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A comparative approach to modelling multiple urban land use changes using tree-based methods and cellular automata: the case of Greater Tokyo Area / Guodong Du in International journal of geographical information science IJGIS, vol 32 n° 3-4 (March - April 2018)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 32 n° 3-4 (March - April 2018) . - pp 757 - 782 Titre : A comparative approach to modelling multiple urban land use changes using tree-based methods and cellular automata: the case of Greater Tokyo Area Type de document : Article/Communication Auteurs : Guodong Du, Auteur ; Kong Joo Shin, Auteur ; Liang Yuan, Auteur ; Shunsuke Managi, Auteur Année de publication : 2018 Article en page(s) : pp 757 - 782 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse comparative [Termes IGN] automate cellulaire [Termes IGN] changement d'utilisation du sol [Termes IGN] Tokyo (Japon) [Termes IGN] utilisation du sol [Termes IGN] zone urbaine Résumé : (Auteur) Urban multiple land use change (LUC) modelling enables the realistic simulation of LUC processes in complex urban systems; however, such modelling suffers from technical challenges posed by complicated transition rules and high spatial heterogeneity when predicting the LUC of a highly developed area. Tree-based methods are powerful tools for addressing this task, but their predictive capabilities need further examination. This study integrates tree-based methods and cellular automata to simulate multiple LUC processes in the Greater Tokyo Area. We examine the predictive capability of 4 tree-based models – bagged trees, random forests, extremely randomised trees (ERT) and bagged gradient boosting decision trees (bagged GBDT) – on transition probability prediction for 18 land use transitions derived from 8 land use types. We compare the predictive power of a tree-based model with multi-layer perceptron (MLP) and among themselves. The results show that tree-based models generally perform better than MLP, and ERT significantly outperforms the three other tree-based models. The outstanding predictive performance of ERT demonstrates the advantages of introducing bagging ensemble and a high degree of randomisation into transition probability modelling. In addition, through variable importance evaluation, we found the strongest explanatory powers of neighbourhood characteristics for all land use transitions; however, the size of the impacts depends on the neighbourhood land use type and the neighbourhood size. Furthermore, socio-economic and policy factors play important roles in transitions ending with high-rise buildings and transitions related to industrial areas. Numéro de notice : A2018-044 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2017.1410550 En ligne : https://doi.org/10.1080/13658816.2017.1410550 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89266 [article]

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