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

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.

KEYWORDS:

• Synthetic population
• spatialization
• social simulation
• multi-agent model

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1. The https://github.com/ANRGenstar library suite (API, templates and GAMA plugin) could be found at Github.

2. The GAMA platform has its own http://gama-platform.org website.

Additional information

Funding

This work is part of the GEN* (‘Generation of Synthetic populations’) research project funded by the French National Research Agency.