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Spatial interpolation of mobile positioning data for population statistics / Anto Aasa in Journal of location-based services, vol 15 n° 4 ([01/10/2021])  

Public [article]  inJournal of location-based services > vol 15 n° 4 [01/10/2021] Titre : Spatial interpolation of mobile positioning data for population statistics Type de document : Article/Communication Auteurs : Anto Aasa, Auteur ; Pilleriine Kamenjuk, Auteur ; Erki Saluveer, Auteur ; et al., Auteur Année de publication : 2021 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] analyse comparative [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] données démographiques [Termes IGN] données GNSS [Termes IGN] interpolation spatiale [Termes IGN] mobilité humaine [Termes IGN] traitement de données localisées Résumé : (auteur) Mobile positioning is recognised to be one of the most promising new sources of data for the production of fast and cost-effective statistics regarding population and mobility. Considerable interest has been shown by government institutions in their search for a way to use mobile positioning data to produce official statistics, although to date there are only few examples of successful projects. Apart from data access and sampling, the main challenges relate to the spatial interpolation of mobile positioning data and extrapolation of recorded data to the level of the entire population. This area of work has to date received relatively little attention in the academic discussion. In the current study, we compare five different methods of spatial interpolation of mobile positioning data. The best methods of describing population distribution and size in comparison with Census data are the adaptive Morton grid and the Random forest model (R2 > 0.9), while the more widely used point-in-polygon and areal-weighted methods produce results that are far less satisfactory (R2 = 0.42; R2 = 0.35). Careful selection of spatial interpolation methods is therefore of the utmost importance for producing reliable population statistics from mobile positioning data. Numéro de notice : A2021-727 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/17489725.2021.1917710 Date de publication en ligne : 10/05/2021 En ligne : https://doi.org/10.1080/17489725.2021.1917710 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98658 [article]

Mapping changes of residence with passive mobile positioning data : the case of Estonia / Pilleriine Kamenjuk in International journal of geographical information science IJGIS, vol 31 n° 7-8 (July - August 2017)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 31 n° 7-8 (July - August 2017) . - pp 1425 - 1447 Titre : Mapping changes of residence with passive mobile positioning data : the case of Estonia Type de document : Article/Communication Auteurs : Pilleriine Kamenjuk, Auteur ; Anto Aasa, Auteur ; Jaanus Sellin, Auteur Année de publication : 2017 Article en page(s) : pp 1425 - 1447 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] Estonie [Termes IGN] géovisualisation [Termes IGN] habitat individuel [Termes IGN] migration pendulaire [Termes IGN] mobilité humaine [Termes IGN] surveillance [Termes IGN] téléphonie mobile [Termes IGN] trace GPS Résumé : (Auteur) Similar to every process involving quantitative research, the study of migration heavily depends on the data available for analysis. The available movement data limit the type of questions that can be asked, and as a result, certain aspects of human spatial mobility have yet to be examined. The development of information and communication technologies and their widespread adoption offers new datasets, methods and interpretations that make it possible to study social processes at a new level. For example, mobile positioning data can aid in overcoming certain constraints embedded in traditional data sources (such as censuses or questionnaires) for study of the connections between daily mobility and change of residence. This study presents a framework for mapping changes of residence using data from passive mobile positioning and an anchor point model to better understand the limits of these methods and their contribution to understanding long-term mobility. The study concludes that the most important considerations in monitoring change of residence using passive mobile position data include the continuity of the time-series data, the varying structure of the mobile tower network and the diversified nature of human mobility. The fine spatial and temporal granularities of passive mobile positioning data allow us to study human movement at a detailed scale. Numéro de notice : A2017-307 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2017.1295308 En ligne : http://dx.doi.org/10.1080/13658816.2017.1295308 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85354 [article]

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