| Summary: | 碩士 === 國立臺灣大學 === 地理環境資源學研究所 === 105 === Source：Micro-data was often used in the microsimulation research, but a large number of population micro-data surveys are difficult. Spatial microsimulation models are being used to create simulation micro-data for geographical areas. The models combine sample records with benchmark data for areas by re-weighting sample records to fit statistical data for each area. However, the ways of validation are debatable. Because those ways are compares the simulated micro-data to the constraint data used in the model, the interaction of variables can’t be verified. Method：We re-conducted the spatial microsimulation for all townships in Taiwan. By using three methods, including Combinatorial Optimization (CO), Iterative Proportional Fitting (IPF) and Generalized Regression (GREGWT), combined sample records with benchmark data for areas with the Taiwan Census raw data in 2000. By the individual scale method, we compared the data structure error and the spatial structure error between the realistic micro-data and the simulation micro-data. We analyze the reason of error distribution by regional differences and variables selection and make the recommendations of spatial microsimulation model. Result：Although Total Absolute Error underestimate the error by IPF and GREGWT, the simulation micro-data can still replace the real micro-data for analysis. The simulation micro-data of CO are significant differences with realistic. The error spatial distribution was affected by the sampling process and regional differences. Variable fields number, data distribution and regional differences are the influence factors of the variable estimated error. Conclusion：Spatial microsimulation model can replace realistic data with selecting the appropriate method, grouping the zones and decreasing the number of variable fields.
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