Sociotope brings your online identity to life

A mass of multi-coloured tentacles against a grey-blue backdrop

While browsing data visualisations on Pinterest the other day, I came across an interesting-looking tool: Sociotope, a social media experiment which takes the data people leave behind in social networks and turns it into an interactive data visualisation.

The free-to-use web app works with Twitter, Facebook and soon Google Plus. It uses your data to build a “virus”-like creature with one tentacle for every post you’ve made, or post that someone else has involved you in, up to a maximum of 150 (though you can choose to load more). The colour scheme is taken from your profile picture, and the length of each tentacle varies depending on the length of the post. The more the tentacles move around, the more people have interacted with that post – providing a slightly bizarre but effective overview of your social media presence.

 A screen capture of me exploring Sociotope and using it to visualise my Twitter activity

Sociotope is functional, but also fun and interesting – you can use your cursor to spin it around in the three-dimensional space, and watch as the tentacles flop about. You can click on each one to see details about that post, although with so many tentacles in the way, it can be difficult to hit the exact one you’re aiming for.

Sociotope also provides a few options for analysing your social media presence, including sorting posts by time and by author. Its design is geared more towards visual impact than straight-forward analysis; but it’s effective as a visualisation and fun to play with, and could serve as an entry point for more casual users into analysing their social media presence, rather than only appealing to professionals, like most analytical tools.

A visual metaphor

Stefan Wagner, the designer who created Sociotope, says he wanted people to gain an understanding of what they leave behind online:

If you browse websites, data is collected about you – lots of data. I think the average user doesn’t ever glimpse how much data that is actually collected … these kind of exceptional visualisations, they gain people’s interest, and they will be interested in this viewing this data and what lies behind it.

Stefan describes Sociotope as a “metaphor” that represents people’s social media activity and their social relevance. “I always liked connecting data visualisation to some sort of metaphor – I like working with metaphors to convey information about something. The idea was created to make a data visualisation of social media and put it in some sort of other form, to shape it differently, so that the viewer would learn something else from it.

“I really hope that people are using it to analyse their own presence and maybe the identity of others. Because social networks, they’re all about social interaction, and I think it’s really important for people to realise how they use this kind of social media, how they interact with their friends, and how deep this interaction goes.”

Does he think that this is a role that data should be playing – in helping people realise these things about themselves? “For me, it’s the only way data should be used. Of course big data is used to do advertisements and stuff but for me, the interesting thing lies in analysing behaviour and getting into how people use this kind of media.”

A colourful Sociotope visualisation with a few tendrils extending out towards the words "tweet", "reply", "tweet with media" and "link"
Sociotope can break your online presence down by type of post and whether or not it contains media | Stefan Wagner / andsynchrony

Sociotope also provides an unexpected insight into how the internet has developed over time and how users’ social media presences have changed with it. By loading posts back far enough, you can play them as a time-lapse which shows the evolution of a person’s social media presence over the years.

“When I started to build the project,” says Stefan, “I saw that in 2009 or 2010, people were writing a lot more text, but now they restrict themselves to posting photos or one-liners – just a few words. People tend to not write so many things any more; they more tend to post photos or videos.

“You can read it out of the visualisation. [Similarly], when you look at websites, how they are structured and how they try to gain attention, photographs or images get a lot more space these days than they did two or three years ago.”

Generating Utopia

Sociotope isn’t Stefan’s only project which uses data visualisation to give insight into how people use social media. In 2013, he created ‘Generating Utopia’, a real-time visualisation of social location data using the social platform Foursquare.

It takes a map of an existing city and alters the topography based on a person’s Foursquare check-ins, elevating the areas where a person checks in the most, to emphasise their importance. The locations are connected by a web of neon lines in primary colours: red for work, blue for recreation and yellow for transport. The overall effect is a dramatic, futuristic cityscape.

“People like to represent themselves from their best side, in social networks,” Stefan explains. “So when they check in somewhere, it’s not like the doctor’s office or something; it’s some awesome place. So people will build up a utopic vision for themselves, and I wanted to build utopian landscapes from the data.”

A bird's-eye view of a cityscape with several buildings perched on top of high mountains, with lines of red, blue and yellow light winding their way around the topography
A still from Generating Utopia | Stefan Wagner / andsynchrony

“I really love provoking people by showing them data in a different way. I like using metaphors and images, strong images, which provoke people’s imagination to make them build up a sensibility towards what data means and how much data they produce. I think it’s really important.”

Stefan says that he would like to see more people creating images and ideas from the data that lies behind a person’s online presence. “Every image which is created helps shape this future idea of how data should be, or how social networks should work. I can only motivate people to try to visualise data.”

Billy Ehrenberg on data journalism’s future and the skills you need

Billy Ehrenberg, ex-Interhacktive and data journalist, has spent the last year working on new data-based projects with City A.M.’s expanding online team.

I caught up with him to ask what his role involves, and what he sees as the future of data journalism.

In his average day, he admitted that he doesn’t do as much data as he’d like.

“There is a common misconception that graphs in stories means that it’s data – but I try to get at least one data piece done a day.

“Some of what I do is trying to find a story in the numbers, but often the story is quite obvious or easy to tease out, and I need to use visuals or explanations to make it accessible and interesting. To do this I use a few different tools.”

“Excel, Google Sheets, QGIS, CartoDB, HighCharts, Quartz Chartbuilder, Outwit Hub, Illustrator – each one has their advantages”

Billy has several different favourite data tools depending on the job at hand. For example, he says he usually prefers Excel for cleaning datasets.

“I’ve used Open Refine a bit, and that’s certainly worth getting into. Excel and Google Sheets have a bunch of functions that let you pull data apart and whip it into shape – so how useful Excel is depends mostly on if you’re boring enough to have fiddled with functions for days on end.”

data journalism at city am

“Fake data”

On what he sees as the future of data journalism, Billy reckons that “it will naturally divide between real data and fake data. You see some people who do things like not adjusting historic financial data (even film revenues) for inflation because they are in a rush or just don’t realise they should. That’s a dangerous thing: people can see a graph or chart and think that what it shows is fact, when it’s as easily manipulated or screwed up as words are.”

“That’s a dangerous thing: people can see a graph or chart and think that what it shows is fact, when it’s as easily manipulated or screwed up as words are.”

“I think you’ll get two sets of people: those who do not do a lot else, with big skillsets like coding, stats, cartography and programming, and those who have to rush out faux data for hits.”

The next ‘hot topic’

Billy told me he’s not sure what the next hot topic is, but he think it’ll be related to coding – “maybe it’s a cop out, as it’s nothing new.

“People wonder if it’s worth coding if you’re a journalist, and even if you are a journalist if you code. I’m obviously pro-learning.”
data journalism at city am

Data principles

“It’s really important to try not to mislead people. Graphics are easy to use to manipulate people. The more complex they are, the more likely you are to mess up and the less likely it is anyone will notice, even if it changes something.”

“Visualising ethically is important too: even the colours on a map or the extents of an axis can make a change look hugely dramatic”

“I try to let the data tell the story as much as I can and if I don’t like what it’s saying I won’t change the message.”

When asked what data-related skill he wishes he could master, Billy said: “it’s got to be D3. It’s so difficult that I get a real buzz out of solving something in it, even if it’s taken hours.

“Probably learning JavaScript is the best way to crack that nut. It’s a work in progress.”

How to get started with D3

If you’re interested in dataviz, you’ve probably been hearing a whole bunch about D3. When you stumble across a really creative-looking visualisation, chances are it’ll have been made with D3.

Yes, it’s hyped, and there are a million tutorials out there and it can all feel a little overwhelming. So where to start?

Luckily, your humble Interhacktive test bunny has taken the plunge, and tried out three ways of learning to use D3. Read on, for the pros and cons of each.

What is D3?

It’s a powerful Javascript library developed by Mike Bostock. D3 stands for “data-driven documents”, which basically means that you’re binding the data you want to visualise to DOM elements and manipulating them.

D3 Data-Driven Documents.

All of which sounds really technical, of course. The short answer is that D3 lets you make beautiful custom visualisations, entirely from scratch.

When should I learn it?

You’re getting the hang of out-of-the-box vis tools like Plotly, Datawrapper and CartoDB, and are starting to feel frustrated by their limitations. You want to have more freedom to make visualisations that look exactly the way you want.

I’ve heard it’s insanely complicated. Should I be worried?

The honest answer? Yes – D3 does have a rather steep learning curve. I felt quietly panicked the first time I read through a tutorial, and you may feel a little bit discouraged at first too, depending on your background.

A section of the 250 lines of code I wrote to make a bar chart.
A section of the 250 lines of code it took to make a bar chart.

You will be writing Javascript, and yes, you will be writing hundreds of lines of code. You’ll want to be somewhat familiar with HTML, CSS, SVG and DOM elements (if any of this sounds scary, don’t worry! Start with Codecademy’s HTML and CSS tutorial).

BUT: You hardly need to be a Javascript expert before you can start learning D3. In fact, lots of people have said that D3 is a great way to get a feel for Javascript in the first place.

Okay, I’m in. How do I get started?

There are roughly speaking a bazillion tutorials – just on Mike Bostock’s GitHub page. Where to turn? I’ve trawled through a whole bunch of them and am back to report on the three I found most useful.

Video tutorials

Ah, Youtube. Reliable source of how-to videos from how best to groom your cat to how to dance salsa. Naturally it’s also happy to instruct you on how to visualise your data. I followed user D3Vienno’s 20 video tutorials, and found his explanations of some of D3’s more bizarre functions rather good.

20 videos sounds like a lot to get through, but they’re easy to follow and he’s managed to fit in a lot of information. By the time you get to the 20th, you’ll have covered ground from very introductory principles up to advanced D3 layouts like tree maps and geomapping.

Pros

D3Vienno is a journalist, which makes his tutorials particularly useful to fellow hacks.

Cons

The tutorials get off to quite a slow start, so if you have any previous experience of Javascript, you may start to feel restless.

Book: Interactive Data Visualisation for The Web

Scott Murray’s book on D3 is a bit of a classic. It’s also surprisingly funny (probably as funny as a book on programming is likely to get).

This hefty tome is intended for absolute beginners and will hold your hand as it guides you from the very basics – like what HTML really is.

Pros 

Murray’s exercises are easy to follow. And like I said, he’ll make you giggle a bit.

Cons 

I didn’t feel afterward that I’d got a whole lot of independent skill. When working through the tutorials I thought that I’d got it all, but when I tried my hand at creating my own viz afterward, I drew a blank. I’d suggest combining the book with some video tutorials.

Go to a workshop!

I was lucky enough to attend a D3 workshop in London run by Peter Cook of AnimatedData, where attendees range from visual journalists to Python developers and Javascript beginners (yours truly).

Cook really goes through the fundamentals. If you’re coming expecting to have made some crazily advanced force network by the afternoon, you’ll be disappointed. By hometime, I had created a scatterplot that I could’ve charted in Excel. BUT: More importantly, he’s talked us through the philosophy behind D3.

My rather sad-looking scatterplot.
My rather sad-looking scatterplot.

When simply following tutorials, you’ll often find yourself typing in strange commands like enter() and exit() without really understanding why they’re doing whatever it is they’re doing.

Attending a session with a professional like Cook takes you through the steep initial learning curve with a firm understanding of the fundamentals.

We also deconstructed how breath-taking visualisations like this one by the New York Times were made.

Screenshot: New York Times
Screenshot: New York Times

“This bridges the gap between what you know now and what’s necessary to do these advanced visualisations,” Cook said.

Pros
Having a chance to ask any stupid questions really gives you a better grasp of how D3 works.

Cons
The cost…(up to £250, to be exact).

So what’s the verdict?

Going to a workshop is fantastic, but has the obvious drawback of being expensive. Still, there really is no good replacement for having someone knowledgeable taking you through it in person. If you’re broke, try to find someone who’ll be your D3 mentor so you can turn to them for questions. Why not join a Meetup?

Is there any reason not to learn D3?

If you really hate coding, maybe give it a rest for now. There are a lot of other cool tools that you can use to make visualisations without writing a line of code.

But if you learn a little bit – even if you can’t do particularly advanced things on your own – you will know enough to have an easier time cooperating with any developers or journocoders you’re working with. That’ll help you know if and when demands that you’re making are unreasonable, and give you an idea of what takes how long. It will make you popular with all the developers and the world a generally happier place.

6 sites that show why data is beta

New to data journalism and keen to learn but unsure about the kind of stories you could uncover with numbers? Well worry not because the Interhacktives have collected the examples of experts in action so you don’t have to.

Here’s a roundup in no particular order of the best news sites that use data journalism and data visualisation in the UK.

 

Guardian Datablog Screen Shot 2014-11-17 at 13.46.07

 

Guardian Data Blog

Data journalism is by no means a new trend. The Guardian is cited as the first major publication to bring data journalism into digital era, with Simon Rogers launching the Datablog in 2009.

The blog covers everything from topics  currently on the news agenda to general interest.

This week saw a report on the record levels of opium harvested in Afghanistan and a visualisation about the lives and reigns of Game of Thrones Targaryen kings.

The Guardian’s Datablog is good for beginners as there tends to be a link to the source of their data on each article, enabling you to access the data and to use it for your own stories.

Amp3d graph - We're eating more chocolate than there is in the world, "Predicted world chocolate deficit"

Ampp3d

This arm of the The Mirror is what its creator Martin Belam calls “socially shareable data journalism”, the successor to his Buzzfeed -esque site UsVSTh3m. Launched last Christmas, after only eight weeks of building, Ampp3d is the tabloid perspective of data journalism.

Stories this week included what makes the Downton Abbey’s perfect episode and the British city where people are most likely to have affairs.

Most importantly, perhaps, is that it’s a site specifically designed for viewing and sharing on a mobile device. As Belam writes on his blog,  80+ per cent of traffic at peak commuting times comes from mobile, which the project aims to capitalise on this attention.

i100 "The list" Screen Shot 2014-11-17 at 14.30.30

i100

i100 is The Independent’s venture into shareable data journalism. It takes stories from The Independent and transforms them into visual, interactive pieces of often data journalism. It also incorporates an upvote system to put the reader in charge of the site’s top stories.

The articles are easily shareable since social media integration is a core part of the reader’s experience.

To upvote an article, you have to log in with one of your social networks (currently Facebook, Twitter, Google Plus, Linkedin, Instagram or Yahoo).

Bureau of Investigative Journalism homepage

Bureau of Investigative Journalism

Championing journalism of a philanthropic kind, the data journalism of the Bureau of Investigative Journalism differs from most of the other publications on this list.

Based at City University London, its focus is not on the visual presentation of data, but the producing of “indepth journalism” and investigations that aim to “educate the public about the abuses of power and the undermining of democratic processes as a result of failures by those in power”. As a result, there is little visualisation and mostly straight reporting.

For data journalists, though, its ‘Get the Data’ pieces are indispensable resources as they allow you to download the relevant Google spreadsheets that you could then turn into data visualisations.

FT Datawatch: the world's stateless people screenshot

The FT

The Financial Times’  Data blog is one of the leading international news sources for data journalism and one of the UK’s leading innovators in data visualisation. It creates pieces of interactive and data-driven journalism based on issues and stories around the world, which include everything from an interactive map showing Isis’ advances in Iraq to UK armed forces’ deaths since World War II.

It describes itself as a “collaborative effort” from journalists from inside the FT, occasionally accepting guest blogs.

Bloomberg screenshot of homepage

Bloomberg

Bloomberg  has perhaps some of the most impressive-looking data visualisations out of all the news sources mentioned. The emphasis on the aesthetic is immediately apparent since a zoomed-in version of each visualisation functions to draw a reader in on the homepage as opposed to a traditional headline/photo set up.

Interactivity is the most defining feature of Bloomberg’s data journalism. Many of its pieces rely on the reader to actively click on parts of the visualisation in order to reveal specific data. For example, its World Cup Predictions and Results article requires the reader to select a game in order to see statistics and information about it.

Pop Culture and Data: The Best Visualisations

Data is data is data

It has long been the contention of the Interhacktives that good data work is good data work, regardless of the subject. While there is prestige associated with using a data visualisation to illustrate a point about obesity across Europe or in election coverage, it is all too easy to forget that good data visualisations can be used to illustrate anything for which there is data.

For instance, here is a quick visualisation thrown together comparing the most commonly used words from the script of The Best of Both Words Part I, the Star Trek: The Next Generation season 3 final, with the same from Part II, the season 4 opener.
Wordle: Star Trek Best of Both Words Part IWordle: Star Trek The Best of Both Worlds Part II

 

Frivolous? Perhaps. But to a Trek enthusiast, that wordcloud can prove a point about Geordi’s near invisibility during season 4, and handily it proves my point too; data can be used to illustrate our favourite pieces of pop-culture.

Here, then are some of our favourite pop-culture data visualisations from the past year. Over the coming week we’ll be featuring interviews with some of the people who made them, with a focus on how they feel data can be used to make us reconsider our favourite films, music and shows.

Scatterplot of the Most Overrated Films

Made by student Benjamin Moore, using the rCharts opensource tool, this great chart shows the correlation (and sometimes startling lack thereof) between film critics and audiences’ opinion of the same film. Made using data gleaned from the Rotten Tomatoes API, it finally offers proof that critics were wrong about Step Up being a mediocre movie, and About a Boy being a good one.

In this write-up of his process, Benjamin notes that the Rotten Tomatoes API is permissive in the number of calls it allows but restricting in the data it actually offers up. He also notes that his method for gathering the data was likely by its nature to underperform, since by searching using the ‘similar films’ term was likely to pull in several films from the same serious, which would likewise redirect to the other. Nevertheless, a fantastic piece of data work that shows us how sharply divided audience and critical consensus can often be.

Rappers’ Vocabularies, Ranked

Designer and data scientist Matt Daniels is keenly aware of the ability of data to change how we look at pop culture. In this analysis, he ranks various rappers based on the diversity of their vocabulary in their first 35,000 lyrics. By his metrics, he shows that many are in fact more lyrically ambitious than Shakespeare was in the first 5,000 words of seven of his plays. A few even rank above Herman Melville’s notoriously verbose Moby Dick.

Click for the interactive version. Courtesy of Matthew Daniels
Click for the interactive version. Courtesy of Matthew Daniels

While there are issues with this method of analysis, some noted in this io9 article by which it came to widespread public attention and others noted in his initial write-up, it’s still a great piece of data work. Matt is no stranger to using data to analyse pop-culture: His [possibly not safe for work] “absurdly nerdy look at how hip-hop invented the most important slang of our time” and data-driven look at Outkast both show how data work can direct attention towards topics that might not otherwise have received it.

Graph TV

Finally, a means to pinpoint the exact moment your favourite television programme took a dip in quality. Created by software engineer and super-fast Rubik’s Cube solver Kevin Wu, this fun data visualisation allows you to  see individual IMDB scores for television programmes, and an aggregated line for each season which shows how critical consensus changed as the season progressed.

Visualisation of Narrative Structure

Good data visualisations can even help us shed new light on old, paper-bound stories. This interactive visualisation, created by neuroscience Ph.D students Natalia Bilenko and Asako Miyakawa, details the character interactions by chapter in books such as Kafka On The Shore and The Hobbit. They also show a sentence by sentence breakdown of how many sentences in each have a positive or negative connotation and, as Natalia’s expertise is in the mind’s response to linguistic ambiguity, she should know how these effect the reader.

Did we miss any great, pop-culture driven data stories? Let us know in the comments below!

Interview with Kiln’s Duncan Clark

Duncan Clark Kiln.it

 

Duncan Clark Kiln.it
Photo: Kiln.it

Kiln is a design studio specialising in data visualisation, digital storytelling, maps and animation. It was founded and is run by Duncan Clark and Robin Houston, creators behind such projects as Women’s Rights or In flight for the Guardian. In this short interview Duncan Clark tells Aleksandra Wisniewska how they go about their projects.

How do you choose what subjects to cover in your visualisations?

It’s a mix. Sometimes we have an idea that we know we want to pursue; sometimes the Guardian or another client will approach us with an idea.

What is key for you in the process of designing information?

One golden rule is to let the information speak for itself. There’s no point making a pretty visualisation if it doesn’t make the data clearer to understand and easier to interrogate.

What is your favourite project that kiln.it worked on so far and why? What do you think makes it interesting for people to explore?

In flight” is certainly the most ambitious thing we’ve done so far and possibly my favourite. I like that fact that almost everyone says “wow” at seeing the sheer number of planes that have flown through the air in the last 24 hours. But I also think it’s interesting as an experiment in combining different approaches to storytelling: it takes elements from documentary making, data visualisation, radio production, live mapping and tries to combine them into a coherent whole.in flight the guardian kiln

What’s your work process? How much leeway do you have in your work? Do you get precise instructions for your projects or do you only accept broadly defined commissions?

It varies. Sometimes the starting point of a commission is just a broad subject area; at other times a client might have a very specific visualisation technique in mind from the outset. Most commonly, though, we’re given a dataset and asked to work out how best to turn it into something compelling.

What advice would you give to a budding data journalist?

It depends what kind of data journalist you want to be. If you’re mainly interested in breaking stories, then getting acquainted with how to get unexplored data via Freedom of Information requests might be a good idea. If you’re more interested in interactives and visualisations then learning to code can’t hurt: access to good developers is always a bottleneck for journalists, so being able to do at least some of the coding yourself is a huge advantage. Try getting started with a free HTML, CSS and JavaScript course at Codecademy.kilnit logo

Two hours of Interhacktivity: #hackshangout

Interhacktivity tutorial #hackshangout

Interhacktivity tutorial #hackshangout

Our tutorial on data journalism will start at 6pm today (Monday 24th March) – click here for the link.

 

To see last week’s tutorial on social media verification, click here.

 

The time for our first hour of Interhacktivity is almost upon us.

The tutorials will be held via a Google Hangout on Air. The exact link to the Hangouts will be posted at the top of this article when they go live, on Monday and Thursday (at 6pm).

Throughout the tutorials, non-presenting Interhacktives will be monitoring Twitter. We’re hoping to keep matters as informal as possible, so, if you have any questions during the event, please tweet using the hashtag #hackshangout. And, of course, if you have any questions, suggestions or comments before or after the event, please do the same, or tweet us directly.

Two hours of Interhacktivity #hackshangout

 

After taking into account the results of our poll, the topics were decided as follows:

Data tutorial (Monday 24th March, 6pm)

– Data cleaning and mapping with Daniele Palumbo

– Data visualisations (Datawrapper and Raw) with Laura Cantadori

Social media tutorial (Thursday 20th March, 6pm)

– Social media verification with Rachel Banning-Lover and Chris Sutcliffe

If you are curious and feel in need of some guidance on how to fit into a modern newsroom, join us on Monday and Thursday.

For more details of the thinking behind the event, click here.

Andrew Hill interview: ‘data journalism can make the most honest, impactful news’

Andrew Hill is a data scientist at mapping software company CartoDB.

What is CartoDB and whom is it for?

We built CartoDB to help democratize map-making online. It is a tool for anyone who wants to create a map. We think everyone has the ability to create interesting visualizations and tell important stories with data. We created CartoDB to make it easier, faster, and less expensive.

What are the broad goals of CartoDB?

First is to allow anyone to make beautiful maps from their data. Second is to help people tell stories from data that wasn’t possible before. Finally, it’s our goal to push the boundaries of mapping online through innovative technology and beautiful design. CartoDB is being built to enable the future of maps online.

Why should the world be excited about data mapping?

There are a lot of reasons to be excited about data journalism. I am by no means an expert, but I’d say I’m more a passionate fan. If you start at the outside and work inwards, I think the field is doing a lot to increase data literacy and public perception of what data is and what it can tell us about the world. It has transformed the way that we are able to consume what used to be a very complex topic, by giving us approachable and often beautiful insights into the data that exist. I think that when done correctly, data journalism can be some of the most honest and impactful pieces of news.

Maps are a small piece of that but I often make the argument that they are one of the best tools for telling stories with data. I would argue that, on average, the public is more prepared to interact with a map than they are to interact with other common forms of data visualization.

What is the best thing about CartoDB?

That someone who has never mapped data before can spend five minutes with the tool and already be tweeting me really interesting maps they created. As a bonus, they are excited by that and the power it gives them.

CartoDB Map

Are there any things you wish CartoDB could do that it currently doesn’t?

I’m anxiously awaiting our public map gallery. Right now, so many people are making great maps online, but we don’t have any automated way to show them in a public gallery. When that happens, I’ll probably have to spend 2 hours a day just looking at them all.

What is the most challenging aspect of creating a data mapping service?

In the early days I wrote a lot more code for CartoDB but I do it a lot less now that we have much better software engineers on the team. So for me, the most challenging part is connecting with all the people that could benefit from the service. Every time I demo CartoDB someone is completely shocked that they didn’t know about the service before, I need to reach those people more efficiently.

What does the job of a data scientist entail?

A lot. I do everything from developer advocacy and outreach to exploring new technologies for CartoDB. One of my favorite things I get to do is play with data and try to find stories to tell with visualizations. Storytelling with maps has been my big focus lately, especially trying to communicate how that can be done with all different types of data and working with the team to make that easier with our platform.

What do you think makes a good data map?

It totally depends on your goals and audience. It always takes design. Whether that comes early or late in your process of thinking about a map, it has to be in there. Second, it takes some understanding of your data – without understanding the data it is going to be hard to figure out if your map is telling the right story. That is why the filter wizard and the full SQL access in CartoDB help me so much: it allows me really to dig into datasets and see the result right on my map.

Are there any areas of CartoDB that you feel are under-utilised by its users?

I’m the biggest SQL fan there is but we try to reduce how much our users need to use it. In fact, you don’t even need to know what SQL is to start making beautiful maps. But all my favorite maps that I’ve created have come from a little to a lot of SQL use. I’m going to be teaching spatial SQL in the Map Academy (cartodb.com/academy) and can’t wait to see people start using it more for their maps.