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Scalable and privacy-respectful interactive discovery of place semantics from human mobility traces / Natalia Andrienko in Information visualization, vol 15 n° 2 (April 2016)  

Public [article]  inInformation visualization > vol 15 n° 2 (April 2016) . - pp 117 - 153 Titre : Scalable and privacy-respectful interactive discovery of place semantics from human mobility traces Type de document : Article/Communication Auteurs : Natalia Andrienko, Auteur ; Gennady Andrienko, Auteur ; Georg Fuchs, Auteur ; Piotr Jankowski, Auteur Année de publication : 2016 Article en page(s) : pp 117 - 153 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse géovisuelle [Termes IGN] analyse multicritère [Termes IGN] données spatiotemporelles [Termes IGN] information sémantique [Termes IGN] migration pendulaire [Termes IGN] mobilité urbaine [Termes IGN] protection de la vie privée [Termes IGN] réseau social [Termes IGN] téléphone intelligent [Termes IGN] trace GPS [Vedettes matières IGN] Géovisualisation Résumé : (auteur) Mobility diaries of a large number of people are needed for assessing transportation infrastructure and spatial development planning. Acquisition of personal mobility diaries through population surveys is a costly and error-prone endeavour. We examine an alternative approach to obtaining similar information from episodic digital traces of people’s presence in various locations, which appear when people use their mobile devices for making phone calls, accessing the Internet or posting georeferenced contents (texts, photos or videos) in social media. Having episodic traces of a person over a long time period, it is possible to detect significant (repeatedly visited) personal places and identify them as home, work or place of social activities based on temporal patterns of a person’s presence in these places. Such analysis, however, can lead to compromising personal privacy. We have investigated the feasibility of deriving place meanings and reconstructing personal mobility diaries while preserving the privacy of individuals whose data are analysed. We have devised a visual analytics approach and a set of supporting tools making such privacy-preserving analysis possible. The approach was tested in two case studies with publicly available data: simulated tracks from the VAST Challenge 2014 and real traces built from georeferenced Twitter posts. Numéro de notice : A2016--019 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1177/1473871615581216 En ligne : https://doi.org/10.1177/1473871615581216 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83874 [article]

Extracting semantics of individual places from movement data by analyzing temporal patterns of visits / Gennady Andrienko (2013)    

Public contenu dans Proceedings of the Sixth ACM SIGSPATIAL International Workshop on Computational Transportation Science / Craig A. Knoblock (2013)   Titre : Extracting semantics of individual places from movement data by analyzing temporal patterns of visits Type de document : Article/Communication Auteurs : Gennady Andrienko, Auteur ; Natalia Andrienko, Auteur ; Georg Fuchs, Auteur ; Ana-Maria Olteanu-Raimond , Auteur ; Juergen (Jürgen) Symanzik, Auteur ; Cezary Ziemlicki, Auteur Editeur : New York [Etats-Unis] : Association for computing machinery ACM Année de publication : 2013 Conférence : IWCTS 2013, 6th ACM SIGSPATIAL International Workshop on Computational Transportation Science 05/11/2013 08/11/2013 Orlando Floride - Etats-Unis Proceedings ACM Langues : Anglais (eng) Résumé : (auteur) Data reflecting movements of people, such as GPS or GSM tracks, can be a source of information about mobility behaviors and activities of people. Such information is required for various kinds of spatial planning in the public and business sectors. Movement data by themselves are semantically poor. Meaningful information can be derived by means of interactive visual analysis performed by a human expert; however, this is only possible for data about a small number of people. We suggest an approach that allows scaling to large datasets reflecting movements of numerous people. It includes extracting stops, clustering them for identifying personal places of interest (POIs), and creating temporal signatures of the POIs characterizing the temporal distribution of the stops with respect to the daily and weekly time cycles and the time line. The analyst can give meanings to selected POIs based on their temporal signatures (i.e., classify them as home, work, etc.), and then POIs with similar signatures can be classified automatically. We demonstrate the possibilities for interactive visual semantic analysis by example of GSM, GPS, and Twitter data. GPS data allow inferring richer semantic information, but temporal signatures alone may be insufficient for interpreting short stops. Twitter data are similar to GSM data but additionally contain message texts, which can help in place interpretation. We plan to develop an intelligent system that learns how to classify personal places and trips while a human analyst visually analyzes and semantically annotates selected subsets of movement data. Numéro de notice : C2013-022 Affiliation des auteurs : LASTIG COGIT+Ext (2012-2019) Thématique : GEOMATIQUE Nature : Communication DOI : 10.1145/2534848.2534851 En ligne : http://dx.doi.org/10.1145/2534848.2534851 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=80134

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