hands-on lab

Spatial Measurements and Spatial Transformations with BigQuery GIS and Python

Difficulty: Beginner

Duration: Up to 1 hour

Students: 41

Rating: 5/5

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Description

This lab will teach you how to perform spatial measurements and spatial transformations in BigQuery GIS using Python and Jupyter notebooks. The lab uses New York City landmark geospatial points to demonstrate the distance measurement function within BigQuery GIS and Google public census places data to demonstrate the area measurement function on a geospatial polygon. Spatial transformation functions are demonstrated by calculating the centroid of geospatial polygons from the Google zip code public data and combining single geospatial points into multipoint GEOGRAPHY objects using the aggregate union transformation function from the Google New York tree census public data.

Learning Objectives

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

Interact with BigQuery GIS datasets within Jupyter notebooks
Perform spatial measurements on GEOGRAPHY data
Perform spatial transformations on GEOGRAPHY data

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 is beneficial but not required