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Identification and classification of routine locations using anonymized mobile communication data / Gonçalo Ferreira in ISPRS International journal of geo-information, vol 11 n° 4 (April 2022)
[article]
Titre : Identification and classification of routine locations using anonymized mobile communication data Type de document : Article/Communication Auteurs : Gonçalo Ferreira, Auteur ; Ana Alves, Auteur ; Marco Veloso, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 228 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] analyse de groupement
[Termes IGN] classification barycentrique
[Termes IGN] données spatiotemporelles
[Termes IGN] migration pendulaire
[Termes IGN] mobilité urbaine
[Termes IGN] origine - destination
[Termes IGN] point d'intérêt
[Termes IGN] Portugal
[Termes IGN] précision sémantique
[Termes IGN] statistiques d'appels détaillés
[Termes IGN] téléphonie mobileRésumé : (auteur) Digital location traces are a relevant source of insights into how citizens experience their cities. Previous works using call detail records (CDRs) tend to focus on modeling the spatial and temporal patterns of human mobility, not paying much attention to the semantics of places, thus failing to model and enhance the understanding of the motivations behind people’s mobility. In this paper, we applied a methodology for identifying individual users’ routine locations and propose an approach for attaching semantic meaning to these locations. Specifically, we used circular sectors that correspond to cellular antennas’ signal areas. In those areas, we found that all contained points of interest (POIs), extracted their most important attributes (opening hours, check-ins, category) and incorporated them into the classification. We conducted experiments with real-world data from Coimbra, Portugal, and the initial experimental results demonstrate the effectiveness of the proposed methodology to infer activities in the user’s routine areas. Numéro de notice : A2022-419 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.3390/ijgi11040228 Date de publication en ligne : 29/03/2022 En ligne : https://doi.org/10.3390/ijgi11040228 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100306
in ISPRS International journal of geo-information > vol 11 n° 4 (April 2022) . - n° 228[article]Finding spatial outliers in collective mobility patterns coupled with social ties / Monica Wachowicz in International journal of geographical information science IJGIS, vol 30 n° 9-10 (September - October 2016)
[article]
Titre : Finding spatial outliers in collective mobility patterns coupled with social ties Type de document : Article/Communication Auteurs : Monica Wachowicz, Auteur ; Tianyu Liu, Auteur Année de publication : 2016 Article en page(s) : pp 1806 - 1831 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] centroïde
[Termes IGN] contenu généré par les utilisateurs
[Termes IGN] données issues des réseaux sociaux
[Termes IGN] données massives
[Termes IGN] interface en langage naturel
[Termes IGN] mobilité humaine
[Termes IGN] statistiques d'appels détaillés
[Termes IGN] traitement de données localisées
[Termes IGN] traitement du langage naturel
[Termes IGN] Twitter
[Termes IGN] valeur aberranteRésumé : (Auteur) Currently the increase in the variety and volume of data sources is demanding new data analytical workflows for exploring them concurrently, especially if the goal is to detect spatial outliers. In this paper, we propose a data analytical workflow for exploring Call Detail Records in conjunction with geotagged tweets. The aim was to investigate how massive data point observations can be analyzed to detect spatial outliers in collective mobility patterns that are coupled with social ties. This workflow consists of analytical tasks that are developed based on the a-priori assumption of two isometric spaces where Natural Language Processing techniques are used to find spatial clusters from geotagged tweets in a Social Space which are later used to aggregate the Call Detail Records generated by antennas located in the Mobility Space. The dynamic weighted centroids that are given by the mean location of the number of calls per hour of all antennas that belong to a particular cluster are used to compute Standard Deviation Ellipses. The longer the period of time a weighted centroid stays outside of the 99.7% probability region of an ellipse, the highest the likelihood that they are spatial outliers. The workflow was implemented for the city of Dakar in Senegal. The results indicate that the further the hourly weighted centroids are skewed from the normal mean of an ellipse, the stronger the influence of a cluster is in finding spatial outliers. Furthermore, the longer the period of time the outliers stays outside of the 99.7% probability region of an ellipse, the highest the likelihood that the outliers are genuine and can be associated to extraordinary events such as natural disasters and national holidays. Numéro de notice : A2016-569 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/SOCIETE NUMERIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2016.1144887 En ligne : http://dx.doi.org/10.1080/13658816.2016.1144887 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=81713
in International journal of geographical information science IJGIS > vol 30 n° 9-10 (September - October 2016) . - pp 1806 - 1831[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 079-2016051 RAB Revue Centre de documentation En réserve L003 Disponible Understanding the bias of call detail records in human mobility research / Ziliang Zhao in International journal of geographical information science IJGIS, vol 30 n° 9-10 (September - October 2016)
[article]
Titre : Understanding the bias of call detail records in human mobility research Type de document : Article/Communication Auteurs : Ziliang Zhao, Auteur ; Shih-Lung Shaw, Auteur ; Yang Xu, Auteur ; et al., Auteur Année de publication : 2016 Article en page(s) : pp 1738 - 1762 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] Chine
[Termes IGN] contenu généré par les utilisateurs
[Termes IGN] données massives
[Termes IGN] erreur systématique
[Termes IGN] mobilité humaine
[Termes IGN] navigation pédestre
[Termes IGN] statistiques d'appels détaillés
[Termes IGN] téléphonie mobileRésumé : (Auteur) In recent years, call detail records (CDRs) have been widely used in human mobility research. Although CDRs are originally collected for billing purposes, the vast amount of digital footprints generated by calling and texting activities provide useful insights into population movement. However, can we fully trust CDRs given the uneven distribution of people’s phone communication activities in space and time? In this article, we investigate this issue using a mobile phone location dataset collected from over one million subscribers in Shanghai, China. It includes CDRs (~27%) plus other cellphone-related logs (e.g., tower pings, cellular handovers) generated in a workday. We extract all CDRs into a separate dataset in order to compare human mobility patterns derived from CDRs vs. from the complete dataset. From an individual perspective, the effectiveness of CDRs in estimating three frequently used mobility indicators is evaluated. We find that CDRs tend to underestimate the total travel distance and the movement entropy, while they can provide a good estimate to the radius of gyration. In addition, we observe that the level of deviation is related to the ratio of CDRs in an individual’s trajectory. From a collective perspective, we compare the outcomes of these two datasets in terms of the distance decay effect and urban community detection. The major differences are closely related to the habit of mobile phone usage in space and time. We believe that the event-triggered nature of CDRs does introduce a certain degree of bias in human mobility research and we suggest that researchers use caution to interpret results derived from CDR data. Numéro de notice : A2016-567 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/SOCIETE NUMERIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2015.1137298 En ligne : http://dx.doi.org/10.1080/13658816.2015.1137298 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=81710
in International journal of geographical information science IJGIS > vol 30 n° 9-10 (September - October 2016) . - pp 1738 - 1762[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 079-2016051 RAB Revue Centre de documentation En réserve L003 Disponible