Spatial Analysis with GeoDa: Part I - Introduction

GeoDa (https://geodacenter.asu.edu/software) is a free and open source cross-platform program for exploratory (spatial) data analysis or EDA/ESDA and maximum likelihood spatial regression. It has been downloaded nearly 150,000 times and is available on Windows, OS X, and Linux.  ASU's GeoDa center is home to Luc Anselin, e.g. Anselin's Moran's I a local indicator of spatial autocorrelation or LISA.

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

Book Review: An Introduction to R for Spatial Analysis and Mapping

I decided to talk a walk on the wild-side and examined R as a GIS for spatial analysis.  I hope to use several of R's spatial statistics packages and to automate tasks--staying within one program.  I highly recommend Brunsdon and Comber's book ($50 on Amazon, Paperback, electronic versions also available).

About the Authors: Chris Brunsdon is the creator of geographically weighted regression or GWR. Lex Comber is a professor at Leeds University.

Four Reasons to choose R as a GIS
1)  You are interested in performing tailored exploratory spatial data analysis (ESDA), spatial statistics, regression analysis, and diagnostics.

• Of course, R is also way better than ArcGIS and QGIS for summary statistics too. (Notably, QGIS has integrated a R processing toolbox into it. ArcGIS  also has an official bridge to R.)

2)  You already use R for non-spatial data, have lots of code written, and need to analyze spatial data.

3)  You do not want to export your data (or results) from one program into another and back again!

4) You want to be able to publish or share your code with a wider audience.

A great cover to a great book!

Reader Accessibility
The content is extremely well-presented, clear and concise, and includes color graphics. It is not overly technical. Still, R as a GIS and spatial analysis are tough material and is definitely not for the faint-of-heart. The authors assume readers may not have either a R or GIS background, or both. I took a R class in graduate school and occasionally use it.

Additional packages that assist in manipulating and reshaping data, such as plyr, are also discussed. The authors also warn readers that R packages can change over time, causing error messages, but many warn users about recent and upcoming changes.

Overview
In the first 40 pages, you will learn R basics, if you don't already have a foundation. Next, you will learn GIS fundamentals, how to plot data to create a map, taking into account scale, and adding and positioning common map elements like a north arrow and scale bar. This may sound basic but in R nothing is easy!  Of course, the advantage with code is that you can reuse it or may only need to modify it slightly for many maps.

Late in Chapter 5-6 the book dives into spatial analysis.  The last few chapters are probably the best of the book, as more advanced statistical techniques are discussed including local indicators of spatial auto correlation (LISAs), geographically weighted summary statistics and regression.

The book providers a great guide and reference, and I am sure I will be re-visiting it frequently!  Overall, it is a great mix of practice and theory.

Disclosures: 
None, I found and purchased the book on my own.