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Spatio-temporal mobility and Twitter: 3D visualisation of mobility flows / Joaquín Osorio Arjona in Journal of maps, vol 16 n° 1 ([02/01/2020])
[article]
Titre : Spatio-temporal mobility and Twitter: 3D visualisation of mobility flows Type de document : Article/Communication Auteurs : Joaquín Osorio Arjona, Auteur ; Juan Carlos García Palomares, Auteur Année de publication : 2020 Article en page(s) : pp 153 - 160 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse spatio-temporelle
[Termes IGN] base de données localisées
[Termes IGN] données localisées des bénévoles
[Termes IGN] espace-temps
[Termes IGN] interface de programmation
[Termes IGN] Madrid (Espagne)
[Termes IGN] migration pendulaire
[Termes IGN] mobilité urbaine
[Termes IGN] réseau social
[Termes IGN] système d'information géographique
[Termes IGN] Time-geography
[Termes IGN] Twitter
[Termes IGN] visualisation 3D
[Vedettes matières IGN] GéovisualisationRésumé : (auteur) Recent progress in computation and the spatio-temporal richness of data obtained from new sources have invigorated Time Geography. It is now possible to visualise and represent movements of people in a dual spatial–temporal dimension. In this work, we use geo-located data from the social media platform Twitter to show the value of new data sources for Time Geography. The methodology consists of visualising space–time paths in 2D and 3D in four study zones, with different land-use profiles, based on tweets compiled over the course of two years. The results provide a view of behaviours occurring in the areas of study throughout the day, with complementary data to show the population's main activity at different times. Numéro de notice : A2020-645 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/17445647.2020.1778549 Date de publication en ligne : 18/06/2020 En ligne : https://doi.org/10.1080/17445647.2020.1778549 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96071
in Journal of maps > vol 16 n° 1 [02/01/2020] . - pp 153 - 160[article]
Titre : Distributed and parallel architectures for spatial data Type de document : Monographie Auteurs : Alberto Belussi, Éditeur scientifique ; Sara Migliorini, Éditeur scientifique ; Damiano Carra, Éditeur scientifique ; et al., Auteur Editeur : Bâle [Suisse] : Multidisciplinary Digital Publishing Institute MDPI Année de publication : 2020 Importance : 170 p. ISBN/ISSN/EAN : 978-3-03936-751-1 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] base de données localisées
[Termes IGN] collecte de données
[Termes IGN] développement durable
[Termes IGN] données localisées
[Termes IGN] données massives
[Termes IGN] entrepôt de données localisées
[Termes IGN] géoportail
[Termes IGN] Hadoop
[Termes IGN] métadonnées
[Termes IGN] modèle numérique de surface
[Termes IGN] objet mobile
[Termes IGN] OLAP
[Termes IGN] OpenStreetMap
[Termes IGN] PostGIS
[Termes IGN] réseau social
[Termes IGN] SQL
[Termes IGN] système d'information géographique
[Termes IGN] téléphone intelligent
[Termes IGN] traitement parallèle
[Termes IGN] zone tamponRésumé : (Editeur) [Préface] In recent years, an increasing amount of spatial data has been collected by different types of devices, such as mobile phones, sensors, satellites, space telescope, and medical tools for analysis, or is generated by social networks, such as geotagged tweets. The processing of this huge amount of information, including spatial properties, which are frequently represented in heterogeneous ways, is a challenging task that has boosted research in the big data area in an attempt to investigate cases and propose new solutions for dealing with its peculiarities. In the literature, many different proposals and approaches for facing the problem have been proposed, addressing different goals and different types of users. However, most are obtained by customizing existing approaches which were originally developed for the processing of big data of the alphanumeric type, without any specific support for spatial or spatiotemporal properties. Thus, the proposed solutions can exploit the parallelism provided by these kinds of systems, but without taking into account, in a proficient way, the space and time dimensions that intrinsically characterize the analyzed datasets. As described in the literature, current solutions include: (i) the on-top approach, where an underlying system for traditional big datasets is used as a black box while spatial processing is added through the definition of user-defined functions that are specified on top of the underlying system; (ii) the from-scratch approach, where a completely new system is implemented for a specific application context; and (iii) the built-in approach, where an existing solution is extended by injecting spatial data functions into its core. This book aims at promoting new and innovative studies, proposing new architectures or innovative evolutions of existing ones, and illustrating experiments on current technologies in order to improve the efficiency and effectiveness of distributed and cluster systems when they deal with spatiotemporal data. Note de contenu : Preface
1- Distributed Processing of Location-Based Aggregate Queries Using MapReduce
2- Towards the Development of Agenda 2063 Geo-Portal to Support Sustainable Development in Africa
3- HiBuffer: Buffer Analysis of 10-Million-Scale Spatial Data in Real Time
4- Mobility DataWarehouses
5- Parallelizing Multiple Flow Accumulation Algorithm using CUDA and OpenACC
6- LandQv2: A MapReduce-Based System for Processing Arable Land Quality Big Data
7- Mr4Soil: A MapReduce-Based Framework Integrated with GIS for Soil Erosion Modelling
8- High-Performance Geospatial Big Data Processing System Based on MapReduceNuméro de notice : 25884 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Monographie DOI : 10.3390/books978-3-03936-751-1 En ligne : https://doi.org/10.3390/books978-3-03936-751-1 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95762 Modelling perceived risks to personal privacy from location disclosure on online social networks / Fatma S. Alrayes in International journal of geographical information science IJGIS, vol 34 n° 1 (January 2020)
[article]
Titre : Modelling perceived risks to personal privacy from location disclosure on online social networks Type de document : Article/Communication Auteurs : Fatma S. Alrayes, Auteur ; A.I. Abdelmoty, Auteur ; B.A. El-Geresy, Auteur ; G. Theodorakopoulos, Auteur Année de publication : 2020 Article en page(s) : pp 150 - 176 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] accès aux données localisées
[Termes IGN] appariement sémantique
[Termes IGN] données issues des réseaux sociaux
[Termes IGN] échantillonnage (statistique)
[Termes IGN] géolocalisation
[Termes IGN] partage de données localisées
[Termes IGN] protection de la vie privée
[Termes IGN] réseau social
[Termes IGN] vulnérabilitéRésumé : (auteur) As users increasingly rely on online social networks for their communication activities, personal location data processing through such networks poses significant risks to users’ privacy. Location tracks can be mined with other shared information to extract rich personal profiles. To protect users’ privacy, online social networks face the challenge of ensuring transparent communication to users of how their data are processed, and explicitly obtaining users’ informed consent for the use of this data. In this paper, we explore the complex nature of the location disclosure problem and its risks to personal privacy. We evaluate, with an experiment involving 715 participants, the contributing factors to the perception of such risks with scenarios that mimic (a) realistic modes of interaction, where users are not fully aware of the extent of their location-related data being processed, and (b) with devised scenarios that deliberately inform users of the data they are sharing and its visibility to others. The results are used to represent the users’ perception of privacy risks when sharing their location information online and to derive a possible model of privacy risks associated with this sharing behaviour. Such a model can inform the design of privacy-aware online social networks to improve users’ trust and to ensure compliance with legal frameworks for personal privacy. Numéro de notice : A2020-009 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2019.1654109 Date de publication en ligne : 22/08/2019 En ligne : https://doi.org/10.1080/13658816.2019.1654109 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94390
in International journal of geographical information science IJGIS > vol 34 n° 1 (January 2020) . - pp 150 - 176[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 079-2020011 RAB Revue Centre de documentation En réserve L003 Disponible
Titre : Recent advances in geographic information system for Earth sciences Type de document : Monographie Auteurs : Yosoon Choi, Éditeur scientifique Editeur : Bâle [Suisse] : Multidisciplinary Digital Publishing Institute MDPI Année de publication : 2020 Importance : 264 p. Format : 16 x 24 cm ISBN/ISSN/EAN : 978-3-03936-490-9 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG
[Termes IGN] analyse spatiale
[Termes IGN] apprentissage profond
[Termes IGN] bassin hydrographique
[Termes IGN] cartographie thématique
[Termes IGN] codage
[Termes IGN] cognition
[Termes IGN] développement durable
[Termes IGN] effondrement de terrain
[Termes IGN] interface homme-machine
[Termes IGN] modèle dynamique
[Termes IGN] modèle numérique de surface
[Termes IGN] oculométrie
[Termes IGN] outil d'aide à la décision
[Termes IGN] planification urbaine
[Termes IGN] QGIS
[Termes IGN] réseau de transport
[Termes IGN] réseau social
[Termes IGN] utilisation du solRésumé : (éditeur) Geographic information systems (GISs) have played a vital role in Earth sciences by providing a powerful means of observing the world and various tools for solving complex problems. The scientific community has used GISs to reveal fascinating details about the Earth and other planets. This book on recent advances in GIS for Earth sciences includes 12 publications from esteemed research groups worldwide. The research and review papers in this book belong to the following broad categories: Earth science informatics (geoinformatics), mining, hydrology, natural hazards, and society. Note de contenu : 1- Recent advances in geographic information system for earth sciences
2- An efficient parallel algorithm for polygons overlay analysis
3- Vector map random encryption algorithm based on multi-scale simplification and Gaussian distribution
4- Evaluation of effective cognition for the QGIS processing modeler
5- Geo-sensor framework and composition toolbox for efficient deployment of multiple spatial context service platforms in sensor networks
6- Review of GIS-based applications for mining: Planning, operation, and environmental management
7- A tightly coupled GIS and spatiotemporal modeling for methane emission simulation in the underground coal mine system
8- Evaluation of reliable digital elevation model resolution for TOPMODEL in two mountainous watersheds, South Korea
9- Spatiotemporal changes of urban rainstorm-related micro-blogging activities in response to rainstorms: A case study in Beijing, China
10- Rainfall induced landslide studies in Indian Himalayan region: A critical review
11- GIS-based evaluation of landslide susceptibility models using certainty factors and functional trees-based ensemble techniques
12- Spatiotemporal dynamics and obstacles of the multi-functionality of land use in Xiangxi, China
13- Analyzing spatial community pattern of network traffic flow and its variations across time based on taxi GPS trajectoriesNuméro de notice : 28439 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Recueil / ouvrage collectif DOI : 10.3390/books978-3-03936-490-9 En ligne : https://doi.org/10.3390/books978-3-03936-490-9 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98876
Titre : Social Media and Machine Learning Type de document : Monographie Auteurs : Alberto Cano, Éditeur scientifique Editeur : London [UK] : IntechOpen Année de publication : 2020 Importance : 96 p. Format : 16 x 24 cm ISBN/ISSN/EAN : 978-1-83880-616-3 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle
[Termes IGN] apprentissage automatique
[Termes IGN] données massives
[Termes IGN] exploration de texte
[Termes IGN] langage naturel (informatique)
[Termes IGN] réseau social
[Termes IGN] sentimentRésumé : (éditeur) Social media has transformed society and the way people interact with each other. The volume and speed in which new content is being generated surpasses the processing capacity of machine learning systems. Analyzing such data demands new approaches coming from natural language processing, text mining, sentiment analysis, etc to understand and resolve the arising challenges. There is a need to develop robust and adaptable systems to tackle these open issues in real time, as well as to provide a meaningful summarization and visualization to the end users. This book provides the reader with a comprehensive overview of the latest developments in social media and machine learning, addressing research innovations, applications, trends, and open challenges in this crucial area. Note de contenu : 1- Introductory chapter: Data streams and online learning in social media
2- Automatic speech emotion recognition using machine learning
3- A case study of using big data processing in education: Method of matching members by optimizing collaborative
learning environment
4- Literature review on big data analytics methods
5- Information and communication based collaborative learning and behavior modeling using machine learning algorithmNuméro de notice : 28481 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE/SOCIETE NUMERIQUE Nature : Recueil / ouvrage collectif DOI : 10.5772/intechopen.78089 En ligne : https://doi.org/10.5772/intechopen.78089 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99165 Volunteered geographic information systems: Technological design patterns / Jose Pablo Gómez‐Barrón in Transactions in GIS, Vol 23 n° 5 (October 2019)PermalinkSpace, time, and situational awareness in natural hazards: a case study of Hurricane Sandy with social media data / Zheye Wang in Cartography and Geographic Information Science, Vol 46 n° 4 (July 2019)PermalinkExploring the uncertainty of activity zone detection using digital footprints with multi-scaled DBSCAN / Xinyi Liu in International journal of geographical information science IJGIS, Vol 33 n° 5-6 (May - June 2019)PermalinkUnderstanding demographic and socioeconomic biases of geotagged Twitter users at the county level / Jiang Juqin in Cartography and Geographic Information Science, vol 46 n° 3 (May 2019)PermalinkA conceptual framework for studying collective reactions to events in location-based social media / Alexander Dunkel in International journal of geographical information science IJGIS, Vol 33 n° 3-4 (March - April 2019)PermalinkModeling and visualizing semantic and spatio-temporal evolution of topics in interpersonal communication on Twitter / Caglar Koylu in International journal of geographical information science IJGIS, Vol 33 n° 3-4 (March - April 2019)PermalinkCarSenToGram: geovisual text analytics for exploring spatiotemporal variation in public discourse on Twitter / Caglar Koylu in Cartography and Geographic Information Science, Vol 46 n° 1 (January 2019)PermalinkNumérique et territoires / Philippe Cohard (2019)PermalinkA vélo au travers des Andes, pour OpenStreetMap / Anonyme in Géomatique expert, n° 126 (janvier - février 2019)PermalinkSpatialities, social Media and sentiment analysis: Exploring the potential of the detection tool SentiStrength / Christina Reithmeier in GI Forum, vol 2018 n° 2 ([01/09/2018])Permalink