what3words is a really simple way to talk about location. We have divided the world into a grid of 3m x 3m squares and assigned each one a unique 3 word address. It means anyone can accurately find any location and share it more quickly, easily and with less ambiguity than any other system.
The service can be used via the free mobile app or online map. It can also be built into any other app, platform or website, with just a few lines of code.
For example, the torch of the Statue of Liberty is located at toned.melt.ship. This combination is unique, no other location across the globe has it. Easier to remember than WGS84 coordinates, and still very precise.
In the Puzzle you need to drag the shape of the territory to the right place. Just like in childhood we collected pictures piece by piece, so here you can collect a country from regions or whole continents from countries!
In the Quiz you need find the country by flag, emblem or the capital. Did you know that Monaco and Indonesia have the same flags? And that the flags of the United States and Liberia differ only in the number of stars? So, these and other interesting things can be learned and remembered after brainstorming right now!
One of the cool things about MySQL 5.7 is the fact that it sports a few spatial convenience functions (since 5.7.6), allowing one to do operations on geometric values.
One of those convenience functions is ST_Distance_Sphere, which allows one to calculate the (spherical) distance between two points.
Recently I was working an project where I needed said function, yet unfortunately they were running some version of MySQL 5.6 … on which ST_Distance_Sphere isn’t available.
Instead of requesting to upgrade the MySQL server, I decided to polyfillST_Distance_Sphere instead. It’s really easy since it’s a function, and it basically is nothing more than the Haversine Formula which I, as a mapping aficionado, know how to juggle.
DROP FUNCTION IF EXISTS `ST_Distance_Sphere`$$
CREATE FUNCTION `ST_Distance_Sphere` (point1 POINT, point2 POINT)
no sql deterministic
declare R INTEGER DEFAULT 6371000;
declare `φ1` float;
declare `φ2` float;
declare `Δφ` float;
declare `Δλ` float;
declare a float;
declare c float;
set `φ1` = radians(y(point1));
set `φ2` = radians(y(point2));
set `Δφ` = radians(y(point2) - y(point1));
set `Δλ` = radians(x(point2) - x(point1));
set a = sin(`Δφ` / 2) * sin(`Δφ` / 2) + cos(`φ1`) * cos(`φ2`) * sin(`Δλ` / 2) * sin(`Δλ` / 2);
set c = 2 * atan2(sqrt(a), sqrt(1-a));
return R * c;
Run the little snippet above on your server after having selected a database first. Once executed it’ll persistently be available (just like a Stored Procedure) for you to use. Usage is the same as if it were natively available:
Ooh I like this: The National Library of Scotland has released Spy Viewer, a tool showcasing a set of historic which maps have been georeferenced so that they line up perfectly on top of the current maps. Using a circular spyglass interface you can watch the old maps peek through.
Pictured below is a historic glance at London’s Tower Bridge:
Don’t let the fact that it’s a tool by the National Library of Scotland fool you: other maps of other parts of the world are also available (select yours using the category dropdown)
The Population Estimation Service is a Web-based service for estimating population totals and related statistics within a user-defined region. It enables users of a wide variety of map clients and tools to quickly obtain estimates of the number of people residing in specific areas without having to download and analyze large amounts of spatial data.
Tested it by drawing a (rough) polygon around Belgium and it yielded a number of nearly 12 million which is quite correct 🙂
In succession to Google Maps’s Quiet Transformation, a new – and very extensive and highly interesting – comparison by the same author. He start off by taking a look at the level of detail when it comes to buildings.
But these buildings are more than just a pretty detail—they appear to be the foundation for one of Google Maps’s newest features…
The fun part begins when you start combining these building shapes with places (such as restaurants, coffee shops, etc.) to create “Areas of Interest” which represent commercial corridors. These AOIs are coloured differently on a map, allowing you to quickly recognise ‘m by just glancing at the map.
What about Apple Maps?
With “Areas of Interest”, Google has a feature that Apple doesn’t have. But it’s unclear if Apple could add this feature to its map in the near future.
The challenge for Apple is that AOIs aren’t collected—they’re created. And Apple appears to be missing the ingredients to create AOIs at the same quality, coverage, and scale as Google.
And “Areas of Interest” is just one of the things the author covers …
Good intro by Arden de Raaij on setting up a basic Mapbox GL JS map with clickable markers that zoom upon getting clicked.
Strava has released a Global Heatmap powered by Mapbox GL, plotting all locations where their users go run / go cycle / do water activities / do winter activities.
The raw input activity streams data comes from a Spark/S3/Parquet data warehouse. This data includes every one of the 3 trillion GPS points ever uploaded to Strava. Several algorithms clean up and filter this data.
The full global heatmap was built across several hundred machines in just a few hours, with a total compute cost of only a few hundred dollars.