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High-temperature risk disaster, a common meteorological disaster, seriously affects people’s productivity, life, and health. However, insufficient attention has been paid to this disaster in urban communities. To assess the risk of high-temperature disasters, this study, using remote sensing data and geographic information data, analyzes 973 communities in downtown Wuhan with the geography-weighted regression method. First, the study evaluates the distribution characteristics of high temperatures in communities and explores the spatial differences of risks. Second, a metrics and weight system is constructed, from which the main factors are determined. Third, a risk assessment model of high-temperature disasters is established from disaster-causing danger, disaster-generating sensitivity, and disaster-bearing vulnerability. The results show that: (a) the significance of the impact of the built environment on high-temperature disasters is obviously different from its coefficient space differentiation; (b) the risk in the old city is high, whereas that in the area around the river is low; and (c) different risk areas should design built environment optimization strategies aimed specifically at the area. The significance of this study is that it develops a high-temperature disaster assessment framework for risk identification, impact differentiation, and difference optimization, and provides theoretical support for urban high-temperature disaster prevention and mitigation.

In previous studies, planners have debated extensively whether compact development can improve air quality in urban areas. Most of them estimated pollution exposure with stationary census data that linked exposures solely to residential locations, therefore overlooking residents’ space–time inhalation of air pollutants. In this study, we conducted an air pollution exposure assessment by scrutinizing one-hour resolution population distribution maps derived from hourly smartphone data and air pollutant concentrations derived from inverse distance weighted interpolation. We selected Wuhan as the study area and used Pearson correlation analysis to explore the effect of compactness on population-weighted concentrations. The results showed that even if a compact urban form helps to reduce pollution concentrations by decreasing vehicle traveling miles and tailpipe emissions, higher levels of building density and floor area ratios may increase population-weighted exposure. With regard to downtown areas with high population density, compact development may locate more people in areas with excessive air pollution. In all, reducing density in urban public centers and developing a polycentric urban structure may aid in the improvement of air quality in cities with compact urban forms.

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