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STICC: a multivariate spatial clustering method for repeated geographic pattern discovery with consideration of spatial contiguity / Yuhao Kang in International journal of geographical information science IJGIS, vol 36 n° 8 (August 2022)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 36 n° 8 (August 2022) . - pp 1518 - 1549 Titre : STICC: a multivariate spatial clustering method for repeated geographic pattern discovery with consideration of spatial contiguity Type de document : Article/Communication Auteurs : Yuhao Kang, Auteur ; Kunlin Wu, Auteur ; Song Gao, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 1518 - 1549 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse de groupement [Termes IGN] analyse multivariée [Termes IGN] champ aléatoire de Markov [Termes IGN] distribution spatiale [Termes IGN] matrice de covariance [Termes IGN] matrice de Toeplitz [Termes IGN] motif séquentiel [Termes IGN] régionalisation (segmentation) Résumé : (auteur) Spatial clustering has been widely used for spatial data mining and knowledge discovery. An ideal multivariate spatial clustering should consider both spatial contiguity and aspatial attributes. Existing spatial clustering approaches may face challenges for discovering repeated geographic patterns with spatial contiguity maintained. In this paper, we propose a Spatial Toeplitz Inverse Covariance-Based Clustering (STICC) method that considers both attributes and spatial relationships of geographic objects for multivariate spatial clustering. A subregion is created for each geographic object serving as the basic unit when performing clustering. A Markov random field is then constructed to characterize the attribute dependencies of subregions. Using a spatial consistency strategy, nearby objects are encouraged to belong to the same cluster. To test the performance of the proposed STICC algorithm, we apply it in two use cases. The comparison results with several baseline methods show that the STICC outperforms others significantly in terms of adjusted rand index and macro-F1 score. Join count statistics is also calculated and shows that the spatial contiguity is well preserved by STICC. Such a spatial clustering method may benefit various applications in the fields of geography, remote sensing, transportation, and urban planning, etc. Numéro de notice : A2022-591 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2022.2053980 Date de publication en ligne : 30/03/2022 En ligne : https://doi.org/10.1080/13658816.2022.2053980 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101282 [article]

Exploring the uncertainty of activity zone detection using digital footprints with multi-scaled DBSCAN / Xinyi Liu in International journal of geographical information science IJGIS, Vol 33 n° 5-6 (May - June 2019)  

Public [article]  inInternational journal of geographical information science IJGIS > Vol 33 n° 5-6 (May - June 2019) . - pp 1196 - 1223 Titre : Exploring the uncertainty of activity zone detection using digital footprints with multi-scaled DBSCAN Type de document : Article/Communication Auteurs : Xinyi Liu, Auteur ; Qunying Huang, Auteur ; Song Gao, Auteur Année de publication : 2019 Article en page(s) : pp 1196 - 1223 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse de groupement [Termes IGN] données issues des réseaux sociaux [Termes IGN] mobilité urbaine [Termes IGN] réseau social [Termes IGN] Twitter Mots-clés libres : density-based spatial clustering of applications with noise (DBSCAN) Résumé : (Auteur) The density-based spatial clustering of applications with noise (DBSCAN) method is often used to identify individual activity clusters (i.e., zones) using digital footprints captured from social networks. However, DBSCAN is sensitive to the two parameters, eps and minpts. This paper introduces an improved density-based clustering algorithm, Multi-Scaled DBSCAN (M-DBSCAN), to mitigate the detection uncertainty of clusters produced by DBSCAN at different scales of density and cluster size. M-DBSCAN iteratively calibrates suitable local eps and minpts values instead of using one global parameter setting as DBSCAN for detecting clusters of varying densities, and proves to be effective for detecting potential activity zones. Besides, M-DBSCAN can significantly reduce the noise ratio by identifying all points capturing the activities performed in each zone. Using the historic geo-tagged tweets of users in Washington, D.C. and in Madison, Wisconsin, the results reveal that: 1) M-DBSCAN can capture dispersed clusters with low density of points, and therefore detecting more activity zones for each user; 2) A value of 40 m or higher should be used for eps to reduce the possibility of collapsing distinctive activity zones; and 3) A value between 200 and 300 m is recommended for eps while using DBSCAN for detecting activity zones. Numéro de notice : A2019-445 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2018.1563301 Date de publication en ligne : 09/01/2019 En ligne : https://doi.org/10.1080/13658816.2018.1563301 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92781 [article]

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A context-based geoprocessing framework for optimizing meetup location of multiple moving objects along road networks / Shaohua Wang in International journal of geographical information science IJGIS, vol 32 n° 7-8 (July - August 2018)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 32 n° 7-8 (July - August 2018) . - pp 1368 - 1390 Titre : A context-based geoprocessing framework for optimizing meetup location of multiple moving objects along road networks Type de document : Article/Communication Auteurs : Shaohua Wang, Auteur ; Song Gao, Auteur ; Xin Feng, Auteur ; Alan T. Murray, Auteur ; Yuan Zeng Année de publication : 2018 Article en page(s) : pp 1368 - 1390 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG [Termes IGN] arbre-R [Termes IGN] chaîne de traitement [Termes IGN] chemin le plus court, algorithme du [Termes IGN] démonstration de faisabilité [Termes IGN] méthode heuristique [Termes IGN] objet mobile [Termes IGN] optimisation (mathématiques) [Termes IGN] position [Termes IGN] prise en compte du contexte [Termes IGN] réseau routier Résumé : (Editeur) Given different types of constraints on human life, people must make decisions that satisfy social activity needs. Minimizing costs (i.e. distance, time, or money) associated with travel plays an important role in perceived and realized social quality of life. Identifying optimal interaction locations on road networks when there are multiple moving objects (MMO) with space–time constraints remains a challenge. In this research, we formalize the problem of finding dynamic ideal interaction locations for MMO as a spatial optimization model and introduce a context-based geoprocessing heuristic framework to address this problem. As a proof of concept, a case study involving identification of a meetup location for multiple people under traffic conditions is used to validate the proposed geoprocessing framework. Five heuristic methods with regard to efficient shortest-path search space have been tested. We find that the R* tree-based algorithm performs the best with high quality solutions and low computation time. This framework is implemented in a geographic information systems environment to facilitate integration with external geographic contextual information, e.g. temporary road barriers, points of interest, and real-time traffic information, when dynamically searching for ideal meetup sites. The proposed method can be applied in trip planning, carpooling services, collaborative interaction, and logistics management. Numéro de notice : A2018-278 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/13658816.2018.1431838 En ligne : https://doi.org/10.1080/13658816.2018.1431838 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90360 [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 IGN] classification par nuées dynamiques [Termes IGN] connaissance thématique [Termes IGN] Etats-Unis [Termes IGN] point d'intérêt [Termes IGN] problème de Dirichlet [Termes IGN] réseau social géodépendant [Termes IGN] trace GPS [Termes IGN] triangulation de Delaunay [Termes 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 Affiliation des auteurs : non IGN 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]

Metadata topic harmonization and semantic search for linked-data-driven geoportals: A case study using ArcGIS online / Yingjie Hu in Transactions in GIS, vol 19 n° 3 (June 2015)  

Public [article]  inTransactions in GIS > vol 19 n° 3 (June 2015) . - pp 398 - 416 Titre : Metadata topic harmonization and semantic search for linked-data-driven geoportals: A case study using ArcGIS online Type de document : Article/Communication Auteurs : Yingjie Hu, Auteur ; Krzysztof Janowicz, Auteur ; Sathya Prasad, Auteur ; Song Gao, Auteur Année de publication : 2015 Article en page(s) : pp 398 - 416 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] ArcGIS [Termes IGN] diffusion de données [Termes IGN] géoportail [Termes IGN] langage naturel (informatique) [Termes IGN] métadonnées géographiques [Termes IGN] partage de données localisées [Termes IGN] traitement du langage naturel [Termes IGN] web sémantique Résumé : (auteur) Geoportals provide integrated access to geospatial resources, and enable both authorities and the general public to contribute and share data and services. An essential goal of geoportals is to facilitate the discovery of the available resources. Such a process relies heavily on the quality of metadata. While multiple metadata standards have been established, data contributers may adopt different standards when sharing their data via the same geoportal. This is especially the case for user-generated content where various terms and topics can be introduced to describe similar datasets. While this heterogeneity provides a wealth of perspectives, it also complicates resource discovery. With the fast development of the Semantic Web technologies, there is a rise of Linked-Data-driven portals. Although these novel portals open up new ways to organize metadata and retrieve resources, they lack effective semantic search methods. This article addresses the two challenges discussed above, namely the topic heterogeneity brought by multiple metadata standards and the lack of established semantic search in Linked-Data-driven geoportals. To harmonize the metadata topics, we employ a natural language processing method, namely Labeled Latent Dirichlet Allocation (LLDA), and train it using standardized metadata from Data.gov. With respect to semantic search, we construct thematic and geographic matching features from the textual metadata descriptions, and train a regression model via a human participants experiment. We evaluate our methods by examining their performances in addressing the two issues. Finally, we implement a semantics-enabled and Linked-Data-driven prototypical geoportal using a sample dataset from Esri's ArcGIS Online. Numéro de notice : A2015-679 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12151 En ligne : http://dx.doi.org/10.1111/tgis.12151 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=78307 [article]

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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Discovering spatial interaction communities from mobile phone data / Song Gao in Transactions in GIS, vol 17 n° 3 (June 2013)  

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