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A spatio-temporal method for crime prediction using historical crime data and transitional zones identified from nightlight imagery / Bo Yang in International journal of geographical information science IJGIS, vol 34 n° 9 (September 2020)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 34 n° 9 (September 2020) . - pp 1740 - 1764 Titre : A spatio-temporal method for crime prediction using historical crime data and transitional zones identified from nightlight imagery Type de document : Article/Communication Auteurs : Bo Yang, Auteur ; Lin Liu, Auteur ; Minxuan Lan, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 1740 - 1764 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] coefficient de corrélation [Termes IGN] criminalité [Termes IGN] données spatiotemporelles [Termes IGN] géostatistique [Termes IGN] historique des données [Termes IGN] image NPP-VIIRS [Termes IGN] krigeage [Termes IGN] modèle dynamique [Termes IGN] nuit [Termes IGN] Ohio (Etats-Unis) [Termes IGN] prédiction [Termes IGN] prévention des risques [Termes IGN] prise de vue nocturne [Termes IGN] test statistique [Termes IGN] zone urbaine Résumé : (auteur) Accurate crime prediction can help allocate police resources for crime reduction and prevention. There are two popular approaches to predict criminal activities: one is based on historical crime, and the other is based on environmental variables correlated with criminal patterns. Previous research on geo-statistical modeling mainly considered one type of data in space-time domain, and few sought to blend multi-source data. In this research, we proposed a spatio-temporal Cokriging algorithm to integrate historical crime data and urban transitional zones for more accurate crime prediction. Time-series historical crime data were used as the primary variable, while urban transitional zones identified from the VIIRS nightlight imagery were used as the secondary co-variable. The algorithm has been applied to predict weekly-based street crime and hotspots in Cincinnati, Ohio. Statistical tests and Predictive Accuracy Index (PAI) and Predictive Efficiency Index (PEI) tests were used to validate predictions in comparison with those of the control group without using the co-variable. The validation results demonstrate that the proposed algorithm with historical crime data and urban transitional zones increased the correlation coefficient by 5.4% for weekdays and by 12.3% for weekends in statistical tests, and gained higher hit rates measured by PAI/PEI in the hotspots test. Numéro de notice : A2020-475 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1737701 Date de publication en ligne : 13/03/2020 En ligne : https://doi.org/10.1080/13658816.2020.1737701 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95622 [article]

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