What are the main data sources for urban heat risk assessment and how are they integrated?

• A. Only census data.
• B. Only ground-based temperature sensors with no GIS.
• C. Only air quality measurements.
• D. Remote sensing land surface temperature, meteorological data, land cover, building heights; integrated in GIS to map exposure, vulnerability, and adaptive capacity.

Answer: (D)

The main idea is that urban heat risk relies on bringing together multiple data types in a GIS framework to map exposure, vulnerability, and adaptive capacity. Remote sensing land surface temperature reveals where heat is most intense across the city, showing spatial patterns and hotspots that ground sensors might miss. Meteorological data provide the actual air temperature, humidity, and wind conditions people experience, capturing short-term variations and the human-perceived heat load. Land cover data identify surface types—vegetation, water, and built surfaces—that influence cooling through shade and evapotranspiration, as well as heat absorption and storage. Building heights reflect urban form and its impact on shading, wind flow, and heat retention, which shape microclimates within neighborhoods. By integrating these layers in GIS, you can create maps that combine where the heat hazard is highest with who is most exposed and most vulnerable, and where adaptive capacity is weaker, guiding targeted actions like increasing green cover, improving reflective surfaces, or establishing cooling centers.

Using only a single data source misses essential dimensions. Census data alone don’t show current heat distribution; ground-based sensors are typically sparse and miss spatial variability; air quality measurements don’t directly measure heat exposure. The integrated, multi-source approach in GIS provides a fuller, actionable view of urban heat risk.