Geofencing to Organize Your Geospatial Data

This lab demonstrates how to group geospatial data based on their geographic attributes in BigQuery GIS using Python and Jupyter notebooks. The lab uses spatial joins to combine information from two of Google’s public datasets – the zip codes table and the Chicago crimes table – to count the number of crimes in each zip code of Chicago by year. In addition, the lab uses the geopandas package in Python to create a choropleth map showing crime hot spots in Chicago.

Learning Objectives

Upon completion of this lab, you will be able to:

• Interact with BigQuery GIS datasets within Jupyter notebooks
• Perform an analysis using spatial joins on GEOGRAPHY data
• Create a choropleth map using geopandas

Intended Audience

This lab is intended for:

• GIS engineers
• Data engineers dealing with location-based data
• Developers looking to leverage geospatial information

Prerequisites

You should possess:

• Basic understanding of relational databases and ANSI SQL
• Basic understanding of Python
• Familiarity with BigQuery GIS's GEOGRAPHY datatype

Updates

August 20th, 2025 - Resvoled issue connecting to the lab's notebook

June 24th, 2025 - Updated GCP VM instance image

May 27th, 2025 - Migrated lab environment, updated lab permissions, and cleared Jupyter Notebook outputs

August 7th, 2024 - Added password protection to notebook