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Towards generating network of bikeways from Mapillary data / Xuan Ding in Computers, Environment and Urban Systems, vol 88 (July 2021)  

Public [article]  inComputers, Environment and Urban Systems > vol 88 (July 2021) . - n° 101632 Titre : Towards generating network of bikeways from Mapillary data Type de document : Article/Communication Auteurs : Xuan Ding, Auteur ; Hongchao Fan, Auteur ; Jianya Gong, Auteur Année de publication : 2021 Article en page(s) : n° 101632 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web [Termes IGN] approche participative [Termes IGN] cycliste [Termes IGN] données localisées des bénévoles [Termes IGN] gestion des itinéraires [Termes IGN] Mapillary [Termes IGN] OpenStreetMap [Termes IGN] Suède [Termes IGN] système d'information géographique Résumé : (auteur) Nowadays, biking is flourishing in many Western cities. While many roads are used for both cars and bicycles, buffered bike lanes are marked for the safety of cyclists. In many cities, segregated paths are built up to have physical separation from motor vehicles. These types of biking ways are regarded as attributes in geographic information system (GIS) data. This information is required and important in the service of route planning, as cyclists may prefer certain types of bikeways. This paper presents a framework for generating networks of bikeways with attribute information from the data collected on the collaborative street view data platform Mapillary. The framework consists of two layers: The first layer focuses on constructing a bikeway road network using Global Positioning System (GPS) information of Mapillary images. Mapillary sequences are classified into walking, cycling, driving (ordinary road), and driving (motorway) trajectories based on the transportation mode with a trained XGBoost classifier. The bikeway road network is then extracted from cycling and driving (ordinary road) trajectories using a raster-based method. The second layer focuses on extracting attribute information from Mapillary images. Cycling-specific information (i.e., bicycle signs/markings) is extracted using a two-stage detection and classification model. A series of quantitative evaluations based on a case study demonstrated the ability and potential of the framework for extracting bikeway road information to enrich the existing OSM cycling road data. Numéro de notice : A2021-432 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1016/j.compenvurbsys.2021.101632 date de publication en ligne : 17/04/2021 En ligne : https://doi.org/10.1016/j.compenvurbsys.2021.101632 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97798 [article]

Constructing and analyzing spatial-social networks from location-based social media data / Xuebin Wei in Cartography and Geographic Information Science, vol 48 n° 3 (May 2021)  

Public [article]  inCartography and Geographic Information Science > vol 48 n° 3 (May 2021) . - pp 258 - 274 Titre : Constructing and analyzing spatial-social networks from location-based social media data Type de document : Article/Communication Auteurs : Xuebin Wei, Auteur ; Xiaobai Yao, Auteur Année de publication : 2021 Article en page(s) : pp 258 - 274 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse de données [Termes IGN] collecte de données [Termes IGN] données issues des réseaux sociaux [Termes IGN] données spatiotemporelles [Termes IGN] Facebook [Termes IGN] géolocalisation [Termes IGN] réseau social géodépendant [Termes IGN] santé [Termes IGN] système d'information géographique [Termes IGN] urbanisme Résumé : (auteur) People interact with each other in space and time. Improved understanding of human interactions in spatial, temporal, and social dimensions are highly beneficial for research and practices in public health, urban planning, and other fields. Traditional methods of collecting social interaction data are time-intensive and resource-consuming, resulting in relatively small sample sizes and limited information. Furthermore, traditional methods often oversimplify the dynamics of human interactions and fail to capture the characteristics of places where the interactions occur. With the popularity of location-based social media (LBSM) platforms, people can publish information about their social events such as time, location, and other participants. This research introduces a framework that formalizes terminologies and concepts related to spatial-social connections for the construction of spatial-social networks from LBSM data in GIS. Supported by the framework, the study presents methods of collecting, analyzing, and visualizing LBSM data in spatial-social dimensions. The methods are implemented and tested in a case study with Facebook data. The case study demonstrates that location-based social media data can be transformed into spatial-social networks and then be analyzed and visualized to answer innovative types of scientific inquiries. Numéro de notice : A2021-316 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/SOCIETE NUMERIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/15230406.2021.1891974 date de publication en ligne : 09/04/2021 En ligne : https://doi.org/10.1080/15230406.2021.1891974 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97538 [article]

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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 IGN] contenu généré par les utilisateurs [Termes IGN] Facebook [Termes IGN] Foursquare [Termes IGN] géomercatique [Termes IGN] New York (Etats-Unis ; ville) [Termes IGN] point d'intérêt [Termes IGN] politique commerciale [Termes 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]

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)  

Public [article]  inISPRS International journal of geo-information > vol 10 n° 1 (January 2021) . - n° 10 Titre : Incorporating memory-based preferences and point-of-interest stickiness into recommendations in location-based social networks Type de document : Article/Communication Auteurs : Hang Zhang, Auteur ; Mingxin Gan, Auteur ; Xi Sun, Auteur Année de publication : 2021 Article en page(s) : n° 10 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web [Termes IGN] approche participative [Termes IGN] comportement [Termes IGN] filtrage d'information [Termes IGN] interprétation (psychologie) [Termes IGN] mémoire [Termes IGN] mobilité [Termes IGN] point d'intérêt [Termes IGN] réseau social géodépendant [Termes IGN] tourisme Résumé : (auteur) In location-based social networks (LBSNs), point-of-interest (POI) recommendations facilitate access to information for people by recommending attractive locations they have not previously visited. Check-in data and various contextual factors are widely taken into consideration to obtain people’s preferences regarding POIs in existing POI recommendation methods. In psychological effect-based POI recommendations, the memory-based attenuation of people’s preferences with respect to POIs, e.g., the fact that more attention is paid to POIs that were checked in to recently than those visited earlier, is emphasized. However, the memory effect only reflects the changes in an individual’s check-in trajectory and cannot discover the important POIs that dominate their mobility patterns, which are related to the repeat-visit frequency of an individual at a POI. To solve this problem, in this paper, we developed a novel POI recommendation framework using people’s memory-based preferences and POI stickiness, named U-CF-Memory-Stickiness. First, we used the memory-based preference-attenuation mechanism to emphasize personal psychological effects and memory-based preference evolution in human mobility patterns. Second, we took the visiting frequency of POIs into consideration and introduced the concept of POI stickiness to identify the important POIs that reflect the stable interests of an individual with respect to their mobility behavior decisions. Lastly, we incorporated the influence of both memory-based preferences and POI stickiness into a user-based collaborative filtering framework to improve the performance of POI recommendations. The results of the experiments we conducted on a real LBSN dataset demonstrated that our method outperformed other methods. Numéro de notice : A2021-148 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi10010036 date de publication en ligne : 15/01/2021 En ligne : https://doi.org/10.3390/ijgi10010036 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97056 [article]

RegNet: a neural network model for predicting regional desirability with VGI data / Wenzhong Shi in International journal of geographical information science IJGIS, vol 35 n° 1 (January 2021)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 35 n° 1 (January 2021) . - pp 175 - 192 Titre : RegNet: a neural network model for predicting regional desirability with VGI data Type de document : Article/Communication Auteurs : Wenzhong Shi, Auteur ; Zhewei Liu, Auteur ; Zhenlin An, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 175 - 192 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] classification par réseau neuronal [Termes IGN] données localisées des bénévoles [Termes IGN] Hong-Kong [Termes IGN] modèle de simulation [Termes IGN] niveau local [Termes IGN] participation du public [Termes IGN] régression [Termes IGN] réseau social géodépendant Résumé : (auteur) Volunteered geographic information can be used to predict regional desirability. A common challenge regarding previous works is that intuitive empirical models, which are inaccurate and bring in perceptual bias, are traditionally used to predict regional desirability. This results from the fact that the hidden interactions between user online check-ins and regional desirability have not been revealed and clearly modelled yet. To solve the problem, a novel neural network model ‘RegNet’ is proposed. The user check-in history is input into a neural network encoder structure firstly for redundancy reduction and feature learning. The encoded representation is then fed into a hidden-layer structure and the regional desirability is predicted. The proposed RegNet is data-driven and can adaptively model the unknown mappings from input to output, without presumed bias and prior knowledge. We conduct experiments with real-world datasets and demonstrate RegNet outperforms state-of-the-art methods in terms of ranking quality and prediction accuracy of rating. Additionally, we also examine how the structure of encoder affects RegNet performance and suggest on choosing proper sizes of encoded representation. This work demonstrates the effectiveness of data-driven methods in modelling the hidden unknown relationships and achieving a better performance over traditional empirical methods. Numéro de notice : A2021-023 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1768261 date de publication en ligne : 18/05/2020 En ligne : https://doi.org/10.1080/13658816.2020.1768261 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96526 [article]

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Los Angeles as a digital place: The geographies of user‐generated content / Andrea Ballatore in Transactions in GIS, Vol 24 n° 4 (August 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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Behavior-based location recommendation on location-based social networks / Seyyed Mohammadreza Rahimi in Geoinformatica [en ligne], vol 24 n° 3 (July 2020)  

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Learning evolving user’s behaviors on location-based social networks / Ruizhi Wu in Geoinformatica [en ligne], vol 24 n° 3 (July 2020)  

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A vélo au travers des Andes, pour OpenStreetMap / Anonyme in Géomatique expert, n° 126 (janvier - février 2019)

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Assessing spatiotemporal predictability of LBSN : a case study of three Foursquare datasets / Ming Li in Geoinformatica [en ligne], vol 22 n° 3 (July 2018)  

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What is so “hot” in heatmap? qualitative code cluster analysis with foursquare venue / Ilyoung Hong in Cartographica, vol 52 n° 4 (Winter 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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Recommendations in location-based social networks: a survey / Jie Bao in Geoinformatica [en ligne], vol 19 n° 3 (July - September 2015)  

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