Getting to know Geostatistical Analyst

EricKrause · ‎09-12-2017

Spatial interpolation is one of the most common workflows in GIS, and the Geostatistical Analyst extension is built specifically to solve this problem. However, there is often confusion about how exactly interpolation should be done. Which interpolation method should I use? Which parameters should I use? How do I know if the interpolated surface can be trusted? These questions frequently seem daunting to people when they first approach spatial interpolation, and the purpose of this blog is to help you get started towards your goal of accurate spatial interpolation.

Don’t have Geostatistical Analyst? Try Geostatistical Analyst for free.

As a starting point, we suggest the Geostatistical Analyst tutorial:

Download the ArcGIS Tutorial Data for Desktop through my.esri.com. You must have an up-to-date license for ArcGIS for Desktop to download the tutorial data.
There are five tutorials that you should complete. It will take a few hours to finish all of them:

Once you have seen the videos and done the tutorial exercises, you should be ready to start using Geostatistical Analyst on your own data. If you need additional education material, the following Web Courses are available:

If you have reviewed the training material above but still have questions, feel free to post your questions to GeoNet at the Geostatistical Analyst Place.

Good luck and happy kriging!

------------------------------------------------------------------------------------

Geostatistical Analyst also has extensive documentation that is available for free online. Here are a few select topics about some of the most important features in the extension:

Learn about the different types of kriging.
Learn about Empirical Bayesian Kriging, a modern kriging technique that automates many of the most difficult aspects of kriging.
Learn about Empirical Bayesian Kriging 3D, the first interpolation method developed by Esri for data collected in 3D.
- Watch of video of Empirical Bayesian Kriging 3D.
Learn about Areal Interpolation, an interpolation method that interpolates data collected in polygons and allows you to predict values in a different set of polygons.

------------------------------------------------------------------------------------

UPDATE (September 2017) - Free textbook and data

I am happy to announce that we have made Spatial Statistical Data Analysis for GIS Users available as a free download.

This textbook, written by Konstantin Krivoruchko, was previously available through Esri Press: "This book presents a practical introduction and guide to spatial statistics for researchers, statisticians, academics, and college students who want to expand their knowledge and skills in geographic information system (GIS) technology to new areas of analysis. More than 1,000 full-color illustrations are included, along with lessons and sample data to help organize courses and lectures."

Download link for the PDF book: https://downloads.esri.com/esripress/pdfs/spatial-statistical-data-analysis-for-gis-users.pdf

Download link for datasets used in the book: https://downloads.esri.com/esripress/pdfs/spatial-statistical-data-analysis-for-gis-users.zip

------------------------------------------------------------------------------------

UPDATE (May 2018, February 2019) - LearnGIS exercises using Geostatistical Analyst

Several LearnGIS exercises have been created that make heavy use of Geostatistical Analyst. LearnGIS lessons are free online exercises that teach different concepts related to geographic analysis in real-world workflows. They are a great way to see geostatistical workflows from beginning to end.

Analyze Urban Heat Using Kriging - Use Simple Kriging, Empirical Bayesian Kriging, and EBK Regression Prediction to interpolate maps of urban heat in Madison, Wisconsin.
Model Water Quality Using Interpolation - Use Kernel Interpolation With Barriers to interpolate dissolved oxygen levels in Chesapeake Bay.
Interpolate 3D Oxygen Measurements in Monterey Bay - Use Empirical Bayesian Kriging 3D to interpolate oxygen measurements taken along vertical columns in the ocean.

-------------------------------------------------------------------------------------

UPDATE (July 2019) - "Evaluation of empirical Bayesian kriging" has been peer-reviewed and published in the August issue of the Journal of Spatial Statistics. This paper presents the theory and principles behind EBK as well as results from controlled simulations. The results show EBK is accurate and valid for a wide variety of data and generally performs very well compared to other modern interpolation methods.

https://www.sciencedirect.com/science/article/pii/S2211675319300168

-------------------------------------------------------------------------------------

UPDATE (June 2020) - "Empirical Bayesian kriging implementation and usage" has been published in the June 2020 edition of "Science of The Total Environment." This peer-reviewed paper contains the details of the mathematical underpinnings of Empirical Bayesian kriging.

https://www.sciencedirect.com/science/article/pii/S0048969720308007