Visualizing Geospatial Data with Python and BigQuery

This lab will teach you how to create maps from geospatial data using the GeoJSON extension for JupyterLabs. The first section of the lab walks through building a map of New York City landmarks, building up from a single geospatial point, creating a feature collection, and adding points to the existing feature collection. The second section uses Google’s zip code public dataset to convert BigQuery GEOGRAPHY objects to GeoJSON format and map geospatial polygons.

Learning Objectives

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

• Use the GeoJSON extension to JupyterLabs to map geospatial data
• Map GeoJSON objects in Jupyter notebooks
• Interact with BigQuery GIS datasets within Jupyter notebooks
• Map geospatial data from BigQuery GIS's GEOGRAPHY data type

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 28th, 2025 - Migrated lab environment and updated screenshots to reflect the latest UI

August 7th, 2024 - Added password protection to notebook