Unit Outline

Geospatial Data Analytics is an innovative unit designed to provide you with foundational knowledge and practical skills in geospatial programming, building on the knowledge gained in KGG212 GIS: Spatial Analysis. With a primary focus on Python, a powerful and widely used programming/scripting language, this unit explores the latest tools and techniques in geospatial data processing and analysis, encompassing GIS and remote sensing applications. In this unit, you will engage with various Python libraries and frameworks specifically created for geospatial data manipulation, visualisation, and analytics. The unit fosters an understanding of custom GIS solution development, automation of geospatial workflows, and insightful analysis of geospatial data, enabling students to thrive as highly competent professionals in the spatial industry.

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	Apply Python programming techniques to effectively manipulate, analyse, and visualise geospatial data, including GIS and remote sensing datasets.
2	Automate geospatial workflows through Python scripting.
3	Develop custom geospatial algorithms and tools using Python scripting for real-world spatial challenges.
4	Justify coding choices to produce code that meets industry standard for coding style and documentation.

Requisites


REQUISITE TYPE	REQUISITES
Pre-requisite	KGG212

Alterations as a result of student feedback

The unit's focus has shifted towards geospatial analytics using open-source Python libraries. In 2026, a new GIS project was introduced, enabling students to investigate a topic of interest in depth through the development of a custom geospatial workflow. This project provides students with an opportunity to apply and demonstrate the skills they have acquired throughout the unit while creating a portfolio piece that can be showcased to future employers. Students are also required to work in a group and communicate their work through an oral presentation and through a web page.

In response to the increasing use of AI, the unit incorporates the appropriate use of AI tools throughout its assessments. Students are encouraged to use AI in ways that support learning and professional practice, while assessment design continues to maintain academic integrity, learning assurance, and authenticity.

As always, the teaching team greatly appreciates feedback provided both informally throughout the semester and formally through the University of Tasmania's student feedback surveys following completion of the unit.

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:	Fundamentals of Python Programming for Geospatial Data Processing	Week 5	20 %	LO1, LO2, LO3, LO4
Assessment Task 2:	Automating GIS Workflows with Python	Week 9	40 %	LO1, LO2, LO3, LO4
Assessment Task 3:	GIS Project	Refer to Assessment Description	40 %	LO1, LO2, LO3, LO4

Assessment details

Assessment Task 1: Fundamentals of Python Programming for Geospatial Data Processing

Task Description:

This assignment showcases the ability to work with geospatial datasets. The assignment focuses on basic Python programming skills within a geospatial context using Jupyter notebooks. You will apply Python fundamentals, such as variables, data types, control flow, functions, and code style, to import and manipulate a GIS point dataset.

The assignment requires you to input and output spatial datasets, manipulate coordinates and attributes, and generate output files for further GIS data handling, while adhering to first principles in programming. Some time will be provided in the practical for this task, but the remaining work is to be done in the your own time.

Task Length:

Python scripts and explanation/documentation in a Jupyter notebook (500 words and 300-800 lines of code). The use of generative artificial intelligence (AI) tools for this assessment is allowed only as specifically instructed, and any unauthorised use may be considered a breach of academic integrity.

Due Date:

Week 5

Weight:

20 %

CRITERION #

CRITERION

MEASURES INTENDED

LEARNING OUTCOME(S)


1	Apply Python programming fundamentals to import and manipulate geospatial data.	LO1
2	Develop an efficient Python script using Jupyter notebooks which inputs, processes, and outputs spatial datasets in the specified format.	LO1, LO2
3	Use first principles in Python programming to create custom solutions for processing spatial data, addressing real-world spatial challenges.	LO1, LO2, LO3
4	Write Python code that adheres to PEP8 style guidelines, alongside documentation, to communicate the purpose and functionality of the code components.	LO1, LO3, LO4

Assessment Task 2: Automating GIS Workflows with Python

Task Description:

In this assignment, you showcase the ability to manipulate spatial datasets, automate geospatial workflows and create custom GIS solutions using PyQGIS. You will integrate Python and QGIS to develop custom geospatial solutions and automate GIS workflows, leveraging the capabilities of PyQGIS. You will create Python code to programmatically link Python and QGIS, demonstrating your ability to solve spatial analysis problems and use Leaflet (web mapping framework).

The assignment requires you to showcase your skills in Python coding, documentation, use of appropriate PyQGIS functions for spatial analysis, and visualisation of the results. Some time will be provided in the practical for this task, but the remaining work is to be done in your own time.

Task Length:

Python scripts, plots and documentation/explanation in a Jupyter notebook (500-1000 words and 300-900 lines of code). The use of generative artificial intelligence (AI) tools for this assessment is allowed only as specifically instructed, and any unauthorised use may be considered a breach of academic integrity.

Due Date:

Week 9

Weight:

40 %

CRITERION #

CRITERION

MEASURES INTENDED

LEARNING OUTCOME(S)


1	Apply Python programming techniques and PyQGIS functions to manipulate and analyse geospatial data	LO1, LO2, LO3
2	Develop and document efficient Python code in a Jupyter notebook	LO1, LO2, LO4
3	Use plotting functions to visualise spatial analysis results and present custom geospatial solutions and insights	LO3
4	Develop Python code that follows PEP8 style and incorporates comprehensive documentation/comments, to communicate the purpose, functionality, and structure of the code in the context of automating GIS workflows	LO1, LO3, LO4

Assessment Task 3: GIS Project

Task Description:

The GIS project applies your knowledge and skills in geospatial programming. This project addresses all learning outcomes of the unit and provides an opportunity to tackle a complex, real-world spatial problem through code-driven analysis. The project is designed as a group geospatial programming study, with a strong emphasis on Python-based spatial analysis, automation, and reproducible workflows. For the GIS project you are expected to select a topic from a range of pre-defined application areas where each project requires you to acquire and prepare spatial datasets, design and implement spatial algorithms or automated workflows, perform quantitative spatial analysis using code, and interpret and communicate results in a clear and reproducible manner. Project outcomes will be communicated through a combination of programming deliverables (e.g. scripts or notebooks) and an oral presentation. This structure reflects professional geospatial practice, where analysts must both develop robust geospatial code in teams and clearly communicate results to diverse audiences. Dedicated practical time is provided for project development under supervision, allowing you to refine your programming approach, troubleshoot workflows, and receive feedback throughout the project lifecycle.

Task Length:

Python-based programming deliverables (e.g. scripts and Jupyter notebooks), group meeting records demonstrating evidence of ongoing collaboration, peer assessment, and an oral presentation communicating the project workflow, analysis, and results. The use of generative artificial intelligence (AI) tools for this assessment is allowed only as specifically instructed, and any unauthorised use may be considered a breach of academic integrity.

Due Date:

Refer to Assessment Description

Weight:

40 %

CRITERION #

CRITERION

MEASURES INTENDED

LEARNING OUTCOME(S)


1	Apply Python programming techniques to acquire, prepare, and manipulate geospatial datasets relevant to a defined real-world spatial problem.	LO1
2	Develop Python-based automated workflows (e.g. scripts or Jupyter notebooks) to process, analyse, and output spatial data in a reproducible manner.	LO1, LO2
3	Design and implement custom spatial algorithms or analytical methods using Python to address a real-world geospatial challenge.	LO1, LO2, LO3
4	Produce well-structured, documented Python code and clearly communicate analytical methods and results through programming deliverables and an oral presentation.	LO1, LO3, LO4
5	Demonstrate professional project practice by maintaining clear and appropriate records of collaborative communication, task coordination, and both individual and group contributions throughout the project lifecycle.	LO3, 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, and pass any hurdle tasks.

The GIS Project is a hurdle task and you will be required to pass in this assessment to successfully complete the unit.

Academic progress review

The results for this unit may be included in a review of your academic progress. For information about progress reviews and what they mean for all students, see Academic Progress Review in the Student Portal.

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.

Academic integrity

Academic integrity is about acting responsibly, honestly, ethically, and collegially when using, producing, and communicating information with other students and staff members.

In written work, you must correctly reference the work of others to maintain academic integrity. To find out the referencing style for this unit, see the assessment information in the MyLO site, or contact your teaching staff. For more detail about Academic Integrity, see Important Guidelines & Support.

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.

Required Resources

Required reading materials

You will be required to purchase a heavily discounted series of textbooks. https://leanpub.com/b/geopython/c/utas. You will work through parts of this textbook through practical sessions.