Looking Forward

Mike Goodchild

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

Towards an infrastructure for sharing models

• Infrastructure for sharing– search– discovery– evaluation of fitness for use– acquisition– execution

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

Current status

• Some archives– some pre-WWW

• No standards• No clearinghouses• www.ncgia.ucsb.edu/~scott

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

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

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,

pu, du

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

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?

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

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

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

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

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.

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…

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

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