Looking Forward

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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. - PowerPoint PPT Presentation

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  • Looking ForwardMike Goodchild

  • Where is ESRI going?9.0massively expanded toolboxscript management and metadataPython, JScript, Perlvisual modeling interface9.1transportation and routingmany improvements to modeling

  • Towards an infrastructure for sharing modelsInfrastructure for sharingsearchdiscoveryevaluation of fitness for useacquisitionexecution

  • Falling through the cracksText-sharing infrastructurelibraries, bookstores, books, journals, WWW, search enginesData-sharing infrastructuremetadata schema, archives, clearinghouses, data centersModel-sharing infrastructuremodels are the highest form of sharable knowledge of the Earth system

  • Current statusSome archivessome pre-WWWNo standardsNo clearinghouseswww.ncgia.ucsb.edu/~scott

  • The locations of computingUser location uthe user interfaceProcessing location p||u-p||1960s < 10mdedicated lines ca 1970
  • Options for pWhere to process?server or client, which server?published servicesdirectorieswww.geographynetwork.comdescription standardsUDDI: Universal Description, Discovery and IntegrationWSDL: Web Service Definition Language

  • p and u||p-u|| = 0computing in the clientusing local data, ||u-d|| = 0using remote data||p-u||>0send data to the service from the clientlink a remote service to a remote data source, pu, du

  • Costs and benefitsMore cycles available remotelyintegrating and exploiting waste cyclesthe GridSETIIntellectual property issuesintellectual value of servicerisk of disseminationcommercial valueUpdate, versioning issuesdistributed service has versioning problemsProcess coupled to data, well defined

  • High-priority geoservicesGeocodingtied to data, update issueGazetteerconversion between general or domain-specific placename and coordinatesgeoparsingidentification and decoding of placename references in textmapping and associating news storiesqueries based on placenameshow far is the capital of Belgium from the capital of France?What else, is there a general model?

  • Evaluation of modelsWhat determines the value of a model?Excess of benefits over costsCost of executiondepends on data volume, model complexityCost of datadepends on spatial resolution

  • Determining benefitsValue of improved decision makingModel accuracyan inaccurate model has no valueNumbers beat no numbers every timeand a picture is worth a thousand wordsand a GIS has both numbers and picturesand results come out of a computer

  • Sources of error and uncertaintyInadequate spatial resolutionnecessary resolution is defined by the process being modeledhow to combine models of different processes with different resolutions?Inadequate temporal resolutionMeasurement error in the dataError in the parameters

  • Error propagationDetermining the effects of errors in input data on the output of modelingconfidence limits on every resultThe butterfly effectnonlinear responsethe effects of spatial autocorrelationrelative accuracy versus absolute accuracyModeling error in datawith Monte Carlo simulationa very simple illustration

  • Sensitivity analysisRepeat the modeling with various values of parametersoriginal value + 10%original value 10%Observe effects on resultsidentifying parameters whose values are most criticalAn exampleJ.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.

  • Other strategiesHind-casting etc.run the model backwards in time, and compare to the historical recordstart the model at some previous time and replicate the historical recordused to calibrate the rules of urban growth modelsBut no-one can predict the future

  • Yet more strategiesThe model is only as good as its conceptual inputsthe rules and dataIf the model doesn't predict correctly it could be because:the rules are wrong or incompletethe data are wrong or have inadequate resolutionthe time steps are too longand there is no way to tell which of these is truelikely they are all true

  • In summary:A model is not a way to find out how the world worksbut a way to implement what we know in a convenient, integrated packagea tool for spatial decision supporta link between basic science and decision making