Decision Based Spatial Analysis of Crime

Xue, Yifei; Brown, Donald E.

doi:10.1007/3-540-44853-5_12

Cited by 7 publications

(4 citation statements)

References 17 publications

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“…Statistical approaches have also been used in crime prediction. Based on spatial choice theory (McFadden, 1973), Xue and Brown (2003) modeled the probability of a criminal choosing a target location as a function of multiple spatial characteristics of the location such as family density per unit area and distance to highway. Using regression analysis, they predicted the locations of future crimes in a city.…”

Section: Crime Spatial and Temporal Miningmentioning

confidence: 99%

Intelligence and Security Informatics (ISI): Research Framework

Chen

2011

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Section: Crime Spatial and Temporal Miningmentioning

confidence: 99%

Intelligence and Security Informatics (ISI): Research Framework

Chen

2011

Dark Web

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“…Although the predictive performance of these models exceed basic hot spot methods (Olligschlaeger and Gorr, 1997), the use of neural networks fails to provide the analyst with a clear understanding of what spatial features are providing the most impact on the predictions (Groff and LaVigne, 2002). In order to improve on the predictive power of hot spotting while also gaining insight into which spatial features are important, we modify the discrete choice theory from economics (McFadden, 1986) to model the criminal's site-selection process (Liu and Brown, 1998;Xue and Brown, 2003;Smith and Brown, 2004;Bernasco and Block, 2009).…”

Section: Spatial Analysis Of Crime Incidentsmentioning

confidence: 99%

“…Understanding spatial crime patterns allows the law enforcement field to adjust policing patrols both to make certain areas less desirable to criminals and to reduce the local community's fear of certain types of crimes (Chainey and Ratcliffe, 2005). Additional details on these and other approaches with respect to criminal analysis can be found in (Xue and Brown, 2003), (Boba, 2005), (Eck et al, 2005), (Tseloni, 2006), (Gorr and Kurland, 2011), and (Mohler et al, 2011).…”

Section: Spatial Analysis Of Crime Incidentsmentioning

confidence: 99%

“…, s n ∈ D. We can further define the random variable Y (s, t) as the observed realization of our spatial-temporal process Z (s, t). As discussed in the previous chapter, the works of Liu and Brown (1998), Xue and Brown (2003), provide a method for calculating the likelihood of future criminal events across a spatial region given a dataset of previous criminal events.…”

Section: Hierarchical Feature-space Modelingmentioning

confidence: 99%

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Temporal Considerations for Spatial Event Prediction

Fox

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A significant amount of research and practice in the law enforcement arena focuses on spatial and temporal event analysis. Although some efforts integrate spatial and temporal analysis, the majority of the previous work focuses on modeling events in one area across time periods or modeling multiple areas within single temporal intervals. This dissertation attempts to close the gap between spatial and temporal analysis by focusing on the challenges of integrating spatial and temporal features in criminal site selection problems. Since the criminal environment contains multiple actors intermingled in a spatial region, the challenge for the analyst is identifying patterns both spatially and temporally based on the criminal's site-selection criteria. This dissertation uses a three-pronged approach to study (1) the identification and inclusion of temporal distances into a feature-space model; (2) the application of hierarchical modeling for temporal intervals; and (3) the consideration of a random field construct for modeling site-selection event data. We evaluate each approach on both a simulated data set and a sample data set from a small US city. We compare results of the different methodologies using a test statistic based on measuring performance across a space-time surveillance plot. We found that a two-stage analytical approach assists us in identifying when spatial-temporal modeling might offer significant improvements in predictive performance. Additionally, the integration of a hierarchical structure with the feature-space generalized linear model provides a method that accounts for temporal considerations in the criminal's site-selection process. These temporal features can be "pulse events" that indicate temporary shifts in spatial-temporal patterns or transitions at the discrete temporal interval level that might account for low-level seasonality within the criminal's site-selection process. Finally the development of an expanded feature-space model provides benefits similar to those of a Markov random field model without the computational demands of the Bayesian construct for hierarchical modeling. These methods for identifying and including temporal information into spatial analysis methodologies offer benefits not only in the law enforcement and military communities, but also to the business and environmental sectors. iii When one takes as long as I did to complete a dissertation, the list of people to thank becomes rather extensive. In the interest of keeping my acknowledgements shorter than my literature review, I would like to highlight three groups. First of all, I am very thankful for my advisor, Donald Brown, and my committee members, Gerry Learmonth, Brian Smith, Greg Gerling, and Matt Gerber. Their individual efforts, assistance, and patience assisted me while in residence at the University, and, even more so, while I was working away from the Grounds. Their collective flexibility and support made completion possible as the Army decided to move me away from Charlottesville a second time. In add...

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Intelligence and security informatics

Chen

2006

Annual Review Info Sci & Tec

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Decision Based Spatial Analysis of Crime

Cited by 7 publications

References 17 publications

Intelligence and Security Informatics (ISI): Research Framework

Intelligence and Security Informatics (ISI): Research Framework

Temporal Considerations for Spatial Event Prediction

Intelligence and security informatics

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