The geospatial sport

An orienteer leaving a control site. 

If you love studying maps or solving puzzles, and you love being outside, then orienteering — the thinking runner's sport — might be the sport you've been looking for.

There are many, many flavours of orienteering (on foot, on skis, in kayaks, etc), but here's how it generally works:

  • Competitors make their way to an event, perhaps on a weekday evening, maybe a weekend morning.
  • Several courses are offered, varying in length (usually 2 to 12 km) and difficulty (from walk-in-the-park to he's-still-not-back-call-search-and-rescue).
  • A course consists of about 20 or so 'controls', which must be visited in order. Visits are recorded on an electronic 'dibber' carried by the orienteer, or by shapes punched on a card.
  • Each person chooses a course , and is allotted a start time.
  • You can't see your course — or the map — until you start. You have 0 seconds to prepare.
  • You walk or run or ski or bike around the controls, at various speeds and in various (occasionally incorrect) directions.
  • After making it to the finish, everyone engages in at least 30 minutes of analysis and dissection of route choices and split times, while eating everything in sight.

The catch is that your navigation system is entirely analog: you are only allowed a paper map and an analog compass, plus a whistle for safety. The only digital components are the timing system and the map-making process — which starts with LiDAR and ends in a software package like OCAD or OOM.

Orienteering maps are especially awesome. They are usually made especially for the sport, typically at 1:5000 or 1:7500, with a 2.5 m or 5 m contour interval. Many small features are mapped, for example walls and fences, small pits and mounds, and even individual trees and boulders.

The sample orienteering map from the  Open Orienteering Mapper  software, licensed GNU GPL. White areas correspond to open, runnable (high velocity) woodland, with darker shades of green indicating slower running. Yellow areas are open. Olive green areas are out of bounds.

The sample orienteering map from the Open Orienteering Mapper software, licensed GNU GPL. White areas correspond to open, runnable (high velocity) woodland, with darker shades of green indicating slower running. Yellow areas are open. Olive green areas are out of bounds.

Other than the contours and paths, the most salient feature is usually the vegetation, which is always carefully mapped. Geophysicists will like this: the colours correspond more to the speed with which you can run than to the type of vegetation. Orienteering maps are velocity maps!

Here's part of another map, this one from Debert, Nova Scotia:

bluenose-map-debert.png

So, sporty cartophile friends, I urge you to get out and give it a try. My family loves it because it's something we can do together — we all get to compete on our own terms, with our own peers, and there's a course for everyone. I'm coming up on 26 years in the sport, and every event is still a new adventure!


World Orienteering Day — really a whole week — is in the last week in May. It's a great time to give orienteering a try. There are events all over the world, but especially in Europe. If you can't find one near you, track down your national organization and check for events near you.

Is geolocation the new lat-long?

location_pick-up.png

If I want to hail a ride-sharing service I can give an address as my location. Unless I am at the rear of the building or in the parking lot across the street, in which case I need to fiddle about with a pin on a slippy map. No problem, I can usually eyeball it and then just look out for the driver.

But there are other situations where an address won't do, and fiddling with pins isn't an option either. Telling the pizza company exactly where the delivery drone should land. Calling an ambulance to a remote area. Specifying a well location in the desert. Doing almost anything in the desert.

Wait, isn't this what latitude and longitude are for?

Sort of. I mean, it is what lat and long are for, but they aren't all that good at it. For one thing, there's the annoying problem of datums, which is even more annoying because hardly anyone realizes it's a problem.

Then there's the fact that to get better than 10 metre accuracy, you need 5 decimal places (or 1 decimal place in the seconds if we're talking DMS). So, even without the all-important datum, your average lat-long pair in N America would need at least 18 characters in decimal notation: 44.44845N64.37565W. This is not terribly user-friendly.

What are the alternatives?

In the last ten years or so, several alternatives to addresses and lat-longs have emerged. For example, one interesting geocoding system — geohash.org — encodes locations as strings of letters and digits. The location of my fictional 'pick up point' would be dxfhz5e4fxs. A bit of a mouthful perhaps but the system helpfully omits some easily confused characters, like lower-case L, and is case-insensitive. Another really nice feature: the string is big-endian so you can remove characters from the right to get a bit less precision.

In 2013, what3words burst onto the geolocation scene with an ingenious proprietary algorithm uniquely transforms the location of every 3 x 3 m square on Earth into three pronounceable words. My pick-up location becomes a cryptic crossword clue: dreadlocks.boarded.pageant. One feature of the scheme is that similar-sounding locations are not neighbours, avoiding near-miss confusion. For example, the square to the west of mine is called lawmakers.sieves.breezes, and the similar-sounding deadlock.boarded.pageant is in the middle of a field in New South Wales. Mercedes and Dominoes Pizza are experimenting with what3words.

More recently still, Open Location Codes have got some traction. Also called plus codes — a clue that they were invented by Google — they are a really nice example of a well-executed open standard, with fully open code, reference implementations in lots of languages, a public-facing website, and great documentation. My location's plus code is 87PQCJXF+9Q. Like the geohash, it can be shortened — but only in particular ways, for example, I could give the code as CJXF+9Q, Mahone Bay. Unlike what3words, Open Location Codes are free to use. My guess is that we'll be seeing them all over the place as self-driving cars and drones become more widespread.

Mapcodes, developed in about 2001, are yet another implementation of geocodes. Their main feature is the use of very short codes for densely populated places. However, there are some problems. For example, codes can be specified with or without country and region codes — but the different versions do not resemble each other.

For comparison, here's how I might describe my pick up location on the map at the top of this post:

 
geocodes_again.png
 

Useful for geoscience?

They certainly seem easier to wield that lat-long, and you don't need to worry about datums anymore, but perhaps they feel too new or ephemeral to catch on for some geospatial practitioners. I also wonder why no-one seems to have thought about the 3D problem yet... Which floor of the apartment building is this pizza going to? How far down this mine is the heart attack victim? At exactly what depth in this lake was the wreckage of the self-driving car found?

What do you think? will any of these schemes gain traction? Might any of them be useful in science or engineering applications? Will you be experimenting with them?


I can't leave the subject of geolocation codes without mentioning geohashing — a sort of cross between geocaching and professional nerdism. Invented in 2008 by xkcd creator, Randall Munroe, geohashing involves generating random locations via an MD5 hash, then visiting that location without getting lost or beaten up.

x lines of Python: read and write a shapefile

Shapefiles are a sort-of-open format for geospatial vector data. They can encode points, lines, and polygons, plus attributes of those objects, optionally bundled into groups. I say 'sort-of-open' because the format is well-known and widely used, but it is maintained and policed, so to speak, by ESRI, the company behind ArcGIS. It's a slightly weird (annoying) format because 'a shapefile' is actually a collection of files, only one of which is the eponymous SHP file. 

Today we're going to read a SHP file, change its Coordinate Reference System (CRS), add a new attribute, and save a new file in two different formats. All in x lines of Python, where x is a small number. To do all this, we need to add a new toolbox to our xlines virtual environment: geopandas, which is a geospatial flavour of the popular data management tool pandas.

Here's the full rundown of the workflow, where each item is a line of Python:

  1. Open the shapefile with fiona (i.e. not using geopandas yet).
  2. Inspect its contents.
  3. Open the shapefile again, this time with geopandas.
  4. Inspect the resulting GeoDataFrame in various ways.
  5. Check the CRS of the data.
  6. Change the CRS of the GeoDataFrame.
  7. Compute a new attribute.
  8. Write the new shapefile.
  9. Write the GeoDataFrame as a GeoJSON file too.

By the way, if you have not come across EPSG codes yet for CRS descriptions, they are the only way to go. This dataset is initially in EPSG 4267 (NAD27 geographic coordinates) but we change it to EPSG 26920 (NAD83 UTM20N projection).

Several bits of our workflow are optional. The core part of the code, items 3, 6, 7, and 8, are just a few lines of Python:

    import geopandas as gpd
    gdf = gpd.read_file('data_in.shp')
    gdf = gdf.to_crs({'init': 'epsg:26920'})
    gdf['seafl_twt'] = 2 * 1000 * gdf.Water_Dept / 1485
    gdf.to_file('data_out.shp')

That's it! 

As in all these posts, you can follow along with the code in the Jupyter Notebook.

Minecraft for geoscience

The Isle of Wight , complete with geology. ©Crown copyright. 

The Isle of Wight, complete with geology. ©Crown copyright. 

You might have heard of Minecraft. If you live with any children, then you definitely have. It's a computer game, but it's a little unusual — there isn't really a score, and the gameplay has no particular goal or narrative, leaving everything to the player or players. It's more like playing with Lego than, say, playing chess or tennis or paintball. The game was created by Swede Markus Persson and then marketed by his company Mojang. Microsoft bought Mojang in September last year for $2.5 billion. 

What does this have to do with geoscience?

Apart from being played by 100 million people, the game has attracted a lot of attention from geospatial nerds over the last 12–18 months. Or rather, the Minecraft environment has. The game chiefly consists of fabricating, placing and breaking 1-m-cubed blocks of various materials. Even in normal use, people create remarkable structures, and I don't just mean 'big' or 'cool', I mean truly remarkable. So the attention from the British Geological Survey and the Danish Geodata Agency. If you've spent any time building geocellular models, then the process of constructing elaborate digital models is familiar to you. And perhaps it's not too big a leap to see how the virtual world of Minecraft could be an interesting way to model the subsurface. 

Still I was surprised when, chatting to Thomas Rapstine at the Geophysics Hackathon in Denver, he mentioned Joe Capriotti and Yaoguo Li, fellow researchers at Colorado School of Mines. Faced with the problem of building 3D earth models for simulating geophysical experiments — a problem we've faced with modelr.io — they hit on the idea of adapting Minecraft models. This is not just a gimmick, because Minecraft is specifically designed for simulating and manipulating landscapes.

The Minecraft model (left) and synthetic gravity data (right). Image ©2014 SEG and Capriotti & Li. Used in acordance with SEG's  permissions . 

The Minecraft model (left) and synthetic gravity data (right). Image ©2014 SEG and Capriotti & Li. Used in acordance with SEG's permissions

If you'd like to dabble in geospatial Minecraft yourself, the FME software from Safe now has a standardized way to get Minecraft data into and out of the environment. Essentially they treat the blocks as point clouds (e.g. as you might get from Lidar or a laser scan), so they can do conventional operations, such as differences or filtering, with the software. They recorded a webinar on the subject yesterday.

Minecraft is here to stay

There are two other important angles to Minecraft, both good reasons why it will probably be around for a while, and probably both something to do with why Microsoft bought Mojang...

  1. It is a programming gateway drug. Like web coding, and image processing, Minecraft might be another way to get people, especially young people, interested in computing. The tiny Linux machine Raspberry Pi comes with a version of the game with a full Python API, so you can control the game programmatically.  
  2. Its potential beyond programming as a STEM teaching aid and engagement tool. Here's another example. Indeed, the United Nations is involved in Block By Block, an effort around collaborative public space design echoing the Blockholm project, an early attempt to explore social city planning in the tool.

All of which is enough to make me more curious about the crazy-sounding world my kids have built, with its Houston-like city planning: house, school, house, Home Sense, house, rocket launch pad...

References

Capriotti, J and Yaoguo Li (2014) Gravity and gravity gradient data: Understanding their information content through joint inversions. SEG Technical Program Expanded Abstracts 2014: pp. 1329-1333. DOI 10.1190/segam2014-1581.1 

The thumbnail image is from an image by Terry Madeley.

UPDATE: Thank you to Andy for pointing out that Yaoguo Li is a prof, not a student.