What Is GeoSimulation

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

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

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

4. What is the performance of new components and design concepts?

5. 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

6. FOR REAL...

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

8. Examples of (simulation) models

9. Examples of (simulation) models

10. Examples of (simulation) models

11. 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?

12. 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, 167-227.

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

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

15. 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.

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

17. 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

18. 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, 163-178.

19. Applications of GeoSimulation Ferguson, N. M., Cummings, A. T., Cauchemez, S., Fraser, C., Riley, S., Meeyai, A., Iamsirithaworn, S. & Burke, D. S. 2005 Strategies for containing an emerging influenza pandemic in Southeast Asia. Nature 437, 209–214.

20. Applications of GeoSimulation Prophylaxis Social Distance

21. 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

22. 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.

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

24. The elements • Moving towards Talisman • Data • Visualisation • Computation • Models • Training

25. The elements • FuturICT

26. 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: www.geotalisman.org