uncertainty in both Schemes for each of the stages 
in Figure 5. Note how there is an assessment for 
all polygons regardless of the Boolean outcome in 
Figure 5. Thus in Scheme A, doubt can be cast on 
the acceptance of unit 6 and in Scheme B doubt can 
be cast on both the acceptance of units 1-7 and the 
rejection of units 8 and 9 in the traditional 
binary form of the analysis. 
The software used in this small example is Genamap 
which provides lineage tables through the use and 
recording of active IDs. Table 4 shows the analysis 
lineage. This type of record would permit 
sensitivity analysis to quickly recompute the «E 
Scores for changing levels of initial uncertainty 
and hence identify what decrease in uncertainty is 
required to improve the end result. Thus for unit 6 
in Scheme A, the observer should collect additional 
data on category B in Layer 1, only in the area of 
unit 6, so that it can be improved to "moderately 
certain' or better from the observer's point of 
view. For Scheme B, category W in Layer 2 must also 
be resurveyed so that it is mapped in as 
"reasonably certain? or better. 
IMPLICATIONS FOR GEO-INFORMATION THEORY 
The model has a number of implications for geo- 
information theory. There is a distinct need to 
improve the ability of GISs to record natural 
variation and uncertainty in data, not at the 
generalised level of the coverage but at the level 
of the individual object or entity. Precise, crisp 
boundaries and totally homogeneous areal units are 
in fact a special case in our mosaic of natural 
environment. Why are we using a tool more suited to 
these special cases for all our spatial analysis? 
The point, line and polygon may be inappropraite 
primitives to record imprecisely bounded objects. 
To have proactive data, observers will need to 
exercise greater responsibility in recording levels 
of uncertainty and will have to develop strategies 
in order to do so. Yet the user must take 
responsibility for assessing fitness-for-use since 
it is the user who is most familiar with the 
prevailing context. Sensitivity analysis of data 
should become a standard routine. Data structures 
and database design will need to be modified in 
order to faciltate this. There is the potential for 
involving expert systems. Current relationships 
between user and data will undoubtedly change. 
Probably the greatest impact of the model is likely 
to be in the area of research. Ways of combining 
uncertainty measures will have to be sought. New 
measures may be developed that can be better 
embedded within the data. There are a number of 
candidate metrics that could be used for 
propagating uncertainty. These will need to be 
investigated, as will the mapping from source 
uncertainty measures. The propagation will need to 
be modelled for a wide range of data 
transformations. The mapping out process and 
visualization of uncertainty distributions is 
already a major concern (Beard et al. 1991). Most 
of all, the model provides a framework around which 
whole or partitial solutions to the problem of 
uncertainty in GIS can be built and tested. 
ACKNOWLEDGEMENTS 
This research has been supported by the Hong Kong 
Polytechnic. 
Genamap is a registered trademark of GENASYS TI 
Inc. Fort Collins, Colorado. 
REFERENCES 
American Society of Civil Engineers, 1983. Map 
Uses, Scales and Accuracies for Engineering and 
Associated Purposes. ASCE, New York, USA. 
American Society of Photogrammetry and Remote 
Sensing, 1985. Accuracy specifications for large 
Scale maps. Photogrammetric Engineering and 
Remote Sensing 51:195-199 
Beard, K., 1989. Use error: the neglected error 
component. Proceedings  AutoCarto 9, Maryland 
:808-817 
Beard, K.; Buttenfield, B.P. & Clapham, S.B., 1991. 
Visualization of spatial data quality. NCGIA 
Technical Paper 91-26, NCGIA, Santa Barbara, USA. 
Bedard, Y., 1987. Uncertainties in land information 
systems. Proceedings AutoCarto 8, Maryland :175- 
184 
Bethel, J.S. & Mikhail, E.M., 1983. On-line quality 
assessment in DTM. ASPRS/ACSM Annual Convention 
:576-584 
Blakemore, M., 1983. Generalisation and error in 
spatial data bases. Cartographica 21 :181-139 
Bureau of Budget, 1947. National Map Accuracy 
Standards, US Government Printing Office, 
Washington D.C., USA. 
Burrough, P.A., 1986. Principles of Geographical 
  
ID Entries 
Selection/Criteria 
  
  
  
  
  
  
Map/ID Selection Table Criteria 
1 12 Liz ATTRIBUTE | TAGV1 | BOUNDED A 
5 12 LiL2 ATTRIBUTE TAGV2 BOUNDED W 
10 25 LiL2 ATTRIBUTE | TAGV1 | BOUNDED $C 
11 25 L1L2 ATTRIBUTE | TAGV2 | BOUNDED #Y 
12 19 1, 65 |" BETS - OR 
13 18 10, 12 ] SETS - AND 
14 17 11, 13 | SETS - AND 
  
  
  
  
Table 4: Simplified extract of the Genamap ACTIVE ID list showing 
lineage of analysis depicted in Figure 5. 
766 
L