x lines of Python: static basemaps with contextily

Difficulty rating: Beginner

Something that is often useful in planning is to have a basemap of the area in which you have data or an interest. This can be made using a number of different tools, up to and including full-fledged GIS software, but we will use Contextily for a quick static basemap using Python. Installation is as simple as using conda install contextily or pip install contextily.

The steps that we want to take are the following, expressed in plain English, each of which will roughly be one line of code:

1. Get a source for our basemap (placenames and similar things)
2. Get a source for our geological map
3. Get the location that we would like to map
4. Plot the location with our geological data
5. Add the basemap to our geological map
6. Add the attribution for both maps
7. Plot our final map

We will start with the imports, which as usual do not count:

import contextily as ctx
import matplotlib.pyplot as plt

Contextily has a number of built-in providers of map tiles, which can be accessed using the ctx.providers dictionary. This is nested, with some providers offering multiple tiles. An example is the ctx.providers.OpenStreetMap.Mapnik provider, which contains the following:

{'url': 'https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png',
 'max_zoom': 19,
 'attribution': '(C) OpenStreetMap contributors',
 'name': 'OpenStreetMap.Mapnik'}

The most important parameter in the dictionary for each provider is the url. These are of the form example.com/{z}/{x}/{y}.png. The {z} is the zoom level, while {x} and {y} relate to the latitude and longitude of a given tile, respectively. Note that these are the same as those used by interactive Slippy maps; contextily just downloads them as a single static image.

The easiest is to use one of these providers, but we can also define our own provider, using the above pattern for the URL. For geological data, the Macrostrat project is a great resource, especially because they have a tileserver supplying some detail. Their tileserver can be added using

geology_tiles = 'https://tiles.macrostrat.org/carto/{z}/{x}/{y}.png'

We also need a place to map. Contextily has a geocoder that can return the tiles covering a given location. It uses OpenStreetMap, so anything that is present there is useable as a location. This includes countries (e.g. 'Paraguay'), provinces/states ('Nova Scotia'), cities ('Lubumbashi'), and so on.

We will use Nova Scotia as our area of interest, as well as giving our desired map tiles. We can also use .plot() on the Place object to get a look at it immediately, using that basemap.

ctx.Place('Nova Scotia', source=ctx.providers.CartoDB.Positron).plot()

The Positron style from Carto for Nova Scotia.

We'll use a different basemap though:

basemap = ctx.providers.Stamen.Toner

We can create the Place with our desired source — geology_tiles in this case — and then plot this on the basemap with some transparency. We will also add an attribution, since we need to credit MacroStrat.

place = ctx.Place('Nova Scotia', source=geology_tiles)

base_ax = place.plot()
ctx.add_basemap(ax=base_ax, source=basemap, alpha=0.5)
text = basemap.attribution + ' | Geological data: MacroStrat.org (CC-BY)'
ctx.add_attribution(ax=base_ax, text=text)

Finally, after a plt.show() call, we get the following:

Obviously this is still missing some important things, like a proper legend, but as a quick overview of what we can expect in a given area, it is a good approach. This workflow is probably better suited for general location maps.

Contextily also plays well with geopandas, allowing for an easy locality map of a given GeoDataFrame. Check out the accompanying Notebook for an example.

 Run the accompanying notebook in MyBinder

The geospatial sport

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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.

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:

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!

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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?

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:

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?

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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.

Update: You can access this algorithm right from Python: from antigravity import geohash