I hadn’t seen this video before. It demonstrates one of the earliest attempts at automated cartography for the display of time with spatial data. Truly ground breaking, the video shows the urban growth of Lansing at 5 yearly intervals from between 1850 and 1965 and was produced by Allan Schmidt at the Michigan State University Urban Regional Research Institute. The visualisation was produced with the synergraphic mapping system (SYMAP) developed by Howard Fisher in the mid 1960s. More details and a fully downloadable version can be found here. A presentation on SYMAP is available here. The sequence starts with a slow version of two minutes forty-five seconds before repeating the sequence more rapidly in forty-five seconds, and finally in five seconds.
Followers of spatialanalysis.co.uk will know that a lot of maps I feature are about London. Many of these maps have caught the eye of those outside of the geography, GIS/ spatial analysis community who don’t really have an interest in the technicalities of making the maps etc. Oliver O’Brien and I have decided to team up to launch the mappinglondon.co.uk blog for people who like to see maps of London without the techie blurb/ code you often see here. This is timely as there are some fantastic London mapping events in the pipeline (stay tuned) that I know will spread the good word about the geography and cartography of this great city.
The plan is to post little and often so that we can share with you the maps that have been catching our attention. I don’t expect things to change much here, but you may find some lighter cartographic relief over at mappinglondon.co.uk.
Buried deep in the ESRI (UK) website is a case study I helped put together showcasing some of the ways we use GIS (specifically ESRI products) within UCL Department of Geography and Centre for Advanced Spatial Analysis. ESRI (UK) co-sponsor my PhD research and I have had a very positive and productive relationship with the company. I know that they are keen to promote the use of their software within higher-education (and at secondary schools) and you can find out more here. Click on the image below for the case study.
Buried in the London Datastore are the population estimates for each of the London Boroughs between 2001 – 2030. They predict a declining population for most boroughs with the exception of a few to the east. I was surprised by this general decline and also the numbers involved- I expected larger changes from one year to the next. I think this is because my perception of migration is of the volume of people moving rather than the net effects on the baseline population of these movements. I don’t envy the GLA for making predictions so far into the future, but can understand why they have to do it (think how long it took to initiate Crossrail!). Last year I produced a simple animation showing past changes in London’s population density (data) and it provides a nice comparison to the above. In total I have squeezed 40 maps on this page!
These maps were all produced to demonstrate the mapping capabilities of R. The first uses ggplot2 (plus classInt + RColorBrewer) and is based on some code (see below) written by Mark Bulling. If you follow the code below you will end up with this map, not the one I have produced above. I will stick my code in a formal tutorial soon. The animation uses the standard plot functions (plus spatial packages) in R as per this example.
The release of the R package “googleVis” has made the production of interactive maps through Google’s Chart Tools a simple task. Ignoring the some basic data manipulation the below map was produced with these two lines of code:
Geo=gvisGeoMap(Map, locationvar=”Country”, numvar=”Percentage”,
This map, although simple to produce, is nontrivial as it shows the percentages of 5-14 year olds in each country conducting child labour. You can download the data for it here, and the rest of the R code here.
Data: Map, Chart ID: GeoMap_2011-01-11-09-36-24
R version 2.12.1 (2010-12-16),
If you print the “Geo” object you will get a load of code that you can then paste into your website. I am amazed by how straightforward it is, thanks to the clever people at Google some great programming from R contributors. It isn’t perfect (I think the Mercator projection is inappropriate here) but it’s a great start.
The TFL data release contained the start point, end point, and duration for around 1.4 million bike journeys. An educated guess has been made about routes between stations using OpenStreetMap data and some routing software. The animation shows the scheme’s busiest day (thanks to a tube strike) and provides an amazing insight into the dynamics of Boris Bike users. You can find more info here.
I suspect this animation will be another big PR win for TFL, it is just a shame that it took a freedom of information request to get the underlying data.
Martin’s viz is one of my favourites but there have been a couple of others released that use similar technologies to show urban transport systems. Chris McDowall has produced an animation on a much larger scale by showing Auckland’s public transport system on a typical Monday.
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Another great animation was produced by fellow CASA researcher Anil Bawa-Cavia. This shows London’s bus network and it makes for a great comparison to Auckland’s transport system above.
The National Geographic Surname Map has generated a lot of discussion both online and via email. The response has been overwhelmingly positive but some people, unsurprisingly, have suggested improvements. A recent post on the great Junk Charts blog acts as a good summary of the comments I have received. For the purpose of this post I have left out the positives in order that I can address some of the suggested limitations of the map. There is always room for improvement but I thought it would be good to outline some of the logic behind relaxing a couple of Tufte’s classic rules on data visualisation. I have pasted each suggested improvements from Junk Charts below and added my responses beneath. “They really ought to have used relative popularity rather than absolute popularity. This is another area of improvement for all word clouds. Today, word clouds plot the number of times a specific word appears in a piece of text. We often try to compare several word clouds against each other; and when we do that, the only sensible measure is the proportion (relative frequency) of time a specific word appear. Say, one compares Obama and McCain speeches by comparing two word clouds. If these two speeches differ significantly in length, then comparing the number of times each candidate use “education” words is silly — we have to compare the number of times per length of the speech.”
The use of relative popularity is something I would agree with in most circumstances. The surname map, however, is designed to give a national impression (rather than state by state) impression of the general distribution of surnames. Had we used a relative measure (such as freq. per million) where would the million come from, the state or the entire US population? If it were the former we would compound the second criticism below. If we wanted a comparison (such as changes over time) we would, of course, have used relative frequencies. “The cutoff of top 25 names in each state suffers a similar problem. The 26th most popular name in California, a populous state, is of more interest than say the 15th most popular name in Montana (or insert your favorite small state). Instead, a more sensible cutoff would be including names that account for at least 2 percent (say) of a state’s population. By doing this, the more populated states would have more entries than the less populated states.”
As another commenter remarked, the long-tailed nature of the surname distribution would mean there is very little difference between the popularity rank and an equally arbitrary cutoff percentage. I also don’t understand why more populated states would have more surnames at the top of their distribution. It is not necessarily the case that population size correlates with surname frequency.
“Given the above bullets, it is not surprising that the word-size scale has serious problems. Because it is an absolute number and not relative to each state’s population, the big words can only show up in populous states. In other words, the size of the words tells us about the geographical distribution of the U.S. population. As I mentioned before (such as here), this insight is available on pretty much every map used to plot data that has ever been produced. The one thing that all these maps never fail to tell us is the fact that most of the U.S. population is bi-coastal. Unfortunately, the real message of the map — in this case, the geography of surnames — is subsumed.”
The message of the map is that surnames are not randomly distributed across the US. Each wave of migrants moving to the US has a clear preference (or necessity) to where they live(d) and this has creates the diverse patchwork of surnames shown in the map. I cannot see how this message has been subsumed by not standardising for population. If this was a map of car theft then it would be nonsense to not account for population density (or car density) but in the context of surnames (due to the nature of their distribution throughout the population) the patterns (and message) would have been similar. “And then, the map invents false data. Notice that there are 1,250 geographic sites on the map (25 names times 50 states). This is a visually prominent feature of the map, and yet there is no rhyme or reason as to where the names are placed, with the exception of respecting state boundaries. The casual reader may think that the appearance of the Chinese name “Lee” in the inner, central part of California implies that Lee-named Chinese-Americans aggregate in those parts of California. Far from the truth!”
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This is the biggest limitation of the map- and one I had tried to address in the London Surnames map. We were constrained by the fact that the map was being designed for print. Had it been designed as an interactive map (and not simply a static image) we would not have gone about it this way.
As with all visualisations you can’t please everyone, but I hope I have provided some insight into why the map developed the way that it did.
Using the release of the “Muslim Populations By Country” dataset from the Guardian Datastore I have produced a cartogram to visualise the data. The size of the country represents its 2010 Muslim population and the colours indicate how much the population is expected to grow/shrink over the next 20 years. It may not be interactive but it is a really compelling way to show the data. It would be good to get hold of data for other religions. For more cartogram fun check out Worldmapper.
This week has been a busy one with the “publication” of a couple of maps I have been involved with alongside the circulation of a few cartographic gems. I thought I would share my mapping highlights.
To have something published in the National Geographic is a great honour. The map of US Surnames has proved hugely popular and was a great project to work on. A real high point in my PhD research so far.
The popularity of a London version of the US Surname Map outstripped all expectations with 10s of thousands of visitors. Cartographically less impressive than the US map but much more detailed, I think the main thing people are most surprised (and perhaps disappointed about) is just how many “Smiths” there are!
I’ve not quite worked out if this map shows anything surprising but I really like the cartography so “Profane Mountains, Polite Plains” gets a shout out here. It shows the frequency of swearwords in people’s Tweets across the US.
This map of scientific collaborations (detailed here) demonstrates nicely the strong academic ties between some countries over others. I think its a great map which I hope (although I can’t seem to confirm) was created with R. The map was actually created using MySQL, Java and Photoshop (thanks @beyondmaps).
Inspired by the What’s in a Surname? map we helped make with the National Geographic, I have created 15 interactive typographic maps to show the most popular surnames across London. What they lack in cartographic brilliance, I hope they make up for in detail. There are 983 geographic units (Middle Super Output Areas) in each map and across all 15 there are 2379 individual surnames (15,000 surname labels in total). The font size for each surname label has been scaled to give an idea of the number of people who have that surname in each place. The surname frequencies come from the 2001 Electoral Rolland won’t contain everyone living in London but it is one of the best datasets available.
London is renowned for being a diverse city but this is barely reflected in the most prevalent surnames- only a few name origins can be discerned from the map. You have to look a little further down the surname rankings for this diversity to become apparent. The surnames shown on all 15 maps can be traced back to one of 38 origins; I have selected unique colours for 10 of the most popular. Surname origins were established using the Onomap classification tool. We are mapping the origins of the surnames, which are not necessarily the same as the origins of the people possessing them. Many people in London have adopted Anglicised surnames.
It is also clear from the maps that the same sorts of surnames tend to cluster together. This is because they often closely reflect the naming preferences of particular groups of people within an area. As you transition through to the less popular surnames things become a little more jumbled and the distinct patterns present in the first map become less distinct.
The final thing that stands out is how surname popularity decreases between the first and second most popular names and every subsequent change after that. You can see this by how quickly the text size reduces until almost all names are written in the smallest font sizes.
The more you study these maps the more interesting, and perhaps complex, they become. My final thoughts therefore appear a little contradictory. The first is that a surprising number of Londoners share the same name (especially with their immediate neighbours). The second is that despite the dominance of relatively few surnames at the top of the rankings, the further down the rankings you get the more you see of London’s population diversity. We are of course only mapping the top 15 surnames in each area of London- there are many thousands more. If you can’t find your surname on these maps, you can see where it is around the world here.There is no doubt that levitralab.com is an excellent drug, with a strong and long-lasting effect. It helps me and my friends to cope with the problem of the severe erection.
The maps were created as part of my ongoing PhD research using the Worldnames Database compiled by University College London’s Department of Geography. Thanks to Oliver O’Brien from CASA for putting the maps online. A high resolution print version of the map (previewed below) is available on request.