Spatio-temporal stochastic differential equations for crime incidence modeling

Julia Calatayud; Marc Jornet; Jorge Mateu

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Spatio-temporal stochastic differential equations for crime incidence modeling

机译：Spatio-temporal stochastic differential equations for crime incidence modeling

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摘要

We propose a methodology for the quantitative fitting and forecasting of real spatio-temporal crime data, based on stochastic differential equations. The analysis is focused on the city of Valencia, Spain, for which 90247 robberies and thefts with their latitude-longitude positions are available for a span of eleven years (2010-2020) from records of the 112-emergency phone. The incidents are placed in the 26 zip codes of the city (46001-46026), and monthly time series of crime are built for each of the zip codes. Their annual-trend components are modeled by Ito diffusion, with jointly correlated noises to account for district-level relations. In practice, this study may help simulate spatio-temporal situations and identify risky areas and periods from present and past data.

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来源
《Stochastic environmental research and risk assessment》 |2023年第5期|1839-1854|共16页
作者
Julia Calatayud; Marc Jornet; Jorge Mateu;
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作者单位

Departament de Matematiques, Universitat Jaume Ⅰ, 12071 Castellon, Spain;

Departament de Matematiques, Universitat de Valencia, 46100 Burjassot, Spain;

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原文格式 PDF
正文语种英语
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关键词
Space-time crime data; Differential equations; Trend time series; Geometric Brownian motion stochastic process; District-level correlations;

Spatio-temporal stochastic differential equations for crime incidence modeling

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