The Bartlett Centre for Advanced Spatial Analysis



The TALISMAN node, which consists of CASA at UCL and CSAP at the University of Leeds, will develop methods for geospatial data analysis and simulation, specifically models of spatial systems that emphasise interactions which reflect potential and flows at and between locations. The methods we will develop are based on spatial interaction, agent-based models (ABM), cellular automata (CA), and microsimulation methods that span the range from aggregate to individual populations, at different spatial scales and over different temporal periods. Cutting across these models are new developments in computer media that emphasise new ways of collecting data from the bottom up, popularly referred to as 'crowd-sourcing' and new methods of visualising models inputs, outputs and the process of model building using new methods of visual analytics. The node will develop these models which are part of a long tradition in social physics and urban and regional economics which has now emerged and converged with the complexity sciences. We will progress these models in various forms, developing more generic types of model and methods for their implementation through calibration and use in forecasting.

Our emphasis will be on developing substantive extensions to these models by embedding them in new media using Web 2.0 and 3.0 technologies as well as improving them through participatory involvement of relevant stakeholders and model builders. In developing methods in this way as the fusion of spatial simulation and spatial data analysis, we will add considerable variety to NCRM and provide a focus for entirely new developments in the way we generate data and models, through various kinds of crowd-scouring.

In this way, we will extend and consolidate the infrastructure we have been building for social science under the UK e-science programme through the National Centre for e-Social Science node GENeSIS. Our node provides a new dimension to National Centre for Research Methods (NCRM) - the geospatial dimension - which is fast becoming one of the main drivers of new internet technologies in terms of data, their visualisation and dissemination. There are important training and capacity building issues that spin off directly from such developments. The project is financed by ESRC as part of its NCRM and runs from September 2011 to August 2014.

Visit the TALISMAN website: http://www.geotalisman.org


Michael Batty
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Steven Gray
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Andrew Hudson-Smith
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Richard Milton
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Alan Wilson
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Anand, S., Batty, M., Crooks, A., Hudson-Smith, A., Jackson, M., Milton, R., Morley, J., and Rosser, J. (2010) Data Mash-Ups and the Future of Mapping, JISC TechWatch, Technology and Standards Watch, Joint Information Systems Committee, Bristol, UK.

Batty, M. (2011) A Generic Framework for Computational Spatial Modelling, in A. Heppenstall, A. Crooks, L. See and M. Batty (Editors) Agent-Based Models of Geographical Systems (Springer, Berlin)

Stanilov, K. and Batty, M. (2011) Exploring the Historical Determinants of Urban Growth Patterns through Cellular Automata, Transactions in GIS, 15(3), 253-271

Wilson, A. G. (2011) A Note on the Bass Model Applied to Retail Dynamics, CASA Working Paper 167 


Our objectives reflect the impacts of our project and we divide these into research an practice objectives, followed by training issues: first for research

  • To develop and extend state of the art geospatial methods in the form of new techniques of data analysis and new simulation models.
  • To develop substantive extensions of these models with respect to new sources of data.
  • To develop substantive extensions with respect to new problem areas. In particular, new types of grand challenge building on climate change, aging, mobility and migration, energy depletion and conservation, as well as new technologies, are taxing existing geospatial models.
  • To develop new techniques of visualisation of model inputs, outputs and processes so that scientists and informed stakeholders alike are able to generate new ways of articulating large scale problems and ensuring that their models are better calibrated and validated.
  • To develop new methods for sourcing models, building on the expertise of stakeholders whose views are essential to the derivation of better and more applicable models through an extended dialogue of model use and applications.

The practice objectives are:

  • To fashion geospatial models so that they are directly applicable to key global and local problems as represented in key commercial and public issues, from retailing and marketing of location based services to aspects of spatial deprivation.
  • To develop new methods for participation in dialogues with stakeholders involved in the sourcing of new data and disseminating model outputs using new forms of visual analytics.
  • To use geospatial models to engage in new dialogue about raising awareness about critical social problems and to generate ways in which chains of such models might be used in more integrated forms of assessment as in our Tyndall Cities climate change work in London.
  • To provide new ways of articulating key social problems through dialogues motivated by geospatial analysis. To fashion new forms of model that source models in the same way that data is currently being sourced.
  • To target geospatial models in particular sectors of government and the private sector which might benefit enormously and quickly from the raising of awareness concerning the geospatial domain.

Training and capacity building objectives involve formal training programme for a variety of professionals from graduates to working social scientists.

agent-based modelling modelling