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.
Spatial analysis is the broad set of GIS operations that derive new information from the spatial relationships among features — it manipulates geometry and location to answer questions that could not be answered from the layers taken separately. Spatial statistics is the narrower, quantitative subset that measures and models spatial pattern, using methods that explicitly incorporate location, distance or adjacency (as opposed to classical statistics, which assume independent observations). Put simply, spatial analysis transforms and combines spatial data, while spatial statistics quantifies how features are distributed and whether a pattern is random, clustered or dispersed. Examples of spatial analysis: polygon overlay (intersect/union), buffering and proximity analysis, network routing and service areas, viewshed and watershed analysis, and map algebra on rasters. Examples of spatial statistics: the mean centre and standard-distance of a point pattern; nearest-neighbour analysis; Moran's I for spatial autocorrelation; Getis-Ord G* hot-spot analysis; and geostatistical interpolation such as kriging, which also returns a prediction variance. Spatial statistics often feeds spatial analysis: for example, a kriged surface (statistics) can be overlaid and buffered (analysis) to support a decision.
