‘Smart cities’ are characterized by pervasive and distributed sensor networks capturing and generating big data for forms of centralized urban management, drawing together such previously unconnected infrastructural systems as video surveillance, meteorological stations, traffic lights and sewage systems. Although presented as largely civic, corporate and managerial, these schemes have a parallel history in military strategic thinking and policing, from crime mapping and predictive policing models, to new forms of urban warfare involving sensor platforms, and computer analytics, to enable forces to get a ‘clear picture’ of the complexities of the urban landscape and its inhabitants. In some cases, these have come together in overt ways, for example in the new ‘Domain Awareness’ initiatives in Oakland, California, and in New York which extends existing port security projects way beyond the military maritime surveillance ‘domain’ into the surrounding city and its governance. This talk examines some of these intersections of big data, smartness and security and argues that the big data city is always also a surveillance city.
David Murakami Wood is Canada Research Chair (Tier II) in Surveillance Studies at Queen’s University. He studies the histories, technologies and practices of surveillance in cross-cultural contexts. His current project, Ubicity, an examination of security in smart cities, is funded by SSHRC.
Turbulence is everywhere: it affects aircraft drag and fuel consumption, blood flow in arteries, the dispersion of pollution in the air, the formation of weather patterns. Yet no theory has been developed that can explain it, except in the simplest scenarios. Numerical models have become one of the most useful tools to analyze its effects in engineering devices and in the natural environment.
In a numerical model, the history of the fluid velocity, temperature and pressure, is calculated on a grid of very closely spaced points that cover the geometry of interest (a pipe, a turbine blade, a heart valve). Hundreds of millions of points may be necessary, and the history may consist of thousands of snapshots of the flow. Heavyweight computer power is required, and very large amounts of data are generated, that allow the researcher to examine in detail the flow development. Too much detail sometimes: the life of each whorl, vortex or eddy is an open book and hunting for answers in such datasets is as challenging as generating the data itself. Examples of such hunting expeditions will be presented, to show how big data can aid in the understanding of turbulence, and how this understanding can be applied to the real life problems that motivate this work.
Ugo Piomelli holds a Tier 1 Canada Research Chair in Turbulence Simulations and Modelling and the HPCVL-Sun Microsystems Chair in Computational Science and Engineering. He is a Fellow of the Royal Society of Canada, the American Physical Society and the American Society of Mechanical Engineers.
The Surveillance Studies Centre
c/o David Murakami Wood
Department of Criminology
University of Ottawa
Canada