GISC101-23S2 (C) Semester Two 2023

Introduction to Spatial Data Science

15 points

Start Date: Monday, 17 July 2023
End Date: Sunday, 12 November 2023
Withdrawal Dates
Last Day to withdraw from this course:
  • Without financial penalty (full fee refund): Sunday, 30 July 2023
  • Without academic penalty (including no fee refund): Sunday, 1 October 2023


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 R programming language.
  • Perform basic spatial analysis using the spatial capabilities of the open-source R 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.

      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 2023

Students must attend one activity from each section.

Lecture A
Activity Day Time Location Weeks
01 Monday 16:00 - 17:00 Meremere 105 Lecture Theatre
17 Jul - 27 Aug
11 Sep - 22 Oct
Lecture B
Activity Day Time Location Weeks
01 Tuesday 10:00 - 11:00 Jack Erskine 443
17 Jul - 27 Aug
11 Sep - 22 Oct
Computer Lab A
Activity Day Time Location Weeks
01 Tuesday 14:00 - 16:00 211A GIS Comp Lab
Ernest Rutherford 211
17 Jul - 27 Aug
11 Sep - 22 Oct
02 Tuesday 16:00 - 18:00 211A GIS Comp Lab
Ernest Rutherford 211
17 Jul - 27 Aug
11 Sep - 22 Oct

Timetable Note

Students are expected to attend both lectures (A and B - 1h each) per week, attend one computer lab per week (2h), and invest on average 8h – 9h per week  in self-study and assignment/project work during the course.

Component Day Time Location
Lecture A Mondays 16:00 – 17:00 Meremere 105 Lecture Theatre
Lecture B Tuesdays 10:00 – 11:00 Jack Erskine 443
Computer Lab A Tuesdays 14:00 – 16:00 211A GIS Comp Lab Ernest Rutherford 211
Computer Lab A Tuesdays 16:00 – 18:00 211A GIS Comp Lab Ernest Rutherford 211

Course Coordinator / Lecturer

Vanessa Bastos


Malcolm Campbell , Lindsey Conrow and Peyman Zawar-Reza


Assessment Due Date Percentage  Description
Online Quiz 1 06 Aug 2023 4%
Online Quiz 2 27 Aug 2023 4%
Online Quiz 3 01 Oct 2023 4%
Online Quiz 4 22 Oct 2023 4%
SDS in the real world 4% Due date to be advised
Labs 1 - 3 23 Aug 2023 10%
Labs 4 - 6 20 Sep 2023 10%
Labs 6 - 9 11 Oct 2023 10%
Assessment Part A 20 Oct 2023 25%
Assessment Part B 27 Oct 2023 25%

Textbooks / Resources

Recommended textbook(s):

1. Pebezma, E., & Bivand, R. (2021). Spatial Data Science with applications in R.    
2. Rowe, F., & Arribas-Bel D. (2021). Spatial Modelling for Data Scientists.  
3. Lovelace, R., Nowosad, J. & Muenchow, J. (2020). Geocomputation with R. Open source book available at  or from CRC Press.
4. Spatial Data Science with R: web materials at:

Indicative Fees

Domestic fee $951.00

International fee $4,750.00

* 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

  • GISC101-23S2 (C) Semester Two 2023