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.
Spatial (geometric) uncertainty is doubt about where a feature is — error or vagueness in the coordinates, geometry and boundaries of features. It arises from measurement error, digitizing and generalization, datum/projection issues and inherently fuzzy boundaries, and is quantified by positional-accuracy statements such as an RMSE or a circular error (e.g., a GNSS point good only to ±5 m, or a soil boundary that is really a gradual transition drawn as a sharp line). Attribute (thematic) uncertainty is doubt about what a feature is — error or vagueness in the descriptive values assigned to features. It arises from measurement or classification error, out-of-date information, sampling and interpolation, and ambiguous class definitions, and for categorical data is summarised in a confusion (error) matrix (e.g., a pixel classified "forest" that is actually "shrub," or a parcel's assessed value that is stale). The key difference is the object of the doubt: spatial uncertainty concerns the position/shape of a feature, attribute uncertainty concerns the descriptive information attached to it. A feature may be positionally exact but thematically wrong, or correctly labelled but poorly located; both propagate through analysis and both must be reported, because either can invalidate a GIS decision.
