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


Welcome to the the new look Spatial Analysis blog. My last post was way back in March so I have a lot of catching up to do! The prolonged absence was due to all my energy being spent on completing my PhD thesis in order that I could get it out of the way before starting my new job at the UCL Centre for Advanced Spatial Analysis in London. I will be teaching a module on GI Systems and Science so may share some resources here or be on the lookout for ideas. In addition to the odd teaching resource to add to the growing list of R Spatial resources I hope to continue to upload visualisations of some of the latest freely available datasets and also scour the internet for great work that others have done.

I have put a lot of effort into transferring my previous posts and material to the new look blog. If you spot any missing images please let me know. In addition I have added a whole new Map Gallery section. This in part is to help fill the gap left by the Map Room blog no longer being updated but mainly to help readers quickly navigate my favourite maps as I upload them. So to kick things off I have uploaded a couple of fresh posts that I hope you enjoy.

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.

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!”

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.

Spatial data are becoming increasingly common, as are the tools available in R to process it. It takes a little time to understand how R handles spatial data; this tutorial is designed to help get people started. It outlines how to create a simple spatial points object from as csv file, load and export a shapefile and alter or add spatial projection information.

Data and Package Requirements:

London Sport Participation Shapefile. Download (requires unzipping).

London Cycle Hire Locations: Download.

Install the following packages (if you haven’t already done so):

maptools, rgdal (Mac users may wish to see here first).

Click here to view the tutorial code.

2011 GB Census: Planning Ahead.

Friday; Session 2231; RGS-IBG Drayson Room; Link to speakers.

Analysing and Visualising Social Change: Postgraduate research in GIScience.*

Thursday; Session 2143; Electrical Engineering, Room 509a; Link to speakers.

Distance, Speed and Time: The Fundamentals of Transport Geography.

Wednesday; Session 354; RGS-IBG Pavilion; Link to Speakers.

Enhancing Complex Social Simulations with Automata Systems.*

Thursday; Session 3175; Electrical Engineering, Room 509a; Link to speakers.

GIS for Environmental Modelling.*

Thursday; Session 4200; Electrical Engineering, Room 509a; Link to speakers.

Governance and the Geoweb.*

Friday; Session 1226; Sherfield Building, Room 7; Link to Speakers.

Postgraduate Research in Transport (1): Mobility, sustainability and behaviour of individuals.

Friday; Session 1219; Sir Alexander Fleming Building, Room 121; Link to Speakers.

Postgraduate Research in Transport (2): Infrastructure, development and urban form.

Friday; Session 2235; Sir Alexander Fleming Building, Room 121; Link to Speakers.

The Spatial Dimensions of Health.

Wednesday; Session 123; Sherfield Building, Room 7; Link to Speakers.

There is no place like home! – Why historians would want to use GIS.

Friday; Session 3252; RGS-IBG Lowther Room; Link to Speakers.

*GIScience Research Group organised session.

Here is a great opportunity to join our research group:

Summary: Creating better Area Classification for the 2011 Census of the Population

A Ph.D. studentship is available commencing end September 2010 for three years to create an updated methodology for the creation of a new area classification that will be employed on the 2011 Census of the Population, when the output data are made available. The current classification can be viewed as the ‘OAC interactive map’ at www.areaclassification.org.uk.

This provides an excellent opportunity to address a range of important methodological issues with geodemographic classifications, and to build better visualisation methods for dissemination that will maximise engagement and create more opportunities for public feedback. As such, the key research deliverables of this award will be: to evaluate alternate clustering methodologies that create more stable assignments of areas into clusters; to design new geographically sensitive methods of measurement that inform the underlying indicator variables used to create the classification; to develop and evaluate new modes of dissemination which better utilise web based technology and new advances in GIS.

The successful candidate is likely to have a background in GIS or computer science, and will have as a minimum an upper second or first class degree. S/he must also be a home/EU student for fee-paying purposes. Applicants should email a curriculum vitae and supporting statement to Professor Paul Longley at the Department of Geography, University College London: plongley@geog.ucl.ac.uk. The closing date for applications is Friday 27 August 2010, with interviews planned for the following week.

For those yet to be Inspired by Iceland, this video should help:

Inspired by Iceland Video from Inspired By Iceland on Vimeo.