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A BiLSTM-CNN model for predicting users’ next locations based on geotagged social media / Yi Bao in International journal of geographical information science IJGIS, vol 35 n° 4 (April 2021)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 35 n° 4 (April 2021) . - pp 639 - 660 Titre : A BiLSTM-CNN model for predicting users’ next locations based on geotagged social media Type de document : Article/Communication Auteurs : Yi Bao, Auteur ; Zhou Huang, Auteur ; Linna Li, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 639 - 660 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes descripteurs IGN] analyse de groupement [Termes descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] données spatiotemporelles [Termes descripteurs IGN] géopositionnement [Termes descripteurs IGN] graphe [Termes descripteurs IGN] modèle de simulation [Termes descripteurs IGN] point d'intérêt [Termes descripteurs IGN] réseau social [Termes descripteurs IGN] service fondé sur la position [Termes descripteurs IGN] utilisateur [Termes descripteurs IGN] Wuhan (Chine) Résumé : (auteur) Location prediction based on spatio-temporal footprints in social media is instrumental to various applications, such as travel behavior studies, crowd detection, traffic control, and location-based service recommendation. In this study, we propose a model that uses geotags of social media to predict the potential area containing users’ next locations. In the model, we utilize HiSpatialCluster algorithm to identify clustering areas (CAs) from check-in points. CA is the basic spatial unit for predicting the potential area containing users’ next locations. Then, we use the LINE (Large-scale Information Network Embedding) to obtain the representation vector of each CA. Finally, we apply BiLSTM-CNN (Bidirectional Long Short-Term Memory-Convolutional Neural Network) for location prediction. The results show that the proposed ensemble model outperforms the single LSTM or CNN model. In the case study that identifies 100 CAs out of Weibo check-ins collected in Wuhan, China, the Top-5 predicted areas containing next locations amount to an 80% accuracy. The high accuracy is of great value for recommendation and prediction on areal unit. Numéro de notice : A2021-268 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1808896 date de publication en ligne : 26/08/2020 En ligne : https://doi.org/10.1080/13658816.2020.1808896 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97324 [article]

Utilizing urban geospatial data to understand heritage attractiveness in Amsterdam / Sevim Sezi Karayazi in ISPRS International journal of geo-information, vol 10 n° 4 (April 2021)  

Public [article]  inISPRS International journal of geo-information > vol 10 n° 4 (April 2021) . - n° 198 Titre : Utilizing urban geospatial data to understand heritage attractiveness in Amsterdam Type de document : Article/Communication Auteurs : Sevim Sezi Karayazi, Auteur ; Gamze Dane, Auteur ; Bauke de Vries, Auteur Année de publication : 2021 Article en page(s) : n° 198 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes descripteurs IGN] Amsterdam [Termes descripteurs IGN] analyse de groupement [Termes descripteurs IGN] analyse spatiale [Termes descripteurs IGN] attractivité (aménagement) [Termes descripteurs IGN] données issues des réseaux sociaux [Termes descripteurs IGN] données localisées des bénévoles [Termes descripteurs IGN] gestion durable [Termes descripteurs IGN] image Flickr [Termes descripteurs IGN] musée [Termes descripteurs IGN] patrimoine [Termes descripteurs IGN] point d'intérêt [Termes descripteurs IGN] régression géographiquement pondérée [Termes descripteurs IGN] tourisme Résumé : (auteur) Touristic cities are home to historical landmarks and irreplaceable urban heritages. Although tourism brings financial advantages, mass tourism creates pressure on historical cities. Therefore, “attractiveness” is one of the key elements to explain tourism dynamics. User-contributed and geospatial data provide an evidence-based understanding of people’s responses to these places. In this article, the combination of multisource information about national monuments, supporting products (i.e., attractions, museums), and geospatial data are utilized to understand attractive heritage locations and the factors that make them attractive. We retrieved geotagged photographs from the Flickr API, then employed density-based spatial clustering of applications with noise (DBSCAN) algorithm to find clusters. Then combined the clusters with Amsterdam heritage data and processed the combined data with ordinary least square (OLS) and geographically weighted regression (GWR) to identify heritage attractiveness and relevance of supporting products in Amsterdam. The results show that understanding the attractiveness of heritages according to their types and supporting products in the surrounding built environment provides insights to increase unattractive heritages’ attractiveness. That may help diminish the burden of tourism in overly visited locations. The combination of less attractive heritage with strong influential supporting products could pave the way for more sustainable tourism in Amsterdam. Numéro de notice : A2021-304 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi10040198 date de publication en ligne : 25/03/2021 En ligne : https://doi.org/10.3390/ijgi10040198 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97424 [article]

A GIS-based system for spatial-temporal availability evaluation of the open spaces used as emergency shelters: The case of Victoria, British Columbia, Canada / Yibing Yao in ISPRS International journal of geo-information, vol 10 n° 2 (February 2021)  

Public [article]  inISPRS International journal of geo-information > vol 10 n° 2 (February 2021) . - n° 63 Titre : A GIS-based system for spatial-temporal availability evaluation of the open spaces used as emergency shelters: The case of Victoria, British Columbia, Canada Type de document : Article/Communication Auteurs : Yibing Yao, Auteur ; Yuyang Zhang, Auteur ; Taoyu Yao, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 63 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG [Termes descripteurs IGN] analyse multicritère [Termes descripteurs IGN] cartographie d'urgence [Termes descripteurs IGN] Colombie-Britannique (Canada) [Termes descripteurs IGN] données spatiotemporelles [Termes descripteurs IGN] effondrement de terrain [Termes descripteurs IGN] planification stratégique [Termes descripteurs IGN] point d'intérêt [Termes descripteurs IGN] répartition géographique [Termes descripteurs IGN] secours d'urgence [Termes descripteurs IGN] sécurité civile [Termes descripteurs IGN] séisme [Termes descripteurs IGN] tsunami [Termes descripteurs IGN] zone urbaine Résumé : (auteur) Canadian emergency management planners have historically ignored the self-motivated evacuation procedures of people who cannot initially choose the safest evacuation areas. In densely developed urban areas, open spaces can be seen as ideal evacuation areas and should thus be included in shelter planning. In this study, the public open spaces in Great Victoria were selected as the study area and evaluated using GIS technologies. A multi-criteria TOPSIS evaluation model was used to conduct comprehensive quantitative evaluations of the open spaces’ safety, accessibility, and availability. Through hybrid process, service area, and POI aggregation coupling analyses, a model is created that provides an overall evaluation at the district level. In addition to providing a model for evaluating open spaces as emergency shelters, applicable to most Canadian cities, this study emphasizes the importance and disadvantages of open space emergency shelters in Canada, which have heretofore been ignored by decision makers. In Great Victoria, we found that the distribution of open spaces does not match the dynamics of the population distribution, meaning that through inadequate preparation some districts lack a safe evacuation place—this in an area where people are at high risk of earthquake disasters and their subsequent effects. Numéro de notice : A2021-150 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi10020063 date de publication en ligne : 02/02/2021 En ligne : https://doi.org/10.3390/ijgi10020063 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97061 [article]

Joint promotion partner recommendation systems using data from location-based social networks / Yi-Chung Chen in ISPRS International journal of geo-information, vol 10 n° 2 (February 2021)  

Public [article]  inISPRS International journal of geo-information > vol 10 n° 2 (February 2021) . - n° 57 Titre : Joint promotion partner recommendation systems using data from location-based social networks Type de document : Article/Communication Auteurs : Yi-Chung Chen, Auteur ; Hsi-Ho Huang, Auteur ; Sheng-Min Chiu, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 57 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web [Termes descripteurs IGN] contenu généré par les utilisateurs [Termes descripteurs IGN] Facebook [Termes descripteurs IGN] Foursquare [Termes descripteurs IGN] géomercatique [Termes descripteurs IGN] New York (Etats-Unis ; ville) [Termes descripteurs IGN] point d'intérêt [Termes descripteurs IGN] politique commerciale [Termes descripteurs IGN] réseau social géodépendant Résumé : (auteur) Joint promotion is a valuable business strategy that enables companies to attract more customers at lower operational cost. However, finding a suitable partner can be extremely difficult. Conventionally, one of the most common approaches is to conduct survey-based analysis; however, this method can be unreliable as well as time-consuming, considering that there are likely to be thousands of potential partners in a city. This article proposes a framework to recommend Joint Promotion Partners using location-based social networks (LBSN) data. We considered six factors in determining the suitability of a partner (customer base, association, rating and awareness, prices and star ratings, distance, and promotional strategy) and developed efficient algorithms to perform the required calculations. The effectiveness and efficiency of our algorithms were verified using the Foursquare dataset and real-life case studies. Numéro de notice : A2021-152 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi10020057 date de publication en ligne : 30/01/2021 En ligne : https://doi.org/10.3390/ijgi10020057 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97063 [article]

A points of interest matching method using a multivariate weighting function with gradient descent optimization / Zhou Yang in Transactions in GIS, Vol 25 n° 1 (February 2021)  

Public [article]  inTransactions in GIS > Vol 25 n° 1 (February 2021) . - pp 359 - 381 Titre : A points of interest matching method using a multivariate weighting function with gradient descent optimization Type de document : Article/Communication Auteurs : Zhou Yang, Auteur ; Mingjun Wang, Auteur ; Chen Zhang, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 359 - 381 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées [Termes descripteurs IGN] algorithme du gradient [Termes descripteurs IGN] appariement automatique [Termes descripteurs IGN] appariement de données localisées [Termes descripteurs IGN] apprentissage automatique [Termes descripteurs IGN] données localisées des bénévoles [Termes descripteurs IGN] données multisources [Termes descripteurs IGN] exploration de données [Termes descripteurs IGN] intégration de données [Termes descripteurs IGN] point d'intérêt [Termes descripteurs IGN] pondération [Termes descripteurs IGN] qualité des données Résumé : (Auteur) Volunteered geographic information contains abundant valuable data, which can be applied to various spatiotemporal geographical analyses. While the useful information may be distributed in different, low‐quality data sources, this issue can be solved by data integration. Generally, the primary task of integration is data matching. Unfortunately, due to the complexity and irregularities of multi‐source data, existing studies have found it difficult to efficiently establish the correspondence between different sources. Therefore, we present a multi‐stage method to match multi‐source data using points of interest. A spatial filter is constructed to obtain candidate sets for geographical entities. The weights of non‐spatial characteristics are examined by a machine learning‐related algorithm with artificially labeled random samples. A case study on Fuzhou reveals that an average of 95% of instances are accurately matched. Thus, our study provides a novel solution for researchers who are engaged in data mining and related work to accurately match multi‐source data via knowledge obtained by the idea and methods of machine learning. Numéro de notice : A2021-189 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12690 date de publication en ligne : 05/10/2020 En ligne : https://doi.org/10.1111/tgis.12690 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97158 [article]

Incorporating memory-based preferences and point-of-interest stickiness into recommendations in location-based social networks / Hang Zhang in ISPRS International journal of geo-information, vol 10 n° 1 (January 2021)  

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Semantic enrichment of secondary activities using smart card data and point of interests: a case study in London / Nilufer Sari Aslam in Annals of GIS, vol 27 n° 1 (January 2021)  

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Semantic trajectory segmentation based on change-point detection and ontology / Yuan Gao in International journal of geographical information science IJGIS, vol 34 n° 12 (December 2020)  

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Follow the road: historical GIS for evaluating the development of routes in the Negev region during the twentieth century / Motti Zohar in Cartography and Geographic Information Science, vol 47 n° 6 (October 2020)  

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A graph convolutional network model for evaluating potential congestion spots based on local urban built environments / Kun Qin in Transactions in GIS, Vol 24 n° 5 (October 2020)  

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Machine‐learning prediction models for pedestrian traffic flow levels: Towards optimizing walking routes for blind pedestrians / Achituv Cohen in Transactions in GIS, Vol 24 n° 5 (October 2020)  

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Impact of extreme weather events on urban human flow: A perspective from location-based service data / Zhenhua Chen in Computers, Environment and Urban Systems, vol 83 (September 2020)  

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Measuring accessibility of bus system based on multi-source traffic data / Yufan Zuo in Geo-spatial Information Science, vol 23 n° 3 (September 2020)  

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A name‐led approach to profile urban places based on geotagged Twitter data / Juntao Lai in Transactions in GIS, Vol 24 n° 4 (August 2020)  

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Tourism land use simulation for regional tourism planning using POIs and cellular automata / Hong Shi in Transactions in GIS, Vol 24 n° 4 (August 2020)  

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Objets connectés et mobilité urbaine : visualiser les déplacements des usagers de Twitter avec des graphes dynamiques / Françoise Lucchini in Mappemonde [en ligne], n° 128 (juillet 2020)  

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Developing shopping and dining walking indices using POIs and remote sensing data / Yingbin Deng in ISPRS International journal of geo-information, vol 9 n° 6 (June 2020)  

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Estimating and interpreting fine-scale gridded population using random forest regression and multisource data / Yun Zhou in ISPRS International journal of geo-information, vol 9 n° 6 (June 2020)  

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Extracting activity patterns from taxi trajectory data: a two-layer framework using spatio-temporal clustering, Bayesian probability and Monte Carlo simulation / Shuhui Gong in International journal of geographical information science IJGIS, vol 34 n° 6 (June 2020)  

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Sketch maps for searching in spatial data / Ali Zare Zardiny in Transactions in GIS, Vol 24 n° 3 (June 2020)  

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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)  

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A comprehensive framework for studying diffusion patterns of imported dengue with individual-based movement data / Haiyan Tao in International journal of geographical information science IJGIS, vol 34 n° 3 (March 2020)  

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A framework for extracting urban functional regions based on multiprototype word embeddings using points-of-interest data / Sheng Hu in Computers, Environment and Urban Systems, vol 80 (March 2020)  

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Heuristic sample learning for complex urban scenes: Application to urban functional-zone mapping with VHR images and POI data / Xiuyuan Zhang in ISPRS Journal of photogrammetry and remote sensing, vol 161 (March 2020)  

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Urban impervious surface estimation from remote sensing and social data / Yan Yu in Photogrammetric Engineering & Remote Sensing, PERS, vol 84 n° 12 (December 2018)  

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A 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)  

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Aggregate keyword nearest neighbor queries on road networks / Pengfei Zhang in Geoinformatica [en ligne], vol 22 n° 2 (April 2018)  

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Graph-based matching of points-of-interest from collaborative geo-datasets / Tessio Novack in ISPRS International journal of geo-information, vol 7 n° 3 (March 2018)  

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Navigation des personnes aux moyens des technologies des smartphones et des données d’environnements cartographiés / Fadoua Taia Alaoui (2018)  

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Suivi d’objet par capteurs visuels et inertiels sur systèmes embarqués / Imane Salhi (2018)    

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Popularity-aware collective keyword queries in road networks / Sen Zhao in Geoinformatica [en ligne], vol 21 n° 3 (July - September 2017)  

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Extracting urban functional regions from points of interest and human activities on location-based social networks / Song Gao in Transactions in GIS, vol 21 n° 3 (June 2017)  

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Information extraction and visualization from twitter considering spatial structure / Hideyuki Fujita in Cartographica, vol 52 n° 2 (Summer 2017)  

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Mapping fine-scale population distributions at the building level by integrating multisource geospatial big data / Yao Yao in International journal of geographical information science IJGIS, vol 31 n° 5-6 (May-June 2017)  

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Recherche des objets mobiles dans les réseaux routiers : Une approche basée sur l’analyse formelle de concepts / Hafedh Ferchichi in Revue internationale de géomatique, vol 27 n° 2 (avril - juin 2017)  

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Assessing crowdsourced POI quality: combining methods based on reference data, history, and spatial relations / Guillaume Touya in ISPRS International journal of geo-information, vol 6 n° 3 (March 2017)  

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Adaptive and optimal combination of local features for image retrieval / Neelanjan Bhowmik (2017)    

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Une deuxième itération du processus photogrammétrique pour améliorer la précision de mise en place des images / Truong Giang Nguyen (2017)  

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Exploiting location-aware social networks for efficient spatial query processing / Liang Tang in Geoinformatica [en ligne], vol 21 n° 1 (January - March 2017)  

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Recherche multi-descripteurs dans les fonds photographiques numérisés / Neelanjan Bhowmik (2017)  

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Spatial co-location pattern mining of facility points-of-interest improved by network neighborhood and distance decay effects / Wenhao Yu in International journal of geographical information science IJGIS, vol 31 n° 1-2 (January - February 2017)  

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Enabling maps/location searches on mobile devices: constructing a POI database via focused crawling and information extraction / Hsiu-Min Chuang in International journal of geographical information science IJGIS, vol 30 n° 7- 8 (July - August 2016)  

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Exploring cell tower data dumps for supervised learning-based point-of-interest prediction (industrial paper) / Ran Wang in Geoinformatica [en ligne], vol 20 n° 2 (April - June 2016)  

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Evaluation of SIFT and SURF for vision based localization / Xiaozhi Qu (2016)    

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Joining spatial distribution visualisation tools with social media data using free and open source software : extended abstract / Mayra Zurbaran (2016) 

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Mapping the impacts of Iceland's Katla subglacial volcano on the Mýrdalsjökull glacier / Chelsi A. McNeill-Jewer in Cartographica, vol 50 n° 3 (Fall 2015)  

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Trajectoires d’objets mobiles dans un espace support fixe / Elodie Buard in Revue internationale de géomatique, vol 25 n° 3 (septembre - novembre 2015)  

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Creating geo-enabled hand-drawn maps: an experiment of user-generated mobile mapping / Min Lu in International journal of cartography, vol 1 n° 1 (August 2015)  

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Querying visible points in large obstructed space / Jianqiu Xu in Geoinformatica [en ligne], vol 19 n° 3 (July - September 2015)  

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POI Pulse: A multi-granular, semantic signature–based information observatory for the interactive visualization of big geosocial data / Grant McKenzie in Cartographica, vol 50 n° 2 (Summer 2015)  

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