Although the positional accuracy of spatial data has long been of fundamental importance in GIS, it is still largely unknown for linear features. This is compromising the ability of GIS practitioners to undertake accurate geographic analysis and hindering GIS in fulfilling its potential as a credible and reliable tool. As early as 1987 the US National Center for Geographic Information and Analysis identified accuracy as one of the key elements of successful GIS implementation. Yet two decades later, while there is a large body of geodetic literature addressing the positional accuracy of point features, there is little research addressing the positional accuracy of linear features, and still no accepted accuracy model for linear features. It has not helped that national map and data accuracy standards continue to define accuracy only in terms of “well-defined points”. This research aims to address these shortcomings by exploring the effect on linear feature positional accuracy of feature type, complexity, segment length, vertex proximity and e-scale, that is, the scale of the paper map from which the data were originally captured or to which they are customised for output. / The research begins with a review of the development of map and data accuracy standards, and a review of existing research into the positional accuracy of linear features. A geographically sensible error model for linear features using point matching is then developed and a case study undertaken. Features of five types, at five e-scales, are selected from commonly used, well-regarded Australian topographic datasets, and tailored for use in the case study. Wavelet techniques are used to classify the case study features into sections based on their complexity. Then, using the error model, half a million offsets and summary statistics are generated that shed light on the relationships between positional accuracy and e-scale, feature type, complexity, segment length, and vertex proximity. Finally, auto-regressive time series modelling and moving block bootstrap analysis are used to correct the summary statistics for correlation. / The main findings are as follows. First, metadata for the tested datasets significantly underestimates the positional accuracy of the data. Second, positional accuracy varies with e-scale but not, as might be expected, in a linear fashion. Third, positional accuracy varies with feature type, but not as the rules of generalisation suggest. Fourth, complex features lose accuracy faster than less complex features as e-scale is reduced. Fifth, the more complex a real-world feature, the worse its positional accuracy when mapped. Finally, accuracy mid-segment is greater than accuracy end-segment.
| Identifer | oai:union.ndltd.org:ADTP/245414 |
| Creators | Lawford, Geoffrey John |
| Source Sets | Australiasian Digital Theses Program |
| Language | English |
| Detected Language | English |
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