Spatial Analysis and Visualization with BigQuery GIS
This course explores geographic information system (GIS) topics and how to query and analyze GIS data within the database environment of BigQuery GIS. We'll start off by defining what GIS is, discussing some key GIS concepts, and introducing some common GIS data types.
We'll then move on to common types of maps and introduce the concept of map projections. You'll get an introduction to Google's BigQuery tool, and finally, we'll put all these topics together and look at how you can perform analysis and visualization using SQL and Python in conjunction with BigQuery GIS.
If you have a use case analyzing and mapping geospatial data or anticipate one in the future, especially if your data is in a relational format (or already in BigQuery), then this course is ideal for you!
Please feel free to contact us at firstname.lastname@example.org with any feedback you may have related to this course.
- Learn about Google BigQuery GIS and its concepts, as well as common GIS data formats
- Understand the common types of maps and the concept of map projections
- Learn about spatial query functionality and spatial visualization
- Understand how BigQuery GIS can be integrated with Python
This course is intended for anyone who wants to:
- Leverage BigQuery GIS for their geospatial analytics needs
- Learn how to visualize data on maps
To get the most out of this course, you should have basic familiarity with SQL, Python, and cloud computing, ideally Google Cloud Platform.
Hello and welcome to Spatial Analysis and Visualization with BigQuery GIS. This course will introduce you to GIS topics and how to query and analyze GIS data within the database environment of BigQuery GIS. If you haven't worked with GIS data before, don't worry. We'll start off by defining what GIS is, discussing some key GIS concepts, and introducing some common GIS data types.
Next, we'll discuss common types of maps and introduce the concept of map projections. Then we'll switch gears and talk about Google's BigQuery tool. Finally, we'll put it all together and talk about BigQuery GIS with lots of examples of how you can perform analysis and visualization using SQL and even Python.
In order to get the most out of this course, you should have a basic familiarity with SQL since we're gonna have lots of SQL examples in the latter half of the course. In addition, you should have some basic familiarity with cloud providers, especially Google. Finally, basic familiarity with Python is helpful, but not required.
So, how do you know if this course is for you? If you have a use case analyzing and mapping geospatial data or anticipate one in the future, especially if your data is in relational format, or already in BigQuery, then this course should be a great place to get you started.
I'm Erica Deckter and I'll be your instructor for this course. I'm the Principal Data Scientist at Calculated Systems and I spend a lot of my time working with data and especially writing SQL queries. My background is in property insurance where I worked with a significant amount of geospatial data since it's hard to assess risk if you don't know where that risk is.
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.