Crime prevention and the built environment

Göksu, Emel

doi:10.1007/s10901-009-9142-4

Cited by 17 publications

(1 citation statement)

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“…Environmental criminology tells us that some essential factors contribute to the occurrence of crime events, such as criminals, victims, law violations, and specific time and places (Goksu, ). Robbery, which is different from other crime events, is first discovered after the criminal action occurs because the criminals tend to choose robbery places that can provide convenience of escape.…”

Section: Experimental Comparisons and Analysismentioning

confidence: 99%

A non‐parametric statistical test method to detect significant cross‐outliers in spatial points

Deng

Yang

Shi

et al. 2018

Transactions in GIS

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In spatial points that describe geographical events, outliers that deviate significantly from the global or local distribution indicate extraordinary geographical phenomena.Existing outlier detection methods cannot statistically identify significant outliers by considering the co-occurrences of multiple categories of spatial points. Therefore, this study develops a non-parametric statistical test method to detect significant cross-outliers from two categories of spatial points divided into primary and reference ones. Firstly, the cross K function is employed to test the significance of co-occurrences of the two categories of points, and the cooccurrence intensity of each primary point is quantitatively defined as the number of reference points in its circular buffer. Then, the edge-constrained Delaunay triangulation is utilized to construct a reasonable spatial neighborhood for each primary point. On this basis, the Monte Carlo method is utilized to simulate the distribution of reference points in the support domain of each primary point delineated using the α-shape algorithm, and those primary points with significantly different local co-occurrence intensity are determined as spatial cross-outliers. Finally, those crossoutliers obtained using different buffers are evaluated further by analysis of living intervals. Experiments on both simulated and real-life datasets demonstrate the effectiveness and practicability of the proposed method.

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Section: Experimental Comparisons and Analysismentioning

confidence: 99%