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Exploration of OpenStreetMap missing built-up areas using twitter hierarchical clustering and deep learning in Mozambique / Hao Li in ISPRS Journal of photogrammetry and remote sensing, vol 166 (August 2020)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 166 (August 2020) . - pp 41-51 Titre : Exploration of OpenStreetMap missing built-up areas using twitter hierarchical clustering and deep learning in Mozambique Type de document : Article/Communication Auteurs : Hao Li, Auteur ; Benjamin Herfort, Auteur ; Wei Huang, Auteur Année de publication : 2020 Article en page(s) : pp 41-51 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web [Termes descripteurs IGN] analyse de groupement [Termes descripteurs IGN] analyse spatiale [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] carte sanitaire [Termes descripteurs IGN] cartographie collaborative [Termes descripteurs IGN] données localisées des bénévoles [Termes descripteurs IGN] géographie sociale [Termes descripteurs IGN] inventaire du bâti [Termes descripteurs IGN] Mozambique [Termes descripteurs IGN] OpenStreetMap [Termes descripteurs IGN] qualité des données [Termes descripteurs IGN] Twitter Résumé : (auteur) Accurate and detailed geographical information digitizing human activity patterns plays an essential role in response to natural disasters. Volunteered geographical information, in particular OpenStreetMap (OSM), shows great potential in providing the knowledge of human settlements to support humanitarian aid, while the availability and quality of OSM remains a major concern. The majority of existing works in assessing OSM data quality focus on either extrinsic or intrinsic analysis, which is insufficient to fulfill the humanitarian mapping scenario to a certain degree. This paper aims to explore OSM missing built-up areas from an integrative perspective of social sensing and remote sensing. First, applying hierarchical DBSCAN clustering algorithm, the clusters of geo-tagged tweets are generated as proxies of human active regions. Then a deep learning based model fine-tuned on existing OSM data is proposed to further map the missing built-up areas. Hit by Cyclone Idai and Kenneth in 2019, the Republic of Mozambique is selected as the study area to evaluate the proposed method at a national scale. As a result, 13 OSM missing built-up areas are identified and mapped with an over 90% overall accuracy, being competitive compared to state-of-the-art products, which confirms the effectiveness of the proposed method. Numéro de notice : A2020-350 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.05.007 date de publication en ligne : 07/06/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.05.007 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95233 [article]

A global analysis of cities’ geosocial temporal signatures for points of interest hours of operation / Kevin Sparks in International journal of geographical information science IJGIS, vol 34 n° 4 (April 2020)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 34 n° 4 (April 2020) . - pp 759 - 776 Titre : A global analysis of cities’ geosocial temporal signatures for points of interest hours of operation Type de document : Article/Communication Auteurs : Kevin Sparks, Auteur ; Gautam Thakur, Auteur ; Amol Pasarkar, Auteur ; Marie Urban, Auteur Année de publication : 2020 Article en page(s) : pp 759 - 776 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes descripteurs IGN] analyse comparative [Termes descripteurs IGN] analyse spatio-temporelle [Termes descripteurs IGN] climat urbain [Termes descripteurs IGN] coutume [Termes descripteurs IGN] démographie [Termes descripteurs IGN] données géophysiques [Termes descripteurs IGN] estimation quantitative [Termes descripteurs IGN] ethnologie [Termes descripteurs IGN] géographie sociale [Termes descripteurs IGN] gestion urbaine [Termes descripteurs IGN] milieu urbain [Termes descripteurs IGN] modèle dynamique [Termes descripteurs IGN] modélisation spatio-temporelle [Termes descripteurs IGN] point d'intérêt [Termes descripteurs IGN] réseau social Résumé : (auteur) The temporal nature of humans interaction with Points of Interest (POIs) in cities can differ depending on place type and regional location. Times when many people are likely to visit restaurants (place type) in Italy, may differ from times when many people are likely to visit restaurants in Lebanon (i.e. regional differences). Geosocial data are a powerful resource to model these temporal differences in cities, as traditional methods used to study cross-cultural differences do not scale to a global level. As cities continue to grow in population and economic development, research identifying the social and geophysical (e.g., climate) factors that influence city function remains important and incomplete. In this work, we take a quantitative approach, applying dynamic time warping and hierarchical clustering on temporal signatures to model geosocial temporal patterns for Retail and Restaurant Facebook POIs hours of operation for more than 100 cities in 90 countries around the world. Results show cities’ temporal patterns cluster to reflect the cultural region they represent. Furthermore, temporal patterns are influenced by a mix of social and geophysical factors. Trends in the data suggest social factors influence unique drops in temporal signatures, and geophysical factors influence when daily temporal patterns start and finish. Numéro de notice : A2020-294 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2019.1615069 date de publication en ligne : 04/06/2019 En ligne : https://doi.org/10.1080/13658816.2019.1615069 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95126 [article]

An IEEE value loop of human-technology collaboration in geospatial information science / Liqiu Meng in Geo-spatial Information Science, vol 23 n° 1 (March 2020)  

Public [article]  inGeo-spatial Information Science > vol 23 n° 1 (March 2020) . - pp 61- 67 Titre : An IEEE value loop of human-technology collaboration in geospatial information science Type de document : Article/Communication Auteurs : Liqiu Meng, Auteur Année de publication : 2020 Article en page(s) : pp 61- 67 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Information géographique [Termes descripteurs IGN] analyse géovisuelle [Termes descripteurs IGN] approche holistique [Termes descripteurs IGN] données localisées numériques [Termes descripteurs IGN] enrichissement sémantique [Termes descripteurs IGN] éthique [Termes descripteurs IGN] géographie sociale [Termes descripteurs IGN] information sémantique [Termes descripteurs IGN] intégration de données [Termes descripteurs IGN] intelligence artificielle [Termes descripteurs IGN] interface homme-machine [Termes descripteurs IGN] recherche interdisciplinaire [Termes descripteurs IGN] web sémantique Résumé : (auteur) Geosensing and social sensing as two digitalization mainstreams in big data era are increasingly converging toward an integrated system for the creation of semantically enriched digital Earth. Along with the rapid developments of AI technologies, this convergence has inevitably brought about a number of transformations. On the one hand, value-adding chains from raw data to products and services are becoming value-adding loops composed of four successive stages – Informing, Enabling, Engaging and Empowering (IEEE). Each stage is a dynamic loop for itself. On the other hand, the “human versus technology” relationship is upgraded toward a game-changing “human and technology” collaboration. The information loop is essentially shaped by the omnipresent reciprocity between humans and technologies as equal partners, co-learners and co-creators of new values. The paper gives an analytical review on the mutually changing roles and responsibilities of humans and technologies in the individual stages of the IEEE loop, with the aim to promote a holistic understanding of the state of the art of geospatial information science. Meanwhile, the author elicits a number of challenges facing the interwoven human-technology collaboration. The transformation to a growth mind-set may take time to realize and consolidate. Research works on large-scale semantic data integration are just in the beginning. User experiences of geovisual analytic approaches are far from being systematically studied. Finally, the ethical concerns for the handling of semantically enriched digital Earth cover not only the sensitive issues related to privacy violation, copyright infringement, abuse, etc. but also the questions of how to make technologies as controllable and understandable as possible for humans and how to keep the technological ethos within its constructive sphere of societal influence. Numéro de notice : A2020-163 Thématique : GEOMATIQUE Nature : Article DOI : 10095020.2020.1718004 date de publication en ligne : 23/01/2020 En ligne : https://doi.org/10.1080/10095020.2020.1718004 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94823 [article]

Mapping society / Laura Vaughan (2018)  

Public contenu dans Directory of Open Access Books DOAB / Directory of Open Access Books DOAB   Titre : Mapping society : The spatial dimensions of social cartography Type de document : Monographie Auteurs : Laura Vaughan, Auteur Editeur : Londres : University College London Année de publication : 2018 Importance : 270 p. ISBN/ISSN/EAN : 978-1-78735-305-3 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Cartographie [Termes descripteurs IGN] analyse visuelle [Termes descripteurs IGN] carte thématique [Termes descripteurs IGN] compréhension de l'image [Termes descripteurs IGN] géographie sociale [Termes descripteurs IGN] interprétation (psychologie) [Termes descripteurs IGN] population urbaine [Termes descripteurs IGN] sciences humaines et sociales [Termes descripteurs IGN] société [Termes descripteurs IGN] sociologie Résumé : (éditeur) From a rare map of yellow fever in eighteenth-century New York, to Charles Booth’s famous maps of poverty in nineteenth-century London, an Italian racial zoning map of early twentieth-century Asmara, to a map of wealth disparities in the banlieues of twenty-first-century Paris, Mapping Society traces the evolution of social cartography over the past two centuries. In this richly illustrated book, Laura Vaughan examines maps of ethnic or religious difference, poverty, and health inequalities, demonstrating how they not only serve as historical records of social enquiry, but also constitute inscriptions of social patterns that have been etched deeply on the surface of cities. The book covers themes such as the use of visual rhetoric to change public opinion, the evolution of sociology as an academic practice, changing attitudes to physical disorder, and the complexity of segregation as an urban phenomenon. While the focus is on historical maps, the narrative carries the discussion of the spatial dimensions of social cartography forward to the present day, showing how disciplines such as public health, crime science, and urban planning, chart spatial data in their current practice. Containing examples of space syntax analysis alongside full colour maps and photographs, this volume will appeal to all those interested in the long-term forces that shape how people live in cities. Note de contenu : 1. Mapping the spatial logic of society 2. Disease, health and housing 3. Charles Booth and the mapping of poverty 4. Poverty mapping after Charles Booth 5. Nationalities, race and religion 6. Crime and disorder 7. Conclusions Numéro de notice : 25798 Thématique : GEOMATIQUE Nature : Monographie En ligne : https://doabooks.org/doab?func=advancedSearch&uiLanguage=en&fromWeb=1&first=1&qu [...] Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95201

Social Distance metric: from coordinates to neighborhoods / Vagan Terziyan in International journal of geographical information science IJGIS, vol 31 n° 11-12 (November - December 2017)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 31 n° 11-12 (November - December 2017) . - pp 2401 - 2426 Titre : Social Distance metric: from coordinates to neighborhoods Type de document : Article/Communication Auteurs : Vagan Terziyan, Auteur Année de publication : 2017 Article en page(s) : pp 2401 - 2426 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes descripteurs IGN] base de données localisées [Termes descripteurs IGN] classification [Termes descripteurs IGN] distance [Termes descripteurs IGN] exploration de données géographiques [Termes descripteurs IGN] géographie sociale [Termes descripteurs IGN] interpolation [Termes descripteurs IGN] métrique [Termes descripteurs IGN] plus proche voisin (algorithme) [Termes descripteurs IGN] système d'information géographique [Termes descripteurs IGN] voisinage (topologie) Résumé : (Auteur) Choice of a distance metric is a key for the success in many machine learning and data processing tasks. The distance between two data samples traditionally depends on the values of their attributes (coordinates) in a data space. Some metrics also take into account the distribution of samples within the space (e.g. local densities) aiming to improve potential classification or clustering performance. In this paper, we suggest the Social Distance metric that can be used on top of any traditional metric. For a pair of samples x and y, it averages the two numbers: the place (rank), which sample y holds in the list of ordered nearest neighbors of x; and vice versa, the rank of x in the list of the nearest neighbors of y. Average is a contraharmonic Lehmer mean, which penalizes the difference between the numbers by giving values greater than the Arithmetic mean for the unequal arguments. We consider normalized average as a distance function and we prove it to be a metric. We present several modifications of such metric and show that their properties are useful for a variety of classification and clustering tasks in data spaces or graphs in a Geographic Information Systems context and beyond. Numéro de notice : A2017-701 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2017.1367796 En ligne : https://doi.org/10.1080/13658816.2017.1367796 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=88082 [article]

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