What Is GeoSimulation Mark Birkin School of Geography, University of Leeds

What happens to a “system” under certain (extreme) conditions?

How can users be trained cost effectively and at low risk?

What is the performance of new components and design concepts?

GeoSimulation Attempts to achieve some of the same objectives as physical simulations through representation of a spatial social system (‘the city’) as a computational model Possible goals: – Better understanding of how the system works and its most important features – Train the drivers of the system (e.g. planners) to make more effective decisions – Impact analysis: ‘what if?’ scenarios

FOR REAL...

What can I do with a GeoSimulation? Long-term analytics Short-term analytics Real-time analytics Performance evaluation Operations Tactics Strategy Housing Transport Emergency services Policing Hospitals Schools Understand policy options Optimise delivery Evaluate scenarios Projection of future trends Observation of historical trends Visualise demand patterns Visualise interaction patterns Visualise supply patterns

Examples of (simulation) models

Bank account? Building plans? Map! – A simplified and abstract representation of a ‘real‘ phenomenon – It can be manipulated in some useful way Can I afford to go on holiday? Will all the children fit into our new house? What time should I set off to get to the match?

History of GeoSimulation Most migrants move only a short distance. There is a process of absorption, whereby people immediately surrounding a rapidly growing town move into it and the gaps they leave are filled by migrants from more distant areas, and so on until the attractive force [pull factors] is spent. There is a process of dispersion, which is the inverse of absorption. Each migration flow produces a compensating counter-flow. Long-distance migrants go to one of the great centers of commerce and industry. Natives of towns are less migratory than those from rural areas. Females are more migratory than males. Economic factors are the main cause of migration. EG Ravenstein (1885) The Laws of Migration, Journal of the Royal Statistical Society, 48,

History of GeoSimulation Upper-middle and Upper Classes. Wealthy. Mixed. Some comfortable, others poor. Lowest class. Vicious semi-criminal. Fairly comfortable. Good ordinary earnings. Very poor, casual. Chronic want. Poor. 18s-21s a week for a moderate family. Middle class. Well-to-do. Charles Booth Online Archive, booth.lse.ac.uk

History of GeoSimulation Park, R., & Burgess, E. (Eds.) (1925). The city. Chicago: University of Chicago Press.

History of GeoSimulation H Fagin (1963). The Penn Jersey Transportation Study: The Launching of a Permanent Regional Planning Process, Journal of the American Institute of Planners.

History of GeoSimulation Hollerith’s tabulating machine – introduced in the US Census 1890

Applications of GeoSimulation Critiques of the modelling approach: – Douglass Lee (1973) Requiem for Large Scale Urban Modelling – Andrew Sayer (1976) Understanding Models versus Understanding Cities – David Harvey (1973) Social Justice and the City Provide a framework for: – articulating the scope and boundaries of the methods – prioritising development and evaluating progress

Applications of GeoSimulation Lee’s Seven Deadly Sins... – Hypercomprehensiveness – Complicatedness – Expensiveness – Hungriness – Wrong-headedness – Grossness – Mechanicalness Lee, D.B. (1973) Requiem for Large Scale Urban Models, Journal of the American Institute of Planners, 39, 3,

Applications of GeoSimulation Ferguson, N. M., Cummings, A. T., Cauchemez, S., Fraser, C., Riley, S., Meeyai, A., Iamsirithaworn, S. & Burke, D. S Strategies for containing an emerging influenza pandemic in Southeast Asia. Nature 437, 209–214.

Applications of GeoSimulation ProphylaxisSocial Distance

Applications of GeoSimulation Ferguson – Challenge – investment (3 month sim) – Limitations – simplistic behavioural interactions? – Weaknesses: morphing of virus? Panic behaviour? But power – strategic planning; assess merits of alternative interventions; a framework for policy action

Applications of GeoSimulation Thompson C, Birkin M, McLaughlin F, Hodgson S (2010) The Impact of Target Hardening Policy on Spatial Patterns of Urban Crime in Leeds, GISRUK, London. Malleson, N., L. See, A. Evans, and A. Heppenstall (2011). Implementing comprehensive offender behaviour in a realistic agent-based model of burglary. Simulation. Malleson, N., Birkin, M., Hirschfield, A. & Newton, A. (2012). GeoCrimeData: Understanding Crime Context with Novel Geo-Spatial Data. Paper presented to the Association of American Geographers (AAG) Annual Meeting, February 2012, New York.

Applications of GeoSimulation Long-term analytics Short-term analytics Real-time analytics Performance evaluation Operations Tactics Strategy Housing Transport Emergency services Policing Hospitals Schools Understand policy options Optimise delivery Evaluate scenarios Projection of future trends Observation of historical trends Visualise demand patterns Visualise interaction patterns Visualise supply patterns

The elements Moving towards Talisman – Data – Visualisation – Computation – Models – Training

The elements FuturICT

TALISMAN TALISMAN is a node of the NCRM and is based at the University of Leeds and University College London. TALISMAN’s key objectives are to: Develop state-of-the-art geospatial models in the form of new data analysis techniques and simulation models. Build new methods of data acquisition and visualisation. Improve the uptake and dissemination of skills in spatial analysis through training and capacity-building activities. For further information about TALISMAN visit: