Uncomment the following line to install geemap if needed.

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.

In [ ]:

# Installs geemap package
import subprocess

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

In [ ]:

import ee
import geemap

TIGER: US Census States¶

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

Displaying data¶

In [ ]:

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

Dispalying vector as raster¶

In [ ]:

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¶

In [ ]:

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

In [ ]:

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

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

Select multiple states¶

In [ ]:

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¶

In [ ]:

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

In [ ]:

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

In [ ]:

import matplotlib.pyplot as plt
%matplotlib notebook

In [ ]:

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

Discriptive statistics of a column¶

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

In [ ]:

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")

In [ ]:

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

Add a new column to the attribute table¶

In [ ]:

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¶

In [ ]:

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': 5e+11,
  '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¶

In [ ]:

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

Formatting the output¶

In [ ]:

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¶

In [ ]:

# 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 configration 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

In [ ]:

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

In [ ]:

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

In [ ]:

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

In [ ]:

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

In [ ]:

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Last update: 2021-02-25