An Automatic Urban Function District Division Method Based on Big Data Analysis of POI
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초록
Along with the rapid development of the economy, the urban scale has extended rapidly, leading to theformation of different types of urban function districts (UFDs), such as central business, residential andindustrial districts. Recognizing the spatial distributions of these districts is of great significance to manage theevolving 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 dataretrieval method was used to improve the efficiency of the data crawling process. Further, a POI spatial featureanalysis method based on the mean shift algorithm is proposed, where data points with similar attributivecharacteristics are clustered to form the function districts. The proposed method was thoroughly tested in anactual urban case scenario and the results show its superior performance. Further, the suitability of fit topractical situations reaches 88.4%, demonstrating a reasonable UFD division result.
Abstract
Along with the rapid development of the economy, the urban scale has extended rapidly, leading to theformation of different types of urban function districts (UFDs), such as central business, residential andindustrial districts. Recognizing the spatial distributions of these districts is of great significance to manage theevolving 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 dataretrieval method was used to improve the efficiency of the data crawling process. Further, a POI spatial featureanalysis method based on the mean shift algorithm is proposed, where data points with similar attributivecharacteristics are clustered to form the function districts. The proposed method was thoroughly tested in anactual urban case scenario and the results show its superior performance. Further, the suitability of fit topractical situations reaches 88.4%, demonstrating a reasonable UFD division result.
참고문헌 (17)
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