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The Internet can be hugely useful to coordinate disaster relief efforts, or to help rebuild affected communities.

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The 6.2 magnitude earthquake that struck the centre of Christchurch on 22 February 2011 claimed 185 lives, damaged 80% of the central city beyond repair, and forced the abandonment of 6000 homes. It was the third costliest insurance event in history. The CEISMIC archive developed at the University of Canterbury will soon have collected almost 100,000 digital objects documenting the experiences of the people and communities affected by the earthquake, all of it available for study. The Internet can be hugely useful to coordinate disaster relief efforts, or to help rebuild affected communities. Paul Millar came to the OII on 21 May 2012 to discuss the CEISMIC archive project and the role of digital humanities after a major disaster (below). We talked to him afterwards. Ed: You have collected a huge amount of information about the earthquake and people’s experiences that would otherwise have been lost: how do you think it will be used? Paul: From the beginning I was determined to avoid being prescriptive about eventual uses. The secret of our success has been to stick to the principles of open data, open access and collaboration—the more content we can collect, the better chance future generations have to understand and draw conclusions from our experiences, behaviour and decisions. We have already assisted a number of research projects in public health, the social and physical sciences; even accounting. One of my colleagues reads balance sheets the way I read novels, and discovers all sorts of earthquake-related signs of cause and effect in them. I’d never have envisaged such a use for the archive. We have made our ontology is as detailed and flexible as possible in order to help with re-purposing of primary material: we currently use three layers of metadata—machine generated, human-curated and crowd sourced. We also intend to work more seriously on our GIS capabilities. Ed: How do you go about preserving this information during a period of…

Small changes in individual actions can have large effects at the aggregate level; this opens up the potential for drawing incorrect conclusions about generative mechanisms when only aggregated patterns are analysed.

One of the big social science questions is how our individual actions aggregate into collective patterns of behaviour (think crowds, riots, and revolutions). This question has so far been difficult to tackle due to a lack of appropriate data, and the complexity of the relationship between the individual and the collective. Digital trails are allowing Social Scientists to understand this relationship better. Small changes in individual actions can have large effects at the aggregate level; this opens up the potential for drawing incorrect conclusions about generative mechanisms when only aggregated patterns are analysed, as Schelling aimed to show in his classic example of racial segregation. Part of the reason why it has been so difficult to explore this connection between the individual and the collective—and the unintended consequences that arise from that connection—is lack of proper empirical data, particularly around the structure of interdependence that links individual actions. This relational information is what digital data is now providing; however, they present some new challenges to the social scientist, particularly those who are used to working with smaller, cross-sectional datasets. Suddenly, we can track and analyse the interactions of thousands (if not millions) of people with a time resolution that can go down to the second. The question is how to best aggregate that data and deal with the time dimension. Interactions take place in continuous time; however, most digital interactions are recorded as events (i.e. sending or receiving messages), and different network structures emerge when those events are aggregated according to different windows (i.e. days, weeks, months). We still don’t have systematic knowledge on how transforming continuous data into discrete observation windows affects the networks of interaction we analyse. Reconstructing interpersonal networks (particularly longitudinal network data) used to be extremely time consuming and difficult; now it is relatively easy to obtain that sort of network data, but modelling and analysing them is still a challenge. Another problem faced by social…