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Auteur Haosheng Huang |
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Affiner la recherche Interroger des sources externesAnalytics of location-based big data for smart cities: Opportunities, challenges, and future directions / Haosheng Huang in Computers, Environment and Urban Systems, vol 90 (November 2021)
Titre : Analytics of location-based big data for smart cities: Opportunities, challenges, and future directions Type de document : Article/Communication Auteurs : Haosheng Huang, Auteur ; Xiaobai Yao, Auteur ; Jukka Mathias Krisp, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 101712 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] données issues des réseaux sociaux
[Termes IGN] données localisées des bénévoles
[Termes IGN] données massives
[Termes IGN] planification urbaine
[Termes IGN] service fondé sur la position
[Termes IGN] téléphonie mobile
[Termes IGN] ville durable
[Termes IGN] ville intelligenteRésumé : (auteur) The growing ubiquity of location/activity sensing technologies and location-based services (LBS) has led to a large volume and variety of location-based big data (LocBigData), such as location tracking or sensing data, social media data, and crowdsourced geographic information. The increasing availability of such LocBigData has created unprecedented opportunities for research on urban systems and human environments in general. In this article, we first review the common types of LocBigData: mobile phone network data, GPS data, Location-based social media data, LBS usage/log data, smart card travel data, beacon log data (WiFi or Bluetooth), and camera imagery data. Secondly, we describe the opportunities fueled by LocBigData for the realization of smart cities, mainly via answering questions ranging from “what happened” and “why did it happen” to “what's likely to happen in the future” and “what to do next”. Thirdly, pitfalls of dealing with LocBigData are summarized, such as high volume/velocity/variety; non-random sampling; messy and not clean data; and correlations rather than causal relationships. Finally, we review the state-of-the-art research trends in this field, and conclude the article with a list of open research challenges and a research agenda for LocBigData research to help achieve the vision of smart and sustainable cities. Numéro de notice : A2021-650 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/URBANISME Nature : Article DOI : 10.1016/j.compenvurbsys.2021.101712 Date de publication en ligne : 08/09/2021 En ligne : https://doi.org/10.1016/j.compenvurbsys.2021.101712 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98368
in Computers, Environment and Urban Systems > vol 90 (November 2021) . - n° 101712[article]
Titre : Inferring user tasks in pedestrian navigation from eye movement data in real-world environments Type de document : Article/Communication Auteurs : Hua Liao, Auteur ; Weihua Dong, Auteur ; Haosheng Huang, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 739 - 763 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] apprentissage automatique
[Termes IGN] calcul d'itinéraire
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] inférence
[Termes IGN] navigation pédestre
[Termes IGN] oculométrie
[Termes IGN] représentation cognitive
[Vedettes matières IGN] GéovisualisationRésumé : (Auteur) Eye movement data convey a wealth of information that can be used to probe human behaviour and cognitive processes. To date, eye tracking studies have mainly focused on laboratory-based evaluations of cartographic interfaces; in contrast, little attention has been paid to eye movement data mining for real-world applications. In this study, we propose using machine-learning methods to infer user tasks from eye movement data in real-world pedestrian navigation scenarios. We conducted a real-world pedestrian navigation experiment in which we recorded eye movement data from 38 participants. We trained and cross-validated a random forest classifier for classifying five common navigation tasks using five types of eye movement features. The results show that the classifier can achieve an overall accuracy of 67%. We found that statistical eye movement features and saccade encoding features are more useful than the other investigated types of features for distinguishing user tasks. We also identified that the choice of classifier, the time window size and the eye movement features considered are all important factors that influence task inference performance. Results of the research open doors to some potential real-world innovative applications, such as navigation systems that can provide task-related information depending on the task a user is performing. Numéro de notice : A2019-214 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2018.1482554 Date de publication en ligne : 26/06/2018 En ligne : https://doi.org/10.1080/13658816.2018.1482554 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92686
in International journal of geographical information science IJGIS > Vol 33 n° 3-4 (March - April 2019) . - pp 739 - 763[article]Exemplaires(2)
Code-barres Cote Support Localisation Section Disponibilité 079-2019031 RAB Revue Centre de documentation En réserve L003 Disponible 079-2019032 RAB Revue Centre de documentation En réserve L003 Disponible
Titre : Designing across map use contexts : a research agenda Type de document : Article/Communication Auteurs : Amy L. Griffin, Auteur ; Travis White, Auteur ; Carolyn Fish, Auteur ; Beate Tomio, Auteur ; Haosheng Huang, Auteur ; et al., Auteur Année de publication : 2017 Article en page(s) : pp 90 - 114 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Cartographie
[Termes IGN] conception cartographique
[Termes IGN] prise en compte du contexte
[Termes IGN] recherche scientifiqueRésumé : (Auteur) The explosion of map use in the past few decades as part of everyday activities, accelerated through the digital production and dissemination of maps and the availability of low-cost, location-aware devices, has made the job of cartographers and map display designers more challenging. Yet, how do these recent changes affect effective map design? Can we accurately predict which designs will work for a given context? We investigate the concepts of design transferability and context and their potential to help us create map design outcomes that are effective for varying map use situations. We then present a model for operationalizing map use context to support evaluating map design transferability and pose several open research questions that need to be answered to support operationalizing map use context. This is followed by a research agenda that identifies research opportunities related to key research needs that will underpin transferable map design. Numéro de notice : A2018-438 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/23729333.2017.1315988 Date de publication en ligne : 15/05/2017 En ligne : https://doi.org/10.1080/23729333.2017.1315988 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90929
in International journal of cartography > vol 3 suppl 1 (May 2017) . - pp 90 - 114[article]
Titre : European handbook of crowdsourced geographic information Type de document : Monographie Auteurs : Cristina Capineri, Éditeur scientifique ; Muki M. Haklay, Éditeur scientifique ; Haosheng Huang, Éditeur scientifique ; Vyron Antoniou, Éditeur scientifique ; Juhani Kettunen, Éditeur scientifique ; Franck O. Ostermann, Éditeur scientifique ; Ross S. Purves, Éditeur scientifique Editeur : Londres : Ubiquity press Année de publication : 2016 Importance : 476 p. Format : 21 x 30 cm ISBN/ISSN/EAN : 978-1-909188-80-8 Langues : Anglais (eng) Note de contenu : Chapter 1. Introduction
Part I: Theoretical and social aspects
Chapter 2. The nature of volunteered geographic information (Cristina Capineri)
Chapter 3. Why is participation inequality important? (Mordechai (Muki) Haklay)
Chapter 4. Social Media Geographic Information: Why social is special when it goes spatial? (Michele Campagna)
Part II: Quality: Criteria and methodologies
Chapter 5. Handling quality in crowdsourced geographic information (Laura Criscuolo, Paola Carrara, Gloria Bordogna, Monica Pepe, Francesco Zucca, Roberto Seppi, Alessandro Oggioni and Anna Rampini)
Chapter 6. Data quality in crowdsourcing for biodiversity research: issues and examples (Clemens Jacobs)
Chapter 7. Semantic Challenges for Volunteered Geographic Information (Andrea Ballatore)
Chapter 8. Quality analysis of the Parisian OSM toponyms evolution (Vyron Antoniou, Guillaume Touya and Ana-Maria Raimond)
Chapter 9. Tackling the thematic accuracy of areal features in OpenStreetMap (Ahmed Loai Ali)
Chapter 10. Enhancing the management of quality of VGI: contributions from context and task modelling (Benedicte Bucher, Gilles Falquet, Claudine Metral and Rob Lemmens)
Part III: Data analytics
Chapter 11. A methodological toolbox for exploring collections of textually annotated georeferenced photographs (Ross S. Purves and William A. Mackaness)
Chapter 12. Gaining Knowledge from Georeferenced Social Media Data with Visual Analytics (Gennady Andrienko and Natalia Andrienko)
Chapter 13. Head/tail Breaks for Visualization of City Structure and Dynamics (Bin Jiang)
Chapter 14. Querying VGI by semantic enrichment (Rob Lemmens, Gilles Falquet, Stefano De Sabbata, Bin Jiang and Benedicte Bucher)
Chapter 15. Extracting Location Information from Crowd-sourced Social Network Data (Pinar Karagoz, Halit Oguztuzun, Ruket Cakici, Ozer Ozdikis, Kezban Dilek Onal and Meryem Sagcan)
Chapter 16. Spatial and Temporal Sentiment Analysis of Twitter data (Zhiwen Song and Jianhong (Cecilia) Xia)
Chapter 17. Social Networks VGI: Twitter Sentiment Analysis of Social Hotspots (Dario Stojanovski, Ivan Chorbev, Ivica Dimitrovski and Gjorgji Madjarov)
Chapter 18. Research on social media feeds – A GIScience perspective (Enrico Steiger, Rene Westerholt and Alexander Zipf)
Part IV: VGI and crowdsourcing in environmental monitoring
Chapter 19. Changing role of citizens in the national environmental monitoring (Juhani Kettunen, Jari Silander, Matti Lindholm, Maiju Lehtiniemi, Outi Setälä and Seppo Kaitala)
Chapter 20. On the contribution of Volunteered Geographic Information to land monitoring efforts (Jamal Jokar Arsanjani and Cidália C. Fonte)
Chapter 21. Discussing the potential of crowdsourced geographic information for urban areas monitoring using the panoramio initiative (Flavio Lupia and Jacinto Estima)
Chapter 22. AtrapaelTigre.com: enlisting citizen-scientists in the war on tiger mosquitoes (Aitana Oltra, John R.B. Palmer and Frederic Bartumeus)
Chapter 23. Crowdsourcing geographic information for disaster risk management and improving urban resilience: an overview of the lessons learned (João Porto de Albuquerque, Melanie Eckle, Benjamin Herfort and Alexander Zipf)
Part V: VGI in mobility
Chapter 24. Crowdsourcing for individual needs - the case of routing and navigation for mobility-impaired persons (Alexander Zipf, Amin Mobasheri, Adam Rousell and Stefan Hahmann)
Chapter 25. Smart Timetable Service Based on Crowdsensed Data (Károly Farkas)
Chapter 26. Mobile crowd-sensing in the Smart City (Imre Lendák)
Chapter 27. Mobile crowd sensing for smart urban mobility (Dragan Stojanovic, Bratislav Predic and Natalija Stojanovic)
Part VI: VGI in spatial planning
Chapter 28. Using mobile crowdsourcing and geotagged social media data to study people’s affective responses to environments (Haosheng Huang and Georg Gartner)
Chapter 29. Integrating Authoritative and Volunteered Geographic Information for spatial planning (Pierangelo Massa and Michele Campagna)
Chapter 30. A Proposed Crowdsourcing Cadastral Model: Taking Advantage of Previous Experience and Innovative Techniques (Sofia Basiouka and Chryssy Potsiou)
Chapter 31. Modelling the world in 3D from VGI/Crowdsourced data (Hongchao Fan and Alexander Zipf)
Glossary (Linda See, Cristina Capineri and Sofia Basiouka)Numéro de notice : 17356 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Recueil / ouvrage collectif DOI : 10.5334/bax En ligne : https://doi.org/10.5334/bax Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83781
- European handbook of crowdsourced geographic information, ch. 8. Quality analysis of the Parisian OSM toponyms evolution / Vyron Antoniou (2016)
- European handbook of crowdsourced geographic information, ch. 10. Enhancing the management of quality of VGI: contributions from context and task modelling / Bénédicte Bucher (2016)
- European handbook of crowdsourced geographic information, ch. 12. Gaining knowledge from georeferenced social media data with visual analytics / Gennady Andrienko (2016)
- European handbook of crowdsourced geographic information, ch. 14. Querying VGI by semantic enrichment / Robert Lemmens (2016)
Documents numériques
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European handbook of crowdsourced geographic informationAdobe Acrobat PDFProceedings of the 13th International Conference on Location-Based Services, Vienna, 14 - 16 november 2016 / Georg Gartner (2016)
Titre : Proceedings of the 13th International Conference on Location-Based Services, Vienna, 14 - 16 november 2016 Type de document : Actes de congrès Auteurs : Georg Gartner, Éditeur scientifique ; Haosheng Huang, Éditeur scientifique Editeur : [s.l.] : [s.n.] Année de publication : 2016 Conférence : LBS 2016, 13th International Conference on Location-Based Services 14/11/2016 16/11/2016 Vienne Autriche Importance : 296 p. ISBN/ISSN/EAN : 978-1-907075-02-5 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Navigation et positionnement
[Termes IGN] analyse spatio-temporelle
[Termes IGN] mobilité humaine
[Termes IGN] navigation
[Termes IGN] service fondé sur la positionNote de contenu : 1- Outdoor and Indoor Navigation
2- Positioning
3- Mobility Analysis
4- VGI, Social Media, and Spatial-Temporal Analysis
5- LBS ApplicationsNuméro de notice : 19799 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE/POSITIONNEMENT Nature : Actes DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85124
- An interactive system for intrinsic validation of citizen science data for species distribution mapping and modelling applications / Hossein Vahidi (2016)
- Joining spatial distribution visualisation tools with social media data using free and open source software : extended abstract / Mayra Zurbaran (2016)
Documents numériques
en open access
Proceedings of the 13th International Conference on Location-Based ServicesAdobe Acrobat PDF
