Using ESTAT to look at the 2004 Election Data:
First, if you haven't yet, launch ESTAT.
Once ESTAT has loaded (it takes a little bit, be patient), click the folder icon in the upper left corner. This will start the ESTAT Data Wizard.
Since this is the first time you're using ESTAT, make sure the "Create New Project" option is selected and click "Next" to proceed.
The Wizard will prompt you to choose the types of data you want to load. I haven't created any time series data, so for now please deselect the "Time Series" option and leave the "Primary Data" option selected. Click "Next" when you're ready.
Now you need to select the paths for the three files ESTAT needs for this particular project. The first file, an "Ob" file, is the database of county records. Click the folder icon for the "Ob" file path selector and navigate to "C:\pcpHome\data\Election2004\2004_Election_Counties_Ob.csv."
Click "Ok" when you've selected this file.
Next, do the same with the "ObMeta" and "Shape" files.
The "ObMeta" path should be "C:\pcpHome\data\Election2004\2004_Election_Counties_ObMeta.csv."
The "Shape" path should be "C:\pcpHome\data\ArcGIS Files\2004_Election_Counties.shp."
Leave the checkbox for "Projected Data" unchecked - as these data are provided in geographic coordinates and need to be projected in order to appear properly.
The final portion of the ESTAT Data Wizard allows you the opportunity to select the variables you'd like to look at from the dataset you have selected. In this case, I have compiled 66 variables across a range of different categories, from a number of different sources for 3111 counties in the lower 48 states. Use the icons and the variable moving tools to sort and select the variables you are interested in. ESTAT features a special visualization tool called a parallel coordinate plot that lets you visualize lots of variables at once, so don't feel restricted by what you might be accustomed to!
Click the "Finish" button when you're ready and ESTAT will load all of the data you've selected. In this case, since you have no temporal data, you'll be shown three panels. The top left corner is a Scatterplot, the bottom left is a Bivariate Map, and the upper right is a parallel coordinate plot.
Our tools are designed to be dynamically linked at all times and provide lots of visual feedback. So I recommend you start mousing over the views and dragging boxes/lines over things to see how it works. Also, usually the default variables that are placed in the scatterplot and map aren't terribly interesting - so change them to something else, say '% voted for Bush' versus 'pcincome.'
If you want to know more about how these things work, check out this tutorial. In the meantime, here are a couple quick screen captures and explanations of the two things you may not be familiar with in ESTAT:
In this example, I've chosen to map the % of votes for John Kerry versus each county's population in 2000. With a bivariate map, each variable is assigned a color ramp, and these ramps converge on each other. Here, the darkest corner (in the upper right of the legend) represents those places that fall into the upper-most class of Kerry votes as well as the upper-most class of 2000 population. Places that are green are high in Kerry votes but low in population, and places that are purple are simply high in population. Mind you - this does not indicate anything about Bush's vote share - it only shows classes within the range of Kerry votes.
Parallel Coordinate Plot:
The Parallel Coordinate Plot (PCP) is a complicated, albeit powerful visualization method. Each line, in this case, represents a county. The axes represent the variables in this particular dataset, and the lines cross these axes depending on the values each county has for a particular variable. For example, if Centre County, PA has a Bush vote percentage of 52% and Kerry percentage of 47%, then the line for centre county will cross those axes at those points. The PCP in ESTAT lets you roll your mouse over the lines to check out the specific values for each variable in a particular county. Also, you can click-hold and draw a line to select a bunch of lines at once and watch how those places play out across the rest of the PCP, the scatterplot, and the bivariate map.