A14a Checkpoint

Import libraries¶

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import ee
import geemap

import ee import geemap

Create an interactive map¶

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Map = geemap.Map(center=[40, -100], zoom=4)

Map = geemap.Map(center=[40, -100], zoom=4)

Add Earth Engine Python script¶

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# Add Earth Engine dataset
image = ee.Image("USGS/SRTMGL1_003")

#*** Start of imports. If edited, may not auto-convert in the playground. ***#
geometry =

    # shown: False #
    ee.Geometry.Point([114.26732477622326, 30.57603159263821])
#**** End of imports. If edited, may not auto-convert in the playground. ****#
#  ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
#  Chapter:      A1.4 Air Pollution and Population Exposures
#  Checkpoint:   A14a
#  Authors:      Zander Venter and Sourangsu Chowdhury
#  ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

#
 # Section 1: data import and cleaning
 #

# Import a global dataset of administrative units level 1.
adminUnits = ee.FeatureCollection(
    'FAO/GAUL_SIMPLIFIED_500m/2015/level1')

# Filter for the administrative unit that intersects
# the geometry located at the top of this script.
adminSelect = adminUnits.filterBounds(geometry)

# Center the map on this area.
Map.centerObject(adminSelect, 8)

# Make the base map HYBRID.
Map.setOptions('HYBRID')

# Add it to the map to make sure you have what you want.
Map.addLayer(adminSelect, {}, 'selected admin unit')

# Import the population count data from Gridded Population of the World Version 4.
population = ee.ImageCollection(
        'CIESIN/GPWv411/GPW_Population_Count') \
    .filter(ee.Filter.calendarRange(2020, 2020, 'year')) \
    .mean()

# Clip it to your area of interest (only necessary for visualization purposes).
populationClipped = population.clipToCollection(adminSelect)

# Add it to the map to see the population distribution.
popVis = {
    'min': 0,
    'max': 4000,
    'palette': ['black', 'yellow', 'white'],
    'opacity': 0.55
}
Map.addLayer(populationClipped, popVis, 'population count')

# Import the Sentinel-5P NO2 offline product.
no2Raw = ee.ImageCollection('COPERNICUS/S5P/OFFL/L3_NO2')

# Define function to exclude cloudy pixels.
def maskClouds(image):
    # Get the cloud fraction band of the image.
    cf = image.select('cloud_fraction')
    # Create a mask using 0.3 threshold.
    mask = cf.lte(0.3); # You can play around with this value.
    # Return a masked image.
    return image.updateMask(mask).copyProperties(image)


# Clean and filter the Sentinel-5P NO2 offline product.
no2 = no2Raw \
    .filterBounds(adminSelect) \
    .map(maskClouds) \
    .select('tropospheric_NO2_column_number_density')

# Create a median composite for March 2021
no2Median = no2.filterDate('2021-03-01', '2021-04-01').median()

# Clip it to your area of interest (only necessary for visualization purposes).
no2MedianClipped = no2Median.clipToCollection(adminSelect)

# Visualize the median NO2.
no2Viz = {
    'min': 0,
    'max': 0.00015,
    'palette': ['black', 'blue', 'purple', 'cyan', 'green',
        'yellow', 'red'
    ]
}
Map.addLayer(no2MedianClipped, no2Viz, 'median no2 Mar 2021')

#  -----------------------------------------------------------------------
#  CHECKPOINT
#  -----------------------------------------------------------------------

# Add Earth Engine dataset image = ee.Image("USGS/SRTMGL1_003") #*** Start of imports. If edited, may not auto-convert in the playground. ***# geometry = # shown: False # ee.Geometry.Point([114.26732477622326, 30.57603159263821]) #**** End of imports. If edited, may not auto-convert in the playground. ****# # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # Chapter: A1.4 Air Pollution and Population Exposures # Checkpoint: A14a # Authors: Zander Venter and Sourangsu Chowdhury # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # # Section 1: data import and cleaning # # Import a global dataset of administrative units level 1. adminUnits = ee.FeatureCollection( 'FAO/GAUL_SIMPLIFIED_500m/2015/level1') # Filter for the administrative unit that intersects # the geometry located at the top of this script. adminSelect = adminUnits.filterBounds(geometry) # Center the map on this area. Map.centerObject(adminSelect, 8) # Make the base map HYBRID. Map.setOptions('HYBRID') # Add it to the map to make sure you have what you want. Map.addLayer(adminSelect, {}, 'selected admin unit') # Import the population count data from Gridded Population of the World Version 4. population = ee.ImageCollection( 'CIESIN/GPWv411/GPW_Population_Count') \ .filter(ee.Filter.calendarRange(2020, 2020, 'year')) \ .mean() # Clip it to your area of interest (only necessary for visualization purposes). populationClipped = population.clipToCollection(adminSelect) # Add it to the map to see the population distribution. popVis = { 'min': 0, 'max': 4000, 'palette': ['black', 'yellow', 'white'], 'opacity': 0.55 } Map.addLayer(populationClipped, popVis, 'population count') # Import the Sentinel-5P NO2 offline product. no2Raw = ee.ImageCollection('COPERNICUS/S5P/OFFL/L3_NO2') # Define function to exclude cloudy pixels. def maskClouds(image): # Get the cloud fraction band of the image. cf = image.select('cloud_fraction') # Create a mask using 0.3 threshold. mask = cf.lte(0.3); # You can play around with this value. # Return a masked image. return image.updateMask(mask).copyProperties(image) # Clean and filter the Sentinel-5P NO2 offline product. no2 = no2Raw \ .filterBounds(adminSelect) \ .map(maskClouds) \ .select('tropospheric_NO2_column_number_density') # Create a median composite for March 2021 no2Median = no2.filterDate('2021-03-01', '2021-04-01').median() # Clip it to your area of interest (only necessary for visualization purposes). no2MedianClipped = no2Median.clipToCollection(adminSelect) # Visualize the median NO2. no2Viz = { 'min': 0, 'max': 0.00015, 'palette': ['black', 'blue', 'purple', 'cyan', 'green', 'yellow', 'red' ] } Map.addLayer(no2MedianClipped, no2Viz, 'median no2 Mar 2021') # ----------------------------------------------------------------------- # CHECKPOINT # -----------------------------------------------------------------------

Display the interactive map¶

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Map

Map