摘要
The penetration of devices integrated with location-based services and internet services has generated massive data about the everyday life of citizens and tracked their activities happening in cities. Crowdsourced data, such as social media data, POIs data and collaborative websites, generated by the crowd, has become fine-grained proxy data of urban activity and widely used in research in urban studies. However, due to the heterogeneity of data types of crowdsourced data and the limitation of previous studies mainly focusing on a specific application, a systematic review of crowdsourced data mining for urban activity is still lacking. In order to fill the gap, this paper conducts a literature search in the Web of Science database, selecting 226 highly related papers published between 2013 and 2019. Based on those papers, the review firstly conducts a bibliometric analysis identifying underpinning domains, pivot scholars and papers around this topic. The review also synthesises previous research into three parts: main applications of different data sources and data fusion; application of spatial analysis in mobility patterns, functional areas and event detection; application of socio-demographic and perception analysis in city attractiveness, demographic characteristics and sentiment analysis. The challenges of this type of data are also discussed in the end. This study provides a systematic and current review for both researchers and practitioners interested in the applications of crowdsourced data mining for urban activity.
牛海沣
讲师/助理教授
中国人民大学公共管理学院讲师/助理教授，剑桥大学土地经济系博士，剑桥大学跨学科空间分析实验室成员，曾任剑桥大学土地经济系副研究员，负责欧盟 Horizon 2020 资助的城市健康项目 Emotional Cities 高级空间分析相关。研究特章包括城市大数据挖掘、空间数据科学、地理可视化、城市感知和城市动态模拟，具有在中国、英国和欧盟地区的应用经验，特别是关注如何通过结合机器学习、人工智能和城市大数据来更好地支持城市规划、政策制定和智能管理。