F53a 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")

#  ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
#  Chapter:      F5.3 Advanced Vector Operations
#  Checkpoint:   F53a
#  Author:       Ujaval Gandhi
#  ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

blocks = ee.FeatureCollection("TIGER/2010/Blocks")
roads = ee.FeatureCollection("TIGER/2016/Roads")
sfNeighborhoods = ee.FeatureCollection("projects/gee-book/assets/F5-0/SFneighborhoods")

geometry = sfNeighborhoods.geometry()
Map.centerObject(geometry)

# Creating a Choropleth Map

# Filter blocks to the San Francisco boundary.
sfBlocks = blocks.filter(ee.Filter.bounds(geometry))

# Visualize with a single color.
Map.addLayer(sfBlocks, {"color": "#de2d26"}, "Census Blocks (single color)")

# Visualize with values in a column using paint().

# Add a pop_density column.


def func_gvn(f):
    # Get the polygon area in square miles.
    area_sqmi = f.area().divide(2.59e6)
    population = f.get("pop10")
    # Calculate population density.
    density = ee.Number(population).divide(area_sqmi)
    return f.set({"area_sqmi": area_sqmi, "pop_density": density})


sfBlocks = sfBlocks.map(func_gvn)


# Calculate the statistics of the newly computed column.
stats = sfBlocks.aggregate_stats("pop_density")
print(stats)

# Create an empty image into which to paint the features.
# Cast to 32-bit integer which supports storing values
# up to 2,147,483,647.

empty = ee.Image().int32()

# use paint() to color image with the values from the
# 'pop_density' column.
sfBlocksPaint = empty.paint(
    {
        "featureCollection": sfBlocks,
        "color": "pop_density",
    }
)

palette = ["fee5d9", "fcae91", "fb6a4a", "de2d26", "a50f15"]
visParams = {"min": 0, "max": 50000, "palette": palette}
Map.addLayer(sfBlocksPaint.clip(geometry), visParams, "Population Density")

# Filter roads to San Francisco boundary.
sfRoads = roads.filter(ee.Filter.bounds(geometry))

Map.addLayer(sfRoads, {"color": "blue"}, "Roads (default)")

# Visualize with draw().
sfRoadsDraw = sfRoads.draw({"color": "blue", "strokeWidth": 1})
Map.addLayer(sfRoadsDraw, {}, "Roads (Draw)")

styles = ee.Dictionary(
    {
        "S1100": {"color": "blue", "width": 3},
        "S1200": {"color": "green", "width": 2},
        "S1400": {"color": "orange", "width": 1},
    }
)
defaultStyle = {"color": "gray", "width": 1}


def func_wwf(f):
    classcode = f.get("mtfcc")
    style = styles.get(classcode, defaultStyle)
    return f.set("style", style)


sfRoads = sfRoads.map(func_wwf)


sfRoadsStyle = sfRoads.style(**{"styleProperty": "style"})
Map.addLayer(sfRoadsStyle.clip(geometry), {}, "Roads (Style)")

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

# Add Earth Engine dataset image = ee.Image("USGS/SRTMGL1_003") # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # Chapter: F5.3 Advanced Vector Operations # Checkpoint: F53a # Author: Ujaval Gandhi # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ blocks = ee.FeatureCollection("TIGER/2010/Blocks") roads = ee.FeatureCollection("TIGER/2016/Roads") sfNeighborhoods = ee.FeatureCollection("projects/gee-book/assets/F5-0/SFneighborhoods") geometry = sfNeighborhoods.geometry() Map.centerObject(geometry) # Creating a Choropleth Map # Filter blocks to the San Francisco boundary. sfBlocks = blocks.filter(ee.Filter.bounds(geometry)) # Visualize with a single color. Map.addLayer(sfBlocks, {"color": "#de2d26"}, "Census Blocks (single color)") # Visualize with values in a column using paint(). # Add a pop_density column. def func_gvn(f): # Get the polygon area in square miles. area_sqmi = f.area().divide(2.59e6) population = f.get("pop10") # Calculate population density. density = ee.Number(population).divide(area_sqmi) return f.set({"area_sqmi": area_sqmi, "pop_density": density}) sfBlocks = sfBlocks.map(func_gvn) # Calculate the statistics of the newly computed column. stats = sfBlocks.aggregate_stats("pop_density") print(stats) # Create an empty image into which to paint the features. # Cast to 32-bit integer which supports storing values # up to 2,147,483,647. empty = ee.Image().int32() # use paint() to color image with the values from the # 'pop_density' column. sfBlocksPaint = empty.paint( { "featureCollection": sfBlocks, "color": "pop_density", } ) palette = ["fee5d9", "fcae91", "fb6a4a", "de2d26", "a50f15"] visParams = {"min": 0, "max": 50000, "palette": palette} Map.addLayer(sfBlocksPaint.clip(geometry), visParams, "Population Density") # Filter roads to San Francisco boundary. sfRoads = roads.filter(ee.Filter.bounds(geometry)) Map.addLayer(sfRoads, {"color": "blue"}, "Roads (default)") # Visualize with draw(). sfRoadsDraw = sfRoads.draw({"color": "blue", "strokeWidth": 1}) Map.addLayer(sfRoadsDraw, {}, "Roads (Draw)") styles = ee.Dictionary( { "S1100": {"color": "blue", "width": 3}, "S1200": {"color": "green", "width": 2}, "S1400": {"color": "orange", "width": 1}, } ) defaultStyle = {"color": "gray", "width": 1} def func_wwf(f): classcode = f.get("mtfcc") style = styles.get(classcode, defaultStyle) return f.set("style", style) sfRoads = sfRoads.map(func_wwf) sfRoadsStyle = sfRoads.style(**{"styleProperty": "style"}) Map.addLayer(sfRoadsStyle.clip(geometry), {}, "Roads (Style)") # ----------------------------------------------------------------------- # CHECKPOINT # -----------------------------------------------------------------------

Display the interactive map¶

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Map

Map