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Understanding, identifying, and tracking the spatio-temporal mobility of criminal offending can have important implications for social scientists concerning both the development of applied policy as well as more general criminological theory. (1) However, there is currently a disjuncture between available data, geographic units of analysis, and the operationalization of analyses in such research. Pertinent to the current examination, we are interested in the ability to link publicly available crime data and tracking the 'mobility' of this data over a given period of time. Here we introduce a novel approach to aggregating, and delineating, reported crime data that is jurisdictionally relevant while making optimum usage of agency level aggregation at which the data are available to the public. By keeping the data at the agency level and creating a new jurisdictionally relevant geography, we have maintained the integrity of the data at the lowest level and linked it directly to the communities that the law enforcement serves.
The correct conceptualization, and subsequent operationalization, communities potentially has the ability to influence our understanding of the effects of the associated ecological context on a variety of social phenomena. Over time, a number of attempts have been made at improving the 'state of the art' in terms of defining what represents a community. Many aggregate level social research projects make use of Office of Management and Budget's (OMB) defined Metropolitan Areas (MA), which rely on social and economic integration across county level data as the primary unit of analysis (GARM, 1994). However, recent research has shown that not only are MA's extremely heterogeneous, in terms of population, economic and social indicators, but the counties within those metro areas suffer from similar measurement problems (for example and review see Porter and Howell, 2009).
One area, among many recently, that has taken an interest in the conceptualization and operationalization of 'communities' as urban and rural is the field of criminology. This increased attention within the field has been largely directed at the social and environmental context in which crimes occur as a way of testing existing ecological theories of crime (Wells and Weisheit, 2004). While a number of researchers have examined both between- and within-county variations in crime rates, still little has been done to account for the point of argument in the literature that claims that the county as a geographic unit is much to large to understand neighbourhood effects and areas like and Census Tracts focus too narrowly on an isolated urban-centric issues to truly understand the phenomena in its entirety (Messner et al., 1999, 2005; Cohen and Tita, 1999; Bailer et al., 2001; Messner and Anselin, 2004; Hipp, 2007).
Due to this disjuncture, most of the attention given to the ecological context of crime has focused primarily on minute portions of the available geographical units of analysis. Furthermore, the extreme heterogeneity, which exists in many of the geographies used in these examinations of crime, such as counties (Land et al. 1991; Messner et al., 1999; Messner and Anselin 2004), makes it evident that a better understanding of all ecologically distinct units is important in order to further our understanding of reported crime in general. It is important to note here, that in response to this issue there have been a number of sub-county approaches to the examination of reported criminal offending; however, often these tend to focus only on urban settings while neglecting areas of a more rural or of less-developed urban character (Clinard, 1942; Wells and Weisheit, 2004).
This oversight has, therefore, neglected to understand crime in the vast majority of place settlements in the U.S. as 77 per cent of all Census places are outside of urban areas and 60 per cent are in places with a population of less than 2500 people, the common Census definition of rural locality (Wells and Weisheit, 2004). That is not to say that these studies do not account for a good deal of the population, as most of the population in the U.S. resides in these MA's, but it is a significant proportion, which when coupled with those that do not reside in Census Places, creates an even more unequal treatment. In relation, it is hypothesized that Census Designated Places (CDP), as with most sub-county level geographies, vary qualitatively based on the context in which they are contained, including regionally and by metropolitan status.
Through the creation of a jurisdictionally relevant geography, this study extends the creative and resourceful work on tracking spatial mobility of crime by Cohen and Tita (1999) by implementing a spatially-centred multivariate approach in comparison to one introduced by the researchers through the multiple implementation of a similar statistic in univariate form (involving dynamic LISA results; see Anselin, 1995). While there are a number of recently developed, and extremely sophisticated, spatial methods aimed at understanding spatio-temporal relationships, the Local Indicator of Spatial Association (explained in greater detail below) has proven to be a consistent and accepted measure of spatial relationships.
This project is interested in modelling the mobility of crime associated with the fluidity of criminal behaviours across areas, between 1990 and 2000, based on their place-level classification; places or non-place territories. (2) Furthermore, within this analysis the mobility of criminal offending is examined via the implementation of analytic techniques situated within the framework of diffusion. Of the two primary types of spatial diffusion, contagion and hierarchical (Cohen and Tita, 1999), the process of interest here involves the contagious type due to the contiguous nature of the units of analysis and the core-periphery relationship associated with the inherent 'downward' transmission of behaviours, and social processes between core places and the periphery non-place territory (Agnew, 1993; Lightfoot and Martinez 2005). It is expected that the results of this analysis, which are aimed at improving the 'real-time' tracking of criminal offending, can lend themselves to future studies involving the use of spatial patterns in the prediction of spatial patterns through simulation models.
Geography, Crime and the Modifiable Ariel Unit Problem
The spatial demography of crime as a subdiscipline has adopted a number of demographic approaches to the study of the patterns, motivations, and spatial spread of crime. A county-level study on the structural covariates of crime by Land et al. (1991) has led to the growing devotion of criminologists, demographers, and other social scientists to the spatial distribution of criminal violence (Baller et al., 2001, Anselin, 2003). Land et al. (1991) pointed out that the general trend in most of the existing literature of the time used states as the primary unit of analysis, due to the fact that state-level data were readily available and often required less data management. However, other studies have argued that a more appropriate measure is the Metropolitan Area (MA) level, based upon the argument that MA's more readily represent community boundaries (Messner et al., 1999). On the other hand, this is further debated as the use of metro areas neglects substantial within-unit variability, often concerning both the structural covariates as well as the dependent variable of interest (crime in this case) (Messner et al., 1999).
More recently, a number of researchers have reduced scale and examined between-county variations in crime rates (Messner et al., 1999, 2005; Baller et al., 2001; Messner and Anselin, 2004). However, there still exists a certain level of within-county variation and a lack of agreement on the community or neighbourhood associated with a particular sub-county boundary (Cohen and Tita, 1999; Messner et al., 1999; Bailer et al., 2001; Anselin, 2003; Hipp 2007). In regard to these works, there is the increasing use of GIS combined with spatial statistics, which is a documented pattern throughout the social sciences (Goodchild and Janelle, 2004). Figuring prominently among these issues is the specification of the optimal unit of analysis (Cohen and Tita, 1999; Messner et al., 1999; Baller et al., 2001; Anselin, 2003; Goodchild and Janelle, 2004; Hipp, 2007). Thus, it is important to add to what is known about more optimal geographies, which will add to our understanding of …