@article{ref1,
title="Spatio-temporal stochastic differential equations for crime incidence modeling",
journal="Stoch. Environ. Res. Risk Assess.",
year="2023",
author="Calatayud, Julia and Jornet, Marc and Mateu, Jorge",
volume="ePub",
number="ePub",
pages="ePub-ePub",
abstract="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 Itô 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.<p /> <p>Language: en</p>",
language="en",
issn="1436-3240",
doi="10.1007/s00477-022-02369-x",
url="http://dx.doi.org/10.1007/s00477-022-02369-x"
}