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) TIN (Triangulated Irregular Network). A TIN is a vector data structure for representing a continuous surface — most often terrain — as a set of non-overlapping, contiguous triangles built from irregularly spaced sample points (mass points), with breaklines and boundaries honoured as constrained edges. Each sample point carries a $z$ value (e.g., elevation); the points are joined into triangles, and within each triangular facet the surface is modelled as a plane, so any $z$ can be interpolated. The triangulation is normally a Delaunay triangulation, which maximises the minimum interior angle (avoiding thin slivers) and is the dual of the Voronoi diagram of the points.
A TIN adapts its density to the terrain — many small triangles where relief is complex, few large ones where it is flat — so it captures ridges, valleys and breaklines efficiently, unlike a fixed-resolution DEM grid. (b) Entity-relationship (ER) data model. An ER model is a conceptual, implementation-independent way of describing the structure of a database in terms of entities (the things of interest — e.g., PARCEL, OWNER, ROAD), their attributes (properties of each entity — e.g., a parcel's PID and area), and the relationships among entities (associations such as "an OWNER owns a PARCEL"). Relationships carry a cardinality — one-to-one, one-to-many or many-to-many — that governs how the design is later implemented as related tables with primary and foreign keys. Drawn as an ER diagram (entities as boxes, relationships as diamonds/links, attributes as fields), it is the blueprint that precedes the logical (relational) and physical database design, ensuring the data is organized consistently and without redundancy before it is built.
