Making Space for Theory: The Challenges of Theorizing Space and Place for Spatial Analysis in Criminology

Tita, George E.; Radil, Steven M.

doi:10.1007/s10940-010-9115-5

Cited by 65 publications

(39 citation statements)

References 71 publications

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“…Even more recent is the notion of Egohoods, overlapping neighborhoods based on spatial proximity, that have been argued to better represent social reality (Hipp & Boessen 2013: 4 Defining boundaries and size of the geographical units of analysis is a difficult problem related to the Modifiable Areal Unit Problem (MAUP) (Openshaw 1996;Tita & Radil 2010;Oberwittler & Wikström 2009;Lupton 2003;Hipp & Boessen 2013). MAUP consist of problems related to zonation, the placement of boundaries between units, and to scale, the size of units (Green & Flowerdew 1996;Oberwittler & Wikström 2009).…”

Section: The Concept Of Neighborhoodmentioning

confidence: 99%

Collective Efficacy, Neighborhood and Geographical Units of Analysis: Findings from a Case Study of Swedish Residential Neighborhoods

Gerell

2014

Eur J Crim Policy Res

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Section: The Concept Of Neighborhoodmentioning

confidence: 99%

Collective Efficacy, Neighborhood and Geographical Units of Analysis: Findings from a Case Study of Swedish Residential Neighborhoods

Gerell

2014

Eur J Crim Policy Res

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“…Mohler et al . (), Tita and Radil (), Bernasco (), Berestycki and Nadal (), Chainey et al . (), Grubesic and Mack (), McLafferty et al .…”

Section: Introductionunclassified

“…These types of analyses use crime data that have been aggregated to census units or police jurisdictions and are useful for police resource allocation and planning (e.g. Mohler et al (2011), Tita and Radil (2010), Bernasco (2010), Berestycki and Nadal (2010), Chainey et al (2008), Grubesic and Mack (2008), McLafferty et al (2000) and Ratcliffe and McCullagh (1999)). Their conclusions are also informative for characterizing, for example, how burglars in a given region generally behave, but they do not address how one individual burglar chooses targets spatially.…”

Section: Introductionmentioning

confidence: 99%

Characterizing Spatial and Chronological Target Selection of Serial Offenders

Hering

Bair²

2013

Journal of the Royal Statistical Society Series C: Applied Statistics

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Using 31 unique crime series from two US cities, the spatial pattern that individual criminal decision makers follow is investigated. Locations within a city vary in the likelihood for crimes of a given type to occur, and this is accounted for by using kernel density estimation based on all crimes of each type. Kernel density estimation is highly influenced by the bandwidth, so an objective approach is used to select the estimate. Then, the kernel density estimate for each type of crime is incorporated in an inhomogeneous K -function that can identify significant spatial clustering and/or uniformity at various spatial scales for each crime series. We find that robbery series are more likely to exhibit uniformity than burglary series, which tend to show strong clustering. In addition, the order in which new crimes are added to a series is found to follow an interesting pattern that does not always support the theory of offender as forager in which criminals first cluster crimes and then gradually disperse them. Half of the robbery series exhibit a spatial distribution that begins dispersed and develops clusters as increasingly more events are added to the series, indicating that they often return to locations of earlier crimes after having committed crimes elsewhere.

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“…Further, the stochasticity observed may provide a more realistic model of the true rivalry structure. Research has demonstrated that the rivalry networks that link gangs tend to be stable over time [63,50,47], and that the activity spaces of gangs are anchored to specific places [62,39]. However, over longer periods of times, the membership ranks of gangs may ebb and flow due to incarceration, individuals "aging out" of active status, or other forms of incapacitation [64].…”

Section: Long Term Behavior Of the Sblnmentioning

confidence: 99%

“…Within this framework, the existence of persistent violence between two gangs becomes an edge connecting two nodes. From this construction, one can view a collection of gangs as a spatially embedded network [63]. The Hollenbeck policing division of eastern Los Angeles is marked by a particularly high degree of violent crimes involving gang members, including homicides and aggravated assaults [50,25].…”

Section: Introductionmentioning

confidence: 99%

Geographical influences of an emerging network of gang rivalries

Hegemann¹,

Smith²,

Barbaro³

et al. 2011

Physica A: Statistical Mechanics and its Applications

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We propose an agent-based model to simulate the creation of street gang rivalries. The movement dynamics of agents are coupled to an evolving network of gang rivalries, which is determined by previous interactions among agents in the system. Basic gang data, geographic information, and behavioral dynamics suggested by the criminology literature are integrated into the model. The major highways, rivers, and the locations of gangs' centers of activity influence the agents' motion. We use a policing division of the Los Angeles Police Department as a case study to test our model. We use common metrics from graph theory to analyze our model, comparing networks produced by our simulations and an instance of a Geographical Threshold Graph to the network existing in the criminology literature.

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Making Space for Theory: The Challenges of Theorizing Space and Place for Spatial Analysis in Criminology

Cited by 65 publications

References 71 publications

Collective Efficacy, Neighborhood and Geographical Units of Analysis: Findings from a Case Study of Swedish Residential Neighborhoods

Collective Efficacy, Neighborhood and Geographical Units of Analysis: Findings from a Case Study of Swedish Residential Neighborhoods

Characterizing Spatial and Chronological Target Selection of Serial Offenders

Geographical influences of an emerging network of gang rivalries

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