Urban Heat Risk Index Assessment of Dhaka District, Bangladesh: GEE Scripts, Python Notebooks and Outputs

This repository contains the complete data collection, analysis and figure generation pipeline for the paper "Urban Heat Risk Assessment of Dhaka District, Bangladesh: A Fine-Scale Spatial Analysis". The study constructs a spatially explicit Urban Heat Risk Index (UHRI) for 247 administrative units of Dhaka District (wards, unions and paurashavas) following the method of Mitchell and Chakraborty (2015): UHRI = LST(z) + NDBI(z) - NDVI(z) Land Surface Temperature (LST), Normalised Difference Built-up Index (NDBI) and Normalised Difference Vegetation Index (NDVI) were derived from Landsat 9 Collection 2 Level-2 imagery (January to May 2026) in Google Earth Engine. District-wide z scores were computed at pixel level before zonal aggregation to the administrative unit level. UHRI was normalised to [0,1] and classified using Jenks Natural Breaks into five risk classes. Spatial clustering was examined using Global Moran's I (I = 0.8687, Z = 31.998, p < 0.0001), Local Moran's I (LISA) andBivariate LISA (LST x NDVI, I = 0.6899, p < 0.0001). Results identify 104 High-High heat risk hot spots (30.0%) concentrated in Dhaka North and South City Corporation core wards, and 53 Low-Low cold spots (15.3%) in peripheral rural unions of Savar, Keraniganj, Dhamrai, Nawabganj and Dohar upazilas. Contents:- GEE script: Landsat 9 LST, NDBI and NDVI zonal statistics export- Notebook 01: UHRI construction, normalisation and Jenks classification- Notebook 02: Pearson correlation, Top 10/Bottom 10, Global Moran's I, LISA and Bivariate LISA- Notebook 03: All figures (study area, indicator maps, risk map)- Outputs: 8 publication-quality figures and 6 summary tables