Unit Outline

This unit focuses on advanced aspects of spatial data analysis, including practical aspects of developing software scripts for GIS analysis and customisation. This unit will start with a field excursion where you will collect your own point sample data with GNSS, which you will analyse in the unit's computer practicals. The lecture and practicals will cover exploratory spatial data analysis (ESDA), interpolation techniques and terrain modelling, geostatistics, and error propagation modelling. The second part of the unit focuses on GIS application development using the Python programming language. These skills enable you to develop new algorithms and tools, automate tasks, and build custom GIS solutions. This unit will provide you with highly valued skills in the spatial industry.

This unit is available to students face-to-face and online. Online students are expected to participate in live, online workshops each week, which are delivered synchronously/simultaneously with face-to-face students. The laboratory practicals will also allow for interaction with the practical supervisor via Zoom and the introduction will be recorded for those who wish to undertake the practicals independently.

In week 2 there is a field day at Home Hill Winery mapping a landslide.

No prior knowledge of programming or scripting is required for this unit.

Intended Learning Outcomes

As per the Assessment and Results Policy 1.3, your results will reflect your achievement against specified learning outcomes.

On completion of this unit, you will be able to:


1	Evaluate and apply techniques in GNSS data sampling, exploratory spatial data analysis, interpolation, and geostatistics to produce continuous raster surfaces from point sample data
2	Create and document Python scripts that implement spatial data input/output methods, spatial data structures, and spatial analysis algorithms
3	Operate geographic information system (GIS) software and implement Python code to solve complex problems and generate enhanced spatial information from vector, raster, and non-spatial datasets
4	Document and evaluate a spatial analysis workflow (including coding) assisted by cartographic maps and other visual means to communicate analysis results

Requisites


REQUISITE TYPE	REQUISITES
Pre-requisite	KGG540

Alterations as a result of student feedback

This unit has improved the field day and the assessments have been improved to ensure they cater to the all levels of prior scripting experience.

How will I be Assessed?

For more detailed assessment information please see MyLO.

Assessment schedule


ASSESSMENT TASK #	ASSESSMENT TASK NAME	DATE DUE	WEIGHT	LINKS TO INTENDED LEARNING OUTCOMES
ASSESSMENT TASK 1:	Assignment 1: Interpolation & Geostatistics	Week 4	35 %	LO1, LO3, LO4
ASSESSMENT TASK 2:	Assignment 2: Introductory Python exercises in QGIS	Week 9	20 %	LO2, LO3, LO4
ASSESSMENT TASK 3:	Assignment 3: Spatial data handling and analysis in Python	Week 13	40 %	LO2, LO3, LO4
ASSESSMENT TASK 4:	Assignment 4: Python and GIS colour ramps	Week 14	5 %	LO2, LO4

Assessment details

Assessment Task 1: Assignment 1: Interpolation & Geostatistics

TASK DESCRIPTION:

This assignment involves deriving terrain surfaces using ArcPro Geostatistical Analyst. You will collect GNSS data on a field excursion of the HomeHill landslide. The collected data will be used throughout the assignment. Assignment 1 consistes of three parts. Part A will focus on data import, cleaning, and exploration. Part B will focus on deterministic interpolation techniques and (cross-)validation of interpolation results. Part C will focus on geostatistical interpolation techniques and change detection of landslide height surfaces.

TASK LENGTH:

2500 - 3000 words Submissions in week 4, 5, and 7

DUE DATE:

Week 4

WEIGHT:

35 %

CRITERION #

CRITERION

MEASURES INTENDED LEARNING OUTCOME


1	Apply exploratory spatial data analysis techniques to identify outliers including justification of the choice of technique and interpretation of statistics	LO1, LO3, LO4
2	Apply deterministic and geostatistical interpolation techniques, including detailed justification of settings	LO1, LO3, LO4
3	Produce visual and cartographic output to document the spatial analysis workflow and final results	LO1, LO4

Assessment Task 2: Assignment 2: Introductory Python exercises in QGIS

TASK DESCRIPTION:

In this assignment, you will learn to work with Python in QGIS. You will read a vector point file (Shapefile) that is a validation point layer of the Home Hill dataset containing validation height measurements from the static GNSS observations and predicted height measurements from a kriging interpolation. In the assignment you will be reading the attribute values of the validation points and calculate several summary statistics. This assignment covers a range of foundational Python coding tasks that you will encounter when programming in Python. The assignment also covers the basics of working with spatial vector data in QGIS with Python. You are expected to complete the DataCamp Python tutorials as you work through this assignment. Also, the lectures will provide you with key examples and practical skills to address the tasks in this assignment.

TASK LENGTH:

Five Python coding exercises

DUE DATE:

Week 9

WEIGHT:

20 %

CRITERION #

CRITERION

MEASURES INTENDED LEARNING OUTCOME


1	Implementation of Python code to address spatial data handling problem	LO2, LO3
2	Import and manipulation of spatial data in appropriate Python data structures	LO2
3	Production of high-quality plots in Python	LO4
4	Explain implementation using comments in Python code to ensure reusability of code.	LO2, LO4

Assessment Task 3: Assignment 3: Spatial data handling and analysis in Python

TASK DESCRIPTION:

Reading/writing spatial datasets in Python (GNSS track log and raster datasets), manipulation of spatial data, implementation of algorithms, plotting of results, and output of results to spatial data files.

TASK LENGTH:

Five coding exercises in Python

DUE DATE:

Week 13

WEIGHT:

40 %

CRITERION #

CRITERION

MEASURES INTENDED LEARNING OUTCOME


1	Implementation of Python code to read spatial datasets	LO2, LO3
2	Implementation of Python code to manipulate spatial data structure in Python and implement spatial analysis algorithms	LO2, LO3
3	Explain implementation using comments in Python code to ensure reusability of code	LO3, LO4
4	Structure Python code into functions	LO2, LO3, LO4
5	High-quality plots and maps produced in Python code	LO2, LO3, LO4

Assessment Task 4: Assignment 4: Python and GIS colour ramps

TASK DESCRIPTION:

The aim of this assignment is to become familiar with the various colour maps available within the Matplotlib Python programming language module and demonstrate understanding of appropriate choices of colour maps for visualisation of data sets.

TASK LENGTH:

Python script and 500 word report

DUE DATE:

Week 14

WEIGHT:

5 %

CRITERION #

CRITERION

MEASURES INTENDED LEARNING OUTCOME


1	Implementation of Python code to visualise spatial raster layers in a range of scientific colour maps	LO2, LO4
2	Explain code implementation using comments in Python code to ensure reusability of code	LO2, LO4
3	Explanation of the choice of colour map for different types of raster attributes	LO4

How your final result is determined

To pass this unit, you need to demonstrate your attainment of each of the Intended Learning Outcomes, achieve a final unit grade of 50% or greater.

Submission of assignments

Where practicable, assignments should be submitted to an assignment submission folder in MYLO. You must submit assignments by the due date or receive a penalty (unless an extension of time has been approved by the Unit Coordinator). Students submitting any assignment in hard copy, or because of a practicum finalisation, must attach a student cover sheet and signed declaration for the submission to be accepted for marking.

Requests for extensions

If you are unable to submit an assessment task by the due date, you should apply for an extension.

A request for an extension should first be discussed with your Unit Coordinator or teaching support team where possible. A request for an extension must be submitted by the assessment due date, except where you can provide evidence it was not possible to do so. Typically, an application for an extension will be supported by documentary evidence: however, where it is not possible for you to provide evidence please contact your Unit Coordinator.

The Unit Coordinator must notify you of the outcome of an extension request within 3 working days of receiving the request.

Late Penalties

Assignments submitted after the deadline will receive a late penalty of 5% of the original available mark for each calendar day (or part day) that the assignment is late. Late submissions will not be accepted more than 10 calendar days after the due date, or after assignments have been returned to other students on a scheduled date, whichever occurs first. Further information on Late Penalties can be found on the Assessments and Results Procedure.

Review of results and appeals

You are entitled to ask for a review of the marking and grading of your assessment task if there is an irregularity in the marking standards or an error in the process for determining the outcome of an assessment. Details on how to request a review of a mark for an assignment are outlined in the Review and Appeal of Academic Decisions Procedure.