Exploratory Space–Time Analysis of Burglary Patterns

Rey, Sergio J.; Mack, Elizabeth A.; Koschinsky, Julia

doi:10.1007/s10940-011-9151-9

Cited by 62 publications

(51 citation statements)

References 66 publications

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“…Next, the score for each slot is calculated by dividing the coverage of that slot with the total coverage, as shown in Equation (5). The sum of the scores for all slots always equals 1.0.…”

Section: Introduction Of Temporal Analysis Methodsmentioning

confidence: 99%

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Evaluating Temporal Analysis Methods Using Residential Burglary Data

Boldt

Borg

2016

IJGI

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Law enforcement agencies, as well as researchers rely on temporal analysis methods in many crime analyses, e.g., spatio-temporal analyses. A number of temporal analysis methods are being used, but a structured comparison in different configurations is yet to be done. This study aims to fill this research gap by comparing the accuracy of five existing, and one novel, temporal analysis methods in approximating offense times for residential burglaries that often lack precise time information. The temporal analysis methods are evaluated in eight different configurations with varying temporal resolution, as well as the amount of data (number of crimes) available during analysis. A dataset of all Swedish residential burglaries reported between 2010 and 2014 is used (N = 103,029). From that dataset, a subset of burglaries with known precise offense times is used for evaluation. The accuracy of the temporal analysis methods in approximating the distribution of burglaries with known precise offense times is investigated. The aoristic and the novel aoristic ext method perform significantly better than three of the traditional methods. Experiments show that the novel aoristic ext method was most suitable for estimating crime frequencies in the day-of-the-year temporal resolution when reduced numbers of crimes were available during analysis. In the other configurations investigated, the aoristic method showed the best results. The results also show the potential from temporal analysis methods in approximating the temporal distributions of residential burglaries in situations when limited data are available.

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“…Next, the score for each slot is calculated by dividing the coverage of that slot with the total coverage, as shown in Equation (5). The sum of the scores for all slots always equals 1.0.…”

Section: Introduction Of Temporal Analysis Methodsmentioning

confidence: 99%

“…Analytical methods for predicting crimes include approaches for making use of historical crime data, e.g., crime mapping, hot spot identification and risk terrain analysis [2][3][4][5]. The impact of hurricanes on burglaries has also been investigated [6].…”

Section: Related Workmentioning

confidence: 99%

Evaluating Temporal Analysis Methods Using Residential Burglary Data

Boldt

Borg

2016

IJGI

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“…Calls-for-service include violent crimes (count = 2696), property crimes (13,704), disorder (18,949), bylaw complaints (10,598), motor-vehicle-related calls (31,105), 911 calls (65,617), and a variety of other police-related services [47,48]. Locations reflect the closest intersection to call location and calls were summed to the DA-scale.…”

Section: Police Call-for-service Datamentioning

confidence: 99%

“…This is analogous to the spatial ICAR model (Equation (4a,b)). Alternative priors where temporal adjacency is conditional only on previous time periods are available [54,55], however ICAR was considered suitable in this context because calls-for-service at time t should be correlated with incidents at both t − 1 and t + 1 [18].…”

Section: Prior Distributionsmentioning

confidence: 99%

Analyzing Local Spatio-Temporal Patterns of Police Calls-for-Service Using Bayesian Integrated Nested Laplace Approximation

Luan

Quick

Law

2016

IJGI

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This research investigates spatio-temporal patterns of police calls-for-service in the Region of Waterloo, Canada, at a fine spatial and temporal resolution. Modeling was implemented via Bayesian Integrated Nested Laplace Approximation (INLA). Temporal patterns for two-hour time periods, spatial patterns at the small-area scale, and space-time interaction (i.e., unusual departures from overall spatial and temporal patterns) were estimated. Temporally, calls-for-service were found to be lowest in the early morning (02:00-03:59) and highest in the evening (20:00-21:59), while high levels of calls-for-service were spatially located in central business areas and in areas characterized by major roadways, universities, and shopping centres. Space-time interaction was observed to be geographically dispersed during daytime hours but concentrated in central business areas during evening hours. Interpreted through the routine activity theory, results are discussed with respect to law enforcement resource demand and allocation, and the advantages of modeling spatio-temporal datasets with Bayesian INLA methods are highlighted.

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“…6 Probability and statistics, as well as stochastic process theory, are found in the core of the quantitative analysis of crime dynamics. [7][8][9] Other epistemological approaches, such as fuzzy set theory 10,11 and complex network theory, 12 have been applied to cope with the inherent uncertainty and complexity of this phenomenon. Chaos theory has been considered an interesting apparatus to clarify several problems in criminology.…”

Section: Introductionmentioning

confidence: 99%

Multifractal Approach to the Analysis of Crime Dynamics: Results for Burglary in San Francisco

Melgarejo

Neira

2017

Fractals

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This paper provides evidence of fractal, multifractal and chaotic behaviors in urban crime by computing key statistical attributes over a long data register of criminal activity. Fractal and multifractal analyses based on power spectrum, Hurst exponent computation, hierarchical power law detection and multifractal spectrum are considered ways to characterize and quantify the footprint of complexity of criminal activity. Moreover, observed chaos analysis is considered a second step to pinpoint the nature of the underlying crime dynamics. This approach is carried out on a long database of burglary activity reported by 10 police districts of San Francisco city. In general, interarrival time processes of criminal activity in San Francisco exhibit fractal and multifractal patterns. The behavior of some of these processes is close to 1/f noise. Therefore, a characterization as deterministic, high-dimensional, chaotic phenomena is viable. Thus, the nature of crime dynamics can be studied from geometric and chaotic perspectives. Our findings support that crime dynamics may be understood from complex systems theories like self-organized criticality or highly optimized tolerance.

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Exploratory Space–Time Analysis of Burglary Patterns

Cited by 62 publications

References 66 publications

Evaluating Temporal Analysis Methods Using Residential Burglary Data

Evaluating Temporal Analysis Methods Using Residential Burglary Data

Analyzing Local Spatio-Temporal Patterns of Police Calls-for-Service Using Bayesian Integrated Nested Laplace Approximation

Multifractal Approach to the Analysis of Crime Dynamics: Results for Burglary in San Francisco

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