hands-on lab

Geofencing to Organize Your Geospatial Data

Up to 1h
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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 – in order 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


You should possess:

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

About the author

Learning paths

Calculated Systems was founded by experts in Hadoop, Google Cloud and AWS. Calculated Systems enables code-free capture, mapping and transformation of data in the cloud based on Apache NiFi, an open source project originally developed within the NSA. Calculated Systems accelerates time to market for new innovations while maintaining data integrity.  With cloud automation tools, deep industry expertise, and experience productionalizing workloads development cycles are cut down to a fraction of their normal time. The ability to quickly develop large scale data ingestion and processing  decreases the risk companies face in long development cycles. Calculated Systems is one of the industry leaders in Big Data transformation and education of these complex technologies.

Covered topics

Lab steps

Starting the Lab's Jupyter Notebook in Google Cloud