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Posted by on Mar 29, 2015 in London, Visualisation | 0 comments

Burger Cartography

Burger Cartography

  I enjoy burgers and have a passion for maps and mapping, which is probably why Andrew Hill’s recent blog post “In Defense of Burger Cartography” offered a sufficiently large piece of bait for me to bite on and respond to (I join Kenneth Field and Taylor Shelton [and others, I am sure] in his cartographers’ keepnet) . In summary, the post says its time to “fall in love with maps all over again” thanks to a “new world of cartography” that has been liberated from old world critiques. I agree with many of Andrew’s points – it’s good to make it easy for people to make maps, traditional cartography can seem a bit crusty in this “new age” of so-called “Big Data” and web mapping, the more people who enjoy maps the better etc. etc. – but I have a few thoughts of my own to add. Firstly, I’m all for “exploratory playfulness” but I am more for thinking critically. Twitter maps are a key example in Andrew’s post – why get hung up...

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

Welcome to DataShine!

Welcome to DataShine!

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 are slick and seamlessly switch between geographies as you zoom. You can have custom colour palettes and even export high-resolution PDFs (go easy on this though, as it is hard work for the server!). For more details about the project see here. It is still a work in progress with more functionality on the way so please sign up for updates...

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

Introduction to Spatial Data and ggplot2

Introduction to Spatial Data and ggplot2

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 a work in progress so we may add improvements as time goes on. We also have a few more tutorials in the pipeline that will be posted here in due course.   [iframe src=”http://rpubs.com/RobinLovelace/intro-spatial” width=”100%”...

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Posted by on Oct 21, 2013 in Featured Maps, Spatial Analysis, Visualisation | 4 comments

Mapping Where We Live

Mapping Where We Live

Showing where we live is, of course, one of the oldest and most useful reasons to create a map. As we bask in the “Big Data” era, the trend for mapping population is increasing simply because there are more data points out there, the bulk of which are generated by people. Population distribution is important because, as xkcd wittily illustrates, if you were to map these points without accounting for it you often just get a population density map. Or worse still, you think you are creating a map that represents the whole world, but instead you only get the parts of  it where people are connected to the internet. Such maps are considered unsurprising by many (in spite of their hype) because simple maps of raw counts rarely offer surprising insights in the phenomena the map is trying to articulate. For examples of this there are some great maps (and data) of Wikipedia entries vs population density here. For this post, however, I want to ignore the many new datasets...

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Posted by on May 8, 2013 in R Spatial, Resources | 2 comments

3D Mapping in R

3D Mapping in R

This tutorial has been kindly contributed by Robin Edwards (from UCL CASA). RGL is R’s box of power-tool for 3D object rendering, with functionality for creating 3d mesh objects and curved surfaces, and for using materials and directional lighting.  For example the line: plot3d(rnorm(100),rnorm(100),rnorm(100)) creates a 3d scatterplot of x-y-z normal distributions, producing: OpenStreetMap provides a nice way to import map tiles via the OSM API (among others). A helpful StackOverLoader (Spacedman) has provided this useful function for adding ‘z’ values to OSM map objects, enabling them to be plotted in 3d: map3d <- function(map, ...){ if(length(map$tiles)!=1){stop("multiple tiles not implemented") } nx = map$tiles[[1]]$xres ny = map$tiles[[1]]$yres xmin = map$tiles[[1]]$bbox$p1[1] xmax = map$tiles[[1]]$bbox$p2[1] ymin = map$tiles[[1]]$bbox$p1[2] ymax = map$tiles[[1]]$bbox$p2[2] xc = seq(xmin,xmax,len=ny) yc = seq(ymin,ymax,len=nx) colours = matrix(map$tiles[[1]]$colorData,ny,nx) m = matrix(0,ny,nx) surface3d(xc,yc,m,col=colours, ...) } A benefit of the approach is that it enables you to adjust the map to the ideal perspective for representing the data in the final rendered image. Here I’ve applied the function to data on London’s rental costs (for the year to December...

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