Uncomment the following line to install geemap if needed.

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# !pip install geemap

How to select features by location and attribute?¶

Contents

• 1  Install geemap
• 2  Create an interactive map
• 3  Add data to the map
• 4  TIGER: US Census States
  • 4.1  Displaying data
  • 4.2  Dispalying vector as raster
  • 4.3  Select by attribute
    • 4.3.1  Select one single state
    • 4.3.2  Select multiple states
    • 4.3.3  Printing all values of a column
    • 4.3.4  Discriptive statistics of a column
    • 4.3.5  Add a new column to the attribute table
    • 4.3.6  Set symbology based on column values
    • 4.3.7  Download attribute table as a CSV
    • 4.3.8  Formatting the output
    • 4.3.9  Download data as shapefile to Google Drive
• 5  TIGER: US Census Blocks
• 6  TIGER: US Census Counties 2018
• 7  TIGER: US Census Tracts
• 8  TIGER: US Census Roads

Install geemap¶

To follow this tutorial, you need to install the geemap Python package. You can use the following conda comands to install geemap.

conda create -n gee python=3.7
conda activate gee
conda install mamba -c conda-forge
mamba install geemap -c conda-forge

Create an interactive map¶

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import ee
import geemap

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

Add data to the map¶

If you have shapefiles on your computer, you can load shapefiles onto the map using the following script. Note that this will work with small shapefiles. If you shapefiles have complicated geometries and numerous verticies, they might exceed the direct upload limit. In that case, you will have to ingest shapefiles to your GEE account. See instructions here.

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shp_path = '../data/us-states.shp'
states_shp = geemap.shp_to_ee(shp_path)
Map.addLayer(states_shp, {}, 'US States SHP')

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layer = Map.find_layer('US States SHP')

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print(layer)

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# Map.remove_layer(layer)

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layer.interact(opacity=(0.0,1.0,0.1))

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del layer

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Map.add_layer(layer)

Let's remove the layer

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Map.remove_layer(Map.layers[-1])

You can also directly load a FeatureCollection from the Earth Engine Data Catalog or your GEE account. For example:

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Map.layers

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states = ee.FeatureCollection('TIGER/2018/States')
Map.addLayer(states, {}, "TIGER/2018/States")

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Map.ee_layer_dict

TIGER: US Census States¶

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

Displaying data¶

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Map = emap.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¶

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Map = emap.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¶

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Map = emap.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

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

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

Select multiple states¶

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Map = emap.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¶

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

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

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import matplotlib.pyplot as plt
%matplotlib notebook

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

Discriptive statistics of a column¶

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

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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")

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

Add a new column to the attribute table¶

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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¶

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Map = emap.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¶

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

Formatting the output¶

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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¶

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# 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

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Map = emap.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

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Map = emap.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

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Map = emap.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

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Map = emap.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

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Last update: 2021-02-25