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

GRASS vs. ArcGIS: Thiessen Polygons

This is the first of a few showdowns, or throwdowns if you prefer, comparing open source GIS' spatial analysis tools to ArcGIS.  This week: Thiessen polygons. You will need an ArcGIS Advanced Desktop (formerly ArcInfo) license to create these, or some patience with open source software.

See below for a comparison.  Unfortunately, QGIS produced some different/strange results.  I'm not sure why this is but I am investigating.  Haven't tried with pysal yet.  Anyway, see below.  Fyi.

Spatial Analysis in QGIS

It is time to talk about spatial analysis.  Many open source GIS software have at least some analytic capability--more functionality is being added frequently.  Earlier, I showed a simple map of wifi locations in New York City using QGIS.  Let's take a look at the density or in this case area surrounding these points.  Since I have had trouble with kernel density, let's use Thiessen/Voronoi polygons.  Interestingly, these are only available with an ArcInfo license in ArcGIS, which is extremely expensive.  I am not going to compare results here, but let's see what the resulting map looks like.  The lighter/whiter the color the less area between wifi locations and the better the wifi availability.  (Of course I don't show whether the wifi locations are free or cost-based on this map).  Not bad for free data and free data analysis!  I used the nifty vector transparency plugin from QGIS so you can also see some of the land cover.

Click on the map and a larger version will appear.