<jupyter_start><jupyter_text>Get population data:<jupyter_code>##Use Google Earth Engine Interface:
# // Load an image
# var getimage = ee.ImageCollection("CIESIN/GPWv411/GPW_Basic_Demographic_Characteristics").first();

# // Create a geometry representing an export region.
# var geometry = ee.Geometry.Rectangle([-67.4, 17.9, -65.1, 18.6])

# // Export the image, specifying scale and region.
# Export.image.toDrive({
# image: getimage,
# description: 'imageToDriveExample',
# scale: 500,
# region: geometry
# });
image = '../data/supplementary_data/tif/ds4g_population.tif'
image_band = rio.open(image).read(1)
plot_scaled(image_band)
#overlay_image_on_puerto_rico(image,band_layer=1)
preview_meta_data('../data/supplementary_data/tif/ds4g_population.tif')<jupyter_output>Metadata for:  ../data/ds4g_population.tif
Bounding Box: BoundingBox(left=-67.40516228845917, bottom=17.892232143725924, right=-65.096492008272, top=18.601901218180345)
Resolution: (0.008983152841195215, 0.008983152841195215)
Tags: {'AREA_OR_POINT': 'Area'}
More Tags: {'COMPRESSION': 'LZW', 'INTERLEAVE': 'BAND'}
Number of Channels = 1<jupyter_text>Get night time lights data:<jupyter_code>##Use Google Earth Engine Interface:
# // Create a geometry representing an export region.
# var geometry = ee.Geometry.Rectangle([-67.4, 17.9, -65.1, 18.6])

# // Load an image
# var getimagecollection = ee.ImageCollection("NOAA/VIIRS/DNB/MONTHLY_V1/VCMSLCFG")
#       .filterDate('2018-07-01', '2019-07-01')
#       .filterBounds(geometry);

# var imageselect = getimagecollection.select('avg_rad').first();

# // var getimage = ee.Image(imageselect).first();


# // Export the image, specifying scale and region.
# Export.image.toDrive({
# image: imageselect,
# description: 'ds4g_nighttime_lights2',
# scale: 1000,
# region: geometry,
# });
image = '../data/supplementary_data/tif/ds4g_nighttime_lights.tif'
image_band = rio.open(image).read(1)
plot_scaled(image_band)
#overlay_image_on_puerto_rico(image,band_layer=1)<jupyter_output><empty_output><jupyter_text>Get Landcover Data:<jupyter_code>##Use Google Earth Engine Interface:
# // Create a geometry representing an export region.
# var geometry = ee.Geometry.Rectangle([-67.4, 17.9, -65.1, 18.6])

# // Load an image
# var getimage = ee.Image("USGS/GFSAD1000_V1")

# var imageselect = getimage.select('landcover');

# // var getimage = ee.Image(imageselect).first();


# // Export the image, specifying scale and region.
# Export.image.toDrive({
# image: imageselect,
# description: 'ds4g_landcover',
# scale: 1000,
# region: geometry,
# });
image = '../data/supplementary_data/tif/ds4g_landcover.tif'
image_band = rio.open(image).read(1)
bounds = np.arange(0,10,1)
plt.imshow(image_band, cmap='tab10', vmin=0, vmax=5)
plt.colorbar()
#overlay_image_on_puerto_rico(image,band_layer=1)
image_band.min()<jupyter_output><empty_output>
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