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This website uses cookies to help us give you the best experience when you visit our website. By continuing to use this website, you consent to our use of these cookies. The app showed him thousands of women. In fact, Michael knows exactly how many women he swiped yes to: 4, out of 9, Out of these 11, one stood him up, one became a flatmate and two became girlfriends. He happens to know these numbers because he spent hours exporting almost three years of his swiping history.

Michael is not alone. Incels believe they have been sexually ignored by women, whom they feel entitled to sleep with. Taken to the extreme, Incels advocate legalising rape. Nearby good bus House has three toilets, a bath and All bills and fast wifi are It's free and we'll email you new rooms as they come in. Available Now. The flatmate finding event. Click on the image below to go to the interactive version. Makeover Monday is good fun, I happened to have a bit more time tonight and I got the data bug.

We see different angles and viewpoints, constraining myself to too narrow a viewpoint felt like I was ignoring an itch that just needed scratching. This weeks Makeover Monday tackles National Debt. Apparently the US National Debt is one-third of the global total. Showing these two values in a pie chart is a good idea as it quickly shows the proportions involved. One slightly critical feature, if we were to pick holes in the visualisation, is that half-way through the view starts showing the shaded blocks to compare to the So making a design decision to add shaded blocks across each comparison would perhaps have been a better choice as opposed to switching half way through.

The dataset provided for the weeks makeover has simply two rows, showing the debt for each area US and Rest of the World. Clearly this presents a visualisation challenge. Visualising small datasets is hard, as there are limited choices. Before we attempt to visualise something though we need to think about the audience and message we want to provide.

Are we simply trying to show the figures without any comment?

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For example we have no context on the direction of movement of these figures. The ROW figure might be dominated by other developed countries, or might be shared equally. How can we comment without further analysis on temporal change or the context of this figure? My criticism of the original is that the number it shows in comparison are equally huge, and equally incomprehensible for a lay person. Given this visualisation is published on a website Visual Capitalist perhaps their audience is more familiar with global oil production or the size of companies but for any visualistion published away from the site a more meaningful figure is needed.

Personally I think the amount per head is an especially powerful metaphor. Now there is an important decision here, how to effectively show those figures in context. However with such small data is there any point in doing so?

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This is the problem with small datasets, any visual comparison is slightly academic given we can quickly mentally interpret the numbers. Ultimately each project that uses data does so because it needs to inform its audience, and it is the success of the transaction from author to audience that deems how successful the project is. In my opinion yes. In my opinion, no. Is the above image a successful Data Visualisation?

Does it do a better job at informing the audience than the original? Again this is hard to answer but I believe I understand more about the size of the debt when it is visualised in terms of dollars per head. I have a Netflix Subscription but sometimes hidden gems can be hard to root out. I have a good track record of finding good movies though it.

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Now the previous night had proved a mixed bag, so I turned to my trusty companion Alteryx to solve my data woes:. What does this spaghetti do? Well it takes the pretty horrible format txt file and turns into into rows and columns of proper data. Then I simply needed to crosstab and rename the data. Then it was to Tableau…I decided to design for mobile first and quickly over a couple of hours designed a few initial drafts of pages then, as it was getting late I posted them to my colleagues for comment.

I love that I have access to such a great and diverse range of opinions and talent from my peers at The Information Lab. As you can see I got loads of useful feedback — if you want to make a visualisation better just share it as much as possible, ideally on a collaboration tool with image commenting so people can highlight their comments with the corresponding piece of the image.

Tonight, Sunday, was all about acting on the feedback and building the Desktop and Tablet views. I designed a background theme of the visualisation using a very quick piece of photoshop work with the Netflix logo which I incorporated into Desktop — the black left panel but I soon realised that there were some limitations with the Device Designer in Tableau in this initial That meant ones overlaid on black looked odd on mobile when overlaid on white.

It was hard work fitting the phone layout to different sized phones. Lack of real estate means having to compromise on design vs functionality.

All aspects of the visualisation, Text, Filters, Logos need justifying in terms of space. I loved the challenge that working on mobile provided and I hope it makes people entering the competition focus on simple KISS visualisations.

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I ended up working on the smallest device and then checking it resized okay onto larger screens. As you can see below the differences are quite big depending on the phone. In the end I decided the best approach was to switch my designs to Floating to overcome this limitation…while not ideal it did allow me to work round most of the problems. All in all I remain pleased with what was just around 12 hours work! We know all too well that without proper care online communities can easily become bubbles, effectively becoming echo-chambers of opinion that, unchecked, can leave unwary users with a very distorted view of how real world opinion differs from that in their online community.

Did I affect anyone elses as a result of my discussions on the subject, or did we simply reinforce our own beliefs? The blunt truth is that I comprehensively failed to either appreciate or engage with any other viewpoint apart from those almost identical to my own. On the political spectrum the two camps were ideologically so far apart that this polarisation of views was reinforced on both sides and arguments that seemed obvious to one side failed to land on the other. This left the Remain vote in disarray as they failed to appreciate their own failings.

Echo Chamber by Christophe Vorlet, Within data visualisation, my field of interest, it is easy to see the same issues play out. For new members of the data visualisation community it can be very easy to become distracted by these echoes and feel that certain techniques or visualisations offer more value based on feedback from the community than others.

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With this in mind I wanted to offer some tips to the discerning social media user in the Data Visualisation world, new or old, on how to avoid the bubble effect and ensure your timeline remains diverse. Remember it. Try and actively switch your viewpoint to that of an outsider at regular intervals in order to try and see your community through a different lens. Showing people who you are outside the community will help people relate in a different way to your online self and give them confidence to challenge your views if they want to.

Twitter recommendations serve to narrow, not broaden, your network.

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Follow a wide range of genders and ages, go outside your normal circles, a diverse network of followers will server to provide a range of views to counterbalance yours. So this is harder, but you really need to make sure you have a wide range of different viewpoints in your follower list. That way your posts are more likely to be debated as opposed to be accepted at face value.