Spatial Analysis Of Crime Research Articles

Spatial distribution and developmental trajectories of crime versus crime severity: do not abandon the count-based model just yet

Purpose/backgroundA new body of research that focuses on crime harm scores rather than counts of crime incidents has emerged. Specifically in the context of spatial analysis of crime, focusing on crime harm suggests that harm is more concentrated than counts, at the level of crime hot spots. It remains presently unclear what drives the concentration distributions, and whether the count-based model should be abandoned.MethodsCross-sectional and longitudinal analysis of 6 year of spatiotemporal crime data in Toronto, Canada, to compare patterns and concentration of crime harm (measured in terms of the Crime Severity Index (CSI) against crime counts. Conditional probabilities, trajectory analyses, power few concentrations, and spatial Global Moran’s I are used to infer generalised trends from the data.FindingsOverall CSI and crime counts tend to exhibit similar concentrations at the spatial micro levels, except against-the-body crimes such as violence which seems to drive nearly all the variations between the two measurement types. Violence harm spots tend to be more dispersed citywide and often do not remain constant year-to-year, whereas overall crime hotspots are more stable over time. Nevertheless, variations in disproportionally high crime hot spots are associated with total variations in crime, with as little as 1% increase in crime levels in these hot spots translating into substantial overall gains in recorded crime citywide.ConclusionsAbandoning count-based models in spatial analysis of crime can lead to an incomplete picture of crime concentrations. Both models are needed not just for understanding spatial crime distributions but also for cost-effective allocation of policing resources.

SafeTy as a New Form of Social Activation in the Area of Public Safety

The aim of this article is to identify the SafeTy programme and application as a tool to enable information sharing and mutual alerting of local communities about public safety threats. Developed at the Pomeranian Academy in Słupsk, the SafeTy mobile application provides opportunities for residents to communicate and cooperate in the area of safety. It is thus intended to increase the activation of the local community and raise the safety culture of residents. It is also a tool for diagnosing the level of public safety. The article also addresses the issue of geographical spatial analysis of crime based on criminological theories and its potential use in the application. It was assumed that the access to the SafeTy mobile application, which enables mutual threat warnings, influences the activation of local communities contributing to their sense of security and to the safety culture of residents. The study was conducted by means of a critical analysis of the literature and a diagnostic survey, using survey questionnaires (interview and questionnaire). Preliminary results of the research indicate that the SafeTy tool responds to the needs of local communities in terms of information about local threats, while also mobilising residents to identify antisocial behaviour, thus creating opportunities to counter these phenomena. Undoubtedly, local communities are looking for spaces to communicate about threats through, among other things, “groups” on social networks. This need is met by the SafeTy application, which is free of information about users’ private lives or advertising material, as it is dedicated to issues that shape public safety and thus refers to the concept – “neighbourhood watch”.

Spatial Analysis of Crime in East Java Province in 2019

Crime is one of the consequences of fluctuations in the economic condition of a country. Crime incidents harm many parties. The number of criminal acts increased in 2019, especially in Sumatra and Java Island. Most provinces experienced an increasing number of criminal acts, one of them was East Java. East Java contributed more than a quarter of the number of crimes throughout Java Island. The number of criminal acts is count data with overdispersion because its variance is higher than its average. This study aims to analyze the number of criminal acts by applying Geographically Weighted Negative Binomial Regression (GWNBR). The result shows that GWNBR formed two regional groups based on significant variables. The four independent variables namely the unemployment rate, the number of poor people, the Gini ratio, and the police population ratio have a significant effect on all districts/cities. However, the mean year of schooling shows a significant effect only in certain districts/cities. The GWNBR is the best model in modelling the number of criminal acts in East Java.

A spatial Analysis of Crime and Neighborhood Characteristics in Detroit Census Block Groups

Abstract. Crime has an inherent geographical quality and when a crime occurs, it happens within a particular space making spatiality essential component in crime studies. To prevent and respond to crimes, it is first essential to identify the factors that trigger crimes and then design policy and strategy based on each factor. This project investigates the spatial dimension of violent crime rates in the city of Detroit for 2019. Crime data were obtained from the City of Detroit Data Portal and demographic data relating to social disorganization theory were obtained from the Census Bureau. In the presence of spatial spill over and spatial dependence, the assumptions of classical statistics are violated, and Ordinary Least Squares estimations are inefficient in explaining spatial dimensions of crime. This paper uses explanatory variables relating to the social disorganization theory of crime and spatial autoregressive models to determine the predictors of violent crime in the City for the period. Using GeoDa 1.18 and ArcGIS Desktop 10.7.1 software package, Spatial Lag Models (SLM) and Spatial Error Models were carried out to determine which model has high performance in identifying predictors of violent crime. SLM outperformed SEM in terms of efficiency with (AIC:5268.52; Breusch-Pagan test: 9.8402; R2: 16% & Log Likelihood: −2627.26) > SEM (AIC: 5275.24; Breusch-Pagan test: 9.7601; R2: 15% & Log Likelihood: −2630.6194). Strong support is found for the spatial disorganization theory of crime. High percent ethnic heterogeneity (% black) and high college graduates are the strongest predictors of violent crime in the study area.

Spatial Analysis of Crime in Socially Distressed Cluster in Oke Ogun Region, Oyo State, Nigeria

The study is a methodological approach to measuring crime in distressed cluster in different geographic resolutions in Oke-Ogun Region of Oyo State with a view to examining spatial variation in crime concentration in the area. Crime reports of Oke-Ogun Region were collated from the Nigeria Police records on eighteen typologies of crime categorised in police blotter into crime against person and property from 2005 to 2015. Analytical techniques adapted to examine crime concentration were Zscore and Location Quotient of Crime (LQC). For the purpose of having the real picture of crime concentration as one moves across different spatial scales of settlements, settlements in the area were spatially disaggregated into three levels; urban, semi urban and rural settlements. Analysis with the use of Z-score showed that store breaking and arson for crime against property and murder for crime against person were more concentrated in rural settlements than every other crime type relatively. House breaking for crime against property, and breach of peace for crime against person were more concentrated in semi urban settlements, while burglary for crime against property, rape and indecent assault and unnatural offence for crime against person were conspicuously concentrated in urban settlements. Concentration of property crime therefore decreases as one move from rural areas to urban areas with Z scores of -1.15, 0.33 and 1.84 in urban, semi urban and rural areas respectively. However, the concentration of crime against person increases as one move from rural areas through semi urban to urban settlement, with Z scores of 4.06, 0.56 and -3.72 in urban, semi urban and rural areas respectively. Further analysis with LQC was done, and it was observed that rural settlements had (LQC =0.98) a disproportionately low share of 2% of crimes against person relative to urban settlement and that armed robbery, arson and false pretense / cheating are endemic nature of both semi urban and rural settlements. The study concluded that the cluster of aggregated crime types conformed to regular spatial pattern with declining crime cluster as one move from urban areas through semi urban to rural settlement.

Crime Concentration and Temporal Stability in Spatial Patterns of Crime in Niš, Serbia

The research objectives were to examine the concentration of crime and its temporal patterns in Nis and compare the related research results in spatial criminology. Longitudinal property and violent crime data were geocoded and assigned to three types of grid cells. Descriptive statistics were used to examine crime concentration, and spatial stability of crime was tested using a spatial point pattern test. The findings suggest an intense concentration of all crime types within all areal units and years of study. The higher rates of concentration were found in the analysis of less common robbery and violent crime. Findings of spatial pattern analysis suggest moderate to a high degree of similarity in spatial patterns among all crime types. However, sensitivity analysis indicates significant changes in the spatial distribution of both property and violent crime. Therefore, an alternative method is applied. Theoretical implications are discussed, highlighting routine activities and crime pattern theory. The study aims to instigate more spatial analysis of crime and evidence-based criminal policies within the region.

A Tale of Two Theories: Whither Social Disorganization Theory and the Routine Activities Approach?

Social disorganization theory and the routine activities approach have been extensively applied separately as theoretical frameworks for the spatial analysis of crime, with general support. As hypothetical explanations for complex social phenomena, criminological theories can impact how studies are framed and how the crime problem is approached. Thus, it is important to evaluate theories continuously in various geographical, as well as contemporary contexts. This study uses both theories in tandem to examine their ability to explain 2016 property crime in Vancouver, Canada, using 2016 census data. Both theories found moderate support. Of particular note is that all of the variables designated as proxies for ethnic heterogeneity in social disorganization theory were either not statistically significant or negative, consistent with the immigration and crime literature. Additionally, almost all variables, when statistically significant, were found to have consistent results across crime types. These results bode well for the continued use of social disorganization theory and the routine activity approach in spatial analyses of crime.

Crime against Businesses: Temporal Stability of Hot Spots in Mexicali, Mexico

In developing countries, crime is a serious problem that affects the operation and viability of firms. Offenses such as vandalism, robbery, and theft raise the operating costs of firms and imposes on them indirect costs. The literature on spatial analysis of crime is vast; however, relatively little research has addressed business crime, especially in developing countries’ cities. Spatial and temporal analysis of crime concentration represents a basic input for the design and implementation of appropriate prevention and control strategies. This article explores the spatial concentration and stability of thefts committed against commercial establishments in the city of Mexicali, Mexico, from 2009 to 2011 using the Gini coefficient, Lorenz curve, and decile maps. Results revealed that thefts were highly concentrated in a small percentage of urban basic geostatistical areas. Moreover, a portion of these areas were classified as having the highest deciles of thefts (hot spots) and remained in this group throughout the period. In both cases, the relationship between crime and place was close to the 80/20 rule, or the Pareto principle.

Applying Spatial Video Geonarratives and Physiological Measurements to Explore Perceived Safety in Baton Rouge, Louisiana.

Spatial crime analysis, together with perceived (crime) safety analysis have tremendously benefitted from Geographic Information Science (GISc) and the application of geospatial technology. This research study discusses a novel methodological approach to document the use of emerging geospatial technologies to explore perceived urban safety from the lenses of fear of crime or crime perception in the city of Baton Rouge, USA. The mixed techniques include a survey, spatial video geonarrative (SVG) in the field with study participants, and the extraction of moments of stress (MOS) from biosensing wristbands. This study enrolled 46 participants who completed geonarratives and MOS detection. A subset of 10 of these geonarratives are presented here. Each participant was driven in a car equipped with audio recording and spatial video along a predefined route while wearing the Empatica E4 wristbands to measure three physiological variables, all of them linked by timestamp. The results show differences in the participants’ sentiments (positive or negative) and MOS in the field based on gender. These mixed-methods are encouraging for finding relationships between actual crime occurrences and the community perceived fear of crime in urban areas.

Too Fine to be Good? Issues of Granularity, Uniformity and Error in Spatial Crime Analysis

Crime counts are sensitive to granularity choice. There is an increasing interest in analyzing crime at very fine granularities, such as street segments, with one of the reasons being that coarse granularities mask hot spots of crime. However, if granularities are too fine, counts may become unstable and unrepresentative. In this paper, we develop a method for determining a granularity that provides a compromise between these two criteria. Our method starts by estimating internal uniformity and robustness to error for different granularities, then deciding on the granularity offering the best balance between the two. Internal uniformity is measured as the proportion of areal units that pass a test of complete spatial randomness for their internal crime distribution. Robustness to error is measured based on the average of the estimated coefficient of variation for each crime count. Our method was tested for burglaries, robberies and homicides in the city of Belo Horizonte, Brazil. Estimated “optimal” granularities were coarser than street segments but finer than neighborhoods. The proportion of units concentrating 50% of all crime was between 11% and 23%. By balancing internal uniformity and robustness to error, our method is capable of producing more reliable crime maps. Our methodology shows that finer is not necessarily better in the micro-analysis of crime, and that units coarser than street segments might be better for this type of study. Finally, the observed crime clustering in our study was less intense than the expected from the law of crime concentration.

Investigating Sherlock Holmes: Using Geographic Profiling to Analyze the Novels of Arthur Conan Doyle

Central to spatial analysis of crime is the assumption that the offender has visited the locations analyzed. We show that even if this is incorrect, meaningful patterns can be identified. Using 390 locations from the Sherlock Holmes stories, we show that geographic profiling (GP) ranks Conan Doyle in the top 13 percent of 2,678 historical figures in London, despite the fact at the time of writing he had not visited all of the sites. Restricting the analysis to thirty authors contemporary with the Sherlock Holmes stories, Conan Doyle ranks first, with a hit score of 2.8 percent (above Holmes’s address at 221b Baker Street). Finally, we show that GP prioritizes sites strongly associated with Conan Doyle (example.g., his home) compared to those more tangentially associated with him, even when sites are close together. Our analysis, although mostly for amusement, underlines the ability of GP to extract useful information from complex data.

Ambient Population and Larceny-Theft: A Spatial Analysis Using Mobile Phone Data

In the spatial analysis of crime, the residential population has been a conventional measure of the population at risk. Recent studies suggest that the ambient population is a useful alternative measure of the population at risk that can better capture the activity patterns of a population. However, current studies are limited by the availability of high precision demographic characteristics, such as social activities and the origins of residents. In this research, we use spatially referenced mobile phone data to measure the size and activity patterns of various types of ambient population, and further investigate the link between urban larceny-theft and population with multiple demographic and activity characteristics. A series of crime attractors, generators, and detractors are also considered in the analysis to account for the spatial variation of crime opportunities. The major findings based on a negative binomial model are three-fold. (1) The size of the non-local population and people’s social regularity calculated from mobile phone big data significantly correlate with the spatial variation of larceny-theft. (2) Crime attractors, generators, and detractors, measured by five types of Points of Interest (POIs), significantly depict the criminality of places and impact opportunities for crime. (3) Higher levels of nighttime light are associated with increased levels of larceny-theft. The results have practical implications for linking the ambient population to crime, and the insights are informative for several theories of crime and crime prevention efforts.

Green Crime Havens: A Spatial Cluster Analysis of Environmental Crime

ObjectivesThe following study contributes to the bridging green criminology's quantitative research gap by analyzing the spatial distribution of environmental crimes throughout the United States. In doing so, we consider the possibility of green crime havens; areas with elevated health risks associated with dense concentrations of green criminal behavior.MethodsUsing data obtained from the U.S. Environmental Protection Agency ECHO database, we conduct a spatial cluster analysis to identify counties bearing a disproportionate number of the Clean Water Act, Clean Air Act, and Resource Conservation and Recovery Act violations.ResultsFindings affirm the existence of numerous green crime havens with violations occurring both within and across different types of pollution cases.ConclusionsThe study concludes with a theoretical reflection of the treadmill of crime and corresponding series of policy recommendations.

Investigating Contextual Effects on Burglary Risks: A Contextual Effects Model Built Based on Bayesian Spatial Modeling Strategy

A contextual effects model, built based on Bayesian spatial modeling strategy, was used to investigate contextual effects on neighborhood burglary risks in Wuhan, China. The contextual effects denote the impact of the upper-level area on the lower-level units of analysis. These effects are often neglected in Bayesian spatial crime analysis. The contextual effects model accounts for the effects of independent variables, overdispersion, spatial autocorrelation, and contextual effects. Both the contextual effects model and the conventional Bayesian spatial model were fitted to our data. Results showed the two models had almost the same deviance information criterion (DIC). Furthermore, they identified the same set of significant independent variables and gave very similar estimates for burglary risks. Nonetheless, the contextual effects model was preferred in the sense that it provides insights into contextual effects on crime risks. Based on the contextual effects model and the map decomposition technique, we identified, worked out, and mapped the relative contribution of the neighborhood characteristics and contextual effects on the overall burglary risks. The research contributes to the increasing literature on modeling crime data by Bayesian spatial approaches.

Border disorder: On urban boundary work and crime in the divided city

This article offers exploration of one spatial aspect of crime in the divided city: the disproportionate concentration of crime events along Jerusalem’s former socio-historical border (known as ‘Green Line’) that is clearly reflected in a spatial analysis of crime. Offering insight into this phenomenon, an ethnographic investigation reveals the manner in which neighbourhood residents cope with crime by blocking entry to it from the east, thereby reinforcing and reproducing already existing urban divisions. This second, qualitative layer of research enables us to follow urban boundary work in action, which is important, as focusing on boundary work (as opposed to borders) offers insight not only into divided cities as fact but into the mechanisms, logic and culture that reproduce and reshape their urban divisions. In contrast to hegemonic analyses that highlight the importance of macro-politics in shaping the lines that divide the divided city, this article considers crime, and the way residents struggle against it from below, as a central mechanism that reinforces and reproduces the divisions of the divided city.

Using tweets to understand changes in the spatial crime distribution for hockey events in Vancouver.

The use of social media data for the spatial analysis of crime patterns during social events has proven to be instructive. This study analyzes the geography of crime considering hockey game days, criminal behaviour, and Twitter activity. Specifically, we consider the relationship between geolocated crime‐related Twitter activity and crime. We analyze six property crime types that are aggregated to the dissemination area base unit in Vancouver, for two hockey seasons through a game and non‐game temporal resolution. Using the same method, geolocated Twitter messages and environmental variables are aggregated to dissemination areas. We employ spatial clustering, dictionary‐based mining for tweets, spatial autocorrelation, and global and local regression models (spatial lag and geographically weighted regression). Findings show an important influence of Twitter data for theft‐from‐vehicle and mischief, mostly on hockey game days. Relationships from the geographically weighted regression models indicate that tweets are a valuable independent variable that can be used in explaining and understanding crime patterns.

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

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

Spatial conjunctive analysis of (crime) case configurations: Using Monte Carlo methods for significance testing

While research has repeatedly demonstrated how spatial distributions of crime can be shaped by the presence of facilities such as bars and public transport hubs, the joint influence of different facility types has rarely been explored. Spatial conjunctive analysis of case configurations (also known as qualitative comparative analysis) offers a means to identify the combinations of facility types that are most commonly found around crime events, and has been used in a small number of studies focusing on street robbery. This study extends this limited evidence base by implementing a significance test based on the Monte Carlo method using street robbery data for Austin, Texas. The results show that some of the top-ranking facility type combinations had observed frequencies that were not significantly greater than chance expectations. The accurate identification of the highest-risk environments has important implications for crime prevention.

Voronoi Diagrams and Spatial Analysis of Crime

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

Gis Application in Spatial Analysis of Crime in a Developing Nation: A Case of Ogbomoso, Nigeria

Against the background of astronomical rise in crime rate, the increasing and dynamic sophistication in the strategies employed by criminals and the inability of the Nigerian Criminal Justice System to analyze this social malady with improve technology, the study explored the application of Geographic Information System in the spatial analysis of crime in Ogbomoso South Local Government Area. Within the context of residential densities, police crime records from 1999 to 2003 were analyzed. The geometric data were captured from the township map of the study area. Information from 240 questionnaire administered formed the supplementary data used as the socioeconomic attributes of respondents. The analyses were carried out using ArcView 3.2a and Statistical Package for Social Scientist (SSPS) packages. Spatial analyses such as spatial search, buffering and overlay were done. The study revealed that there is a marked variation in the distribution of crime between and within residential areas. Five hot spots of crime were identified. Further observation was made on areas that may not be sufficiently covered when there is a distress call due to inadequate number of police post or station which is a major security infrastructure that signifies the presence of Government Crime Control Personnel. With this observation the study suggests provision of more police post in areas that are not covered. The study posits that Nigeria Police Authority should be equipped with adequate training and technology in order to enhance a wholesome control of crime within the context of the world’s dynamic technologies. This will be effective if other activities within the urban environment are coordinated in order to reduce criminal opportunities.