The effect of seasonality in predicting the level of crime : A spatial perspective
Delgado de la Torre, Rosario (Universitat Autònoma de Barcelona. Departament de Matemàtiques)
Sánchez-Delgado, Héctor (Data Quality and Statistics Executive)

Data:	2023
Resum:	This paper presents an innovative methodology to study the application of seasonality (the existence of cyclical patterns) to help predict the level of crime. This methodology combines the simplicity of entropy-based metrics that describe temporal patterns of a phenomenon, on the one hand, and the predictive power of machine learning on the other. First, the classical Colwell's metrics Predictability and Contingency are used to measure different aspects of seasonality in a geographical unit. Second, if those metrics turn out to be significantly different from zero, supervised machine learning classification algorithms are built, validated and compared, to predict the level of crime based on the time unit. The methodology is applied to a case study in Barcelona (Spain), with month as the unit of time, and municipal district as the geographical unit, the city being divided into 10 of them, from a set of property crime data covering the period 2010-2018. The results show that (a) Colwell's metrics are significantly different from zero in all municipal districts, (b) the month of the year is a good predictor of the level of crime, and (c) Naive Bayes is the most competitive classifier, among those who have been tested. The districts can be ordered using the Naive Bayes, based on the strength of the month as a predictor for each of them. Surprisingly, this order coincides with that obtained using Contingency. This fact is very revealing, given the apparent disconnection between entropy-based metrics and machine learning classifiers.
Ajuts:	Agencia Estatal de Investigación PID2021-123733NB-I00
Drets:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.
Llengua:	Anglès
Document:	Article ; recerca ; Versió publicada
Publicat a:	PloS one, Vol. 18, Num. 5 (May 2023) , art. e0285727, ISSN 1932-6203

DOI: 10.1371/journal.pone.0285727
PMID: 37256849

28 p, 2.9 MB

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