GISC101-24S1 (C) Semester One 2024

Introduction to Spatial Data Science

15 points

Start Date: Monday, 19 February 2024
End Date: Sunday, 23 June 2024
Withdrawal Dates
Last Day to withdraw from this course:
  • Without financial penalty (full fee refund): Sunday, 3 March 2024
  • Without academic penalty (including no fee refund): Sunday, 12 May 2024


Spatial Data Science deals with the processing, manipulation, analysis and visualization of spatial data in a variety of forms. Spatial data are those which contain geographical coordinates enabling them to be used for spatial analysis and mapping and include, for example, images from remote sensing, coordinates collected using navigation technologies, or census information by area, among many others. Spatial data are fundamental to many geographical analyses and spatial data science draws strongly from key geographical concepts - such as Tobler’s classic 1970 law: "everything is related to everything else, but near things are more related than distant things". This course provides a practical introduction to concepts and methods in data science for the analysis of spatial data. By completing the course, you will gain an understanding of the key concepts in spatial data and their collection, how to represent the environment and the world in spatial data, and the ability to apply basic spatial analysis techniques to geographic data using open source platforms such as R, QGIS, and Python. You will develop skills such as importing, manipulating, analyzing, and visualizing spatial data particularly using algorithms in R and Python. You will also develop an awareness of the current limitations and implications of geographic technology, its future development and data stewardship (particularly bi-cultural aspects of stewardship).

Learning Outcomes

  • The overarching goal of this course is to foster the understanding of key concepts in spatial data science (SDS). The course offers a good balance between theory and practice by exploring SDS theory during the lectures and applying learned concepts in computer labs.

    After successfully completing this course, you will be able to:
  • Represent and analyse real-world phenomena using computational models, such as raster and vector representation.
  • Import, manipulate and visualize spatial data using the open-source Python programming language.
  • Perform basic spatial analysis using the spatial capabilities of the open-source Python programming language.
  • Critically reflect on the biases and limitations of different SDS analysis and visualization methods.
  • Understand the current and future limitations and implications of SDS
    • University Graduate Attributes

      This course will provide students with an opportunity to develop the Graduate Attributes specified below:

      Critically competent in a core academic discipline of their award

      Students know and can critically evaluate and, where applicable, apply this knowledge to topics/issues within their majoring subject.

      Employable, innovative and enterprising

      Students will develop key skills and attributes sought by employers that can be used in a range of applications.

      Biculturally competent and confident

      Students will be aware of and understand the nature of biculturalism in Aotearoa New Zealand, and its relevance to their area of study and/or their degree.

      Globally aware

      Students will comprehend the influence of global conditions on their discipline and will be competent in engaging with global and multi-cultural contexts.

Timetable 2024

Students must attend one activity from each section.

Lecture A
Activity Day Time Location Weeks
01 Monday 09:00 - 10:00 Ernest Rutherford 465
19 Feb - 31 Mar
22 Apr - 2 Jun
Lecture B
Activity Day Time Location Weeks
01 Tuesday 13:00 - 14:00 A5 Lecture Theatre
19 Feb - 31 Mar
22 Apr - 2 Jun
Computer Lab A
Activity Day Time Location Weeks
01 Tuesday 09:00 - 12:00 211A GIS Comp Lab
Ernest Rutherford 211
19 Feb - 31 Mar
22 Apr - 9 Jun
02 Wednesday 09:00 - 12:00 211A GIS Comp Lab
Ernest Rutherford 211
19 Feb - 31 Mar
22 Apr - 9 Jun

Course Coordinator

Vanessa Bastos


Assessment Due Date Percentage  Description
Online Quizzes 20% Online Quiz 1 (5%), Week 4, Sat 5pm Online Quiz 2 (5%), Week 6, Sat 5pm Online Quiz 3 (5%), Week 9, Sat 5pm Online Quiz 4 (5%), Week 11, Sat 5pm
Use of Gen-AI and Coding Forum 5% Due by assigned week (to be advised)
Data Sovereignty and Ethics Reflection 9% Due Week 7, Fri 5pm
Lab Assignments 36% Labs 1 & 3 (12%), Week 6, Fri 5pm Labs 4 & 5 (12%), Week 8, Fri 5pm Labs 6 & & (12%), Week 10, Fri 5pm
Group Investigative Journalism Article 30% Die Weel 12 + 1, Fri 5pm

* Lab assignment grades will be weighted by lab attendance according to the following multipliers:
Lab attendance
>= 9 (Grade multiplier 1.00)
8 (Grade multiplier 0.88)
7 (Grade multiplier 0.82)
6 (Grade multiplier 0.76)
5 (Grade multiplier 0.70)
4 (Grade multiplier 0.64)
3 (Grade multiplier 0.58)
2 (Grade multiplier 0.52)
1 (Grade multiplier 0.46)
0 (Grade multiplier 0.40)

Textbooks / Resources

Recommended Reading

McClain, Bonny P; Python for Geospatial Data Analysis : theory, tools, and practice for location intelligence ; O'Reilly Media, Inc., 2022 (

Parker, James R; Python : an introduction to programming ; Mercury Learning & Information, 2017 (

Rey, S., Arribas-Bel, D., & Wolf, L.J; Geographic Data Science with Python ; 2020 (

Severance, C. R; Python for Everybody: Exploring Data in Python 3 ; 2016 (

Tenkanen, H., Heikinheimo, V. & Whipp, D; Introduction to Python for Geographic Data Analysis ; 2022 (

Indicative Fees

Domestic fee $978.00

International fee $4,988.00

* All fees are inclusive of NZ GST or any equivalent overseas tax, and do not include any programme level discount or additional course-related expenses.

For further information see School of Earth and Environment .

All GISC101 Occurrences

  • GISC101-24S1 (C) Semester One 2024