A Literature Review of Big Data-Based Urban Park Research in Visitor Dimension

Hongxu Guo, Zhuoqiao Luo, Mengtian Li, Shumin Kong and Haiyan Jiang
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Hongxu Guo: School of Architecture and Urban Planning, Guangdong University of Technology, 729 East Dongfeng Road, Guangzhou 510090, China
Zhuoqiao Luo: School of Architecture and Urban Planning, Guangdong University of Technology, 729 East Dongfeng Road, Guangzhou 510090, China
Mengtian Li: School of Architecture and Urban Planning, Guangdong University of Technology, 729 East Dongfeng Road, Guangzhou 510090, China
Shumin Kong: School of Architecture and Urban Planning, Guangdong University of Technology, 729 East Dongfeng Road, Guangzhou 510090, China
Haiyan Jiang: School of Architecture and Urban Planning, Guangdong University of Technology, 729 East Dongfeng Road, Guangzhou 510090, China

Land, 2022, vol. 11, issue 6, 1-17

Abstract: Urban parks provide multiple benefits to human well-being and human health. Big data provide new and powerful ways to study visitors’ feelings, activities in urban parks, and the effect they themselves have on urban parks. However, the term “big data” has been defined variably, and its applications on urban parks have so far been sporadic in research. Therefore, a comprehensive review of big data-based urban park research is much needed. The review aimed to summarize the big data-based urban park research in visitor dimension by a systematic review approach in combination with bibliometric and thematic analyses. The results showed that the number of publications of related articles has been increasing exponentially in recent years. Users’ days data is used most frequently in the big data-based urban park research, and the major analytical methods are of four types: sentiment analysis, statistical analysis, and spatial analysis. The major research topics of big data-based urban park research in visitor dimension include visitors’ behavior, visitors’ perception and visitors’ effect. Big data benefits urban park research by providing low-cost, timely information, a people-oriented perspective, and fine-grained site information. However, its accuracy is insufficient because of coordinate, keyword classification and different kinds of users. To move forward, future research should integrate multiple big data sources, expand the application, such as public health and human–nature interactions, and pay more attention to the big data use for overcoming pandemic. This review can help to understand the current situation of big data-based urban park research, and provide a reference for the studies of this topic in the future.

Keywords: perception; social media; mobile phone data; health (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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