| Summary: | 碩士 === 國立臺灣大學 === 電機工程學研究所 === 103 === With the advancement of location-based social networks (LBSNs), users are allowed to “check in” at points of interest (POI) with mobile devices. Compared with conventional demographics, social network data increases in unprecedented pace which resulting in user information. Therefore, human mobility has recently attracted much attention to be studied through LBSNs in spatial, temporal and social aspects. Prior work in urban studies suggests that there is a strong correlation between people dynamics and crime activities. Most works used kernel density estimation to calculate crime density distribution and
predicted crime occurrence with it. Due to the proliferation of social media data, some studies implement crime prediction system through Twitter records. However, there is no research on to quantify human activities. In our model, the human activities and buildings are simulated by Gowalla and Foursquare data respectively for San Francisco and Chicago and each city is characterized by a set of grids. Five temporal periods, two geographic, five social and nine categorical factors are encoded in every grid. To retrieve relevance score of all factors to crime rate, Normalized Discounted Cumulative Gain metric is introduced for ranking relevant instances. Three machine learning models (Support vector machine, linear regression and random forest) are employed to build models to evaluate the predict crimes. The result shows data sparsity can affect the precision and collaborative factors have better predictive power than individual one. And the precision might slightly dropped attributed to less relevant factors.
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