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Exploring the heterogeneity of human urban movements using geo-tagged tweets / Ding Ma in International journal of geographical information science IJGIS, vol 34 n° 12 (December 2020)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 34 n° 12 (December 2020) . - pp 2475 -2 496 Titre : Exploring the heterogeneity of human urban movements using geo-tagged tweets Type de document : Article/Communication Auteurs : Ding Ma, Auteur ; Toshihiro Osaragi, Auteur ; Takuya Oki, Auteur ; Bin Jiang, Auteur Année de publication : 2020 Article en page(s) : pp 2475 -2 496 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse spatio-temporelle [Termes IGN] données issues des réseaux sociaux [Termes IGN] données localisées des bénévoles [Termes IGN] espace urbain [Termes IGN] flux de données [Termes IGN] géobalise [Termes IGN] géolocalisation [Termes IGN] hétérogénéité [Termes IGN] Londres [Termes IGN] migration humaine [Termes IGN] modèle de simulation [Termes IGN] modèle orienté agent [Termes IGN] population urbaine [Termes IGN] Tokyo (Japon) [Termes IGN] Twitter Résumé : (auteur) The availability of vast amounts of location-based data from social media platforms such as Twitter has enabled us to look deeply into the dynamics of human movement. The aim of this paper is to leverage a large collection of geo-tagged tweets and the street networks of two major metropolitan areas—London and Tokyo—to explore the underlying mechanism that determines the heterogeneity of human mobility patterns. For the two target cities, hundreds of thousands of tweet locations and road segments were processed to generate city hotspots and natural streets. User movement trajectories and city hotspots were then used to build a hotspot network capable of quantitatively characterizing the heterogeneous movement patterns of people within the cities. To emulate observed movement patterns, the study conducts a two-level agent-based simulation that includes random walks through the hotspot networks and movements in the street networks using each of three distance types—metric, angular and combined. Comparisons of the simulated and observed movement flows at the segment and street levels show that the heterogeneity of human urban movements at the collective level is mainly shaped by the scaling structure of the urban space. Numéro de notice : A2020-692 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1718153 Date de publication en ligne : 24/01/2020 En ligne : https://doi.org/10.1080/13658816.2020.1718153 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96233 [article]

Classification methods in GIS: information loss minimization for spatial data representation / Toshihiro Osaragi in GIS Geo-Informations-Systeme, vol 2002 n° 9 (September 2002)

Public [article]  inGIS Geo-Informations-Systeme > vol 2002 n° 9 (September 2002) . - pp 22 - 29 Titre : Classification methods in GIS: information loss minimization for spatial data representation Type de document : Article/Communication Auteurs : Toshihiro Osaragi, Auteur Année de publication : 2002 Article en page(s) : pp 22 - 29 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Statistiques [Termes IGN] analyse comparative [Termes IGN] classification [Termes IGN] données localisées [Termes IGN] méthode déterministe [Termes IGN] modèle stochastique [Termes IGN] représentation cartographique 2D [Termes IGN] système d'information géographique Résumé : (Auteur) In the process of representing quantitative spatial data on a map, it is necessary to classify attribute values into some class divisions. When a number of classes are employed, the characteristics of spatial distribution of original data can be expressed faithfully. However, its legends might become rather complicated and the delicate color differences in the represented map would be difficult to distinguish. On the other hand, when employing a few classes, the information such as small vibrating factors or local peaks might be ignored ; namely, much information of original data will be lost. Hence, we should discuss how many classes are necessary to represent spatial data. In this paper, a new classification method using an evaluation function based on Akaike's Information Criterion is proposed, and is applied to actual spatial data. Next, based on the consideration about its result, another classification method minimizing information loss of original data is proposed. Furthermore, numerical examples of its applications are achieved through the comparison with existing classification methods. Numéro de notice : A2002-340 Affiliation des auteurs : non IGN Thématique : MATHEMATIQUE Nature : Article DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=22251 [article]

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