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Understanding an Urban Park through Big Data

Author

Listed:
  • Jisoo Sim

    (Landscape Architecture Program, College of Architecture and Urban Studies, Virginia Tech, 800 Drillfield Dr., Blacksburg, VA 24060, USA)

  • Patrick Miller

    (Landscape Architecture Program, College of Architecture and Urban Studies, Virginia Tech, 800 Drillfield Dr., Blacksburg, VA 24060, USA)

Abstract

To meet the needs of park users, planners and designers must know what park users want to do and how they want the park to offer different activities. Big data may help planners and designers gain this knowledge. This study examines how big data collected in an urban park could be used to identify meaningful implications for planning and design. While big data have emerged as a new data source, big data have not become an accepted source of data due to a lack of understanding of big data analytics. By comparing a survey as a traditional data source with big data, this study identifies the strengths and weaknesses of using big data analytics in park planning and design. There are two research questions: (1) what activities do park users want; and (2) how satisfied are users with different activities. The Gyeongui Line Forest Park, which was built on an abandoned railway, was selected as the study site. A total of 177 responses were collected through the onsite survey, and 3703 tweets mentioning the park were collected from Twitter. Results from the survey show that ordinary activities such as walking and taking a rest in the park were the most common. These findings also support existing studies. The results from social media analytics found notable things such as positive tweets about how the railway was turned into a park, and negative tweets about diseases that may occur in the park. Therefore, a survey as traditional data and social media analytics as big data can be complementary methods for the design and planning process.

Suggested Citation

  • Jisoo Sim & Patrick Miller, 2019. "Understanding an Urban Park through Big Data," IJERPH, MDPI, vol. 16(20), pages 1-16, October.
    • Handle: RePEc:gam:jijerp:v:16:y:2019:i:20:p:3816-:d:274875
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    References listed on IDEAS

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    Cited by:

    1. Ruochen Ma & Katsunori Furuya, 2024. "Social Media Image and Computer Vision Method Application in Landscape Studies: A Systematic Literature Review," Land, MDPI, vol. 13(2), pages 1-22, February.
    2. Yunseon Choe & Jiyoon Lee & Gyehee Lee, 2022. "Exploring Values via the Innovative Application of Social Media with Parks Amid COVID-19: A Qualitative Content Analysis of Text and Images Using ATLAS.ti," Sustainability, MDPI, vol. 14(20), pages 1-16, October.
    3. Siqi Lai & Yifan Zhu & Brian Deal, 2023. "Did COVID-19 Reshape Visitor Preferences in Urban Parks? Investigating Influences on Sentiments in Shanghai, China," Sustainability, MDPI, vol. 15(23), pages 1-16, November.
    4. Yingxue Wang & Jiaheng Du & Jingxing Kuang & Chunxu Chen & Maobiao Li & Jin Wang, 2023. "Two-Scaled Identification of Landscape Character Types and Areas: A Case Study of the Yunnan–Vietnam Railway (Yunnan Section), China," Sustainability, MDPI, vol. 15(7), pages 1-18, April.
    5. Diego Martín Sánchez & Noemí Gómez Lobo, 2023. "Urban Forest Tweeting: Social Media as More-Than-Human Communication in Tokyo’s Rinshinomori Park," Land, MDPI, vol. 12(4), pages 1-15, March.
    6. Mariusz Korzeń & Maciej Kruszyna, 2023. "Modified Ant Colony Optimization as a Means for Evaluating the Variants of the City Railway Underground Section," IJERPH, MDPI, vol. 20(6), pages 1-15, March.

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