JIPS (한국정보처리학회)
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| 한글제목(Korean Title) |
An Automatic Urban Function District Division Method Based on Big Data Analysis of POI |
| 영문제목(English Title) |
An Automatic Urban Function District Division Method Based on Big Data Analysis of POI |
| 저자(Author) |
Hao Guo
Haiqing Liu
Shengli Wang
Yu Zhang
|
| 원문수록처(Citation) |
VOL 17 NO. 03 PP. 0645 ~ 0657 (2021. 06) |
한글내용
(Korean Abstract) |
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영문내용
(English 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. |
| 키워드(Keyword) |
Big Data Analysis
Dichotomy Method
Mean Shift Algorithm
POI Data
Urban Function District
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| 파일첨부 |
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