GEOG 771 : Spatial Analysis and Geocomputation


2020 Semester One (1203) (15 POINTS)

Course Prescription

Approaches and challenges to analysing spatial data. Specific techniques will include spatial autocorrelation, geographical regression, point pattern analysis, interpolation, overlay analysis, and newer geocomputation methods. Students will gain an advanced knowledge of spatial analysis and be well-prepared for postgraduate research or professional practice. No formal prerequisite but an understanding equivalent to GEOG 318 will be assumed.

Course Overview

Course Objectives: (1) To understand fundamental concepts and theories underpinning spatial data analysis; (2) To learn how to use some important software systems/packages to undertake common types of spatial analysis, and to know how to interpret the results properly; and (3) To gain analytical skills in undertaking a small independent study on a chosen topic; and (4) To develop skills in communicating the research results to others in both written and oral forms.
Course Structure: This course consists of scheduled lectures during which the active participation of students in the discussion is mandatory. Lectures   and labs take place in computer laboratory in which students will learn how to make use relevant computer systems/packages and run the analyses taught in class and to interpret the results properly by completing five assignments. In addition, relevant readings on some topics will be provided for those who would like to gain an in-depth knowledge on the topic throughout the semester.

Course Requirements

No pre-requisites or restrictions

Capabilities Developed in this Course

Capability 1: Disciplinary Knowledge and Practice
Capability 2: Critical Thinking
Capability 3: Solution Seeking
Capability 4: Communication and Engagement
Capability 5: Independence and Integrity
Capability 6: Social and Environmental Responsibilities

Learning Outcomes

By the end of this course, students will be able to:
  1. Understand spatial data and their unique properties (Capability 1, 2, 3 and 6)
  2. Understand how different types of spatial data can be analysed descriptively and inferentially (Capability 1, 2 and 3)
  3. Apply a wide range of spatial analysis in a number of computing environments (Capability 1, 2, 3, 4, 5 and 6)
  4. Undertake a supervised study on a selected spatial analysis topic; (Capability 1, 2, 3, 4, 5 and 6)
  5. Explain and communicate spatial analysis results to the audience in both written and verbal languages effectively (Capability 1, 2, 4, 5 and 6)


Assessment Type Percentage Classification
Laboratories 50% Individual Coursework
Presentation 20% Individual Coursework
Project 30% Individual Coursework
Assessment Type Learning Outcome Addressed
1 2 3 4 5


As part of the University-wide Tuākana community, The School of Environment Tuākana Programme aims to provide a welcoming learning environment for, and enhance the success of, all of our Māori and Pacific students. We are led by the principles of tautoko (support) and whanaungatanga (connection), and hope you find a home here at the School. Students who have identified as Māori and/or Pacific will receive an invitation to our online portal introducing the Programme, the resources we have available, and how you can get involved.

This course is supported by our Programme Coordinator, Kaiāwhina/Māori student adviser, and Pacific student adviser. They are able to organise group study and facilitate direct assistance regarding material taught in this course. For more information regarding the Programme feel free to email our Programme Coordinator:

Key Topics

Geocomputation, spatial analysis, spatial data manipulation, spatial data visualisation, GeoAI, spatial statistics

Learning Resources

1. DeMers, M N, 2004. Fundamentals of Geographic Information Systems (3rd edition), New York: John Wiley (Chapter 13: Cartographic Modelling),
2. Fischer, M, Scholten H. and Unwin D (ed), 1996. Spatial Analysis Perspectives on GIS. Taylor and Francis, London
3. Fischer, M., 2006. Spatial Analysis and Geocomputation: Selected Essays. Berling: Springer.
4. Fotheringham, A. S., and P A. Rogerson (eds), 2009. The SAGE Handbook of Spatial Analysis. London: SAGE Press, 513 p.
5. Fotheringham, S, and Rogerson, P (ed), 1994. Spatial Analysis and GIS, London: Taylor and Francis, 281.
6. Fotheringham, A. S. and O’Kelly, M. E. (1989) Spatial Interaction Models, International Encyclopedia of the Social & Behavioral Sciences. Elsevier. doi: 10.1016/B0-08-043076-7/02519-5.
7. Fotheringham, A. S. and O’Kelly, M. E. (1989) Spatial interaction models: formulation and applications, Studies in operational regional science 5. Kluwer Academic Pub.
8. Fotheringham, A. S., Brunsdon, C. and Charlton, M. E. (2000) Quantitative Geography: Perspectives on Spatial Data Analysis, Applied Geography. doi: 10.1016/S0143-6228(97)90005-9.
9. Fotheringham, A.S., Brunsdon, C., and Charlton M. (2002). “Geographically weighted regression: the analysis of spatially varying relationships.” Wiley & Sons. 2002.
10. Haining, R, Wise, S, and Ma, J, 1998. Exploratory spatial data analysis in a geographic information system environment. The Statistician, 47, pp. 457-469.
11. Longley, P A, and Batty, M (eds), 2003. Advanced Spatial Analysis: the CASA book of GIS, ESRI Press.
12. Longley, P., Goodchild, M, Maguire, D.J., and Rhind, D., W., 2015. Geographic Information Science and Systems (4th edition), Chapter 15: Spatial Modelling with GI Systems, Wiley, p 339-357.
13. Oliver, M. A., and R Webster, 1990. Kriging: a method of interpolation for geographical information systems. International Journal of Geographical Information Systems, 4(3): 313-332.
14. Openshaw, S, and Abrahart ,R.J. (eds), 2000. GeoComputation, Taylor and Francis, London
15. Smith, M. J. De, M. F. Goodchild, and P. Longley, 2007. Geospatial Analysis: A Comprehensive Guide to Principles, Techniques and Software Tools. Leicester: Winchelsea Press, 398 p.

Special Requirements

Students must complete practical work and present two assigned topics.

Workload Expectations

This course is a standard 15 point course and students are expected to spend 10 hours per week involved in each 15 point course that they are enrolled in.

For this course, you can expect 24 hours of lectures, a  two-hour lab each week (24h), 15 hours of reading and thinking about the content and 87 hours of work on assignments and/or test preparation.

Digital Resources

Course materials are made available in a learning and collaboration tool called Canvas which also includes reading lists and lecture recordings (where available).

Please remember that the recording of any class on a personal device requires the permission of the instructor.


The content and delivery of content in this course are protected by copyright. Material belonging to others may have been used in this course and copied by and solely for the educational purposes of the University under license.

You may copy the course content for the purposes of private study or research, but you may not upload onto any third party site, make a further copy or sell, alter or further reproduce or distribute any part of the course content to another person.

Academic Integrity

The University of Auckland will not tolerate cheating, or assisting others to cheat, and views cheating in coursework as a serious academic offence. The work that a student submits for grading must be the student's own work, reflecting their learning. Where work from other sources is used, it must be properly acknowledged and referenced. This requirement also applies to sources on the internet. A student's assessed work may be reviewed against online source material using computerised detection mechanisms.

Inclusive Learning

All students are asked to discuss any impairment related requirements privately, face to face and/or in written form with the course coordinator, lecturer or tutor.

Student Disability Services also provides support for students with a wide range of impairments, both visible and invisible, to succeed and excel at the University. For more information and contact details, please visit the Student Disability Services’ website at

Special Circumstances

If your ability to complete assessed coursework is affected by illness or other personal circumstances outside of your control, contact a member of teaching staff as soon as possible before the assessment is due.

If your personal circumstances significantly affect your performance, or preparation, for an exam or eligible written test, refer to the University’s aegrotat or compassionate consideration page:

This should be done as soon as possible and no later than seven days after the affected test or exam date.

Student Feedback

During the course Class Representatives in each class can take feedback to the staff responsible for the course and staff-student consultative committees.

At the end of the course students will be invited to give feedback on the course and teaching through a tool called SET or Qualtrics. The lecturers and course co-ordinators will consider all feedback.

Your feedback helps to improve the course and its delivery for all students.

Student Charter and Responsibilities

The Student Charter assumes and acknowledges that students are active participants in the learning process and that they have responsibilities to the institution and the international community of scholars. The University expects that students will act at all times in a way that demonstrates respect for the rights of other students and staff so that the learning environment is both safe and productive. For further information visit Student Charter (


Elements of this outline may be subject to change. The latest information about the course will be available for enrolled students in Canvas.

In this course you may be asked to submit your coursework assessments digitally. The University reserves the right to conduct scheduled tests and examinations for this course online or through the use of computers or other electronic devices. Where tests or examinations are conducted online remote invigilation arrangements may be used. The final decision on the completion mode for a test or examination, and remote invigilation arrangements where applicable, will be advised to students at least 10 days prior to the scheduled date of the assessment, or in the case of an examination when the examination timetable is published.

Published on 11/01/2020 03:09 p.m.