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A quantitative comparison of regionalization methods / Orhun Aydun in International journal of geographical information science IJGIS, vol 35 n° 11 (November 2021)
[article]
Titre : A quantitative comparison of regionalization methods Type de document : Article/Communication Auteurs : Orhun Aydun, Auteur ; Mark V. Janikas, Auteur ; Renato Martins Assuncao, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 2287 - 2315 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] analyse de groupement
[Termes IGN] données localisées
[Termes IGN] écorégion
[Termes IGN] exploration de données
[Termes IGN] partition d'image
[Termes IGN] partitionnement
[Termes IGN] segmentation en régionsRésumé : (auteur) Regionalization is the task of partitioning a set of contiguous areas into spatial clusters or regions. The theoretical and empirical literature focusing on regionalization is extensive, yet few quantitative comparisons have been conducted. We present a simulation study and explore the quality of frequently used and state-of-the-art regionalization algorithms, namely AZP, AZP-SA, AZPTabu, ARISEL, REDCAP, and SKATER, where the number of regions is an exogenous variable. The simulated benchmark data set consists of model realizations that represent various complexities in spatial data. Model families are defined with respect to regions’ shapes, value-mixing between regions, and the number of underlying spatial clusters. We evaluate the performance of different regionalization methods for realizations families using internal and external measures of regionalization quality. A large number of regionalization quality metrics expose a detailed profile of the analyzed methods’ strengths and weaknesses. We investigate the computational efficiency of every method as a function of the number of spatial units studied. We summarize results for different region families and discuss circumstances that make a certain method more desirable. We illustrate different regionalization algorithms’ implications on defining ecological regions for the conterminous US and compare them against expert-defined ecoregions. Numéro de notice : A2021-760 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2021.1905819 Date de publication en ligne : 05/04/2021 En ligne : https://doi.org/10.1080/13658816.2021.1905819 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98789
in International journal of geographical information science IJGIS > vol 35 n° 11 (November 2021) . - pp 2287 - 2315[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 079-2021111 SL Revue Centre de documentation Revues en salle Disponible Spatially–encouraged spectral clustering: a technique for blending map typologies and regionalization / Levi John Wolf in International journal of geographical information science IJGIS, vol 35 n° 11 (November 2021)
[article]
Titre : Spatially–encouraged spectral clustering: a technique for blending map typologies and regionalization Type de document : Article/Communication Auteurs : Levi John Wolf, Auteur Année de publication : 2021 Article en page(s) : pp 2356 - 2373 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] analyse de groupement
[Termes IGN] apprentissage automatique
[Termes IGN] exploration de données
[Termes IGN] méthode fondée sur le noyau
[Termes IGN] optimisation spatiale
[Termes IGN] régionalisation (segmentation)Résumé : (auteur) Clustering is a central concern in geographic data science and reflects a large, active domain of research. In spatial clustering, it is often challenging to balance two kinds of ‘goodness of fit:’ clusters should have ‘feature’ homogeneity, in that they aim to represent one ‘type’ of observation, and also ‘geographic’ coherence, in that they aim to represent some detected geographical ‘place’. This divides ‘map typologization’ studies, common in geodemographics, from ‘regionalization’ studies, common in spatial optimization and statistics. Recent attempts to simultaneously typologize and regionalize data into clusters with both feature homogeneity and geographic coherence have faced conceptual and computational challenges. Fortunately, new work on spectral clustering can address both regionalization and typologization tasks within the same framework. This research develops a novel kernel combination method for use within spectral clustering that allows analysts to blend smoothly between feature homogeneity and geographic coherence. I explore the formal properties of two kernel combination methods and recommend multiplicative kernel combination with spectral clustering. Altogether, spatially encouraged spectral clustering is shown as a novel kernel combination clustering method that can address both regionalization and typologization tasks in order to reveal the geographies latent in spatially structured data. Numéro de notice : A2021-762 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2021.1934475 Date de publication en ligne : 05/07/2021 En ligne : https://doi.org/10.1080/13658816.2021.1934475 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98795
in International journal of geographical information science IJGIS > vol 35 n° 11 (November 2021) . - pp 2356 - 2373[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 079-2021111 SL Revue Centre de documentation Revues en salle Disponible Spatial structure system of land use along urban rail transit based on GIS spatial clustering / Yu Gao in European journal of remote sensing, vol 54 sup 2 (2021)
[article]
Titre : Spatial structure system of land use along urban rail transit based on GIS spatial clustering Type de document : Article/Communication Auteurs : Yu Gao, Auteur ; Ying Zhang, Auteur ; Haidjar Alsulaiman, Auteur Année de publication : 2021 Article en page(s) : pp 438 - 445 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG
[Termes IGN] aménagement du territoire
[Termes IGN] analyse de groupement
[Termes IGN] Chine
[Termes IGN] exploration de données géographiques
[Termes IGN] modélisation spatiale
[Termes IGN] optimisation (mathématiques)
[Termes IGN] planification urbaine
[Termes IGN] réseau ferroviaire
[Termes IGN] système d'information géographique
[Termes IGN] utilisation du sol
[Termes IGN] zone urbaineRésumé : (auteur) During the past 30 years of reform and opening up, the level of urbanization in our country has made considerable progress, and more cities have had development conditions of the rail transit. In order to solve various problems caused by urbanization and optimize the allocation of urban resources, in this paper, the significance of spatial planning in big cities was analyzed from the perspective of land space utilization along with the rail transit. Based on GIS spatial clustering mining technology and combined with the basic characteristics of geographic information system, a GIS spatial mining search model that can solve the spatial land use was proposed. Then, by combining the clustering algorithm of some association rules, the land planning and utilization along the urban rail transit were calculated. The actual case was taken to establish the grid elements along with the rail transit, and the GIS spatial clustering algorithm was used to verify the model. The results show that GIS spatial clustering algorithm can effectively verify and calculate urban rail transit land planning programs. Numéro de notice : A2021-820 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/22797254.2020.1801356 Date de publication en ligne : 14/11/2020 En ligne : https://doi.org/10.1080/22797254.2020.1801356 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98925
in European journal of remote sensing > vol 54 sup 2 (2021) . - pp 438 - 445[article]Stop-and-move sequence expressions over semantic trajectories / Yenier Torres Izquierdo in International journal of geographical information science IJGIS, vol 35 n° 4 (April 2021)
[article]
Titre : Stop-and-move sequence expressions over semantic trajectories Type de document : Article/Communication Auteurs : Yenier Torres Izquierdo, Auteur ; Grettel Monteagudo Garcia, Auteur ; Marco A. Casanova, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 793 - 818 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web
[Termes IGN] appariement sémantique
[Termes IGN] exploration de données
[Termes IGN] image Flickr
[Termes IGN] information sémantique
[Termes IGN] intelligence artificielle
[Termes IGN] langage de requête
[Termes IGN] RDF
[Termes IGN] SPARQLRésumé : (auteur) Stop-and-move semantic trajectories are segmented trajectories where the stops and moves are semantically enriched with additional data. A query language for semantic trajectory datasets has to include selectors for stops or moves based on their enrichments and sequence expressions that define how to match the results of selectors with the sequence the semantic trajectory defines. This article addresses the problem of searching semantic trajectories, using stop-and-move sequence expressions. The article first proposes a formal framework to define semantic trajectories and introduces stop-and-move sequence expressions, with well-defined syntax and semantics, which act as an expressive query language for semantic trajectories. Then, it describes a concrete semantic trajectory model in RDF, defines SPARQL stop-and-move sequence expressions and discusses strategies to compile such expressions into SPARQL queries. Lastly, the article specifies user-friendly keyword search expressions over semantic trajectories based on the use of keywords to specify stop-and-move queries, and the adoption of terms with predefined semantics to compose sequence expressions. It then shows how to compile such keyword search expressions into SPARQL queries. Finally, it provides a proof-of-concept experiment over a semantic trajectory dataset constructed with user-generated content from Flickr, combined with Wikipedia data. Numéro de notice : A2021-270 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1793157 Date de publication en ligne : 20/07/2020 En ligne : https://doi.org/10.1080/13658816.2020.1793157 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97328
in International journal of geographical information science IJGIS > vol 35 n° 4 (April 2021) . - pp 793 - 818[article]Automating and utilising equal-distribution data classification / Gennady Andrienko in International journal of cartography, vol 7 n° 1 (March 2021)
[article]
Titre : Automating and utilising equal-distribution data classification Type de document : Article/Communication Auteurs : Gennady Andrienko, Auteur ; Natalia Andrienko, Auteur ; Ibad Kureshi, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 100 - 115 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Cartographie thématique
[Termes IGN] analyse spatiale
[Termes IGN] attribut géomètrique
[Termes IGN] attribut sémantique
[Termes IGN] carte choroplèthe
[Termes IGN] classification
[Termes IGN] exploration de données géographiques
[Termes IGN] intervalle de classe
[Termes IGN] répartition géographiqueRésumé : (Auteur) Data classification, i.e. organising data items in groups (classes), is a general technique widely used in data visualisation and cartography, in particular, for creation of choropleth maps. Conventionally, data are classified by dividing the data range into intervals and assigning the same symbol or colour to all data falling within an interval. For instance, the intervals may be of the same length or may include the same number of data items. We propose a method for defining intervals so that some quantity represented by values of another attribute is equally distributed among the classes. This kind of classification supports exploratory analysis of relationships between the attribute used for the classification and the distribution of the phenomenon whose quantity is represented by the additional attribute. The approach may be especially useful when the distribution of the phenomenon is very unequal, with many data items having zero or low quantities and quite a few items having larger quantities. With such a distribution, standard statistical analysis of the relationships may be problematic. We demonstrate the potential of the approach by analysing data referring to a set of spatially distributed people (patients) in relationship to characteristics of the areas in which the people live. Numéro de notice : A2021-184 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/23729333.2020.1863000 Date de publication en ligne : 05/01/2021 En ligne : https://doi.org/10.1080/23729333.2020.1863000 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97114
in International journal of cartography > vol 7 n° 1 (March 2021) . - pp 100 - 115[article]A points of interest matching method using a multivariate weighting function with gradient descent optimization / Zhou Yang in Transactions in GIS, Vol 25 n° 1 (February 2021)PermalinkPermalinkCluttering reduction for interactive navigation and visualization of historical Images / Evelyn Paiz-Reyes (2021)PermalinkConnecting images through time and sources: Introducing low-data, heterogeneous instance retrieval / Dimitri Gominski (2021)PermalinkPermalinkPermalinkExtracting event-related information from a corpus regarding soil industrial pollution / Chuanming Dong (2021)PermalinkIntégration et analyse de données massives et hétérogènes pour une observation intelligente du territoire / Rodrigue Kafando (2021)PermalinkIntelligent sensors for positioning, tracking, monitoring, navigation and smart sensing in smart cities / Li Tiancheng (2021)PermalinkMéthodes et outils pour l’analyse spatiale exploratoire en géolinguistique : contributions aux humanités numériques spatialisées / Clément Chagnaud (2021)Permalink