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.
Rubber sheeting is a geometric adjustment that locally stretches, warps and bends a spatial layer so that a set of identifiable "from" locations is moved onto their true "to" positions on a reference dataset, removing local misalignment that a single global transformation cannot. It uses control (link) points and applies a piecewise or elastic transformation — commonly a triangulated (finite-element) warp or a spline — so that displacement is largest near the control points and blends smoothly between them, like stretching a rubber sheet. Conflation is the broader process of combining two overlapping datasets into a single improved dataset, aligning their geometry (often via rubber sheeting) and reconciling or transferring their attributes. Problems to consider when rubber sheeting: (1) the number, accuracy and especially the spatial distribution of control points — sparse or clustered control leaves areas poorly constrained and can introduce distortion; (2) preserving topology and avoiding self-intersections or slivers as geometry is stretched; (3) matching features correctly between datasets before warping (mis-paired links warp the map wrongly); (4) differential distortion and over-fitting, which can degrade areas that were already correct; and (5) reconciling attribute conflicts and edge effects where the adjusted layer meets unadjusted neighbours.
