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Uncertain Environmental Variables in GIS

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Encyclopedia of GIS

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...

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Author information

Authors and Affiliations

  1. Environmental Sciences Group, Wageningen University, Wageningen, The Netherlands

    Gerard B.M. Heuvelink

  2. National Weather Service, National Oceanic and Atmospheric Administration, Silver Spring, MD, USA

    James D. Brown

Authors
  1. Gerard B.M. Heuvelink
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  2. James D. Brown
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Editor information

Editors and Affiliations

  1. Faculty of Computer Science and Engineering, University of Minnesota, Minneapolis, MN, 55455, USA

    Shashi Shekhar (McKnight Distinguished University Professor) (McKnight Distinguished University Professor)

  2. Management Science and Information Systems Department, Rutgers Business School Newark and New Brunswick Rutgers, the State University of New Jersey, Newark, NJ, 07102, USA

    Hui Xiong (Assistant Professor) (Assistant Professor)

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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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