ArticleStochastic environmental research and risk assessment : research journal2023
Modeling noisy time-series data of crime with stochastic differential equations.
Article in Stochastic environmental research and risk assessment : research journal, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Dynamic transfer learning with co-occurrence-guided multi-source fusion for urban spatio-temporal crime prediction.Frontiers in big data · 2026Article
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3 authors.
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Abstract
We develop and calibrate stochastic continuous models that capture crime dynamics in the city of Valencia, Spain. From the emergency phone, data corresponding to three crime events, aggressions, stealing and women alarms, are available from the year 2010 until 2020. As the resulting time series, with monthly counts, are highly noisy, we decompose them into trend and seasonality parts. The former is modeled by geometric Brownian motions, both uncorrelated and correlated, and the latter is accommodated by randomly perturbed sine-cosine waves. Albeit simple, the models exhibit high ability to simulate the real data and show promising for crimes-interaction identification and short-term predictive policing.
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