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SURV411 Advanced Spatial Analysis and Modelling

Spatial analysis, including geostatistics and network analysis. Environmental modelling incorporating spatial principal components analysis, spatial regression and AI-based techniques such as fuzzy logic and expert systems.

Many real-world problems incorporate location as a fundamental component of their representation. The analysis and modelling of these problems involves specific knowledge and technical skills that are addressed in this paper.

Paper title Advanced Spatial Analysis and Modelling
Paper code SURV411
Subject Surveying
EFTS 0.1334
Points 18 points
Teaching period Semester 2 (On campus)
Domestic Tuition Fees (NZD) $1,173.39
International Tuition Fees Tuition Fees for international students are elsewhere on this website.

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216 points (including SPIN 201 or SURV 208 or SURV 218)
SPIN 402, SURV 310, SURV 508, SURV 511
Recommended Preparation
SURV 319
Schedule C
Commerce, Science

This paper supports the 400-level courses in the BAppSc degree, GIS minor and BSurv degree.


Teaching staff

Convenor: Associate Professor Tony Moore
Lecturers: Aubrey Miller
Dr Pascal Sirguey
Associate Professor Tony Moore
Professor Christina Hulbe
Dr Robert Odolinski

Paper Structure
Many real-world problems incorporate location as a fundamental component of their representation. The analysis and modelling of these problems, therefore, extends beyond considering just the attributes or objects of a problem and must also address the spatial relationships and resulting patterns that derive explicitly from spatial interactions. Spatial analysis is, thus, a set of techniques that allow summary descriptions of spatial data; visualisation; transformations of spatial data; predictive models with spatial and temporal extent; theory testing; generalisation; and model assessment.
Teaching Arrangements

This paper builds upon the understanding of GIS concepts that students gained in SURV 208 through weekly lectures and lab sessions. The paper consists of compulsory lectures, and in addition, hands-on work will be completed in four practical exercises to be completed during weekly laboratory sessions. This will introduce the students to spatial analysis techniques using the specialist software ESRI ArcGIS, as well as other software that may be appropriate (e.g. Matlab).


Textbooks are not required for this paper, and the majority of readings will be supplied. However, it is recommended that students have access to the following books:

  • Paul Longley et al, Geographic Information Systems and Science, 4th edition, 2015 (or equivalent introductory text on GIS) for background information and as a resource on fundamental operations in GIS. (online access)
  • Stein A., van der Meer F., and Gorte B., Spatial Statistics for Remote Sensing, Springer, Netherlands, 1999, 284pp. (on reserve at the library)
  • Fotheringham, A., Brundson, C, and M. Charlton, Geographically weighted regression: the analysis of spatially varying relationships, John Wiley and Sons, 2002, is available from Central library. (on reserve at the library)
Graduate Attributes Emphasised
Global perspective, Interdisciplinary perspective, Lifelong learning, Scholarship, Communication, Critical thinking, Environmental literacy, Information literacy, Research, Self-motivation.
View more information about Otago's graduate attributes.
Learning Outcomes
Students who successfully complete the paper will
  • Gain a strong foundation in spatial analysis and modelling techniques
  • Develop practical experience in developing and assessing a range of analysis methods using ArcGIS and third-party software
  • Develop an understanding of the assumptions and limitations of spatial analysis techniques
  • Be able to formalise the assessment of errors resulting from these methods
  • Develop skills in selecting appropriate analysis techniques for a given problem

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Semester 2

Teaching method
This paper is taught On Campus
Learning management system


Stream Days Times Weeks
A1 Wednesday 10:00-11:50 28-34, 36-41


Stream Days Times Weeks
A1 Thursday 09:00-10:50 28-34, 36-41