GISC404-23S1 (C) Semester One 2023

Spatial Analysis

15 points

Start Date: Monday, 20 February 2023
End Date: Sunday, 25 June 2023
Withdrawal Dates
Last Day to withdraw from this course:
  • Without financial penalty (full fee refund): Sunday, 5 March 2023
  • Without academic penalty (including no fee refund): Sunday, 14 May 2023


This course provides an introduction to a range of statistical techniques used in the analysis of spatial data. A comprehensive lab programme uses a variety of software packages to explore visualisation, exploratory spatial data analysis, spatial autocorrelation, point pattern analysis, spatial statistics and the modifiable areal unit problem (MAUP).

Nau mai ki GISC404-STAT450 - welcome to GISC404-STAT450. This course provides an introduction to a range of statistical techniques used in the analysis of spatial data. It will cover the basic concepts and techniques of spatial data analysis (SDA) and provide a wide range of applications examples from various fields such as geology, demographics, epidemiology and environmental sciences. A comprehensive lab programme uses a variety of software packages (including ArcGIS, Geoda, geoR) to explore and analyse spatial data using the techniques taught in the course.

Learning Outcomes

  • Upon successful completion of this course, you will have:
  • Knowledge of a range of statistical techniques used in the analysis of spatial data
  • An understanding of issues in analysing spatial data
  • The capacity to apply basic statistical and spatial analysis methods to simple research questions
  • Confidence in utilising a range of software packages for analysing spatial data
  • Knowledge and ability to explore, describe and model spatial data
  • Acquired skills to adequately report on and communicate spatial analytical research
    • 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.


Subject to the approval of the Programme Director. RP: GEOG205 or GISC422, GEOG323

Recommended Preparation

Timetable 2023

Students must attend one activity from each section.

Lecture A
Activity Day Time Location Weeks
01 Thursday 13:00 - 15:00 Jack Erskine 111 (23/2)
Rehua 529 (9/3)
Jack Erskine 446 (23/3, 4/5, 18/5-1/6)
20 Feb - 26 Feb
6 Mar - 12 Mar
20 Mar - 26 Mar
1 May - 7 May
15 May - 21 May
29 May - 4 Jun
Computer Lab A
Activity Day Time Location Weeks
01 Wednesday 13:00 - 16:00 Ernest Rutherford 211A GIS Comp Lab (15/3-29/3)
Rehua 008 Computer Lab (10/5)
13 Mar - 19 Mar
27 Mar - 2 Apr
8 May - 14 May

Timetable Note

12 hrs lectures (6 x 2 hour lectures)
9 hrs labs (3 x 3 hour labs)
129 hrs self-study and project work (lab reporting, article reviews, take home assignment)

Course Coordinator / Lecturer

Ioannis Delikostidis


Varvara Vetrova


Assessment Due Date Percentage 
Report for Lab 1 22 Mar 2023 15%
Journal article review 1 31 Mar 2023 15%
Report for Lab 2 24 Apr 2023 15%
Report for Lab 3 10 May 2023 15%
Journal article review 2 03 Jun 2023 15%
Take-home Assignment 05 Jun 2023 25%

Textbooks / Resources

Required Texts

Illian, Janine; Statistical analysis and modelling of spatial point patterns ; John Wiley, 2008.

Recommended Reading

Bivand, Roger S. , Pebesma, Edzer J., Gomez-Rubio, Virgilio; Applied spatial data analysis with R ; Springer, 2008.

Grekousis, George; Spatial analysis methods and practice : describe - explore - explain through GIS ; First edition; Cambridge University Press, 2020.

O'Sullivan, David , Unwin, D; Geographic information analysis ; 2nd ed; John Wiley & Sons, 2010.

There are no prescribed books for this course however all of the topics covered in the course are included in the four texts listed below. Please refer to these books when sourcing reading material for each topic in addition to the ‘Selected Reading’ lists provided.
• O’Sullivan, D. & Unwin, D. (2014). Geographic Information Analysis (2nd ed). London: John Wiley.
• Grekousis, G. (2020). Spatial analysis methods and practice: describe - explore - explain through GIS (Ebook). Cambridge University Press.
• Illian, J., Penttinen, A., Stoyan, H. & Stoyan D. (2008). Statistical Analysis and Modelling of Spatial Point Patterns. Wiley
• Bivand R., Pebesma E. & Gomez-Rubio V. (2008). Applied Spatial Data Analysis with R (Use R). Springer


Prerequisites: Subject to the approval of the PMGST/PGDipGST Programme Director
Recommended preparation: GEOG205 or GISC422, GEOG323

Indicative Fees

Domestic fee $1,114.00

* 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 on the departments and faculties page .

All GISC404 Occurrences

  • GISC404-23S1 (C) Semester One 2023