A31s2 Exercise 2

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")

# Import roads.
grip4_africa = ee.FeatureCollection("projects/sat-io/open-datasets/GRIP4/Africa")
grip4_europe = ee.FeatureCollection("projects/sat-io/open-datasets/GRIP4/Europe")
grip4_north_america = ee.FeatureCollection(
    "projects/sat-io/open-datasets/GRIP4/North-America"
)


# Function to add line length in km
def addLength(feature):
    return feature.set({"lengthKm": feature.length().divide(1000)})  # km


# Calculate line lengths for all roads in Africa
grip4_africaLength = grip4_africa.map(addLength)

# Convert to roads to raster
empty = ee.Image().float()

grip4_africaRaster = empty.paint(
    {"featureCollection": grip4_africaLength, "color": "lengthKm"}
)

# Import simplified countries
countries = ee.FeatureCollection("USDOS/LSIB_SIMPLE/2017")

# Filter to Africa
Africa = countries.filter(ee.Filter.eq("wld_rgn", "Africa"))

# Import global power transmission lines
transmission = ee.FeatureCollection(
    "projects/sat-io/open-datasets/predictive-global-power-system/distribution-transmission-lines"
)

# Filter transmission lines to Africa
transmissionAfrica = transmission.filterBounds(Africa)

# Calculate line lengths for all transmission lines in Africa
transmissionAfricaLength = transmissionAfrica.map(addLength)

# Convert to transmission lines to raster
transmissionAfricaRaster = empty.paint(
    {"featureCollection": transmissionAfricaLength, "color": "lengthKm"}
)

# Add roads and transmission lines together into one image
# Clip to Africa feature collection
stack = (
    grip4_africaRaster.addBands(transmissionAfricaRaster)
    .rename(["roads", "transmission"])
    .clipToCollection(Africa)
)

# Calculate spatial statistics: local Geary's C
# Create a list of weights for a 9x9 kernel.
list = [1, 1, 1, 1, 1, 1, 1, 1, 1]

# The center of the kernel is zero.
centerList = [1, 1, 1, 1, 0, 1, 1, 1, 1]

# Assemble a list of lists: the 9x9 kernel weights as a 2-D matrix.
lists = [list, list, list, list, centerList, list, list, list, list]

# Create the kernel from the weights.
# Non-zero weights represent the spatial neighborhood.
kernel = ee.Kernel.fixed(9, 9, lists, -4, -4, False)

# Use the max among bands as the input.
maxBands = stack.reduce(ee.Reducer.max())

# Convert the neighborhood into multiple bands.
neighs = maxBands.neighborhoodToBands(kernel)

# Compute local Geary's C, a measure of spatial association
# – 0 indicates perfect positive autocorrelation/clustered
# – 1 indicates no autocorrelation/random
# – 2 indicates perfect negative autocorrelation/dispersed
gearys = (
    maxBands.subtract(neighs).pow(2).reduce(ee.Reducer.sum()).divide(math.pow(9, 2))
)

# Convert to a -/+1 scale by: calculating C* = 1 – C
# – 1 indicates perfect positive autocorrelation/clustered
# – 0 indicates no autocorrelation/random
# – -1 indicates perfect negative autocorrelation/dispersed
gearysStar = ee.Image(1).subtract(gearys)

# Import palettes
palettes = require("users/gena/packages:palettes")

# Create custom palette, blue is negative while red is positive autocorrelation/clustered
palette = palettes.colorbrewer.Spectral[7].reverse()

# Normalize the image and add it to the map.
visParams = {"min": -1, "max": 1, "palette": palette}

# Import custom basemap
basemap = require("users/erintrochim/GEE_workshops:backgroundMaps")

# Add basemap
basemap.addCustomBasemap("BlackAndWhite")

# Display
Map.setCenter(3.6, 32.5, 11)
Map.addLayer(gearysStar.focalMax(1), visParams, "local Gearys C*")

# LGTM (nclinton)

# Add Earth Engine dataset image = ee.Image("USGS/SRTMGL1_003") # Import roads. grip4_africa = ee.FeatureCollection("projects/sat-io/open-datasets/GRIP4/Africa") grip4_europe = ee.FeatureCollection("projects/sat-io/open-datasets/GRIP4/Europe") grip4_north_america = ee.FeatureCollection( "projects/sat-io/open-datasets/GRIP4/North-America" ) # Function to add line length in km def addLength(feature): return feature.set({"lengthKm": feature.length().divide(1000)}) # km # Calculate line lengths for all roads in Africa grip4_africaLength = grip4_africa.map(addLength) # Convert to roads to raster empty = ee.Image().float() grip4_africaRaster = empty.paint( {"featureCollection": grip4_africaLength, "color": "lengthKm"} ) # Import simplified countries countries = ee.FeatureCollection("USDOS/LSIB_SIMPLE/2017") # Filter to Africa Africa = countries.filter(ee.Filter.eq("wld_rgn", "Africa")) # Import global power transmission lines transmission = ee.FeatureCollection( "projects/sat-io/open-datasets/predictive-global-power-system/distribution-transmission-lines" ) # Filter transmission lines to Africa transmissionAfrica = transmission.filterBounds(Africa) # Calculate line lengths for all transmission lines in Africa transmissionAfricaLength = transmissionAfrica.map(addLength) # Convert to transmission lines to raster transmissionAfricaRaster = empty.paint( {"featureCollection": transmissionAfricaLength, "color": "lengthKm"} ) # Add roads and transmission lines together into one image # Clip to Africa feature collection stack = ( grip4_africaRaster.addBands(transmissionAfricaRaster) .rename(["roads", "transmission"]) .clipToCollection(Africa) ) # Calculate spatial statistics: local Geary's C # Create a list of weights for a 9x9 kernel. list = [1, 1, 1, 1, 1, 1, 1, 1, 1] # The center of the kernel is zero. centerList = [1, 1, 1, 1, 0, 1, 1, 1, 1] # Assemble a list of lists: the 9x9 kernel weights as a 2-D matrix. lists = [list, list, list, list, centerList, list, list, list, list] # Create the kernel from the weights. # Non-zero weights represent the spatial neighborhood. kernel = ee.Kernel.fixed(9, 9, lists, -4, -4, False) # Use the max among bands as the input. maxBands = stack.reduce(ee.Reducer.max()) # Convert the neighborhood into multiple bands. neighs = maxBands.neighborhoodToBands(kernel) # Compute local Geary's C, a measure of spatial association # – 0 indicates perfect positive autocorrelation/clustered # – 1 indicates no autocorrelation/random # – 2 indicates perfect negative autocorrelation/dispersed gearys = ( maxBands.subtract(neighs).pow(2).reduce(ee.Reducer.sum()).divide(math.pow(9, 2)) ) # Convert to a -/+1 scale by: calculating C* = 1 – C # – 1 indicates perfect positive autocorrelation/clustered # – 0 indicates no autocorrelation/random # – -1 indicates perfect negative autocorrelation/dispersed gearysStar = ee.Image(1).subtract(gearys) # Import palettes palettes = require("users/gena/packages:palettes") # Create custom palette, blue is negative while red is positive autocorrelation/clustered palette = palettes.colorbrewer.Spectral[7].reverse() # Normalize the image and add it to the map. visParams = {"min": -1, "max": 1, "palette": palette} # Import custom basemap basemap = require("users/erintrochim/GEE_workshops:backgroundMaps") # Add basemap basemap.addCustomBasemap("BlackAndWhite") # Display Map.setCenter(3.6, 32.5, 11) Map.addLayer(gearysStar.focalMax(1), visParams, "local Gearys C*") # LGTM (nclinton)

Display the interactive map¶

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Map

Map