hands-on lab
Visualizing Geospatial Data with Python and BigQuery
Difficulty: Intermediate
Duration: Up to 1 hour
Students: 51
Rating: 3/5
Get guided in a real environmentPractice with a step-by-step scenario in a real, provisioned environment.
Learn and validateUse validations to check your solutions every step of the way.
See resultsTrack your knowledge and monitor your progress.
Description
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, then creating a feature collection and finally 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
GEOGRAPHYdata 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
GEOGRAPHYdatatype
Updates
August 7th, 2024 - Added password protection to notebook
Covered topics
Lab steps
Starting the Lab's Google Cloud Hosted Jupyter Notebook