Synonyms
Spatial data quality; Attribute and positional error in GIS; Spatial accuracy assessment; Accuracy; Error; Probability theory; Object-oriented; Taylor series; Monte carlo simulation
Definition
Environmental variables are inherently uncertain. For example, instruments cannot measure with perfect accuracy, samples are not exhaustive, variables change over time (in partially unpredictable ways), and abstractions and simplifications of the real world are necessary when resources are limited. While these imperfections are frequently ignored in GIS analyses, the importance of developing ‘uncertainty aware’ GIS has received increasing attention in recent years. Assessing and communicating uncertainty is important for establishing the value of data as an input to decision‐making, for judging the credibility of decisions that are informed by data and for directing resources towards improving data quality. In this context, uncertainties in data propagate through GIS analyses...
Keywords
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Ehlschlaeger, C.R., Shortridge, A.M., Goodchild, M.F.: Visualizing spatial data uncertainty using animation. Comput. & Geosci. 23, 387–395 (1997)
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Foody, G.M., Atkinson, P.M. (eds.): Uncertainty in Remote Sensing and GIS. Wiley, Chicester (2002)
Goodchild, M.F., Guoqing, S., Shiren, Y.: Development and test of an error model for categorical data. Int. J. Geogr. Inf. Syst. 6, 87–10 (1992)
Heuvelink, G.B.M.: Error Propagation in Environmental Modelling with GIS. Taylor & Francis, London (1998)
Heuvelink, G.B.M., Burrough, P.A. (eds.) Developments in Statistical Approaches to Spatial Uncertainty and its Propagation. Themed issue Int. J. Geogr. Inf. Sci. 16, number 2 (2002)
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Karssenberg, D., De Jong, K.: Dynamic environmental modelling in GIS. 2: Modelling error propagation. Int. J. Geogr. Inf. Sci. 19, 623–637 (2005)
Kros, J., Pebesma, E.J., Reinds, G.J., Finke, P.F.: Uncertainty assessment in modelling soil acidification at the European scale: a case study. J. Environ. Qual. 28, 366–377 (1999)
Kyriakidis, P.C., Dungan, J.L.: A geostatistical approach for mapping thematic classification accuracy and evaluating the impact of inaccurate spatial data on ecological model predictions. Environ. Ecol. Stat. 8, 311–330 (2001)
Longley, P.A., Goodchild, M.F., Maguire, D.J., Rhind, D.W.: Geographic Information Systems and Science, 2nd edn. Wiley, New York (2005)
Miehle, P., Livesley, S.J., C.S. Li, Feikema, P.M., Adams, M.A., Arndt, S.K.: Quantifying uncertainty from large-scale model predictions of forest carbon dynamics. Global Change Biol. 12, 1421–1434 (2006)
Shi, W., Liu, W.B.: A stochastic process-based model for the positional error of line segments in GIS. Int. J. Geogr. Inf. Sci. 14, 51–66 (2000)
Shi, W., Fisher, P.F., Goodchild, M.F. (eds.): Spatial Data Quality. Taylor & Francis, London (2002)
Strebelle, S.: Conditional simulation of complex geological structures using multiple-point statistics. Math. Geo. 34, 1–21 (2002)
Van Oijen, M., Rougier, J., Smith, R.: Bayesian calibration of process-based forest models: bridging the gap between models and data. Tree Physiol. 25, 915–927 (2005)
Wickham, J.D., Stehman, S.V., Smith, J.H., Yang, L.: Thematic accuracy of the 1992 National Land-Cover Data for the western United States. R. Sens. Environ. 91, 452–468 (2004)
Zhang, J.X., Goodchild, M.F.: Uncertainty in Geographic Information. Taylor & Francis, New York (2002)
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© 2008 Springer-Verlag
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Heuvelink, G., Brown, J. (2008). Uncertain Environmental Variables in GIS. In: Shekhar, S., Xiong, H. (eds) Encyclopedia of GIS. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-35973-1_1422
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DOI: https://doi.org/10.1007/978-0-387-35973-1_1422
Publisher Name: Springer, Boston, MA
Print ISBN: 978-0-387-30858-6
Online ISBN: 978-0-387-35973-1
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