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Exploring Spatial Patterns of Property Crime Risks in Changchun, China
International Journal of Applied Geospatial Research
Abstract
Urban crime has increasingly become a major issue for Chinese cities. Using crime data collected at police precincts in 2008, the main aim of this research is to examine the spatial distribution of property crime which accounted for almost 82% of all crimes in the city of Changchun, and analyze the relationship between the spatial patterns of property crime and neighborhood characteristics. Standardized property crime rates SCR were applied to assess the relative risk of property crime across the city. Statistically significant clusters of high-risk areas or hot-spots were detected. A global ordinary least squares OLS regression model and a geographically weighted regression GWR model were calibrated to explore the risk of property crime as a function of contextual neighborhood characteristics. The analytical results show that significant local variations exist in the relationship between the risk of property crime and several neighborhood socioeconomic variables.
... Research Focus Based on research design and questions, the extant research can be classified into the following six categories: (a) formulating hypotheses, and directly assessing the applicability of the theory by analyzing data from surveys (Lu and Miethe 2001;Zhang et al. 2007;Jiang et al. 2010;Jiang et al. 2013;Messner et al. 2017;Zhang et al. 2017); (b) examining the spatial distribution of crime, and then assessing if the covariates identified by the theory are related to the spatial patterns (Song and Liu 2013;Liu and Zhu 2016); (c) comparing different types of neighborhoods, and utilizing the factors proposed by the theory to account for the differences (Zhong 2009;Xiong 2016); (d) focusing on one particular area and applying the theory to explain the phenomena of social disorganization in the area of interest (Liu 2010;Mao and Jin 2014;Cui and Shi 2017); (e) describing and assessing the neighborhood-based crime prevention programs (Situ and Liu 1996;Zhong and Broadburst 2007); and (f) using the structural covariates of social disorganization to predict crime rates and identify high risk areas (Wang et al. 2017). ...
... As shown in Appendix 1 Table 3, some studies use streets (jiedao) as units of analysis, even though their ecological units of interest are neighborhoods (Song and Liu 2013;Liu t al. 2016). In China's urban areas, sub-district offices (jiedao banshichu) are set up as agencies of local governments of municipal districts or cities not divided into districts and are responsible for the administration of neighborhood committees within their jurisdictions. ...
This paper assesses and synthesizes the cumulative results from the empirical research on social disorganization and crime-related phenomena at the neighborhood level in China. Our review identified 17 relevant quantitative, qualitative, and mixed method studies published in journals and books from the late-1990s to date. Our goal is to take stock of the cumulative knowledge to inspire future research in China, thereby advancing social disorganization theory. We synthesize the main findings about the effects of structural factors and intervening mechanisms from quantitative studies, summarize briefly conclusions from qualitative and mixed methods research to crosscheck our synthesis, and identify methodological and theoretical limitations. Our conclusions point to promising directions for future research with special attention to prospects for theory development through comparative criminological inquiry.
... Seven independent variables are investigated in the study and their description is listed in Table 2. Based on social disorganization theory, four dimensions are examined including population heterogeneity, residential instability, socioeconomic status, and residential land use. First, taking Chinese urban environment into consideration, Migration, measured by percentage of population who are registered in other provinces, indicates population heterogeneity (Wei & Daqian, 2013). Second, RentalHousing, measured by percentage of rental properties among all house units, captures residential instability according to aforementioned analysis and the fact that migrant population favors rental houses (Wang, Wang, & Wu, 2010;Wu, 2006;Zheng, Long, Fan, & Gu, 2009). ...
... In many Western countries, variables related to ethnicity are discussed intensively (Sampson et al., 1997;Sampson & Groves, 1989;Sampson & Raudenbush, 1999;Steenbeek & Hipp, 2011). However, in non-Western countries like China, issues on rural to urban migration are requisite to be considered as a result of economic reform and rapid process of urbanism (Chen et al., 2017a;Wei & Daqian, 2013;Xu, 2009;Zhang, Messner, & Liu, 2007). It is possible that many non-Western countries such as China experience less ethnic issue but more internal migration issue. ...
... GeoDa software was used to run a global autocorrelation analysis on the spatial distribution of crimes, roads, and public facilities in Liangshan Prefecture [17][18][19]. The results are shown in Table 1. ...
Crime prevention and governance play critical roles in public security management. Liangshan Yi Autonomous Prefecture in Sichuan Province has a high crime rate, and spatio-temporal analysis of crime in this region could assist with public security management. Therefore, Liangshan Prefecture was selected as the research object in this study. The spatial crime data were obtained from China Judgments Online, and property crime, violent crime, and special crime (i.e., pornography, gambling, drugs, and guns) were analyzed. The findings were as follows. In terms of time characteristics (month, day, and hour), property crime tended to occur in autumn and winter, in the early month, on Wednesdays and Fridays, and at early morning. Violent crime tended to occur in winter and spring, on Mondays and Thursdays, and at night. Special crime occurred in spring and autumn, on Tuesdays, and in the daytime. In terms of spatial features, the central region of Liangshan Prefecture was the focal area for crime. There were obvious low-aggregation areas in the western region for special crime. The eastern region exhibited a high incidence of various crimes. Regarding the spatio-temporal evolution characteristics from 2013 to 2019, there were some obvious hotspots of violent and property crime in downtown and surrounding townships of Xichang City, which is the capital of Liangshan Prefecture. During the study period, the incidence of special crime has an obvious downward trend which shows that there are more new cold spots.
... Similar to the findings of studies conducted in Western countries, the distribution of crimes or hotspots in some Chinese cities displays hourly, daily, weekly and seasonal variation, and some factors that may have impacts on the spatial and temporal pattern of crime have also been verified [7][8][9][10]. The applicability of western criminological theories, such as social disorganization theory [11] and routine activities theory [12], have also been demonstrated by studies conducted in Chinese context [13][14][15]. Nevertheless, few of these studies have focused on long-term changes in crime patterns in China. ...
Chinese cities have been undergoing extraordinary changes in many respects during the process of urbanization, which has caused crime patterns to evolve accordingly. This research applies a Bayesian spatiotemporal model to explore and understand the spatiotemporal patterns of crime risk from 2008 to 2017 in Changchun, China. The overall temporal trend of crime risk, the effects of land use covariates, spatial random effects, and area-specific differential trends are estimated through a Bayesian spatiotemporal model fitted using the Integrated Nested Laplace Approximation (INLA). The analytical results show that the regression coefficient for the overall temporal trend of crime risk changed from significantly positive to negative after the land use variables are incorporated into the Bayesian spatiotemporal model. The covariates of road density, commercial and recreational land per capita, residential land per capita, and industrial land per capita are found to be significantly associated with crime risk, which relates to classic theories in environmental criminology. In addition, some areas still exhibit significantly increasing crime risks compared with the general trend even after controlling for the land use covariates and the spatial random effects, which may provide insights for law enforcement and researchers regarding where more attention is required since there may be some unmeasured factors causing higher crime trend in these areas.
... The urban area has undergone a lot of changes including in the aspect of the social dimension, economy, rising residential mobility, and more heterogeneous urban dwellers. This transformation has impacted the rise of crime and the change of crime patterns in urban areas (Song and Liu, 2013). The residential burglary in Kuching, Sarawak tends to be clustered in 2015 and 2017 due to the opportunity of crime. ...
Residents in Malaysia are mostly concentrated in urban areas. However, people do feel worried about residential burglary despite living in urban areas. Although burglary cases have recorded a decline, the fears of crime are still high among the people in Kuching, Sarawak. The objective of this study is to identify the spatial pattern of residential burglary in Kuching, Sarawak. This study is using Global Moran’s I method as it can evaluate the spatial autocorrelation of residential burglary in the global context. The spatial data consists of Kuching district boundary, road data, police station boundary and police sector boundary, while non-spatial data includes residential burglary from 2015-2017, time, addresses of the crime occurrences, latitude and longitude. The result revealed that the strong global spatial patterns were found for residential burglary in Kuching, Sarawak between 2015 and 2017. However, there was no global spatial pattern found for residential burglary in 2016.
... 徐 冲 等：微观空间因素对街头抢劫影响的空间异质性 防范的缺失。由于人们日常活动的变化使得这三种条件时空汇合，即同时被满足的机会 增加，造成了犯罪行为的发生和犯罪率的提高。结合两个理论可以得出：通常潜在的犯 罪者是在对受害者以及犯罪场所进行理性选择之后来确定是否实施犯罪行为的，由此可 见犯罪场所作为犯罪发生的承载空间的重要性。是否理性的选择犯罪发生场所将很大程 度决定犯罪是否发生。而近年来蓬勃发展的犯罪地理学恰恰是以其独特的地理学空间视 角，对犯罪发生场所的空间位置进行了大量的研究工作，进一步发展了传统的"理性选 择理论"与"日常活动理论"中关于犯罪产生的空间影响因素的研究与讨论。现有研究 表明，犯罪行为的发生总在某些特定的空间环境下表现出集聚的态势 [6][7][8][9] 。在确定了犯罪 发生空间上存在集聚特征现象后，部分学者开始尝试探索犯罪的发生究竟和哪些特定的 空间要素有关，也开始关注微观空间要素对于犯罪发生的影响，例如重要的空间节点 (交通枢纽、车站) 对于抢劫犯罪的影响 [10,11] ，不同土地利用类型地块上的空间设施对于 犯罪发生的影响等 [12] 。此外，对于空间要素对犯罪发生的影响程度在空间上存在异质性 现象的研究成果也不断丰富 [13][14][15][16][17][18][19][20][21] [25] ： Abstract: Urban crime has increasingly become a major issue in the context of rapid urbanization in China. Investigating the patterns and effects of spatial factors on urban crime is of great importantce for urban public safety and security. ...
在快速城镇化的背景下,日益严重的城市犯罪问题已经严重影响了城市的安定与繁荣,深入研究城市犯罪的空间影响因素对于未来城市安全发展具有重要意义。以H市DP半岛上2006-2011年发生的373起街头抢劫案件为研究对象,通过将研究区域网格划分为233个样本单元,以核密度处理方式将原始案件点转化为每个格网单元的犯罪强度(密度)作为因变量,结合"日常活动理论"与"理性选择理论"选取微观空间因素作为自变量,最终采用地理加权回归模型分析微观空间因素对街头抢劫案件发生强度的空间异质性现象。研究表明:公交站点个数变量、交叉口个数变量、土地利用混合程度变量与最近出岛口距离变量,对街头抢劫发生的影响程度存在空间异质性现象,尤其是公交站点个数变量在GWR模型中表现出随空间位置的不同呈现显著的正负两种影响效果。警务部门可以参照该结果针对不同局部区域的高影响微观空间因素进行重点防控,提高警务效率,从而更有效地防范和抑制街头抢劫犯罪的发生。
... The existing research has also focused on the overall crime rate, rather than separating crimes by type [17,18], and has tended to be based simply on questionnaires or field surveys, due to limited access to official crime data in China [19,20]. Recently, with increased data disclosure, researchers have begun to use more accurate data for crime analysis, such as crime data provided by the Public Security Bureau [21]. However, there remains inadequate geographic research focused on the spatial relationship between the floating population and crime. ...
With the rapid development of China’s economy, the demand for labor in the coastal cities continues to grow. Due to restrictions imposed by China’s household registration system, a large number of floating populations have subsequently appeared. The relationship between floating populations and crime, however, is not well understood. This paper investigates the impact of a floating population on residential burglary on a fine spatial scale. The floating population was divided into the floating population from other provinces (FPFOP) and the floating population from the same province as ZG city (FPFSP), because of the high heterogeneity. Univariate spatial patterns in residential burglary and the floating population in ZG were explored using Moran’s I and LISA (local indicators of spatial association) models. Furthermore, a geographically weighted Poisson regression model, which addressed the spatial effects in the data, was employed to explore the relationship between the floating population and residential burglary. The results revealed that the impact of the floating population on residential burglary is complex. The floating population from the same province did not have a significant impact on residential burglary in most parts of the city, while the floating population from other provinces had a significantly positive impact on residential burglary in most of the study areas and the magnitude of this impact varied across the study area.
... Analysis into the diffusion of offencesdwhich can be thought of generally as spatialetemporal emergence, growth and spreading of outbreaks of rioting in space over timedcould provide valuable insights into how riots may evolve: insights that would be of value to scholars and the police alike. Several studies have identified spaceetime clustering in patterns of different types of offences, many of which emphasise the importance of fine-grained data analysis as a means of setting policing priorities (Johnson & Bowers, 2004;Song & Liu, 2013;Wu, Ye, & Webb, 2012). Using a computational approach, in this paper, we characterise the spatial diffusion patterns observed during the 2011 London riots to estimate if and how the disorder spread through space and time, and how such patterns might have changed as the disorderdand the police response to itdcontinued. ...
Surprisingly little research has examined the localised diffusion of riots within cities. In this paper, we investigate such patterns during the 2011 London riots, and consider how they changed as police numbers increased. Understanding how offences spread in space and time can provide insights regarding the mechanisms of contagion, and of the risk of events spreading between contiguous areas. Using spatial–temporal grids of varying resolution, and a Monte Carlo simulation, we compare observed patterns with those expected assuming the timing and location of events are independent. In particular, we differentiate between four space–time signatures: “flashpoints” of disorder which appear out of nowhere, “containment” whereby already affected areas experience further events, “escalation” whereby rioting continues in affected areas and spreads to those nearby, and “relocation” whereby the disorder moves from one locality to those adjacent. During the first half of the disorder, fewer counts of relocation diffusion were observed than expected, but patterns of containment, escalation, and flashpoints were all more prominent. For the second half of the disorder, when police capacity increased roughly three-fold, observed patterns did not differ from expectation. Our results show support for theories of spatial contagion, and suggest that there was a degree of coordination amongst rioters. They also show that police activity did not just suppress rioting, but dampened the influence of contagion, without displacement.
La escasez de recursos humanos y técnicos de la policía dificulta el estudio de patrones delictivos. Para apoyar esta tarea, Chamberlayne (2008) introdujo la automatización a la técnica estadística del análisis de umbral. Esta técnica calcula el número previsto de delitos en una región concreta utilizando la media móvil ponderada, de forma que permite estimar, a partir de años anteriores, el volumen de delitos para la zona respecto del volumen esperado. Por tanto, facilita detectar aquellas zonas donde existe un aumento o descenso de delitos fuera de lo esperado. Debido al problema de la delincuencia patrimonial en Barcelona, el objetivo de este artículo es aplicar la técnica del análisis de umbral para detectar volúmenes inusuales de esta tipología. Para este estudio, se analizaron un total de 164.788 delitos contra la propiedad en vía urbana de los distritos de Barcelona entre los años 2015 y 2017. Los resultados obtenidos sugieren prestar mayor atención al distrito de Ciutat Vella y Sant Martí, así como a los barrios de la Barceloneta el Raval y El Parc i la Llacuna del Poblenou, debido a unos niveles de delincuencia muy superiores a los esperados. También se destacan reducciones significativas en otras zonas. Por consiguiente, el análisis de umbral es una técnica de análisis de la delincuencia útil y fácil de utilizar en la gestión policial. Además, permite identificar aquellas zonas en las que la variación de la delincuencia requiere un estudio y un análisis detallado.
The shortage of human and technical resources in the police forces complicates the study of crime patterns. To assist in this task, Chamberlayne (2008) introduced automation to the statistical technique of threshold analysis, which calculates the expected number of crimes in a region by using the weighted moving average. The volume of crime in an area can thus be estimated based on previous years in relation to the expected volume in the current year. It therefore facilitates the detection of areas with an increase or decrease in crime beyond expectations. Due to the problem of property crime in Barcelona, the objective of this paper is to apply the technique of threshold analysis to detect unusual volumes of this type of offence. We analysed 164'788 street offences against the property in districts of Barcelona between 2015 and 2017. The results suggest that greater attention should be paid to the districts of Ciutat Vella and Sant Martí, as well as to the neighbourhoods of Barceloneta, el Raval and El Parc i la Llacuna del Poblenou, due to higher crime levels than expected in these zones. Significant reductions are also noted in other areas. Threshold analysis is therefore a useful and easy-to-use crime analysis technique in police management. Furthermore, it allows the identification of areas where the variation in crime requires further detailed study and analysis.
This paper reviews several of the key issues that underlie the development and expansion of think tanks in China from both a domestic and international perspective. Substantively, the review focuses on the need to develop criminological think tanks in China due to the well-documented relationship between rapid urbanization, social displacement, and crime. Though work on urbanization, social disorganization, and crime has existed for over half a decade in Western criminology, it suffers from a lack of research outside of the Western and usually more specifically American, cultural context. To advance this call for research, the paper identifies 14 generative research programs in the areas of the Routine Activities Theory, Social Bonds Theory, and Institutional Anomie Theory pursuable by Chinese criminological think tanks. The paper concludes with a brief discussion of some potential barriers to the successful production and dissemination of criminological research in China.
Prior work in architectural and urban studies suggests that there is a strong correlation between people dynamics and crime activities in an urban environment. These studies have been conducted primarily using qualitative evaluation methods, and as such are limited in terms of the geographic area they cover, the number of respondents they reach out to, and the temporal frequency with which they can be repeated. As cities are rapidly growing and evolving complex entities, complementary approaches that afford social scientists the ability to evaluate urban crime theories at scale are required. In this paper, we propose a new method whereby we mine telecommunication data and open crime data to quantitatively observe these theories. More precisely, we analyse footfall counts as recorded by telecommunication data, and extract metrics that act as proxies of urban crime theories. Using correlation analysis between such proxies and crime activity derived from open crime data records, we can reveal to what extent different theories of urban crime hold, and where. We apply this approach to the metropolitan area of London, UK and find significant correlations between crime and metrics derived from theories by Jacobs (e.g., population diversity) and by Felson and Clarke (e.g., ratio of young people). We conclude the paper with a discussion of the implications of this work on social science research practices.
The interaction of [Pd(bpy)(n-pr-dtc)]Br (I) and ([Pd(phen)(n-pr-dtc)]Br (II) (bpy=2,2′-bipyridine, phen=1,10-phenanthroline and n-pr-dtc=n-propyldithiocarbamate) with human serum albumin (HSA) was investigated using fluorescence, UV–vis absorption and circular dichroism (CD) spectroscopy techniques under simulative physiological conditions (pH=7.4). It was observed that the two complexes interact with HSA via static fluorescence quenching. The thermodynamic parameters indicate that the binding process was spontaneous and that hydrogen bonds and van der Waals forces play a major role in the association of the HSA–Pd(II) complexes. The activation energy (Ea), binding constant (Kb) and number of binding sites (n) of the HSA–Pd(II) complexes were calculated from fluorescence data at 293 K, 303 K and 311 K. The conformational alternations of protein secondary structure in the presence of Pd(II) complexes were demonstrated using synchronous fluorescence, three-dimensional fluorescence spectra, UV–vis absorption and circular dichroism techniques. Furthermore, the apparent distance between donor (HSA) and acceptor (Pd(II) complexes) was determined using fluorescence resonance energy transfer (FRET). The binding studies between these complexes and HSA give us key insights into the transportation, distribution and toxicity of newly design antitumor Pd(II) complexes in human blood.
Crime is one of the major concerns facing Chinese cities. Using crime data compiled at police precinct level in 2008, this research examines spatial patterns of violent crimes in Changchun, and explores the relationship between the spatial distribution of violent crimes and neighborhood characteristics. Crime rates are applied as a measure of the intensity of violent crimes. Spatial statistics and geographic information systems are used to detect violent crime hot spots, or statistically significant locales of high violent crime rates in Changchun. A multiple linear regression model is calibrated to assess the impacts of contextual neighborhood characteristics on violent crimes. The analytical results demonstrate that the risk or intensity of violent crimes is strongly concentrated in the central city area, and neighborhood socioeconomic, demographic, especially land use characteristics are effective in accounting for the spatial variation in the distribution of violent crimes across the city of Changchun. These findings are largely in line with the routine activities theory, social disorganization theory, and the framework of crime prevention through environmental design, which emphasize the importance of opportunities, local social context, and environmental design in shaping the spatial pattern of and reducing urban crimes.
Although there is a growing body of research based on social disorganization theory that relates the neighborhood context to juvenile crime and delinquency, it is unknown whether neighborhood social processes operate in a similar way across all types of disadvantaged neighborhoods. It is possible that some social processes are unique to economically depressed areas. This research attempts to explain theoretically and test empirically the relationships between neighborhood social structure, social processes, delinquent opportunity structures, and rates of adolescent delinquency among structurally disadvantaged neighborhoods. The hypotheses are tested using neighborhood-level parent and youth data from 44 Denver neighborhoods. A series of regression models are constructed to estimate the effects of the neighborhood on rates of delinquency. The results show that for this high-risk sample the most consistent predictor of rates of problem behavior is youths' perceptions of limited opportunities for the future.
Two general theoretical perspectives, criminal opportunity and social disorganization, have been widely used to explain the level of crime in cities and temporal changes in their crime rates. Using a pooled cross-sectional and time-series design of 584 U.S. cities for the years 1960, 1970, and 1980, the present study evaluates the empirical adequacy of these theories. The cross-sectional findings were far more supportive of social disorganization theories than criminal opportunity theories. However, neither perspective was able to consistently explain changes in crime rates over time. Ethnic heterogeneity, household size, and the rate of crowding in households were the strongest predictors of the level and changes in official rates of homicide, robbery, and burglary. The results are discussed in terms of their implications for future research.
Hotspot mapping is a popular analytical technique that is used to help identify where to target police and crime reduction resources. In essence, hotspot mapping is used as a basic form of crime prediction, relying on retrospective data to identify the areas of high concentrations of crime and where policing and other crime reduction resources should be deployed. A number of different mapping techniques are used for identifying hotspots of crime – point mapping, thematic mapping of geographic areas (e.g. Census areas), spatial ellipses, grid thematic mapping and kernel density estimation (KDE). Several research studies have discussed the use of these methods for identifying hotspots of crime, usually based on their ease of use and ability to spatially interpret the location, size, shape and orientation of clusters of crime incidents. Yet surprising, very little research has compared how hotspot mapping techniques can accurately predict where crimes will occur in the future. This research uses crime data for a period before a fixed date (that has already passed) to generate hotspot maps, and test their accuracy for predicting where crimes will occur next. Hotspot mapping accuracy is compared in relation to the mapping technique that is used to identify concentrations of crime events (thematic mapping of Census Output Areas, spatial ellipses, grid thematic mapping, and KDE) and by crime type – four crime types are compared (burglary, street crime, theft from vehicles and theft of vehicles). The results from this research indicate that crime hotspot mapping prediction abilities differ between the different techniques and differ by crime type. KDE was the technique that consistently outperformed the others, while street crime hotspot maps were consistently better at predicting where future street crime would occur when compared to results for the hotspot maps of different crime types. The research offers the opportunity to benchmark comparative research of other techniques and other crime types, including comparisons between advanced spatial analysis techniques and predic-tion mapping methods. Understanding how hotspot mapping can predict spatial patterns of crime and how different mapping methods compare will help to better inform their application in practice.
This paper analyzed the spatial patterns of four types of crime (assault, robbery, autotheft, and burglary) and their relationship with neighbourhood characteristics in the City of Omaha, Nebraska by using geographic information systems procedures and ordinary least square regression methods. Location quotients of crime and crime density were employed as two alternative measures of crime rates. This article has three important findings: First, the rationale of the employment of official crime rates for neighbourhood crime study is questionable; Second, while location quotients can be used to highlight the prevalent types of crime across urban neighbourhoods, they have limited use for the statistical analysis; and third, crime density focuses on the spatial intensity of crime and is more appropriate as the indicator of neighbourhood level crime than population-standardized crime rates and location quotients. This article not only presents important insights into the enhanced interpretation of the geography of neighbourhood crime, but also can be considered as testing the social disorganization theory and routine activity theory by using different measures instead of crime rates. Policy implications pertaining to neighbourhood crime mapping and law enforcement intervention are discussed at the end.
Since 1987, the Chinese government has begun to liberalize its approach to land markets, allowing for the development of commercial residential housing and sparking greater distinctions in housing quality. At the same time, there have been growing income differentials among Beijing's residents. This paper discusses how Beijing's new urban affluent have begun to take advantage of greater housing choices. It reports on a survey conducted in 1997 that uncovers the occupational characteristics, consumption patterns, housing characteristics, and residential locations of affluent residents. The results indicate that Beijing's new urban affluent are beginning to share some of the consumption features of middle-class Westerners. At the same time, social segregation is still nascent in Beijing. There is now only a sprinkling of affluent neighborhoods in the city, and a slight majority of wealthier respondents continue to live in public housing. Moreover, few are able to afford detached single-family housing or a private automobile. The continuation of current trends could both deepen and broaden social segregation in Beijing, as class divisions come to be manifested in separate neighborhoods.
In this paper we present a "routine activity approach" for analyzing crime rate trends and cycles. Rather than emphasizing the characteristics of offenders, with this approach we concentrate upon the circumstances in which they carry out predatory criminal acts. Most criminal acts require convergence in space and time of likely offenders, suitable targets and the absence of capable guardians against crime. Human ecological theory facilitates an investigation into the way in which social structure produces this convergence, hence allowing illegal activities to feed upon the legal activities of everyday life. In particular, we hypothesize that the dispersion of activities away from households and families increases the opportunity for crime and thus generates higher crime rates. A variety of data is presented in support of the hypothesis, which helps explain crime rate trends in the United States 1947-1974 as a byproduct of changes in such variables as labor force participation and single-adult households.
Present versions of the routine activity approach to deviant behavior do not account for the social context of situational motivation and opportunity. Using concepts from social bonding and differential association theories, we argue that the patterning of routine activities is guided in part by the same factors that cause deviant behavior, namely differential social relations. We extend the routine activity approach by arguing that the effect of routine activities on deviant behavior is contingent on people's differential social relations. Based on cross-sectional survey data from a nationally representative sample of Icelandic adolescents, our findings support these claims. First, there is a considerable decrease in the effect of our routine activities indicator (unstructured peer interaction in the absence of authority figures) on both violent behavior and property offending when we control for differential social relations, namely bonding with conventional agents and associations with deviant peers. Second, the findings indicate that differential social relations play a critical role in moderating the effects of routine activities on deviance.
Crime can be analyzed and mapped in a number of different ways. This article compares maps of violent crime across the cities of Brit- ish Columbia utilizing three crime measures: counts, rates and crime loca- tion quotients (LQCs). The LQC, adapted from regional planning, provides views of crime patterns not obtained with the two more traditional meas- ures of crime. When used in conjunction with crime counts and rates, the LQC offers a way of understanding how one area is different from another for purposes of research and deployment of prevention and control re- sources. Crime and its contextual backcloth exist at many spatial and tem- poral levels of resolution, from the international scene to the individ- ual crime site, from the trends of centuries to the patterns of seconds (Brantingham and Brantingham, 1993, 1984; Brantingham et al.,
We extend the routine activity perspective's situational analysis of crime to individual offending and to a broad range of deviant behaviors. In this view, unstructured socializing with peers in the absence of authority figures presents opportunities for deviance: In the presence of peers, deviant acts will be easier and more rewarding; the absence of authority figures reduces the potential for social control responses to deviance; and the lack of structure leaves rime available for deviant behavior. To determine whether individuals who spend more time in unstructured socializing activities engage in deviant behaviors more frequently, we analyzed within-individual changes in routine activities and deviance across five waves of data for a national sample of more than 1,700 18- to 26-year-olds. Participation in these routine activities was strongly associated with criminal behavior, heavy alcohol use, use of marijuana and other illicit drugs, and dangerous driving. Furthermore, routine activities accounted for a substantial portion of the association between these deviant behaviors and age, sex, and socioeconomic status.
The impact that natural disasters have on crime is still little known. In general, it is assumed that crime declines shortly after the disaster and slowly increases to pre-disaster levels over time. However, this assumption is not always confirmed by the few empirical studies that have been conducted to date. In this paper we analyze the impacts that Hurricanes Katrina and Rita had on the temporal and spatial distribution of reported crimes in the city of Houston, TX. Crime data were collected before, during, and after the landfall of both hurricanes. The modeling part of this paper focused on spatial, temporal, and spatio-temporal models, at both global and local scales. The results show that while Hurricane Katrina did not have any apparent impact on crime, Hurricane Rita led to a significant short-term increase in burglaries and auto thefts. Spatial analysis indicated that the increase in burglaries was mostly concentrated in African American and, to a lesser degree, in Hispanic neighborhoods. There is some indication that the mandatory evacuation order that was issued prior to the arrival of Hurricane Rita motivated crime “prone ” individuals, who did not follow the evacuation order, to burglarize unprotected residences of individuals, who evacuated. No mandatory evacuation order was issued for Hurricane Katrina. 1.
The present research examines a structural model of violent crime in Portland, Oregon, exploring spatial patterns of both crime and its covariates. Using standard structural measures drawn from an opportunity framework, the study provides results from a global ordinary least squares model, assumed to fit for all locations within the study area. Geographically weighted regression (GWR) is then introduced as an alternative to such traditional approaches to modeling crime. The GWR procedure estimates a local model, producing a set of mappable parameter estimates and t-values of significance that vary over space. Several structural measures are found to have relationships with crime that vary significantly with location. Results indicate that a mixed model— with both spatially varying and fixed parameters—may provide the most accurate model of crime. The present study demonstrates the utility of GWR for exploring local processes that drive crime levels and examining misspecification of a global model of urban violence.
In order to extend the study of community social disorganization and crime beyond its exclusive focus on large urban centers, we present an analysis of structural correlates of arrest rates for juvenile violence in 264 nonmetropolitan counties of four states. Findings support the generality of social disorganization theory: Juvenile violence was associated with rates of residential instability, family disruption, and ethnic heterogeneity. Though rates of poverty were not related to juvenile violence, this is also in accord with social disorganization theory because, unlike urban settings, poverty was negatively related to residential instability. Rates of juvenile violence varied markedly with population size through a curvilinear relationship in which counties with the smallest juvenile populations had exceptionally low arrest rates. Analyses used negative binomial regression (a variation of Poisson regression) because the small number of arrests in many counties meant that arrest rates would be ill suited to least-squares regression.
The main aim of this article is to analyze the relationships between the spatial patterns of residential burglaries and the socioeconomic characteristics of neighborhoods in London, Ontario. Relative risk ratios are applied as a measure of the intensity of residential burglary. The variation in the risks of burglary is modeled as a function of contextual neighborhood variables. Following a conventional (global) regression analysis, spatial variations in the relationships are examined using geographically weighted regression (GWR). The GWR results show that there are significant local variations in the relationships between the risk of residential burglary victimization and the average value of dwellings and percentage of the population in multifamily housing. The results are discussed in the context of four hypotheses, which may explain geographical variations in residential burglary. The practical implication of the GWR analysis is that different crime prevention policies should be implemented in different neighborhoods of the city.
The possibility that homicides can spread from one geographic area toanother has been entertained for some time by social scientists, yetsystematic efforts to demonstrate the existence, or estimate the strength,of such a diffusion process are just beginning. This paper uses exploratoryspatial data analysis (ESDA) to examine the distribution of homicides in 78counties in, or around, the St. Louis metropolitan area for two timeperiods: a period of relatively stable homicide (1984–1988) and aperiod of generally increasing homicide (1988–1993). The findingsreveal that homicides are distributed nonrandomly, suggestive of positivespatial autocorrelation. Moreover, changes over time in the distribution ofhomicides suggest the possible diffusion of lethal violence out of onecounty containing a medium-sized city (Macon County) into two nearbycounties (Morgan and Sangamon Counties) located to the west. Althoughtraditional correlates of homicide do not account for its nonrandom spatialdistribution across counties, we find some evidence that more affluentareas, or those more rural or agricultural areas, serve as barriers againstthe diffusion of homicides. The patterns of spatial distribution revealedthrough ESDA provide an empirical foundation for the specification ofmultivariate models which can provide formal tests for diffusion processes.
This paper proposes that individuals who report that they live in neighborhoods characterized by disorder—by crime, vandalism, graffiti, danger, noise, dirt, and drugs—have high levels of fear and mistrust. It further proposes that an individual's alliances and connections with neighbors can buffer the negative effects of living in a neighborhood characterized by disorder on fear and mistrust. Results from a representative sample of 2482 Illinois residents collected by telephone in 1995 support the propositions. Living in a neighborhood with a lot of perceived disorder significantly affects mistrust and the fear of victimization, adjusting for sociodemographic characteristics. Perceived neighborhood disorder and social ties significantly interact: informal social ties with neighbors reduce the fear‐ and mistrust‐producing effects of disorder. However, formal participation in neighborhood organizations shows little buffering effect.
Book description: The growing potential of GIS for supporting policing and crime reduction is now being recognised by a broader community. GIS can be employed at different levels to support operational policing, tactical crime mapping, detection, and wider-ranging strategic analyses. With the use of GIS for crime mapping increasing, this book provides a definitive reference.
GIS and Crime Mapping provides essential information and reference material to support readers in developing and implementing crime mapping. Relevant case studies help demonstrate the key principles, concepts and applications of crime mapping.
This book combines the topics of theoretical principles, GIS, analytical techniques, data processing solutions, information sharing, problem-solving approaches, map design, and organisational structures for using crime mapping for policing and crime reduction. Delivered in an accessible style, topics are covered in a manner that underpins crime mapping use in the three broad areas of operations, tactics and strategy.
* Provides a complete start-to-finish coverage of crime mapping, including theory, scientific methodologies, analysis techniques and design principles.
* Includes a comprehensive presentation of crime mapping applications for operational, tactical and strategic purposes.
* Includes global case studies and examples to demonstrate good practice.
* Co-authored by Spencer Chainey, a leading researcher and consultant on GIS and crime mapping, and Jerry Ratcliffe, a renowned professor and former police officer.
This book is essential reading for crime analysts and other professionals working in intelligence roles in law enforcement or crime reduction, at the local, regional and national government levels. It is also an excellent reference for undergraduate and Masters students taking courses in GIS, Geomatics, Crime Mapping, Crime Science, Criminal Justice and Criminology.
Chapter 1 Table of Contents Chapter 2 Preface Chapter 3 1 Basic Issues Chapter 4 2 The Criminal Behavior of Neighborhood Residents Chapter 5 3 Neighborhood Opportunities for Criminal Behavior Chapter 6 4 Neighborhood Dynamics and the Fear of Crime Chapter 7 5 The Neighborhood Context of Gang Behavior Chapter 8 6 Neighborhood-Based Responses to Crime: Policy Issues Chapter 9 Epilogue Chapter 10 Notes Chapter 11 References Chapter 12 Acknowledgments Chapter 13 Index Chapter 14 About the Author
Recent theories posit that social differentiation in the risks of criminal victimization is due to variation in routine activities/lifestyles which place some persons or their property in proximity to motivated offenders. For a sample of 107,678 residents in thirteen U.S. cities, measures of the nature and quantity of routine activities outside the home (major daytime activity, frequency of nighttime activity) are introduced to assess the mediational effects of these variables on the demographic correlates of victimization. Routine activities/lifestyle variables have relatively strong direct and mediational effects on individuals' risks of property victimization but not for violent victimization. These findings are discussed in terms of their implications for further research on the relationship between demographic variables, routine activities/lifestyles, and criminal victimization.
The purpose of this paper is to apply a "routine activities" approach to explain the relations between macro-level indicators of leisure activities and rates of serious crime. We hypothesize that indicators of the volume of leisure activities that typically take place within households will exhibit negative relations with rates of crime, whereas indicators of the volume of leisure activities that are usually conducted away from households will yield positive associations with rates of crime. Regression analyses for a sample of the largest Standard Metropolitan Statistical Areas (SMSAs) in the United States provide strong support for these hypotheses. A "household activities" index and a "nonhousehold activities" index exhibit negative and positive associations, respectively, with SMSA crime rates. We suggest that while these results are fully consistent with the routine activities approach, they are not readily interpretable with reference to other general perspectives on the relations between leisure and crime.
Multi-level research has shown that individual and community factors are important predictors of the risk for violent victimization. However, previous analyses have drawn different conclusions about the role of any given factor. These differences likely are related to variations in how violence is measured and to the fact that the data are drawn from different locales. The research presented here uses the 1995 National Crime Victimization Survey and tract-level census data to examine (1) how the risk of violence is distributed across persons and places in the United States and (2) whether empirical findings are sensitive to the operationalization of violence. Results show that some individual-level predictors (e.g., gender and race) are sensitive to the operationalization of violence, whereas others (e.g., age and marital status) are not. In addition, the impact of community characteristics on violence depends on central-city residence. In central cities, persons most at risk are in disadvantaged tracts, with lower proportions of immigrants. Outside central cities, the proportion of immigrants in an area increases risk, while community disadvantage has no independent influence. The importance of an empirical foundation for the development of theories of risk is discussed.
The past decade has seen a rapid growth in the use of a spatial perspective in studies of crime. In part this growth has been driven by the availability of georeferenced data, and the tools to analyze and visualize them: geographic information systems (GIS), spatial analysis, and spatial statistics. In this paper we use exploratory spatial data analysis (ESDA) tools and Bayesian models to help better understand the spatial patterning and predictors of property crime in Seattle, Washington for 1998-2000, including a focus on built environment variables. We present results for aggregate property crime data as well as models for specific property crime types: residential burglary, nonresidential burglary, theft, auto theft, and arson. ESDA confirms the presence of spatial clustering of property crime and we seek to explain these patterns using spatial Poisson models implemented in WinBUGS. Our results indicate that built environment variables were significant predictors of property crime, especially the presence of a highway on auto theft and burglary.
This study discusses methods for predicting local crime rates, measures of fear of crime and measures of disorder that are based on regression models which make use of local census variables and regional dummies. The crime types for which predictions are made are personal crime, total household crime, burglary and vehicle crime. The information on crime, fear of crime and disorder is drawn from the 2000 British Crime Survey. The local census variables (relating to postcode sectors) are drawn from the 1991 England and Wales Census. The results show that the modelling of measures of fear and disorder is more robust than the modelling of crime. Therefore, it appears more possible to predict types of area where concern about crime and problems of disorder are likely to be highest than to predict those areas most at risk of crime.
This sociohistorical study of the development of criminology in the People’s Republic of China not only considers available primary and secondary sources but also directly draws upon fieldwork interviews conducted with prominent scholars in China in 2007. Crime has been a silent partner in Chinese modernization, and law and order have been as central to the Chinese ruling elite’s priorities as the promise of prosperity and economic growth. Criminology as a field of study with recognized scholars and research publications has been established in China, and our article critically examines the development, focus and scope, direction and trends, and underpinning theories.
The systemic model posits that informal control reduces crime and that social networks reduce crime indirectly by stimulating informal control. The systemic literature consistently supports the informal control-crime relationship but reveals wider variation in the measurement and effects of network dimensions. Recognizing this pattern, some scholars advocate an explicit distinction between networks and informal control. We formally address that issue with analysis of the measurement structure of multiple network and informal control indicators using data collected in 300 Seattle neighborhoods. Results reveal several distinct network dimensions that are themselves distinct from informal control. Regression analysis supports the systemic model: informal control reduces crime victimization, and networks exhibit an indirect, negative effect through informal control. Consistent with prior research, some network measures have a positive, direct effect on crime. We conclude that a distinction between networks and informal control is essential when testing and evaluating the systemic model.
The risk of property crime victimization is examined from a routine activities approach using data from six neighborhoods in Atlanta, Georgia. Indicators of the concepts of motivated offenders, suitable targets, and capable guardians are identified, and their individual and combined explanatory power are examined. The findings reveal that measures of neighborhood crime and housing type are the only variables that consistently relate to victimization in the hypothesized direction; employment status is the only guardianship measure that has the predicted effect on victimization. The analyses provide limited support for the routine activities approach, but are also consistent with hypotheses derived from other theoretical perspectives on criminal victimizations.
The social disorganization perspective assumes that social interaction among neighbors is a central element in the control of community crime. Moreover, social interaction among neighbors that occurs frequently, such as every day, is assumed to be most effective. This analysis tests that assumption by exploring the consequences of frequent and infrequent interaction. I construct 10 alternative measures of social interaction and separately examine the effect of each on the rates of three serious crimes across 60 urban neighborhoods. Findings suggest that type of interaction matters. Getting together once a year or more with neighbors has the most consistent and generally strongest effect on burglary, motor vehicle theft, and robbery. Further this form of interaction mediates a significant proportion of the effect of ecological characteristics on community crime. Implications for community crime research are discussed.
In “Toward a Theory of Race, Crime, and Urban Inequality,” Sampson and Wilson (1995) argued that racial disparities in violent crime are attributable in large part to the persistent structural disadvantages that are disproportionately concentrated in African American communities. They also argued that the ultimate causes of crime were similar for both Whites and Blacks, leading to what has been labeled the thesis of “racial invariance.” In light of the large scale social changes of the past two decades and the renewed political salience of race and crime in the United States, this paper reassesses and updates evidence evaluating the theory. In so doing, we clarify key concepts from the original thesis, delineate the proper context of validation, and address new challenges. Overall, we find that the accumulated empirical evidence provides broad but qualified support for the theoretical claims. We conclude by charting a dual path forward: an agenda for future research on the linkages between race and crime, and policy recommendations that align with the theory’s emphasis on neighborhood level structural forces but with causal space for cultural factors.
■ Abstract This paper assesses and synthesizes the cumulative results of a new "neighborhood-effects" literature that examines social processes related to problem behaviors and health-related outcomes. Our review identified over 40 relevant studies published in peer-reviewed journals from the mid-1990s to 2001, the take-off point for an increasing level of interest in neighborhood effects. Moving beyond traditional char- acteristics such as concentrated poverty, we evaluate the salience of social-interactional and institutional mechanisms hypothesized to account for neighborhood-level varia- tions in a variety of phenomena (e.g., delinquency, violence, depression, high-risk behavior), especially among adolescents. We highlight neighborhood ties, social con- trol, mutual trust, institutional resources, disorder, and routine activity patterns. We also discuss a set of thorny methodological problems that plague the study of neigh- borhood effects, with special attention to selection bias. We conclude with promising strategies and directions for future research, including experimental designs, taking spatial and temporal dynamics seriously, systematic observational approaches, and benchmark data on neighborhood social processes.
The increased availability of digital data and the increased scrutiny of public expenditure are opening new opportunities for detailed spatial analysis of social behaviour and policy initiatives to target resources where they are most needed. Two such policy areas in which the use of GIS combined with spatial analysis tools has made significant progress are health and police services, which are at the top of the political agenda due to increasing 'demand' and spiralling costs. Against this background, this paper presents the results of a collaborative research project carried out in Sheffield on the use of GIS for crime pattern analysis. The research described is significant in a number of respects: it is based on high-quality detailed crime data and geographical data for the whole of Sheffield; it compares two different methodologies for crime pattern analysis, one developed specifically for crime, the other for health research; and it demonstrates the policy value of this transfer of methodologies across disciplines.
This study explores causes of variation in auto theft rates using spatial data with face blocks as a unit of analysis. An integration of routine activity theory and social disorganization theory is proposed, premised on an empirical basis of interaction effects and a pattern of automobile theft diffusion. The results show that the integration of social disorganization theory and routine activity theory significantly increases the predictive power of the analyses and reveals several new socioecological implications for how and why auto theft occurs.
Contextual analysis is widely endorsed as a research tool to bridge the macro-micro gap in studies of social phenomena. Using
a multistage sample of 5,098 Seattle residents, we extend previous work by examining how individuals' risks of criminal victimization
are influenced by their personal life-styles and by the characteristics of their neighborhood. Although several types of contextual
effects were observed, a major finding is that lower levels of guardianship and higher target attractiveness strongly increased
the risks of burglary for residents of more affluent areas, whereas these factors had little net impact on the burglary risks
of residents of more socially disorganized areas. There were no major differences in the predictors of violent victimization
across different neighborhood contexts. We conclude with a discussion of the results as they relate to future research on
theoretical integration and crime control policy.
As a scholarly discipline, criminology in China is growing in stature, maturity and utility. In the short thirty years since 1979, China has successfully established criminology as a scientific field of study with well-defined subjects, recognised scholars and copious research/publications. To date, there are very few systematic and comprehensive studies of criminology (in English language) as an emerging and important field of academic discipline in China. As a result, we know very little about its focus and scope; direction and trend; theories and findings; and problems and issues. This is a first attempt to do so. The article (in two parts, published separately) investigates into: ‘Literature on law, crime and punishment in China’, ‘The idea of crime (Fanzui)’, ‘Traditional thinking of crime and punishment in imperial China’, ‘Nature, structure and development of criminology’, ‘Contemporary theories on crime and punishment’ and ‘Fundamental issues and challenges’ facing criminological research in China.
In this study, the author tests the significance of several concepts as predictors of neighborhood burglary rates using multivariate regression and spatial analysis techniques. The characteristics the author investigates have been identified by studies on the social ecology of crime and by more recent research evaluating community-oriented policing. He draws heavily from recent work by Sampson, Raudenbush, and Earls as well as others who have advanced the social disorganization theory of crime. This perspective views the presence of “community” as a key factor that helps maintain order in neighborhoods, even in the presence of structural criminogenic conditions such as concentrated poverty.
We examined the spatial distribution of crime incidents on USDA Forest Service lands using a geographic information system and several spatial analysis techniques. Our primary objective was to examine whether patterns existed in the spatial distribution of crime and to explore the relationship of patterns to other geographic features using the Forest Service and other databases. We analyzed a database containing over 45,000 spatially referenced crimes such as felonies, infractions, and misdemeanors. Other spatial data layers included transportation networks, administrative boundaries, hydrology, elevation, and digital orthophotographs. Results at a regional scale showed crime densities concentrated in forests adjacent to population centers and transportation corridors. Nearest neighbor, quartic kernel density estimation, and quadrat analyses identified crime patterning and hot spots. Our results suggest that managers can use these spatial techniques as decision support tools to better understand the relationship between natural resources and crime.
This research assesses the impact that one natural disasterHurricane Katrinaand subsequent population movements have had on crime in the state of Louisiana. Using Index Crimes from the Louisiana Commission on Law Enforcement and population estimates from the U.S. Census Bureau, time series of violent and nonviolent crime rates were first analyzed using autoregressive, integrated, and moving average (ARIMA) models. Cumulative percentile maps were created next to analyze spatial trends of crime hot and cold spots in the study area. Overall, results from this research support theories that suggest that crime rates remain stable or actually decline in regions receiving evacuees from areas hardest hit by the hurricane. In the case of Orleans Parish, results are inconclusive due to unreliable crime rates for the period following Hurricane Katrina until the beginning of 2006. It is suggested that crime rates in Orleans Parish fell drastically after the storm. However, some crime types, including robbery, burglary, and larceny, returned to pre-Katrina levels and murder and aggravated assault even exceeded prestorm averages by the end of December 2007.
This article investigates the spatial dimension of automotive theft, break and enter, and violent crime in Vancouver, British Columbia in 1996. The article uses and synthesizes social disorganization theory and routine activity theory as a theoretical backcloth and employs a spatial autoregressive regression procedure that accounts for spatial autocorrelation between crime rates and socio-economic characteristics at the census tract level. Strong support is found for synthesizing these two most common spatial theories of crime. In particular, high unemployment (social disorganization theory) and the presence of young populations (routine activity theory) are the strongest predictors of criminal activity.Le présent article examine la dimension spatiale du vol automobile, du cambriolage et de crimes violents recensés en 1996 dans la ville de Vancouver, Colombie-Britannique. L'article s'appuie sur des théories de la désorganisation sociale et des activités courantes et en fait une synthèse servant de base théorique. Il utilise une méthode de régression spatiale autorégressive qui permet d'expliquer l'autocorrélation spatiale entre les taux de criminalité et les caractéristiques socio-économiques selon les secteurs de recensement. La synthèse de ces deux théories spatiales les plus répandues en matière de criminologie est validée. Le taux de chômage élevé (la théorie de la désorganisation sociale) et la présence de jeunes (théorie des activités courantes) constituent les facteurs les plus importants pour prédire la criminalité.
Recent contextual analyses of victimization survey data are extended by application of hierarchical logistic model techniques. Using a multi-stage sample of 5,090 Seattle residents, we estimate models for individuals' risks of violent crime and burglary victimization as a function of both individual crime opportunity factors (routine activity and personal lifestyle) and contextual indicators of neighborhood social disorganization (neighborhood incivilities on conditions of disorder, ethnic heterogeneity, and neighborhood density in terms of both residents and strangers). Strong contextual direct effects of density, disorder, and heterogeneity are observed for violent and or burglary risks. Further, the hierarchical method used here provides a richer type of contextual analysis, indicating that neighborhood factors also “condition” the impact of crime opportunity factors for risk of both violent and burglary victimization. Implications for theoretical integration, victimization prevention strategies, and crime control policies are discussed.
This research analyzes changes in crime rates by city size and determines the extent to which these changes can be explained by socioeconomic variables. More particularly it addresses rates of change in mean crime rates for violent and property crime between 1976–1984 and 1985–1994 for all U. S. cities, then compares results to Ohio cities. It provides a detailed analysis of changing crime rates in 111 Ohio cities with populations between 10,000 and 99,999 inhabitants and attempts to account for crime differentials between these cities employing linear regression and factor analysis. Results indicate that crime is significantly related to poverty and its associated conditions and processes.
Abstract Although the overall crime rate dropped between 1993 and 2000, both adolescent violence and violent crime in rural areas has been on the rise. However, little research has been conducted on the determinants of rural violence using targeted regional samples of rural youth. This study examines the applicability of lifestyle/routine activities (RA) theory to a large sample of rural adolescents from Alabama. Multivariate logistic regression analyses indicate that: (1) social guardianship reduces the risk of assault and robbery victimization; (2) blacks are less likely to be assault and robbery victims; and (3) males are less likely to be robbery victims. Social isolation at the individual level is also a strong risk factor for both robbery and assault victimization. The theoretical implications of these findings and suggestions for future research are also discussed.
Attempts to integrate the two predominant spatial theories of crime, social disorganization and routine activity theories, may benefit from examining empirical relationships at units of analysis smaller than the relatively large units characteristic of most ecological research (cities, SMSAs, census tracts, multiple city blocks). Small units of analysis, specifically, face blocks (both sides of a city block between two intersections) are analyzed in a study of street robbery within a medium-size southeastern U.S. city. Models of street robbery and street-robbery “potential” suggest a crime diffusion process. Several interaction effects between variables of social disorganization and routine activity theory are found, which may form the basis in future research for successful theoretical integration.
Abstract This research explores violent and property crime rates in nonmetropolitan counties. It is argued that crime rates are lower in these counties because of higher levels of social integration. We test the hypothesis that predictors of crime from social disorganization theory exert different effects on violent and property crimes at different levels of population change in nonmetropolitan counties. We use a spatial lag regression model to predict the 1989–1991 average violent and property crime rates for these counties, taken from the Uniform Crime Reports (UCR). The results show that a factor-analyzed index of resource disadvantage (poverty rate, income inequality, unemployment, percent female-headed households) has different effects on both violent and property crime at different levels of population change in nonmetropolitan counties. Contrary to expectations, we find that resource disadvantage exerts a greater positive effect on both violent and property crimes in nonmetropolitan counties that lost population between 1980 and 1990. Implications for theory and research are discussed.
A leading sociological theory of crime is the “routine activities” approach (Cohen and Felson, 1979). The premise of this ecological theory is that criminal events result from likely offenders, suitable targets, and the absence of capable guardians against crime converging nonrandomly in time and space. Yet prior research has been unable to employ spatial data, relying instead on individual- and household-level data, to test that basic premise. This analysis supports the premise with spatial data on 323,979 calls to police over all 115,000 addresses and intersections in Minneapolis over 1 year. Relatively few “hot spots” produce most calls to Police (50% of calls in 3% of places) and calls reporting predatory crimes (all robberies at 2.2% of places, all rapes at 1.2% of places, and all auto thefts at 2.7% of places), because crime is both rare (only 3.6% of the city could have had a robbery with no repeat addresses) and concentrated, although the magnitude of concentration varies by offense type. These distributions all deviate significantly, and with ample magnitude, from the simple Poisson model of chance, which raises basic questions about the criminogenic nature of places, as distinct from neighborhoods or collectivities.
After a period of decline in the discipline, the social disorganization model of Shaw and McKay is again beginning to appear in the literature. This paper examines five criticisms of the perspective and discusses recent attempts to address those issues and problems that are still in need of resolution.
The capabilities for visualization, rapid data retrieval, and manipulation in geographic information systems (GIS) have created the need for new techniques of exploratory data analysis that focus on the “spatial” aspects of the data. The identification of local patterns of spatial association is an important concern in this respect. In this paper, I outline a new general class of local indicators of spatial association (LISA) and show how they allow for the decomposition of global indicators, such as Moran's I, into the contribution of each observation. The LISA statistics serve two purposes. On one hand, they may be interpreted as indicators of local pockets of nonstationarity, or hot spots, similar to the Gi and G*i statistics of Getis and Ord (1992). On the other hand, they may be used to assess the influence of individual locations on the magnitude of the global statistic and to identify “outliers,” as in Anselin's Moran scatterplot (1993a). An initial evaluation of the properties of a LISA statistic is carried out for the local Moran, which is applied in a study of the spatial pattern of conflict for African countries and in a number of Monte Carlo simulations.
Highlighting resource inequality, social processes, and spatial interdependence, this study combines structural characteristics from the 1990 census with a survey of 8,872 Chicago residents in 1995 to predict homicide variations in 1996–1998 across 343 neighborhoods. Spatial proximity to homicide is strongly related to increased homicide rates, adjusting for internal neighborhood characteristics and prior homicide. Concentrated disadvantage and low collective efficacy—defined as the linkage of social control and cohesion—also independently predict increased homicide. Local organizations, voluntary associations, and friend/kinship networks appear to be important only insofar as they promote the collective efficacy of residents in achieving social control and cohesion. Spatial dynamics coupled with neighborhood inequalities in social and economic capacity are therefore consequential for explaining urban violence.