| Summary: | 碩士 === 國立臺灣大學 === 生物環境系統工程學研究所 === 99 === Spatial data plays a crucial role in many research fields because of its ability of expressing multiple types of information such as location and other attributes at the same time. Point data is one major type of spatial data that is capable of demonstrating accurate and precise locations. However, this could probably contribute to a serious problem in privacy and security. Consequently, the government usually adopts aggregated data to release public information for privacy protection. Nevertheless, under the perspective of academic research, aggregated data is somehow easily influenced by spatial scale and the extent.
This study aims to figure out the best measurement for point data spatial analysis, meanwhile protecting the privacy from releasing unexpected information. Random error introduction is proposed in this study to improve the spatial pattern distortion and privacy protection. Average nearest neighbor distance was applied as the threshold for privacy leak. Monte-Carlo simulation was used for simulating the spatial pattern distortion after introducing random error to the original point data. According to the spatial distribution of the original point data, the study area was separated in to three areas: mountain, county, and city. The result shows density of city and mountain areas grow fastest with type 1 and type 2 error; on the other hand, the country area grows slower with both type 1 and type 2 error. For the purpose of maintaining the spatial pattern and protecting privacy at the same time, the simulation result of city and mountain areas are relatively better than that of county area. In the conclusion, random error introduction can sucessfully protect privacy and meanwhile keep the spatial pattern effectively. However, the effect depends on the property of individual area.
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