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Urban expansion in Auckland, New Zealand: a GIS simulation via an intelligent self-adapting multiscale agent-based model / Tingting Xu in International journal of geographical information science IJGIS, vol 34 n° 11 (November 2020)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 34 n° 11 (November 2020) . - pp 2136 - 2159 Titre : Urban expansion in Auckland, New Zealand: a GIS simulation via an intelligent self-adapting multiscale agent-based model Type de document : Article/Communication Auteurs : Tingting Xu, Auteur ; Jay Gao, Auteur ; Giovanni Coco, Auteur ; Shuliang Wang, Auteur Année de publication : 2020 Article en page(s) : pp 2136 - 2159 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG [Termes IGN] agent (intelligence artificielle) [Termes IGN] Auckland [Termes IGN] croissance urbaine [Termes IGN] étalement urbain [Termes IGN] modèle de simulation [Termes IGN] modèle orienté agent [Termes IGN] occupation du sol [Termes IGN] réseau neuronal artificiel [Termes IGN] simulation dynamique [Termes IGN] utilisation du sol Résumé : (auteur) When modelling urban expansion dynamics, cellular automata models focus mostly on the physical environments and cell neighbours, but ignore the ‘human’ aspect of the allocation of urban expansion cells. This limitation is overcome here using an intelligent self-adapting multiscale agent-based model. To simulate the urban expansion of Auckland, New Zealand, a total of 15 urban expansion drivers/constraints were considered over two periods (2000–2005, 2005–2010). The modelling takes into consideration both a macro-scale agent (government) and micro-scale agents (residents of three income levels), and their multi-level interactions. In order to achieve reliable simulation results, ABM was coupled with an artificial neural network to reveal the learning process and heterogeneity of the multi-sub-residential agents. The ANN-ABM accurately simulated the urban expansion of Auckland at both the global and local scales, with kappa simulation value at 0.48 and 0.55, respectively. The validated simulation result shows that the intelligent and self-adapting ANN-ABM approach is more accurate than an ABM with a general type of agent model (kappa simulation = 0.42) at the global scale, and more accurate than an ANN-based CA model (kappa simulation = 0.47) at the local scale. Simulation inaccuracy stems mostly from the outdated master land use plan. Numéro de notice : A2020-613 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1748192 Date de publication en ligne : 17/04/2020 En ligne : https://doi.org/10.1080/13658816.2020.1748192 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95984 [article]

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