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The usefulness of any geographic data depends on its fitness for a particular purpose. The critical measure of that fitness is referred to as data quality. Data quality may be expressed in terms of several indicators such as attributes, temporal or positional accuracies. In this research, positional accuracy assessment was carried out on two datasets using Root Mean Square Error (RMSE) technique. Coordinates of nineteen ground controls points were measured in the field using Differential Global Positioning System technique which served as a reference base. The coordinates of these points were compared with their corresponding positions extracted from the two datasets, Town Sheet (1: 2500) and orthophoto (1: 5000). The Town Sheet was scanned, rescaled (1:5000) and georeferenced in Ghana Meter Grid coordinate system to conform to the orthophoto. The digitised Town Sheet and the reference base were superimposed with the orthophoto serving as backdrop in GIS environment. Positional error of 1.23 m was obtained for points extracted from the Town Sheet, while an error of 2.79 m was registered for points from the orthophoto. Shoreline features extracted from these two datasets and appended for shoreline change analysis recorded a total positional error of 3.98 m. The study has shown that the original scale (large) of the Town Sheet may have contributed significantly to the quality of data extracted. In the orthophoto, though geometrically rectified, the scale representation of a unit measure on the photo explains the uncertainties in the dataset. The integrated dataset obviously bore the cumulative effect of the input datasets. It is concluded that for the purpose of shoreline change analysis, such as shoreline change trends, large scale data sources should be used where possible for accurate decision-making. It is recommended that the positional accuracy of any spatial data be ascertained before using it to support decision.

Positional Accuracy Assessment for Effective Shoreline Change Analyses