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Posted by on Feb 16, 2011 in Featured Maps, London, R Spatial, Visualisation | 6 comments

Mapping London’s Population Change 1801-2030

Mapping London’s Population Change 1801-2030

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 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! Technical Stuff 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...

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Posted by on Jan 7, 2011 in Featured Maps, London, Visualisation | 4 comments

Boris Bikes/Barclays Cycle Hire Average Journey Times

Boris Bikes/Barclays Cycle Hire Average Journey Times

The visualisation above shows the average relative duration of Boris Bikers’ weekday journeys over a 4 month period at hourly intervals. For each time step the average journey time (in seconds) from each docking station has been calculated.This information is interesting because it shows the preference for short journeys around the City of London, whilst people on the outskirts of the the scheme (especially to the west) take longer journeys. I also like the the fact that journey times around Soho and the West End are longest around 23:00- perhaps correlating with the number of after-work drinks consumed. In one visualisation you get to see the changes in the cyclists behaviour- from the early morning commuters through to the late night cruisers The data come from Transport for London’s recent release of 1.4 million Barclays Cycle Hire journeys to their developers area (thanks to this FOI request). The data are said include all the journeys between 30 July 2010 and 3 November 2010, except those starting between midnight and...

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Posted by on Jan 3, 2011 in R Spatial | 9 comments

Exporting KML from R

Exporting KML from R

Google Earth has become a popular way of disseminating spatial data. KML is the data format required to do this. It is possible to load almost any type of spatial data format into R and export it as a KML file. In my experience R seems much quicker at doing this than many well-known GIS platforms, such as ArcGIS. The worksheet below explains how. Data and Package Requirements: 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...

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Posted by on Sep 8, 2010 in R Spatial, Resources | 6 comments

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Clipping a Surface By a Polygon

Clipping a Surface By a Polygon

Background: A common function in standard GIS software enables users to create a raster surface and extract values or clip it based on a set of polygons. This may be used in cases where you want analysis to be constrained to within a town’s boundaries or a coastline. This tutorial will outline how to create a surface using kernel density estimation (KDE) and then clip the surface so that it is constrained within the City of London Boundary. Data Requirements: City of London Boundary Shapefile: Download (requires unzipping). London Cycle Hire Locations: Download. Install the following packages (if you haven’t done so already): sm, maptools.? Click here to view the...

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Posted by on Sep 7, 2010 in R Spatial, Resources | 2 comments

Writing a Spatial Function: The Location Quotient

Background: In some cases it is necessary to conduct the same analysis multiple times on either the same or different data. In such circumstances it is worth writing a function to simplify the code. In this example the location quotient provides a simple calculation easily written in to a function. The location quotient (LQ) is an index for comparing a region’s share of a particular activity with the share of that same activity found at a more aggregate spatial level (a good book on this kind of thing is Burt et al.). In this example we take a shapefile of London Boroughs that contains information on the population of each borough and the percentage of sports participation in each borough. In this case there is little point in calculating the LQ as the percentage alone would be more meaningful. The focus here is how to undertake the methods, not their appropriate use, or the validity of the results. Data Requirements: London Sport Participation Shapefile: Download (requires unzipping) Install the...

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