Predictive crime mapping

Geographic Information Systems (GIS) have emerged as a key tool in intelligence-led policing and spatial predictions of crime are being used by many police services to reduce crime. Break and entries (BNEs) are one of the most patterned and predictable crime types, and may be particularly amendable to predictive crime mapping. A pilot project was conducted to spatially predict BNEs and property crime in Vancouver, Canada. Using detailed data collected by the Vancouver Police Department on where and when observed crimes occur, the statistical model was able to predict future BNEs for residential and commercial locations. Ideally implemented within a mobile GIS, the automated model provides continually updated predictive maps and may assist patrol units in self-deployment decisions. Future research is required to overcome computational and statistical limitations, and to preform model validation.     

Spatial analysis of violent crimes in Metro Manila,Philippines

Spatial patterns of murder and physical injury in Metro Manila, Philippines were visualised through conditional choropleth maps. Relationship of both crime rates with some demographic variables were investigated while accounting for possible spatial autocorrelation using spatial lag models. Results show that both crime rates tend to cluster in the northern cities of Metro Manila. Furthermore, significant spatial lag coefficients were found only for physical injury rates, with values ranging from 0.49 to 0.62, signifying a positive city-level spatial dependence of physical injury rates in Metro Manila. Moreover, some demographic covariates, such as population density, percentage of young males, education, marriage, and immigration were found to be associated with both crime rates. These results could serve as useful indicators of crime incidence; thus it is recommended that crime monitoring systems include them to aid in resource allocation and program planning for better crime prevention and security management.     

Geographical patterns of incendiary and accidental fires in Springfield,Massachusetts, 1980–1984

Arson is a serious crime occurring with increasing frequency in urban America today. To date, this crime remains poorly documented and seldom discussed in the literature, particularly from a geographical viewpoint. This study examined the spatial distribution and underlying factors associated with 440 arson and 732 accidental fires recorded in Springfield, Massachusetts, between 1980 and 1984. Based upon a series of dot and choropleth maps, there was evidence that both incendiary and accidental fires were clustered not only in specific Springfield neighborhoods but also along individual city streets. In an attempt to understand better the varying frequency of arson fires among the 36 census tracts in the Springfield study area, a multiple regression analysis was performed using census data reflecting a variety of social, economic, and housing characteristics. Two variables, representing housing vacancy and tenement-type housing, entered into the final regression equation. Together, these two variables accounted for 70.4% of the reported arson cases. A subsequent inspection of the regression residuals revealed a random geographical pattern, thereby precluding any simple explanation for the remaining unexplained arson. Other factors such as insurance, building ownership, and length of occupancy were thought to warrant examination in future research.     

GIS supporting intelligence-led policing

Tightening budgets and increased demand for public accountability has placed additional stress on already limited police department resources. Web-based crime mapping provides significant improvement over previous methods of information dissemination, allowing police departments to continue to work quickly and efficiently within these limitations. This modern technology has enabled a more proactive approach to policing, including intelligence led-policing and public facing crime maps. As such, officers are now able to better consider spatial patterns related to historic crime, and determine more informedly where crimes may occur in the future, and allocate their limited resources accordingly.     

Crime Mapping and the Training Needs of Law Enforcement

This paper explores some of the more recent developments within crime mapping and the broader application of geographical information technology within law enforcement. The information technology (IT) revolution and the reduction in computing costs since the 1980s has brought a range of analytical tools within the budgets of most police services, and one of the most significant changes has been in the way that spatial data are handled. Law enforcement has strong geographic currents at all levels of the organisation, and this paper examines three applications of geographical information systems (GIS) within policing: hotspot mapping; CompStat; and geographic profiling. The paper concludes by discussing the future training needs using a simple model of intelligence-led crime reduction. This model suggests that training for managers to enable a greater understanding of the analyses presented to them, and how to use mapping to further crime prevention and reduction, may be as important as increasing the technical ability of crime analysts. The challenge for the immediate future of crime reduction practice in law enforcement is less to worry about the training of analysts, and more to address the inability of law enforcement management to understand and act on the crime analysis they are given.     

Detecting Spatial Movement of Intra-Region Crime Patterns Over Time

Many of the traditional measures of the degree to which crime patterns change over space and time have limitations. In particular most are unable to determine any change in spatial crime pattern within an areal unit. Usually studies measure the change in crime levels in contiguous areas (expressed as discrete sub-divisions of a study area), but this can become problematic due to difficulties such as the Modifiable Areal Unit Problem (MAUP). This paper describes a technique developed to allow researchers to examine intra-study region changes in crime patterns between two time periods without the need to aggregate crime counts to within-city areal boundaries. The method presented uses a random point nearest neighbor test combined with a Monte Carlo simulation. The process resolves problems of patterning and the MAUP that are common with a number of spatial displacement and pattern movement studies. This technique is demonstrated with example data from a city-wide police burglary crackdown in the Australian capital.     

A randomized experimental study of sharing crime data with citizens: Do maps produce more fear?

The larger roles of the community in crime prevention and improvements in technology have increased police–citizen communication and the distribution of information from police departments to private citizens. Combined, these changes have led to the current movement among law enforcement agencies toward sharing both summary reports and maps of crime with community groups. Although the dissemination of crime information is intended to benefit community members, there is a lack of empirical evidence demonstrating the effects of crime mapping on citizen perceptions and fear of crime. This experiment compared three formats for disseminating crime data; two popular types of crime maps (i.e., graduated symbol and density) and the traditional tabular format of crime statistics. A randomized experimental design was used to measure residents fear of crime and their perception of the safety of different areas of Redlands, CA. Overall, residents who viewed either type of map reported less fear than those who viewed tabular statistics. Respondents who viewed graduated symbol maps consistently reported less fear than either density maps or tabular statistics. However, there were differences depending on the type of map. While graduated symbol maps were associated with the lowest levels of fear of robbery, theft or assault; density maps produced different reactions depending on the area of the city. Finally, the maps did not stigmatize high crime areas of Redlands. Where statistically significant differences existed, respondents who were given maps were more likely to recommend someone move into an area than those who were given tabular statistics.     

Risk Terrain Modeling: Brokering Criminological Theory and GIS Methods for Crime Forecasting

The research presented here has two key objectives. The first is to apply risk terrain modeling (RTM) to forecast the crime of shootings. The risk terrain maps that were produced from RTM use a range of contextual information relevant to the opportunity structure of shootings to estimate risks of future shootings as they are distributed throughout a geography. The second objective was to test the predictive power of the risk terrain maps over two six‐month time periods, and to compare them against the predictive ability of retrospective hot spot maps. Results suggest that risk terrains provide a statistically significant forecast of future shootings across a range of cut points and are substantially more accurate than retrospective hot spot mapping. In addition, risk terrain maps produce information that can be operationalized by police administrators easily and efficiently, such as for directing police patrols to coalesced high‐risk areas.     

Spatializing the Social Networks of Gangs to Explore Patterns of Violence

The majority of spatial studies of crime employ an inductive approach in both the modeling and interpretation of the mechanisms of influence thought to be responsible for the patterning of crime in space and time. In such studies, the spatial weights matrix is specified without regard to the theorized mechanisms of influence between the units of analysis. Recently, a more deductive approach has begun to gain traction in which the theory of influence is used to model influence in geographic space. Using data from Los Angeles, we model the spatial distribution of gang violence by considering both the relative location of the gangs in space while simultaneously capturing their position within an enmity network of gang rivalries. We find that the spatial distribution of gang violence is more strongly associated with the socio-spatial dimensions of gang rivalries than it is with adjacency-based measures of spatial autocorrelation.     

Where the Action is in Crime? An Examination of Variability of Crime Across Different Spatial Units in The Hague, 2001–2009

Objectives

To identify how much of the variability of crime in a city can be attributed to micro (street segment), meso (neighborhood), and macro (district) levels of geography. We define the extent to which different levels of geography are important in understanding the crime problem within cities and how those relationships change over time.

Methods

Data are police recorded crime events for the period 2001–2009. More than 400,000 crime events are geocoded to about 15,000 street segments, nested within 114 neighborhoods, in turn nested within 44 districts. Lorenz curves and Gini coefficients are used to describe the crime concentration at the three spatial levels. Linear mixed models with random slopes of time are used to estimate the variance attributed to each level.

Results

About 58–69 % of the variability of crime can be attributed to street segments, with most of the remaining variability at the district level. Our findings suggest that micro geographic units are key to understanding the crime problem and that the neighborhood does not add significantly beyond what is learned at the micro and macro levels. While the total number of crime events declines over time, the importance of street segments increases over time.

Conclusions

Our findings suggest that micro geographic units are key to understanding the variability of crime within cities—despite the fact that they have received little criminological focus so far. Moreover, our results raise a strong challenge to recent focus on such meso geographic units as census block groups.

     

EGOHOODS AS WAVES WASHING ACROSS THE CITY: A NEW MEASURE OF “NEIGHBORHOODS”

Defining “neighborhoods” is a bedeviling challenge faced by all studies of neighborhood effects and ecological models of social processes. Although scholars frequently lament the inadequacies of the various existing definitions of “neighborhood,” we argue that previous strategies relying on nonoverlapping boundaries such as block groups and tracts are fundamentally flawed. The approach taken here instead builds on insights of the mental mapping literature, the social networks literature, the daily activities pattern literature, and the travel to crime literature to propose a new definition of neighborhoods: egohoods. These egohoods are conceptualized as waves washing across the surface of cities, as opposed to independent units with nonoverlapping boundaries. This approach is illustrated using crime data from nine cities: Buffalo, Chicago, Cincinnati, Cleveland, Dallas, Los Angeles, Sacramento, St. Louis, and Tucson. The results show that measures aggregated to our egohoods explain more of the variation in crime across the social environment than do models with measures aggregated to block groups or tracts. The results also suggest that measuring inequality in egohoods provides dramatically stronger positive effects on crime rates than when using the nonoverlapping boundary approach, highlighting the important new insights that can be obtained by using our egohood approach.     

SPREADING THE WEALTH: THE EFFECT OF THE DISTRIBUTION OF INCOME AND RACE/ETHNICITY ACROSS HOUSEHOLDS AND NEIGHBORHOODS ON CITY CRIME TRAJECTORIES*

This study tests the effect of the composition and distribution of economic resources and race/ethnicity in cities, as well as how they are geographically distributed within these cities, on crime rates during a 30‐year period. Using data on 352 cities from 1970 to 2000 in metropolitan areas that experienced a large growth in population after World War II, this study theorizes that the effect of racial/ethnic or economic segregation on crime is stronger in cities in which race/ethnicity or income are more salient (because of greater heterogeneity or inequality). We test and find that higher levels of segregation in cities with high levels of racial/ethnic heterogeneity lead to particularly high overall levels of the types of crime studied here (aggravated assaults, robberies, burglaries, and motor vehicle thefts). Similarly, higher levels of economic segregation lead to much higher levels of crime in cities with higher levels of inequality.     

Risk Clusters,Hotspots, and Spatial Intelligence: Risk Terrain Modeling as an Algorithm for Police Resource Allocation Strategies

The study reported here follows the suggestion by Caplan et al. (Justice Q, 2010) that risk terrain modeling (RTM) be developed by doing more work to elaborate, operationalize, and test variables that would provide added value to its application in police operations. Building on the ideas presented by Caplan et al., we address three important issues related to RTM that sets it apart from current approaches to spatial crime analysis. First, we address the selection criteria used in determining which risk layers to include in risk terrain models. Second, we compare the “best model” risk terrain derived from our analysis to the traditional hotspot density mapping technique by considering both the statistical power and overall usefulness of each approach. Third, we test for “risk clusters” in risk terrain maps to determine how they can be used to target police resources in a way that improves upon the current practice of using density maps of past crime in determining future locations of crime occurrence. This paper concludes with an in depth exploration of how one might develop strategies for incorporating risk terrains into police decision-making. RTM can be developed to the point where it may be more readily adopted by police crime analysts and enable police to be more effectively proactive and identify areas with the greatest probability of becoming locations for crime in the future. The targeting of police interventions that emerges would be based on a sound understanding of geographic attributes and qualities of space that connect to crime outcomes and would not be the result of identifying individuals from specific groups or characteristics of people as likely candidates for crime, a tactic that has led police agencies to be accused of profiling. In addition, place-based interventions may offer a more efficient method of impacting crime than efforts focused on individuals.     

A methodological model

Clues derived from the locations connected to violent repeat criminal offenders, such as serial murderers, rapists, and arsonists, can be of significant assistance to law enforcement. Such information allows police departments to focus their activities, geographically prioritize suspects, and to concentrate saturation or directed patrolling efforts in those zones where the criminal predator is most likely to be active. By examining spatial data connected to a series of crime sites, this methodological model generates a choropleth probability map that indicates the areas most likely to be associated to the offender—home, work site, or travel routes. Based on the Brantingham theoretical structure and the routine activities approach, the model goes beyond simple cluster or centroid analysis by employing specific serial murder research, overlapping modified Pareto functions, and Manhattan distances. The methodology is also sensitive to the target/victim opportunity backcloth, landscape issues, and problems of spatial “outliers.”     

ROLE OF THE STREET NETWORK IN BURGLARS' SPATIAL DECISION‐MAKING*

Explaining why crime is spatially concentrated has been a central theme of much criminological research. Although various theories focus on neighborhood social processes, environmental criminology asserts that the physical environment plays a central role by shaping people's activity patterns and the opportunities for crime. Here, we test theoretical expectations regarding the role of the road network in shaping the spatial distribution of crime and, in contrast to prior research, disentangle how it might influence offender awareness of criminal opportunities and the supply of ambient guardianship. With a mixed logit (discrete choice) model, we use data regarding (N = 459) residential burglaries (for the first time) to model offender spatial decision‐making at the street segment level. Novel graph theory metrics are developed to estimate offender awareness of street segments and to estimate levels of ambient guardianship, distinguishing between local and nonlocal guardianship. As predicted by crime pattern theory, novel metrics concerning offender familiarity and effort were significant predictors of residential burglary location choices. And, in line with Newman's (1972) concept of defensible space, nonlocal (local) pedestrian traffic was found to be associated with an increase (decrease) in burglary risk. Our findings also demonstrate that “taste” preferences vary across offenders, which presents a challenge for future research to explain.     

Disentangling the Crime-arrest Relationship: The Influence of Social Context

Drawing on the economic and conflict perspectives of crime control, as well as insights from the tipping effect literature, the present investigation examines the extent to which the social context within which potential offenders operate tempers the macro-level, reciprocal relationship between crime and arrests. We use autoregressive integrated moving average techniques to assess the extent to which the April 2001 race-related riot in Cincinnati, Ohio conditions the reciprocal relationship between property crime and arrests for the entire city and disaggregated by police district. Consistent with a majority of prior longitudinal studies, our analyses for the entire length of the times series reveal no evidence of an association between our measures of crime and arrest, regardless of the level of spatial aggregation. In contrast to the results from our baseline models, the post-riot transfer function models indicate that there is a reciprocal association between crime and arrests that is contingent upon the social context. The implications of our findings for the further study of the reciprocal relationship between crime and arrests are discussed.     

Is it Important to Examine Crime Trends at a Local “Micro” Level?: A Longitudinal Analysis of Street to Street Variability in Crime Trajectories

Over the last 40 years, the question of how crime varies across places has gotten greater attention. At the same time, as data and computing power have increased, the definition of a ‘place’ has shifted farther down the geographic cone of resolution. This has led many researchers to consider places as small as single addresses, group of addresses, face blocks or street blocks. Both cross-sectional and longitudinal studies of the spatial distribution of crime have consistently found crime is strongly concentrated at a small group of ‘micro’ places. Recent longitudinal studies have also revealed crime concentration across micro places is relatively stable over time. A major question that has not been answered in prior research is the degree of block to block variability at this local ‘micro’ level for all crime. To answer this question, we examine both temporal and spatial variation in crime across street blocks in the city of Seattle Washington. This is accomplished by applying trajectory analysis to establish groups of places that follow similar crime trajectories over 16 years. Then, using quantitative spatial statistics, we establish whether streets having the same temporal trajectory are collocated spatially or whether there is street to street variation in the temporal patterns of crime. In a surprising number of cases we find that individual street segments have trajectories which are unrelated to their immediately adjacent streets. This finding of heterogeneity suggests it may be particularly important to examine crime trends at very local geographic levels. At a policy level, our research reinforces the importance of initiatives like ‘hot spots policing’ which address specific streets within relatively small areas.     

组织体刑事责任论及其应用

在我国刑法明文规定了单位犯罪及其处罚的情况下,再提倡单位犯罪否定论,意义不大。就单位犯罪研究而言,现在面临的重大问题是,如何在自然人刑法之下,合理认定刑法第30条、第31条所规定的单位犯罪及其处罚。单位是由人和物复杂结合而成的法律实体,具有自己独特的制度特征、文化气质和环境氛围,这些要素能够对单位中的自然人的思想和行为产生影响。在我国刑法的规定之下,单位犯罪的认定和处罚,首先应考虑单位组成人员在业务活动中实施的侵害法益行为或结果,其次必须甄别作为单位“手足”的自然人的行为是否体现单位意思。只有在单位组成人员的行为体现了单位意思时,才能处罚单位自身。在判断单位意思时,必须依据单位的结构、制度、宗旨,单位高级管理人员的决定乃至单位的政策等客观要素进行推定。在单位业务活动中出现违法结果时,应首先考虑成立自然人犯罪,之后再考虑成立单位犯罪。     

GENERAL THEORY OF SPATIAL CRIME PATTERNS*

I propose a general theory for examining the spatial distribution of crime by specifically addressing and estimating the spatial distribution of the residences of offenders, targets, guardians, and their respective expected movement patterns across space and time. The model combines information on the locations of persons, typical spatial movement patterns, and situational characteristics of locations to create estimates of crime potential at various locations at various points in time and makes four key contributions. First, the equations make the ideas involved in the theory explicit, and they highlight points at which our current state of empirical evidence is lacking. Second, by creating measures of spatial “potentials” of offenders, targets, and guardians, this theory provides a precise grounding for operationalizing spatial effects in studies of place and crime. Third, the equations provide an explicit consideration of offenders and where they might travel and, therefore, incorporates offenders into crime‐and‐place research. Fourth, these equations suggest ways that researchers could use simulations to predict stable patterns, as well as changes, in the levels of crime at both micro and macro scales. Finally, I provide an empirical demonstration of the added explanatory power provided by the theory to a study of place and crime.     

The Influence of Community Areas,Neighborhood Clusters,and Street Segments on the Spatial Variability of Violent Crime in Chicago

Objectives

The influence of three hierarchical units of analysis on the total spatial variability of violent crime incidents in Chicago is assessed. This analysis seeks to replicate a recent study that found street segments, rather than neighborhood units of analysis, accounted for the largest share of the total spatial variability of crime in The Hague, Netherlands (see Steenbeek and Weisburd J Quant Criminol. doi: 10.1007/s10940-015-9276-3, 2015).

Methods

We analyze violent crime incidents reported to the police between 2001 and 2014. 359,786 incidents were geocoded to 41,926 street segments nested within 342 neighborhood clusters, in turn nested within 76 community areas in Chicago. Linear mixed models with random slopes of time were estimated to observe the variance uniquely attributed to each unit of analysis.

Results

Similar to Steenbeek and Weisburd, we find 56–65 % of the total variability in violent crime incidents can be attributed to street segments in Chicago. City-wide reductions in violence over the observation period coincide with increases in the spatial variability attributed to street segments and decreases in the variability attributed to both neighborhood units.

Conclusions

Our results suggest that scholars interested in understanding the spatial variation of crime across urban landscapes should be focused on the small places that comprise larger geographic areas. The next wave of “neighborhood-effects” research should explore the role of hierarchical processes in understanding crime variation within larger areas.