Effectiveness of Support Vector Machine for Crime Hot-Spots Prediction

Kianmehr, Keivan; Alhajj, Reda

doi:10.1080/08839510802028405

Cited by 41 publications

(14 citation statements)

References 24 publications

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“…SVM is used for crime hotspot prediction in ref. [65], where they find it outperforming neural networks and spatial auto-regressionbased approaches. In ref.…”

Section: Support Vector Machines (Svm)mentioning

confidence: 99%

A review of data mining applications in crime

Hassani

Huang

Silva

et al. 2016

Statistical Analysis

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Crime continues to remain a severe threat to all communities and nations across the globe alongside the sophistication in technology and processes that are being exploited to enable highly complex criminal activities. Data mining, the process of uncovering hidden information from Big Data, is now an important tool for investigating, curbing and preventing crime and is exploited by both private and government institutions around the world. The primary aim of this paper is to provide a concise review of the data mining applications in crime. To this end, the paper reviews over 100 applications of data mining in crime, covering a substantial quantity of research to date, presented in chronological order with an overview table of many important data mining applications in the crime domain as a reference directory. The data mining techniques themselves are briefly introduced to the reader and these include entity extraction, clustering, association rule mining, decision trees, support vector machines, naive Bayes rule, neural networks and social network analysis amongst others

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“…SVM is used for crime hotspot prediction in ref. [65], where they find it outperforming neural networks and spatial auto-regressionbased approaches. In ref.…”

Section: Support Vector Machines (Svm)mentioning

confidence: 99%

A review of data mining applications in crime

Hassani

Huang

Silva

et al. 2016

Statistical Analysis

View full text Add to dashboard Cite

show abstract

“…There is even a nascent literature of algorithmic approaches to time- and place-specific crime hot spot prediction on which to build. Exploratory data-mining work by Corcoran et al (2003) and Olligschlaeger (1998), involving artificial neural network modelling, has been followed by studies that seek to apply an increasingly wide range of machine learning techniques to a motley assortment of crime datasets (see Almanie et al, 2015; Kianmehr and Alhajj, 2008; Yu et al, 2014). This work typically privileges method over meaning by adopting a non-critical approach to the spatial and temporal features of the data under interrogation.…”

Section: Big Data For Big Issues?mentioning

confidence: 99%

Place and time in the Criminology of Place

Bannister

O’Sullivan

Bates

2017

Theoretical Criminology

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This article evaluates developments in the ecological analysis of crime, which have found their most recent expression in a Criminology of Place. We argue that theoretical and methodological deficiencies are evident in the Criminology of Place and associated literatures with respect to their underlying treatment of place, time and causation. Big Data holds promise for helping address these shortfalls, but dangers also. The successful advance of the Criminology of Place requires elevating the why question to equal status with those of where and what in the analysis of crime. Ultimately, the paper positions the progress towards and prospects for a multi-scalar and time sensitive theoretical and empirical model of the Criminology of Place.

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“…In terms of individual, each occurrence of a crime is certainly a (p, 1-p) binary event, which is a common probability event in reality [5] . In addition, generally if a certain event possesses a characteristic that its occurrence is "rare", a Poisson stream may be used to approximate it [6] .…”

Section: Attenuation Based Profile Vectormentioning

confidence: 99%

A Novel Algorithm for Criminal Statistical Processing

Lin¹,

Chen²

2015

Proceedings of the 2015 International Conference on Electrical, Computer Engineering and Electronics

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Social order tendency analysis is studied based on intensive probe to present criminal information research jobs. The phenomenon of "routine fluctuation" and "warning fluctuation" in the field of social public order is extensively studied. An urban crime distribution vector generation algorithm, which based on exponential attenuation, is generated by combining the historic statistic data and with the present one. a Composite Statistic Profiling-Vector Hypothesis-test Algorithm, consist of a system anomaly testing expression and a corresponding referenced anomaly testing expression, is presented to exam the present statistics value set, thus social system anomaly index is obtain. Prototype system and experiment results show its precision and credibility.

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Effectiveness of Support Vector Machine for Crime Hot-Spots Prediction

Cited by 41 publications

References 24 publications

A review of data mining applications in crime

A review of data mining applications in crime

Place and time in the Criminology of Place

A Novel Algorithm for Criminal Statistical Processing

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