Evaluation of Sustainable Development of the Urban Ecological Environment and Its Coupling Relationship with Human Activities Based on Multi-Source Data

Abstract

Combining the travel modes of human activities, fully mining multi-source data, and analyzing the relationship between the urban ecological environment and human activities are important topics in urban ecological environment planning. Human activity indicators were constructed based on the data of POI points, OSM road network, and residential areas. Machine learning models such as support vector regression machine, extreme gradient boosting regression, polynomial regression, and random forest regression were combined with remote sensing images to construct an urban ecological environment indicator system. These models were used to conduct regression analysis of urban ecological environment indicators and human activity indicators in Chengdu, China. The research shows that the three indicators of human activities all show a trend of increasing in the center and gradually decreasing in the surrounding areas, while the sustainable urban ecological environment indicators show the opposite trend. On the relationship between urban ecological environment and human activities, XGB has the best effect; the correlation between the street vitality index and the urban function mixing index and the sustainable urban ecological environment is stronger, and the correlation between the walkability measure index of the residential area and the sustainable urban ecological environment is even worse.