Uncomment the following line to install geemap if needed.

In [ ]:

# !pip install geemap

# !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  Displaying 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¶

In [ ]:

import ee
import geemap

import ee import geemap

In [ ]:

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

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 vertices, they might exceed the direct upload limit. In that case, you will have to ingest shapefiles to your GEE account. See instructions here.

In [ ]:

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

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

In [ ]:

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

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

In [ ]:

print(layer)

print(layer)

In [ ]:

# Map.remove_layer(layer)

# Map.remove_layer(layer)

In [ ]:

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

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

In [ ]:

del layer

del layer

In [ ]:

Map.add_layer(layer)

Map.add_layer(layer)

Let's remove the layer

In [ ]:

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

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:

In [ ]:

Map.layers

Map.layers

In [ ]:

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

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

In [ ]:

Map.ee_layer_dict

Map.ee_layer_dict

TIGER: US Census States¶

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

Displaying data¶

In [ ]:

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

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

Displaying vector as raster¶

In [ ]:

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

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¶

In [ ]:

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

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

In [ ]:

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

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

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

Select multiple states¶

In [ ]:

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

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¶

In [ ]:

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

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)

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

In [ ]:

import matplotlib.pyplot as plt
%matplotlib notebook

import matplotlib.pyplot as plt %matplotlib notebook

In [ ]:

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

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

Discriptive statistics of a column¶

For example, we can calculate 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")

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)

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)

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 = 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

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¶

In [ ]:

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

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))

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 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()

# 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

In [ ]:

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

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

In [ ]:

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

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

In [ ]:

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

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

In [ ]:

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

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

In [ ]:

---

Last update: 2021-10-26