No offerings have been identified for this subject in 2016

SPA409 Introductory Mathematics for Spatial Analysis (8)


This subject provides an introduction to the basic mathematical and statistical techniques required for spatial analysis applications in GIS, image analysis and remote sensing.

+ Subject Availability Modes and Location

Continuing students should consult the SAL for current offering details prior to contacting their course coordinator: SPA409
Where differences exist between the handbook and the SAL, the SAL should be taken as containing the correct subject offering details.

Subject information

Duration Grading System School:
One sessionHD/FLSchool of Computing and Mathematics

Enrolment restrictions

Must be enrolled in a Graduate Certificate, Graduate Diploma or Masters course

Learning Outcomes

Upon successful completion of this subject, students should:
Understand the mathematical and statistical concepts that underpin many procedures used in the fields of GIS, image processing and remote sensing;
Be able to apply mathematical and statistical techniques to assist in the solution of a range of probems in spatial analysis.


The subject will cover the following topics:
Introduction to differential calculus: historical background, functions, limits, slope of a tangent, derivative, stationary points, applications of differentiation; Introduction to integral calculus: indefinite integrals, approximate areas, definite integrals, applications of integration; Introduction to vectors and matrices: elementary matrix operations, determinant, inverse, solutions of systems of linear equations; Introduction to linear algebra: linear maps, eigenvectors, eigenvalues, diagonalization of a matrix; Simple probability concepts: complement, addition rule, product rule; Representation of data - histogram, stem-and-leaf plot, boxplot, scatterplot; Descriptive statistics: mean, standard deviation, variance, covariance, correlation and standardised values; The normal distribution with applications; Other distributions including chi-square, F and multivariate normal distributions; Introduction to regression: simple linear regression.


The information contained in the 2016 CSU Handbook was accurate at the date of publication: 06 September 2016. The University reserves the right to vary the information at any time without notice.