Ecological well-being Performance in a Chinese Urban Agglomeration: Spatiotemporal Analysis and Policy Insights from an Orange-based Machine Learning Framework

Key Points

• Ecological well-being performance shows significant variation across different time periods and locations, impacting urban planning.
• The machine learning framework utilized effectively identifies trends and anomalies in ecological data across urban regions.
• Urban agglomerations were analyzed for spatial and temporal variations using an innovative machine learning technique, enhancing understanding of ecological health.
• Results highlight the need for targeted policy interventions in urban areas to improve overall ecological well-being.