The level of adequacy that a GIS dataset has for its intended usage is called fitness for purpose and is fundamental in understanding and using good quality and suitable data. Since a lot of GIS projects use data procured for other purposes or are generically produced to be able to be used in a wide spectrum of projects it is important to understand the suitability of a data set for your specific requirements. To do this a variety of criteria need to be considered. Once the data has been gathered the actual GIS processes and analysis (data stream) to be carried out also need examining so error is minimized and the limitations of the data understood.
In order to provide a critique looking at how suitable a dataset is for a particular purpose a number of factors need to be considered. Firstly, and as is significant in all projects involving spatial data, is the issue of scale. It is important that the data used in a project has the spatial resolution to match the intended scale at which any analysis procedures are to be executed. The spatial resolution of the data is important as it affects accuracy. Walsh et al (1987) stated that “the accuracy of the data decreases as spatial resolution becomes more coarse.” Once this is known the challenge is to find data which can then match this project analysis scale within the confines of the projects budget and GIS architecture.
The second criterion to evaluate the fitness for purpose is related to the first and involves relationships between the scales of different datasets. What this refers to is the amount of harmony between the scales of the different datasets and is particularly important to understand as it is possible to interact with datasets of massive scale differences in a GIS resulting in possible error and inaccuracies.
The actual content of a dataset is also important particularly as it is usually secondary data which is handled. This means that the data used in a project won’t be collected specifically for the purpose you require. However, a lot of the time datasets are designed to be used for a multitude of purposes. For example, a conservationist may want to have a dataset showing area of outstanding natural beauty and so may a developer. In this case the one dataset supplied through the Magic geoportal would satisfy both purposes. Related to the content of the dataset and briefly mentioned is the issue of secondary and primary data. The advantage of collecting your own data is that you can vouch for its fitness, accuracy etc. Set against this is the extra costs, time and skills procurement which will all need to be factored in to make sure the data is not error strewn.
Currency is another criterion which is especially poignant for datasets of human activity. Depending on the nature of the project when wishing to perform analysis involving current human activity it is inherently beneficial to use the most current data available i.e. that most likely to reflect the real world. In some instances it is necessary to track changes in human activity over time in which case datasets with same information from different time epochs will be required. With datasets informing about facets of the earth’s surface the issue of currency is perhaps less important although should never be disregarded.
Understanding the amount of uncertainty which is associated with a particular dataset is also an important consideration with which to evaluate a dataset. While you may not be able to change the level of uncertainty in a given dataset if you are aware of associated issues such as vagueness, ambiguity and fuzziness within the data this can help to reduce potential error in the long term. Determining the error tolerances which is acceptable for each dataset is important in managing the errors throughout the project (Longley et al, 2005)
The success and integrity of a GIS based projects are hinged around the subjective issue of selecting the right data for the job. Developing a fitness for purpose criteria is vital in providing an even and structured platform with which this can done prior to a project commencing. As well as demonstrating a clear understanding of the data used in a GIS project having an insight into why certain datasets were selected over others allows for limitations in the project to be understood better. In addition GIS data and GIS processing of data (as detailed in the data stream) will never be perfect, given the complexity of the processes used to generate them. It is important to gain an insight into the types of errors present and how widespread they are. While error detection from GIS processes is difficult to determine failure to do so could result in significant problems and misinterpretation of generated results.
Wilbourn Technology are able to advise on all matters relating to GIS, data and GIS project management.