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Modeling spatiotemporal information generation / Simon Scheider in International journal of geographical information science IJGIS, vol 30 n° 9-10 (September - October 2016)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 30 n° 9-10 (September - October 2016) . - pp 1980 - 2008 Titre : Modeling spatiotemporal information generation Type de document : Article/Communication Auteurs : Simon Scheider, Auteur ; Benedikt Gräler, Auteur ; Edzer J. Pebesma, Auteur ; Christophe Stasch, Auteur Année de publication : 2016 Article en page(s) : pp 1980 - 2008 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées [Termes IGN] base de données dérivée [Termes IGN] données hétérogènes [Termes IGN] exploration de données géographiques [Termes IGN] information géographique [Termes IGN] mise à jour de base de données [Termes IGN] mise à jour en continu [Termes IGN] regroupement de données [Termes IGN] source de données Résumé : (Auteur) Maintaining knowledge about the provenance of datasets, that is, about how they were obtained, is crucial for their further use. Contrary to what the overused metaphors of ‘data mining’ and ‘big data’ are implying, it is hardly possible to use data in a meaningful way if information about sources and types of conversions is discarded in the process of data gathering. A generative model of spatiotemporal information could not only help automating the description of derivation processes but also assessing the scope of a dataset’s future use by exploring possible transformations. Even though there are technical approaches to document data provenance, models for describing how spatiotemporal data are generated are still missing. To fill this gap, we introduce an algebra that models data generation and describes how datasets are derived, in terms of types of reference systems. We illustrate its versatility by applying it to a number of derivation scenarios, ranging from field aggregation to trajectory generation, and discuss its potential for retrieval, analysis support systems, as well as for assessing the space of meaningful computations. Numéro de notice : A2016-573 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2016.1151520 En ligne : http://dx.doi.org/10.1080/13658816.2016.1151520 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=81729 [article]

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enviroCar: A citizen science platform for analyzing and mapping crowd-sourced car sensor data / Arne Bröring in Transactions in GIS, vol 19 n° 3 (June 2015)  

Public [article]  inTransactions in GIS > vol 19 n° 3 (June 2015) . - pp 362 – 376 Titre : enviroCar: A citizen science platform for analyzing and mapping crowd-sourced car sensor data Type de document : Article/Communication Auteurs : Arne Bröring, Auteur ; Albert Remke, Auteur ; Christophe Stasch, Auteur ; et al., Auteur Année de publication : 2015 Article en page(s) : pp 362 – 376 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web [Termes IGN] acquisition de données [Termes IGN] capteur terrestre [Termes IGN] diagnostic [Termes IGN] données localisées des bénévoles [Termes IGN] interface web [Termes IGN] milieu urbain [Termes IGN] réseau de capteurs [Termes IGN] trafic routier [Termes IGN] véhicule automobile Résumé : (auteur) This article presents the enviroCar platform for collecting geographic data acquired from automobile sensors and openly providing those data for further processing and analysis. By plugging a low-cost On-Board Diagnostics (OBD-II) adapter into a car and using an Android smartphone, various kinds of sensor data measured by today's cars can be collected and uploaded on to the Web. Once available on the Web, these data can be used to monitor traffic and related environmental parameters. We analyse the OBD-II interface and its potential usage for environmental monitoring, e.g. to estimate fuel consumption and resulting inline image emissions, noise emission, and standing times. Next, we present the main contribution of this article, the system design of the enviroCar platform. This system design consists of the enviroCar app and the enviroCar server, which allows for flexible geoprocessing of the uploaded data. We focus in this article on the description of the spatiotemporal RESTful Web Service interface and underlying data model specifically designed for handling the mobile sensor data. Finally, we present application scenarios in which the enviroCar platform can act as a powerful tool, e.g. regarding traffic monitoring and smarter cities (e.g. the detection of pollutant emission hotspots in the city), or towards applications for a quantified self (e.g. monitoring fuel consumption). We started the enviroCar project in 2013 and have been able to attract a growing number of participants since then. In a crowd-funding initiative, enviroCar was successfully funded by volunteers, demonstrating the interest in this platform. Numéro de notice : A2015-678 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12155 En ligne : http://dx.doi.org/10.1111/tgis.12155 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=78304 [article]