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
GEOGRAPHYdata - 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
GEOGRAPHYdatatype
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