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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)  

Public [article]  inGeoinformatica [en ligne] > vol 22 n° 3 (July 2018) . - pp 541 - 561 Titre : Assessing spatiotemporal predictability of LBSN : a case study of three Foursquare datasets Type de document : Article/Communication Auteurs : Ming Li, Auteur ; Rene Westerholt, Auteur ; Hongchao Fan, Auteur ; Alexander Zipf, Auteur Année de publication : 2018 Article en page(s) : pp 541 - 561 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes descripteurs IGN] comportement [Termes descripteurs IGN] jeu de données localisées [Termes descripteurs IGN] modèle de simulation [Termes descripteurs IGN] prévision [Termes descripteurs IGN] réseau social géodépendant [Termes descripteurs IGN] ville Résumé : (Auteur) Location-based social networks (LBSN) have provided new possibilities for researchers to gain knowledge about human spatiotemporal behavior, and to make predictions about how people might behave through space and time in the future. An important requirement of successfully utilizing LBSN in these regards is a thorough understanding of the respective datasets, including their inherent potential as well as their limitations. Specifically, when it comes to predictions, we must know what we can actually expect from the data, and how we could maximize their usefulness. Yet, this knowledge is still largely lacking from the literature. Hence, this work explores one particular aspect which is the theoretical predictability of LBSN datasets. The uncovered predictability is represented with an interval. The lower bound of the interval corresponds to the amount of regular behaviors that can easily be anticipated, and represents the correct predication rate that any algorithm should be able to achieve. The upper bound corresponds to the amount of information that is contained in the dataset, and represents the maximum correct prediction rate that cannot be exceeded by any algorithms. Three Foursquare datasets from three American cities are studied as an example. It is found that, within our investigated datasets, the lower bound of predictability of the human spatiotemporal behavior is 27%, and the upper bound is 92%. Hence, the inherent potentials of the dataset for predicting human spatiotemporal behavior are clarified, and the revealed interval allows a realistic assessment of the quality of predictions and thus of associated algorithms. Additionally, in order to provide further insight into the practical use of the dataset, the relationship between the predictability and the check-in frequencies are investigated from three different perspectives. It was found that the individual perspective provides no significant correlations between the predictability and the check-in frequency. In contrast, the same two quantities are found to be negatively correlated from temporal and spatial perspectives. Our study further indicates that the heavily frequented contexts and some extraordinary geographic features such as airports could be good starting points for effective improvements of prediction algorithms. In general, this research provides novel knowledge regarding the nature of the LBSN dataset and practical insights for a more reasonable utilization of the dataset. Numéro de notice : A2018-349 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s10707-016-0279-5 date de publication en ligne : 25/11/2016 En ligne : https://doi.org/10.1007/s10707-016-0279-5 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90758 [article]

What is so “hot” in heatmap? qualitative code cluster analysis with foursquare venue / Ilyoung Hong in Cartographica, vol 52 n° 4 (Winter 2017)  

Public [article]  inCartographica > vol 52 n° 4 (Winter 2017) . - pp 332 - 348 Titre : What is so “hot” in heatmap? qualitative code cluster analysis with foursquare venue Type de document : Article/Communication Auteurs : Ilyoung Hong, Auteur ; Jin-Kyu Jung, Auteur Année de publication : 2017 Article en page(s) : pp 332 - 348 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] analyse spatiale [Termes descripteurs IGN] carte de densité de clics [Termes descripteurs IGN] distribution spatiale [Termes descripteurs IGN] réseau social géodépendant [Termes descripteurs IGN] Seattle (Washington) [Vedettes matières IGN] Géovisualisation Résumé : (Auteur) Foursquare is a popular Web service and a representative location-based social network (LBSN) service using position data. Heatmap is a widely used means of geovisualization for analyzing social data with locational values. Until now, heatmap analysis of LBSN has focused on identifying quantitative distribution and patterns, with little consideration of the qualitative analysis of data content. Based on a case study of Foursquare venues and user-created content in Seattle, WA, this study conducts analyses assessing both the quantitative spatial distribution and the qualitative characteristics of coffee shops in the Seattle metropolitan area. It specifically proposes a new analytical method referred to as “code cluster,” which is designed to employ quantitative and qualitative approaches simultaneously. The significance of this method is its capacity to explain geographical differences in terms of qualitative traits in cluster regions, in addition to analyzing their spatial characteristics and distributions. In introducing this new hybrid approach, our aims are to reflect the original intent and essence of the data throughout the research process and to make further efforts to analyze and interpret the contextualized meanings. This will be possible through integration of advanced spatial analysis, geovisualization, and qualitative research that build on current geographic and geovisual research with big data. Numéro de notice : A2017-830 Thématique : GEOMATIQUE Nature : Article DOI : 10.3138/cart.52.4.2016-0005 En ligne : https://doi.org/10.3138/cart.52.4.2016-0005 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89364 [article]

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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)  

Public [article]  inTransactions in GIS > vol 21 n° 3 (June 2017) . - pp 446 - 467 Titre : Extracting urban functional regions from points of interest and human activities on location-based social networks Type de document : Article/Communication Auteurs : Song Gao, Auteur ; Krzysztof Janowicz, Auteur ; Helen Couclelis, Auteur Année de publication : 2017 Article en page(s) : pp 446 - 467 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes descripteurs IGN] classification par nuées dynamiques [Termes descripteurs IGN] connaissance thématique [Termes descripteurs IGN] Etats-Unis [Termes descripteurs IGN] point d'intérêt [Termes descripteurs IGN] problème de Dirichlet [Termes descripteurs IGN] réseau social géodépendant [Termes descripteurs IGN] trace GPS [Termes descripteurs IGN] triangulation de Delaunay [Termes descripteurs IGN] zone urbaine Résumé : (Auteur) Data about points of interest (POI) have been widely used in studying urban land use types and for sensing human behavior. However, it is difficult to quantify the correct mix or the spatial relations among different POI types indicative of specific urban functions. In this research, we develop a statistical framework to help discover semantically meaningful topics and functional regions based on the co-occurrence patterns of POI types. The framework applies the latent Dirichlet allocation (LDA) topic modeling technique and incorporates user check-in activities on location-based social networks. Using a large corpus of about 100,000 Foursquare venues and user check-in behavior in the 10 most populated urban areas of the US, we demonstrate the effectiveness of our proposed methodology by identifying distinctive types of latent topics and, further, by extracting urban functional regions using K-means clustering and Delaunay triangulation spatial constraints clustering. We show that a region can support multiple functions but with different probabilities, while the same type of functional region can span multiple geographically non-adjacent locations. Since each region can be modeled as a vector consisting of multinomial topic distributions, similar regions with regard to their thematic topic signatures can be identified. Compared with remote sensing images which mainly uncover the physical landscape of urban environments, our popularity-based POI topic modeling approach can be seen as a complementary social sensing view on urban space based on human activities. Numéro de notice : A2017-623 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12289 En ligne : http://dx.doi.org/10.1111/tgis.12289 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86938 [article]

Recommendations in location-based social networks: a survey / Jie Bao in Geoinformatica [en ligne], vol 19 n° 3 (July - September 2015)  

Public [article]  inGeoinformatica [en ligne] > vol 19 n° 3 (July - September 2015) . - pp 525 - 565 Titre : Recommendations in location-based social networks: a survey Type de document : Article/Communication Auteurs : Jie Bao, Auteur ; David Wilkie, Auteur ; Mohamed Mokbe, Auteur Année de publication : 2015 Article en page(s) : pp 525 - 565 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web [Termes descripteurs IGN] analyse comparative [Termes descripteurs IGN] analyse de données [Termes descripteurs IGN] géoétiquetage [Termes descripteurs IGN] performance [Termes descripteurs IGN] positionnement automatique [Termes descripteurs IGN] réseau social géodépendant [Termes descripteurs IGN] source de données [Termes descripteurs IGN] système de recommandation [Termes descripteurs IGN] utilisateur Résumé : (auteur) Recent advances in localization techniques have fundamentally enhanced social networking services, allowing users to share their locations and location-related contents, such as geo-tagged photos and notes. We refer to these social networks as location-based social networks (LBSNs). Location data bridges the gap between the physical and digital worlds and enables a deeper understanding of users’ preferences and behavior. This addition of vast geo-spatial datasets has stimulated research into novel recommender systems that seek to facilitate users’ travels and social interactions. In this paper, we offer a systematic review of this research, summarizing the contributions of individual efforts and exploring their relations. We discuss the new properties and challenges that location brings to recommender systems for LBSNs. We present a comprehensive survey analyzing 1) the data source used, 2) the methodology employed to generate a recommendation, and 3) the objective of the recommendation. We propose three taxonomies that partition the recommender systems according to the properties listed above. First, we categorize the recommender systems by the objective of the recommendation, which can include locations, users, activities, or social media. Second, we categorize the recommender systems by the methodologies employed, including content-based, link analysis-based, and collaborative filtering-based methodologies. Third, we categorize the systems by the data sources used, including user profiles, user online histories, and user location histories. For each category, we summarize the goals and contributions of each system and highlight the representative research effort. Further, we provide comparative analysis of the recommender systems within each category. Finally, we discuss the available data-sets and the popular methods used to evaluate the performance of recommender systems. Finally, we point out promising research topics for future work. This article presents a panorama of the recommender systems in location-based social networks with a balanced depth, facilitating research into this important research theme. Numéro de notice : A2015-497 Thématique : GEOMATIQUE Nature : Article En ligne : http://link.springer.com/article/10.1007/s10707-014-0220-8/fulltext.html Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=77309 [article]