Census Geocoding

Last updated on 2026-01-14 | Edit this page

Estimated time: 102 minutes

Overview

Questions

  • What is geocoding and why is it essential for census analysis?
  • How can we convert addresses into spatial coordinates?
  • How do we combine census data with OpenStreetMap features?
  • How can spatial context improve demographic analysis?

Objectives

  • Understand what geocoding is and how it works
  • Convert address-based census data into geographic coordinates
  • Query OpenStreetMap (OSM) features using Python
  • Combine census points with OSM layers for spatial analysis
  • Visualize geocoded census data alongside urban infrastructure

Introduction

Census and demographic datasets are often non-spatial — they exist as tables containing addresses, place names, or administrative units. To analyze these data geographically, we must first geocode them: converting text-based locations into latitude and longitude coordinates.

Once census data are geocoded, they can be enriched with contextual information from OpenStreetMap (OSM), such as roads, buildings, parks, schools, or hospitals. This enables deeper spatial insights into population distribution, accessibility, and urban structure.

In this lesson, you will learn how to:

  1. Geocode address-based census data
  2. Convert results into spatial objects
  3. Query OpenStreetMap features
  4. Visualize census data in its geographic context

Emphasize ethical use of geocoding services and rate limits. Remind learners that geocoding accuracy varies by location and data quality.

Why Census Geocoding Matters ?

Census data becomes far more powerful when location is explicitly included. Geocoding allows researchers to move from spreadsheets to spatial insight.

What Census Geocoding Helps Us Understand

  • Population distribution and density patterns
  • Access to services (schools, hospitals, transit, parks)
  • Spatial inequality and environmental justice
  • Urban growth and land-use change
  • Neighborhood-level demographic trends
  • Relationships between people and infrastructure

Why Researchers Combine Census Data with OSM

  • Census data provides who and what
  • OpenStreetMap provides where and how
  • Together, they enable:
    • Accessibility studies
    • Urban planning analysis
    • Public health assessments
    • Infrastructure equity evaluations
    • Place-based policy analysis

Geocoding transforms census data from static tables into spatial evidence.

1. Installing Required Libraries

PYTHON 

!pip install geopandas geopy osmnx matplotlib

2. Load Census Data or Address Data

PYTHON 

import pandas as pd

df = pd.read_csv("census_addresses.csv")
df.head()

This dataset should contain an address column (e.g., street, city, state).

3. Geocode Addresses Using Nominatim

PYTHON 

from geopy.geocoders import Nominatim

geolocator = Nominatim(user_agent="census_geocoding_tutorial")

def geocode_address(address):
    try:
        location = geolocator.geocode(address)
        return location.latitude, location.longitude
    except:
        return None, None

df["lat"], df["lon"] = zip(*df["address"].apply(geocode_address))

Note: Geocoding services may return None for incomplete or ambiguous addresses.

4. Convert to a GeoDataFrame

PYTHON 

import geopandas as gpd

gdf = gpd.GeoDataFrame(
    df,
    geometry=gpd.points_from_xy(df.lon, df.lat),
    crs="EPSG:4326"
)

gdf.head()

Plot the geocoded points:

PYTHON 

gdf.plot(figsize=(6,6), color="red")

5. Query OpenStreetMap Features

OpenStreetMap provides free, global geographic data.

Example: download buildings in a city.

PYTHON 

import osmnx as ox

place = "Lafayette, Indiana, USA"

buildings = ox.geometries_from_place(
    place,
    tags={"building": True}
)

Plot buildings with census points:

PYTHON 

ax = buildings.plot(color="lightgray", figsize=(8,6))
gdf.plot(ax=ax, color="red", markersize=10)

6. Adding Spatial Context to Census Data

You can buffer census points to analyze nearby features.

PYTHON 

gdf_buffer = gdf.copy()
gdf_buffer["geometry"] = gdf_buffer.geometry.buffer(200)  # meters (after projection)

Spatial join example:

PYTHON 

join = gpd.sjoin(buildings, gdf_buffer, predicate="within")
join.head()

This links buildings to nearby census locations.

Challenge

Challenge

Challenge 1 — Query a Different OSM Feature

Choose one:

  • Roads → {"highway": True}

  • Schools → {"amenity": "school"}

  • Parks → {"leisure": "park"}

Plot the feature with census points.

PYTHON 

parks = ox.geometries_from_place(place, tags={"leisure": "park"})
parks.plot()
Challenge

Challenge (continued)

Challenge 2 — Accessibility Analysis

For each census point:

  • Create a buffer

  • Count how many buildings fall inside

  • Interpret spatial differences

Higher counts suggest higher accessibility or density.

Math

Geocoding transforms a text location L into coordinates (x,y)

Spatial joins evaluate relationships between geometries:

  • within
  • intersects
  • contains These operations allow census attributes to be analyzed spatially.
Key Points

  • Geocoding converts census addresses into spatial coordinates

  • GeoPandas enables spatial operations on tabular data

  • OpenStreetMap provides rich contextual geographic layers

  • Combining census + OSM reveals spatial patterns and inequality

  • Spatial context transforms demographic data into actionable insight

Module Overview

Lesson Overview
Beginner Introduction to Address Geocoding
Intermediate Introduction to OSM Overpass API
Advanced Introduction to Advanced Batch-Geocoding