There are two main ways we translate and understand information in our minds. One - verbalisation, through reading words. Two – visualisation, through looking at images. Both have benefits, but numerous studies have shown that for many people, understanding and retaining information is easier through visualisation – transferring words and numbers into visual imagery.
This is particularly true when it comes to data. In today’s world, data is just about everywhere, giving birth to the likes of the Big Data phenomenon and endless data analytics companies. With so much data available to us, however, we can easily become overwhelmed by it.
This is where data visualisation comes into play. By presenting often complex and large datasets in a pictorial or graphical format, our brains can more easily understand the data that is in front of us, and so better grasp the meaning behind it.
With this in mind, we have found 5 of the most impressive examples of data visualisation for you to enjoy:
1. The Rhythm of Food
The Rhythm of Food is a project analysing the seasonality and trends of different types of food and recipes. A collaboration between Google News Lab and Truth & Beauty, the project’s data comes from Google search data concerning hundreds of ingredients, recipes and other food related search terms, collected over the past 12 years.
Collecting this data together and presenting it by food type within a radial ‘year clock’ chart, the data reveals seasonal food trends, allowing the user to spot patterns during the year and developments through the years. Has the hype over Kale come to an end yet? What other foods are Americans interested in at Thanksgiving apart from turkey? (Spoiler in the image below) And when are we most obsessed with chocolate?
2. Ve’s Conversion Tracker
Ve’s solutions engage visitors online around the world and drive them to convert, from onsite engagement to programmatic display. In order to track the success of this on a global scale, Ve’s data team created a conversion map visualisation.
Functioning as a live tracker, every time a dot flashes up, this indicates that a conversion has been directly driven by Ve’s solutions. This allows Ve to gauge the number of conversions, as well as the global reach of its tools.
3. YouGov Profiles
YouGov Profiles has created a data visualisation that acts as an audience segmentation tool for brands and consumers to better understand their audiences. Using 120,000 integrated data points from over 200,000 individuals, YouGov Profiles recreates the persona of an individual who likes a brand, person or thing, from demographics to their favourite movies.
Take David Cameron, for example. From the data collected, people who like David Cameron are most likely to enjoy golfing, wear Tommy Hilfiger clothing and have a pet dog. On the other hand, those who like Donald Trump prefer to watch NASCAR, enjoy Fox News and are most likely to have a profession in military and defense.
Selfiecity is a data visualisation project that analyses the demographics, poses and expressions of selfies. Exploring tens of thousands of selfies from 5 different cities around the world, the data is analysed through a combination of human judgement and automatic image analysis.
Once collected, this data is then revealed within pictorial graphs to more easily understand the results. The age of selfie takers, for example as shown in the graph below, reveals that selfie takers in Bangkok are on average younger than those in Berlin. Other graphs determine the facial expressions of selfies by city, revealing that those who take selfies in Bangkok smile the most, whilst those in Moscow smile the least.
5. A Day in the Life of Americans
Wanting to understand in granular detail how Americans spend their day, Nathan Yau put together the data visualisation captioned ‘A Day in the Life of Americans’. Using microdata from the American Time Use Survey that asked thousands of people what they did during a 24-hour period, Yau was able to recreate the average American’s day to the minute.
However, in order to easily understand this data, consisting of 1,440 transition matrices, Yau drew lines to represent them, in the process creating the diagram as seen below. The diagram below shows the individual at 6:30 to 7:30am, so lines are heavy on eating and drinking, housework and personal care as they get up for the day. In the diagram representing 10pm to midnight however, a much stronger focus can be seen on leisure, personal care and sleeping.
As can be seen just in the 5 examples of data visualisations above, there are endless numbers of ways to present data and make the sometimes overwhelming amount of data not only more manageable, but more insightful.
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