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.

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.

If I said a country was 1594719800 metres squared it would mean a lot less to you than if I said it was about the size of Greater London (so long as you know about how big Greater London is). For this reason the media tend to report the extent of a flood in relation to the size of the Isle of Wight or Icebergs in relation to the size of Wales (or Luxembourg) so that we can imagine the extent and scale of a disaster or news story. Despite plenty of comment on how ridiculous such comparisons are, and a great website that will convert standard measurements into the fractions or multiples of the size of Wales, I am yet to see a mapped representation of our increasingly standard units of area. The one I produced above is not meant to be definitive, just a starting point to what I hope will be a new system to replace the frankly inadequate* metric measures we are used to.

A much more effective alternative to simply stating an area in terms of its relative size to another area is of course to produce a map.  Geographically correct maps contain most of this information in the first place but they aren’t much good if you want to compare two things at either ends of the World or even the Solar System. With loads of mapping data online it is now easy to start shifting things around and laying them on top of each other in the same way the BBC’s How Big Really? website does.

This is fine if you want to compare a couple of things, but the map gets messy if you want to do more than that. For more complex comparisons you need to start with a fresh map (be careful of the projection) and shifitng everything around to fit on a single page. Doing this can have a big impact as Kai Krause did with his “True Size of Africa” map.

…sparsely populated areas (UK Cities on top of the Highland region of Scotland by Alasdair Rae)…

…and their transport systems such as subways (by Neil Freeman)

and cycle hire schemes (by Oliver O’Brien).

I think they offer a new perspective on the world and use maps as more abstract forms of information visualisation, so lets hope we see them more often to accompany the usual descriptive “relative to Wales” statements.

*I don’t seriously mean this.

It would be a shame to end the year without a festive map! Jack Harrison (@jacksfeed) is studying for a research masters in “Advanced Spatial Analysis and Visualisation” at UCL. I teach on the course and it obviously hasn’t worked Jack hard enough this term as he has had time to slack off and produce this great map of Christmas festivities based on information from Wikipedia. As with all good maps, it speaks for itself and I think it is a great way of showing the difference in celebrations, especially as the Anglo-American view of Christmas is (unsurprisingly) so dominant. It also serves as a splendid last post on the blog for this year. Merry Christmas, and see you in 2012!

As 2011 draws to a close it is worth reflecting on what, I think, has been a defining year for mapping and spatial analysis. Geographic data have become open, big, and widely available, leading to the production of new and interesting maps on an almost daily basis. The increasing utilisation of technology such as Google Fusion Tables has made it easier than ever to map data. Sadly the number of bad maps is on the increase as a result (largely thanks to the web’s preference for the Mercator projection and push-pins) and I hope things will improve (over to you Google!) next year. To inspire another year of mapping, and in no particular order, here is the spatialanalysis “Best of 2011″. The maps here have been popular, engaged users, innovated, and raised the bar for cartographic standards. I bet I have missed some so feel free to link to your best map in the comments section.

Paul Butler’s Facebook Connections Map

This just sneaks in as it was produced in December 2010. The map is important for what it doesn’t show (most of Africa for example) rather than what it does. It has served as an inspiration for many others, and raised the bar in terms of the detail and extent of social media mapping.

National Geographic Surnames Map

I think the National Geographic Surnames Map is one of an increasing number of brilliant typographic maps that have been produced in the past year.  Typographic maps can show many variables (using colour, font size etc) and are often instantly engaging. This one was especially popular alongside its “sister” map of London Surnames.

Galaxy Survey Fly Through 

I really like this video as it serves to demonstrate just how vast the universe is. I spend my life mapping a few things over relatively small geographic areas and there is plenty for me to do. We have barely even started mapping the universe and I think this video captures the immensity of the undertaking.

iPhone Tracker

This map is not featured for its cartographic brilliance but for its unveiling of the volume of data our electronic devices, in this case iPhones, are capable of collecting. It served as a wake up call for many that data about our locations are collected all the time and it is easy to track where you have been.

Fedex Cartograms

Cartograms are becoming an increasingly popular way of mapping population data. I don’t have a problem with advertising so long as it is informative as well. I think these maps tick the box as they provide the best animations I have seen of cartograms morphing from one dataset to the other so I’m happy to give fedex a plug for this one.

Naming Rivers

The “Naming Rivers” map shows how different cultural and linguistic factors have influenced the naming of geographic features in the US. We talk about how we live in a “world without borders” but this plainly isn’t true as things we encounter on a daily basis are still influenced by the uneven movements of various populations over time.

Scientific Collaboration

This map, inspired by the Facebook connections map (above), demonstrates the dominance of a few countries within the scientific literature and the limited collaborations between a few countries. This pattern is seen in many datasets and is another illustration that  “global” is often only a minority of countries.

Eric Fischer’s Twitter Language Map

I really liked these maps both for their cartography but also for their demonstration that linguistic and national borders can be seen online as well. There has also been a tendency for fine scale mapping of Twitter data so it is nice to get a global perspective.

ITO 10 Years of Road Casualties UK (and US)

As I was writing this, the BBC have launched their own visualisations with this depressing data. It is often said that in the context of modern health and safety standards the car would never have been allowed. With maps such as the above it is easy to see why. ITO World have tried to be more intelligent with their use of icons- they have moved beyond the simple “pins on maps” we often see. It doesn’t work so well at the regional level, but as you zoom in clear accident hotspots unfortunately emerge.

NOAA Japanese Tsunami Wave Height

This year saw a devastating earthquake and subsequent tsunami hit Japan. NOAA produced a series of excellent maps and visualisations to help chart and explain the events. The map shows likely tsunami wave heights. I found it interesting as it shows both the extent of the waves and the way in which they appear as tentacles circling Earth.

BBC Brief History of Time Zones

Good maps help to educate and I found the above interactive globe from the BBC a really great way to learn about time zones. The BBC are becoming increasingly ambitious with their maps and I think they have excelled themselves with this one.

xkcd’s What Your Favourite Map Projection Says About You

This captures the different opinions on some of the many map projections perfectly. You may have gathered from the opening lines of this post that projections are really important and often considered too complicated to bother with. I’m all for the Winkel-Tripel although I can’t claim to have been a fan before the National Geographic adopted it, as I would have been too young to care at the time.

Transport for London have just released their performance data (link here) for the London Underground network. It is in the form of a really detailed file that contains, amongst other things, the “Peak Operated Kilometres” and “Peak Passenger Journeys” for the past 6 years or so. If you total the distances covered by the Tube rolling stock at peak times, this map shows how far they travel each day. The Central Line wins with over 13,000km – the equivalent of almost reaching Australia! In humble last place is the Waterloo and City Line that just passes Dublin with a little over 500km travelled. There are many more cool things to be done with the data, but I thought this is a neat way of showing the gargantuan task of shifting London commuters!

I think mapping Twitter’s engagement with the academic literature is important as it echos the map below and provides another example of the dominance of researchers (both in terms of access and production) from a few countries in the academic literature.

You can see how the “hotspots” of collaboration allign with the hotspots of tweets below.

One thing academics strive for is “impact”. This can mean many things, but one often applied criterion is the number of people outside of your immediate academic community that read your work. Twitter is becoming increasingly cited as an example of impact (for example how many followers an academic has), but as these maps demonstrate, much of the engaged audience is already where work is relatively easily accessible.

About the data from Altmetric:

“This dataset lists the ~ 58k tweets that mentioned a scientific article (broadly speaking anything with a DOI, PMID or arxiv ID) between the 1st and 31st of July 2011.

Recall isn’t 100%: my best estimate is that it’s missing another ~ 6k tweets where the article couldn’t be identified, the link was malformed or the journal involved is new or gets very low traffic.

Twitter’s TOS prohibit re-distribution of the tweets themselves but the dataset contains the extracted links, the tweet ID and some information about the tweeter (screen name, country & lat/lng derived from their location using Yahoo! Placemaker).“

The number of rivers in the US, combined with Derek’s brilliant design, make this a really compelling map. Inspired by this work I have quickly (with much less cartographic flair) extracted the major rivers and streams in Great Britain from the Ordnance Survey’s Strategi dataset and coloured them according to whether they are a “river”, “canal” (not sure if this really counts in terms of naming), “water”, “afon” (Welsh for river) and “brook”. You can see that a clear geography exists. I was not surprised by all the “afons” being in Wales but I was surprised to see so many “waters” in Scotland.

On the topic of naming, settlement names also have a clear geography as they, like rivers in the US, reflect the different settlers (or invaders!) of the British Isles over millennia. The map below (taken from my thesis) shows the different naming influences on settlements in Britain. The most striking aspect is the abrupt end to the Viking settlement names along what is called the Danelaw Line. So if you live north of this line you will be using more Viking words on a daily basis than those to the south. There are loads of people studying and recording the different place naming conventions in Britain- I would recommend you check out the “Institute for Name Studies” if you want more information.