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.
The two operations act on opposite problems. Map generalization reduces the detail and complexity of existing features so that data captured at one scale can be shown clearly at a smaller scale, keeping the map legible and meaningful. It applies operators such as simplification (dropping vertices), smoothing, aggregation, selection/elimination, collapse (an area to a point or line), and displacement (nudging features apart so they remain distinguishable). It works on data you already have, and it necessarily discards information. Spatial interpolation, by contrast, creates new information: from a set of known sample values at discrete locations it estimates values at the unsampled locations between them, producing a continuous surface. Methods include inverse-distance weighting, spline, Thiessen (nearest-neighbour) and kriging. So generalization simplifies and abstracts existing geometry to suit a display scale (fewer details), whereas interpolation predicts unknown values to fill gaps and build a surface (more coverage). Generalization is chiefly a cartographic/data-management task tied to scale, while interpolation is an analytical/geostatistical task tied to prediction and comes with its own estimation uncertainty.
