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

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.