Country crime analysis using the self-organizing map, with special regard to demographic factors

Li, Xingan; Juhola, Martti

doi:10.1007/s00146-013-0441-7

Cited by 17 publications

(15 citation statements)

References 19 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%

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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%

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)

Self Cite

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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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“…Communities worldwide suffer the effects of crime and the deterrence of crime has become one of the most important global tasks, along with the concern for public security [1]. How immigration affects and increases the crime rates has historically been an important consideration in criminological theory and is a major concern of political and public discourse and debate on immigration policy [2].…”

Section: Introductionmentioning

confidence: 99%

Are Immigrants More Likely to Be Involved in Criminal Activity in Saudi Arabia?

Algahtany¹,

Kumar²,

Khormi³

2016

JSS

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This paper aims to explain the effects of immigration on the overall crime rate and in the six most significant categories of crime in Saudi Arabia, being drugs activity, theft, murder, assault, alcohol and outrageous issues, during a ten-year period from 2003 to 2012, in all thirteen administrative provinces, as well as establish the provinces most affected by criminal activities of immigrants during this period. In our finding, no positive association between immigrants and criminal cases was noted. It is clearly visible that the highest rate of overall criminal activities is in the south, north and Makkah areas, where there is high probability of illegal immigrants, supporting the basic criminological theory that areas with high levels of immigrants also experience high rates of crime. The study provides suggestions to the Saudi government, policy and decision makers, and immigration authorities, which could assist in reducing crimes perpetrated by immigrants.

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Country crime analysis using the self-organizing map, with special regard to demographic factors

Cited by 17 publications

References 19 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

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

Are Immigrants More Likely to Be Involved in Criminal Activity in Saudi Arabia?

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