Error, uncertainty, accuracy and precision are very important concepts in GIScience, but the issues associated with them are often overlooked.  This becomes very apparent when you discuss GISystems with the typical GIS user. The idea that there is uncertainty present in the data that could influence analysis outcomes– a result of the error and uncertainty in the source data and introduced by the processing of that data–is often mentioned only in passing in official reports. However, the issues that arise from uncertainty–error, accuracy and precision– are very important.  The quality of the decisions that can be made, based on any set of data, depend to a large extent upon the quality of the information that was used to make those decisions. This situation is not unique to GIScience, of course, but is highlighted when spatial data are collected, analyses conducted, and spatial output created. Since it is relatively easy to combine data from a wide range of sources, GIS analyses and presentations (e.g., a map) will often bring to light the poor quality of the information that people have been using over the years.

In order to know whether data are appropriate for an analysis, it is important to look at its metadata (data about data). Using metadata you can reduce the error and uncertainty in your analyses, since you’ll know about the source of the data, what modifications have been made to the data, etc.

When overlaying two layers obtained from different agencies you often end up with a situation such as that illustrated below (Question: are the polygon boundaries supposed to line up, or do they reflect actual differences in the soil types / land use polygons?):

The differences between the polygon boundaries are referred to as slivers:

The questions you need to ask–are the differences meaningful or simply the result of uncertainties associated with data collection (possibly collected at different dates, at different scales) and/or generalization (mandate issues)? What set of polygon boundaries should you accept as ‘real’, and which should you reject? Or, should you take the average of the two sets of polygons? Most often answers to these questions are left up to the person doing the work, since there are no formal guidelines available.