18-Geom-A7 Geospatial Information Systems · May 2017
Nivaar worked solution (AI-drafted; not reviewed by a licensed engineer)
National Exams — May 2017 — 04-Geom-A7 Geospatial Information Systems. Closed-book; any non-communicating calculator permitted. Format: fifteen questions of varied value totalling 100 marks; fifteen questions constitute a complete paper and all fifteen are solved in full below. Most answers are required in essay form. 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); H. Samet, The Design and Analysis of Spatial Data Structures (Addison-Wesley, 1990); 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.
Data quality is described by a standard set of "elements" (ISO 19157/19113) that let a user judge fitness for use. At least five commonly used criteria are:
(1) Positional (spatial) accuracy — how close feature coordinates are to their true real-world positions, usually reported as a horizontal/vertical RMSE or a circular error; it governs whether layers register correctly. (2) Attribute (thematic) accuracy — how correct the descriptive values are (e.g., a land-cover class or a pipe diameter); for categorical data it is summarised in a confusion (error) matrix and an overall/kappa accuracy. (3) Logical consistency — the degree to which the data obey structural and topological rules: no dangling arcs, no sliver polygons, closed boundaries, valid domain values and referential integrity. (4) Completeness — whether all features that should be present are present and none are duplicated or extraneous (errors of omission and commission), assessed against the feature-capture specification. (5) Lineage (provenance) — the source, processing history, methods and dates by which the dataset was produced, so its reliability can be traced. (6) Temporal accuracy / currency — how up to date the data are and the validity of their timestamps. (7) Resolution / scale — the source scale or cell size, which sets the smallest feature that is reliably captured. Together these criteria are recorded in the dataset's metadata.
