GISCI 242 : Principles of GIScience


2020 Semester One (1203) (15 POINTS)

Course Prescription

Spatial analysis and GIScience applications of spatial data handling for human environments within the context of a theoretical framework for understanding the caveats and ethics of mapping persons in place. Develops advanced practical knowledge of techniques and methodologies in a vector model spatial analysis environment as developed through applications for housing, social wellbeing and inequality, disease, and access to public transportation.

Course Overview

Course Objectives: (1) To understand fundamental concepts and theories underpinning spatial data analysis; (2) To learn how to use important software systems/packages to undertake common types of spatial analysis, and to know how to interpret the results; and (4) To develop communication  skills of research results to as both written and oral forms. This course is the second stage of the GIScience curriculum and builds upon the skills and concepts of Geog 140, refining the broad base of knowledge given in that course. Course topics will contain example from across GIScience, including Human Geography, Physical Geography, Transport Geography, Qualitatative GIS, Geovisualisation, and others. 

Course Requirements

Prerequisite: 15 points from EARTHSCI 210, GEOG 140, 210, GISCI 140 Restriction: GEOG 318

Capabilities Developed in this Course

Capability 1: Disciplinary Knowledge and Practice
Capability 2: Critical Thinking
Capability 4: Communication and Engagement
Capability 5: Independence and Integrity
Capability 6: Social and Environmental Responsibilities
Graduate Profile: Bachelor of Science

Learning Outcomes

By the end of this course, students will be able to:
  1. Demonstrate independent theoretical and practical knowledge of and proficiency in the use of spatial statistics and spatial analytic methodologies (vector model), including and understanding of the appropriateness and limitations of their applications. (Capability 1, 2 and 5)
  2. Hone an ethical geospatial practice in the context of the caveats of the real-world consequences of mapping and analysing people in place, and when handling and mapping socially and environmentally sensitive spatial data. (Capability 2, 5 and 6)
  3. Evaluate critically, and interpret the results/outputs/products of the application of spatial analyses methodologies (spatial statistics, geovisualization) to spatial datasets. (Capability 2)
  4. Identify opportunities for the deployment of spatial data science methodologies and technologies as core components of broader promotions of social wellbieng and environmental sustainability. (Capability 6)
  5. Use maps and geovisualisation as effective means of communicating the results of spatial data analysis. (Capability 4)
  6. Develop an understanding of current developments and shifts in the field of GIScience. (Capability 1)


Assessment Type Percentage Classification
Quizzes 30% Individual Test
Laboratories 60% Individual Coursework
Presentation 10% Group & Individual Coursework
Assessment Type Learning Outcome Addressed
1 2 3 4 5 6


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 a designated Tuākana tutor with appropriate knowledge of the course and related skills. They will organise group study sessions 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

  1. Exploratory spatial data analysis
  2. Spatial statistics
  3. Spatial and non-spatial queries
  4. Network analysis
  5. Multi-Criteria Evaluation models
  6. Interpolation methods
  7. Web Mapping and Scripting
  8. Neogeography and Web 2.0
  9. 2D and 3D Geovisualisation

Learning Resources

1. DeMers, M N, 2004. Fundamentals of Geographic Information Systems (3rd edition), New York: John Wiley 
2. Haining, R, Wise, S, and Ma, J, 1998. Exploratory spatial data analysis in a geographic information system environment. The Statistician, 47, pp. 457-469
3. Longley, P., Goodchild, M, Maguire, D.J., and Rhind, D., W., 2015. Geographic Information Science and Systems 
4. Haklay M, Singleton A & Parker C (2008) Web mapping 2.0: the Neogeography of the Geoweb. Geography Compass 2: 2011-2039
5. O’Sullivan D & Unwin DJ (2010) Geographic Information Analysis, 2nd edition. Hoboken, NJ: John Wiley & Sons.
6. Schuurman N (2004) “Bringing it all together: using GIS to analyze and model spatial phenomena.” In GIS: A Short Introduction. Malden, MA: Wiley Blackwell.

Special Requirements

Students must complete practical work and participate in labs and lectures. 

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,  24 hours of lab time, 15 hours of reading and reflecting on course content as well as 87 hours of assignment work 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.