Patterns of Urban Green Space Use Applying Social Media Data: A Systematic Literature Review

Gabriele Zabelskyte, Nadja Kabisch and Zaneta Stasiskiene
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Gabriele Zabelskyte: Institute of Environmental Engineering, Kaunas University of Technology, K. Donelaičio g. 73, LT-44249 Kaunas, Lithuania
Nadja Kabisch: Geography Department, Humboldt-Universität zu Berlin, Unter den Linden 6, 10099 Berlin, Germany
Zaneta Stasiskiene: Institute of Environmental Engineering, Kaunas University of Technology, K. Donelaičio g. 73, LT-44249 Kaunas, Lithuania

Land, 2022, vol. 11, issue 2, 1-21

Abstract: Scientific interest in the potential of urban green spaces, particularly urban parks, to improve health and well-being is increasing. Traditional research methods such as observations and surveys have recently been complemented by the use of social media data to understand park visitation patterns. We aimed to provide a systematic overview of how social media data have been applied to identify patterns of urban park use, as well as the advantages and limitations of using social media data in the context of urban park studies. We used the PRISMA method to conduct a systematic literature analysis. Our main findings show that the 22 eligible papers reviewed mainly used social media data to analyse urban park visitors’ needs and demands, and to identify essential park attributes, popular activities, and the spatial, social, and ecological coherence between visitors and parks. The review allowed us to identify the advantages and limitations of using social media data in such research. These advantages include a large database, real-time data, and cost and time savings in data generation of social media data. The identified limitations of using social media data include potentially biased information, a lack of socio-demographic data, and privacy settings on social media platforms. Given the identified advantages and limitations of using social media data in researching urban park visitation patterns, we conclude that the use of social media data as supplementary data constitutes a significant advantage. However, we should critically evaluate the possible risk of bias when using social media data.

Keywords: urban green spaces; social media data; park use; systematic review; big data (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 (4)

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