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Posted by on Jul 9, 2012 in Resources, Spatial Analysis, Visualisation | 0 comments

Editorial: Visualisation Tools for Understanding Big Data

Editorial: Visualisation Tools for Understanding Big Data

I recently co-wrote an editorial (download the full version here) with Mike Batty (UCL CASA) in which we explored some of the current issues surrounding the visualisation of large urban datasets. We were inspired to write it following the CASA Smart Cities conference and we included a couple of visualisations I have blogged here. Much of the day was devoted to demonstrating the potential of data visualisation to help us better understand our cities. Such visualisations would not have been possible a few years ago using desktop computers their production has ballooned as a result of recent technological (and in the case of OpenData, political) advances. In the editorial we argue that the many new visualisations, such as the map of London bus trips above, share much in common with the work of early geographers and explorers whose interests were in the description of often-unknown processes. In this context, the unknown has been the ability to produce a large-scale impression of the dynamics of London’s bus network. The pace of exploration is largely determined by...

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Posted by on Dec 10, 2010 in Visualisation | 0 comments

Creating Regions from Social Relationships

Creating Regions from Social Relationships

The video above accompanies interesting research, led by the MIT SENSEable City Lab, that shows how the linkages between landline calls in the UK can be used to create a “new” regional geography. The main conclusion I drew from the map is the fact that people phone those nearest to them more than those further away; but this is unsurprising. To me, the map is important for two reasons. Firstly, it is an excellent example of our data rich culture and our ability to use this data for meaningful analysis. Not so long ago the clustering of 12 billion phone calls would have been a laughable undertaking. The second outcome is the demonstration of the fact that we no longer need to rely on arbitrary administrative boundaries in our data analysis, we are increasingly able to create our own data-based geographies. Thanks to Jon Reades (one of the project researchers) for alerting me to this...

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Posted by on Jun 7, 2010 in Resources, Visualisation | 0 comments

Top 60 Chinese Cities

Top 60 Chinese Cities

Cities are one of the many phenomena that follow a long-tailed distribution. In simple terms there are a few big cities and lots of small ones. The classic way of showing a long tailed distribution (and the method from which the name is taken) is to produce as plot such as that below: The infographic at the top of the post by chinfographics.com demonstrates the distribution in a more engaging and constructive way. One method I have used in the past to demonstrate data with a long tailed distribution is the excellent Wordle tool. I have created a Wordle (below) for the same data (downloaded from Chinfographics). Whilst it does not compete with the Chinfographics infographic in terms of quality,  I still think Wordles provide a very simple, and effective, method of displaying data with a “long...

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