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Lessons and challenges in land change modeling derived from synthesis of cross-case comparisons

Gilmore Pontius Jr R., Castella J.C., De Nijs T., Duan Z., Fotsing E., Goldstein N., Kok K., Koomen E., Lippitt C.D., McConnell W., Mohd Sood A., Pijanowski B., Verburg P.H., Veldkamp T.A.. 2018. In : Behnisch Martin (ed.), Gotthard Meinel (ed.). Trends in spatial analysis and modelling: Decision-support and planning strategies. Cham : Springer International Publishing, p. 143-164. (Geotechnologies and the environnement, 19).

This chapter presents the lessons and challenges in land change modeling that emerged from years of reflection and numerous panel discussions at scientific conferences concerning a collaborative cross-case comparison in which the authors have participated. We summarize the lessons as nine challenges grouped under three themes: mapping, modeling, and learning. The mapping challenges are: to prepare data appropriately, to select relevant resolutions, and to differentiate types of land change. The modeling challenges are: to separate calibration from validation, to predict small amounts of change, and to interpret the influence of quantity error. The learning challenges are: to use appropriate map comparison measurements, to learn about land change processes, and to collaborate openly. To quantify the pattern validation of predictions of change, we recommend that modelers report as a percentage of the spatial extent the following measurements: misses, hits, wrong hits and false alarms. The chapter explains why the lessons and challenges are essential for the future research agenda concerning land change modeling. (Résumé d'auteur)

Mots-clés : modèle mathématique; méthode statistique; cartographie de l'occupation du sol; utilisation des terres; paysage; environnement

Thématique : Conservation de la nature et ressources foncières; Méthodes mathématiques et statistiques

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