Simplifying a Simple Map

You only need to do a few things to get away from the “conventional” GIS map and improve it’s legibility and aesthetics. In this example below I wanted to make the top map easier to read and more interesting to look at, so I added some topography, got rid of the callout box, deleted the different colors for different states and the north arrow (both of which are unnecessary), simplified city points, and made the roads less visually fragmenting. The one thing that I like about the original map that I should have kept in my revision is the size of the highway symbols. I believe the symbols that I created are a bit too small. But with simple changes there is an overall effect of toning down secondary information and bringing the primary story to light.

Lookout Fire

The USDA Forest Service (in particular the Pacific Southwest Region Remote Sensing Lab) puts out some great GIS data, and they are always updating, so it’s good to check back often to see their new offerings. I have been doing quite a lot of work with forest fires over the past year and a half, and the data available are pretty incredible. Below, we see some examples of these data. The top image shows the perimeter of the Lookout Fire that occurred in the Sierra Nevada mountains in 1999. And the image below shows the fire perimeter plus fire severity, measured by canopy mortality. With these data, we can begin to tell the story of how this landscape changed, and try to make sense of the patterns of forest fires.

Design through Data

There is so much that you can do with data, and I tend to lean toward using data for design and cartography. Most of the time, I use high quality data to create maps for greater understanding of an area, showing an annotated, generalized map (yes, redundant) for a specific purpose. But sometimes I am fascinated at simply using geographic data for design purposes. We do not gain a greater understanding of a particular location through the images below, but I do think this is still an interesting portrayal of data.

California Hot Springs

So, this is an interesting data set that my boss came across. It is public data hosted by the California Department of Conservation under the guise of geothermal springs/wells. Mostly used for industrial purposes, the data also show hot springs/wells used for baths and non-commercial uses. Tucked back in the Department of Conservation’s FTP site is a DBF that has coordinates for the springs. Though, as happens often, the longitudes are in the 120’s instead of the -120’s. But with a little bit of tweaking and converting to shapefile, I was able to get some points to visualize on a map. Now, it’s just as easy for me to then toss these points over to Google Earth, post it right here, and have a really accurate layer of hot springs data for the entire state. But I guess this is where Google and I go our separate ways. If you’re feeling really desperate for some time in the hot springs, though, and want to know where to go, you could georeference the larger linked map in Google Earth for more generalized locations. But beyond the individual data points and their locations, I do think that these maps show a very interesting, dynamic picture of California’s landscape.

California GNIS

This data set is one of my favorites. Ever wonder how Google got all of the names of mountains and lakes and geysers, etc. into their Terrain View maps? No? Okay. Well, they use the Geographic Names Information System (GNIS) created by the United States Geological Survey (USGS) and the United States Board on Geographic Names. GNIS points show both cultural and physical features, which is why you will see a concentration of points in and around cities, and more scattered points throughout the more mountainous regions of California. You will see that there are fewer points in the Central Valley and the Desert, mostly because, there are more indistinguishable characteristics (example 1, example 2) across the landscape. Moving on, if we strip everything away, and just show California’s GNIS points, this is what we get…