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.
Both concepts measure statistical association, but they differ in how many variables are involved. Spatial autocorrelation describes the correlation of a single variable with itself across space: it measures the degree to which the value at one location resembles the values at nearby locations. Positive autocorrelation (the common case, expressing Tobler's First Law of Geography — "near things are more related than distant things") means high values cluster near high and low near low; negative autocorrelation means neighbours tend to be dissimilar (a checkerboard); zero means the pattern is spatially random. It is quantified by indices such as Moran's I and Geary's C, computed with a spatial-weights (neighbour) matrix. Spatial correlation (spatial cross-correlation) describes the relationship between two different variables measured over space — for example, whether elevation and mean temperature co-vary geographically, or whether soil moisture and vegetation vigour track together. The essential distinction is therefore: autocorrelation asks "does this one variable depend on its own values at neighbouring locations?", whereas spatial correlation asks "do these two distinct variables vary together across space?". Autocorrelation is a property that must be respected before applying many statistical tests, because it violates the independence assumption.
