Global urbanisation has accelerated in recent years, especially in rapidly growing coastal cities, and the destruction of habitat and natural resources has intensified. Although much attention has been paid to the study of habitat quality, there are still gaps in our understanding of the factors that influence it and their interactions. In this study, the InVEST habitat quality evaluation model and the GeoDetector model were used to construct a framework for analysing the dynamic changes in habitat quality and their influencing factors from 1992 to 2015. Wenzhou City, Zhejiang Province, China, was selected as the study area. The new framework extends studies on habitat quality change to annual analysis and reduces the lag between the actual change and the mapping time. The interactions between natural and anthropogenic factors are explored, and the effects of different types of land use conversion on habitat quality are further discussed. The results show that: (1) During the study period, cultivated and construction land areas in Wenzhou City increased the most, and forest land area decreased the most. (2) Habitat quality in Wenzhou City was generally good during the study period, but it showed a declining trend from year to year, and the distribution of habitat quality decreased from west to east. (3) The interactions between land use change and annual precipitation change and those between land use change and population density change have the most significant impact on habitat quality. The conversion of forest land to cultivated land, conversion of water area to cultivated land, and conversion of forest land to building land have the greatest impact on habitat quality. The results of the study can provide recommendations for ecological restoration, optimal integration of protected areas, and provide a reference for the healthy and sustainable development of coastal regions.

The assessment of habitat quality is instrumental in preserving regional species diversity and ecosystem health, thereby forming the theoretical foundation for sustainable urban development. While the Invest model is a commonly employed tool for habitat quality evaluation, it fails to consider the terrain. This study, centered on Haitan Island, introduces the terrain diversity index to rectify the Invest model’s lack of terrain evaluation. The terrain diversity index, encompassing indices for terrain slope, undulation, and humidity, combined with the Invest model, was applied for a comprehensive assessment of the study area’s habitat quality. Furthermore, the distribution characteristics of habitat quality on Haitan Island, China, were examined using Moran’s I and LISA indices. The research indicates that forest land is the primary land cover type on Haitan Island, with blue-green space comprising forests, farmland, water bodies, and grassland, making up 66.8% of the island’s area, thus implying a positive overall ecological base. Habitat quality distribution within the study area displays spatial heterogeneity, with regions of superior habitat quality primarily found in the northeast areas such as Junshan. Compared to the standalone Invest model, the combined method considering terrain and vegetation cover types yields a more sensitive impact on habitat quality evaluation and improves the precision of identifying superior habitat quality by 56.7%. Spatial autocorrelation analysis revealed that the comprehensive habitat quality index in the study area exhibited clustered distribution. Hotspots were mainly identified in areas like Junshan and the western mangrove wetland, regions with a high concentration of habitat quality values, while low-value clusters were mostly found in the central city and southwestern plains. This study offers a novel methodology for habitat quality evaluation, compensating for the traditional Invest model’s neglect of terrain factors, and enriching the research on island habitat quality. It can provide fresh approaches and references for future habitat-related studies.

The continuous change process in the impact of differences in public transport accessibility has not been explained specifically in previous studies. This study reveals that the interaction between two continuous explanatory variables has a significant impact on the explained variable in the hedonic model. The study takes the accessibility variable in the house price model as an instance, dividing the accessibility variable of the residential community into two parts. The first part is the rail accessibility defined by the Euclidean distance from the residential community to the nearest rail transportation station. The second part is the road accessibility defined by two Space Syntax indicators, connectivity and carrying capacity, according to the spatial pattern of the road network. As demonstrated by the spatial interactive regression model, this research finds that road connectivity has a significant regulating effect on the impact of the distance to the closest rail station on house prices based on the empirical evidence from Fuzhou, China.