Error Propagation Modelling In Raster Gis
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Download Full-text PDF Error Propagation Modelling in Raster GIS: Overlay OperationsArticle (PDF Available) in International Journal of Geographical Information Science 12(2):145-167 · March 1998 with 123 ReadsDOI: 10.1080/136588198241932 · Source: DBLP1st Giuseppe Arbia21.55 · Catholic University of the Sacred Heart 2nd Daniel A. Griffith38.44 · University of Texas at Dallas3rd Robert Haining36.82 · University of CambridgeAbstractPerforming data manipulations on maps that possess error as a result of the process of data collection leads to error propagation. The errors that are present in maps are modified by such operations in ways that may undermine the purposeofanalysisand http://www.tandfonline.com/doi/abs/10.1080/136588198241932 lead to increased uncertainty in thevalidity ofthe conclusions that are drawn. This paper analyses how source map error propagates as a result of overlay operations. Geman and Geman's corruption model for individual source map error is used for the analysis which allows for attribute measurement error and location error that can then interact with the (true) source map geography. This paper reports https://www.researchgate.net/publication/220649922_Error_Propagation_Modelling_in_Raster_GIS_Overlay_Operations theoretical results on the univariate overlay problem and then extends these results through simulation. Throughout a set of source maps and error processes are used with specified properties in order to examine in detail the interactions that can take place between the different elements of the source map structure and the error process. The paper uses ANOVA methods to quantify the contribution made by different components of the map and the measurement process to spatial (join count) and non-spatial (proportion of misclassified pixels and the Kappa index) descriptors of error. The paper concludes with a discussion of the usefulness of these results for managing error in spatial databases and for the development of automated uncertainty reporting.Discover the world's research10+ million members100+ million publications100k+ research projectsJoin for free CitationsCitations78ReferencesReferences33Propagation of positional error in 3D GIS: estimation of the solar irradiation of building roofs"Propagated errors are defined as the discrepancies between performing identical operations on the true and on the observed, error-contaminated data layers. Error propagation modelling is the formal process of representing the transformations in data quality that occur through GIS operations on data layer
computer program is no longer available for download. The program became too unstable to function with upgrades to Python and the Environmental System Research Institute (ESRI) ArcGIS Desktop application. INTRODUCTION Graphical representation http://co.water.usgs.gov/projects/REPtool/ of raster processing with REPTool. The U.S. Geological Survey Raster Error Propagation Tool (REPTool) is a public domain, Python-based geoprocessing tool (computer program) for use with the Environmental System Research Institute (ESRI) ArcGIS Desktop application (ESRI; Redlands, Calif.) to estimate error propagation and prediction uncertainty in raster processing operations and geospatial modeling. REPTool is designed to introduce concepts of error and uncertainty in geospatial data and error propagation modeling and provide users of ArcGIS Desktop a geoprocessing tool and methodology to consider how error affects geospatial model output. REPTool uses Latin Hypercube sampling (LHS) (McKay and others, 1979) within a probabilistic framework to track error propagation in geospatial models and quantitatively estimate the uncertainty of the model output. Users may specify error for each input raster or model coefficient represented in the geospatial model. The error error propagation modelling for the input rasters may be specified as either spatially invariant or spatially variable across the spatial domain. Users may specify model output as a distribution of uncertainty for each raster cell. REPTool uses the Relative Variance Contribution (RVC) method (van Horssen and others, 2002) to quantify the relative error contribution from the two primary components in the geospatial model — errors in the model input data and coefficients of the model variables. REPTool is appropriate for many types of geospatial processing operations, modeling applications, and related research questions, including applications that consider spatially invariant or spatially variable error in geospatial data as follows. A. Analyses of error propagation, uncertainty, and sensitivity to understand and estimate: • How error in geospatial model input propagate through Map Algebra expressions. • The magnitude of spatially variable uncertainty of geospatial model predictions from error introduced as model input. • The relations between spatially variable uncertainty of geospatial model predictions and error introduced from individual model inputs. B. Given uncertainty in geospatial model predictions: • Evaluate the range of probable predictions from geospatial models to environmental standards or regulatory limits. • Determine the type and location of data needed to improve model prediction confidence. • A
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