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Path dependence and the validation of agent-based spatial models of land use / Daniel G. Brown 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 153 - 174 Titre : Path dependence and the validation of agent-based spatial models of land use Type de document : Article/Communication Auteurs : Daniel G. Brown, Auteur ; S. Page, Auteur ; M. Zellner, Auteur ; W. Rand, Auteur Année de publication : 2005 Article en page(s) : pp 153 - 174 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] agent (intelligence artificielle) [Termes IGN] analyse spatiale [Termes IGN] incertitude des données [Termes IGN] instance [Termes IGN] Michigan (Etats-Unis) [Termes IGN] modèle stochastique [Termes IGN] prédiction [Termes IGN] système complexe [Termes IGN] système multi-agents [Termes IGN] urbanisation [Termes IGN] utilisation du sol Résumé : (Auteur) In this paper, we identify two distinct notions of accuracy of land-use models and highlight a tension between them. A model can have predictive accuracy: its predicted land-use pattern can be highly correlated with the actual land-use pattern. A model can also have process accuracy : the process by which locations or land-use patterns are determined can be consistent with real world processes. To balance these two potentially conflicting motivations, we introduce the concept of the invariant region, i.e., the area where land-use type is almost certain, and thus path independent; and the variant region, i.e., the area where land use depends on a particular series of events, and is thus path dependent. We demonstrate our methods using an agent-based land-use model and using multitemporal land-use data collected for Washtenaw County, Michigan, USA. The results indicate that, using the methods we describe, researchers can improve their ability to communicate how well their model performs, the situations or instances in which it does not perform well, and the cases in which it is relatively unlikely to predict well because of either path dependence or stochastic uncertainty. Numéro de notice : A2005-046 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/13658810410001713399 En ligne : https://doi.org/10.1080/13658810410001713399 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=27184 [article]

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