There is an entire world of data visualisations in the form of graphs and animations out there. Cleverly crafted, helping us to discover beautifully nuanced patterns and happenings.

Check out the DataIsBeautiful subreddit to peek into the world of amazingly insightful visualisations that are produced everyday across the world. summary: An ongoing project that is looking into capturing data on harassment inside Yelahanka, Bengaluru

Utilising the interstices between what we consider true, worthwhile and what we see as being abstract uselessness, I’ve begun to play around with popular and the standard forms of visualising data: Maps, Network Graphs, anything-graphs. And making them useless, with a tinge of normalcy, seeing that the data is true. Whether it is the locations of earthquakes across the world for a month, or weather data across a the world, or the conversations in the movie Inside Out…..the data is true and is represented without lies. But does the representation make any sense? Probably not.

It is that tiny mark between sense-full and sense-less that tingles our mind. We often term it bad, or use-less……but it tingles nonetheless.

Representing earthquakes across the world

With mighty help from Daniel Shiffman’s video on mapping projections.

Earthquakes over a period of a month

Creative tweaks to the first map visualisation

Building another layer on top of it.

I then went on to displaying a mis-tiled or mixed up map of clouds(i) and temperature(ii) in real time. I used the OpenWeatherMap database to access the map. Does this garbled up tile count as misrepresentation? Does it add the same amount tingle to such a Sense-less visualisation?

mis-tiled or mixed up map of clouds(i)

mis-tiled or mixed up map of temperature(i)

Presenting the conversations in Inside Out

I had begun work on cataloguing all the dialogues and statements from the movie Inside Out mid-cycle as a stepping stone for learning to use data visualisation techniques.

These visualisations are a graph of all the interactions between the characters of the movie Inside Out for the first 25 minutes of the movie.

How do I play around with this data and ’mis’represent it? What happens when I begin to exaggerate graphs? What macro-visions/elements emerge?