A FRAMEWORK FOR THE ASSESSMENT OF LOCAL SPATIAL UNCERTAINTY USING A POLYGON APPROACH 
   
R.C. Allan & G.P. Ellis 
RMIT Centre for Remote Sensing & GIS 
RMIT University 
124 Latrobe Street, Melbourne, Victoria, Australia 3000 
E-mail: rod.allan@rmit.edu.au 
Commission lll, Working Group III/IV 
KEY WORDS: Error, Classification, Accuracy, Recognition, Spatial Accuracy. 
ABSTRACT: 
For remotely sensed data to be effectively used in a GIS the user needs to know the reliability of the product. Typically, 
in the production of a thematic layer in a land resource data base, an overall accuracy assessment of the product is 
undertaken in which the user determines its fitness for use. However neither the magnitude of source errors at each 
stage of data handling nor the within spatial variability is known from this assessment. 
This paper proposes a methodology to elicit relative measures of error in the various stages of the data processing flow 
and the extent of local spatial variability in the input data layer by identifying and then measuring the error source in an 
iterative scheme. The process utilises overlays of independent realisations by image interpreters of the same scene to 
create polygons in disagreement between the interpreters. Geometric characteristics of these polygons are investigated 
to establish as to whether any changes in geometry are attributable to a particular source. Preliminary results from a 
case study are discussed. 
1. INTRODUCTION 
An important but complex issue, when using Geographic 
Information Systems (GIS) to integrate, analyse and 
display spatial data, is the definition and quantification of 
errors. With increasing emphasis placed on spatial 
information processing, data are being used for purposes 
they were never intended (Goodchild 1993). The resultant 
products often have no indication as to their suitability for 
use in the decision making process. 
With the integration of disparate data sources required 
for a GIS, error propagation and control throughout the 
processes are not readily understood nor easily imposed. 
1.1 Spatial Data Bases - A User's Perspective 
For many applications, the effective use of spatial data 
bases is dependent upon the data user who determines 
its fitness for use (Chrisman 1994). A data user's own 
perception of its worth for an application is based on 
some a priori knowledge of the user about GIS and about 
the data bases themselves (Coward & Heywood 1991). 
Spatial data bases can represent multiple versions of 
reality and the operation of a basic GIS function such as 
generalisation for example, creates a less representative 
version of reality. Indeed, for many users, these 
operations on the data base are necessary to achieve the 
required product. 
The lack of any detailed knowledge of the extent to which 
error is introduced and its magnitude, particularly at its 
source, is one of the impediments in understanding error 
propagation. Source errors enter in the data processing 
flow at various stages and, importantly, not solely at the 
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International Archives of Photogrammetry and Remote Sensing. Vol. XXXI, Part B3. Vienna 1996 
data acquisition stage. With land resource data bases, 
for example, the classification phase which includes the 
operator's interpretative skills and bias can be a 
significant source of error. 
1.2 Local and Boundary Errors 
The production of thematic maps through spatial data 
processing is primarily based upon the nominal 
categorisation of discrete classes with boundaries and, 
by implication, is representative of what exists in reality. 
In fact, the representation of contiguous classes 
(polygons) is “nothing more than a construct of 
cartographic convenience” (Trotter 1991). The 
interpretive techniques employed to delineate classes in 
the production of a thematic map are subjective. The 
degree of subjectivity is largely dependent on the 
heterogeneity of the image pixels, the scale of 
representation and the number of allocated classes. 
Cherrill and McClean (1995) found that, in interpreting 
land cover change, smaller class areas yielded less 
precise results. They also suggest that classification 
(attribute) error is more significant than positional error 
with land cover types. 
The present problems in using maps with fixed 
categorical attributes results in a binary (yes/no) 
response to a spatial query rather than a measure of the 
likelihood of a certain characteristic being at that location 
(Lowell 1992). Boundary representation between the 
classes, in this instance, would not be a cartographic line 
but rather a transition zone of width dependent on the 
spectral similarity of contiguous classes. However, 
Goodchild (1994) asserts that the ‘blurring’ of a boundary 
may give the user a false impression of the extent of 
  
   
  
    
   
    
    
  
    
  
  
    
   
    
     
  
   
    
    
  
    
  
  
    
  
    
  
    
     
  
  
   
   
    
   
    
    
   
  
    
     
   
    
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