利用地理标签数据感知城市活力

doi:10.11947/j.AGCS.2020.20190051

测绘学报 ›› 2020, Vol. 49 ›› Issue (3): 365-374.doi: 10.11947/j.AGCS.2020.20190051

利用地理标签数据感知城市活力

朱婷婷^1,2,3, 涂伟^1,2,3, 乐阳^1,2,3, 钟晨⁵, 赵天鸿^1,3, 黎秋平^1,3, 李清泉^1,2,3,4

1. 深圳大学建筑与城市规划学院城市空间信息工程系, 广东深圳 518060;
2. 人工智能与数字经济广东省实验室(深圳), 广东深圳 518060;
3. 广东省城市空间信息工程重点实验室, 广东深圳 518060;
4. 武汉大学测绘遥感信息工程国家重点实验室, 湖北武汉 430079;
5. 伦敦大学国王学院地理学院, 英国伦敦

收稿日期:2019-01-31 修回日期:2019-10-24 发布日期:2020-03-24
通讯作者: 涂伟 E-mail:tuwei@szu.edu.cn
作者简介:朱婷婷(1994-),女,硕士生,研究方向为城市时空大数据分析。E-mail:zhutingting2017@email.szu.edu.cn
基金资助:
国家自然科学基金（71961137003）；深圳市科技创新委员会基础研究项目（JCYJ20180305125113883）

Sensing urban vibrancy using geo-tagged data

ZHU Tingting^1,2,3, TU Wei^1,2,3, YUE Yang^1,2,3, ZHONG Chen⁵, ZHAO Tianhong^1,3, LI Qiuping^1,3, LI Qingquan^1,2,3,4

1. Department of Urban Informatics, School of Architecture and Urban Planning, Shenzhen University, Shenzhen 518060, China;
2. Guangdong Laboratory of Artificial Intelligence and Digital Economy(SZ), Shenzhen University, Shenzhen 518060, China;
3. Guangdong Key Laboratory of Urban Informatics, Shenzhen University, Shenzhen 518060, China;
4. State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan 430079, China;
5. Department of Geography, King's College London, London WC2R 2LS, UK

Received:2019-01-31 Revised:2019-10-24 Published:2020-03-24
Supported by:
The National Natural Science Foundation of China (No. 71961137003);The Basic Research Projects of Shenzhen Technology Innovation Commission (No. JCYJ20180305125113883)

摘要/Abstract

摘要： 高度活跃的城市是社会稳定发展的基础。基于地理标签感知的城市活力能够量化城市发展现状，探索城市活力的影响机制，为精细化城市治理提供技术支撑。传统城市活力研究依赖于街区的活力调查，时间长，费用高。本文研究利用兴趣点和社交媒体签到等地理标签数据，提出了城市活力度量指标，探索性分析城市活力的分布模式。基于土地利用、道路和建筑物等数据计算建成环境指标，构建城市活力和建成环境之间的普通线性回归与空间自回归模型，揭示了影响城市活力的建成环境因素。基于深圳市的试验结果表明：兴趣点和社交媒体签到数据能够较好地指示城市活力。深圳市的城市活力主要受商业用地、工业用地、土地混合利用以及路网密度、地铁站点密度的影响。住宅用地和建筑物占地密度对基于POI的城市活力具有显著影响。

关键词: 城市活力, POI, 位置签到数据, 空间自回归

Abstract: Promoting neighborhood vibrancy is vital for urban development. Recently, geotagged data provide unprecedented opportunities for discovering urban vibrancy patterns and their affecting mechanism. However, traditional urban vibrancy studies rely on fields survey therefore are time-consuming and highly-cost. This study constructs two urban vibrancy indices using point-of-interest and social media check in data. The spatial patterns of urban vibrancy are explored with spatial auto-regression analytic. Ordinary regression models (OLS) and spatial autoregression models (SAM) are established for revealing the influences of built environment on urban vibrancy by using geospatial data such as land use, roads and buildings. An empirical study in Shenzhen was implemented. The results show that:commercial land, industry land, mixed land use, the road density, and metro stations are five main factors highly influencing Shenzhen vibrancy. Residential land use and building footprints only have significant effects on vibrancy exhibited by POI. These exploratory findings demonstrate that urban vibrancy should be assessed and improved for different consideration.

Key words: urban vibrancy, POI, geotagged check in data, spatial auto-regression

中图分类号:

P208

朱婷婷, 涂伟, 乐阳, 钟晨, 赵天鸿, 黎秋平, 李清泉. 利用地理标签数据感知城市活力[J]. 测绘学报, 2020, 49(3): 365-374.

ZHU Tingting, TU Wei, YUE Yang, ZHONG Chen, ZHAO Tianhong, LI Qiuping, LI Qingquan. Sensing urban vibrancy using geo-tagged data[J]. Acta Geodaetica et Cartographica Sinica, 2020, 49(3): 365-374.

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利用地理标签数据感知城市活力

Sensing urban vibrancy using geo-tagged data

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