Spatial Patterns Of Crime Research Articles

An Analysis of the Relationship between Weather and Aggressive Crime in Cleveland, Ohio

Abstract This study investigated the relationship between weather and aggressive crime for the period from 1999 through 2004 for the city of Cleveland, Ohio. The majority of the analysis focused on meteorological summer (June–August), because this is the time when the most oppressive conditions occur. Citywide analysis (nonspatial) was performed for many temporal variations, which accounted for season, time of day, and day of week (weekend or weekday). The linear regression model explored the relationship between apparent temperature and aggressive crime counts. Results show that summer has the highest aggressive crime counts, while winter has the lowest crime counts. Aggressive crime generally increases linearly as apparent temperature increases, with nonaggravated assaults and domestic violence assaults having the largest response as the weather becomes hotter. The midday and early night hours (i.e., 0300–1200 LT) have the greatest significant findings relating apparent temperature to aggressive crime. Further analysis was performed at the subcity level. A threshold of mean apparent temperature of 24°C was used in order to investigate spatial patterns of aggressive crime when it is “hot” compared to when it is “cold.” Overall, the spatial patterns of crime counts are minimally influenced by hotter weather. Despite the numerous different spatial analyses that were performed, there was no significant evidence suggesting that spatial patterns of aggressive crime are greatly affected by hotter weather. Rather, it appears that warmer weather brings relatively similar percentage increases in aggressive crime activity citywide. Further exploration and analysis of the weather–crime relationship could be of significant benefit to law enforcement officials and emergency response personnel, who increasingly use geographic information system (GIS)-based tools in their work to assist in determining where and when intervention is most beneficial.

Disaggregating the Relationship Between Schools and Crime

Although an extensive literature exists on crime in schools, surprisingly few studies have examined crime within the vicinity of schools. Schools, like other urban facilities, can generate crime by providing youth opportunities to congregate with little supervision, particularly before and after school hours. Some noteworthy studies have demonstrated that crime is more likely around schools, but the distinctive patterns based on school, time, and spatial contexts have not been fully addressed. The current study examines the differential crime generating potential of schools by type (public/private) and by level (elementary, middle, and high school), taking into account both spatial and temporal indicators. The authors employ a unique methodology for spatial modeling using the matrix exponential spatial expansion. Results indicate that there are distinct patterns of crime associated with schools, which suggests that disaggregating schools is important for understanding spatial patterns of crime.

THE DYNAMICS OF CRIME REGIMES*

Crimes have many features, and the mix of those features can change over time and space. In this article, we introduce the concept of a crime regime to provide some theoretical leverage on collections of crime features and how the collections of features can change. Key tools include the use of principal components analysis to determine the dimensions of crime regimes, visualization methods to help reveal the role of time, summary statistics to quantify crime regime patterns, and permutation procedures to examine the role of chance. Our approach is used to analyze temporal and spatial crime patterns for the city of Los Angeles during an 8‐year period. We focus on the number of violent crimes over time and their potential lethality.

The “Hottest” Part of a Hotspot: Comments on “The Utility of Hotspot Mapping for Predicting Spatial Patterns of Crime”

The recent article by Chainey et al. (2008) in the Security Journal made an important contribution to the analysis of crime hotspots by developing a criterion for comparing methods that identify hotspots. However, the methodology they adopted to compare several specific hotspot techniques was limited and, consequently, raised doubts about the conclusions they drew.

A Brief Commentary on “The Utility of Hotspot Mapping for Predicting Spatial Patterns of Crime”

Even though this article (Chainey et al., 2008) is extremely interesting and thought provoking, I have to admit that it includes a couple of mistakes that may potentially mislead uninformed readers. When it tackles the hotspot mapping techniques the authors confuse the "spatial ellipses" with some sort of spatial clustering technique.

Crime and Regeneration in Urban Communities: The Case of the Big Dig in Boston, Massachusetts

Theory in environmental criminology postulates that changes in the urban structure will produce changes in spatial and temporal patterns of crime. This research examined spatial patterns of crime in Boston, Massachusetts using location quotients and buffer analysis. The focus of this reseach ivas how the central artery project ('Big Dig') changed accessibility to a unique neighbourhood in Boston known as the North End. Theoretically, as a result of the increased accessibility, the North End would be likely to experience changes in spatial patterns of crime. Traditionally, however, the population of the North End has remained homogenous in nature, a modifying influence on crime. Analysis using location quotients and location time quotients found that block groups in the North End could not be characterized as 'troubled areas' when compared with the rest of the city. The buffer analysis revealed high rates of certain types of crimes, and high relative concentrations of certain types of crimes around the former location of the central artery. Methodological issues and measurement problems pertaining to this unique situation are also discussed. Theory in environmental criminology postul ates that changes in the urban structure will produce changes in spatial and temporal patterns of crime. This research examined spatial patterns of crime in Boston, Mas sachusetts and focused especially on docu menting the effects of the central artery project on spatial patterns of crime. The 'Big Dig' project (as the demolition of the central artery project has been colloquially known) was conducted over a prolonged period of time and involved a complete demolition of

The Utility of Hotspot Mapping for Predicting Spatial Patterns of Crime

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.

Using Geographically Weighted Regression to Explore Local Crime Patterns

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.

Spatial Pattern of Violent Crime (Homicide & Dacoity) in Chennai City: A Geographical Information System Approach

Spatial Pattern of Violent Crime (Homicide & Dacoity) in Chennai City: A Geographical Information System Approach

Job Access and Homicide Patterns in Chicago: An Analysis at Multiple Geographic Levels Based on Scale-Space Theory

Economists and criminologists have long tried to establish linkages between job markets and crime. Most prior research, however, was across large areas (e.g., states, metropolitan areas) or across time. This research focuses on examining the variation of job markets within a city (i.e., Chicago) and whether it is related to the spatial pattern of crime (i.e., homicide). The job market condition is measured by job accessibility, an index computed by a Geographic Information System (GIS) method. Multivariate regressions, controlling for other socioeconomic covariates, are used to analyze the relationship between job access and homicide rates. Considering problems with analysis of rare events such as homicide in small populations, this research constructs various levels of geographic areas from census tracts to generate more stable homicide rates with larger base populations. In particular, a spatial clustering method based on the scale-space theory is used to merge adjacent tracts of similar attributes into new geographic areas. The study shows an inverse relationship between job accessibility and homicide rates across census tracts and the newly-constructed geographic areas in Chicago.

Assessing spatial patterns of crime in Lima, Ohio

Police departments, city officials and policy makers all recognize the importance of a better understanding of the dynamics of crime. Both theoretical and applied approaches, or combinations of the two, which provide insight into why and where crime takes place are much in demand. Macrolevel analysis helps to identify problem neighborhoods. Microlevel analysis helps to isolate precise trouble spots within neighborhoods and, as a result, allows for better evaluation of crime and specific socioeconomic, demographic, land use and environmental characteristics associated with these trouble spots. This paper details a framework for better understanding the spatial characteristics of crime based upon the use of geographical information systems (GIS) and quantitative techniques. Lima, Ohio, provides a case study for a number of reasons. Many smaller communities have serious crime problems, so the study of crime in these communities is much needed. Lima (population 40,263) has violent crime rates well above average for its size, experiencing rates of violent crime equal to or above larger cities such as Columbus, Cincinnati and Toledo. Also, crime has an inordinately strong influence in the social and economic performance of small cities and hinders economic recovery efforts. The establishment of an analytical and theoretical framework for evaluating the relationship between aspects of place and the clustering of crime will undoubtedly lead to enhanced crime prevention strategies.

Spatial Crime Patterns and the Introduction of the UK Minimum Wage*

AbstractThis paper provides an empirical evaluation of whether one can uncover a link between crime and the labour market using a research methodology that is different to that utilized in existing work. We exploit a large regulatory change that was made to the UK labour market when a national minimum wage was introduced in April 1999. This minimum wage introduction provided pay increases for quite a large number of low paid workers. From a theoretical perspective we argue that this wage boost could have altered individual incentives to participate in crime. We then go on to develop empirical tests of this hypothesis comparing spatial crime patterns, measured at police force area level, in the years before and after the introduction of the minimum wage floor. Our empirical study of area‐level crime rates before and after the minimum wage introduction uncovers a statistically significant link between changes in crime and the extent of area low pay before the minimum wage was introduced. Overall our results are in line with the notion that altering wage incentives can affect crime and therefore that there exists a link between crime and the low wage labour market.

Ｍｏｒａｎ’ｓ Ｉ統計量による犯罪分布パターンの分析

This paper examined the spatial patterns of crime in a part of Tokyo. 68400 cases reported to the police were classified by 15 crime types, and then Moran's I, one of the indicators of the spatial autocorrelation was calculated using CrimeStat software. Moran's I for almost crime types including burglary, theft, snatching indicates significantly positive autocorrelation. To examine the effects of aggregation unit's size on Moran's I, data aggregated by both residential blocks and chome districts are used for calculation.The results from two types of spatial weight matrix are also discussed.

Mapping an Opportunity Surface of Residential Burglary

The use of geographic information systems (GIS) to understand spatial patterns of crime and criminal behavior has become more prevalent in recent years, but with a few exceptions these analyses fall short of serving as predictive tools. The recent introduction of user-friendly, raster-based mapping software, designed primarily for environmental and planning purposes, offers new tools for examining and predicting crime and criminal behavior. By applying opportunity theories to the crime of residential burglary, this article examines the utility of raster-based mapping software for predicting desirable and undesirable locations of burglaries, as well as likely locations for crime displacement or diffusion. The findings reveal that the model holds promise for serving these prediction purposes.

A General Misspecification Test for Spatial Regression Models: Dependence, Heterogeneity, and Nonlinearity

There is an increasing awareness of the potentials of nonlinear modeling in regional science. This can be explained partly by the recognition of the limitations of conventional equilibrium models in complex situations, and also by the easy availability and accessibility of sophisticated computational techniques. Among the class of nonlinear models, dynamic variants based on, for example, chaos theory stand out as an interesting approach. However, the operational significance of such approaches is still rather limited and a rigorous statistical‐econometric treatment of nonlinear dynamic modeling experiments is lacking. Against this background this paper is concerned with a methodological and empirical analysis of a general misspecification test for spatial regression models that is expected to have power against nonlinearity, spatial dependence, and heteroskedasticity. The paper seeks to break new research ground by linking the classical diagnostic tools developed in spatial econometrics to a misspecification test derived directly from chaos theory—the BDS test, developed by Brock, Dechert, and Scheinkman (1987). A spatial variant of the BDS test is introduced and applied in the context of two examples of spatial process models, one of which is concerned with the spatial distribution of regional investments in The Netherlands, the other with spatial crime patterns in Columbus, Ohio.

Crime: A threat to tourism in South Africa

Tourism can play a significant role in overcoming the many socio‐economic challenges South Africa faces, but only if the reality and perception of tourism safety are addressed urgently and adequately as a strategic priority. Here, I analyse the spatial pattern of crime against tourists in South Africa from 1993 to 1996. ‘No‐go’ areas for tourists are identified and tourist perceptions of personal safety are examined on a region by region basis. The analysis shows that, except for the Gauteng region (the inland economic core region), most other cases of reported crime against tourists are in the coastal areas of the country. Gauteng and KwaZulu‐Natal are perceived by foreign tourists to be the most ‘unsafe’ areas in South Africa. Tourists from Australia, the Pacific, Asia and Scandinavia expressed the greatest concern for their safety in South Africa. In spite of the current positive tourism marketing climate of ‘post‐apartheid’ South Africa, there is increasing concern that if the personal safety and security of tourists are not addressed in a concerned and coordinated manner, tourism to South Africa will be adversely affected.

The relationship between unemployment and crime

Various authors have tested for the presence of a relationship between unemployment and crime, employing time‐series or cross‐sectional data. However, little use has been made of the British Crime Survey. Consequently, in the present paper, we test for the presence of a relationship between male unemployment and crime, using regional and area‐level crime data from the 1992 British Crime Survey, with socio‐economic and demographic data being taken from the 1991 census. Use of these data sets allows us to look at four differing measures of crime: we can study possible relationships between male unemployment and the incidence, and prevalence, of both property and personal crimes. Area‐level data are also employed to test more ecological explanations of spatial crime patterns.

Investigating spatial patterns of crime through police statistics, crime surveys and census profiles: Findings from Dundee housing estates

Abstract This paper addresses two major issues in the field where environmental criminology and the analysis of local population structures interact. First, it discusses three key sources of criminological and demographic data for investigating spatial patterns of crime within cities, and operationalises them in a particular urban area to assess their compatibility. Second, it provides some data on housebreaking to indicate that there can be appreciable differences in levels of crime, between adjacent, public‐sector housing estates in one particular, but representative, Scottish urban setting.

Toward an Environmental View of Inner-City Crime

Modern studies of the spatial patterns of crime have drawn much of their inspiration from the empirical tradition established by Shaw and McKay (1942). Theoretical principally in their kinship with the economic/ecological focus of the “Chicago School” of sociology, the Shaw-McKay studies carefully plotted crime locations and residences of criminals in relation to a model of urban functional land use. Their findings, and those of other investigators writing in the same tradition, show strong and lasting correlations among crime locations, offenders' residences, inner-city areas, nonwhite populations, and urban poverty. With its emphasis on simply locating criminals and their activity in urban space, this approach encourages a pathological view of the problem. Crime is observable wrong behavior and its spatial patterning an indictment of the inner city.

The Minimum Wage Law and Youth Crimes: Time-Series Evidence

Previous articleNext article No AccessThe Minimum Wage Law and Youth Crimes: Time-Series EvidenceMasanori HashimotoMasanori Hashimoto Search for more articles by this author PDFPDF PLUS Add to favoritesDownload CitationTrack CitationsPermissionsReprints Share onFacebookTwitterLinkedInRedditEmail SectionsMoreDetailsFiguresReferencesCited by The Journal of Law and Economics Volume 30, Number 2Oct., 1987 Sponsored by The University of Chicago Booth School of Business and The University of Chicago Law School Article DOIhttps://doi.org/10.1086/467144 Views: 50Total views on this site Citations: 28Citations are reported from Crossref Copyright 1987 The University of ChicagoPDF download Crossref reports the following articles citing this article:Zachary S. Fone, Joseph J. Sabia, Resul Cesur The unintended effects of minimum wage increases on crime, Journal of Public Economics 219 (Mar 2023): 104780.https://doi.org/10.1016/j.jpubeco.2022.104780Nicolás Corona Juárez, Henrik Urdal, Krishna Chaitanya Vadlamannati The significance of age structure, education, and youth unemployment for explaining subnational variation in violent youth crime in Mexico, Conflict Management and Peace Science 39, no.11 (Aug 2020): 49–73.https://doi.org/10.1177/0738894220946324Pallab Ghosh, Kevin Grier, Jaeho Kim Heterogeneous endogeneity, Statistical Papers 62, no.22 (Jun 2019): 847–886.https://doi.org/10.1007/s00362-019-01116-9Chae M. Jaynes The Relationship between Job Quality and Crime: Examining Heterogeneity in Treatment and Treatment Effect, Journal of Research in Crime and Delinquency 57, no.66 (Apr 2020): 693–740.https://doi.org/10.1177/0022427820918899Pallab K. Ghosh, Gary A. 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