By Frederick E. Petry, Vincent B. Robinson, Maria A. Cobb
This booklet specializes in examine advances in methods for incorporating particular dealing with of uncertainty, particularly by way of fuzzy units, to handle geographic problems.Over the prior numerous years curiosity within the use of fuzzy units ways has grown throughout a wide spectrum of fields that use spatial details to handle geographic difficulties. not just has there been significant study on incorporating fuzzy units within the geographic info structures (GIS) helping geographic challenge fixing, yet more and more the uncertainties inherent in modeling geographic difficulties were addressed utilizing smooth computing methods.This booklet has goals. One is to stimulate additional learn in either the speculation and alertness of fuzzy units to spatial details administration and geographic challenge fixing. the opposite is to spotlight the advances in examine that have matured to the purpose that we discover fuzzy modeling getting used by way of geoscientists, machine scientists, geographers, ecologists, engineers, and others. The ebook contains examples of using fuzzy units in representational concerns reminiscent of terrain positive aspects, panorama morphology, spatial extents and methods for spatial interpolation. a number of functions utilizing fuzzy units are coated together with info mining, spatial determination making, ecological simulation, and reliability in GIS.
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Extra info for Fuzzy Modeling with Spatial Information for Geographic Problems
Sample text
Fuzzy Sets and Systems 113(1) Cohn AG (1995) The Challenge of Qualitative Spatial Reasoning. Computing Surveys 27(3):323-327 Cohn AG, Bennett B, Gooday J, Gotts NM (1997) Representing and Reasoning with Qualitative Spatial Relations about Regions. In: Stock O (ed) Spatial and Temporal Reasoning. Kluwer, pp 97-134 Dutta S (1991) Approximate Spatial Reasoning: Integrating Qualitative and Quantitative Constraints. International Journal of Approximate Reasoning 5:307-331 Freeman J (1975) The Modeling of Spatial Relations.
The question, therefore, is how to define Fr. Let us describe a very simple idea. Consider two segments I and J on an oriented line. We have either IrJ or (IrJ). The first case can be rewritten r(I,J)=1 and the second case r(I,J)=0. Now, assume the oriented line is 'T(v) and I and J are the longitudinal sections AT(v) and BT(v). m Ii. n Jj. , Fr (T,AT(v),BT(v)=1) if and only if there exist two segments Ii and Jj such that r(Ii,Jj)=1 (and r(I,J)=0 otherwise). The idea, obviously, is not very satisfactory.
16(c)). The topological relationships along direction T (on the X-axis) are described by the vector composed of the thirteen FAr B(T) values (on the Y-axis). Usually, most of these values are zero. 15. It tells us to what extent the objects are involved in some spatial relationships along T. 16(d)). Figs. 16 and 17 show two object pairs and the corresponding diagrams. 17(c) illustrate well the symmetric nature of the histograms. For any T, we have: AB oi and F FA>B (T) = FA