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Comparing Social Media Data and Survey Data in Assessing the Attractiveness of Beijing Olympic Forest Park

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  • Zhifang Wang

    (College of Architecture and Landscape Architecture, Peking University, Beijing 100871, China)

  • Yue Jin

    (College of Architecture and Landscape Architecture, Peking University, Beijing 100871, China
    Regal Lloyds International Real Estate Consultants CO., LTD. 19thFl. Tower A, Full-link Plaza, 18 Chaoyangmenwai Ave, Beijing 100020, China)

  • Yu Liu

    (Institute of Remote Sensing and Geographical Information Systems, Peking University, Beijing 100871, China)

  • Dong Li

    (Innovation Center for Technology, Beijing Tsinghua Tongheng Urban Planning & Design Institute, 2203 West Tower, No. 1 Qinghe Jiayuan, Qinghe Zhongjie, Haidian, Beijing 100085, China)

  • Bo Zhang

    (Horticulture and Landscape Architecture Department, Oklahoma State University, Stillwater, OK 74078, USA)

Abstract

Together with the emerging popularity of big data in numerous studies, increasing theoretical discussions of the challenges and limitations of such data sources exist. However, there is a clear research gap in the empirical comparison studies on different data sources. The goal of this paper is to use “attractiveness” as a medium to examine the similarity and differences of Social media data (SMD) and survey data in academic research, based on a case study of the Beijing Olympic Forest Park, in Beijing, China. SMD was extracted from two social media platforms and two surveys were conducted to assess the attractiveness of various locations and landscape elements. Data collection, keyword extraction and keyword prioritization were used and compared in the data gathering and analysis process. The findings revealed that SMD and survey data share many similarities. Both data sources confirm that natural ambience is more appreciated than cultural elements, particularly the naturalness of the park. Spaces of practical utility are more appreciated than facilities designed to have cultural meanings and iconic significance. Despite perceived similarities, this study concludes that SMD exhibits exaggerated and aggregated bias. This resulted from the intrinsic character of SMD as volunteered and unstructured data selected through an emotional process rather than from a rational synthesis. Exciting events were reported more often than daily experiences. Reflecting upon the strength and weakness of SMD and survey data, this study would recommend a combined landscape assessment process, which first utilizes SMD to build up an assessment framework, then applies conventional surveys for supplementary and detailed information. This would ultimately result in comprehensive understanding.

Suggested Citation

  • Zhifang Wang & Yue Jin & Yu Liu & Dong Li & Bo Zhang, 2018. "Comparing Social Media Data and Survey Data in Assessing the Attractiveness of Beijing Olympic Forest Park," Sustainability, MDPI, vol. 10(2), pages 1-18, February.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:2:p:382-:d:129797
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    References listed on IDEAS

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    1. Paulina Guerrero & Maja Steen Møller & Anton Stahl Olafsson & Bernhard Snizek, 2016. "Revealing Cultural Ecosystem Services through Instagram Images: The Potential of Social Media Volunteered Geographic Information for Urban Green Infrastructure Planning and Governance," Urban Planning, Cogitatio Press, vol. 1(2), pages 1-17.
    2. Yu Liu & Xi Liu & Song Gao & Li Gong & Chaogui Kang & Ye Zhi & Guanghua Chi & Li Shi, 2015. "Social Sensing: A New Approach to Understanding Our Socioeconomic Environments," Annals of the American Association of Geographers, Taylor & Francis Journals, vol. 105(3), pages 512-530, May.
    3. Anne L. Washington, 2014. "Government Information Policy in the Era of Big Data," Review of Policy Research, Policy Studies Organization, vol. 31(4), pages 319-325, July.
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    4. Arpan Kumar Kar, 0. "What Affects Usage Satisfaction in Mobile Payments? Modelling User Generated Content to Develop the “Digital Service Usage Satisfaction Model”," Information Systems Frontiers, Springer, vol. 0, pages 1-21.
    5. Arpan Kumar Kar, 2021. "What Affects Usage Satisfaction in Mobile Payments? Modelling User Generated Content to Develop the “Digital Service Usage Satisfaction Model”," Information Systems Frontiers, Springer, vol. 23(5), pages 1341-1361, September.
    6. Qureshi, Salman & Tarashkar, Mahsa & Matloobi, Mansour & Wang, Zhifang & Rahimi, Akbar, 2022. "Understanding the dynamics of urban horticulture by socially-oriented practices and populace perception: Seeking future outlook through a comprehensive review," Land Use Policy, Elsevier, vol. 122(C).
    7. Wang, Zhifang & Fu, Hongpeng & Jian, Yuqing & Qureshi, Salman & Jie, Hua & Wang, Lu, 2022. "On the comparative use of social media data and survey data in prioritizing ecosystem services for cost-effective governance," Ecosystem Services, Elsevier, vol. 56(C).

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