18-Geom-A7 Geospatial Information Systems · May 2014
Nivaar worked solution (AI-drafted; not reviewed by a licensed engineer)
National Exams — May 2014 — 04-Geom-A7 Geospatial Information Systems. Closed-book; no calculator permitted. Format: twenty-three short-answer questions of equal value (5 marks each); a candidate answers any twenty, but all twenty-three 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); 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 (positional) uncertainty is the doubt about where a feature actually is — the error or vagueness in the coordinates and geometry of features. It arises from measurement error, digitizing and generalization error, datum/projection issues, and the inherent fuzziness of some boundaries, and it is usually quantified by a positional accuracy statement such as an RMSE or a circular error. Attribute (thematic) uncertainty is the doubt about what a feature is — the error or vagueness in the descriptive values assigned to features. It arises from measurement or classification error, outdated information, sampling and interpolation, and ambiguous definitions, and for categorical data is often summarised in a confusion (error) matrix. Examples of spatial uncertainty: a GNSS-collected point good to only ±5 m; a coastline whose position shifts with the tide; a soil-polygon boundary that is really a gradual transition digitized as a sharp line. Examples of attribute uncertainty: a land-cover pixel classified "forest" when it is actually "shrub"; a parcel's assessed value that is out of date; an interpolated pollutant concentration whose estimate carries a prediction error. Both forms must be tracked because they propagate through analysis and affect the confidence of any GIS decision.
