| Title | A systematic review of big data-based urban sustainability research: State-of-the-science and future directions |
|---|---|
| ID_Doc | 43116 |
| Authors | Kong, LQ; Liu, ZF; Wu, JG |
| Title | A systematic review of big data-based urban sustainability research: State-of-the-science and future directions |
| Year | 2020 |
| Published | |
| Abstract | The future of humanity depends increasingly on the performance of cities. Big data provide new and powerful ways of studying and improving coupled urban environmental, social, and economic systems to achieve urban sustainability. However, the term big data has been defined variably, and its urban applications have so far been sporadic in terms of research topic and location. A comprehensive review of big data-based urban environment, society, and sustainability (UESS) research is much needed. The aim of this study was to summarize the big data-based UESS research using a systematic review approach in combination with bibliometric and thematic analyses. The results showed that the numbers of publications and citations of related articles have been increasing exponentially in recent years. The most frequently used big data in UESS research are human behavior data, and the major analytical methods are of five types: classification, clustering, regression, association rules, and social network analysis. The major research topics of big data-based UESS research include urban mobility, urban land use and planning, environmental sustainability, public health and safety, social equity, tourism, resources and energy utilization, real estate, and retail, accommodation and catering. Big data benefit UESS research by proving a people-oriented perspective, timely and real-time information, and fine-resolution spatial dynamics. In addition, several obstacles were identified to applying big data in UESS research, which are related to data quality and acquisition, data storage and management, data security and privacy, data cleaning and preprocessing, and data analysis and information mining. To move forward, future research should integrate multiple big data sources, develop and utilize new methods such as deep learning and cloud computing, and expand the application fields to focus on the interactions between human activities and urban environments. This review can contribute to understanding the current situation of big data-based UESS research, and provide a reference for studies of this topic in the future. (C) 2020 Elsevier Ltd. All rights reserved. |
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