definition of “harm” is based on the Sentencing Guidelines for England and Wales, as each crime category (n = 415) attracts a different severity weight. Both “hotspots” and “harmspots” are defined as being at least 2 standard deviations from the mean distribution within each city: This procedure creates comparable datasets. The data suggest that half of all crime events are concentrated within 3% of all street segments in the selected councils, yet harm is even more heavily concentrated, with half of all harm located in just 1% of each council [OR = 3.49; 95% CI 3.268–3.728]. The intra-unit variance was also reduced by approximately half — from 0.75% to 0.45%. We discuss the implications of using harm, in addition to counts, for research and policy by arguing that a shift in focus is required both for the development of theories and for cost-effective prevention strategies.     

Some thoughts on inference in the analysis of spatial data

Statistical inference is important for all those who engage in the analysis of spatial data. The issue is becoming increasingly important given the explosion in the availability of spatial data and the proliferation of Geographic Information Systems (GIS) across different academic disciplines and application areas. The aim of this paper is to provide a brief overview of some of the concepts and controversies inherent in statistical inference in the hope of raising the level of awareness within the geographic information science community that different points of view exist when it comes to inference. We argue that the concept of statistical inference in spatial data analysis and spatial modelling is perhaps broader than many GIS users imagine. In particular, we argue that different types of inference exist and that process inference is just as valid as sample inference, even though the latter appears to dominate the GIS literature.     

ZG市诈骗犯罪的时空分布与影响因素

诈骗犯罪是现代城市中亟待解决的一大社会难题。现有研究多从社会学角度,基于微观层次探讨影响诈骗受害的个人属性特征,缺乏从地理学视角对诈骗受害者生存环境中的中宏观层次因素的考虑。本文基于犯罪地理学视角,综合运用平均最近邻、核密度以及负二项回归等研究方法,对ZG市5类诈骗警情的时空分布特征和基于建成环境与社会环境两大维度的影响因素进行了深入探讨。结果表明：① 各类诈骗的时间分布规律各异,但整体上呈现出工作日高于休息日、下午或晚上高于上午、凌晨时段为最低的特征;② 和其他类型的城市犯罪类似,诈骗犯罪整体呈现出与老城区距离衰减的“多中心”显著性集聚特征;各类型诈骗热点的空间分布大同小异,多分布在城市的老城区、CBD、火（汽）车站、城中村或高校布局的周边地带;③ 各类诈骗犯罪的空间分布影响因素各异,但整体表现出与银行网点、旅游景点、道路密度、土地利用混合度、高校等建成环境因素高度相关;与购买商品房家庭占比、受教育水平、人口老龄化和城镇化水平等社会环境显著相关的特性。本文拓展了犯罪地理学领域对诈骗犯罪的研究成果,证实了各类诈骗犯罪符合日常活动理论的理论假设,对诈骗受害情景预防具有重要意义。     

The concentration of urban crime in space by race: evidence from South Africa

Crime inequality in neighborhoods by race is blamed on social inequalities borne out of segregation and economic discrimination. South Africa is a country synonymous with racial-spatial segregation and discrimination as a result of legislatively enforced policies of the former apartheid government. This study examines whether urban crime inequalities by race exist in the city of Tshwane, South Africa and identifies the empirical causes of these crime inequalities. Violent and sexual crime was found to concentrate in Black African neighborhoods, while property crime was concentrated in neighborhoods classified as “Mixed”. The causes of crime in neighborhoods were found to vary across racial groups with results suggesting non-uniformity in the extent to which the various constructs impact crime based on race. The results challenge the notion that segregation and economic discrimination uniformly impacts affected communities. Explanations for the findings are provided in the context of an increasingly eclectic post-apartheid South African city.     

Temporal stability of model parameters in crime rate analysis: An empirical examination

Two common practices in modeling of crime when crime data is available for multiple years are using single-year crime data corresponding to census data and taking the average of crime rate (or count) over multiple years. Current theoretical and empirical literature provides little, if any, rationale in support of either practice. Averaging multiple years is purported to reduce heterogeneity and minimize the measurement error in the year-to-year emergence of crime. However, it is unclear how useful the analysis of averaged and smoothed data is for revealing the relationship between crimes and socio-demographic and economic characteristics of every single year. In order to more clearly understand these two approaches, this paper applies a seemingly unrelated regression model to assess the temporal stability of model parameters. The model accounts for spatial autocorrelation among crime rates and social disorganization variables at the block group level.     

Localized income inequality, concentrated disadvantage and homicide

Several theories (e.g., routine activities, strain and social disorganization theories) suggest that there are effects of income inequality and concentrated disadvantage on crime. This study follows primarily the long line of social disorganization theorizing to explain the intraurban variation of homicide rates in Chicago. In addition to poverty, income inequality adds stress to individuals when they compare their situation to those around them. A localized income inequality (LII) index is implemented in a Geographic Information System (GIS) to measure the inequality between contiguous areas. Defining inequality in a localized context is important because geographic proximity permits close contacts between residents and thus reinforces the awareness of relative deprivation. This inequality index adds a new dimension to concentrated disadvantage in urban areas. The study shows that the new concentrated disadvantage measure is a strong predictor of homicide rates across three areal units of analysis in Chicago, i.e., census tracts, community areas and neighborhood clusters.     

2000年以来中外犯罪地理研究进展回顾与展望

以"Web of ScienceTM核心合集"和CNKI核心文献库为数据源,运用CiteSpace软件进行文献计量分析,从发文时间、地区分布、学科分布、研究机构、关键词共现与高被引文献等方面,总结比较了2000年以来中外犯罪地理研究进展,并展望了未来的研究趋势.结果发现:1)国内外犯罪地理发文量整体呈现持续增长态势,美...     

Spatial crime distribution and prediction for sporting events using social media

ABSTRACT

Sporting events attract high volumes of people, which in turn leads to increased use of social media. In addition, research shows that sporting events may trigger violent behavior that can lead to crime. This study analyses the spatial relationships between crime occurrences, demographic, socio-economic and environmental variables, together with geo-located Twitter messages and their ‘violent’ subsets. The analysis compares basketball and hockey game days and non-game days. Moreover, this research aims to analyze crime prediction models using historical crime data as a basis and then introducing tweets and additional variables in their role as covariates of crime. First, this study investigates the spatial distribution of and correlation between crime and tweets during the same temporal periods. Feature selection models are applied in order to identify the best explanatory variables. Then, we apply localized kernel density estimation model for crime prediction during basketball and hockey games, and on non-game days. Findings from this study show that Twitter data, and a subset of violent tweets, are useful in building prediction models for the seven investigated crime types for home and away sporting events, and non-game days, with different levels of improvement.     

The crime kaleidoscope: A cross-jurisdictional analysis of place features and crime in three urban environments

Research identifies various place features (e.g., bars, schools, public transportation stops) that generate or attract crime. What is less clear is how the spatial influence of these place features compares across relatively similar environments, even for the same crime. In this study, risk terrain modeling (RTM), a geospatial crime forecasting and diagnostic tool, is utilized to identify place features that increase the risk of robbery and their particular spatial influence in Chicago, Illinois; Newark, New Jersey; and Kansas City, Missouri. The results show that the risk factors for robbery are similar between environments, but not necessarily identical. Further, some factors were riskier for robbery and affected their surrounding landscape in different ways that others. Consistent with crime pattern theory, the results suggest that the broader organization of the environmental backcloth affects how constituent place features relate to and influence crime. Implications are discussed with regard to research and practice.     

入室盗窃多尺度地理因子分析

选取N市作为研究区域,以该市2013年全年的入室盗窃案件作为研究对象。将研究区域内入室盗窃与地理因子在不同空间尺度上进行关联性对比分析。首先,从环境犯罪学理论和现有犯罪地理特征研究两方面出发,分析现有研究中的尺度依赖性。每一套环境犯罪学理论均有其适用的空间尺度,在犯罪地理特征研究中会有相应的地理因子与之关联;其次,引入似不相关回归模型获取入室盗窃与地理因子在小区、街区、社区、街道以及区等多个空间尺度上的关联性。并用瓦尔德检验来测试各个关联系数之间差异的显著性。实验结果表明,随着尺度的变化,有些地理因子与入室盗窃的关联性并未变化,有些则发生了显著变化。这些变化可用不同尺度的犯罪地理学理论进行解释;最后,将多个尺度地理因子反演入室盗窃空间分布的结果进行综合,并将之与单一尺度的反演结果进行比较。结果证明,与单一尺度的地理因子反演犯罪空间分布的精度相比,利用多个尺度地理因子解释犯罪空间分布的精度更高。研究表明：在犯罪地理学研究中,环境犯罪学理论、地理因子分析以及犯罪空间分布反演均存在尺度依赖性。综合多个尺度的结果进行分析,不仅有利于准确认识犯罪的地理因子及其对应理论,对于犯罪反演精度也有所提高。