18-Geom-A7 Geospatial Information Systems · December 2015
Nivaar worked solution (AI-drafted; not reviewed by a licensed engineer)
National Exams — December 2015 — 04-Geom-A7 Geospatial Information Systems. Closed-book; an approved Casio or Sharp calculator is permitted. Format: twelve short-answer questions of varying value totalling 100 marks; all questions constitute a complete exam and are solved in full below. Datum and coordinate conventions follow the Canadian spatial reference framework — NAD83(CSRS) horizontally and CGVD2013 vertically.
Reference texts: P. A. Longley, M. F. Goodchild, D. J. Maguire & D. W. Rhind, Geographic Information Systems and Science (4th ed., Wiley, 2015); P. Bolstad, GIS Fundamentals: A First Text on Geographic Information Systems (6th ed., XanEdu, 2019); P. A. Burrough, R. A. McDonnell & C. D. Lloyd, Principles of Geographical Information Systems (3rd ed., Oxford, 2015); M. Worboys & M. Duckham, GIS: A Computing Perspective (2nd ed., CRC, 2004); J. P. Snyder, Map Projections — A Working Manual (USGS PP 1395); ISO 19115 Geographic information — Metadata.
Question text not reproduced: the examination questions are © Engineers and Geoscientists BC. Open the official past paper (linked at the top of this page) to read the question, then follow the worked solution below.
(a) Representing data uncertainty. Uncertainty is captured and made usable in several complementary ways. It is documented in metadata as quality statements to a standard such as ISO 19115 — positional accuracy (RMSE, circular error), attribute/thematic accuracy, completeness, logical consistency and temporal accuracy. It is quantified for positional error by an RMSE or an error ellipse/buffer around features, and for categorical attributes by a confusion (error) matrix with overall and per-class accuracy and the kappa statistic. It can be stored as an attribute on each feature (a confidence code, a ± value, a data-quality field), visualized (error bars, transparency/fuzziness, reliability diagrams, probability surfaces), and modelled/propagated through analysis by error-propagation formulae or Monte-Carlo simulation so that the uncertainty of a derived product is known. In short, uncertainty is represented as documented quality metrics, per-feature confidence values, statistical summaries and visual/analytical error models. (b) Lineage. Lineage is the recorded history and provenance of a dataset — its sources, the dates of capture, the processing steps, transformations, edits and the responsible parties that produced its current state. It matters because it lets a user judge fitness for use: knowing whether a layer came from a 1:50 000 map, a GNSS survey or a satellite classification, and what was done to it, is essential to trusting it. Lineage supports reproducibility and auditing (the processing chain can be reviewed or repeated), error tracing (a defect can be tracked to the step that introduced it), currency assessment (how up-to-date the data is), and legal/liability defensibility for cadastral, environmental and engineering decisions. Without lineage, a dataset's reliability is undocumented and its quality-control chain is broken, so lineage is a mandatory element of GIS metadata.
