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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]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
079-2020091	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Interactive discovery of sequential patterns in time series of wind data / Norhakim Yusof in International journal of geographical information science IJGIS, vol 30 n° 7- 8 (July - August 2016)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 30 n° 7- 8 (July - August 2016) . - pp 1486 - 1506 Titre : Interactive discovery of sequential patterns in time series of wind data Type de document : Article/Communication Auteurs : Norhakim Yusof, Auteur ; Raul Zurita-Milla, Auteur ; Menno-Jan Kraak, Auteur ; Bas Retsios, Auteur Année de publication : 2016 Article en page(s) : pp 1486 - 1506 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG [Termes IGN] carte interactive [Termes IGN] direction [Termes IGN] météorologie [Termes IGN] vent [Termes IGN] visualisation de données statistiques [Termes IGN] vitesse Résumé : (Auteur) Wind speed and direction vary over space and time due to the interactions between different pressures and temperature gradients within the atmospheric layers. Near the earth’s surface, these interactions are modulated by topography and artificial structures. Hence, characterizing wind behaviour over large areas and long periods is a complex but essential task for various energy-related applications. In this study, we present a novel approach to discover wind patterns by integrating sequential pattern mining and interactive visualization techniques. The approach relies on the use of the Linear time Closed pattern Miner sequence algorithm in conjunction with a time sliding window that allows the discovery of all sequential patterns present in the data. These patterns are then visualized using integrated 2D and 3D coordinated multiple views and visually explored to gain insight into the characteristics of the wind from a spatial, temporal and attribute (type of wind pattern) point of view. This proposed approach is used to analyse 10 years of hourly wind speed and direction data for 29 weather stations in the Netherlands. The results show that there are 15 main sequential patterns in the data. The spatial task shows that weather stations located in the same region do not necessarily experience similar wind pattern. For within the selected time interval, similar wind patterns can be observed in different stations and in the same station at different times of occurrence. The attribute task discovered that the repetitive occurrences of chosen pattern indicate as regular wind behaviour at different weather stations that persisted continuously over time. The results of these tasks show that the proposed interactive discovery facilitates the understanding of wind dynamics in space and time. Numéro de notice : A2016-316 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2015.1135928 En ligne : http://dx.doi.org/10.1080/13658816.2015.1135928 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=80936 [article]

Exemplaires(2)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
079-2016042	RAB	Revue	Centre de documentation	En réserve L003	Disponible
079-2016041	RAB	Revue	Centre de documentation	En réserve L003	Disponible

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]

Using geographically weighted regression kriging for crop yield mapping in West Africa / Muhammad Imran in International journal of geographical information science IJGIS, vol 29 n° 2 (February 2015)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 29 n° 2 (February 2015) . - pp 234 - 257 Titre : Using geographically weighted regression kriging for crop yield mapping in West Africa Type de document : Article/Communication Auteurs : Muhammad Imran, Auteur ; Alfred Stein, Auteur ; Raul Zurita-Milla, Auteur Année de publication : 2015 Article en page(s) : pp 234 - 257 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG [Termes IGN] agriculture [Termes IGN] analyse de données [Termes IGN] Burkina Faso [Termes IGN] carte agricole [Termes IGN] cartographie statistique [Termes IGN] image SPOT-Végétation [Termes IGN] krigeage [Termes IGN] régression géographiquement pondérée [Termes IGN] rendement agricole [Termes IGN] sorgho (céréale) Résumé : (Auteur) Geographical information systems support the application of statistical techniques to map spatially referenced crop data. To do this in the optimal way, errors and uncertainties have to be minimized that are often associated with operations on the data. This paper applies a spatial statistical approach to upscale crop yields from the field level toward the scale of Burkina Faso. Observed yields were related to the Normalized Difference Vegetation Index derived from SPOT-VEGETATION. The objective was to quantify the uncertainties at the subsequent steps. First, we applied a point pattern analysis to examine uncertainties due to the sampling network of field surveys in the country. Second, geographically weighted regression kriging (GWRK) was applied to upscale the yield observations and to quantify the corresponding uncertainty. The proposed method was demonstrated with the mapping of sorghum yields in Burkina Faso and results were compared with those from regression kriging (RK) and kriging with external drift using a local kriging neighborhood (KEDLN). The proposed method was validated with independent yield observations obtained from field surveys. We observed that the lower uncertainty range value increased by 39%, and the upper uncertainty range value decreased by 51%, when comparing GWRK with RK and KEDLN. Moreover, GWRK reduced the prediction error variance as compared to RK (20 vs. 31) and to KEDLN (20 vs. 39). We found that climate and topography had a major impact on the country’s sorghum yields. Further, the financial ability of farmers influenced the crop management and, thus, the sorghum crop yields. We concluded that GWRK effectively utilized information present in the covariate datasets and improved the accuracies of both the regional-scale mapping of sorghum yields and was able to quantify the associated uncertainty. Numéro de notice : A2015-578 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2014.959522 En ligne : http://www.tandfonline.com/doi/full/10.1080/13658816.2014.959522 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=77840 [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]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
030-2013031	RAB	Revue	Centre de documentation	En réserve L003	Disponible