Tutorial 4: Working with Geometries¶

cendat can fetch geographic boundaries alongside your data for mapping.

Goal: Get race data with geometries for US regions.

Setup¶

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import os
from cendat import CenDatHelper
from dotenv import load_dotenv

load_dotenv()
cdh = CenDatHelper(key=os.getenv("CENSUS_API_KEY"))
cdh.list_products(years=[2011], patterns=r"acs/acs5\)")
cdh.set_products()

import os from cendat import CenDatHelper from dotenv import load_dotenv load_dotenv() cdh = CenDatHelper(key=os.getenv("CENSUS_API_KEY")) cdh.list_products(years=[2011], patterns=r"acs/acs5\)") cdh.set_products()

Step 2: Select Data and Geography¶

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cdh.list_groups(patterns=r"^race")

cdh.list_groups(patterns=r"^race")

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cdh.set_groups(["B02001"])
cdh.describe_groups()

cdh.set_groups(["B02001"]) cdh.describe_groups()

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# 020 = Region
cdh.set_geos(["020"])

# 020 = Region cdh.set_geos(["020"])

Step 3: Get Data with Geometry¶

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response = cdh.get_data(
    include_names=True,
    include_geometry=True  # This fetches boundaries from TIGERweb
)

response = cdh.get_data( include_names=True, include_geometry=True # This fetches boundaries from TIGERweb )

Step 4: Convert to GeoDataFrame¶

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# to_gpd() returns a GeoDataFrame ready for mapping
gdf = response.to_gpd(destring=True, join_strategy="inner")
print(gdf)

# to_gpd() returns a GeoDataFrame ready for mapping gdf = response.to_gpd(destring=True, join_strategy="inner") print(gdf)

In [ ]:

# Use with matplotlib, folium, or your preferred mapping library
gdf.plot(column="B02001_001E", legend=True)

# Use with matplotlib, folium, or your preferred mapping library gdf.plot(column="B02001_001E", legend=True)

Note - Supported Geographies

Geometry fetching currently supports: regions (020), divisions (030), states (040), counties (050), county subdivisions (060), census tracts (140), block groups (150), and places (160).