Abstract:
Environmental injustice, a significant social and environmental issue, refers to the disproportionate distribution of environmental hazards and benefits among different populations. This study aims to investigate and highlight environmental injustices in Philadelphia's urban parks by leveraging social media data and machine learning techniques.
This research contributes to the growing body of knowledge on environmental justice in the context of urban planning and park equity by utilizing social media data as a novel data source. This approach enables the analysis of public sentiment and concerns regarding the environmental quality and accessibility of urban parks in Philadelphia.
Key Findings:
The study identified significant disparities in park quality and accessibility across different neighborhoods in Philadelphia.
Areas with lower socioeconomic status and higher minority populations often had fewer and smaller parks, limited amenities, and poorer maintenance.
Social media data analysis revealed that residents in underserved neighborhoods frequently expressed dissatisfaction with the quality and accessibility of nearby parks.
Machine learning algorithms successfully categorized social media posts into various themes related to park amenities, cleanliness, safety, and accessibility, providing valuable insights into specific issues faced by different communities.
These findings highlight the need for targeted urban planning policies that prioritize equitable access to quality urban parks for all residents, regardless of their socioeconomic status or neighborhood.
Conclusion:
This study demonstrates the potential of using social media data and machine learning to identify environmental injustices in urban parks. By combining these innovative approaches with traditional research methods, we can improve our understanding of the complex social and environmental factors that contribute to unequal access to green spaces and inform the development of more equitable and sustainable urban planning policies.
