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Mapped: Journeys to Work

Posted by on Jul 25, 2014 in Slideshow, Visualisation | 0 comments

Today the Office for National Statistics released the long awaited journey to work data collected by the 2011 Census in England and Wales. Here it is in all its glory. En masse you can really see the dominance of London in the South East as well as the likes of Manchester, Liverpool and Birmingham further north. If you want to pick out specific flows between areas you can use our “Commute.DataShine” tool developed by Oliver...

Welcome to DataShine!

Posted by on Jun 17, 2014 in Resources, Slideshow, Spatial Analysis, Visualisation | 1 comment

Last October I was fortunate enough to be awarded an ESRC “Future Research Leaders” grant. These run for up to 3 years and offer the opportunity for early career researchers to focus on their research interests and personal development activities. My area of interest is the analysis and visualisation of large-scale and open demographic datasets, so the project is called Big Open Data: Mining and Synthesis (BODMAS). The first output from the project is now ready and was developed with Oliver O’Brien. It’s called DataShine: Census and is a mapping platform for the key 2011 Census variables in England and Wales. Oliver has implemented a number of technical innovations to produce maps that...

Front Page of the British Medical Journal

Posted by on Feb 22, 2014 in Media, Visualisation | 2 comments

Oliver O’Brien and I have managed to sneak a map on the front page of this week’s issue of the British Medical Journal. The graphic shows the modelled flows of bikes between docking stations and accompanies a paper on the positive health effects of the Boris Bike cycle scheme in London. The trick it seems was to make the cycle flows look as much like veins as possible…...

Stunning Maps of World Topography

Posted by on Feb 7, 2014 in Featured Maps, R Spatial, Slideshow, Visualisation | 4 comments

Robin Edwards, a researcher at UCL CASA, has created these stunning topographic maps using the high resolution elevation data provided by the British Oceanographic Data Centre. The transitions from black (high areas) to blue (low areas) give the maps a slightly ethereal appearance to dramatic effect. All but the highest areas of Europe appear to blend into the sea, and there is a loss in the sense of scale that makes the Pacific ranges look like small water channels in a shallow sea. The best thing about these graphics (and the main reason I have featured them here) is that they were completely produced using the R software program with just a...

Coxcomb Plots and Spiecharts in R

Posted by on Jan 20, 2014 in Visualisation | 2 comments

I am not a great fan of pie charts since they are often used for the sake of it in circumstances where a chart is not needed at all! That said, I am willing to make an exception for “Coxcomb Plots” as pioneered by Florence Nightingale since they increase the data density on the plot and can enable comparisons across variables.  Robin Lovelace has written a neat tutorial on how to create them in R, I think it’s well worth a look. He and I also recently posted this ggplot2 and spatial data tutorial, and we have more on the way! [iframe src=”http://rpubs.com/RobinLovelace/11641″ width=”100%”...

Introduction to Spatial Data and ggplot2

Posted by on Dec 9, 2013 in R Spatial, Resources, Spatial Analysis | 12 comments

For those starting out with spatial data in R, Robin Lovelace and I have prepared this tutorial (funded as part of the University of Leeds and UCL Talisman project). Here we introduce a range of analysis skills before demonstrating how you can deploy the powerful graphics capabilities of ggplot2 to visualise your results. There is also some “bonus” material at the end to show how you can use ggplot2 for descriptive statistics and so on. The tutorial covers: -Introduction to ggplot2 -Map projections -Adding Google and Stamen basemaps -Clipping and joining spatial data -Aggregating spatial data -ggplot2 for descriptive statistics Download the data you need from here. This is...