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Calibration of a spatial simulation model with volunteered geographical information

2011; Taylor & Francis; Volume: 25; Issue: 8 Linguagem: Inglês

10.1080/13658816.2011.559169

1365-8824

Mark Birkin, Nick Malleson, Andrew Hudson‐Smith, Steven Gray, Richard Milton,

Urban Transport and Accessibility

Abstract 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. Keywords: crowd-sourcingsimulationcalibratione-infrastructure Acknowledgements The research reported in this article has been funded through the JISC Information Environments Programme, e-Infrastructure for Social Simulation. Notes 1. The penetration rates for all seven OAC groups were as follows: Blue Collar Communities, 69; City Living, 171; Countryside, 107; Prospering Suburbs, 102; Constrained by Circumstances, 104; Typical Traits, 107; Multicultural, 108. Penetration of 200 indicates that members of this group are twice as likely to complete the survey, while 50 shows that they are half as likely. 2. For the purpose of estimating the models in this article, it has been assumed that stated preferences are a reliable indicator of revealed behaviours. Although this simplifies our analysis, the integrity of the model is not seriously compromised by this assumption.