Update #1: It looks like an older version of GeoDa's source code is available (circa 2014) but not more current versions: https://code.google.com/archive/p/geoda/source
Why use GeoDa?
You are interested in spatial analysis of vector data (points, lines, polygons) and statistics. This includes looking for clusters of count or rate data, which have similar attribute values, performing regression (asking why a certain pattern exists), observed/predicted values, residuals, and diagnostics. Spatial statistics are commonly used in mainly fields including health, criminology, and pretty much everything!
If you are using GIS for a problem, at some point, you should consider spatial statistics. The human brain and eye can only see so much. Some patterns aren't easily apparent.
Spatial analysis can come at a cost ($), and this is why GeoDa is so great! It is free, open source, and has great capabilities It even includes some advanced options which you can't currently find in ArcGIS.
Features
GeoDa includes the ability to make choropleth maps, graphs, Thiessen polygons, creating spatial weights using queen and rook contiguity (which requires a high level license in ArcGIS), graphing features by number of neighbors, linked graphs you can 'brush,' LISAs, and regression. We will dive deeper into features later--there is a lot to cover.
A list of GeoDa's features can be found at: https://geodacenter.asu.edu/general-features. Also, here is a list of its modeling features: https://geodacenter.asu.edu/node/397.
Examples of Use
In 2014, I wrote about a simple use case: examining health insurance rates at the county-level:
http://opensourcegisblog.blogspot.com/2014/04/exploring-health-insurance-estimates-by.html.
More to come...
This is the first part in a series that explores GeoDa's functions and spatial statistics. If there is something you would like to see, leave it in the comment section below.
Want blog or YouTube updates? You can follow me @jontheepi: https://twitter.com/jontheepi
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