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Pattern-based identification and mapping of landscape types using multi-thematic data / Jakub Nowosad in International journal of geographical information science IJGIS, vol 35 n° 8 (August 2021)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 35 n° 8 (August 2021) . - pp 1634 - 1649 Titre : Pattern-based identification and mapping of landscape types using multi-thematic data Type de document : Article/Communication Auteurs : Jakub Nowosad, Auteur ; Tomasz F. Stepinski, Auteur Année de publication : 2021 Article en page(s) : pp 1634 - 1649 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Cartographie thématique [Termes IGN] gestion des ressources [Termes IGN] gestion foncière [Termes IGN] matrice de co-occurrence [Termes IGN] modèle mathématique [Termes IGN] modélisation spatiale [Termes IGN] occupation du sol [Termes IGN] paysage [Termes IGN] régionalisation (segmentation) [Termes IGN] regroupement de données [Termes IGN] segmentation en régions Résumé : (auteur) Categorical maps of landscape types (LTs) are useful abstractions that simplify spatial and thematic complexity of natural landscapes, thus facilitating land resources management. A local landscape arises from a fusion of patterns of natural themes (such as land cover, landforms, etc.), which makes an unsupervised identification and mapping of LTs difficult. This paper introduces the integrated co-occurrence matrix (INCOMA) – a signature for numerical representation of multi-thematic categorical patterns. INCOMA enables an unsupervised identification and mapping of LTs. The region is tessellated into a large number of local landscapes – patterns of themes over small square-shaped neighborhoods. With local landscapes represented by INCOMA signatures and with dissimilarities between local landscapes calculated using the Jensen-Shannon Divergence (JSD), LTs can be identified and mapped using standard clustering or segmentation techniques. Resultant LTs are typically heterogeneous with respect to categories of contributing themes reflecting the human perception of a landscape. LTs calculated by INCOMA are more faithful abstractions of actual landscapes than LTs obtained by the current method of choice – the map overlay. The concept of INCOMA is described, and its application is demonstrated by an unsupervised mapping of LT zones in Europe based on combined patterns of land cover and landforms. Numéro de notice : A2021-549 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2021.1893324 Date de publication en ligne : 02/03/2021 En ligne : https://doi.org/10.1080/13658816.2021.1893324 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98064 [article]

Spatial association between regionalizations using the information-theoretical V-measure / Jakub Nowosad in International journal of geographical information science IJGIS, vol 32 n° 11-12 (November - December 2018)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 32 n° 11-12 (November - December 2018) . - pp 2386 - 2401 Titre : Spatial association between regionalizations using the information-theoretical V-measure Type de document : Article/Communication Auteurs : Jakub Nowosad, Auteur ; Tomasz F. Stepinski, Auteur Année de publication : 2018 Article en page(s) : pp 2386 - 2401 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse de groupement [Termes IGN] analyse de variance [Termes IGN] distribution spatiale [Termes IGN] logiciel libre [Termes IGN] régionalisation (segmentation) [Termes IGN] variable Résumé : (Auteur) There is a keen interest in calculating spatial associations between two variables spanning the same study area. Many methods for calculating such associations have been proposed, but the case when both variables are categorical is underdeveloped despite the fact that many datasets of interest are in the form of either regionalizations or thematic maps. In this paper, we advance this case by adapting the so-called -measure method from its original information-theoretical formulation to the analysis of variance formulation which provides more insight for spatial analysis. We present a step-by-step derivation of the -measure from the perspective of the analysis of variance. The method produces three indices of global association and two sets of local association indicators which could be mapped to indicate spatial distribution of association strength. The open-source software for calculating all indices from vector datasets accompanies the paper. To showcase the utility of the -measure, we identified three different application contexts: comparative, associative, and derivative, and present an example of each of them. The -measure method has several advantages over the widely used Mapcurves method, it has clear interpretations in terms of mutual information as well as in terms of analysis of variance, it provides more precise assessment of association, it is ready-to-use through the accompanying software, and the examples given in the paper serves as a guide to the gamut of its possible applications. Two specific contributions stemming from our re-analysis of the -measure are the finding of the conceptual flaw in the Geographical Detector—a method to quantify associations between numerical and categorical spatial variables, and a proposal for the new, cartographically based algorithm for finding an optimal number of regions in clustering-derived regionalizations. Numéro de notice : A2018-526 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2018.1511794 Date de publication en ligne : 30/08/2018 En ligne : https://doi.org/10.1080/13658816.2018.1511794 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91353 [article]

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