Research on measurement and optimization strategy of spatial health performance of winter city blocks in cold climate based on human factor data detection

Urban problems such as environmental pollution and extreme weather events caused by accelerated urbanization have further caused public health crises, so the healthy city becomes an inevitable goal for sustainable development. Studies have confirmed that health performance at block scale greatly affects the health quality of urban settlements, and human factors data is more intuitive and accurate at detecting it. Current studies mostly focus on cities with hot or mild climates, while winter cities suffer greater health impact because of the larger proportion of cold climate they deal with throughout the year, so the need to improve their health performance is more urgent. Accordingly, this study takes Harbin, a typical winter city, as an example, to track sample block experiencers’ physiological stress response in cold climate with an E4 wristband to quantify health performance, then construct an “urban block space–health performance” assessment frame through the MOS and propose a suitable optimization strategy. The research shows that the block sample health performance in cold climate is related to spatial type and crowd structure and highlights people’s concern for walking safety, thermal comfort, and transportation convenience in urban blocks. Meanwhile, an optimization strategy based on the assessment frame can enrich optimization perspectives and provide a scientific basis for the practice of health performance.