Dates: 8-12 May 2017

Location: Computer lab, room 314, 3rd floor, School of Earth Sciences, University of Melbourne

Time: Blended lectures and practical exercises from 9:30am till 5pm each day

The abundance of digital spatial data coupled with the development of technologies like Geographical Information Systems (GIS) has changed the way in which information about spatial phenomena is collected, managed, analysed and depicted. A GIS is not simply a computer system for making maps; although it can readily and very effectively accomplish this. The main difference between a GIS and computer mapping or drafting systems is that a GIS enable analysis of complex spatial interrelationships that exist between phenomena in the real world as well as their non-spatial attributes. This allows us to go beyond making static digital maps from digital data, by providing a technology with the capacity to answer questions that relate to what objects are, where they occur, and how they relate to each other.

For example, a digital map can depict the magnitude and distribution of earthquakes relative to major faults and the earths topography. A GIS can also do this, and it can be used to answer questions about the various phenomena shown on the map. How many quakes of a chosen minimum magnitude occur within a specified area and specified time period? Is there a correlation between the density of quakes and faults in a particular orientation? What is the relationship between the topographic gradient and elevation within a chosen area? Are Cu anomalies in a sediment geochemistry survey correlated with a particular lithology, regolith type or structure, and if so, where do these phenomena occur together?

This course will introduce the concept of a GIS as a problem solving technology within the geosciences, and through hands-on practical classes and lectures will provide the basic hands-on skills needed to design and implement a GIS project. Specific topics will include map projections and georeferencing, distortions in image data, raster and vector data models, incorporating digital terrain models and geophysical data, introduction to boolean logic and functions, data accuracy and access issues and limitations of GIS. The course will include examination of case histories of GIS projects and students will also build a GIS project of their own to solve a simulated exploration problem using QGIS and real world data sets.

If you have your own data for your research projects please bring it along to the course. We will schedule some time during the week to discuss and assist you with your own GIS projects.

Special Requirements: None; however basic computer skills and some knowledge of statistics would be an advantage. 

For further course and assessment information please contact Robin Armit

University of Melbourne course information can be found at:

Location: University of Melbourne

Venue: Please see the handbook entry (timetable) for lecture and workshop locations

This subject introduces the fundamental concepts of computing programming and how to solve simple problems using the high-level procedural languate Python, with a specific emphasis on data manipulation, transformation and the visualisation of scientific data.

Fundamental programming constructs; fundamental data structures; abstraction; basic program structures; algorithmic problem solving, solving differential equations; use of modules.

The subject assumes no prior knowledge of computer programming.

This course will run from Monday 13th February to the 26th of February and will be held at The University of Melbourne, School of Earth Sciences, McCoy building in the 3rd floor computing lab.

The course material and computational resources will be available online. You will be given the required passwords and links when you arrive. You are welcome to bring your own laptop but you will need to arrange your own access to the University of Melbourne wireless network (or eduroam). 

Further course and assessment information can be found at:

For information on this course, please contact Tim Miller.

For information on how this course fits with PYE and GEO, please contact Louis Moresi.

Location:  Parkville - on campus

This course builds upon a basic knowledge of python (see Introduction to Python - INP) to develop key expertise in scientific applications of python, particularly for the Earth sciences. We will do all of our work within the literate programming environment of jupyter (formerly ipython) notebooks.

We will introduce/review the 'standard' scientific python toolkits such as numpy, scipy, matplotlib, pandas. We will teach you how to manipulate and transform data in simple ways, plotting, mapping, visualisation, interpolation, gridding, function fitting, and exporting data / images into common, interchangeable data formats such as hdf5 and netcdf, geotiff

We will learn how to orchestrate common earth science python software applications including plate reconstruction (pygplates), seismic data set acquisition and analysis (obspy), meshing and interpolation (stripy).

We will learn how to solve very simple differential equations with application to geothermal energy and ground water flow, statistical analysis of data sets, online data repository.


We are going to make extensive use of jupyter notebooks. If you haven't used these before, it will be helpful to watch this tutorial on the benefits of notebooks:  .

You do not need to come along to the course knowing how to use the notebooks, but you should understand why this is a great environment for learning and a springboard to your use of python in the future. 

This article introduces numpy and scipy for users of python [Read the first 5 pages and skim the rest if you wish as this is more detail than you need]

Oliphant, T. E. (2007), Python for scientific computing, Computing In Science and Engineering, 9(3), 10–20, doi:10.1109/MCSE.2007.58. 

We'll be using matplot lib for graphs and cartopy for maps. Please take a look at the gallery for each one and take note if there are any examples that particularly relate to your work or interests: matplotlib and cartopy.

For further information, please see