Is my city susceptible to over-heating?

Excessive temperatures, especially in an urban setting can be dangerous. As higher temperatures put vulnerable populations at an increased risk of death, city planners need to know which areas are likely to be most impacted and guide interventions like tree planting and the use of cooler building materials.

A heat risk index or a  Heat Vulnerability Index  (HVI) can be used to map neighborhoods whose residents are more at risk during and immediately following extreme heat. Heat risk is determined by environmental factors like surface temperature, vegetation cover and population density.

As we have seen PLACE data provides the ability to map vegetation and population in great detail and when combined with other data sources that record things like surface temperature, it can be used to help develop city wide HVI's, pinpointing  heat islands . In this example we look at Zomba in Malawi. Our approach and follows a similar study conducted for  Athens. 

A heat index was generated by combining three (3) datasets. Normalized Difference Vegetation Index (NDVI) and Land Surface Temperatures (LST) data from Landsat 8 satellite data with population density derived from PLACE data.

•  NDVI  is used to quantify vegetation greenness. NDVI is the ratio between red (R) and near infrared (NIR) bands. Using Landsat 8 data NDVI is derived from: (Band 5 – Band 4) / (Band 5 + Band 4) where Band 4 is red and Band 5 is near infrared.
•  LST  measures the Earth’s surface temperature in  Kelvin . Using Landsat 8 data LST is derived from Band 10 (thermal infrared band).
• Population density estimates were derived from building detections using PLACE imagery and household size from census data as outlined  here . These estimates were converted to a raster (cell based) dataset.

Using ArcGIS Pro's  ModelBuilder  we built an additive processing workflow, as shown below, normalizing the three inputs and combining them as a raster based heat index.

The result is a HVI map that shows at risk areas in Zomba ranked from 1 - 5, with 5 being the most at risk of excessive heat and 1 the least, represented by a 60m by 60m grid.

In the map below the darker the cell the more at risk an area to the affects of heat. Looking at the map you can see concentrations of risk areas running from west to east across the city, intersected by a park in the center (use the + and - tools to zoom in/out on the map).

Very quickly and combined other datasets, PLACE imagery can give new actionable insights into the risk of rising temperatures by understanding which populations will be worst affected.