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Space-time multiple regression model for grid-based population estimation in urban areas / Ko Ko Lwin in International journal of geographical information science IJGIS, vol 30 n° 7- 8 (July - August 2016)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 30 n° 7- 8 (July - August 2016) . - pp 1579 - 1593 Titre : Space-time multiple regression model for grid-based population estimation in urban areas Type de document : Article/Communication Auteurs : Ko Ko Lwin, Auteur ; Komei Sugiura, Auteur ; Koji Zettsu, Auteur Année de publication : 2016 Article en page(s) : pp 1579 - 1593 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] appareil portable [Termes IGN] densité de population [Termes IGN] estimation statistique [Termes IGN] milieu urbain [Termes IGN] modèle conceptuel de données spatio-temporelles [Termes IGN] population [Termes IGN] régression multiple [Termes IGN] Twitter Résumé : (Auteur) We can collect, store, and analyze a huge amount of information about human mobility and social interaction activities due to the emergence of information and communication technologies and location-enabled mobile devices under cyber physical system frameworks. The high spatial resolution of population data on a multi-temporal scale is required by transport planners, human geographers, social scientists, and emergency management teams. In this study, we build a space-time multiple regression model to estimate grid-based (500 m × 500 m) spatial resolution at multi-temporal scale (30-min intervals) population data based on the space-time relationship among geospatially enabled person trip (PT) survey data and incorporate both mobile call (MC) and geotagged Twitter (GT) data. Since using geospatially enabled PT survey data as dependent variables enables us to acquire actual population amounts, which strongly depend on MCs and social interaction activities. Although many grids have a strong correlation between PT and MC/GT, some show fewer correlation results, especially where the grids have factories, schools, and workshops in which fewer MCs are found but a large population is presented. Although GT data are sparser than MCs, people from amusement and tourist areas can be detected by GT data. The space-time multiple regression model can also estimate the different amounts of populations based on human travel behavior that changes over space and time. According to accuracy assessments, the night-time estimated results, especially between 00:00 and 06:30, strongly correlate with national census data except in places where the grids have railway and subway stations. Numéro de notice : A2016-319 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2016.1143099 En ligne : http://dx.doi.org/10.1080/13658816.2016.1143099 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=80939 [article]

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