raster_chunk_processing.py Examples

This is part 3 of a three-part post. Read part 1 (Introduction) here and part 2 (Installation and Usage) here.

Ok, enough dry words. Let's see the goods!

These are all clips of the Grand Canyon of the Yellowstone River/Dunraven pass area from the USGS 1/3rd arc-second DEMs. For the smoothed DEMs, I created a hillshade with the gdaldem hilllshade -combined command. The elevation shade is the North_America_State_Utah_2 ramp from the ESRI Terrain Tools download.

Original Hillshade

Mean Radius 10 Hillshade

The simplest smoothing is averaging the value of the nearby pixels. This gets rid of noise, but also gets rid of the ridges and valleys. The sharp brown line is an error from importing the Terrain Tool's ArcMap styles into ArcGIS Pro.

Gauss Radius 10 Hillshade

A Gaussian kernel does a better job at smoothing away the high-frequency noise (ie, small sudden changes in elevation) while preserving the broader changes. Note the smoothing in the sides of the canyon, and the dampening of the contour-like artifacts in the gulleys.

mdenoise Hillshade

This is an aggressive smoothing setting; a similar effect might be possible with a higher-radius Gaussian blur.

Skymodel Hillshade

Behold the bold shades of the skymodel (using the original, unsmoothed DEM). With such tweakability available, this is but one possible output from this DEM. Much experimentation is needed to see if it's worth using for your data. Note that this particular output can be approximated by a simple slope calculation, with a white-to-black, shallow-to-steep-slope color ramp

CLAHE
This is a CLAHE stretch on the unsmoothed DEM's hillshade with a radius of 30 and a clip of 0.01. CLAHE gives a slightly different look than what's possible with the contrast stretcher tools in ArcGIS. It also permanently alters the file, which can be useful for bump-mapping imagery with a hillshade.

TPI 10
TPI gives different looks depending on the radius you use. It's usually symbolized with a green-yellow-red color ramp, but I like to use it as a layer in a terrain stack (set partially transparent, it can help highlight some of the more exciting terrain areas) either as is or hillshaded. This is the raw output from a kernel radius of 10.

TPI 10 Hillshade
Here's the raw hillshade from the TPI 10 dataset. Definitely not something useful on it's own, what it's really showing is channels and ridglines (which is what the raw TPI is often used to identify).

TPI 10 Combined
And here's the TPI 10 hillshade at 75% transprency layered on top of a smoothed skymodel hillshade. Look at how well-defined the draws and ridge lines are; this could make for a great basemap for a stream map.

TPI 30 Hillshade
We can get a slightly different look just by increasing the kernel radius to 30. We lose the sense of height in Mount Washburn, but the ridge lines really stand out.

Default Hillshade
Here's the default hillshade (without the -combined option) just to remind us of where we came from. Quite the snoozer, if terrain is your thing. Also note how the massive amounts of grey midtones muddle the colors of the elevation ramp. This is particularly abhorrent when laying it over imagery.

raster_chunk_processing.py Installation and Usage

This is part 2 of a three-part post. Read part 1 (Introduction) here and part 3 (Example Output) here.

Installation

Git Repo
RCP lives in the https://github.com/jacobdadams/rcp git repo, along with some other playground scripts and the environment.yml file for easily recreating the conda environment.

Prerequisites
RCP is written in Python 3 and relies on the numpy, astropy, scikit-image, numba, and gdal libraries. The recommended way of installing these packages is from the conda-forge channel using the conda package management system.

After you've installed conda, use the environment.yml file in the git repo to automatically create the rcp conda environment:

(NOTE: The environment.yml file currently does not include the numba packages. It will be updated later)
conda env create -f environment.yml

Or, just install the needed packages thus:
conda install numpy numba astropy scikit-image gdal

The package solver will take a while to get this set up. This environment is configured to avoid problems with the default channel's GDAL not supporting bigtiffs, an issue with the solver not handling the conda-forge channel properly (see https://github.com/conda-forge/gdal-feedstock/issues/219), and changes to numpy that are throwing warnings in astropy (see https://github.com/astropy/astropy/issues/7816). Just using the normal gdal installation from conda-forge seems to work now.

See my earlier post on conda for a brief intro if you're new to this world.

It may be possible to run RCP in Python 2.7, but this has not been tested. Various small tweaks would probably be required to get it running.

The mdenoise option requires Sun et al's mdenoise executable from the author's webpage. Download the appropriate version, and note it's location—you'll need to set a variable to this path.

The skymodel option requires an illumination file, which can be generated by the skyLum.exe program written by Dr. James Stewart. This can be found in the skyModels.zip archive on the repo listed below. Thank you to Dr. Stewart for allowing me to redistribute this program. skyLum.exe is only compiled for Windows.

Installing

1. Clone the git repo: https://github.com/jacobdadams/rcp
2. Change the mdenoise_path variable to point to your downloaded mdenoise executable (it's towards the end of the script).
3. Create the conda environment (if you haven't done so already): conda env create -f environment.yml
1. Or, install the needed packages in an existing environment: conda install numpy numba astropy scikit-image gdal

Usage
python raster_chunk_processing.py -m [method] {general options} {method-specific options} input_file output_file

RCP is run by your python interpreter and takes two types of options: general and method-specific. General options control the chunk size and processing, and they are required for most methods (they usually won't choose defaults if you don't specify anything). The method-specific options control the parameters for each

For a brief overview of all the options, run python raster_chunk_processing.py -h.

General Options
-m The processing method (blur_mean, blur_gauss, blur_toews, mdenoise, hillshade, skymodel, clahe, TPI).
-s The size of the chunks that the raster will be divided into for processing. mdenoise seems to fail on chunks larger than 1500, but for other methods it depends on how much memory you have and (for small rasters) how many cores you want to use at a single time.
-o The number of pixels to be read beyond each edge of the calculated chunk to avoid edge artifacts. I find something like 20 or 25 is sufficient. Some methods overwrite this setting to ensure they have good data.
-p Number of concurrent processes to run. Values larger than 1 will allow parallel processing if you have the cores available. I usually set this to n-1, where n is the number of cores I have. This is the one option with a default (1).
--verbose Toggles verbosity on. By default, RCP reports when each chunk was started, it's index (ie, 1 of 220), and the percentage of chunks already started/finished (ie, 25%; this gets a little gray because when running parallel a previously-ordered chunk in a separate stream may not be completed before this message is printed). With verbosity enabled, it reports the value of all the options passed to it, and for each chunk it adds the pixel indices of the chunk (including overlaps) and the process ID. The output from the mdenoise executable will only be shown if the verbose flag is set.

Kernel Size Option
-r Radius (in pixels) to use for blur_mean, blur_toews, blur_gauss, and TPI. This is the main parameter to control the blurring effect. Play around with this on a subset of your data to get a feel for the best look.

blur_gauss Option
-d Standard deviation of the Gaussian distribution (sigma). Start with 1, and experiment from there. A large sigma for a given radius (say, greater than 1/2 the radius value) effectively turns it into a mean filter.

mdenoise Options
-n Iterations for normal updating. Start with something around 10 and play around from there. Large values of -n and -v will increase the runtime for each chunk.
-t Threshold value, between 0 and 1. Try starting with 0.6.
-v Iterations for vertex updating. Start with 20 and play around.

CLAHE Options
-c Clip limit, between 0 and 1. I usually use really small limits, like 0.01 or 0.05; 0.1 is usually as high as I'll go.
-k Kernel size for clahe. Start with 30.

Hillshading Options
-az Azimuth of the light source (defaults to the standard of 315 degrees).
-alt Altitude of the light source (defaults to the standard of 45 degrees).

Skymodel Options
-l Path to the luminance file generated by SkyLum.exe. The header lines must be deleted so that every line in the file is a az/elevation/weight tuple.

File Options
input_file The input DEM. This must currently be a single-band file readable and createable by GDAL.
output_file The output file that will be created. A full-size but blank file is created at the beginning of the program and is then opened and updated each time a chunk is finished. The source file is used to determine the file type, projection, extent, and cell size of the output file (unless it's a VRT, in which case it defaults to a GeoTiff). If a GeoTiff is used, the output file is compressed with LZW, tiled, and bigtiff-enabled (-co compress=lzw, -co tiled=yes, -co bigtiff=yes). If you are using another format, you can edit the program to support different options (search for lzw_opts in the ParallelRCP() function).

Usage Examples
Ok, that was a lot of explanation. Here are some examples in action:

Gaussian Blur
python raster_chunk_processing.py -m blur_gauss -s 2000 -o 50 -p 3 -r 10 -d 1 dem_state.tif dem_state_gauss10.tif
(Took about a minute to process a 10k pixel by 10k pixel DEM).

mdenoise
python raster_chunk_processing.py -m mdenoise -s 1500 -o 50 -p 3 -n 20 -t 0.6 -v 30 dem_state.tif dem_state_md206030.tif
(Took about 8 minutes on the 10k by 10k DEM).

skymodel
python raster_chunk_processing.py -m skymodel -s 2000 -o 600 -p 3 -l 1_35_215_150.csv dem_state.tif dem_state_skymodel.tif

Introducing raster_chunk_processing.py (aka, RCP)

This is part 1 of a three-part post. Read part 2 (Installation and Usage) here and part 3 (Example Output) here.

After over a year of off-and-on development, I've finally got my second-favorite project ready for the masses (ie, I finally added command-line argument parsing). I present to you the new, the shiny, raster_chunk_processing.py!

Edit, May 2019:
That feeling when you think you've got something working, only to discover that its not doing what you think it's doing. rcp's skymodeling algorithm has been updated to properly compute shadows. Updated examples will be forthcoming.


Ok, so I need to work on my naming skills.

What Makes RCP Special?
RCP applies any one of several raster processing methods to arbitrarily-large rasters by automatically breaking up the raster into smaller chunks. This a) avoids out-of-memory errors with large rasters, 32-bit programs, and/or 8 GB or 16 GB memory loadouts in normal desktop computers and b) facilitates parallel processing when feasible.

What makes RCP better than re-tiling a large raster and processing the chunks individually? Many of these algorithms modify or create a new value for each pixel based on the pixels around them. When you run these on individual tiles and then merge them, the pixels on the edges of the tiles don't have all the needed info and produce a noticeable line in the final image. You also spend time and storage space tiling and merging.

RCP avoids these downsides by dividing the raster into chunks of a user-defined size, adding a few pixels (again, a user-defined amount) to each side of the chunk to avoid the aforementioned edge problems, then reading only this chunk into memory and working on it, and finally writing it into it's proper location within the output file.

RCP relies on GDAL for all the file access heavy lifting, and I owe a huge shout-out to Frank Warmerdam, Even Rouault, and the rest of the GDAL team.

Processing Methods
Currently, RCP can do several really cool things. The first is a selection of DEM smoothers (blur_mean, blur_gauss, blur_toews, and mdenoise). Next, and really, really fun, is Kennelly and Stewart's skymodel hillshade (along with a bog-standard hillshade algorithm copied shamelessly from the GDAL source). It will also calculate Topographic Position Index (TPI) with a user defined window, which is a step up on the default gdaldem TPI which uses a fixed window size. Finally, it provides a really esoteric contrast stretcher called Contrast Limited Adaptive Histogram Equalization (CLAHE).

DEM Smoothing
Often, a DEM comes to us with some imperfections. Maybe that new half-foot Lidar is too detailed. Maybe you're tired of those contour-line-esque artifacts in the NED 1/3 arc-second DEMs. Maybe you don't want to show so much detail, but want to maintain the crisp mountain ridges. This is where smoothing helps: in preparation for hillshading or some other cartographic, non-analytical process.

blur_mean smoothes the DEM by taking the average of all the pixels within a user-dfined radius/diameter circle around each pixel. This is similar to the ArcGIS Focal Statistics (Mean) tool with a circular neighborhood.

blur_gauss is similar to blur_mean, but it uses a 2d Gaussian distribution kernel for it's average, providing more detailed results than a regular blur_mean. You could do this with a custom neighborhood in Focal Statistics, but RCP makes it trivial to change the neighborhood size.

blur_toews is a smoothing kernel provided by Mike T. at https://gis.stackexchange.com/a/10467. It is slightly different from blur_mean and blur_gauss and may give better results depending on the terrain.

mdenoise uses the mdenoise.exe program that implements Sun et al (2007)'s Mesh Denoise algorithm, again as inspired by Mike T. at https://gis.stackexchange.com/a/12856. The compiled .exe is 32 bit and can only handle small rasters before blowing up and running out of memory. By limiting the size of the chunk we're working on, RCP allows us to use this executable as-is (along with automating the tif->asc->tif conversion process) and to parallelize the process.

Hillshades
My first impetus for this script was Kennelly and Stewart's skymodel hillshade technique, as featured on the cover of the 2016 ESRI Map Book. More optimization work needs to be done, but my ultimate goal is to get this working much faster than ESRI's Terrain Tools script tool through parallelization (and maybe offloading to the GPU).

skymodel is the aforementioned implementation of the skymodel technique, using Dr. Stewart's SkyLum.exe to generate a list of different hillshade azimuths and altitudes that are then combined into the final image.

hillshade is the bog-standard hillshading algorithm lifted from the gdaldem code. This is mainly used to generate all the different hillshades for the skymodel function.

Other Utilities
There are two other utilities I've included as I've played around with different raster techniques.

TPI is the normal Topographic Position Index calculation of the pixel value minus the mean of the surrounding pixels, which can then be categorized and displayed as desired. Coincidentally, this is also a high-pass version of the mean filter (http://fourier.eng.hmc.edu/e101/lectures/Fourier_Analysis/node10.html, http://northstar-www.dartmouth.edu/doc/idl/html_6.2/Filtering_an_Imagehvr.html). Hillshading the results of TPI can be rather interesting, depending on the TPI kernel width.

CLAHE is some really cool image magic that increase the contrast of your image in neat ways (https://imagej.net/Enhance_Local_Contrast_(CLAHE), https://en.wikipedia.org/wiki/Adaptive_histogram_equalization#Contrast_Limited_AHE) Yeah, I don't quite understand all the implications myself... but it can look cool! I've used it to increase the contrast of hillshades that I've then multiplied into aerial imagery to "bumpmap" the imagery. Increasing the contrast allows me to darken the shadows without increasing the darkness of the grey midtones.

WARNING: CLAHE relies on knowing the historgam of the entire image, so running it with chunks can cause visibly different stretches for each chunk. If you make your chunks large enough that the histograms are roughly the same, this difference nearly disappears.

For example, if one chunk covers mainly valley floors (where there are few hills and thus few shadows) and another chunk covers mountains (with lots of whites and lots of darks), they're going to have different histograms and thus different stretches. However, if your chunks are big enough that they cover both valley and mountains, the histograms should be similar enough to get away with.

Check the next posts for how to use it and semi-cartographic examples!

PS: Any feedback on RCP is welcome! 

Wonderland Post 2: More Terrain

Terrain Tricks

Two tips that I picked up from Tom Patterson's page on texture shading are texture shading (obviously) and adding other elevation-derived products to the map using transparency (and, if you've got the time/inclination, multiply blend mode in Photoshop/GIMP—paging the ArcGIS Pro developers...).

Tonight we'll explore a couple different products added to our hillshade. First, for reference, is the hillshade placed under the DEM, with some transparency applied to both:


Slope
After some playing around, I've found that slope makes a great addition to hillshades (see my Zions Fireworks post). To generate this, I run gdaldem slope -p dem.tif out.tif, which generates a raster showing percent slope.

In ArcGIS, invert the standard black to white color ramp so that it's now showing white to black, and give it a pretty high transparency value. This adds a little bit of shading to steep slopes, which helps show off all the little nooks and crannies in the terrain:


TPI: Poor Man's Texture Shading
Texture shading was originally developed by Leland Brown and uses a fractional Laplacian operator to generate a raster that gives more of a "ridge and canyon" network feel (see the original paper for more info), with the side effect of calling out great little details if your elevation raster is clean enough/high-res enough.

The texture shading programs provided by Brown take a while to run, and struggle with large rasters. I've found I can get 80% of the way there with the GDAL Topographic Position Index tool: gdaldem TPI dem.tif out.tif. This poster gives more info about what TPI is, but for our purposes it outputs a great raster that can emphasize areas of abrupt topographic change—cliffs, ridges, canyon walls, and the like.

By using a black to white color ramp with the middle grays set to about 75% transparency (so they don't muddle things too much) and the whole layer set to about 80% transparency, this gives a subtle but noticeable improvement to the ridges and small details:


One of the tricks with TPI is that it's scale dependent. As the poster linked above describes, different window sizes give different results. GDAL's utility doesn't allow for changing the window. I'm working on a custom implementation that will allow you to fiddle with this parameter for analytic or artistic purposes.

So, do these additions make a map better? It depends on what you want to show. I like them, but I can see that some people may think it adds too much visual clutter.

Wonderland Post 1

Last month I visited the Yellowstone area with some family. On this trip I discovered The Story of Man in Yellowstone, published in 1949 by Merril Beal. It gives a great (if aged) review of the recorded history of human (or at least non-Native American) exploration of the Yellowstone area. He calls the area Wonderland, which I think is great and I'm totally using from now on.

While reading this book, I had to constantly refer to a map because the landmarks Beal references were all mountains, rivers and lakes—there was no Norris Junction or Old Faithful Village in the early 1800s. I realized that my (and I imagine most everyone else's) mental map of Yellowstone is heavily influenced by the standard NPS park map.

This is no knock on the NPS map—it is a beautiful map that is fantastic at doing it's job (I'd love to chat with Tom Patterson someday about the NPS maps and terrain mapping). However, that job is to show you how to get to a very select group of relatively easily accessible attractions. Most mountains, mountain ranges, rivers, and lakes are buried deep in the visual hierarchy.

So, after all that intro, my goal with this map is to show the Wonderland that Colter (probably) saw, the Wonderland that the Folsom and Washburn expeditions later explored. I want to flip the NPS map's hierarchy. Natural features will be the focus, while man-made roads and arbitrarily-delineated attractions will fade into the background as reference points.

While we were visiting, we drove to the Grand Canyon of the Yellowstone. A sign between Norris and Canyon Village said we were crossing the caldera rim. While I could pull the NPS map out and see where the rim was, the terrain was so muted I had no way to relate that to what I was seeing out the window, or mentally zoom out and situate my experience into the physical area as a whole. This map should fix that.

Elevation

The first and most important part of the map is the underlying terrain. If we're going to talk about mountains and ridges, they'd better pop. Also, thanks to the caldera and other tectonic goodies that make the area what it is, there are some interesting stories to be told about the area. Existing maps just don't do these justice.

Elevation maps (hypsometric tints) are tough to do. You often see a kinda-natural-but-definitely-not color ramp, like on those giant roll-up physical relief wall maps at school (do schools still have those? I sure hope so, even if only so the current generation knows the frustration of trying to get one to roll back up). Or, you see some that try to be realistic as possible. Unfortunately, land cover is only partially determined by elevation, so this often strays into the uncanny valley.

For Wonderland, I chose to approach the uncanny valley head on, but I'm really pleased with the colors I finally came up with. I think they do a good job showing the elevation changes while sticking to a mountain forest color pallet. You can really see the uplands in the southwest part of the park, and how the caldera really dominates the center while the northern and eastern edges are more "traditional" mountainous areas.

I'm still playing around with the bright greens, but everything else is really working for me. This was the result of lots of little tweaks to the North America County San Bernardino 3 ramp from ESRI's Terrain Tools download.

Hillshade

Hillshading is another vital part of terrain maps, the thing that tweaks your brain into seeing mountains and valleys. I usually prefer to use the GDAL hillshade command with the combined option, which takes out a lot of the grays from the default algorithm. Because I'm focusing on the terrain, I chose a z scale of 5 to try to make it pop a little more.

The default 1/3 arc-second DEMs from the USGS can have lots of artifacts that aren't natural (look at the ripple effect along the gullies in the unsmoothed image below). To fix this, I smoothed the DEM using a DEM super-tool I've created and will probably write more about in the future. My first try is a Gaussian blur with a radius of 30 pixels (which is a different blur than the standard focal mean tool in ArcGIS, if I understand that tool correctly).

Comparing it to the unsmoothed image, I may try tightening the radius to preserve some of the edges. Or I may try some other cool smoothing techniques. We'll see.

Unsmoothed


Gaussian Blur, 30 Pixels


Finally for today, one oddity.

One of my biggest requests/hopes/desires/dreams for ArcGIS Pro is for different blend modes for each layer, instead of the standard transparency slider. This is especially useful for hillshade and elevation mashups like this, where a multiply mode would make my heart sing for joy (QGIS has it!).

I've heard rumblings that it may be in the long-term plan, but until then I seem to have a bug in my setup that does it for me already:

This image has the exact same settings as the ones above, just for some reason occasionally it draws weird. I have to turn different layers on and off before it reverts to what I'd expect.

Zion's Fireworks: A Map of Canyons in Zion National Park

I recently created this map of Zion Canyon in Zion National Park for a good friend who loves canyoneering, and I really like the result; it ticks all the right boxes for me. The hillshade exposes the drama and excitement of the terrain, and I think I got the elevation color ramp pretty close to awesome.

As a canyoneer you read about all these great canyons, but sometimes it's hard to get a good mental map of where they are in relation to more common points of interest. The USGS topos usually label canyons and routes that took their name from an old established canyon name, like Heaps, Imlay, and Behunin Canyons. However, names like Das Boot and Misery Canyon were created by the canyoneering community and are nowhere to be found. Fortunately, the Geographic Names Information System (GNIS) from the USGS includes most of these canyons, allowing us to create our own map:

(Keep reading after the map for a little bit more on the creation process)

Click here for a higher-resolution image

The Process

I really wanted to show of the majesty of the landscape, to show how the canyons cut sharp clefts into the landscape. I found 1-meter elevation data from the USGS for the Park, but it had some annoying artifacts that appear to come from the lidar data or processing:

However, it smoothed up quite nicely at this scale using the MDenoise program created by Sun et al (2007) while still retaining a lot of the sharp details of the sheer red-rock cliffs:

I added a slope layer as well, symbolized as flat = white to steep = black and made it about 80% transparent. This doesn't show up much in the canyons, but brought out a little bit more detail in the lower cliff face of Zion Canyon.

Then, I created a custom color ramp based on the colors from the NPS' geology guide for the park to echo the actual rock layers and formations. It does a decent job, but can't quite do it justice without some representation for the vegetation on the plateau tops. It's also not properly indexed; the slight tilt in the strata relative to a flat sea-level "zero elevation point" means that, on opposite ends of the park, you could see different formations at the same elevation. In this map, every point of a given elevation is the same color.

The roads and rivers layers should hopefully help orient you to places you remember if you've visited the Park: the switchbacks up to the tunnel, and the end of the road at the Temple of Sinawava. I've let the flow of the labels, along with the hillshade, reveal the canyons themselves by setting the canyon lines to "No Color." Finally, a couple points of interest, like The Sentinel and Angels Landing, are identified to give a little bit more context and orientation.

And a Little Bit of Map Philosophy

All maps are, to an extent, an abstracted representation of reality. Some try to eliminate this as much as possible, using satellite imagery and high-accuracy polygons for every feature imaginable. Others embrace their abstractness, using labels in place of lines and funky symbols that represent an entire town.

With its color ramp and hillshade, I want this map to straddle the border between representational and realistic. The colors don't match exactly with what formations are found in the exposed strata at any given exact place in the canyon. But, with the whites and greens capping the domes and plateaus and the changing shades of tans and brownish-reds descending to the canyon floor, your mind (hopefully) is reminded of the grand wonders that exist in the real world.

I hope we never lose the ability to be amazed of these wonders.

Mental Maps and The Shadows of Human Impact

I like maps. I like to stare at them, try to figure out what their story is. I especially like maps of the physical environment—I've spent hours on Google Maps' terrain view exploring different areas, trying to read the earth, what the bare ground is telling us.

But even with Google Maps, the roads are still there, still distracting us, still saying "look what humans have created! Look at this mighty road we built in this impossible canyon!" So I went to make my own terrain maps, free of the influence of humans.

My first attempt actually ended up telling more of humankind's story than I intended.

Looking for good-but-not-overwhelmingly-detalied elevation data, I downloaded the 5-meter auto-correlated DEM data for the Salt Lake valley from the Utah AGRC. Auto-correlated elevation models are created examining the differences in stereo image pairs (see https://gis.stackexchange.com/a/61993) and thus show various parts of the built environment. What this means is that my attempt to scrub away humankind was beautifully flawed and ended up showing  some really cool artifacts.

Let's start with freeways, because every post about the evils of human influence has to talk about freeways, right? This is an overpass. Even without showing the roads, the massive amount of earthmoving that's been done to raise one high-volume stream of metal, cloth, and human beings over another stream is a little stupefying.

Even old-school grid systems, the darling child of walkability nerds and new urbanists, get in on the act. They're easier to see when they're cut into a hillside and the streets square off the lots to accommodate our discomfort of weird, unordered shapes.

This is a wonderfully disjointed bit of geomorphology. Freeway planners of the 50s and 60s took advantage of a natural stream channel/canyon to build a freeway without having to tear down homes. The steep canyon walls gradually give way to the valley floor, however, and are replaced by the flat bed and smooth, flowing, engineered curves of the Interstate Freeway system.

For all our love of attacking modern human's impact on the environment, let's not forget that something as Utah-Pioneer-'Merica!-ish as farming shows up as well. This is one of the last center-pivot irrigation fields I could find in the valley, though the latest satellite imagery shows it's been dug up in preparation for some sort of development.

File this one in the "What the ...?" department. A look at the satellite imagery shows this massive, angular cut into the hillside was made for... a park. A park?! I'm sure there's a story and reason behind it, but I can't for the life of me figure it out.

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But the point of this post isn't to tell this micro story, the story of how we've shaped the earth. No, the story I want to tell is this one.

Full-size link (8mb)

Look familiar? This is the Salt Lake valley. I've always wondered what it looked like before 1847, when it the only human inhabitants were the Northwestern Shoshone. Like I said above, it's not perfect, but it still gives us a much better picture of what the land beneath our tires looks like.

The human mind is amazing at creating mental maps of areas. We know the routes that are familiar, the topological links between our house, the roads, the freeway, and our office or favorite shopping center. We know the canyons and tall mountains are "east," that Ogden is "north" and Provo is "south." We recognize the Bingham Canyon Mine slag piles as "west."

But this plays tricks on us as well. For years, I always though of going downtown as "going up" somehow. Looking at the elevation color (AKA hypsometric tint), downtown is lower than most of the valley! My mental map doesn't match reality, and it doesn't even match the word "downtown." But the "downtown is up" concept was so engrained in my brain that in Portland I always thought I was going north when I got off the train at Pioneer Square and walked uphill to the PSU campus... on the south end of downtown. But I was going up!

What I like about this map is that except for the few highly-identifiable freeway cuts and raises, you can't use any of your normal mental map clues to figure out where you are. You have to stop thinking about a home, the road, or an arbitrary address grid coordinate, and start thinking about river channels, glacial moraines, and canyons.

After looking at this map in detail, someone told me they couldn't figure out where was where. That's the point.

As Yoda once said, "you must unlearn what you have learned." Let go of your east side vs west side mentality, your downtown vs suburbs bias, your industrial wastelands vs green-grass dreamworld. Release your a priori knowledge, and see what you can find.

Read the story of the valley.

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I didn't start intending to write about such abstract ideas as mental maps and such conscientious topics as humankind's impact on the world. No, I wanted to share the discoveries I've made in this map of the place I've lived for over twenty years of my life.

Looking at this map, I realize I barely know it.

I meant to write about the cartographic tools and techniques I used, the thrill of getting something to work after trying half a dozen different ways. Someday I'll write that, and the many other techniques I've discovered in the two years since I first wrote this on my personal blog. But for now, I'll keep exploring.