Quality and its Impact in GIS

The unique advantage of GIS is the capability to analyze and answer geo-spatial questions. If no geo-spatial data is available for a region, of course, it is not possible to use GIS. On the other hand, the validity of the analysis and quality of the answers in GIS are closely related to the quality of the geo-spatial data used and the quality of the embedded models and the external models. If poor quality or incomplete data were used, the query and analysis would provide poor or incomplete results. The same will happen if the quality of the models was poor.

Therefore, it is fundamental to know the quality of the information in a GIS and the quality of the models. Generally, the quality of the embedded models in commercial GIS is unknown. In many cases, a GIS user has no way to know how good the embedded models of the system are, which is problematic in GIS because perfect geo-spatial data used with poor-quality embedded models generates poor results and the user may not be aware of that.

From the viewpoint of data, quality is defined by the U.S. National Committee Digital Cartographic Data Standard (NCDCDS)(19) as ‘‘fitness for use.’’ This definition states that quality is a relative term: Data may be fit to use in a particular application but unfit for another. Therefore, we need to have a very good understanding of the scope of our application to judge the quality of the data to be used. The same committee identifies, in the Spatial Data Transfer Standard (SDTS), five quality components in the context of GIS: lineage, positional accuracy, attribute accuracy, logical consistency, and completeness.

SDTS is the U.S. Federal Information Processing Standard-173 and states ‘‘lineage is information about the sources and processing history of the data.’’ Positional accuracy is ‘‘the correctness of the spatial (geographic) location of features.’’ Attribute accuracy is ‘‘the correctness of semantic (non-positional) information ascribed to spatial (geographic) features.’’

Logical consistency is ‘‘the validity of relationships (especially topological ones) encoded in the data,’’ and completeness is ‘‘the mapping and selection rules and exhaustiveness of feature representation in the data.’’

The International Cartographic Association (ICA) has added two more quality components: semantic accuracy and temporal information. As indicated by Guptill and Morrison (20), ‘‘semantic accuracy describes the number of features, relationships, or attributes that have been correctly encoded in accordance with a set of feature representation rules.’’

Guptill and Morrison (20) also indicate ‘‘temporal information describes the date of observation, type of update (creation, modification, deletion, unchanged), and validity periods for spatial (geographic) data records.’’ Most of our understanding about the quality of geo-spatial information is limited to positional accuracy, specifically point positional accuracy. Schmidley (21) has conducted research in line positional accuracy. Research in attribute accuracy has been done mostly in the remote sensing area, and some in GIS (see Chapter 4 of Ref. 20). Very little research has been done in the other quality components (see Ref. 20).

To make the problem worse, because of limited digital vector geo-spatial coverage worldwide, GIS users combine, many times, different sets of geo-spatial information, each set of a different quality level. Most GIS commercial products have no tools to judge the quality of the data used; therefore, it is up to the GIS user to judge and keep track of information quality.

Another limitation of GIS technology today is the fact that GIS systems, including analysis and query tools, are sold as ‘‘black boxes.’’ The user provides the geo-spatial data, and the GIS system provides results. In many cases, the methods, algorithms, and implementation techniques are considered proprietary and there is no way for the user to judge their quality. More and more users are starting to recognize the importance of quality GIS data. As result, many experts are conducting research into the different aspects of GIS quality.

Quality of external models usually can be evaluated. Generally, the user knows in detail the external model to be used and can derive means to evaluate its quality. Models can be evaluated by comparing their results with data of higher quality. For example, a rain prediction model can be evaluated by comparing the predicted rain with the actual rain. If this comparison is done enough times, it is possible to have a good estimator of the quality of the model.