us_census_data.ipynb

Kernel: Python 3

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U.S. Census Data

The United States Census Bureau Topologically Integrated Geographic Encoding and Referencing (TIGER) dataset contains the 2018 boundaries for the primary governmental divisions of the United States. In addition to the fifty states, the Census Bureau treats the District of Columbia, Puerto Rico, and each of the island areas (American Samoa, the Commonwealth of the Northern Mariana Islands, Guam, and the U.S. Virgin Islands) as the statistical equivalents of States for the purpose of data presentation. Each feature represents a state or state equivalent.

For full technical details on all TIGER 2018 products, see the TIGER technical documentation.

• TIGER: US Census States: ee.FeatureCollection("TIGER/2018/States")

• TIGER: US Census Counties: ee.FeatureCollection("TIGER/2018/Counties")

• TIGER: US Census Tracts: ee.FeatureCollection("TIGER/2010/Tracts_DP1")

• TIGER: US Census Blocks: ee.FeatureCollection("TIGER/2010/Blocks")

• TIGER: US Census Roads: ee.FeatureCollection("TIGER/2016/Roads")

• TIGER: US Census 5-digit ZIP Code: ee.FeatureCollection("TIGER/2010/ZCTA5")

Install Earth Engine API and geemap

Install the Earth Engine Python API and geemap. The geemap Python package is built upon the ipyleaflet and folium packages and implements several methods for interacting with Earth Engine data layers, such as Map.addLayer(), Map.setCenter(), and Map.centerObject(). The following script checks if the geemap package has been installed. If not, it will install geemap, which automatically installs its dependencies, including earthengine-api, folium, and ipyleaflet.

Important note: A key difference between folium and ipyleaflet is that ipyleaflet is built upon ipywidgets and allows bidirectional communication between the front-end and the backend enabling the use of the map to capture user input, while folium is meant for displaying static data only (source). Note that Google Colab currently does not support ipyleaflet (source). Therefore, if you are using geemap with Google Colab, you should use import geemap.foliumap. If you are using geemap with binder or a local Jupyter notebook server, you can use import geemap, which provides more functionalities for capturing user input (e.g., mouse-clicking and moving).

# Installs geemap package
import subprocess

try:
    import geemap
except ImportError:
    print("geemap package not installed. Installing ...")
    subprocess.check_call(["python", "-m", "pip", "install", "geemap"])

# Checks whether this notebook is running on Google Colab
try:
    import google.colab
    import geemap.foliumap as emap
except:
    import geemap as emap

# Authenticates and initializes Earth Engine
import ee

try:
    ee.Initialize()
except Exception as e:
    ee.Authenticate()
    ee.Initialize()

TIGER: US Census States

https://developers.google.com/earth-engine/datasets/catalog/TIGER_2018_States

Displaying data

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

states = ee.FeatureCollection("TIGER/2018/States")

Map.centerObject(states, 4)
Map.addLayer(states, {}, "US States")

Map.addLayerControl()  # This line is not needed for ipyleaflet-based Map
Map

Displaying vector as raster

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

states = ee.FeatureCollection("TIGER/2018/States")
image = ee.Image().paint(states, 0, 2)

Map.centerObject(states, 4)
Map.addLayer(image, {}, "US States")

Map.addLayerControl()
Map

Select by attribute

Select one single state

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

tn = ee.FeatureCollection("TIGER/2018/States").filter(ee.Filter.eq("NAME", "Tennessee"))

Map.centerObject(tn, 6)
Map.addLayer(tn, {}, "Tennessee")

Map.addLayerControl()
Map

tn = (
    ee.FeatureCollection("TIGER/2018/States")
    .filter(ee.Filter.eq("NAME", "Tennessee"))
    .first()
)

props = tn.toDictionary().getInfo()
print(props)

Select multiple states

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

selected = ee.FeatureCollection("TIGER/2018/States").filter(
    ee.Filter.inList("NAME", ["Tennessee", "Alabama", "Georgia"])
)

Map.centerObject(selected, 6)
Map.addLayer(selected, {}, "Selected states")

Map.addLayerControl()
Map

Printing all values of a column

states = ee.FeatureCollection("TIGER/2018/States").sort("ALAND", False)
names = states.aggregate_array("STUSPS").getInfo()
print(names)

areas = states.aggregate_array("ALAND").getInfo()
print(areas)

import matplotlib.pyplot as plt

%matplotlib notebook

plt.bar(names, areas)
plt.show()

Descriptive statistics of a column

For example, we can calculate the total land area of all states:

states = ee.FeatureCollection("TIGER/2018/States")
area_m2 = states.aggregate_sum("ALAND").getInfo()
area_km2 = area_m2 / 1000000
print("Total land area: ", area_km2, " km2")

states = ee.FeatureCollection("TIGER/2018/States")
stats = states.aggregate_stats("ALAND").getInfo()
print(stats)

Add a new column to the attribute table

states = ee.FeatureCollection("TIGER/2018/States").sort("ALAND", False)
states = states.map(lambda x: x.set("AreaKm2", x.area().divide(1000000).toLong()))
first = states.first().toDictionary().getInfo()
print(first)

Set symbology based on column values

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

states = ee.FeatureCollection("TIGER/2018/States")

visParams = {
    "palette": ["purple", "blue", "green", "yellow", "orange", "red"],
    "min": 500000000.0,
    "max": 5e11,
    "opacity": 0.8,
}

image = ee.Image().float().paint(states, "ALAND")
Map.addLayer(image, visParams, "TIGER/2018/States")

Map.addLayerControl()
Map

Download attribute table as a CSV

states = ee.FeatureCollection("TIGER/2018/States")
url = states.getDownloadURL(
    filetype="csv",
    selectors=["NAME", "ALAND", "REGION", "STATEFP", "STUSPS"],
    filename="states",
)
print(url)

Formatting the output

first = states.first()
props = first.propertyNames().getInfo()
print(props)
props = states.first().toDictionary(props).getInfo()
print(props)

for key, value in props.items():
    print("{}: {}".format(key, value))

Download data as shapefile to Google Drive

# function for converting GeometryCollection to Polygon/MultiPolygon
def filter_polygons(ftr):
    geometries = ftr.geometry().geometries()
    geometries = geometries.map(
        lambda geo: ee.Feature(ee.Geometry(geo)).set("geoType", ee.Geometry(geo).type())
    )

    polygons = (
        ee.FeatureCollection(geometries)
        .filter(ee.Filter.eq("geoType", "Polygon"))
        .geometry()
    )
    return ee.Feature(polygons).copyProperties(ftr)


states = ee.FeatureCollection("TIGER/2018/States")
new_states = states.map(filter_polygons)

col_names = states.first().propertyNames().getInfo()
print("Column names: ", col_names)

url = new_states.getDownloadURL("shp", col_names, "states")
print(url)

desc = "states"

# Set configuration parameters for output vector
task_config = {
    "folder": "gee-data",  # output Google Drive folder
    "fileFormat": "SHP",
    "selectors": col_names,  # a list of properties/attributes to be exported
}

print("Exporting {}".format(desc))
task = ee.batch.Export.table.toDrive(new_states, desc, **task_config)
task.start()

TIGER: US Census Blocks

https://developers.google.com/earth-engine/datasets/catalog/TIGER_2010_Blocks

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

dataset = ee.FeatureCollection("TIGER/2010/Blocks").filter(
    ee.Filter.eq("statefp10", "47")
)

pop = dataset.aggregate_sum("pop10")

print("The number of census blocks: ", dataset.size().getInfo())
print("Total population: ", pop.getInfo())

Map.setCenter(-86.79, 35.87, 6)
Map.addLayer(dataset, {}, "Census Block", False)

visParams = {
    "min": 0.0,
    "max": 700.0,
    "palette": ["black", "brown", "yellow", "orange", "red"],
}

image = ee.Image().float().paint(dataset, "pop10")

Map.setCenter(-73.99172, 40.74101, 13)
Map.addLayer(image, visParams, "TIGER/2010/Blocks")

Map.addLayerControl()
Map

TIGER: US Census Counties 2018

https://developers.google.com/earth-engine/datasets/catalog/TIGER_2018_Counties

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

Map.setCenter(-110, 40, 5)
states = ee.FeatureCollection("TIGER/2018/States")
#     .filter(ee.Filter.eq('STUSPS', 'TN'))
# // Turn the strings into numbers
states = states.map(lambda f: f.set("STATEFP", ee.Number.parse(f.get("STATEFP"))))

state_image = ee.Image().float().paint(states, "STATEFP")

visParams = {
    "palette": ["purple", "blue", "green", "yellow", "orange", "red"],
    "min": 0,
    "max": 50,
    "opacity": 0.8,
}

counties = ee.FeatureCollection("TIGER/2016/Counties")
# print(counties.first().propertyNames().getInfo())

image = ee.Image().paint(states, 0, 2)
# Map.setCenter(-99.844, 37.649, 4)
# Map.addLayer(image, {'palette': 'FF0000'}, 'TIGER/2018/States')
Map.addLayer(state_image, visParams, "TIGER/2016/States")
Map.addLayer(ee.Image().paint(counties, 0, 1), {}, "TIGER/2016/Counties")

Map.addLayerControl()
Map

TIGER: US Census Tracts

https://developers.google.com/earth-engine/datasets/catalog/TIGER_2010_Tracts_DP1

http://magic.lib.uconn.edu/magic_2/vector/37800/demogprofilehousect_37800_0000_2010_s100_census_1_t.htm

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

dataset = ee.FeatureCollection("TIGER/2010/Tracts_DP1")
visParams = {
    "min": 0,
    "max": 4000,
    "opacity": 0.8,
    "palette": [
        "#ece7f2",
        "#d0d1e6",
        "#a6bddb",
        "#74a9cf",
        "#3690c0",
        "#0570b0",
        "#045a8d",
        "#023858",
    ],
}

# print(dataset.first().propertyNames().getInfo())

# Turn the strings into numbers
dataset = dataset.map(
    lambda f: f.set("shape_area", ee.Number.parse(f.get("dp0010001")))
)

# Map.setCenter(-103.882, 43.036, 8)
image = ee.Image().float().paint(dataset, "dp0010001")

Map.addLayer(image, visParams, "TIGER/2010/Tracts_DP1")

Map.addLayerControl()
Map

TIGER: US Census Roads

https://developers.google.com/earth-engine/datasets/catalog/TIGER_2016_Roads

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

fc = ee.FeatureCollection("TIGER/2016/Roads")
Map.setCenter(-73.9596, 40.7688, 12)
Map.addLayer(fc, {}, "Census roads")

Map.addLayerControl()
Map