Building GIS Platforms for Spatial Business: A Focus on the Science of Maximizing Location Intelligence Benefits Through Risk/Cost Management
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2022-01-04
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An ensemble model for aggregating weighted risks and costs is tested in a Monte Carlo simulation with Tomlinson's 22 lower-order risk factors for GIS implementations. The basic assumption of the model is that practitioners incorrectly manipulate and transpose risk and cost factors contributing to less than optimum implementation results. Such examples include: (1) violation of Lusser's probability product law, (2) non-use of Galton's 50th percentile/median as the "wisdom of the crowd" estimate, (3) incorrect use of weighting (if at all), (4) dubious ranking of lower-order risk factor importance and (5) the inability to automatically predict a Bayesian posterior adjusted cost projection. The ensemble model corrects for these and other errors. Life data analysis and reliability functions from reliability engineering are built into the model for further enhancement of results.
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Geospatial Big Data Analytics, ensemble model, gis implementation, help, risk factor, spatial business
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10 pages
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Proceedings of the 55th Hawaii International Conference on System Sciences
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Attribution-NonCommercial-NoDerivatives 4.0 International
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