Tag Archives: data visualisation

Twitter Data Drawings – VIDEO

I’ve put together a quick time-lapse video to show some of the process behind producing the twitter data drawings I exhibited during my final MA show.

I shows the machine in action so should hopefully help people understand how the marks were made when looking at the final products.

The video features 3 pieces from the exhibition. The first is a drawing of all the ‘love’ shared in Wales on the 9/9/15. The second comprises of 4 larger drawings showing all of the ‘love’ and ‘hate’ shared in New York City on 4 consecutive days leading up to and following the anniversary of 9/11. The final piece shows the ‘love’ and ‘hate’ shared in 4 different cities around the world on the 9/9/15. The cities (clockwise from top left) are: Singapore, London, San Francisco and Paris.

For more information on the process behind this series you can read my other blog posts on the project:

Draw bots and Data Visualisation (part 1 of 3)

or see more images in my portfolio.


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Postgraduate Exhibition – Photos

A lot of my posts recently have been pretty process/theory heavy, so I thought I’d keep this one simple. Here are some photos of my final work in the Postgraduate Exhibition.

For more photos and videos of finished and in-progress work take a look at my Instagram: benpartridgebtp

I am currently putting together a time-lapse of the drawing machine in action in the exhibition that I will upload later this week.


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Polarograph drawing machine – Wales 9/9/15

This drawing machine is drawing all the love shared on Twitter in Wales on 6/9/15. The drawing will depict 24 hours with each ‘pixel’ documenting one minute, the darker the ‘pixel’ the more love was share in that minute. The ‘pixels’ are drawn chronologically from the top left hand corner of the drawing to the bottom right.


New York –  9/9/15, 10/9/15, 11/9/15, 12/9/15
These drawings show the love and hate shared on Twitter in New York on four consecutive days. Clockwise from top 9/9/15, 10/9/15, 11/9/15 and 12/9/15 . Each drawing depicts 24 hours with each ‘pixel’ documenting one minute. The darker the ‘pixel’ the more love or hate was shared in that minute. Love is drawn in red and hate in blue. The ‘pixels’ are drawn chronologically from the centre of the grid to the outmost edge of each drawing.


Paris, London, San Francisco, Singapore – 6/9/15
This drawing shows the love and hate shared on Twitter in four cities around the world on 6/9/15. Clockwise from top these cities are Paris, London, San Francisco and Singapore. Each drawing depicts 24 hours with each ‘pixel’ documenting one minute. The larger the pixel the more love or hate was shared in that minute. Love is drawn in red and hate in blue. These pixels are drawn chronologically from the centre of the grid to the outmost edge of each drawing.


San Francisco, New York, Sao Paolo, Cardiff, London, Paris Jakarta, Hong Kong, Tokyo – 9/9/15

This drawing shows the love and hate shared on Twitter over one hour in nine cities around the world on 9/9/15. The drawings are arranged according to their time zone with +0000 GMT in the centre. Each drawing depicts one hour with each ‘pixel’ documenting one minute. The larger the pixel the more love or hate was shared in that minute. Love is drawn in red and hate in blue.

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For more information about this project see my blog posts:
Draw bots and Data visualisation – Part 1

Drawbots and Data Visualisation – Part 2

Drawbots and Data Visualisation – Part 3

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Twitter Visualisations

My recent work has looked at turning digital data from twitter into drawings, as a result I’ve been doing a lot of research into artists who use data in exciting a visually engaging ways. In this post I’ve collated a few of my favourites:

Inflated Egos (2015) – Chris Cairns

Each candidates’ head inflates or deflates depending on their popularity rating in the polls on on Twitter. I like how this instillation brings the digital data into the physical world. The simplicity of the the concept of the candidates heads getting bigger or small depending on their popularity makes an accessible, effective and engaging piece of data visualisation.

Listening Post ‘I am’ (2003) – Ben Rubin

Searching for the phrases ‘I am’ in public chat rooms and other public forums. Listening post displays these messages decontextualised in real time, either as text or ‘read’ by a synthesised voice. The result is a powerful reflection on the immediacy and anonymity of digital communication.

Good Morning – Jer Thorp

Nice animation showing people tweeting ‘good morning’ around the world. It’s interesting how you can track day breaking around the world by people’s activity on Twitter.

Just Landed _ Jer Thorp

Another from Jer Throp this time plotting people’s movements based on tweeting ‘just landed in’. I find it interesting how Jer’s projects expose some of the hidden information that can be extracted from publicly shared twitter data.



Not so much a visualisation but a stream, Twistori displays real time tweets featuring the phrases I love, I hate, I think, I believe, I feel and I wish. I fins it as a strange hypnotic quality watching the anonymous stream of statements ranging from the emotional to the banal pop up in your browser.

Equity BotScott Kidall 

Again, not strictly a visualisation project but I thought I’d include it Scott Kidall’s Equity bot tracks emotions on Twitter and correlates them to changes in the stock market. The algorithm ‘buys’ and ‘sells’ these emotions as they rise and fall in value.

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Drawbots and Data Visualisation – Part 3

This is the final part of a three part post on the work I have been doing over the summer towards my final postgraduate exhibition. In the previous posts I explained how I built my drawing machine and why I decided to try my hand at some data visualisation. In this post I will explain a bit about my process in collating and visualising the data to make drawings the illustrate the amount of love and hate shared over 24 hours on twitter in different cities around the world.

Essentially I will try to explain how I got from this:

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to this:

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to this:


If you haven’t already guess this post my get a bit techy, but I’ll try keep it brief and punctuated with lots of pictures.

So it started to dawn on me that I was going to have to do some coding for this project, of which my sum experience to date was a bit of HTML and copying and pasting Arduino sketches. So I began to tentatively look for a platform/language that relatively quick to learn with good online documentation and, as a broke student, ideally something free and open source.

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Processing fits the bill perfectly, it’s an open source platform based on Java designed for artists and designers. I won’t go into it in too much detail here, but I’ll definitely do a post of my favourite art projects built with it at some point. Tou can check out loads on the Exhibition section of their website.

One of the Processing’s biggest advantages is the huge online community using the programme, as an open source programme users are constantly contributing, by sharing projects and offering support through forums and building new libraries.

Once I had decided that I would use twitter as my data source I needed a way to interact with it’s API, after reading Jer Throp’s great tutorial on this, I decided to us the Twitter4J library.


Inspired by Twistori (above) I began playing around making programmes  that searched twitter for different terms on twitter and displaying the tweets on screen. seeing all this data pop up was a pretty spooky experience, of course it was all made public by the users and could be found ‘by hand’, but seeing it pulled from the internet by an autonomous programme got me thinking again about how much info we are happy to put out there. It also made me realise how banal most of the stuff people on twitter talk about, and just how much people love One Direction (it’s a LOT):

WARNING: as you can probably guess, tracking all the hate on Twitter is not a particularly life affirming past time and some of the language in this video is pretty unpleasant. Don’t blame me, blame society. You have been warned…

However I was finding that the same tweets were coming up again and again and if I wanted to create something that could keep track of the times a term was used over a set period I would have to utilise Twitter streaming api. This proved slightly more complicated especially when filtering very popular terms such as ‘love’. The word was being used so often globally that the programme couldn’t keep up. This meant I was getting very similar values each time (the maximum number of requests the programme could handle in the time frame), you can see it in the images below (love is shown to a different scale to the others otherwise the whole box would be black):

To bring the numbers down to a more manageable level I implemented a location filter, which used longitude and latitude to put a bounding box over an area. The twitter API then sent all geotagged tweets from within that area. As I couldn’t combine a key word and a location filter I had to then make the programme break each tweet down and ‘read’ it for the key words.

This gave me the raw data I needed, a long list of how many times ‘love’ and ‘hate’ were mentioned on twitter on different cities around the world over a day. I was feeling pretty smug, but the only problem was that on its’ own, the data looked pretty uninspiring:

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I experimented with representing the data in a number of different ways:


‘Love’, ‘hate’ and ‘envy’ shared on twitter in San Francisco over 24 hours

These were just visual aids for my benefit, it was the polargraph that would need to interpret and draw the data:

Looking at the way the pendulum-like way polargraph drew, I (with help from my dad) developed the programme so that it would plot the data diagonally following the line of the pen, rather than simply from left to right top to bottom (see below). I was hoping this would make the images easier to ‘read’ but it also opened up interesting possibilities for displaying the finished drawings.

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I decided to present the drawings in grids of four, the drawing below shows four consecutive days in New York:


and this one shows the same day in 4 different cities (clockwise from top right; Paris, London, San Francisco and New York):


I felt presenting them in sets allowed for an easy way to compare the images without being too prescriptive. Individually these images are difficult to interpret, when presented together they given one another meaning providing a frame of reference that allows interpretation to be a creative rather than scientific process. Time flows from the centre of the grid out, like ripples in a pond. There is both an individuality on commonality to each location/day that I find intriguing. I like how visual pieces of information they provoke questions and challenge preconceptions. what are the concentrated areas of love in Paris? Why does San Francisco, a city with a historical reputation for free love, have such a proportionally high concentration of hate?

Of course, the whole notion of measuring our emotions digitally and distilling them into a drawing is a little tongue in cheek, but it does raise some interesting questions about the limits between computer and human interaction, especially in the nuanced ways we utilise language. The programme does not have a sarcasm filter for example. It did get me thinking about how extraordinarily complex language is, it’s a wonder we understand each other at all.

I will be showing a number of drawings and the polargraph in action in my final postgraduate show at Aberystwyth University School of Art:



For more information about this project see my blog posts:
Draw bots and Data visualisation – Part 1

Drawbots and Data Visualisation – Part 2

Drawbots and Data Visualisation – Part 3

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Drawbots and Data Visualisation – Part 2

This is the second part of a three part post on the work I have been doing over the summer towards my final postgraduate exhibition.

In the previous post I explained how I had built my own drawing machine, something that could take a digital input and make a physical analogue drawing from it. In this post I will explain why I decided to try and draw data.

IMG_3997I really liked how the mechanical mathematical way the machine interpreted an image and handled tone, especially when those images happened to be well known works of art:


Although fun, I knew this type of drawing was only a means to an end, I wanted to create drawings that tapped into the innate nature of digital media rather than copying existing images. A lot of the digital processes we use to make art in film, photography and illustration were developed in response to their analogue counterparts. To establish themselves they were framed as equivalent or superior to the analogue processes that proceeded them (I recommend Vilem Flusser’s ‘Towards a Philosophy of Photography‘ on this).

In a sense this framing of digital processes in relation to analogue ones shapes most of our interactions with digital technology, even today we still save ‘files’ into ‘folders’ on our ‘desktop’. Even the visual interface we use references processes and technology that are increasingly obsolete. In most cases we still use icon of a floppy disk to signify ‘save’, yet for anyone under 20 the action and icon it references will have no correlation. Cut and and paste is another good example, their digital uses are now far more common than the analogue processes it references.

I aimed to combine the serendipity of the drawn image with the accuracy and speed of digital technology when completing repetitive computational analysis of real time data. This would be an attempt to create physical representations of digital data.

i first became interested in data when making my polargraph, while the design and software is open-source and free I still needed to buy the hardware, the stepper motors, motor shield, counterweights etc. At the time some teaching I had agreed to do had fallen through so I was pretty broke, so I was completing online surveys in exchange for Amazon vouchers which I used to buy the components I needed. Not glamorous, or quick, but it got the job done.

Completing these surveys started changing how I viewed my personal data, my demographic data, age, gender, nationality became a commodity with economic value as did my opinions; whether I thought a certain brand was trustworthy, or ‘for people like me’. I also began to realise how much of this we give away for free to companies such as Google, Facebook and Apple I wanted to know if this data is so valuable to companies, allowing them to improve marketing strategies and increase profits could it be used for more positive ends? It while researching this that I came across the Open Data Institute and in particular their Data as Culture exhibition, see the catalogue below:

Data as Culture 2014 – Catalogue


I was really excited to see artists using data as medium, I especially liked YoHas’ Invisible Airs, a dada-like interpretation of Bristol City Councils’ expenditure database.

I decided that I would try and use twitter data as the raw material for the final drawings, it seemed like a good source of constantly changing information. I was also inspired my a number of other artists who had used Twitter in their data visualisation work.

In the final part of this post I get a bit techy and explain how I interpreted the twitter data in Processing and created drawings mapping the love and hate shared on Twitter in different cities around the world (below).



Drawing showing all of the Love and Hate shared on Twitter in New York – 31/8/15.


For more information about this project see my blog posts:
Draw bots and Data visualisation – Part 1

Drawbots and Data Visualisation – Part 2

Drawbots and Data Visualisation – Part 3

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