@article{fdi:010072823,
  title = {{G}en*: a generic toolkit to generate spatially explicit synthetic populations},
  author = {{C}hapuis, {K}. and {T}aillandier, {P}. and {R}enaud, {M}. and {D}rogoul, {A}lexis},
  editor = {},
  language = {{ENG}},
  abstract = {{A}gent-based models tend to integrate more and more data that can deeply impact their outcomes. {A}mong these data, the ones that deal with agent attributes and localization are particularly important, but are very difficult to collect. {I}n order to tackle this issue, we propose a complete generic toolkit called {G}en* dedicated to generating spatially explicit synthetic populations from global (census and {GIS}) data. {T}his article focuses on the localization methods provided by {G}en* that are based on regression, geometrical constraints and spatial distributions. {T}he toolkit is applied for a case study concerning the generation of the population of {R}ouen ({F}rance) and shows the capabilities of {G}en* regarding population spatialization.},
  keywords = {{S}ynthetic population ; spatialization ; social simulation ; multi-agent model},
  booktitle = {},
  journal = {{I}nternational {J}ournal of {G}eographical {I}nformation {S}cience},
  volume = {32},
  numero = {6},
  pages = {1194--1210},
  ISSN = {1365-8816},
  year = {2018},
  DOI = {10.1080/13658816.2018.1440563},
  URL = {https://www.documentation.ird.fr/hor/fdi:010072823},
}