magazinelogo

Journal of Humanities, Arts and Social Science

ISSN Print: 2576-0556 Downloads: 1365480 Total View: 9212764
Frequency: monthly ISSN Online: 2576-0548 CODEN: JHASAY
Email: jhass@hillpublisher.com
ArticleOpen Access http://dx.doi.org/10.26855/jhass.2025.03.001

Evaluating the Influence of Park Attributes on User Satisfaction Using Flickr Data: A Case Study of Southeast Michigan

Jieping Yang

School for Environment and Sustainability, University of Michigan, Ann Arbor, MI 48109, USA.

*Corresponding author: Jieping Yang

Published: March 26,2025

Abstract

The integration of social data in urban studies presents new opportunities for assessing public perceptions of green spaces. This research investigates how park attributes influence visitor sentiment using Flickr data from Southeast Michigan. By analyzing geo-tagged photos and user comments, this study applies sentiment analysis and linear regression modeling to identify the key features that enhance or detract from user satisfaction. The results indicate that natural areas, historical monuments, and walking trails significantly improve user experiences, whereas BMX areas and equestrian activities tend to reduce satisfaction levels. These findings suggest that urban planners and landscape architects should emphasize natural elements and cultural landmarks in park design to foster positive user engagement. Future research should integrate data from multiple social platforms and apply more advanced machine-learning techniques to further validate these insights. This study underscores the potential of social analytics as a valuable tool for urban planning and landscape development.

Keywords

Social analysis; park attributes; user satisfaction; sentiment analysis; urban planning

References

Chen, Y., Parkins, J. R., & Sherren, K. (2019). Using geo-tagged social media posts to assess urban green space values. Landscape and Urban Planning, 170, 283-292.

Gliozzo, G., Pettorelli, N., & Haklay, M. (2016). Using crowdsourced imagery to detect cultural ecosystem services: A case study in South America. Science of the Total Environment, 573, 1103-1114.

Johnson, M., Fox, N., & Meerow, S. (2021). Leveraging social media analytics for urban landscape assessment. Journal of Urban Planning, 48(3), 112-127.

Kaczynski, A. T., & Havitz, M. E. (2008). Investigating the relationship between park attributes and physical activity. Journal of Leisure Research, 40(4), 556-576.

Li, X., & Yang, J. (2022). Social media sentiment analysis in urban planning: Challenges and opportunities. Urban Informatics, 5(1), 45-68.

Meerow, S., Newell, J. P., & Stults, M. (2020). Urban resilience and social media data integration. Urban Studies, 57(2), 289-310.

Montgomery, D. C., Peck, E. A., & Vining, G. G. (2021). Introduction to Linear Regression Analysis. John Wiley & Sons.

Sun, Z., Wang, P., & Li, J. (2022). Enhancing sentiment analysis with deep learning techniques. AI & Society, 37(2), 289-310.

Xiang, Z., Du, Q., Ma, Y., & Fan, W. (2017). Sentiment analysis in tourism: A study of online travel reviews. Journal of Travel Research, 56(6), 727-744.

How to cite this paper

Evaluating the Influence of Park Attributes on User Satisfaction Using Flickr Data: A Case Study of Southeast Michigan

How to cite this paper: Jieping Yang. (2025) Evaluating the Influence of Park Attributes on User Satisfaction Using Flickr Data: A Case Study of Southeast Michigan. Journal of Humanities, Arts and Social Science9(3), 420-426.

DOI: http://dx.doi.org/10.26855/jhass.2025.03.001