Application of the self-organising map to visualisation of and exploration into historical development of criminal phenomena in the USA, 1960-2007

Li, Xingan; Juhola, Martti

doi:10.1504/ijsss.2014.062435

Cited by 6 publications

(11 citation statements)

References 23 publications

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“…The SOMine software can automatically handle the missing values and generate a map from the given data. SOM was selected for this study due to its good performance in publications [1], [2], [3], [4], [5], [6], [7].…”

Section: B Preprocessing and Generating Clustersmentioning

confidence: 99%

“…In [1] and [2], the Self-Organising Map (SOM), assisted with some additional data mining techniques, was applied in the research of crime based on international databases. The research, composed of a series of papers, dealt with the relationship between crime and demographic factors [3], [4], economic factors [5], historical developments [6], and that between a particular offence, homicide and its social context [7]. The suitability and the evidence of the performance of SOM in aforementioned studies convinced us to choose SOM as a main machine learning technique for this paper.…”

Section: Introductionmentioning

confidence: 99%

“…The number of recorded crimes continued to increase, until it reached the 2003 peak of 6 million [17]. Similar to that in the United States, a steady decrease started after the crime climax of the early 2000s [6].…”

Section: Introductionmentioning

confidence: 99%

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Development of crime in England and Wales 1898–2001: Data mining using self-organising map

Joutsijoki

Laurikkala

et al. 2017

2017 IEEE Symposium Series on Computational Intelligence (SSCI)

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The aim of this article is to inquire about historical development of criminal phenomena in England and Wales, and relationship between different crime rates, based on a set of English and Welsh historical data. This national-level study uses a dataset covering 103 years (1898-2001, with data of 1939 missing and not counted) and 50 attributes. The collected data are clustered with Self-Organizing Map (SOM) and the features are assessed using Scatter algorithm. Several machine learning methods are applied to verify the clustering result obtained by the SOM. Accuracy of 96.2% gained by one-vs-one least-squares support vector machines shows that the clusters obtained by the SOM are valid. The article is an exploratory application of the SOM in research of criminal phenomena through processing of multivariate data. The research showed that SOM was able to cluster efficiently the present data and to characterize these different clusters. This is the accepted manuscript of the article, which has been published in 2017 IEEE Symposium Series on Computational Intelligence (SSCI). https://doi.

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Section: B Preprocessing and Generating Clustersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Development of crime in England and Wales 1898–2001: Data mining using self-organising map

Joutsijoki

Laurikkala

et al. 2017

2017 IEEE Symposium Series on Computational Intelligence (SSCI)

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“…Clustering known as data segmentation or partitioning is the task to discover groups and structures in the data that are in some way or another "Similar", without using known structures in the data. Some of the common clustering techniques are the K-mean and self-organizing map (Li et al, 2010;Li and Juhola, 2014a). Outlier detection uses specific measurement to study data that differs markedly from the rest of the data (Chen et al, 2004).…”

Section: Classification For Data Mining Applications and Techniquesmentioning

confidence: 99%

A Review on a Classification Framework for Supporting Decision Making in Crime Prevention

No¹,

Hamid²,

Mohemad³

et al. 2014

J. of Artificial Intelligence

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Increasing volume of crimes has brought a serious problem to many countries across the world. Crime prevention is an important component of an overall strategy to reduce crime and to strengthen public safety. Although, Supporting Decision Making (SDM) in crime prevention is an important topic but a comprehensive literature review on the subject has yet to be implemented. Thus, this study presents a systematic and comprehensive review on a classification framework for SDM in crime prevention. Forty four journal articles on the subject published between 2000 and October, 2015 were analyzed and classified into two categories of index crime (violent crime and property crime) and six classes of data mining techniques (prediction, classification, visualization, regression, clustering and outlier detection). The results of this study clearly show that data mining especially prediction and clustering techniques have been applied most extensively in both index crime categories. The main data mining techniques used for SDM in crime prevention are Bayesian, neural network and nearest neighbor. This study also addresses the gaps between SDM in crime prevention and the needs of practitioners to encourage more researches in crime analysis. Finally, it concludes with some suggestions for future research on SDM in crime prevention.

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“…Li and Juhola (2013) and Li (2014) applied the SOM in the study of criminal phenomena based on international databases, assisted with some other data mining techniques. The research dealt with the relationship between crime and demographic factors (Li and Juhola, 2014a;Li et al 2015a), economic factors (Li and Juhola, 2015), historical developments of criminal phenomena in the USA (Li and Juhola, 2014b), and that between a particular offence, homicide and its social context (Li et al, 2015b). These studies concluded that the SOM, in addition to its application in microscopic research, could be a helpful instrument for research in crime at international and national levels based on relevant statistical data.…”

Section: Introductionmentioning

confidence: 99%

GDP growth vs. criminal phenomena: data mining of Japan 1926–2013

Joutsijoki

Laurikkala

et al. 2017

AI & Soc

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The aim of this article is to inquire about potential relationship between change of crime rates and change of GDP growth rate, based on historical statistics of Japan. This national-level study used a dataset covering 88 years (1926-2013) and 13 attributes. The data were processed with the Self-Organizing Map (SOM), separation power checked by our ScatterCounter method, assisted by other clustering methods and statistical methods for obtaining comparable results. The article is an exploratory application of the SOM in research of criminal phenomena through processing of multivariate data. The research confirmed previous findings that SOM was able to cluster efficiently the present data and characterize these different clusters. Other machine learning methods were applied to ensure clusters computed with SOM. The correlations obtained between GDP and other attributes were mostly weak, with a few of them interesting.

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Application of the self-organising map to visualisation of and exploration into historical development of criminal phenomena in the USA, 1960-2007

Cited by 6 publications

References 23 publications

Development of crime in England and Wales 1898–2001: Data mining using self-organising map

Development of crime in England and Wales 1898–2001: Data mining using self-organising map

A Review on a Classification Framework for Supporting Decision Making in Crime Prevention

GDP growth vs. criminal phenomena: data mining of Japan 1926–2013

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