Global Population Vulnerability Mapping for Public Health Risk Assessment

// Define a study area ROI over a representative metropolitan area
var roi = ee.Geometry.Rectangle([72.7, 18.8, 73.0, 19.3]); // Mumbai Metro Region
Map.centerObject(roi, 10);
Map.setOptions('HYBRID');

// Load WorldPop Population Dataset (2020)
var pop = ee.ImageCollection('WorldPop/GP/100m/pop')
  .filter(ee.Filter.date('2020-01-01', '2020-12-31'))
  .select('population')
  .mean()
  .clip(roi);

// Load Sentinel-5P NO2 Air Pollution Data (2020)
var no2 = ee.ImageCollection('COPERNICUS/S5P/OFFL/L3_NO2')
  .filterBounds(roi)
  .filterDate('2020-01-01', '2020-12-31')
  .select('NO2_column_number_density')
  .mean()
  .clip(roi);

// Load ERA5-Land Air Temperature and convert Kelvin to Celsius
var temp = ee.ImageCollection('ECMWF/ERA5_LAND/MONTHLY_AGGR')
  .filterBounds(roi)
  .filterDate('2020-05-01', '2020-06-30') // Warmest summer season
  .select('temperature_2m')
  .mean()
  .subtract(273.15)
  .clip(roi);

// Resample air quality and temperature to match 100m WorldPop spatial scale
var targetScale = 100;
var popResampled = pop.reproject({crs: 'EPSG:4326', scale: targetScale});
var no2Resampled = no2.resample('bilinear').reproject({crs: 'EPSG:4326', scale: targetScale});
var tempResampled = temp.resample('bilinear').reproject({crs: 'EPSG:4326', scale: targetScale});

// Define helper function for Min-Max normalization (0 to 1 scaling)
function normalize(img, region, scale) {
  var stats = img.reduceRegion({
    reducer: ee.Reducer.minMax(),
    geometry: region,
    scale: scale,
    maxPixels: 1e9
  });
  var minKey = img.bandNames().get(0).getInfo() + '_min';
  var maxKey = img.bandNames().get(0).getInfo() + '_max';
  var minVal = ee.Number(stats.get(minKey));
  var maxVal = ee.Number(stats.get(maxKey));
  return img.subtract(minVal).divide(maxVal.subtract(minVal)).clamp(0, 1);
}

// Normalize baseline exposure components
var normPop = normalize(popResampled, roi, targetScale);
var normNO2 = normalize(no2Resampled, roi, targetScale);
var normTemp = normalize(tempResampled, roi, targetScale);

// Visualization settings
Map.addLayer(normPop, {min: 0, max: 1, palette: ['white', 'blue']}, 'Normalized Population Density');
Map.addLayer(normNO2, {min: 0, max: 1, palette: ['white', 'yellow', 'orange']}, 'Normalized NO2 Exposure');
Map.addLayer(normTemp, {min: 0, max: 1, palette: ['white', 'red']}, 'Normalized Thermal Heat Stress');

// Combine normalized factors into a multi-criteria Vulnerability Index
// Weights: Population Sensitivity (0.4), Air Quality (0.3), Heat Stress (0.3)
var vulnerabilityIndex = normPop.multiply(0.4)
  .add(normNO2.multiply(0.3))
  .add(normTemp.multiply(0.3))
  .rename('Vulnerability_Index');

// Apply spatial smoothing filter to reduce high-frequency grid noise
var smoothVulnerability = vulnerabilityIndex.reduceNeighborhood({
  reducer: ee.Reducer.mean(),
  kernel: ee.Kernel.square({radius: 200, units: 'meters'})
});

// Define color palette for public health mapping
var healthPalette = [
  '#2b83ba', // Very Low Vulnerability (Blue)
  '#abdda4', // Low Vulnerability (Green)
  '#ffffbf', // Moderate Vulnerability (Yellow)
  '#fdae61', // High Vulnerability (Orange)
  '#d7191c'  // Very High Vulnerability (Red)
];

// Display composite vulnerability raster layer
Map.addLayer(smoothVulnerability, {
  min: 0.1,
  max: 0.8,
  palette: healthPalette
}, 'Population Vulnerability Index');

// Calculate zonal mean vulnerability across sub-administrative areas
var adminUnits = ee.FeatureCollection('FAO/GAUL/2015/level2')
  .filterBounds(roi);

var adminVulnerability = smoothVulnerability.reduceRegions({
  collection: adminUnits,
  reducer: ee.Reducer.mean().combine({
    reducer2: ee.Reducer.stdDev(),
    sharedInputs: true
  }),
  scale: targetScale
});

// Highlight high vulnerability administrative units
Map.addLayer(adminUnits.style({color: 'black', fillColor: '00000000'}), {}, 'District Boundaries');
print('Mean Vulnerability by District:', adminVulnerability);

// Export final vulnerability index raster layer to Google Drive
Export.image.toDrive({
  image: smoothVulnerability,
  description: 'Global_Population_Vulnerability_Index_2020',
  folder: 'GEE_PublicHealth_Outputs',
  scale: 100,
  region: roi,
  fileFormat: 'GeoTIFF',
  maxPixels: 1e9
});