US That Could’ve Been: Creating Timeline X’s Map with GIS

I’ve been dabbling around with an alternate historical geography of the United States that I’ve called “The United States That Could’ve Been.” So far, I’ve laid out the initial concept map of the altered U.S., and I’ve drafted an alternate timeline of events, called “Timeline X,” which is almost certain to be updated and improved upon a little later. I decided that I’d like to go a few more steps with this and create some bare basic demographic analyses in a GIS, and perhaps put together a little almanac with entries about each of the states. To start, I decided to use existing data from these places just to see, assuming that all other migration events and such else remained largely the same in Timeline X as it did in our reality, what the populations of these new states would be.

For sake of completeness and to jog your memory, here’s the initial concept map that I created for this little project. You can click to make it bigger if you like:

So, how to turn that, which was created on Adobe Illustrator, into a GIS file? I could either draw the shapes each from scratch (ewww, no), or I could work with things a little bit from existing data, specifically TIGER files from the U.S. Census Bureau. If you look at the map above, assuming you’re familiar with the county line boundaries of the United States, you’ll note while many work well within county boundaries, a number of these new states don’t really follow them at all. That could get messy. What to do?

Well, for one thing, it’s FICTION, which means I can just change the rules… and I did, for a number of the places. See those nice, neat lines creating the borders like those of Absaroka? I simply altered those the follow the closest county boundaries. In most of those cases, the states that are new in Timeline X have seceded from their original states, and generally a secession of that sort assumedly would follow county boundaries. The one time it’s happened in Timeline Alpha (our timeline) was when West Virginia left Virginia during the Civil War, and that split followed county boundaries.

In other cases, such as the eastern boundary of Indiana or the boundaries of the state of Navajo, I used county subdivisions to follow the intended path as accurately as possible, and altered the GIS shapefile accordingly.

GIS Procedures

If you’re not interested in knowing exactly how I created the data in a program called QGIS, skip ahead. This is going to take a while.

    1. Downloaded TIGER shapefile data from the U.S. Census Bureau that included all of the counties in the country.
    2. Using the map above as a guide, I carefully selected the counties composing each state in Timeline X, adding to the attribute table a column called “XState” that contained the name of the state to which the county belongs in the timeline.
    3. Several states (Indian Stream, Indiana, Ohio, Navajo, New Hampshire, Newington and Rye, Red Wisconsin, Vermont, Winneconne) required that further divisions were made at political units smaller than the county level. For these, I downloaded a TIGER shapefile from the U.S. Census Bureau that included all county subdivisions, and created a layer including only those needed to draw the boundaries.
    4. All shapes selected in that layer of county subdivisions were assigned an “XState” attribute in the attribute table.
    5. Using the “Union” geoprocessing tool, I combined the shapes of the two layers, and edited the attribute table using the field calculator to assign all shapes a common “XState” column that contained the appropriate attribute according to which state that county or county subdivision belonged.
    6. Then, I joined a county and county subdivision demographic tables downloaded from the U.S. Census bureau to associate a full set of demographics for each geographic unit to the shapefile. Importantly, by doing so, I also associated these demographics with the “XState” attribute.

From here, I needed to accomplish two separate goals: creation of a new map of states that could both be exported to Illustrator to be “neatened” up, and the creation of state-level demographic data to allow the silly analyses of this fictional timeline that I had planned. In this case, because of how the “Dissolve” geoprocessing feature works in QGIS these are, in a way, mutually exclusive goals. To dissolve the shapes into state-sized entities, I’d get the mapping aspect right but I’d lose the demographic information that I’d added to the table (because for some reason, summing the attributes isn’t an option). To get a compiled demographic database at the state level, though, I’d have to maintain the geographic component of those shapes to ensure that the numbers for each state were getting to where they should be.

To accomplish this, I basically split the data in half, and did both.

First, making the shapes:

      1. I copied the .dbf file (the attribute table) from the combined, unioned shapefile to a separate place on my computer.
      2. On the shapefile, I deleted all attributes in the table except for the “XState attribute”
      3. Using the “Dissolve” geoprocessing tool, I used the “XState” attribute to combine the county and county subdivisions with the same state name into single features that represented the state in the map document.

Then, making the demographics:

      1. Taking that .dbf file, I opened it with OpenOffice and sorted all of the demographics by the “XState” attribute. For many of the states, it was simply a matter of creating a new record, summing the demographics from all of that state’s pieces in that record, then deleting the individual records for counties and county parts.
      2. For others, thoguh, I had to do a little more math. Remember those states that needed the county subdivisions to lay out the boundaries (Indian Stream, Indiana, Ohio, Navajo, New Hampshire, Newington and Rye, Red Wisconsin, Vermont, Winneconne)? These required that I not only added the demographics to the correct XState, but also subtracted them from the state whose counties I had subdivided.
      3. I ended up with a simple table, 124 records (one for each state) that were identified by the “XState” attribute.

Once these edits were complete, I took the shapefile and joined it to the table using the “XState” attribute. This created a shapefile that includes all of the state boundaries, plus demographic data for each state. Well, Chihuahua, Sonora and Baja California aren’t complete since IGENI doesn’t do the same categories as the U.S. Census does, but it’s still not too bad.

Then, of course, I exported the shapes to Illustrator format, where I made them look nicer in terms of cartography.


So, how did the GIS alteration, done specifically to make things easier for analysis, change the shapes of the states in Timeline X? Some. Here’s the new map to check out, and again, you can click to make it bigger:

But perhaps most importantly, by altering the map in that way, I enabled myself to create GIS data of this project, which will lend itself to other uses as I continue messing around with Timeline X. If you’re interested, you can download the complete shapefile used to create the map right here, or check out the Google Earth layer.


I’ll be using this data to create an almanac with a listing of all of the states, and I might eventually create demographic data from scratch that goes more with Timeline X as I flesh it out.

It’s fun to create data!

Author: Andrew Shears

Andrew Shears is an Assistant Professor of Geography at Mansfield University in Mansfield, Pennsylvania. His research interests lie at an intersection of the human-environmental nexus, and includes branches of mapping, technological, memorialization and urban geographies. He lives in Wellsboro, Pennsylvania with his wife Amy, a professional photographer.

4 thoughts on “US That Could’ve Been: Creating Timeline X’s Map with GIS”

  1. Andrew, I really enjoy these maps and the imaginary history you've developed. Just one comment – is the red state above Sonora supposed to be Baja Arizona? It's not marked as such. I look forward to your next installment.

  2. It would be pretty sick to render this map in D3.js and add tooltips with information about what historical event might have created each state. Is that what you mean by "almanac" in your post?

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