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# SPA409 Introductory Mathematics for Spatial Analysis (8)

### Abstract

 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

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.

### Syllabus

 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.

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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.