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A simple line clustering method for spatial analysis with origin-destination data and its application to bike-sharing movement data / Biao He in ISPRS International journal of geo-information, vol 7 n° 6 (June 2018)
[article]
Titre : A simple line clustering method for spatial analysis with origin-destination data and its application to bike-sharing movement data Type de document : Article/Communication Auteurs : Biao He, Auteur ; Zhang Yan, Auteur ; Yu Chen, Auteur ; Zhihui Gu, Auteur Année de publication : 2018 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] analyse de groupement
[Termes IGN] analyse spatio-temporelle
[Termes IGN] bicyclette
[Termes IGN] entropie
[Termes IGN] extraction de modèle
[Termes IGN] origine - destination
[Termes IGN] raisonnement spatial
[Termes IGN] voisinage (relation topologique)Résumé : (Auteur) Clustering methods are popular tools for pattern recognition in spatial databases. Existing clustering methods have mainly focused on the matching and clustering of complex trajectories. Few studies have paid attention to clustering origin-destination (OD) trips and discovering strong spatial linkages via OD lines, which is useful in many areas such as transportation, urban planning, and migration studies. In this paper, we present a new Simple Line Clustering Method (SLCM) that was designed to discover the strongest spatial linkage by searching for neighboring lines for every OD trip within a certain radius. This method adopts entropy theory and the probability distribution function for parameter selection to ensure significant clustering results. We demonstrate this method using bike-sharing location data in a metropolitan city. Results show that (1) the SLCM was significantly effective in discovering clusters at different scales, (2) results with the SLCM analysis confirmed known structures and discovered unknown structures, and (3) this approach can also be applied to other OD data to facilitate pattern extraction and structure understanding. Numéro de notice : A2018-345 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi7060203 Date de publication en ligne : 29/05/2018 En ligne : https://doi.org/10.10.3390/ijgi7060203 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90568
in ISPRS International journal of geo-information > vol 7 n° 6 (June 2018)[article]Inference and analysis across spatial supports in the big data era : Uncertain point observations and geographic contexts / Colin Robertson in Transactions in GIS, vol 22 n° 2 (April 2018)
[article]
Titre : Inference and analysis across spatial supports in the big data era : Uncertain point observations and geographic contexts Type de document : Article/Communication Auteurs : Colin Robertson, Auteur ; Rob Feick, Auteur Année de publication : 2018 Article en page(s) : pp 455 - 476 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] contenu généré par les utilisateurs
[Termes IGN] incertitude des données
[Termes IGN] prise en compte du contexteRésumé : (Auteur) The ways in which geographic information are produced have expanded rapidly over recent decades. These advances have provided new opportunities for geographical information science and spatial analysis—allowing the tools and theories to be expanded to new domain areas and providing the impetus for theory and methodological development. In this light, old problems of inference and analysis are rediscovered and need to be reinterpreted, and new ones are made apparent. This article describes a new typology of geographical analysis problems that relates to uncertainties in the relationship between individual‐level data, represented as point features, and the geographic context(s) that they are associated with. We describe how uncertainty in context linkage (uncertain geographic context problem) is also related to, but distinct from, uncertainty in point‐event locations (uncertain point observation problem) and how these issues can impact spatial analysis. A case study analysis of a geosocial dataset demonstrates how alternative conclusions can result from failure to account for these sources of uncertainty. Sources of point observation uncertainties common in many forms of user‐generated and big spatial data are outlined and methods for dealing with them are reviewed and discussed. Numéro de notice : A2018-213 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12321 Date de publication en ligne : 23/03/2018 En ligne : https://doi.org/10.1111/tgis.12321 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90003
in Transactions in GIS > vol 22 n° 2 (April 2018) . - pp 455 - 476[article]Mapping hourly dynamics of urban population using trajectories reconstructed from mobile phone records / Zhang Liu in Transactions in GIS, vol 22 n° 2 (April 2018)
[article]
Titre : Mapping hourly dynamics of urban population using trajectories reconstructed from mobile phone records Type de document : Article/Communication Auteurs : Zhang Liu, Auteur ; Ting Ma, Auteur ; Yunyan Du, Auteur ; Tao Pei, Auteur ; et al., Auteur Année de publication : 2018 Article en page(s) : pp 494 - 513 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] analyse spatio-temporelle
[Termes IGN] carte thématique
[Termes IGN] cartographie des flux
[Termes IGN] classification par réseau neuronal
[Termes IGN] mobilité urbaine
[Termes IGN] population urbaine
[Termes IGN] régression
[Termes IGN] téléphone intelligent
[Termes IGN] trace numérique
[Termes IGN] trajet (mobilité)Résumé : (Auteur) Understanding the spatiotemporal dynamics of urban population is crucial for addressing a wide range of urban planning and management issues. Aggregated geospatial big data have been widely used to quantitatively estimate population distribution at fine spatial scales over a given time period. However, it is still a challenge to estimate population density at a fine temporal resolution over a large geographical space, mainly due to the temporal asynchrony of population movement and the challenges to acquiring a complete individual movement record. In this article, we propose a method to estimate hourly population density by examining the time‐series individual trajectories, which were reconstructed from call detail records using BP neural networks. We first used BP neural networks to predict the positions of mobile phone users at an hourly interval and then estimated the hourly population density using log‐linear regression at the cell tower level. The estimated population density is linearly correlated with population census data at the sub‐district level. Trajectory clustering results show five distinct diurnal dynamic patterns of population movement in the study area, revealing spatially explicit characteristics of the diurnal commuting flows, though the driving forces of the flows need further investigation. Numéro de notice : A2018-215 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12323 Date de publication en ligne : 26/02/2018 En ligne : https://doi.org/10.1111/tgis.12323 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90006
in Transactions in GIS > vol 22 n° 2 (April 2018) . - pp 494 - 513[article]The characteristics of asymmetric pedestrian behavior : A preliminary study using passive smartphone location data / Nick Malleson in Transactions in GIS, vol 22 n° 2 (April 2018)
[article]
Titre : The characteristics of asymmetric pedestrian behavior : A preliminary study using passive smartphone location data Type de document : Article/Communication Auteurs : Nick Malleson, Auteur ; Anthony Vanky, Auteur ; Behrooz Hashemian, Auteur ; Paolo Santi, Auteur ; Santosh K. Verma, Auteur ; et al., Auteur Année de publication : 2018 Article en page(s) : pp 616 - 634 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] chemin le plus court, algorithme du
[Termes IGN] matrice
[Termes IGN] piéton
[Termes IGN] téléphone intelligent
[Termes IGN] trace numériqueRésumé : (Auteur) Understanding the movements of people is essential for the design and management of urban areas. This article presents a novel approach to understanding the asymmetry in route choice (i.e., the degree to which people choose different walking routes for their outbound and return journeys). The study utilizes a large volume of traces of individual routes, captured using a smartphone application. The routes are aggregated to a regular grid, and matrix statistics are developed to estimate the aggregate degree of route asymmetry for different types of route (shortest, longest, weekday, weekend, etc.). The results suggest that people change their route approximately 15% of the time. Although this varied little when observing trips made at the weekend or on a weekday, people taking journeys that deviated substantially from the shortest possible path were 6 percentage points less likely to change their routes than those taking journeys that were closest to the shortest path (14 and 20% asymmetry, respectively). The absolute length also impacted on the asymmetry of journeys, but not as substantially. This result is important because, for the first time, it reports a correlation between deviation from shortest route and aggregate pedestrian choice. Numéro de notice : A2018-218 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12336 Date de publication en ligne : 06/04/2018 En ligne : https://doi.org/10.1111/tgis.12336 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90010
in Transactions in GIS > vol 22 n° 2 (April 2018) . - pp 616 - 634[article]The national geographic characteristics of online public opinion propagation in China based on WeChat network / Chuan Ai in Geoinformatica, vol 22 n° 2 (April 2018)
[article]
Titre : The national geographic characteristics of online public opinion propagation in China based on WeChat network Type de document : Article/Communication Auteurs : Chuan Ai, Auteur ; Bin Chen, Auteur ; Lingnan He, Auteur ; et al., Auteur Année de publication : 2018 Article en page(s) : pp 311 - 334 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] caractérisation
[Termes IGN] Chine
[Termes IGN] interaction spatiale
[Termes IGN] réseau social
[Termes IGN] villeRésumé : (Auteur) Offline networks have been the subject of intense academic scrutiny for many decades, but we still know little about the nationwide spatial interaction patterns and its application for public opinion management of online social networks. With the aim of uncovering the geographic interaction characteristics of online public opinion propagation, we analyze a large dataset obtained from WeChat, the most popular social media application in China, and construct the spatial interaction network G, which contains 359 city-nodes. It is found that the communities in the network and the administrative division corresponded well with each other, and cities with high betweenness and degree also develop well in the economy. Public opinion propagation depends on the state of online interaction. The findings indicate that public opinion should be managed separately by regions divided according to the community division, and different regions should adopt different management methods according to their economic, historical and political characteristics. In our work, the possibility and opportunity is presented to study the spatial interaction patterns of online public opinion propagation with the massive behavioral data and the methods of complex network. Numéro de notice : A2018-366 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s10707-017-0311-4 En ligne : https://doi.org/10.1007/s10707-017-0311-4 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90731
in Geoinformatica > vol 22 n° 2 (April 2018) . - pp 311 - 334[article]A comparative approach to modelling multiple urban land use changes using tree-based methods and cellular automata: the case of Greater Tokyo Area / Guodong Du in International journal of geographical information science IJGIS, vol 32 n° 3-4 (March - April 2018)PermalinkThe effect of acquisition error and level of detail on the accuracy of spatial analyses / Filip Biljecki in Cartography and Geographic Information Science, Vol 45 n° 2 (March 2018)PermalinkPermalinkLeveraging correlation across space and time to interpolate geophysical data via CoKriging / Sonja Pravilovic in International journal of geographical information science IJGIS, vol 32 n° 1-2 (January - February 2018)PermalinkPermalinkModélisation spatio-temporelle multi-niveau à base d'ontologies pour le suivi de la dynamique en imagerie satellitaire / Fethi Ghazouani (2018)PermalinkSimulation de formes réalistes de développement résidentiel, de l'échelle du bâtiment à celle de l'ensemble d'une région urbaine / Maxime Colomb (2018)PermalinkPermalinkUtilisation de véhicules traceurs pour la détection et la localisation de l'infrastructure routière par apprentissage automatique / Yann Méneroux (2018)PermalinkIdentification of rainwater harvesting sites using SCS-CN methodology, remote sensing and Geographical Information System techniques / Tarun Kumar in Geocarto international, vol 32 n° 12 (December 2017)PermalinkCalibrating a Land Parcel Cellular Automaton (LP-CA) for urban growth simulation based on ensemble learning / Yimin Chen in International journal of geographical information science IJGIS, vol 31 n° 11-12 (November - December 2017)PermalinkKnowledge-guided consistent correlation analysis of multimode landslide monitoring data / Shuangxi Miao in International journal of geographical information science IJGIS, vol 31 n° 11-12 (November - December 2017)PermalinkSocial Distance metric: from coordinates to neighborhoods / Vagan Terziyan in International journal of geographical information science IJGIS, vol 31 n° 11-12 (November - December 2017)PermalinkSpatiotemporal model for assessing the stability of urban human convergence and divergence patterns / Zhixiang Fang in International journal of geographical information science IJGIS, vol 31 n° 11-12 (November - December 2017)PermalinkHub Labels on the database for large-scale graphs with the COLD framework / Alexandros Efentakis in Geoinformatica, vol 21 n° 4 (October - December 2017)PermalinkLocalisation des caméras ANPR sur le réseau routier pour le profilage géographique / Marie Trotta in Revue internationale de géomatique, vol 27 n° 4 (octobre - décembre 2017)PermalinkSnapshot and continuous points-based trajectory search / Shuyao Qi in Geoinformatica, vol 21 n° 4 (October - December 2017)PermalinkUncertain Voronoi cell computation based on space decomposition / Klaus Arthur Schmid in Geoinformatica, vol 21 n° 4 (October - December 2017)PermalinkA GPU-accelerated adaptive kernel density estimation approach for efficient point pattern analysis on spatial big data / Guiming Zhang in International journal of geographical information science IJGIS, vol 31 n° 9-10 (September - October 2017)PermalinkThe geometry of space-time prisms with uncertain anchors / Bart Kuijpers in International journal of geographical information science IJGIS, vol 31 n° 9-10 (September - October 2017)Permalink