Spatial Analysis with GeoDa: Part II - Importing Data and Tools

GeoDa opens as a "floating bar" which you will find nice as you do analysis and realize multiple linked windows can be arranged.  The maps and graphs are interactive, as I'll show in later posts, show selecting features in one window will highlight the same parts in other windows.

When I learn a new piece of software, I always go from left to right.  

File Menu
The "File" menu allows you to import data, save and load projects (self-named *.gda files), and export selected data. In addition, there is a nice Project Information option that tells the title, data source and type, project name, number of observations and fields.

Data Formats
Users can import a wide array of file formats: shapefile, SQLite/SpatialLite, *.csv , .xls, .dbf, .json, .gml, .kml, and MapInfo files.  Remember, are analyzing vector data, so points, lines, and polygons. Remember map projections matter, since spatial weights are created based on distance!

GeoDa does a great job of offering multiple file types to import.

Tools Menu: Spatial Weights
Spatial weights are used to model spatial relationships. Using GeoDa, we can create spatial weights based on contiguity/bordering (think chess moves: rook or queen), distance, and the number of nearest neighbors.  Imagine a grid or matrix that has a row and column for every feature.  The cells are populated using 0/1 for weights based on contiguity (where a feature borders another) or distances for distanced based weights.

Tips:

• Generally, do not go above 2nd order of contiguity: 1st order contiguity is neighbors, 2nd order is neighbors of neighbors.  Anything beyond this becomes extremely difficult to interpret.
• The GeoDa Center also has PySAL an open source Python library that can be used to create spatial weights and perform spatial analysis.

The first option is "Select" if you have already created weights.  The second option is "Create."  Here you will a couple of options to examiner spatial relationships in your data.  Which one you choose should be based on the phenomenon you are studying. Like other types of analysis, you will also want to examine how different spatial weights affect your results.

• More about contiguity based spatial weights: https://geodacenter.asu.edu/node/380 and distance-based weights: https://geodacenter.asu.edu/node/384

Connectivity Histogram
Another one of GeoDa's cool features is a histogram that shows the number of features with a specific number of features.  It can also help you clear up any questions you have about different types of contiguity and how spatial relationships are modeled.

On the histogram a right, the bar/bin for two neighbors is selected.
On the map at left the county is highlighted. Selecting other bars would highlight more features.
Users can also see the distribution of the spatial weights from the histogram.

Shape
In case you tabular data, you can create points from this menu. You can also create a bounding box or grid.  Next time, we'll look at the Table and Map toolbars.

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ESRI's Open Data vs.Data.gov

ESRI recently announced its open data website (http://opendata.arcgis.com/), which was in beta in mid-2014, so I decided it was a good time to take a look.  More and more open data is being published in a variety of places, by different organizations.

I thought it was only fair to make some comparisons. Because ESRI's user base and audiences are large, I decided the best comparison is Data.gov.  The only drawback is that Data.gov has been in existence longer.

Of course, open data is important regardless of the platform.  Moreover, open data can be imported into any free and open source GIS.

"[Open data's] impacts include... cost savings, efficiency, fuel for business, improved civic services, informed policy, performance planning, research and scientific discoveries, transparency and accountability, and increased public participation in the democratic dialogue." - Data.gov

One big difference between the sites is ESRI's contains lots of data from States vs. federal (state, and local) data found in Data.gov.  ESRI touts that it works with more than 380,000 organizations across the globe, so more open data is on its way!

A table comparing ESRI's Open Data vs. Data.gov

One big advantage of ESRI's Open Data page is being able to view geographic data in your browser immediately and even see some attribute data.

Data.gov's metrics pages are really neat including one on data sets published by agency by month: . Most data sets come from NOAA and USGS, which can be accessed in several different ways.   Obviously, a lot of the pages linked from Data.gov either use ESRI formats or are driven by ESRI products.  For developers it is also important to note that Data.gov has challenges/competitions. So be sure to check their website and social media! 

Data.gov helps you get started with browsing categories.

Currently, Data.gov has more data sets but it will be interesting to see how much ESRI can catch-up in the months to come.  It is a win-win situation for any data scientist or GIS analyst.  As open data sites get larger, they can become harder to search and navigate.  In sum, both sites will have to keep innovating to help bring out the best in open data and analysis.

SaTScan 9.4 released, better than ever!

SaTScan is a program for detecting clusters over space, time, and space-time.  It is available for Windows, Mac OS X, and Linux. SaTScan 9.4 was recently released and it is better than ever!  The data import wizard now allows shapefiles to be read and and a graphing feature has been added to help examine temporal trends. Visit the link for a better look at the rundown of new features.

The Import Wizard now reads shapefiles.

In previous posts, I've covered the types of files you will need and how to aggregate data in preparation for importing it. Since version 9.2, SaTScan has had the ability to export *.kml and *.shp so that the most likely clusters can be viewed in GIS software. (Aside: Google Earth Pro is now free! https://www.google.com/work/mapsearth/products/earthpro.html)

Below is an example looking at clusters of low immunization rates in California from the journal Pediatrics. Free full-text: http://pediatrics.aappublications.org/content/135/2/280.full.pdf+html

In SaTScan, using lat/long coordinates, allows users to export to *.kml and *.shp.
Google Earth opens the *.kml automatically when a run is complete.

A few tutorials are being made, http://www.satscan.org/tutorials.html and sample data is available. Be sure to read the expertly written user's guide before running: http://goo.gl/rHg7M6. and the long and varied bibliography of analyses conducted with SaTScan: http://www.satscan.org/references.html

Update #1 (2/20/15)
Scan statistics can also be implemented in R's Spatial Epi Package and rsatscan.