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Gen*: a generic toolkit to generate spatially explicit synthetic populations
- Source :
- International Journal of Geographical Information Science, International Journal of Geographical Information Science, Taylor & Francis, 2018, 32 (6), pp.1194-1210. ⟨10.1080/13658816.2018.1440563⟩, International Journal of Geographical Information Science, 2018, 32 (6), pp.1194-1210. ⟨10.1080/13658816.2018.1440563⟩
- Publication Year :
- 2018
- Publisher :
- HAL CCSD, 2018.
-
Abstract
- International audience; 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.
- Subjects :
- 010504 meteorology & atmospheric sciences
Computer science
Geography, Planning and Development
Population
0211 other engineering and technologies
spatialization
02 engineering and technology
Library and Information Sciences
computer.software_genre
social simulation
01 natural sciences
Social simulation
education
multi-agent model
021101 geological & geomatics engineering
0105 earth and related environmental sciences
education.field_of_study
Multi-agent model
[SHS.GEO]Humanities and Social Sciences/Geography
Synthetic population
Spatialization
[INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation
Regression
Order (business)
[INFO.INFO-MA]Computer Science [cs]/Multiagent Systems [cs.MA]
Multi agent model
Data mining
computer
Information Systems
Subjects
Details
- Language :
- English
- ISSN :
- 13658816 and 13658824
- Database :
- OpenAIRE
- Journal :
- International Journal of Geographical Information Science, International Journal of Geographical Information Science, Taylor & Francis, 2018, 32 (6), pp.1194-1210. ⟨10.1080/13658816.2018.1440563⟩, International Journal of Geographical Information Science, 2018, 32 (6), pp.1194-1210. ⟨10.1080/13658816.2018.1440563⟩
- Accession number :
- edsair.doi.dedup.....22f6f4e21abff4601e8e607f27e67180