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Geo-spatial modelling with unbalanced data: modelling the spatial pattern of human activity during the Stone Age / Jaroslav Jasiewicz in Open geosciences, vol 7 n° 1 (January 2015)  

Public [article]  inOpen geosciences > vol 7 n° 1 (January 2015) . - pp 289 - 307 Titre : Geo-spatial modelling with unbalanced data: modelling the spatial pattern of human activity during the Stone Age Type de document : Article/Communication Auteurs : Jaroslav Jasiewicz, Auteur ; Iwona Sobkowiak-Tabaka, Auteur Année de publication : 2015 Article en page(s) : pp 289 - 307 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] apprentissage automatique [Termes IGN] géomorphométrie [Termes IGN] incertitude des données [Termes IGN] modélisation spatiale [Termes IGN] préhistoire [Termes IGN] site archéologique Résumé : (auteur) With the increasing availability of data, geoscience provides many methods to model the spatial extent of various phenomena.Acquiring representative, high quality data is the most important criterion to assess the value of any spatial analysis, however, there are many situations in which these criteria cannot be fulfilled. Archived data, collected in the past, for which analysis cannot be repeated or supplemented is a very common information source. Archaeological data collected at a regional extent during years of field work and superficial observations are an additional example. Such data rarely provide representative samples and are usually imbalanced; only very few examples contain useful data, while many examples remain without any archaeological traces. In spite of these limitations archaeological information presented in the form of maps can be a useful and helpful tool to analyse the spatial patterns of some phenomena and, from a more practical point of view, a tool to predict the location of undiscovered occurrences. The primary goal of this paper is to present a methodology for modelling spatial patterns based on imbalanced categorical data which do not fulfil the criteria of spatial representation and incorporates uncertainty in its decision process. This concept will be discussed using a collection of Stone Age sites and set of environmental variables from the postglacial lowlands in Western Poland. We will propose a machine-learning system which adopts CART through bootstrap simulation to incorporate uncertainty into the spatial model and utilise that uncertainty in the decision-making process. Finally, we will describe the relationships between the model and environmental variables and present our results in cartographic form using the principles of decision-tree cartography. Numéro de notice : A2015-440 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1515/geo-2015-0031 En ligne : https://doi.org/10.1515/geo-2015-0031 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=77040 [article]