# 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 Outlines
Current CSU students can view Subject Outlines for recent sessions. Please note that Subject Outlines and assessment tasks are updated each session.

No offerings have been identified for this subject in 2018.

Where differences exist between the Handbook and the SAL, the SAL should be taken as containing the correct subject offering details.

## Subject Information

HD/FL

One session

##### School

School 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

##### This 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.