Looking Forward

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Looking Forward

• Mike Goodchild

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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

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Towards an infrastructure for sharing models

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

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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

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Current status

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

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The locations of computing

• User location u
• the user interface
• Processing location p
• u-p
• 1960s lt 10m
• dedicated lines ca 1970 lt10km
• now no limit
• Data storage location d
• independent of u, p
• Subject location s
• independent of u, p, d

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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

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p and u

• p-u 0
• computing in the client
• using local data, u-d 0
• using remote data
• p-ugt0
• send data to the service from the client
• link a remote service to a remote data source,
  p?u, d?u

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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

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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?

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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

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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

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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

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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

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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)
  211230.

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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

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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

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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