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Using ChatGPT for Data Visualisation: Antarctic Sea Ice through the years

In the last 12 months ChatGPT has been established as the go-to generative AI solution for the mainstream, with over 1 billion monthly active users, it's no surprise everyone is talking about the impact of ChatGPT on the future of humanity. I am one of those billion users, and I have been using it multiple times each week for the last year, exploring its utility and trying to identify some of the pitfalls.  The premium membership, ChatGPT plus, has gone through several different guises, but recently the ways of interacting with the advanced features of GPT-4 have been greatly streamlined through the addition of custom GPTs, the GPT store and the integration of image generation, code interpreter and bing search all within a single chat.  So, with that greatly streamlined approach I wanted to re-run a little task I gave ChatGPT several months ago, to see how much of an improvement had been made. The task was based on a weekly challenge from the data visualisation community, WorkoutWe
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#ThanksToVideoGames shows Twitter gaming community has a sincere side

National Video Games Day was Thursday 7th July, 2020. A day where people in the gaming community can reflect on what video games mean to them. For me, video games are a way of staying in touch with friends I no longer live close to and provides an escape from the everyday stresses of the world.  It's clear that I'm not the only one, with #ThanksToVideoGames making waves on Twitter, people from all over the world shared their own reasons to be grateful for having video games in their lives. My curiosity got the better of me; I wanted to find out more about why people were grateful for video games and I wanted to show people that (shockingly) gamers can also be very sincere online.  I created this visualisation of the most common words that appeared in tweets with #ThanksToVideoGames to show the different themes coming through in discussions on Twitter. Make sure you hit the full screen icon to show the full picture; you  c
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[Exploring my physical activity data: Step 3] What can I learn from my 2019 physical activity data?

This is part 3 in a series of posts focusing on an exploration of my own physical activity data. You can read the whole series here . This time, I'll be looking at what I can learn from my data, how my physical activity data can be combined with other data sets and some more powerful data visualisations.  You can see the full datasets and interactive versions of the visualisations on Tableau Public. The first thing that struck me from my initial exploration was that there seem to be a number of extreme values that relate to days when I'm outside of my usual routine.  I looked back and categorised my data for each day in 2019 as working days, weekends, holidays and travel for work days. As you can see from the animation below: workdays have a fairly tight distribution with most values falling from 4,000–8,000 steps; weekends are very lazy at 0-4,000 steps; holidays show a much wider and higher spread of values than a usual working day; and travel for work sees the widest spread
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[Step 2]: Starting to explore my own physical activity data

Welcome to an exploration of my own physical activity data form 2019, captured by Google Fit.  This is Step 2 in a multi-part blog series in which I take my first steps into data visualization by analysing my own physical activity data recording by Google Fit. In Step 1, I downloaded my own data and highlighted some oddities about how Google records physical activity data. You can see the first post here .  You can also see the full data set, as well as interactive versions of these graphs on my Tableau Public profile .  First, let's look at the daily step count across the whole time period, July 2018–January 2020.  My daily step count from July 2018–January 2020 Just looking at this graph shows me quite a few things, even without any analysis or looking individual data points. Most of the major spikes in activity are related to holidays or travel - the spikes in July, August and November are all trips away from home. You can also clearly see the expected drop in activity seen arou
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Which countries have the most room for social distancing?

The inspiration for this article came from a meme I saw online saying that Flat Earthers are worried that social distancing measures will push people over the edge!  Obviously they are referring to the huge psychological and economic hardship that billions of us are facing after enduring weeks of social distancing (I am currently on week 6 of working from home). But it got me thinking, how much room do we currently have to keep up social distancing guidelines, and what countries have the most room for social distancing?  First, t o keep at least 2 metres away from the next nearest person, each person would need 4 m 2 . What would the world look like if you were to keep everyone exactly 2 metres apart? Based on a rough calculation, that would mean you could get up to a population density of 250,000 people per km 2 . The most densely populated city in the world is Manila, at  46,178 people per km 2 . So there's no need for the Flat Earthers to worry.  In fact, if you gave t
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[Step 1]: Gaining access to my physical activity data

In this second post in my series " Exploring my physical data ", I take  my first steps in exploring my own physical activity data.  The first hurdle in my exploration is gaining access to my own physical activity data. I don't use any smart wearables or actively track my own activity, but I've allowed Google Fit to passively track my physical activity since 2018.  Google Fit, the source of all my physical activity data Thankfully, Google has its own service, Google Takeout , that allows you to access and download pretty much all of the data they have on you in a simple and easy to use interface.  I used this guide to quickly find out how to export my Fit data, but Google Support has a really useful guide as well.  I decided to only use the daily aggregated data (ie daily steps, daily distance walked, etc.) as the info on individual activities seems a bit like overkill at this stage.  You can download your own physical activity data and have a look
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Taking my first steps into Data Visualisation

Like many, the social distancing guidance during the COVID-19 pandemic has left me with more free time around the house than usual. I've also found myself thinking about how important data can be in shaping our perceptions of the world.  The challenge I've given myself is to get started with making interactive data visualisations, in the form of a visual essay. These data-rich, beautiful, interactive stories are fast becoming the go-to method for effectively communicating the results of an investigation or experiment using large data sets (such as crime rates, climate science, electoral coverage).  The Pudding  have a great repository of data visualisations and a number of how-to's that I'm surely going to need down the line!  BBC News have a fantastic set of visualisations to explain the COVID-19 pandemic  that have been part of my inspiration to finally give this a go.  BBC Data Viz showing how confirmed coronavirus cases have spread up until 10th April. S
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