1. Looking Forward Mike Goodchild

2. Where is ESRI going? • 9.0 • massively expanded toolbox • script management and metadata • Python, JScript, Perl • visual modeling interface • 9.1 • transportation and routing • many improvements to modeling

3. Towards an infrastructure for sharing models • Infrastructure for sharing • search • discovery • evaluation of fitness for use • acquisition • execution

4. Falling through the cracks • Text-sharing infrastructure • libraries, bookstores, books, journals, WWW, search engines • Data-sharing infrastructure • metadata schema, archives, clearinghouses, data centers • Model-sharing infrastructure • models are the highest form of sharable knowledge of the Earth system

5. Current status • Some archives • some pre-WWW • No standards • No clearinghouses • www.ncgia.ucsb.edu/~scott

7. The locations of computing • User location u • the user interface • Processing location p • ||u-p|| • 1960s < 10m • dedicated lines ca 1970 <10km • now no limit • Data storage location d • independent of u, p • Subject location s • independent of u, p, d

8. Options for p • Where to process? • server or client, which server? • published services • directories • www.geographynetwork.com • description standards • UDDI: Universal Description, Discovery and Integration • WSDL: Web Service Definition Language

9. p and u • ||p-u|| = 0 • computing in the client • using local data, ||u-d|| = 0 • using remote data • ||p-u||>0 • send data to the service from the client • link a remote service to a remote data source, pu, du

10. Costs and benefits • More cycles available remotely • integrating and exploiting waste cycles • the Grid • SETI • Intellectual property issues • intellectual value of service • risk of dissemination • commercial value • Update, versioning issues • distributed service has versioning problems • Process coupled to data, well defined

12. High-priority geoservices • Geocoding • tied to data, update issue • Gazetteer • conversion between general or domain-specific placename and coordinates • geoparsing • identification and decoding of placename references in text • mapping and associating news stories • queries based on placenames • how far is the capital of Belgium from the capital of France? • What else, is there a general model?

13. Evaluation of models • What determines the value of a model? • Excess of benefits over costs • Cost of execution • depends on data volume, model complexity • Cost of data • depends on spatial resolution

14. Determining benefits • Value of improved decision making • Model accuracy • an inaccurate model has no value • Numbers beat no numbers every time • and a picture is worth a thousand words • and a GIS has both numbers and pictures • and results come out of a computer

15. Sources of error and uncertainty • Inadequate spatial resolution • necessary resolution is defined by the process being modeled • how to combine models of different processes with different resolutions? • Inadequate temporal resolution • Measurement error in the data • Error in the parameters

16. Error propagation • Determining the effects of errors in input data on the output of modeling • confidence limits on every result • The butterfly effect • nonlinear response • the effects of spatial autocorrelation • relative accuracy versus absolute accuracy • Modeling error in data • with Monte Carlo simulation • a very simple illustration

19. Sensitivity analysis • Repeat the modeling with various values of parameters • original value + 10% • original value – 10% • Observe effects on results • identifying parameters whose values are most critical • An example • J.C.J.H. Aerts, M.F. Goodchild, and G.B. M. Heuvelink (2003) Accounting for spatial uncertainty in optimization with spatial decision support systems. Transactions in GIS 7(2): 211–230.

20. Other strategies • Hind-casting etc. • run the model backwards in time, and compare to the historical record • start the model at some previous time and replicate the historical record • used to calibrate the rules of urban growth models • But no-one can predict the future…

21. Yet more strategies • The model is only as good as its conceptual inputs • the rules and data • If the model doesn't predict correctly it could be because: • the rules are wrong or incomplete • the data are wrong or have inadequate resolution • the time steps are too long • and there is no way to tell which of these is true • likely they are all true

22. In summary: • A model is not a way to find out how the world works • but a way to implement what we know in a convenient, integrated package • a tool for spatial decision support • a link between basic science and decision making