Generating synthetic population at individual and household levels with aggregate data

Population synthesis is a process to create data records of individual persons and households with associated attributes that closely resemble the real population. It is the basis of microsimulation models for various applications such as urban planning, crime modeling and epidemiology. This work aims to create synthetic Thai population at the provincial scale. Our synthetic population generator is based on the synthetic reconstruction method, which is most suitable where only aggregate census data are available, as in Thailand. With available census tabulations from various government agencies, the generator is configured to combine 16 tabulation data at individual and household levels using conditional probabilities. The order of conditional probabilities is designed according to dependencies between the attributes and the difference in resolutions of the data from multiple sources. The main contribution of this work is the method to generate complex household types. Many family related attributes are used to create family relationships among individuals. Then, families and individuals are assigned into households according to household statistics. The generator is evaluated by creating synthetic population of Phitsanulok, a province with 835,555 individuals and 296,807 households localized in 18 municipality areas of 9 districts. The aggregated tabulations of the synthetic population are compared to the original ones. The results show that the distributions of their aggregated attributes are very close to the source data. Therefore, the synthetic population is a good approximate of the real population.