Crime and Place

As mentioned above, spatial criminology as we know it today began almost 200 years ago in France by Quetelet and Guerry. One of the trajectories of spatial criminology over these many years has been the identification of spatial heterogeneity within the common spatial units of analysis of the day. For many years, the standard spatial unit of analysis within this literature has been the neighborhood or census tract. One of the great advantages of these units of analysis is the availability of census data for analysis such as with the crime rate and location quotient studies discussed above. Twenty-five years ago, the use of yet another smaller spatial unit of analysis began to emerge, called the micro-place, this literature is often referred to as the crime and place literature. These micro-places could be addresses, street intersections, or street segments, and widely used for citywide analyses because it became apparent that neighborhoods were far from being spatially homogeneous with regard to criminal activity.

A key factor in the research of the influence of the environment on the level and nature of crime is the assumed distance of influence. From a theoretical perspective in spatial criminology, the micro-place makes a lot of sense. Within routine activity theory (Cohen and Felson. 1979), a criminal event occurs when a motivated offender and a suitable target converge in time and space. That convergence, of course, does not occur in a neighborhood, but at a discrete location. In the geometric theory of crime (Brantingham and Brantingham, 1981), criminal events dominantly occur at our activity nodes and the pathways between them, but there are also particular places within these activity nodes where the majority of criminal events would occur. And lastly, rational choice theory (Clarke and Cornish, 1985) postulates that criminal events are the result of context specific choices. That context can vary significantly within a “neighborhood” such that the context of microplaces will be important for criminal decision making.

The first known research study to systematically investigate the micro-place in a city wide analysis was propounded by Sherman et al. (1989) who presented an interesting analysis of predatory crime in Minneapolis. In this study, Sherman et al. (1989) found that 50% of calls for police sendee were generated from 3% of street segments. This profound concentration of criminal events showed that even in the neighborhoods with the greatest criminal event levels, it had areas within its periphery that were crime free. Smith et al. (2000) investigated the integration of routine activity theory and social disorganization theory considering the micro-place rather than the more tradition census tract or other census defined unit of analysis. They found that the integration of these two theories was far more successful using the micro-place. Weisburd et al. (2004) and Groff et al. (2009) analyzed the micro-place in Seattle using trajectory analysis. Similar to Sherman et al. (1989), these authors found that 50% of all criminal events were accounted for by approximately 5% of street segments, over a 14-year period. Moreover, Groff et al. (2009) found that street segments with the same trajectory tended to cluster together.

Crime Hot Spots

One of the most common and innovative uses of crime mapping is to aggregate numerous crime events into hot spot maps. Modem tools of visual and spatial analysis, strongly supports the law enforcement and controlling agencies in their entire decision-making process concerned with crime. Crime records including crime history and crime pattern are valuable information required much for collective and integrated mapping of criminal events in spatio-temporal framework. Hot spot mapping of crime events is a common and innovative application of spatial crime mapping (Fig. 2.3). Areas on a crime map that are shaded with the same color are deemed to contain approximately the same density or frequency of crime. A step-wise statistical analysis and visualization process is essential to identify general areas affected by high criminal activity and to focus on these areas. Such as Lucy and Alexander (2014) used two packages of the R language: the Spatial Relative Risk and ggplot2 to analyze and visualize the concentration of crime, respectively, and estimated relative clustering of crime with Kernel Density Estimation (KDE).

FIGURE 2.3 Crime Hot Spots.

Risk Terrain Modeling: Caplan and Kennedy (2016) developed a risk terrain modeling (RTM) as a statistically valid approach to identify spatially vulnerable places to crime through the creation of a risk of crime score based upon criminogenic features from the physical environment of an area. Caplan et al. (2011) indicated that RTM is more accurate at predicting crime than retrospective crime hot spot mapping. Anderson and Tarah (2018) applied the RTM to property crime victimization (i.e., residential burglary) in Vancouver. Canada.

Development of recent research in this area indicates the importance of the micro place in understanding the spatial dynamics of crime. Weisburd et al. (2013) in the context of crime prevention, expressed that over a 16-years’ time period, 1% of street segments in Seattle represented 23% of all criminal activity across the city. This is an incredible concentration of criminal events in any context. In another set of analyses in Canada, Andresen and Malleson (2011) and Andresen and binning (2012) revealed how concentrated crime can be in two Canadian cities, Vancouver and Ottawa. In Vancouver, in 2001, 50% of the criminal events under study were accounted for by just 5% of street segments an almost identical result compared to the work done on Seattle. This concentration varied by crime type from approximately 1% of street segments (sexual assault and robbery) to almost 8% (burglary). In Ottawa, in 2006, the concentrations were even greater: 50% of the criminal events under study were accounted for by 1.7% of street segments, ranging from

0.01% of street segments (commercial robbery) to 1.67% of street segments (burglary), the average is greater than the range because different crime types have their concentrations at different places.

In Vancouver, just over 60% of street segments have any criminal events whereas less than 10% of street segments in Ottawa have any criminal events. This is an incredible concentration w'ithin Ottawa that may be due to the limited number of crime types under analysis, but is noteworthy none the less. It is found that the percentage of street segments with any criminal events that account for 50% of criminal events as the base for the percentage, not all street segments in each city. These percentages represent concentrations within concentrations and indicate that there are clearly hot spots of crime within hot spots of crime and that spatial heterogeneity can even be present considering the micro-spatial unit of analysis.