Advancements in remote sensing techniques and urban data analysis tools have enabled the successful monitoring and detection of green spaces in a city. This study aims to develop an index called the urban green accessibility (UGA) index, which measures people’s accessibility to green space and represents the citywide or local characteristics of the distribution pattern of green space. The index is defined as the sum of pedestrians’ accessibility to all vegetation points, which consists of the normalized difference vegetation index (NDVI) with integration and choice values from angular segment analysis. In this study, the proposed index is tested with cases of New York, NY, and San Francisco, CA, in the US. The results reveal differences based on the significance of streets. When analysis ranges are on a neighborhood scale, a few hotspots appear in well-known green areas on commonly accessible streets and in local neighborhood parks on residential blocks. The appearance of high-accessibility points in low-NDVI areas implies the potential of the efficient and proper distribution of green spaces for pedestrians. The proposed measure is expected to help in planning and managing green areas in cities, taking people’s accessibility and spatial relationships into consideration.

In this study we examined the relationships between the built environment and urban air temperature in Seoul city, Korea. We developed multivariate regression models that address the relationship between built environment characteristics and the ambient air temperature with spatial statistics techniques. In addition, we analyzed the difference in daytime and nighttime air temperature to identify the built environment characteristics that affect the intensity of the nocturnal urban heat island effect (UHI). The large sample size of AWS locations in Seoul makes it possible to analyze the factors that influence ambient air temperature and UHI effect. The analysis results indicate that the sky view factor (SVF) and gross floor area significantly influence the daytime air temperature, while the building coverage and albedo showed strong relationships with the nocturnal air temperature. This study also demonstrated the importance of advanced spatial statistics techniques that control spatial autocorrelation and spatial heteroscedasticity in urban air temperature research. Our models confirmed the need to capture the effects of spatial autocorrelations within our spatial data. The findings of this study are valuable for understanding the complicated associations between the built environment and urban air temperature and to develop public policies to mitigate UHI effects.