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Understanding collective human movement dynamics during large-scale events using big geosocial data analytics / Junchuan Fan in Computers, Environment and Urban Systems, vol 87 (May 2021)  

Public [article]  inComputers, Environment and Urban Systems > vol 87 (May 2021) . - n° 101605 Titre : Understanding collective human movement dynamics during large-scale events using big geosocial data analytics Type de document : Article/Communication Auteurs : Junchuan Fan, Auteur ; Kathleen Stewart, Auteur Année de publication : 2021 Article en page(s) : n° 101605 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] collecte de données [Termes IGN] données GPS [Termes IGN] données issues des réseaux sociaux [Termes IGN] données localisées des bénévoles [Termes IGN] données massives [Termes IGN] dynamique spatiale [Termes IGN] échantillonnage de données [Termes IGN] éclipse solaire [Termes IGN] estimation par noyau [Termes IGN] Etats-Unis [Termes IGN] événement [Termes IGN] géolocalisation [Termes IGN] migration humaine [Termes IGN] mobilité territoriale [Termes IGN] téléphonie mobile Résumé : (auteur) Conventional approaches for modeling human mobility pattern often focus on human activity and movement dynamics in their regular daily lives and cannot capture changes in human movement dynamics in response to large-scale events. With the rapid advancement of information and communication technologies, many researchers have adopted alternative data sources (e.g., cell phone records, GPS trajectory data) from private data vendors to study human movement dynamics in response to large-scale natural or societal events. Big geosocial data such as georeferenced tweets are publicly available and dynamically evolving as real-world events are happening, making it more likely to capture the real-time sentiments and responses of populations. However, precisely-geolocated geosocial data is scarce and biased toward urban population centers. In this research, we developed a big geosocial data analytical framework for extracting human movement dynamics in response to large-scale events from publicly available georeferenced tweets. The framework includes a two-stage data collection module that collects data in a more targeted fashion in order to mitigate the data scarcity issue of georeferenced tweets; in addition, a variable bandwidth kernel density estimation(VB-KDE) approach was adopted to fuse georeference information at different spatial scales, further augmenting the signals of human movement dynamics contained in georeferenced tweets. To correct for the sampling bias of georeferenced tweets, we adjusted the number of tweets for different spatial units (e.g., county, state) by population. To demonstrate the performance of the proposed analytic framework, we chose an astronomical event that occurred nationwide across the United States, i.e., the 2017 Great American Eclipse, as an example event and studied the human movement dynamics in response to this event. However, this analytic framework can easily be applied to other types of large-scale events such as hurricanes or earthquakes. Numéro de notice : A2021-275 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/SOCIETE NUMERIQUE Nature : Article DOI : 10.1016/j.compenvurbsys.2021.101605 Date de publication en ligne : 05/02/2021 En ligne : https://doi.org/10.1016/j.compenvurbsys.2021.101605 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97358 [article]

Thinking about space-time connections : spatiotemporal scheduling of individual activities / Kathleen Stewart in Transactions in GIS, vol 17 n° 6 (December 2013)  

Public [article]  inTransactions in GIS > vol 17 n° 6 (December 2013) . - pp 791 - 807 Titre : Thinking about space-time connections : spatiotemporal scheduling of individual activities Type de document : Article/Communication Auteurs : Kathleen Stewart, Auteur ; Junchuan Fan, Auteur ; Emily White, Auteur Année de publication : 2013 Article en page(s) : pp 791 - 807 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] accessibilité [Termes IGN] analyse spatio-temporelle [Termes IGN] généralisation [Termes IGN] géovisualisation [Termes IGN] mise à jour [Termes IGN] mobilité urbaine [Termes IGN] modèle conceptuel de données spatio-temporelles [Termes IGN] ontologie [Termes IGN] personnalisation [Termes IGN] requête spatiotemporelle [Termes IGN] SWRL Résumé : (Auteur) This article presents a spatiotemporal model for scheduling applications that is driven by the events and activities individuals plan and manage every day. The framework is presented using an ontological approach where ontologies at different levels of generalization, e.g. domain, application, and task ontologies, are linked together through participation and inheritance relationships. S_Events are entered into a schedule as a new S_Entry, or modifications can be made to existing entries including reschedule, postpone, change location, and delete as schedules vary over time. These schedule updates are formalized through changes to planned start and end times and the planned locations of S_Entries are expressed using SWRL, a semantic web rule language. SWRL is also used for reasoning about schedule changes and the space-time conflicts that can occur. The sequence of entries in a schedule gives rise to S_trajectories representing the locations that individuals plan to visit in order to carry out their schedule, adding an additional spatial element to the framework. A prototype Geoscheduler application maps S_Entries against a timeline, offering a spatiotemporal visualization of scheduled activities showing the evolution of a schedule over space-time and affecting spatiotemporal accessibility for individuals. Numéro de notice : A2013-671 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12028 Date de publication en ligne : 17/05/2013 En ligne : https://doi.org/10.1111/tgis.12028 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32807 [article]