Maps are one of the most powerful and ubiquitous types of data visualisations used in the media today. It’s also one of the oldest ways to visualise information which is being extremely disrupted today. Mapping that started as manual data collection and mapping centuries ago, is becoming increasingly data-driven and predictive.
In this post, I will show how visualising geospatial information has changed over time, how it’s being used in data journalism today and where we are heading. I’ve also listed some useful resources to get you get started with mapping and visualising geodata.
Let’s start with a little bit of history. The cholera map by John Snow is perhaps one of the best-known examples of maps bringing significant insight and one of the earliest cases of data journalism. It changed the way we see the disease and set a milestone in data visualisation.
Another map which at the first glance doesn’t look like map at all is this graphic by Charles Joseph Minard which portrays the losses suffered by Napoleon’s army in the Russian campaign of 1812. Edward Tufte, one of the most famous statisticians of our time, calls it “probably the best statistical graphic ever drawn”.
Maps in the news today
Another good example is the parlamentary elections map for Berlin by Berliner Morgenpost. Apart from visualising election results for the whole city as well for each election poll, the interactive news team at the Morgenpost newsroom did something else: They offered their users an extremely detailed search for each address and added additional layers which opened up new possibilities for exploration of the hidden patterns. For example, you could see how Berlin residents with a migration background or neighbourhoods with a high proportion of unemployed residents voted. You can find a tutorial for this map here (in German).
When maps shouldn’t be maps
Sometimes, maps shouldn’t be maps though. As the deputy graphics director at The New York Times Matthew Ericson put it in his essay on modern news maps,
Sometimes the reflexive impulse to map the data can make you forget that showing the data in another form might answer other — and sometimes more important — questions.
First, sometimes the interesting patterns aren’t geographic patterns at all as in this visualization on the election results.
On election night, people don’t want to know just “Who’s winning my district?,” but also “Which party is doing better than expected tonight?” And the results map, as good as it is at answering the first question, has a much harder time answering the second.
That’s why a different visual representation proves to be much more effective in answering this question.
Second, in some cases the geographic data itself should be analysed (rather than just displayed) and mashed up with other data such as demographics or income.
In this case, the analysis of geographical and other data attempted to answer the question “Who in New Orleans had been most severly affected by the flooding?” and resulted in a simple table:
Yet another example of structuring geographical patterns on something different than a map is a visualisation of the gay rights in the US, broken down to states and regions by the Guardian. This visualization also follows a classic rule of dataviz composition mentioned by Alberto Cairo:
Overview first, zoom and filter, then details on demand.
The Guardian visualisation allows to quickly understand the big picture of gay rights patterns in the US based on geographical distribution, after which readers can compare separate rights and states and, when scrolling down, find more details on each of the categories. What’s also worth mentioning is the possibility to personalise the information by connecting to Facebook and seeing rights of states where your friends live thus engaging readers.
The future of maps: predictive analysis & enhanced data collection
The big question is: what will be happening to maps in the next years? The session on Maps & Data at the recent DLD conference attempted to answer these questions. Here are some key take-aways.
How maps are changing:
- Map design is changing due to new technologies and gadgets like wearables and is becoming more compact.
- Maps are growing because of mobile – people need to see where they are (for example as in the Runkeeper app).
- The open Google Maps API has been the driving force for the explosion for map-derivative companies.
- There’s an increasing need for for smarter, information-rich maps. People want to actively pull information rather than just take what is being offered.
- Maps are used to make decisions. For example, half of the business of The Weather Company are subscriptions.
- Map is becoming your orientation device showing you where you should move and helping you make decisions. You can add additional dimensions to the map, such as business locations or a time dimension.
- The ability to build comprehensive predictive models that help people to acquire additional information will be one of the characteristics of the maps of the future.
- Companies need to be able to sense what’s going on in the environment to make it emotional. For example, The Weather Company tries to find out what people feel based on what’s going on in their environment. They’ve discovered that, when blizzards come, man become boys and start playing around whereas women flip out worrying how they’re going to handle everything. With that kind of knowledge, companies can make special offers and deliver more value to the users. That means moving from observing from predicting.
- Being able to show real time feedback on the map wasn’t possible a few years ago. It’s reality now.
Sources of information for maps:
- Sources of information for maps are expanding rapidly: drones, sensors , smart watches and micro satellites have the potential to totally change the image and geodata collection.
- The open data is disrupting the ways maps are being produced. With government opening up data and user generated content flowing in, companies can use this data to create their own applications.
Here’s the full video of the session:
My personal take-away from this session is that journalists are still too focused on producing and showcasing content instead of using this content combined with data to offer even more value to their audiences. In his new book “Geeks Bearing Gifts: Imagining New Futures for News” Jeff Jarvis is putting it this way:
Apple’s Siri, Google’s Glass, smartwatches, and their coming competitors may break the association of mobile with the phone when we can just speak to the air, asking questions and getting answers without having to haul out a device, without having to type or click, without going to a site or, for that matter, without ending up at a page. What happens to our notions of our products and services in news media when they are no longer built with pages at all?
So I guess it’s high time media start to think in a more consumer-oriented way and offer solutions which will simplify the live of thousands and potentially millions of people. That doesn’t mean media companies should all strive to invent the new Google Maps or the new Waze (although I personally wouldn’t mind), but rather use existing technologies to offer more value to consumers by combining geodata with context-based content and direct them in their daily routine.
Where can I learn more about maps?
If you want to dive deep into map-making and understanding cartography and geospatial concepts as well as use those to produce data journalism pieces, you can start here:
- To learn more about the types of maps and the basics of cartography and geospatial analysis, look up this explanation of map types by University of Muenster and check out these cartography & visualisation concepts by the PennState University.
- Also check out this short introduction to making maps by Noah Veltman (a little geeky, but very cool)
- To take a deep dive into maps and spatial thinking, you can take the free online course “Maps and the Geospatial Revolution” on Coursera.
- I can also highly recommend a cheat sheet for producing geodata visualisations and a list of useful tools by Achim Tack and Patrick Stotz from Mappable.