18-Geom-A7 Geospatial Information Systems · December 2018
Nivaar worked solution (AI-drafted; not reviewed by a licensed engineer)
Paper format. National Exams, December 2018 — 04-Geom-A7, Geospatial Information Systems. Closed book (one approved Casio or Sharp calculator permitted); duration 3 hours. Fifteen (15) questions are provided and any ten (10) constitute a complete paper; each question is of equal value (10 marks), so a complete paper totals 100 marks. Most answers are essay-format; clarity and organization count. All fifteen questions are solved below for completeness.
Longley, Goodchild, Maguire & Rhind, Geographic Information Systems and Science (4th ed.); P. Bolstad, GIS Fundamentals (5th ed.); Worboys & Duckham, GIS: A Computing Perspective (2nd ed.); Burrough, McDonnell & Lloyd, Principles of Geographical Information Systems (3rd ed.); de Smith, Goodchild & Longley, Geospatial Analysis; OGC Simple Feature Access (ISO 19125); ISO 19157 Geographic information — Data quality; ISO 19115 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) Spatial versus attribute uncertainty. Every geographic object in a GIS has two components — where it is (geometry) and what it is (attributes) — and each carries its own uncertainty. Spatial (geometric) uncertainty is uncertainty in the position, shape or extent of a feature: the coordinates of a surveyed corner known only to ±0.3 m, a digitized coastline that has been generalized, a parcel boundary whose true line is fuzzy. It is usually quantified as a positional error (horizontal/vertical RMSE, an error ellipse, or an ε-band around a line). Attribute uncertainty is uncertainty in the descriptive value attached to a feature that is otherwise correctly located: a land-cover pixel classified as "forest" that is really "shrub", an out-of-date zoning code, a measured soil pH with instrument error. It is quantified by classification accuracy (a confusion matrix, percent correctly classified) for categorical attributes, or measurement error for continuous ones. In short, spatial uncertainty asks "is it in the right place?" and attribute uncertainty asks "is the label/value correct?"; a feature can be perfectly located yet mis-classified, or correctly classified yet mis-placed.
(b) Metadata and its role. Metadata is "data about data" — structured documentation that describes a dataset's content, extent, coordinate reference system, source, lineage (how it was captured and processed), capture/publication dates, resolution/scale, completeness and, most importantly here, its quality. It is recorded to recognized standards such as ISO 19115 (metadata) with quality reported per ISO 19157. Its role in identifying uncertainty is that uncertainty is not visible in the geometry or the attribute table itself; the only place a user learns how good the data are is the metadata. The positional-accuracy element (e.g., "horizontal RMSE 2 m, mapped at 1:20 000") reveals the spatial uncertainty, while the thematic-accuracy element (e.g., "overall classification accuracy 85 %, see confusion matrix") reveals the attribute uncertainty; lineage and currency expose additional error sources. Metadata therefore lets a user judge fitness-for-use and propagate error before combining layers, rather than discovering the problem in the final map.
