This subject provides an introduction to the basic mathematical and statistical techniques required for spatial analysis applications in GIS, image analysis and remote sensing.
No offerings have been identified for this subject in 2019.
HD/FL
One session
School of Computing and Mathematics
Must be enrolled in a Graduate Certificate, Graduate Diploma or Masters course
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.
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.
For further information about courses and subjects outlined in the CSU handbook please contact:
The information contained in the CSU Handbook was accurate at the date of publication: May 2019. The University reserves the right to vary the information at any time without notice.