Every project I have been involved thus far, I have helped people to ask the question – ‘Are maps really the right tool for us to tell this story?’ And I must say, there are not many people who are convinced. Maps are cool, they look nice, you can make them interactive, they may go viral (for good or bad), and yes, people like maps. Agree and that’s one of the many reasons why I love making maps and telling stories through them. If you do not ask the question, several things can go wrong.

I put together a repository to start gathering few examples of situations when maps go wrong. And spoke at an event in Bangalore and it was exciting. We will see some of those in this blog post. I am not intending to provide solutions to most of these, that will make a better blog post later. Broadly, there are six lists –

Misrepresentation of data

Careless handling of images and data can cause terrible mistakes, like the one below from the CNN a few weeks back.

CNN - Hong Kong is now in Brazil

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Our tutorials so far have been focused on several aspects of cartography, from data structures to their analysis and representation. Not surprisingly, most of them are aligned to web technologies, and client browsers expect the applications to consume relatively low resources. Spatial data have comparatively higher memory footprints owing to the structure and the amount of information they hold. For instance, the taluk boundary level data for India is 46.3 MB in GeoJSON format. This means that it cannot be used directly in a web project; it needs to be optimised first.

Optimising spatial data essentially translates to simplifying the geometries in the file. Since, in a web context, it is not using it for analysis, a slight difference in the area or shape of corners will not make a huge difference. Users may not even realise that the shapes are simplified, if it is done in just the right way.

To give you an idea of the process, have a look at the following maps of Florida. The first row showcases the original data from the Florida Geographic Data Library, converted to GeoJSON (8.2MB). The second set of images shows the simplification of the geometry (note the sharp edges) in the GeoJSON (now 427KB). This really hasn’t changed the way the map looks on the whole, which is exactly what we need for web representation.

florida_combined florida_optimised_combined

In this article I will quickly look at a few easy methods to simplify geometries.


TopoJSON, developed by Mike Bostock, is an extension of GeoJSON with encoded Topology.

Rather than representing geometries discretely, geometries in TopoJSON files are stitched together from shared line segments called arcs.

This simplifies the structure of the data by identifying the relationships and storing them in the same file, thus eliminating redundancy. TopoJSON works seamlessly with D3.js and can be integrated with pretty much any other web application.

Simplify using QGIS

The QGIS vector processing suite comes with a tool for simplifying geometries. It employs the popular Ramer–Douglas–Peucker algorithm which reduces the number of points in a curve. You have to select the layer that you want to simplify and pick a tolerance level. The higher the tolerance, the lesser the number of points and the lower the size of the file.


PostGIS ST_Simplify

In case you are serving spatial data from a PostgreSQL database through an API to the client-side, PostGIS implements the previously mentioned Ramer-Douglas-Peucker algorithm through the procedure called ST_Simplify. For example, to apply ST_Simplify on a geometry called ‘state’ of id 1, with a tolerance of 0.002 from a table called ‘country, the PostGIS command would be:

SELECT ST_Simplify(state, 0.002) from country where id=1;

These techniques are very essential when you deal with large amounts of spatial data that are required to be rendered in the browser. If you have more ideas or questions, let us know in the comments!