Emergent Internet of Things (IoT) based technologies offer the potential for new ways in engaging with places, spaces and objects. The use of mobile and tablet computing linked specifically to objects and memory, comment and narrative creation opens up a potentially game-changing methodology in user interaction above and beyond the traditional 'kiosk' type approach. In this position statement we detail the QRator project in the Grant Museum at University College London. The QRator project explores how handheld mobile devices and Internet enabled interactive digital labels can create new models for public engagement, personal meaning-making and the construction of narrative opportunities inside museum spaces. The project won the United Kingdom National Museum and Heritage Award for Innovation for exploring the cultural shift that is anticipated as society moves to a ubiquitous form of computing in which every device is 'on', and every device is connected in some way to the Internet.

For many scientific disciplines, the continued progression of information technology has increased the availability of data, computation and analytical methodologies including simulation and visualisation. Geographical information science is no exception. In this article, we investigate the possibilities for deployment of e-infrastructures to inform spatial planning, analysis and policy-making. We describe an existing architecture that feeds both static and dynamic simulation models from a variety of sources, including not only administrative datasets but also attitudes and behaviours which are harvested online from crowds. This infrastructure also supports visualisation and computationally intensive processing. The main aim of this article is to illustrate how spatial simulation models can be calibrated with crowd-sourced data. We introduce an example in which popular attitudes to congestion charging in a major UK city (Manchester) were collected, with promotional support from a high-profile media organisation (the BBC). These data are used to estimate the parameters of a transport simulation model, using a hungry estimation procedure which is deployed within a high-performance computational grid. We indicate how the resulting model might be used to evaluate the impact of alternative policy options for regulating the traffic in Manchester. Whilst the procedure is novel in itself, we argue that greater credibility could be added by the incorporation of open-source simulation models and by the use of social networking mechanisms to share policy evaluations much more widely.