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    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.

    About this paper

    Paper title Advanced Spatial Analysis and Modelling
    Subject Surveying
    EFTS 0.1482
    Points 20 points
    Teaching period Semester 2 (On campus)
    Domestic Tuition Fees ( NZD ) $1,340.02
    International Tuition Fees Tuition Fees for international students are elsewhere on this website.
    216 points (including SURV 208 or SURV 220)
    SURV 310, SURV 411, SURV 508
    Recommended Preparation
    One of SURV 319, SURV 519

    This paper supports the 500-level courses in the BSc (Hons) GIS, PGDipAppSci in GIS, MAppSc in GIS and MSc in GIS.


    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


    Semester 2

    Teaching method
    This paper is taught On Campus
    Learning management system


    Stream Days Times Weeks
    A1 Wednesday 10:00-11:50 29-35, 37-42


    Stream Days Times Weeks
    A1 Thursday 09:00-10:50 29-35, 37-42
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