I’ve moved to my own domain! Aaand I’ve been busy working on thesis-related things lately, so I haven’t had time to post anything. I initially saw this blog as an expanded portfolio of sorts, but given my situation I’ve decided to try aim for shorter, more Tumblr-esque posts.
An interesting thing about this map is that each layer is contained in one 23,000 pixel tall spritesheet to reduce load time. An uninteresting thing is that my workflow was to export black and white density images from QGIS (which I’ve been working with more lately), generalize in Illustrator, export each slice and then stitch them together into one image with ImageMagick. I grabbed the population data from here.
Let me know what you think!
EDIT: I incorrectly listed the data as density per square miles originally, but it looks like the GPW data is in fact in kilometers. How embarrassing – my mistake!
I’ve never done any interactive mapping before, and wanted to test the waters using Processing. After the positive reactions to my post office expansion video, I thought some people might find an interactive version interesting or useful.
I used the code that produced the video as a foundation to build interactivity into the map. The most significant visual change was the move to a dark background with bright data points. While I’m not entirely happy with the “civilization illuminating a dark continent” effect that might be communicated by this choice, I wanted to provide some variation in how opening dates were represented, and on monitors I’ve always found bright colors easier to distinguish from one another than dark.
Interaction allows a more in-depth exploration of the data: this map includes the ability to set the range of dates displayed, zooming and panning, clickable points, and user-controlled playback for a given date range. I tried to make things relatively straightforward: use the buttons at the top or drag and scroll with the mouse to pan and zoom. Click on a highlighted point to show more information. High and low dates can be changed by dragging them along the timeline, while dragging the selected range will move it while maintaining the date gap. Use the buttons to the left of the timeline to play, stop, or change the speed of automated playback.
I don’t have much to add in the way of analysis that wasn’t mentioned in the thread of my previous post. I’d encourage you to explore different date ranges, though – you can see some interesting patterns of activity that were lost in the previous video by zooming and selecting specific dates.
I’m happy with the final result, I learned a lot during this process, and I’m excited to apply what I’ve learned to some other projects I have in mind. I wish the performance were a little better, and in the future I’d like to try my hand at some more browser-friendly interactive projects using Processing.js. I’d welcome any criticisms or suggestions on the design – particularly regarding the user interface and implementation of interactivity.
This visualization shows how formal US territorial control expanded in North America from 1700 to 1900, as seen through changes in the spatial distribution of post offices:
(HD and 1080p download here. It’s much prettier!)
A few months ago, I scraped post office location information from the USPS Postmaster Finder, and then extracted lat/long coordinates by correlating placenames to the USGS GNIS. Recently I remembered I had this data sitting around. I’ve been experimenting with Processing a lot lately as a tool for geographic visualization, and decided this would be an interesting dataset to use as a first stab at animation/dynamic mapping. I used parts of zipdecode from Ben Fry’s excellent book Visualizing Data as a foundation for my code. After some frustrations, this visualization is the result.
Of course, there are a few caveats: the USPS admits that their post office data is a constant work in progress, so there are likely many offices that aren’t shown on this map. About 10% of the placenames in the USPS data failed to find any coordinate matches in the GNIS data, so those don’t show up either. Finally, the USPS data includes closing dates for many offices, but these aren’t represented on the map, which might give a false sense of density in some areas.
Still, I think the results are pretty interesting. I’m no historian, but here are a few interesting patterns I’ve noticed aside from general westward expansion:
- 1776 – Several new post offices crop up along the east coast after the Revolution.
- 1846 – Rash of openings in Texas after statehood and the end of the Mexican-American War.
- 1848 – First offices established on the west coast, with lots of activity afterwards, likely due to gold rushes and CA statehood.
- 1851 – New Mexico and Utah start to see some activity as they become territories. I especially like the lines extending from Santa Fe along the Rio Grande / El Camino Real.
- 1860s – No activity in the South during the Civil War; also an interesting sweep across the Great Plains, with Oklahoma remaining conspicuously quiet.
- 1870s – Distinct traces along railroads in Nebraska and Kansas.
- 1890s – Oklahoma lights up due to several land rushes.
Anyone notice anything else interesting? Let me know.
Update: Very cool parallels to this visualization of newspapers in the U.S. 1690 to 2011, from Stanford’s Rural West Initiative!
I figured I’d jump right in with my first post and share a map I made a while back that I finally got around to polishing up:
Generic place names (or toponyms) such as Cumberland Gap or Mount Rainier provide general categorical descriptions of a geographic feature, in contrast to specific toponyms, which provide a unique identifier: Lake Huron. This map taps into the place names contained in the USGS National Hydrography Dataset to show how the generic names of streams vary across the lower 48. Creeks and rivers are symbolized in gray due to their ubiquity (although the etymology behind the American use of creek is interesting), while bright colors symbolize other popular toponyms.
Lite-Brite aesthetic notwithstanding, I like this map because it illustrates the range of cultural and environmental factors that affect how we label and interact with the world. Lime green bayous follow historical French settlement patterns along the Gulf Coast and up Louisiana streams. The distribution of the Dutch-derived term kill (dark blue) in New York echoes the colonial settlement of “New Netherland” (as well as furnishing half of a specific toponym to the Catskill Mountains). Similarly, the spanish-derived terms rio, arroyo, and cañada (orange hues) trace the early advances of conquistadors into present-day northern New Mexico, an area that still retains some unique cultural traits. Washes in the southwest reflect the intermittent rainfall of the region, while streams named swamps (desaturated green) along the Atlantic seaboard highlight where the coastal plain meets the Appalachian Piedmont at the fall line.
There are a few patterns on the map that I haven’t been able to figure out. West Virginia shows a sharp north to south division between runs and branches that continues to puzzle me. Some other geographic patterns I’ve noticed in WV largely run parallel to the Appalachians, from the SE to NW. I don’t know much about the area, though, and I have no idea what could be behind such a distinct division. Any West Virginia-ites willing to take a stab? I’m also intrigued by the patch of branches in southwestern Wisconsin, which I suspect may have something to do with the diffusion of naming practice by way of branch-loving Appalachian miners during a regional lead mining boom in the early 19th century.
This map originally came from a late 2009 project in a class by Joby Bass. If I remade it now, I’d probably try to negotiate some of the overlaps in symbology that happens in very crowded areas, but I still think it’s interesting as-is. If you are interested in learning more about toponyms, George Stewart’s Names on the Land is an engaging classic on naming practices in the US, and there are more specific articles about stream names from Wilbur Zelinsky and Robert West.
Edit: It inexcusably slipped my mind, but a tip o’ the hat to Bill Rankin for design inspiration!
Update: James Cheshire over at Spatial Analysis has posted a version for the UK. Very cool to see which names did – or more interestingly, the ones that didn’t – make the jump to the US. For example, why did brook have such influence in New England, when it doesn’t seem to be very pervasive on James’ map? Was brook more popular in the UK in the past? Interesting stuff!
Update 2: Pfly posted this link to his flickr page in the comments below, with some interesting maps of toponyms pulled from the Geographic Names Information System (the underlying dataset for the names I queried from the NHD). Some stream patterns are much easier to see when only one or two names are mapped at a time, and the set also includes some nice maps of non-watery toponyms, as well.