domain, are of the correct data class and fall 
within predefined ranges. Although these types of 
tests can detect blunders, their main aim is 
ensuring logical consistency of the data rather 
than improving accuracy. 
Metadata is ‘data about data’ (Medyckyj-Scott et 
al., 1991). Coming under the umbrella of metadata 
are general descriptions of the data, provenance 
lineage reports, specifications of Source materials 
and the data dictionary. Its purpose is to allow 
potential users to assess the suitability of the 
data for a specific task. Metadata is therefore an 
integral component of data standards. The proposed 
standard for Digital Cartographic Data Quality 
(Moellering, 1988) requires that measures of 
accuracy (positional and attribute), consistency, 
completeness and the data's lineage be recorded. 
Whilst such information is clearly desirable, the 
true place for quality information is embedded with 
the data it pertains to. Only in this way can data 
be proactive about its uncertainty. 
FURTHER PERSPECTIVES ON POSSIBLE SOLUTIONS 
The above short review, though somewhat selective, 
indicates that many issues are outstanding, the 
most fundamental of which is a theoretical basis 
from which to implement the means to record, 
propagate and visualize uncertainty in GIS. Before 
presenting a general model proposed to fulfill this 
function, additional perspectives on the solution 
to be sought are discussed. 
As commented above, much of the concern has been 
with testing accuracy and eradicating error. This 
assumes there is 'truth' against which to measure, 
that is the existence of a binary right or wrong. 
Most spatial data does not fall into mutually 
exclusive sets with exact boundaries. Soils and 
other geomorphic data are typical. More research 
could be fruitfully expended on how to preserve 
natural variation and fuzzy boundaries in GIS 
rather than the current practice of making all data 
fit a crisp representation in a digital database. 
Existing techniques of using confusion matrices are 
inapplicable to aerial photographic interpretive 
data such as flood susceptibility or slope 
instability, as the chances of being able to sample 
flooding events or landsliding may be short-lived 
or rare and in any case hazardous. Using a panel of 
experts to review all such interpretive data may 
actually increase the uncertainty if a consensus 
cannot be reached! 
Recourse to check surveys of higher accuracy may 
not be the answer either for this type of data. For 
each inexact phenomenon there is a characteristic 
resolution (linked to scales of space and time) at 
which confidence in identification and delineation 
is at a maximum (Davis et al., 1991). Trying to 
survey at a different resolution will only increase 
uncertainty. The object ‘village’ breaks down into 
buildings and sub-landuses in one direction making 
delineation more difficult and gradually reduces to 
a dot in the other. In a similar way, increasing 
the number of classes to account for variation may 
not be helpful at the analytical stage. 
Aggregate measures of quality produced by existing 
testing schemes are not very meaningful as they say 
nothing about variability of error in space. For 
large coverages compiled possibly by several 
individuals, such variability is likely to be an 
important component in decision making from 
derivative maps. 
762 
Who should hold responsibility for the data? A 
commonly held view is that users are at the mercy 
of someone else's data collection. Inadequate 
metadata or measures of quality leave the user 
uncertain as to the fitness-for-use of the data. 
Even with quality measures present, there is no 
saying how different users may interpret them. The 
reverse side of the coin is that data collectors 
have little control over misuse of their data once 
it is in the GIS (Beard, 1989). 
Clearly some quality data is required, but how 
much? With complex measures of different dimensions 
of the data, the database may become 
disproportionately loaded with quality data. After 
all, given the nature of most GIS data the level of 
uncertainty in any uncertainty measures is likely 
to remain high. Spurious accuracy in quality data 
should be avoided and a more general approach 
adopted. An informed user should not be committing 
large resources from decisions based only on maps. 
Such maps should provide information on likely 
Sites or appropriate scenarios so that limited 
funds for detailed studies can be deployed with 
maximum benefit. Such a user requires guidelines as 
to where potentially suitable sites carry a high 
risk of abortive work and where potentially 
unsuitable sites may be usefully explored. In this 
situation the data must be proactive about its 
uncertainty over every part of the map, must be 
capable of propagation and can be interpreted 
within the specific context. 
A GENERAL MODEL FOR HANDLING UNCERTAINTY AND 
FITNESS-FOR-USE IN GIS 
A general model for handling uncertainty in GIS is 
presented in Figure 2. This model is designed with 
regard to the issues discussed in the previous 
sections and will hopefully provide a focus and 
coordinating principle for future research as 
discussed in the final section. 
The overall structure is based on a communication 
model (Bedard, 1987). It recognizes that data 
collection is carried out within a specific context 
and yet observers will have their own view of the 
real world (W). Observers will either generate 
uncertainty measures (if appropriate for the data) 
or at worst be able to verbalize their uncertainty 
about a number of dimensions of the data. This 
information about the uncertainty in the data may 
be global or pertaining to individual objects or 
entities. It is further recognized that a different 
metric (or choice of metrics) may be used 
internally in the GIS to propagate uncertainty 
during data analysis. There must then be a mapping 
from the observers stated uncertainty to the 
propagation metric. This allows flexibility in 
collecting uncertainty measures at the observation 
stage, provided a suitable mapping into the 
internal metric can be found, whilst remembering 
that high levels of precision in such 
transformation may be spurious. 
Following propagation the resulting metric and its 
spatial distribution may not be easily intelligible 
to the user. A second mapping is therefore required 
So that any visualization fits the user's real 
world model in the context of the specific task. 
The user can then assess fitness for use and take 
responsibility for the data. Sensitivity analysis 
is possible if the lineage of analysis is stored 
and on the basis of the results, issue requirements 
for improved data. This can relate to a specific 
portion of a coverage if the distribution of 
uncertainty is known. 
TI 
e 
VE 
re