Courses & Units

Environmental Geographic Information Science C KGG541

Introduction

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.

Summary

Unit name	Environmental Geographic Information Science C
Unit code	KGG541
Credit points	12.5
College/School	College of Sciences and Engineering School of Geography, Planning, and Spatial Sciences
Discipline	Geography, Planning, and Spatial Sciences
Coordinator	Doctor Steve Harwin
Delivered By	University of Tasmania
Level	Postgraduate

Availability

This unit is currently unavailable.

Note

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* The Final WW Date is the final date from which you can withdraw from the unit without academic penalty, however you will still incur a financial liability (refer to How do I withdraw from a unit? for more information).

Unit census dates currently displaying for 2024 are indicative and subject to change. Finalised census dates for 2024 will be available from the 1st October 2023. Note census date cutoff is 11.59pm AEST (AEDT during October to March).

About Census Dates

Learning Outcomes

Evaluate and apply techniques in GNSS data sampling, exploratory spatial data analysis, interpolation, and geostatistics to produce continuous raster surfaces from point sample data
Create and document Python scripts that implement spatial data input/output methods, spatial data structures, and spatial analysis algorithms
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
Document and evaluate a spatial analysis workflow (including coding) assisted by cartographic maps and other visual means to communicate analysis results

Fee Information

Field of Education	Commencing Student Contribution ^1,3	Grandfathered Student Contribution ^1,3	Approved Pathway Course Student Contribution ^2,3	Domestic Full Fee ⁴
			not applicable

¹ Please refer to more information on student contribution amounts.
² Please refer to more information on eligibility and Approved Pathway courses.
³ Please refer to more information on eligibility for HECS-HELP.
⁴ Please refer to more information on eligibility for FEE-HELP.

If you have any questions in relation to the fees, please contact UConnect or more information is available on StudyAssist.

Please note: international students should refer to What is an indicative Fee? to get an indicative course cost.

Requisites

Prerequisites

KGG540

Teaching

Teaching Pattern	13 x 2-hr workshops, 13 x 3-hr practicals. Workshops and practical introductions will be recorded and made available on MyLO. In week 2 there will be a Field Day at the Home Hill landslide.
Assessment	Assignment 4: Python and GIS colour ramps (5%)\|Assignment 2: Introductory Python exercises in QGIS (20%)\|Assignment 1: Interpolation & Geostatistics (35%)\|Assignment 3: Spatial data handling and analysis in Python (40%)
Timetable	View the lecture timetable \| View the full unit timetable

Teaching Pattern

13 x 2-hr workshops, 13 x 3-hr practicals. Workshops and practical introductions will be recorded and made available on MyLO.

In week 2 there will be a Field Day at the Home Hill landslide.

Assessment

Assignment 4: Python and GIS colour ramps (5%)|Assignment 2: Introductory Python exercises in QGIS (20%)|Assignment 1: Interpolation & Geostatistics (35%)|Assignment 3: Spatial data handling and analysis in Python (40%)

Timetable

View the lecture timetable | View the full unit timetable

Textbooks

Required	Required readings will be listed in the unit outline prior to the start of classes.
Recommended	You will be working through Python tutorials provided by DataCamp: https://www.datacamp.com/ (access instructions will be provided)
Links	Booktopia textbook finder

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