The price of a house is affected by both the subjective and objective factors of the street environment in a neighborhood. However, the relationships between these factors and housing prices are not fully understood. Street view imagery (SVI) has recently emerged as a new data source for housing price studies. The SVI contains both objective and subjective information and can be used to extract objective measurements describing the physical environment and subjective measurements depicting human perceptions. Compared to conventional methods, there is consistency between subjective and objective information extracted from SVIs, and the two types of information are acquired from the perspective of the human visual perceptual system. Therefore, using both objective and subjective information extracted from street view images to study their relationship with housing prices has several advantages. In this study, focusing on the city of Suzhou, China, we extracted subjective perception and objective view indices from SVIs and systematically assessed their effects on housing prices. The global ordinary least squares (OLS) regression model and the local geographically weighted regression (GWR) model were used to model the correlations between these measures and housing prices. The OLS reveals that overall objective measures have stronger explanatory power, and built environment factors have a greater impact on housing prices. GWR shows that subjective factors can explain more variance in housing prices on the local scale and that home buyers care more about the subjective perceptions of the neighborhood’s surroundings. The map of the GWR local coefficients demonstrates that the perception indicators have both positive and negative effects on housing prices in different places. In addition, a Monte Carlo test was performed to verify the spatially varying relationships between these measures. Our findings provide important references for urban designers and guide various applications, such as safe neighborhood design and sustainable city planning.

Interactions among ecosystem services (ESs) involve tradeoffs and synergies. Quantitatively studying the trade-off and synergistic relationships between land use/land cover change (LULC) and ESs enables the precise identification of the quality status and driving factors of ESs within the region, which is crucial for rational resource allocation and environmental protection. In this study, the spatial and temporal change characteristics of the three ESs of carbon storage (CS), soil retention (SR) and habitat quality (HQ) are explored by using the InVEST model and GIS technology in the region around Taihu Lake, and the tradeoffs and synergies among the three are determined based on the difference comparison. The results indicate that: (1) The study area has a downward trajectory in CS and HQ from 1990 to 2020, while SR experiences some fluctuations. The spatial distribution of the three ESs exhibits high levels in the southwest and low levels in the northeast. (2) The most sensitive regions where tradeoffs and synergies are most pronounced occur primarily in the newly construction land regions and the southwestern mountainous and hilly areas. In newly construction land regions, there are often tradeoffs relationships observed between CS and SR, as well as between HQ and SR. Conversely, a predominantly negative synergy is mainly observed between CS and HQ. In the southwestern hilly terrain, due to changes in landscape patterns, HQ and SR exhibit higher levels of negative synergistic relationships. (3) LULC is a significant driver of spatial and temporal changes in ESs, as well as changes in tradeoffs and synergies in the study area, necessitating integrated research from economic, social and climate change perspectives.