on mapping. They argue that Web 2.0 mash-ups, layering geographically tagged social data on top of digital maps, enable quick and simple visualization of data, presenting research outcomes in ways that can be easily understood by diverse audiences.
Many of the examples the authors present emphasize how much researchers can achieve using simple, generic technologies and services such as Google Maps and Fusion Tables. Helpfully, Batty and his colleagues at UCL’s Centre for Advanced Spatial Analysis (CASA) have packaged these services into useful tools (such as MapTube5), which not only enable the geo-mapping of datasets with a few button clicks, but also provide ways for researchers to share and re-use each other’s efforts.
Another way in which advances in visualization techniques have harnessed the increase in computer power and new sources of data is the creation of fly-through, 3D models and visualizations of, for example, urban environments. More mundanely, but perhaps of greater value to researchers and planners involved in urban science, and the latest of many research areas predicted to be transformed by the advent of big data,6 are CASA’s ‘city dashboards’, which integrate diverse sources of data to create a real-time visualization of the state of the city and its inhabitants. Example applications include visualizing in real-time the state of mass transit systems. Such tools can provide powerful and intuitive front-ends to the simulations and models presented in Chapter 6, allowing, for example, exploration of the impact of closure of parts of the system.
Batty and his co-authors stress the importance of crowdsourcing and ‘citizen science’ for creating resources accessible to the public and illustrate this with the example of Open Street Map, a free map of the world.7 They conclude with some thoughts on the future of visualization as a tool for social scientific investigation and understanding. They predict the emergence of radically different kinds of tools that make use of more abstract forms of visualization, with an increasing emphasis on the use of non-spatial data as the way forward for understanding how social systems function.
Chapter 12: Ethical Praxis in Digital Social Research
Current approaches to ethics no longer seem adequate for twenty-first century social research. We have already noted the concern registered by the authors of preceding chapters about the privacy and confidentiality threats raised by the proliferation of social data. There is an emerging consensus that a new ethical framework for the conduct of social research is necessary in order to protect citizens from harm but, as yet, there is little agreement on what changes it should embody, and how it should be promulgated and enforced.
In this chapter, Jirotka and Anderson examine the ethical issues raised by e-Research methods and what steps the social research community might take to address them. They use three case studies to illustrate the issues. The first describes a flagship UK e-Science project eDiaMoND and the process of gaining ethical approval for its work. The second concerns a recent controversy regarding social science researchers’ use of Facebook data called the ‘Harvard Meltdown’. The final case study is about developing prototype assistive technology for vulnerable people. Jirotka and Anderson draw several conclusions from these studies: managing ethics in large scale, multi-disciplinary research projects is particularly difficult and some of the founding principles of research ethics, such as informed consent, can be burdensome; protecting the identity of sources using conventional techniques for anonymization is becoming progressively less reliable as more and more information about subjects and settings becomes openly available via the Web (identification is always possible given enough correlated data); consenting to take part in research must be done in a principled way and, having consented, participants must have the power in practice – and not just in principle – to withdraw it; and finally, where a project involves interventions in people’s lives, researchers must consider what may happen once the project finishes.
They conclude with a discussion of the ethics of big social data. They underline the importance of the well-rehearsed arguments about threats to privacy and confidentiality. They ask what rules should apply to the use of social media in research: does publishing thoughts and opinions in public render informed consent irrelevant? However, their key insight goes further: it questions whether the lure of big social data is persuading researchers to relax their professional judgment about what conclusions are warrantable from the data. Jirotka and Anderson’s fundamental argument is that we need to bring ethical considerations into the heart of how we conduct research, from the point where decisions are being made about research goals, through to the collection and analysis of the data and the making sense of the findings.
Chapter 13: Sociology and the Digital Challenge
This final chapter examines the implications of massively increased computational and data resources for social research methods, including the impact on its established practices and future of its disciplines. In it, Savage returns to themes that he and his co-author, Burrows, first raised in their subsequently much-cited paper, ‘On the coming crisis of empirical sociology’ (Savage and Burrows, 2007). His aim, in part, is to ground expectations of the changes in social research that may follow from digital innovations and, not least, to question their inevitability. As the contributions of the authors of the chapters in this volume convincingly demonstrate, the future of digital sociology is contested: they all agree that the discipline is undergoing a sustained period of innovation, but its future direction is unknown. Together, they make a powerful case for Savage’s assertion that the future of digital sociology is not a given, but lies in the hands of current and subsequent generations of practitioners.
1.4 Future directions
1.4.1 Technical Developments
The other chapters in this book, described above, confirm that e-Research has moved on from an early focus on grid computing to encompass a very diverse set of tools, some of which are enhancements of previous software and others that are entirely new. A factor that suggests that this diversity will persist and even grow is the lack of central co-ordination and oversight. In the UK, the national e-Science Centre, which was the hub for the core programme, ceased operating in 2011, as did the NCeSS Hub in 2010. Other national centres still exist, for example the New Zealand eScience Infrastructure (www.nesi.org.nz), as do several international initiatives, such as the Open Grid Forum (www.ogf.org) and the European Grid Infrastructure (www.egi.eu). The emphases of these centres and programmes, however, are largely high performance computing, providing cloud services and codifying grid standards; areas of limited relevance to the social sciences. Outside these programmes, technical developments are either mostly modest refinements to existing tools, updates to commercial packages driven by competition for market share, or the adoption and adaptation of whatever generic or specialized tools and services researchers find can smooth the path of their own research. The future path of technical developments is therefore impossible to predict, though the drive to harness computing power to enable better research is unlikely to abate.
1.4.2 The Data Deluge
As reiterated in most of the chapters in this volume, we live in an information age characterized by a deluge of digital data (Hey and Trefethen, 2004; Hey, Tansley and Tolle, 2009). The chapters set out many of the potential research benefits to be obtained by collecting and analysing artificially produced and naturally occurring big data of many kinds from numerous sources. However, these benefits will only be realized if the wealth of data is managed in ways that ensure that it is discoverable,
