GU Yanyan, JIAO Limin, DONG Ting, WANG Yandong, XU Gang. Spatial Distribution and Interaction Analysis of Urban Functional Areas Based on Multi-source Data[J]. Geomatics and Information Science of Wuhan University, 2018, 43(7): 1113-1121. DOI: 10.13203/j.whugis20160192
Citation: GU Yanyan, JIAO Limin, DONG Ting, WANG Yandong, XU Gang. Spatial Distribution and Interaction Analysis of Urban Functional Areas Based on Multi-source Data[J]. Geomatics and Information Science of Wuhan University, 2018, 43(7): 1113-1121. DOI: 10.13203/j.whugis20160192

Spatial Distribution and Interaction Analysis of Urban Functional Areas Based on Multi-source Data

Funds: 

The National Natural Science Foundation of China 41571385

The National Natural Science Foundation of China 41271399

China Special Fund for Surveying, Mapping and Geoinformation Research in the Public Interest 201512015

the National Key Research and Development Program of China 2016YFB0501400

More Information
  • Author Bio:

    GU Yanyan, PhD candidate, specializes in spatial data mining and urban spatial structure. E-mail: yyg@whu.edu.cn

  • Corresponding author:

    JIAO Limin, PhD, professor. E-mail:lmjiao027@163.com

  • Received Date: September 04, 2016
  • Published Date: July 04, 2018
  • With the rapid development of economy, urban internal space structure has been optimized significantly. It is of great significance to identify the spatial distribution and interaction rules of diffe-rent functional regions (DFR) for urban structure analysis and rational planning. We identify the spatial distribution of DFR by analyzing points of interest(POI) data based on kernel density estimation and head/tail breaks. On this basis, we analyze the spatio-temporal discipline of attraction and mutual relationship between typical DFR based on taxi trajectory data. Inside the 5th ring road of urban region of Beijing, the study reveals that:①Typical DFR Xidan, Guomao, Zhongguancun are business-oriented districts, Wangjing is a residential district with a significantly commuting characteristic. ②Guomao has the robust gravity (39.4%) on itself, which indicates that Guomao has more comprehensive urban functions. ③The attraction of DFR within the scope of resident trip distance decreases with the increase of distance, which conforms to the experience cognition and geographic spatial attenuation law. The results show that using kernel density estimation and head/tail breaks to analyze POI data and taxi trajectory data and identify the spatial distribution of DFR is reasonable and effective.
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