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Termes IGN > informatique > intelligence artificielle > ingénierie des connaissances > découverte de connaissances > exploration de données > exploration de données géographiques
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HiPerMovelets: high-performance movelet extraction for trajectory classification / Tarlis Tortelli Portela in International journal of geographical information science IJGIS, vol 36 n° 5 (May 2022)
[article]
Titre : HiPerMovelets: high-performance movelet extraction for trajectory classification Type de document : Article/Communication Auteurs : Tarlis Tortelli Portela, Auteur ; Jonata Tyska Carvalho, Auteur ; Vania Bogorny, Auteur Année de publication : 2022 Article en page(s) : pp 1012 - 1036 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] classification
[Termes IGN] exploration de données géographiques
[Termes IGN] jeu de données localisées
[Termes IGN] trace numérique
[Termes IGN] trajet (mobilité)Résumé : (auteur) In the last decade, trajectory classification has received significant attention. The vast amount of data generated on social media, the use of sensor networks, IOT devices and other Internet-enabled sources allowed the semantic enrichment of mobility data, making the classification task more challenging. Existing trajectory classification methods have mainly considered space, time and numerical data, ignoring the semantic dimensions. Only recently proposed methods as Movelets and MASTERMovelets can handle all types of dimensions. MASTERMovelets is the only method that automatically discovers the best dimension combination and subtrajectory size for trajectory classification. However, although it outperformed the state-of-the-art in terms of accuracy, MASTERMovelets is computationally expensive and results in a high dimensionality problem, which makes it unfeasible for most real trajectory datasets that contain a big volume of data. To overcome this problem and enable the application of the movelets approach on large datasets, in this paper we propose a new high-performance method for extracting movelets and classifying trajectories, called HiPerMovelets (High-performance Movelets). Experimental results show that HiPerMovelets is 10 times faster than MASTERMovelets, reduces the high-dimensionality problem, is more scalable, and presents a high classification accuracy in all evaluated datasets with both raw and semantic trajectories. Numéro de notice : A2022-332 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/13658816.2021.2018593 Date de publication en ligne : 03/01/2022 En ligne : https://doi.org/10.1080/13658816.2021.2018593 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100608
in International journal of geographical information science IJGIS > vol 36 n° 5 (May 2022) . - pp 1012 - 1036[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 079-2022051 SL Revue Centre de documentation Revues en salle Disponible Discovering co-location patterns in multivariate spatial flow data / Jiannan Cai in International journal of geographical information science IJGIS, vol 36 n° 4 (April 2022)
[article]
Titre : Discovering co-location patterns in multivariate spatial flow data Type de document : Article/Communication Auteurs : Jiannan Cai, Auteur ; Mei-Po Kwan, Auteur Année de publication : 2022 Article en page(s) : pp 720 - 748 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] analyse bivariée
[Termes IGN] analyse de groupement
[Termes IGN] analyse univariée
[Termes IGN] autocorrélation spatiale
[Termes IGN] Chicago (Illinois)
[Termes IGN] co-positionnement
[Termes IGN] données de flux
[Termes IGN] données socio-économiques
[Termes IGN] dynamique spatiale
[Termes IGN] enquête
[Termes IGN] exploration de données géographiques
[Termes IGN] migration pendulaire
[Termes IGN] origine - destination
[Termes IGN] voisinage (relation topologique)Résumé : (auteur) Spatial flow co-location patterns (FCLPs) are important for understanding the spatial dynamics and associations of movements. However, conventional point-based co-location pattern discovery methods ignore spatial movements between locations and thus may generate erroneous findings when applied to spatial flows. Despite recent advances, there is still a lack of methods for analyzing multivariate flows. To bridge the gap, this paper formulates a novel problem of FCLP discovery and presents an effective detection method based on frequent-pattern mining and spatial statistics. We first define a flow co-location index to quantify the co-location frequency of different features in flow neighborhoods, and then employ a bottom-up method to discover all frequent FCLPs. To further establish the statistical significance of the results, we develop a flow pattern reconstruction method to model the benchmark null hypothesis of independence conditioning on univariate flow characteristics (e.g. flow autocorrelation). Synthetic experiments with predefined FCLPs verify the advantages of our method in terms of correctness over available alternatives. A case study using individual home-work commuting flow data in the Chicago Metropolitan Area demonstrates that residence- or workplace-based co-location patterns tend to overestimate the co-location frequency of people with different occupations and could lead to inconsistent results. Numéro de notice : A2022-256 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2021.1980217 Date de publication en ligne : 20/09/2021 En ligne : https://doi.org/10.1080/13658816.2021.1980217 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100229
in International journal of geographical information science IJGIS > vol 36 n° 4 (April 2022) . - pp 720 - 748[article]Detecting and visualizing observation hot-spots in massive volunteer-contributed geographic data across spatial scales using GPU-accelerated kernel density estimation / Guiming Zhang in ISPRS International journal of geo-information, vol 11 n° 1 (January 2022)
[article]
Titre : Detecting and visualizing observation hot-spots in massive volunteer-contributed geographic data across spatial scales using GPU-accelerated kernel density estimation Type de document : Article/Communication Auteurs : Guiming Zhang, Auteur Année de publication : 2022 Article en page(s) : n° 55 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web
[Termes IGN] données localisées des bénévoles
[Termes IGN] données massives
[Termes IGN] estimation par noyau
[Termes IGN] exploration de données géographiques
[Termes IGN] géovisualisation
[Termes IGN] processeur graphique
[Termes IGN] qualité des données
[Termes IGN] réseau social
[Termes IGN] tâche claireRésumé : (auteur) Volunteer-contributed geographic data (VGI) is an important source of geospatial big data that support research and applications. A major concern on VGI data quality is that the underlying observation processes are inherently biased. Detecting observation hot-spots thus helps better understand the bias. Enabled by the parallel kernel density estimation (KDE) computational tool that can run on multiple GPUs (graphics processing units), this study conducted point pattern analyses on tens of millions of iNaturalist observations to detect and visualize volunteers’ observation hot-spots across spatial scales. It was achieved by setting varying KDE bandwidths in accordance with the spatial scales at which hot-spots are to be detected. The succession of estimated density surfaces were then rendered at a sequence of map scales for visual detection of hot-spots. This study offers an effective geovisualization scheme for hierarchically detecting hot-spots in massive VGI datasets, which is useful for understanding the pattern-shaping drivers that operate at multiple spatial scales. This research exemplifies a computational tool that is supported by high-performance computing and capable of efficiently detecting and visualizing multi-scale hot-spots in geospatial big data and contributes to expanding the toolbox for geospatial big data analytics. Numéro de notice : A2022-091 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi11010055 Date de publication en ligne : 12/01/2022 En ligne : https://doi.org/10.3390/ijgi11010055 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99507
in ISPRS International journal of geo-information > vol 11 n° 1 (January 2022) . - n° 55[article]La photogrammétrie appliquée au récolement des réseaux enterrés : retour d’expérience d’une méthode industrialisée / Jérôme Leroux in XYZ, n° 169 (décembre 2021)
[article]
Titre : La photogrammétrie appliquée au récolement des réseaux enterrés : retour d’expérience d’une méthode industrialisée Type de document : Article/Communication Auteurs : Jérôme Leroux, Auteur ; Maxime Chauvin, Auteur ; Valentin Poitevin, Auteur Année de publication : 2021 Article en page(s) : pp 16 - 24 Note générale : Bibliographie Langues : Français (fre) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] appariement d'images
[Termes IGN] données localisées 3D
[Termes IGN] exploration de données géographiques
[Termes IGN] intelligence artificielle
[Termes IGN] portail
[Termes IGN] reconstruction 3D
[Termes IGN] réseau technique souterrain
[Termes IGN] semis de points
[Termes IGN] téléphone intelligent
[Termes IGN] téléphonie mobileRésumé : (Auteur) De nouvelles solutions existent aujourd’hui pour mesurer les réseaux enterrés. L’acquisition d’images avec un smartphone permet de reconstruire en 3D une fouille ouverte grâce aux algorithmes de photogrammétrie par corrélation dense. Les enjeux de fiabilité et d’industrialisation pour ce type de production de données nous ont poussés à améliorer une méthode d’acquisition préexistante. L’étude menée ici vise principalement à déterminer la meilleure technique de captation d’images. Les éléments clés de la réussite de la méthode reposent sur 1. L’utilisation de marqueurs dimensionnels à travers la pose des balises et 2. La détection de ces marqueurs dans les images via des algorithmes basés sur de l’Intelligence Artificielle. Les données 3D produites et accessibles via un portail web permettent de dessiner les infrastructures existantes en réduisant le plus possible l’intervention humaine. Numéro de notice : A2021-847 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueNat DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99171
in XYZ > n° 169 (décembre 2021) . - pp 16 - 24[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 112-2021041 RAB Revue Centre de documentation En réserve L003 Disponible Spatial structure system of land use along urban rail transit based on GIS spatial clustering / Yu Gao in European journal of remote sensing, vol 54 sup 2 (2021)
[article]
Titre : Spatial structure system of land use along urban rail transit based on GIS spatial clustering Type de document : Article/Communication Auteurs : Yu Gao, Auteur ; Ying Zhang, Auteur ; Haidjar Alsulaiman, Auteur Année de publication : 2021 Article en page(s) : pp 438 - 445 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG
[Termes IGN] aménagement du territoire
[Termes IGN] analyse de groupement
[Termes IGN] Chine
[Termes IGN] exploration de données géographiques
[Termes IGN] modélisation spatiale
[Termes IGN] optimisation (mathématiques)
[Termes IGN] planification urbaine
[Termes IGN] réseau ferroviaire
[Termes IGN] système d'information géographique
[Termes IGN] utilisation du sol
[Termes IGN] zone urbaineRésumé : (auteur) During the past 30 years of reform and opening up, the level of urbanization in our country has made considerable progress, and more cities have had development conditions of the rail transit. In order to solve various problems caused by urbanization and optimize the allocation of urban resources, in this paper, the significance of spatial planning in big cities was analyzed from the perspective of land space utilization along with the rail transit. Based on GIS spatial clustering mining technology and combined with the basic characteristics of geographic information system, a GIS spatial mining search model that can solve the spatial land use was proposed. Then, by combining the clustering algorithm of some association rules, the land planning and utilization along the urban rail transit were calculated. The actual case was taken to establish the grid elements along with the rail transit, and the GIS spatial clustering algorithm was used to verify the model. The results show that GIS spatial clustering algorithm can effectively verify and calculate urban rail transit land planning programs. Numéro de notice : A2021-820 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/22797254.2020.1801356 Date de publication en ligne : 14/11/2020 En ligne : https://doi.org/10.1080/22797254.2020.1801356 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98925
in European journal of remote sensing > vol 54 sup 2 (2021) . - pp 438 - 445[article]Automating and utilising equal-distribution data classification / Gennady Andrienko in International journal of cartography, vol 7 n° 1 (March 2021)PermalinkPermalinkMéthodes et outils pour l’analyse spatiale exploratoire en géolinguistique : contributions aux humanités numériques spatialisées / Clément Chagnaud (2021)PermalinkSTME: An effective method for discovering spatiotemporal multi‐type clusters containing events with different densities / Chao Wang in Transactions in GIS, Vol 24 n° 6 (December 2020)PermalinkA framework for group converging pattern mining using spatiotemporal trajectories / Bin Zhao in Geoinformatica, vol 24 n° 4 (October 2020)PermalinkGeovisualization and harmonic analysis for the exploratory search of localized cyclic recurrences in spatio-temporal event data / Jacques Gautier in Geomatica, vol 74 n° 3 (September 2020)PermalinkBreaking the eyes: how do users get started with a coordinated and multiple view geovisualization tool? / Izabela Golebiowska in Cartographic journal (the), Vol 57 n° 3 (August 2020)PermalinkLos Angeles as a digital place: The geographies of user‐generated content / Andrea Ballatore in Transactions in GIS, Vol 24 n° 4 (August 2020)PermalinkMining spatiotemporal association patterns from complex geographic phenomena / Zhanjun He in International journal of geographical information science IJGIS, vol 34 n° 6 (June 2020)PermalinkPermalinkLa modélisation en géographie / Denise Pumain (2020)PermalinkPermalinkChamps et objets pour mieux représenter les phénomènes dans leur contexte géographique / Anne Ruas in Revue internationale de géomatique, vol 29 n° 2 (avril - juin 2019)PermalinkConception et évaluation de techniques d'interaction non-visuelle basées sur un dispositif personnel / Sandra Bardot (2019)PermalinkA hybrid ensemble learning method for tourist route recommendations based on geo-tagged social networks / Lin Wan in International journal of geographical information science IJGIS, vol 32 n° 11-12 (November - December 2018)PermalinkNRand‐K : Minimizing the impact of location obfuscation in spatial analysis / Mayra Zurbaran in Transactions in GIS, vol 22 n° 5 (October 2018)PermalinkSpatial mining of migration patterns from web demographics / T. Edwin Chow in International journal of geographical information science IJGIS, vol 32 n° 9-10 (September - October 2018)PermalinkMining and visual exploration of closed contiguous sequential patterns in trajectories / Can Yang in International journal of geographical information science IJGIS, vol 32 n° 7-8 (July - August 2018)PermalinkGenerating vague neighbourhoods through data mining of passive web data / Paul Brindley in International journal of geographical information science IJGIS, vol 32 n° 3-4 (March - April 2018)PermalinkThe influence of domain expertise on visual overviews of spatiotemporal data / Susanne Bleisch in International journal of cartography, vol 3 n° 2 (December 2017)PermalinkA cloud-enabled automatic disaster analysis system of multi-sourced data streams: An example synthesizing social media, remote sensing and Wikipedia data / Qunying Huang in Computers, Environment and Urban Systems, vol 66 (November 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)PermalinkAn iterative method for obtaining a mean 3D axis from a set of GNSS traces for use in positional controls / A. Mozas-Calvache in Survey review, vol 49 n° 355 (October 2017)PermalinkDiscovering non-compliant window co-occurrence patterns / Reem Y. Ali in Geoinformatica, vol 21 n° 4 (October - December 2017)PermalinkAggregation-based information retrieval system for geospatial data catalogs / Javier Lacasta in International journal of geographical information science IJGIS, vol 31 n° 7-8 (July - August 2017)PermalinkPerSE : visual analytics for calendar related spatiotemporal periodicity detection and analysis / Brian Swedberg in Geoinformatica, vol 21 n° 3 (July - September 2017)PermalinkRobust point cloud classification based on multi-level semantic relationships for urban scenes / Qing Zhu in ISPRS Journal of photogrammetry and remote sensing, vol 129 (July 2017)PermalinkExploring spatiotemporal clusters based on extended kernel estimation methods / Jay Lee in International journal of geographical information science IJGIS, vol 31 n° 5-6 (May-June 2017)PermalinkEvaluating data stability in aggregation structures across spatial scales: revisiting the modifiable areal unit problem / Jonathan K. Nelson in Cartography and Geographic Information Science, Vol 44 n° 1 (January 2017)PermalinkA review of volunteered geographic information quality assessment methods / Hansi Senaratne in International journal of geographical information science IJGIS, vol 31 n° 1-2 (January - February 2017)PermalinkPermalinkMining spatiotemporal co-occurrence patterns in non-relational databases / Berkay Aydin in Geoinformatica, vol 20 n° 4 (October - December 2016)PermalinkOn discovering co-location patterns in datasets : a case study of pollutants and child cancers / Jundong Li in Geoinformatica, vol 20 n° 4 (October - December 2016)PermalinkModeling spatiotemporal information generation / Simon Scheider in International journal of geographical information science IJGIS, vol 30 n° 9-10 (September - October 2016)PermalinkUnsupervised classification of airborne laser scanning data to locate potential wildlife habitats for forest management planning / Jari Vauhkonen in Forestry, an international journal of forest research, vol 89 n° 4 (August 2016)PermalinkGrid pattern recognition in road networks using the C4.5 algorithm / Jing Tian in Cartography and Geographic Information Science, Vol 43 n° 3 (June 2016)PermalinkIntegrating geo web services for a user driven exploratory analysis / Simon Moncrieff in ISPRS Journal of photogrammetry and remote sensing, vol 114 (April 2016)PermalinkImage based geo-localization in the Alps / Olivier Saurer in International journal of computer vision, vol 116 n° 3 (February 2016)PermalinkPersonal mobility pattern mining and anomaly detection in the GPS era / Dong-He Shih in Cartography and Geographic Information Science, Vol 43 n° 1 (January 2016)PermalinkQGIS 2 cookbook / Alex Mandel (2016)PermalinkRevisiting cartography : towards identifying and developing a modern and comprehensive framework / Melih Basaraner in Geocarto international, vol 31 n° 1 - 2 (January - February 2016)PermalinkCo-clustering geo-referenced time series: exploring spatio-temporal patterns in Dutch temperature data / Xiaojing Wu in International journal of geographical information science IJGIS, vol 29 n° 4 (April 2015)PermalinkSENTERRITOIRE pour la détection d’opinions liées à l’aménagement d’un territoire / Eric Kergosien in Revue internationale de géomatique, vol 25 n° 1 (mars - mai 2015)PermalinkGeo-located community detection in Twitter with enhanced fast-greedy optimization of modularity: the case study of typhoon Haiyan / Mohamed Bakillah in International journal of geographical information science IJGIS, vol 29 n° 2 (February 2015)PermalinkMining trajectory data and geotagged data in social media for road map inference: Mining social media for road map inference / Jun Li in Transactions in GIS, vol 19 n° 1 (February 2015)Permalink