The United States: Her Natural and Industrial Resources

The Inspiration

This map came about when I read Marty's post on MapHugger entitled "The Cartographic Aesthetic." He posited that maps intentionally retain a precise visual language in order to appear authoritative, "rejecting the inclusion of any appearance of human touch" due to it's inherent imperfection.

Included in that post was this beautiful map from the 1940's of Great Britain.

Great Britain Her Natural and Industrial Resources
Source: Boston Public Library (Flickr)

I was enthralled with the style.
I had to replicate it.
For the United States.

The Colors

The palette was

Using Adobe Color I extracted the key colors from the map.

I added a few of my own colors too

The Legend

Now comes the tricky part

Good cartography is 90% data, 5% preparation, and 5% luck.




Looking at the legend, I knew that the data would be extra tricky in this case. Not only were some of the terms foreign to me, some were extremely specific.

Great Britain Her Natural and Industrial Resources legend

Where do I find the "Moorland" data for the US? Where are all the food processing plants? Smelting facilities? Cement factories? Is there a map of potteries?
If there was data for all of this, I couldn't find it.

I had to pivot.

The Background

The hunt for data begins.

The original contained data too obscure or specific for me to easily include in my map, so I had to figure out an alternative vision for what my map could be.

I decided to start with the background. The original contains flowy, generalized, land use/land cover data. That's easy enough to reproduce for the United States.

flowy land use

Enter the USDA Cropland data layer

This amazingly rich dataset contains a wealth of information about the land use of the lower 48 United States at 30 meters per pixel. The legend is extensive, containing 125 possible values, from corn to cantaloupes, apples to watermelon, and perennial ice/snow to dense urban.

But how to take advantage of this data? It looks really nice as-is, but the original map had much more general categories and shapes.

Here's how I processed the land use raster into flowy vectors:

  1. Reclassify
    • Reduce and simplify the categories
  2. Resample
    • 30m → 15km by majority rule
  3. Raster to Polygon
  4. Smooth Polygon
  5. Manually stitch together


Cropscape data layer national image


Cropscape data layer national processed and smoothed

This took some time

Reference Data

This was the easy part

Thanks to NaturalEarth Data, basic background information is extremely easy to come up with. I highly recommend using their data for all your small-scale mapping needs.

Cropscape data layer national processed and smoothed
Source: NaturalEarth Data

Data Mining

Digging for data.

Inspired by the other items in the original legend, I started hunting for relevant datasets that would fall under the "Natural & Industrial Resources" theme. Looking in the "Industries" section, I thought it would probably be possible to find mining related data somewhere.

Great Britain Legend section

Prospecting for mining data.

After some searching I came across the United States Geological Survey (USGS) Mineral Resources Data System (MRDS), which is a "collection of reports describing metallic and nonmetallic mineral resources throughout the world."

Perfect. I should be able to find things like granite and cement and iron in there.

usgs mrds screenshot

That's a lot of dots.

Magnificent Minerals

A rich vein of data

With this dataset I found much more than just granite, cement, and iron. This was a veritable smorgasbord of resource data. Uranium, smectite, shale, exhalite, welded tuff, quartz, pegmatite, gabbro, hornblend, marble...

Reminds me of Dwarf Fortress

dwarf fortress animation

To keep things simple, I reduced this gigantic dataset down to a few key categories.

minerals legend

The map still looked massively cluttered, so I used Esri's Cartographic Representation system to Disperse Markers to make them more readable.

When the points were still too numerous, I used a definition query like this:


The % represents the modulo arithmetic operation, which returns the remainder after a division operation. In this case, the defition query divides the OBJECTID by 5 and returns the remainder. The result being that only features with OBJECTID's ending in 0 or 5 were displayed, reducing the clutter by 80%.

Artistic License

"Wait, that's cheating"

"You can't just decide to eliminate data from your map because it's too cluttered!"

Yes I can.

It's art.

Other data

There's a lot.

Instead of boring you with the history of every remaining dataset in the map, here's a comprehensive list:



  • Esri ArcGIS 10.2.1
  • Inkscape


It made a splash



  • ESRI User Conference 2014 - Map Gallery
    • 2nd Place - Most Unique
    • 2nd Place - People's Choice
  • AASHTO GIS-T 2014 - Map Gallery
    • Best Cartography
    • People's Choice

Free download

I'm happy to share this striking, award winning, world famous map with you completely hassle free! Below is a 5K, high definition version for you to download! Just click the image below to get the 5K jpg for your personal use. It's perfect for all your desktop wallpaper needs.

But wait, there's more!

For the first time ever

I'm making a limited number of prints available.

Download the wallpaper

Are you sure you don't want a print?


That's ok, we're still friends. Enjoy your wallpaper!

usa resources thumb 730px

© Mapsmith 2014