My biggest concern about some previous books on infographics (and much of what is available online) relates to their uncritical promotion as brilliant ways of displaying factual information based on complex data. I was therefore pleased to see that in her introduction, Sandra Rendgen, stresses the problems with “Suspicious Data” stating that:

“…general popularisation (of infographics) brings with it a level of denigration, and content-related weaknesses are frequently found in graphic representations”.   

After reading this I was reassured that there was a strong editorial policy in terms of the graphics selected and their associated commentary. This shows and, despite the many pages to be filled, that vast majority of what is featured can be held up as best practice. The essays and associated images at the beginning offer good historical context (data visualisation is nothing new) and the location section alone would make for a good book for cartographers (sad not to see an Swiss mountain maps in there though). I also found the Category section (especially the work of Stefanie Posavec, below) particularly interesting by showcasing a range of examples of visualising more qualitative data.

Everyone who comes by my office has opened the book at least once and been tempted to buy it. For £29 on Amazon (RRP £44) I think you would be mad not to. For those producing data visualisations the book provides some great inspiration for future projects, whilst those who simply enjoy looking at them will not be disappointed.

Blood is everywhere when it comes to describing cities. We have arterial roads, pulsing transport flows, and cities with different metabolisms. Thanks to great new datasets and visualisation software the analogy of cities with pulsing flows is being increasingly utilised for explanatory mapping. For example the work of UCL CASA’s Jon Reades above depicts the London Underground network as a set of arteries that thicken as passenger volumes passing through the network increase, whilst Pedro Miguel Cruz has taken it one step further to depict Lisbon’s roads as “blood vessels” (complete with their own clots).

I think these visualisations offer a neat conceptualisation of city flows provided the metaphor isn’t stretched too far. As the slime mould (below) shows us, we have a lot to learn from comparing our cities to biological processes- just so long as it doesn’t get too gory!

Thanks to an Xbox Kinect, Google Earth and some programming wizardry from UCL CASA researcher George MacKerron it is now possible to fly over London. The video below shows “Pigeon Sim” which has been developed to offer a fresh way of interacting with London’s urban data. Using Peter-Pan like arm gestures (above) users can fly over London’s landmarks passing various data feeds (such as real-time tweets or travel information) as they go. Pigeon Sim is one of the most fun methods I have seen for interacting with spatial data in a long time and I can’t wait to see more of CASA’s research outputs integrated into the system. Best viewed full screen.

I recently had the pleasure of presenting at the first Data Visualisation London Meetup event where I spoke about some of work we do at UCL CASA. A fair chunk of the slides were movies so I thought it best to stick them in a blog post. If you like what you see you can sign up for the CASA masters course or check out our other blogs.

First up was my interactive surname map of London.  I used this to demonstrate that “Big Data” (the general theme of the meetup) is nothing new (we have collected large- scale population data for over a century) and that we can use visualisation to demonstrate complex data.

Next, was the now famous animation of London’s transport flows produced by Joan Serras.

I then went on to say that we can begin to build more sophisticated maps of public transport by utilising routing algorithms. We took this approach to map the 114 thousand or so bus trips in London each day.

I then showed a couple of top-secret visualisations produced by Jon Reades and others at CASA. Stay tuned for when these are released. Twitter data featured in all talks and my chosen animation was produced by Anders Johansson in collaboration with Steven Gray and Fabian Neuhaus.

Next up were a couple of visualisations of cycle hire data in London (animation by Martin Zaltz-Austwick),

and other cities (below) to see how people utilize the schemes. You can see Oliver O’Brien’s live map here.

The final slide demonstrates how we are bringing all these themes together with the “City Dashboard” project. Click here (or image below) to take a real-time look at your city.

Last year Eric Fischer produced a great map (see below) visualising the language communities of Twitter. The map, perhaps unsurprisingly, closely matches the geographic extents of the world’s major linguistic groups. On seeing these broad patterns I wondered how well they applied to the international communities living in London. The graphic above shows the spatial distribution of about 470,000 geo-located tweets (collected and georeferenced by Steven Gray) grouped by the language stated in their user’s profile information*. Unsurprisingly, English is by far the most popular. More surprising, perhaps, is the very similar distributions of most of the other languages- with higher densities in central areas and a gradual spreading to the outskirts (I expected greater concentrations in particular areas of the city). Arabic (and Farsi) tweets are much more concentrated around the Hyde Park, Marble Arch and Edgware Road areas whilst the Russian tweeters tend to stick to the West End. Polish and Hungarian tweets appear the most evenly spread throughout London.

Even though the maps represent close to half a million tweets they are still based on a selective sample- they only include people who have a good location (either through GPS or a specific address) and those who are connected to the internet. I expect the latter requirement will exclude many short term visitors to London, and may explain why there aren’t so many hotspots around London’s landmarks (as is the case with Flickr where people can upload georeferenced images when they get home). In spite of this, I think the information in these maps is useful as a basis for comparison to other cities and it helps to reveal some of the finer patterns within the broad regions mapped by Fischer.

*this is slightly different to Eric Fischer’s method. He used Google’s translation tools to determine the language of each tweet whereas I have taken the stated language of each user because I am more interested in what users feel their preferred language is. I often see English tweeters post in French for example. Google also hasn’t quite mastered the slang or abbreviations that often crop up in Londoner’s tweets.

I recently stumbled upon a fascinating dataset which contains digitised information from the log books of ships (mostly from Britain, France, Spain and The Netherlands) sailing between 1750 and 1850. The creation of this dataset was completed as part of the Climatological Database for the World’s Oceans 1750-1850 (CLIWOC) project. The routes are plotted from the lat/long positions derived from the ships’ logs. I have played around with the original data a little to clean it up (I removed routes where there was a gap of over 1000km between known points, and only mapped to the year 1800). As you can see the British (above) and Spanish and Dutch (below) had very different trading priorities over this period. What fascinates me most about these maps is the thousands (if not millions) of man hours required to create them. Today we churn out digital spatial information all the time without thinking, but for each set of coordinates contained in these maps a ship and her crew had to sail there and someone had to work out a location without GPS or reliable charts.

These maps were produced with the latest version of R‘s ggplot2, maptools, geosphere and png packages. Formatting the data took the most work (it was a very large MS Access database). I used ggplot’s annotation_raster() to add the compass rose and title.

Update: For some nice animations and a much better critical analysis of the data see Ben Schmidts blog.

The explicit message of this map is that surprisingly large distances are covered across the network and that the Central Line rolling stock travels furthest.  It is up to the reader to work out why this may be the case. Slow thinking maps require the viewer to think a little harder. These can range from more complex or unfamiliar ways of projecting the world as demonstrated by this gridded population cartogram of Africa from Benjamin Hennig’s PhD thesis

or the seemingly impenetrable (from a distance at least), but wonderfully intricate hand drawn work of Steven Walter (click image for interactive version).

I have seen bad examples of both slow thinking and fast thinking maps but there is undoubtedly more rubbish in the latter category. I blame the rise of infographics in addition to the increasing ease with which data can be mapped (I note, this latter point has also facilitated many great maps).  It’s not all bad though, much like tabloid newspaper headlines I think clever fast thinking visualisations have required a lot of slow thinking by their creators and are good for portraying simple but important messages. My concern, however, is that slow thinking data visualisations are on the decline, especially online, because they do not grab the attention of potential viewers quickly enough or  a similar impact (in terms of internet traffic) can be achieved with less data processing or, in the case of geography, cartographic flair.  In addition there is a (perhaps legitimate) fear that producing complicated visualisations will intimidate or confuse readers. This latter point is important in the context of the media and is a problem the New York Times Graphics Department (also at the AAG conference) seem to have grappled with. Their approach is to introduce the unfamiliar or complex alongside graphics people are used to. This was done to excellent effect with their 2008 election coverage- look out for the cartogram towards the bottom.

So do the renowned folks at the NY Times Graphics Dept. prefer fast or slow thinking visualisations? I asked them what they think makes a successful map. Archie Tse said what I hoped he would: the best maps readable, or interpretable, at a number of levels. They grab interest from across the room and offer the headlines before drawing the viewer ever closer to reveal intricate detail. I think of these as rare visualisations for fast and slow thinking. The impact of such excellent maps is manifest by the popularity of atlases and why they inspire so many to become cartographers and/or travel the world. The work of David Ismus offers a classic example.

From a distance (above) the road network reveals major population centres, the shading mountains and the colours the depths of water surrounding/ within the US. You want to know more and each time you look something new catches your eye.

In addition Ismus offers something else- he has painstakingly visited (cartographically rather than physically) every part of the map through manual labelling. Most of the maps we see are the product of automated cartography and can therefore make sense computationally but are less intuitive to use. This relates to the final commonality in the most interesting visualisation talks I went to at the AAG – all great maps, fast or slow, “feel right” to those who created them. There is a gut instinct at work that, perhaps, cannot be taught or acted on quickly. So next time you see a map, give it the time it deserves. Is it fast, slow, or the best of both?

Finding ways to effectively map population data is a big issue in spatial data visualization.  The standard practice uses choropleth maps that simply colour administrative units based on the combined characteristics of the people that live there (see below).

These maps are popular with cartographers for a couple of reasons. You get a clear sense that the map is depicting some form of aggregation (or grouping) so readers of the map are (hopefully) less tempted to think that everything or everyone in that particular unit are the same. Mapping in this way is often the simplest option as names of the administrative units often come with the data you are interested in so they can be easily linked. Ultimately the underlying data are at household level and choropleth’s colour areas (such as parks etc) where nobody lives. For example the River Thames is running through the map above. Oliver O’Brien has sought to remedy some if these drawbacks by clipping the standard choropleth to building outlines (see first map and below).

I think this has resulted in a great visual improvements to the standard maps, and they closely resemble the iconic maps of Charles Booth. The question is, has Ollie gone too far? The reason the maps look better is because they have massively increased the implied precision of the data. This is what makes the increasingly popular dot density maps so eye-catching (but potentially very misleading). You are more likely to think that the inhabitants of each building (if, indeed there are any) are exactly as the colour suggests, but we know that the final colour is based on a number of the surrounding households (approx. 125 in this case). The obvious solution is to map household level data but this clearly isn’t possible for reasons of confidentiality in addition to the fact that grouping households makes statistical sense in many applications. The counter to this argument is that if people are encouraged to look for their own house it will be abundantly clear (to them at least) that the implied category is unrepresentative and they view the map more critically. This implied precision, called the ecological fallacy, affects our lives daily with anything from insurance premiums, to public services and marketing but we don’t notice it because it isn’t mapped. By revealing it in such a visually appealing way, do these maps compound the problem or educate us about it? Click here for Ollie’s explanation of the maps.

As a cyclist in London you can do your best to avoid left turning buses and dozy pedestrians. One thing you can’t really avoid though is pollution (although I accept cyclists probably aren’t much worse off than pedestrians and drivers in this respect). To illustrate this I have taken data for 3.2 million journeys from the Barclays Cycle Hire scheme and combined it with GLA pollution data for particulate matter. Unsurprisingly, pollution is worse at junctions and where there is lots of static traffic, with the popular cycling routes around Waterloo Bridge and the Strand particularly affected. Most of the journeys are subject to relatively low (by London standards) levels because cyclists try and avoid the busiest routes, such as Euston Road. The loop around Hyde Park is really popular with Boris Bikers and fortunately one of the least polluted but clearly more could be done to sort out the pollution hotspots around the west end.

The routes have been guessed using routing algorithms and OpenStreetMap data and optimised for cyclists (ie we assumed that people would prefer cycle lanes over roads etc). Thanks to Ollie O’Brien for this analysis. You can see more of his work here. I produced this map using the R software package and blog about how I did it here.

The above map (and this one) was produced using R and ggplot2 and serve to demonstrate just how sophisticated R visualisations can be. We are used to seeing similar maps produced with conventional GIS platforms or software such as Processing but I hadn’t yet seen one from the R community (feel free to suggest some in the comments). The map contains three layers: buildings, water and the journey segments. The most challenging aspect was to change the standard line ends in geom_segment from “butt” to “round” in order that the lines appeared continuous and not with “cracks” in, see below.

I am grateful to Hadley and the rest of the ggplot2 Google Group for the solution. You can see it here. From this point I layered the plots using the geom_polygon() command for the buildings and water bodies and my new function geom_segment2() for the journey segments- these were simply the start and end latitudes and longitudes for each node in the road network and the number of times a cyclist passed between them. I have included the code below
 


#Code supplied by james cheshire Feb 2012
#load packages and enter development mode
library('devtools')
dev_mode()
library(ggplot2)
library(proto)

plon2