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Exploring spatiotemporal clusters based on extended kernel estimation methods / Jay Lee in International journal of geographical information science IJGIS, vol 31 n° 5-6 (May-June 2017)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 31 n° 5-6 (May-June 2017) . - pp 1154 - 1177 Titre : Exploring spatiotemporal clusters based on extended kernel estimation methods Type de document : Article/Communication Auteurs : Jay Lee, Auteur ; Junfang Gong, Auteur ; Shengwen Li, Auteur Année de publication : 2017 Article en page(s) : pp 1154 - 1177 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse spatio-temporelle [Termes IGN] données spatiotemporelles [Termes IGN] estimation par noyau [Termes IGN] exploration de données géographiques [Termes IGN] groupe [Termes IGN] implémentation (informatique) [Termes IGN] infraction [Termes IGN] Ohio (Etats-Unis) [Termes IGN] système d'information géographique Résumé : (auteur) We examined three different ways to integrate spatial and temporal data in kernel density estimation methods (KDE) to identify space–time clusters of geographic events. Spatial data and time data are typically measured in different units along respective dimensions. Therefore, spatial KDE methods require special extensions when incorporating temporal data to detect spatiotemporal clusters of geographical event. In addition to a real-world data set, we applied the proposed methods to simulated data that were generated through random and normal processes to compare results of different kernel functions. The comparison is based on hit rates and values of a compactness index with considerations of both spatial and temporal attributes of the data. The results show that the spatiotemporal KDE (STKDE) can reach higher hit rates while keeping identified hotspots compact. The implementation of these STKDE methods is tested using the 2012 crime event data in Akron, Ohio, as an example. The results show that STKDE methods reveal new perspectives from the data that go beyond what can be extracted by using the conventional spatial KDE. Numéro de notice : A2017-243 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2017.1287371 En ligne : http://dx.doi.org/10.1080/13658816.2017.1287371 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85179 [article]

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