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http://dx.doi.org/10.3745/JIPS.04.0219

An Automatic Urban Function District Division Method Based on Big Data Analysis of POI  

Guo, Hao (College of Geomatics, Shandong University of Science and Technology)
Liu, Haiqing (College of Transportation, Shandong University of Science and Technology)
Wang, Shengli (Ocean Science and Engineering College, Shandong University of Science and Technology)
Zhang, Yu (College of Transportation, Shandong University of Science and Technology)
Publication Information
Journal of Information Processing Systems / v.17, no.3, 2021 , pp. 645-657 More about this Journal
Abstract
Along with the rapid development of the economy, the urban scale has extended rapidly, leading to the formation of different types of urban function districts (UFDs), such as central business, residential and industrial districts. Recognizing the spatial distributions of these districts is of great significance to manage the evolving role of urban planning and further help in developing reliable urban planning programs. In this paper, we propose an automatic UFD division method based on big data analysis of point of interest (POI) data. Considering that the distribution of POI data is unbalanced in a geographic space, a dichotomy-based data retrieval method was used to improve the efficiency of the data crawling process. Further, a POI spatial feature analysis method based on the mean shift algorithm is proposed, where data points with similar attributive characteristics are clustered to form the function districts. The proposed method was thoroughly tested in an actual urban case scenario and the results show its superior performance. Further, the suitability of fit to practical situations reaches 88.4%, demonstrating a reasonable UFD division result.
Keywords
Big Data Analysis; Dichotomy Method; Mean Shift Algorithm; POI Data; Urban Function District;
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