To create the Heatwave Vulnerability Index, the methodology involved several steps designed to integrate various data sources and provide an assessment of vulnerability across different districts. Here’s an overview of the methodology: 1) Data Collection: Gathering the following data sets - Land Surface Temperature over summer across India (2021), district boundaries (2023), socio-economic data (income and type of labour - 2023), and population per district (2023) 2) Data Preprocessing: a) Processing the LST layer - Converting the TIFF image of land surface temperature (LST) data for analysis by first transforming it into integer format (INT), then reclassifying this INT data into a raster format. Subsequently, converting this raster into a polygon layer to align it with administrative boundaries, and finally, performing a spatial join with the district boundaries of India to obtain district-wise LST data. b) Standardizing and normalizing the data to ensure consistency across different formats and scales. This includes georeferencing the data to a common coordinate system 3) Indicator Selection: Choosing relevant indicators such as population density, Income level (number of households with highest earning member earning less than INR 10,000) and type of labour (number of households engaged in manual casual labour), and median land surface temperature over April and May. 4) Normalizing Datasets using Z-Scores: Calculating z-scores for population density, income level, and type of labor, and LST datasets to normalize these factors onto a common scale. Creating new fields in each dataset's attribute table to store z-scores. 4) Weight Assignment: Assigning weights to each indicator based on their relative importance in contributing to vulnerability. Currently, the weights are as follows LST: 50% Population Density: 25% Number of households with highest earning member less than Rs. 10,000: 15% Number of households engaged in manual casual labour: 10% 5) Index Calculation: Combining the indicators using the assigned weights to calculate a composite vulnerability index for each district. This is done through a weighted sum approach, where each indicator value is multiplied by its weight and summed to produce an overall vulnerability score. 6) Validation and Calibration: Validating the index by comparing it with historical heatwave impact data, such as records of heat-related illnesses and deaths. Adjust the weights and indicators if necessary to improve accuracy.