| 摘要: |
| 基于社交媒体位置数据，采用K-means聚类方法，通过分析在500 m×500 m网格尺度上，城市不同时间的腾讯用户密度热力值变化规律，识别上海城市功能区，将不同区域按功能区类型划分为产业园区、城市居住区、郊区居住区、城市综合功能区、农村村落地区、农田、滩涂及未利用地分布区.通过将识别结果与高分辨率卫星影像和兴趣点（POI）数据的对比分析，证明了使用社交媒体位置数据进行城市功能区识别的可行性.本方法获取数据成本低，运用简便，为对其他地区主体功能区的划分提供了一种新的思路与方法. |
| 关键词: 社交媒体位置数据 城市功能区 K-means算法 上海市 |
| DOI：10.3969/J.ISSN.1000-5137.2022.04.019 |
| 分类号:P208;TU984 |
| 基金项目:国家自然科学基金（72074151）；上海自然科学基金（20ZR1441500）；国家社会科学基金（18ZDA105） |
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| Identification of urban functional areas based on social media location data: a case study of Shanghai |
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NIU Yanyan1, YANG Yicheng1, YU Jia1,2, WANG Chenyu1, SUN Haiqing1
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1.School of Environmental and Geographical Science, Shanghai Normal University, Shanghai 200234, China;2.Key Innovation Group of Digital Humanities Resource and Research, Shanghai Normal University, Shanghai 200234, China
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| Abstract: |
| Based on social media location data,this paper uses K-means clustering method to achieve the identification of urban functional areas in Shanghai by analyzing the change pattern of Tencent user density heat values at the grid scale of 500 m×500 m at different times in the city. The different areas of Shanghai are divided into industrial areas,urban residential areas,suburban residential areas,integrated urban functional areas,rural village areas,agricultural land,mudflat and unused land areas based on the functional area types. The feasibility of using social media location data for urban functional area identification is demonstrated by comparing and analyzing the identification results with high-resolution satellite images and point of interest(POI) data. The cost of data acquisition of this method is low. And it is easy to be used,which provides a new idea and methodology for the division of the main functional areas in other urban regions. |
| Key words: social media location data urban functional area K-means algorithm Shanghai |
