Abstract
In this subject spatial scientists are introduced to solving complex spatial problems using programming techniques. Two open source programming languages, R and Python, are introduced to develop a life-long learning capability in spatial programming. Open source spatial analysis software is also explored to provide the student with a more comprehensive … For more content click the Read More button below.
Syllabus
fundamentals of spatial analysis using the R programming language; fundamentals of spatial analysis using the Python programming language; open source spatial analysis software and its integration with R and Python; and application of programming techniques to spatial modelling problems.
Learning outcomes
Upon successful completion of this subject, students should:
1.
be able to describe and compare open source spatial software;
2.
be able to utilise open source spatial software to carry out spatial analysis and modelling;
3.
be able to apply programming skills to provide spatial analysis solutions; and
4.
be able to design, construct and implement programming techniques to solve a spatial modelling problem.
Assumed knowledge
Intermediate level knowledge of Geographic Information Science, equivalent to SPA432 or SPA308, and fundamentalknowledge of remote sensing, equivalent to SPA441 or SPA217, are assumed.
Learning resources
Additional resources required by students
Spatial information software is used in this subject, software is supplied.