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Explicit Spatializing Heat-Exposure Risk and Local Associated Factors by coupling social media data and automatic
Zheng Cao1, Zhifeng Wu1, Shaoying Li1
1School of Geographical Sciences, Guangzhou University, Guangzhou 510006, China; Guangdong Province Engineering Technology Research Center for Geographical Conditions Monitoring and Comprehensive Analysis, Guangzhou 510006, China; Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), Guangzhou 511458, China.
High temperatures pose significant public health risks. This study developed a high-resolution method using weather and population mobility data to assess temperature risk, identifying key risk areas and influencing factors for mitigation strategies.
Area of Science:
- Environmental Health
- Urban Planning
- Public Health
Background:
- Extreme high temperatures are increasing in frequency and intensity, posing significant weather-related public health risks.
- Effective and low-cost risk assessment methods are crucial for mitigating the adverse impacts of extreme heat events.
- Existing methods may lack the spatiotemporal resolution needed for targeted interventions.
Purpose of the Study:
- To develop a high spatiotemporal resolution temperature risk assessment method.
- To integrate automatic meteorological station data with population mobility data for enhanced accuracy.
- To identify high-temperature risk areas and influencing factors for developing mitigation strategies.
Main Methods:
- Applied automatic meteorological station data and population mobility data to create a high-resolution (100m) temperature risk assessment.
- Analyzed population mobility patterns to understand working/residential complexes and identify spatial clustering hotspots.
- Utilized the geographically weighted regression model to investigate factors influencing heat exposure distribution.
Main Results:
- The developed method identified high-temperature risk areas, with over 36% of total mortality occurring in these zones.
- Key risk areas were concentrated in the southwest of the study area, with peak risks before dawn and in the evening.
- Individual factors explained over 22.4% of heat exposure spatial distribution, with services (catering, transportation, living) being significant; joint factors explained over 47.9%.
Conclusions:
- The introduced spatial-specific method effectively quantifies high-temperature risk, considering population mobility.
- Understanding the mechanisms behind spatial heat risk distribution is vital for urban planning and public health.
- Findings provide theoretical and management implications for urban designers and energy governors to develop heat mitigation strategies.
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