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Calibrating a neural network-based urban change model for two metropolitan areas of the upper Midwest of the United States / B.C. Pijanowski in International journal of geographical information science IJGIS, vol 19 n° 2 (february 2005)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 19 n° 2 (february 2005) . - pp 197 - 215 Titre : Calibrating a neural network-based urban change model for two metropolitan areas of the upper Midwest of the United States Type de document : Article/Communication Auteurs : B.C. Pijanowski, Auteur ; S. Pithadia, Auteur ; B.A. Shellito, Auteur ; K. Alexandridis, Auteur Année de publication : 2005 Article en page(s) : pp 197 - 215 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG [Termes IGN] Détroit [Termes IGN] Kappa de Cohen [Termes IGN] métropole [Termes IGN] Minneapolis (Minnesota) [Termes IGN] modèle de simulation [Termes IGN] occupation du sol [Termes IGN] pondération [Termes IGN] spatial metrics [Termes IGN] système d'information géographique Résumé : (Auteur) We parameterized neural net-based models for the Detroit and Twin Cities metropolitan areas in the US and attempted to test whether they were transferable across both metropolitan areas. Three different types of models were developed. First, we trained and tested the neural nets within each region and compared them against observed change. Second, we used the training weights from one area and applied them to the other. Third, we selected a small subset (- 1 %) of the Twin Cities area where a lot of urban change occurred. Four model performance metrics are reported : (1) Kappa; (2) the scale which correct and paired omission/commission errors exceed 50%; (3) landscape pattern metrics; and (4) percentage of cells in agreement between model simulations. We found that the neural net model in most cases performed well on pattern but not location using Kappa. The model performed well only in one case where the neural net weights from one area were used to simulate the other. We suggest that landscape metrics are good to judge model performance of land use change models but that Kappa might not be reliable for situations where a small percentage of urban areas change. Numéro de notice : A2005-048 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/13658810410001713416 En ligne : https://doi.org/10.1080/13658810410001713416 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=27186 [article]

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