Gen*: a generic toolkit to generate spatially explicit synthetic populations

Kevin Chapuis (1) , Patrick Taillandier (2) , Renaud Misslin (3) , Alexis Drogoul (4)

1 UMMISCO - Unité de modélisation mathématique et informatique des systèmes complexes [Bondy]
2 MIAT INRA - Unité de Mathématiques et Informatique Appliquées de Toulouse
3 IDEES - Identité et Différenciation de l’Espace, de l’Environnement et des Sociétés
4 ICTLab - USTH - Information, Communication and Technology Laboratory [Hanoi]

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.

Mots clés: Social simulation; Multi-agent model; Synthetic population; Spatialization

Domaines: Modélisation et simulation; Système multi-agents [cs.MA]; Géographie

Kevin Chapuis, Patrick Taillandier, Renaud Misslin, Alexis Drogoul. Gen*: a generic toolkit to generate spatially explicit synthetic populations. International Journal of Geographical Information Science, 2018, 32 (6), pp.1194-1210. ⟨10.1080/13658816.2018.1440563⟩. ⟨hal-01821597⟩