The MRes course I direct, the ASAV (Advanced Spatial Analysis and Visualisation) has undergone some changes and emerged, butterfly-like, as the MRes in Spatial Data Science and Visualisation. Ta-da!
The first thing to say is that the visualisation component is not any smaller. It was important to me that the course maintained the balance it has between science, social science and creativity. There is a lot of technical material on the course, but we’ve changed the structure in the first term so that students take modules on GIS (digital mapping and spatial analysis), quantitative methods (mathematical approaches to the world), and introductory coding (programming for visualisation and drawing) – and in doing so learn bits of Processing, R and Python. This isn’t to put off the designers, architects and geographers that study the course every year – rather, it’s to make sure they have the same skills as the people coming in from GIS, computer science or software development, and go into the second term with everything they need to do great technical work.
The other big change is that our students will no longer study spatial simulation – mathematical modelling of spatial systems – but instead will take a more data-driven approach. Data science for spatial systems will teach students how to work with larger datasets in databases and how to structure, query and analyse them. This will tie into the visualisation work as they understand and communicate large and complex data.
Finally, the dissertation is now assessed by a report and a paper – which means that research-minded students can work with supervisors to submit their work to journals and conferences all the more easily.
I’m excited about this new setup, and if it appeals to you, you can find more information on our course website, ask me about it, or apply here before July 31st if you want to start in September 2015. See you there!
Open City Documentary Festival ran last week, and I attended their excellent panel event A Smart Portrait of London, knowing that visualisations from CASA staff and students (including some lovely work by our MRes students) featured at the end of the evening, but somehow having forgotten that filmmaker Kevin Biderman had interviewed me for his excellent interactive film piece, Data Futures†. I contributed to the discussion led by the panel, talking about how brilliant it will be when Google run our lives*, it only gradually dawning on me that following the group’s rather on-point critique of the challenges around the ethics of big data, a huge image of my face was going to appear talking about how cool datavis is; a rather less critical perspective. I think Kevin has done me a real service in making me look like an enthusiast for the potential of data and visualisation rather than a technocratic cheerleader; but it’ll certainly be worth watching the full piece when it is completed and how my upbeat message is tempered by more cautious commentators. Either way, you can see it above.
If you’d like to hear more about data, privacy and politics, I’m chairing a session of the same name at the Practising Ethics in Built Environment Research conference on Tuesday, featuring Emma Uprichard (an interdisciplinary social scientist based in Warwick), Nathan Lea or the Institute of Health Informatics at UCL, and Jack Stilgoe (from UCL’s Science and Technology Studies department) – or if you’re more interested in the technical/practical side of data visualisation, you might want to consider our new MRes course, which is open for applications until the end of July.
† the full piece is not yet available, but I will link to it here or via my twitter account @sociablephysics when it is
*that’s slightly lacking nuance; it was more a comment about how, to me, “the public” still seem generally happy to trade their data for services, even following the Snowdon GCHQ/NSA revelations
The data visualisation projects this year were, as ever, excellent. The theme was “a day in the life”, and the groups took that in different directions – I’ll focus on just one of those projects, a group who decided to explore the data behind the London Marathon to draw out its story.
Heidi, Kaisa, Yuefeng (“Jeff”) and Aditya wanted to understand the marathon from a data-driven perspective – both as an athletic event, and as one which has impact across the world. They set about gathering data from the Marathon site about runners, and in parallel, mining Twitter for buzz running up to the event by searching on marathon-related hashtags across the world. The also gathered data on race day in London in order to see live tweets as they happened over the course of the race.
These were the datasets, and from this the team was able to generate a diverse but coherent set of visualisations: maps of tweets around the world, visualisations of international runners travelling to the UK, and visualisations of the race itself drawn from real running data. The 2D version of this incorporates weather and curated tweets of encouragement; the 3D version has a detailed 3D model of London and a high-resolution basemap.
The group then synthesised these elements into the coherent and concise video you see above. Choosing a consistent colour palette and base map glue the individual pieces together; the use of a voice over draws to viewer in and ties the elements together further.
In the course of this project, the students used elements from all aspects of their visualisation course – 3D environments, data collection, and programmatic visualisation – but it’s what the elements have in common that makes for an effective project.
This post originally appeared at http://spatialdatascience.org/ in June 2015.
This review of Alex Pentland’s Social Physics: How Good Ideas Spread – the Lessons from a New Science was published in the December 2014 issue of Physics World – which can be accessed here if you sign up for free!
Alex “Sandy” Pentland is a computer scientist with an impressive academic record and an even more impressive history of translating academic outputs into business and consultancy. To say he has entrepreneurial flair would seem to be an understatement; his previous book was a bestseller, and his career is sprinkled liberally with consultancies and spin-outs from his research group. His career defies easy categorization, but he calls the work that he does on network analysis and computational social science “social physics”. In his latest book, Social Physics: How Good Ideas Spread – the Lessons from a New Science he outlines his vision of a discipline that has a history of infighting and intellectual land-grabbing.
“My problem is that I’ve been persecuted by an integer”
With this, George Miller, like a Franz Kafka of cognitive psychology, launched one of the most influential papers in the field. Miller’s persecutor was the number seven – which, in test after test of absolute judgement, appeared as approximately the number of categories people could tell apart.
GPS bike tracks visualisation in Madrid (work in progress) – Martin Zaltz Austwick and Gustavo Romanillos (c) 2014
I’ve written in the past (and here) about how difficult I’ve found it to get my hands on a book about visualisation that’s a real page turner – this must be a problem that greater minds than I have struggled with for at least a century or two, but I find the writing about design I see curiously unsatisfying. Like travel writing, I’d rather be doing the thing than reading about it. Which is as fine a way as any to learn, but a bit solipsistic.
Critique of Knowledge Production etched into sustainable bamboo
In this guest post by anthropologist Charlotte Johnson, she discusses her perspectives on the PICKs project (published earlier this month in Sustainability: The Journal of Record and available open source).
When asked to ‘map all the research related to cities and resources at UCL’ my first thought was ‘eek’, and not just due to the overwhelming size of the task (UCL has a research body of over 4000 people). But from a critical perspective mapping can be an act of epistemic violence – what gets put on the map and why, who gets to decide on the parameters? For me, a map is an object fraught with imperial overtones not to mention the hubris in attempting to comprehensively represent an ever-shifting landscape.