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An overview of clustering methods for geo-referenced time series: from one-way clustering to co- and tri-clustering / Xiaojing Wu in International journal of geographical information science IJGIS, vol 34 n° 9 (September 2020)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 34 n° 9 (September 2020) . - pp 1822 - 1848 Titre : An overview of clustering methods for geo-referenced time series: from one-way clustering to co- and tri-clustering Type de document : Article/Communication Auteurs : Xiaojing Wu, Auteur ; Changxiu Cheng, Auteur ; Raul Zurita-Milla, Auteur Année de publication : 2020 Article en page(s) : pp 1822 - 1848 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse de groupement [Termes IGN] analyse spatio-temporelle [Termes IGN] classification barycentrique [Termes IGN] classification par nuées dynamiques [Termes IGN] exploration de données [Termes IGN] géoréférencement [Termes IGN] modélisation spatio-temporelle [Termes IGN] regroupement de données [Termes IGN] série temporelle [Termes IGN] taxinomie Résumé : (auteur) Even though many studies have shown the usefulness of clustering for the exploration of spatio-temporal patterns, until now there is no systematic description of clustering methods for geo-referenced time series (GTS) classified as one-way clustering, co-clustering and tri-clustering methods. Moreover, the selection of a suitable clustering method for a given dataset and task remains to be a challenge. Therefore, we present an overview of existing clustering methods for GTS, using the aforementioned classification, and compare different methods to provide suggestions for the selection of appropriate methods. For this purpose, we define a taxonomy of clustering-related geographical questions and compare the clustering methods by using representative algorithms and a case study dataset. Our results indicate that tri-clustering methods are more powerful in exploring complex patterns at the cost of additional computational effort, whereas one-way clustering and co-clustering methods yield less complex patterns and require less running time. However, the selection of the most suitable method should depend on the data type, research questions, computational complexity, and the availability of the methods. Finally, the described classification can include novel clustering methods, thereby enabling the exploration of more complex spatio-temporal patterns. Numéro de notice : A2020-477 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1726922 Date de publication en ligne : 16/02/2020 En ligne : https://doi.org/10.1080/13658816.2020.1726922 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95624 [article]

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Co-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)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 29 n° 4 (April 2015) . - pp 624 - 642 Titre : Co-clustering geo-referenced time series: exploring spatio-temporal patterns in Dutch temperature data Type de document : Article/Communication Auteurs : Xiaojing Wu, Auteur ; Raul Zurita-Milla, Auteur ; Menno-Jan Kraak, Auteur Année de publication : 2015 Article en page(s) : pp 624 - 642 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse de groupement [Termes IGN] analyse spatio-temporelle [Termes IGN] exploration de données géographiques [Termes IGN] regroupement de données [Termes IGN] série temporelle [Termes IGN] température de l'air [Termes IGN] visualisation de données Résumé : (Auteur) Clustering allows considering groups of similar data elements at a higher level of abstraction. This facilitates the extraction of patterns and useful information from large amounts of spatio-temporal data. Till now, most studies have focused on the extraction of patterns from a spatial or a temporal aspect. Here we use the Bregman block average co-clustering algorithm with I-divergence (BBAC_I) to enable the simultaneous analysis of spatial and temporal patterns in geo-referenced time series (time evolving values of a property observed at fixed geographical locations). In addition, we present three geovisualization techniques to fully explore the co-clustering results: heatmaps offer a straightforward overview of the results; small multiples display the spatial and temporal patterns in geographic maps; ringmaps illustrate the temporal patterns associated to cyclic timestamps. To illustrate this study, we used Dutch daily average temperature data collected at 28 weather stations from 1992 to 2011. The co-clustering algorithm was applied hierarchically to understand the spatio-temporal patterns found in the data at the yearly, monthly and daily resolutions. Results pointed out that there is a transition in temperature patterns from northeast to southwest and from ‘cold’ to ‘hot’ years/months/days with only 3 years belonging to ‘cool’ or ‘cold’ years. Because of its characteristics, this newly introduced algorithm can concurrently analyse spatial and temporal patterns by identifying location-timestamp co-clusters that contain values that are similar along both the spatial and the temporal dimensions. Numéro de notice : A2015-590 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2014.994520 En ligne : http://www.tandfonline.com/doi/full/10.1080/13658816.2014.994520 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=77876 [article]

Visual discovery of synchronisation in weather data at multiple temporal resolutions / Xiaojing Wu in Cartographic journal (the), vol 50 n° 3 (August 2013)  

Public [article]  inCartographic journal (the) > vol 50 n° 3 (August 2013) . - pp 247 - 256 Titre : Visual discovery of synchronisation in weather data at multiple temporal resolutions Type de document : Article/Communication Auteurs : Xiaojing Wu, Auteur ; Raul Zurita-Milla, Auteur ; Menno-Jan Kraak, Auteur Année de publication : 2013 Article en page(s) : pp 247 - 256 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse diachronique [Termes IGN] calcul matriciel [Termes IGN] carte de Kohonen [Termes IGN] distribution spatiale [Termes IGN] données hétérogènes [Termes IGN] données météorologiques [Termes IGN] données multitemporelles [Termes IGN] série temporelle [Termes IGN] synchronisation Résumé : (Auteur) Analysing spatio-temporal weather patterns is fundamental to better understand the system Earth. Such patterns depend on the spatial and temporal resolution of the available data. Here, we study a particular spatio-temporal pattern, namely, synchronisation, and how this is affected by different temporal resolutions and temporal heterogeneity. Twenty years of daily temperature data collected in 28 Dutch meteorological stations are used as case study. Given the complexity of the analysis, we propose a geovisual analytic approach based on self-organizing maps (SOMs). This approach allows exploring the data from two perspectives: (1) station-based, in which spatially synchronous weather stations are grouped into clusters; and (2) year-based, in which temporal synchronisation is analysed using a calendar year as basic unit and similar years are clustered. Clusters are identified using the SOM U-matrices and maps. Next, the spatial distribution of synchronous stations is displayed in the geographic space. Trend plots are used to illustrate trends in every cluster and the temperatures of stations and years are compared with the corresponding cluster representative values to identify anomalies in the temperature records. The analysis is repeated at daily, weekly and monthly resolutions to study the effects of different temporal resolutions on synchronisation. Also daily spatial synchronisation results for all years with those for groups of daily synchronous years are analysed to study the effects of temporal heterogeneity. Results show that synchronisation results are different at different temporal resolutions. Monthly results are the most stable ones both in station-based and year-based. It is also observed that spatial synchronisation results are simplified when considering temporal heterogeneity. Numéro de notice : A2013-464 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1179/1743277413Y.0000000067 En ligne : https://doi.org/10.1179/1743277413Y.0000000067 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32602 [article]

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