// Google Earth Engine code for estimating greenspace exposure
// Code link: https://code.earthengine.google.com/11189060e7835ef091d66601c97546e6
// load three-level administrative units
// county level
var block = ee.FeatureCollection("FAO/GAUL/2015/level2")
// country level
// var block = ee.FeatureCollection("FAO/GAUL/2015/level0")
// state level
// var block = ee.FeatureCollection("FAO/GAUL/2015/level1")  

// load 10-m esa worldCover
var esa = ee.Image("ESA/WorldCover/v100/2020")

// group all forests, shrubs, grasses to vegetation
var green = esa.remap([10,20,30,40,50,60,70,80,90,95,100], [1,1,1,0,0,0,0,0,0,0,0])  //tree, shrubs and grass

// load worldpop
var pop = ee.ImageCollection("WorldPop/GP/100m/pop").filterDate('2020-01-01','2021-01-01').mosaic()

// aggregate 10-m greenspace to 100-m worldpop
var pop_prj = ee.ImageCollection("WorldPop/GP/100m/pop").filterDate('2020-01-01','2021-01-01').first().projection()
var esa_prj = ee.Image("ESA/WorldCover/v100/2020").projection()

var green100 = green.reproject(esa_prj).reduceResolution({
      reducer: ee.Reducer.mean(),
      maxPixels: 1024
    })
    // reproject to worldpop at 100 m
    .reproject({
      crs: pop_prj
    });

// estimate population weighted greenspace exposure

// (1) perfect match population weighted greenspace exposure
// 100 m
var popgreen = green100.multiply(pop)

// (2) different buffered sizes for estimating greenspace exposure
// Define a kernel of circle or square
// 500 m
var kernelcore = ee.Kernel.circle({
  radius: 5, units: 'pixels', normalize: true
});
// Smooth the image by convolving with the kernel core.
var smooth = green100.convolve(kernelcore);
var popgreen500m = smooth.multiply(pop)

// Define a kernel of circle or square
// 1000 m
var kernelcore = ee.Kernel.circle({
  radius: 10, units: 'pixels', normalize: true
});
// Smooth the image by convolving with the kernel core.
var smooth = green100.convolve(kernelcore);
var popgreen1000m = smooth.multiply(pop)


// Define a kernel of circle or square - 2km
// 1500 m
var kernelcore = ee.Kernel.circle({
  radius: 15, units: 'pixels', normalize: true
});
// Smooth the image by convolving with the kernel core.
var smooth = green100.convolve(kernelcore);
var popgreen1500m = smooth.multiply(pop)

// statistics
// population
var popsum = pop.reduceRegions({
  collection: block,
  reducer:ee.Reducer.sum(),
  scale: 100
});

// drop .geo column
var polyOut = popsum.select(['.*'],null,false);

// Export
Export.table.toDrive({
  collection: polyOut,
  description: 'Population_sum',
  folder: 'Global_Greenspace_Expo', 
  fileFormat: 'CSV'
});

// perfect pixel match 100 m
var countygreen = popgreen.reduceRegions({
  collection: block,
  reducer:ee.Reducer.sum(),
  scale: 100
});

// drop .geo column
var polyOut = countygreen.select(['.*'],null,false);

// Export
Export.table.toDrive({
  collection: polyOut,
  description: '100m_greenspace_exposure',
  folder: 'Global_Greenspace_Expo',
  fileFormat: 'CSV'
});

// 500m 
var countygreen500m = popgreen500m.reduceRegions({
  collection: block,
  reducer:ee.Reducer.sum(),
  scale: 100
});

// drop .geo column
var polyOut = countygreen500m.select(['.*'],null,false);

// Export
Export.table.toDrive({
  collection: polyOut,
  description: '500m_greenspace_exposure',
  folder: 'Global_Greenspace_Expo',
  fileFormat: 'CSV'
});  

// 1km
var countygreen1km = popgreen1000m.reduceRegions({
  collection: block,
  reducer:ee.Reducer.sum(),
  scale: 100
});

// drop .geo column
var polyOut = countygreen1km.select(['.*'],null,false);

// Export
Export.table.toDrive({
  collection: polyOut,
  description: '1000m_greenspace_exposure',
  folder: 'Global_Greenspace_Expo',
  fileFormat: 'CSV'
});   

// 1500 m
var countygreen1500m = popgreen1500m.reduceRegions({
  collection: block,
  reducer:ee.Reducer.sum(),
  scale: 100
});

// drop .geo column
var polyOut = countygreen1500m.select(['.*'],null,false);

// Export
Export.table.toDrive({
  collection: polyOut,
  description: '1500m_greenspace_exposure',
  folder: 'Global_Greenspace_Expo',
  fileFormat: 'CSV'
});