Exploring crash statistics - an excellent interactive visualization

This series of interactive visualizations by the BBC is quite stunning. The first shows a map of fatal car accidents in the UK. For the area chosen you can see age, sex, and vehicle type breakdowns. Mouseovers show details of the accident as well as links to the news story about it.

The year slicer on the bottom is excellent, allowing you not only to quickly jump between years, but also to see how accidents vary over the months in each year. My only wish is that you could show the markers for all years, allowing you to better identify accident hotspots, and that they superimposed a line across years to show a trend of total accidents.

On another tab, they have the breakdown of accidents by time of day. The buttons on the side allow you to see the pie chart and time of day statistics by the category chosen. A pie chart is not my first choice here - a bar chart would be better, but you can click a section of the pie to show the time of day data by just that segment - mouseover the image below to see when young people kill themselves vs. the older population. I think the radial bar chart works very well in this case, especially with the radial gridlines.



In a previous career I was a consultant focusing on fatigue and safety, so these data are of particular interest to me - for example, if you select vehicles, then the pie segment for goods vehicles, you can see the spike in accidents at 4AM as truckers fall asleep at the wheel and crash. The only thing missing (which you rarely see in data like these) is normalization - the risk of driving in the early hours is so much greater than at other times of the day considering the amount of traffic. An excellent example of data visualization.

Visualizing data that obscures trends (pie charts are "better")

I've been playing around with Google trends for a while now. It's an incredibly powerful tool for market research, especially when you're looking at new markets, or how the popularity of a brand is changing.  You can look at how a particular search term has trended over five years, where the term is more popular (countries, cities), compare several search terms, and how news stories that bear relevance to the search terms may have impacted the search volume.

The search terms I chose to compare for this example were "pie chart" and "bar chart", partially in response to the friendly back and forwards that Chandoo and Jon Peltier have had recently concerning appropriate use of pie charts. My data adds nothing to their conversation apart from what the public considers 'search worthy', so apologies for the misleading blog title, but the data are perfect for demonstrating how you can better visualize data that have seasonal and other variations that hide overall trends.

Here are the data - relative search popularity of the two terms on a week to week basis since 2004. Some things come out immediately - people search for pie charts more than they search for bar charts. There seems to be seasonal variation within each year, and there seems to be a trend over the years. To investigate these trends further I summarized data by month/year/etc. by grouping the dates in pivot charts.

Here's the seasonal variation - taken by looking at the average search popularity by month of the year, across all five years. Both terms are searched for less in the summer (for the northern hemisphere, but the data are overwhelmingly dominated by users in this hemisphere), and at the end of the year. This would coincide with both school holidays and lower business search volume (as people take vacations). The lines suggest that "bar chart" is less affected during the summer than "pie chart". To confirm this, I've added another series that compares how the two terms vary compared to each other. If the bar for a month is lower than 1, "bar chart" has dropped less relative to its average popularity over the year when compared to "pie chart", so yes, even when you account for the fact that absolute height variation of "pie chart" is greater, "bar chart" does not experience as great a drop-off mid-year.

As a nod towards the bar-chart vs. pie chart argument, perhaps one could argue tongue-in-cheek that "pie chart" is searched for by school children more than business users as the drop-off is larger in the school summer vacation, so therefore bar charts must be more important to business.. No, probably not.

Before we move on though, a quick comment on what we've lost from the 'raw' data. From our summary chart you could conclude that the search volume is about the same in December as it is in the summer months. However, look at the top chart - search volume only drops in December in the last two weeks of the month, whereas search volume is lower for every week of July. For that reason, you must carefully consider the level of summary you chose, whether an average or sum is better for comparisons, and whether you should also show the raw data.

Next we have year to year variation. There is a dip in 2006 and 2007 (for which I have no explanation..) The pattern is the same for bar and pie searches. It is likely to be a reflection of real search volume, not a collection artifact, because two unrelated search terms do not show this dip. I wondered if "bar chart" was catching up to "pie chart", so the line compares the absolute difference. Interestingly, without the average line plotted across, it really looks like this line is dropping - "bar chart" is catching "pie chart" up, but it's misleading - because both the other lines are headed upwards, even a straight-ish line looks like it's dropping. The addition of the straight average line reveals that there is little, if any catching up being done.

Similarly to the prior chart, you have to be mindful of what you've lost in this summary. If you use an absolute count of searches, rather than an average then the results will be different to some degree (and not only because we are missing December 2009 data).

I did also plot these data by day of the month - searches are more slightly popular towards the beginning of a month, but I would attribute that to the two main US holidays, Thanksgiving and Christmas, both occurring the later parts of the month.

How else could you summarize these data?

Visually exciting and useful? Google's image swirl

When I find a new visualization or graphically based tool on the web, I like to jump straight in without looking at what it's supposed to do, or for that matter, how it's supposed to work.

Google's image swirl is superb - so obvious, and yet very useful. Search for an image - e.g. Statue of Liberty, and it gives you a selection of stacked images, with each stack having a different image characteristic - aspect ratio, colors, light level (night/day), for example. Select one of these and you can drill down further until you can get to a range of images that all are pretty similar, jump back up a level if you still want to refine it. In the example above I've navigated to pictures taken at day that also have nice fluffy clouds and the Statue of Liberty (or some interpretation of it)

CNN's Newspulse

CNN recently updated its homepage. I'm not a great fan of how it looks, but they did add 'Newspulse', a listing of the most popular stories. The screenshot is of the tool - they have a smaller add-in for the front page with the top five stores and the bar chart. I'm not sure what the 'popularity' measure is - page clicks I would presume, but it's an interesting slice and dice affair.

I'm not really sure I care about the data - the whole point of the web, as compared to television or radio, is that I get to chose what I consider important, not other people. And if it's popular, surely I've already seen it? Anyway, if you are going to do it then I'd make some changes.






I've just focussed on the charts - there are other changes I'd make to layout. I've kept the bar charts as comparing the relative popularity of stories is of interest, but I've de-emphasized them. I've added sparklines (a type of information graphic characterized by its small size and data density) which show how the popularity of a story has changed over the time period chosen. You can see when a story broke compared to others, how quickly it gained and lost momentum, and whether it's still on its way up, or is old news..