Projecting Households of Synthetic Population on Buildings Using Fundamental Geospatial Data

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In this paper, we propose a method to project households of synthetic population using fundamental geospatial data for real-world social simulations. That is, we assign each generated household on a building in a geographical map. When we try to conduct a real-scale social simulation, we need attributes of agents and their locations on a geographical map. We have already proposed a synthetic population method that generates attributes of agents or citizens from the statistics of the real world. To determine the locations of agents, we propose, in this paper, a threefold method to project generated households on buildings in a geographical map using the fundamental geospatial data. We apply the proposed method to project households generated from the statistics of Takatsuki City, Osaka, Japan and project them on buildings in the map. In order to cope with a problem of random assignment of households on buildings, we propose a modified method to consider types and area of buildings. Projection results show that households are assigned more reasonably to isolated houses and apartments.