Year: 2018
Type: article
Abstract: Agent-based models tend to integrate more and more data that can deeply impact their outcomes. Among these data, the ones that deal with agent attributes and localization are particularly important, but are very difficult to collect. In order to tackle this issue, we propose a complete generic toolkit called Gen* dedicated to generating spatially explicit synthetic populations from global (census and GIS) data. This article focuses on the localization methods provided by Gen* that are based on regression, geometrical constraints and spatial distributions. The toolkit is applied for a case study concerning the generation of the population of Rouen (France) and shows the capabilities of Gen* regarding population spatialization.
Institutions: Unité de Modélisation Mathématique et Informatique des Systèmes Complexes, Institut de Recherche pour le Développement, Université Pierre-et-Marie-Curie, Institut National de la Recherche Agronomique, Université de Toulouse, Identités et Différenciation de l'Environnement des Espaces et des Sociétés, Université de Rouen Normandie, Vietnam Academy of Science and Technology
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: green
Funder: Agence Nationale de la Recherche
OpenAlex ID: W2791295239