Once installed, you run GeoDa by clicking an icon or double-clicking a shortcut in the usual way. If the GeoDa installer did not make an entry in the Start Menu, you can create a shortcut by navigating to C:\Program Files\GeoDa\geoda_version.exe (or wherever you find the .exe file on your computer) then right-clicking and selecting Create Shortcut.
When GeoDa starts up, Connect to a data source using the File tab. If the 'Connect to a data source' window does not automatically appear, Choose File-New and it should open. Choose a shapefile to examine.
Making maps in GeoDa is simple: select the type of map you want from the Map menu. With the datasets you are working with in this project, only the following four options, Quantile, Percentile, Box Map and Standard Deviation make sense. Each of these makes a choropleth with the class intervals based on a different statistical view of the data.
Be particularly careful in your interpretation of a Quantile or Percentile map if you make one: the class intervals do not relate to the percent values but to the ranking of data values.
In some versions of GeoDa, I have been unable to get the Cartogram to work with the Census Area Unit shapefiles used in this project. [NB: It does work in the most recent version: 1.14.0].
I believe that this is a problem with the shapefiles, and not with GeoDa. Specifically, when ArcGIS is used to aggregate polygon shapefiles from smaller units (here, I made the CAUs from the mesh block data), it often shifts polygon boundaries sufficiently that they no longer touch one another. The cartogram tool relies on polygons touching one another for its simplified picture of the map. If you are interested in making a cartogram, the akCity_MB01_ethnic shapefile works, or try the sample data sets supplied with GeoDa.
The main focus of GeoDa is exploratory spatial data analysis (ESDA). To get a flavor of this, try making a histogram or scatterplot using the named options in the Explore menu. Once you have a histogram or scatterplot in one window, you can select data points in the statistical display, and see those selections highlighted in the map views. In general, any selection in any window in GeoDa will be highlighted in all map views. This is called linked-brushing and is a key feature of exploratory data analysis.
Linked-brushing can help you to see patterns in spatial data more readily, particularly spatial autocorrelation effects. When data is positively spatially autocorrelated, moving the 'brush' in an area in a statistical display (say a scatterplot) will typically show you sets of locations in the map views that are also close together. Moving the brush around can help you to spot cases that do not follow the trend.
For a moving brush, make a selection in any view while holding down the <CTRL> key (CMD key if you are working on a Mac). Once you have made the selection, you can let go of the <CTRL> key and then move the selection area around by dragging with the mouse. To stop the moving selection, click again, anywhere in the current view.
However, seeing a pattern is not the same as it really being there. You will see repeated examples of this in lessons in this course. In the case of spatial autocorrelation, that is the role of the measures we have covered in this lesson's reading, and in particular, Moran's /, which we will look at more closely in the remainder of this project.