Overview
Advanced Geographic Information Science topics in spatial analysis, modelling and visualisation, including geostatistics, principal components analysis, spatial AI, visual analytics, map cognition and art-enhanced visualisation.
This is an advanced level Geographic Information Science (GIScience) paper, that explores the state-of-the-art in two major functions of GIS: the extraction of valuable information and knowledge from data through analysis, and geographic communication through visualisation.
The paper is a theoretical and practical grounding in a selection of important techniques at the cutting edge of GIScience, with a research-led perspective from the paper teaching team. Towards the end of the paper will be the opportunity to develop and implement your own project in analysis or visualisation. Broad topics in the paper include geostatistics, principal component analysis, spatial AI, geovisual analytics, cognition and usability, and advanced geographic visualisation (including non-visual maps, games, abstracted representations and art).
This paper and the introductory and intermediate GIS papers together form the complete geospatial package, covering the essential GIS aspects of data, analysis and visualisation, explored in depth to research level, for surveyors, geographers, geologists, life scientists and health scientists, amongst others, who will need GIS knowledge and skills going forward.
About this paper
| Paper title | Advanced Geographic Information Science |
|---|---|
| Subject | Surveying |
| EFTS | 0.1334 |
| Points | 18 points |
| Teaching period | Semester 2 (On campus) |
| Domestic Tuition Fees ( NZD ) | $1,278.51 |
| International Tuition Fees | Tuition Fees for international students are elsewhere on this website. |
- Pre or Corequisite
- SURV 320 or SURV 520
- Restriction
- SURV 518, SURV 411, SURV 511, SURV 412, SURV 512
- Schedule C
- Science
- Eligibility
This paper assumes introductory-level GIS and intermediate-level GIS, at least as a co-requisite, and is suitable for surveyors, geographers, geologists, life scientists and health scientists, amongst others, who need specialist GIS knowledge and skills going forward.
- Contact
- More information link
- Teaching staff
Course coordinator and lecturer: Antoni Moore
Lecturers: Pascal Sirguey, Christina Hulbe
- Paper Structure
The paper covers the following topics:
- Geostatistics and autocorrelation
- Kriging interpolation and error
- Spatial Principal Component Analysis Theory and Application
- Spatial artificial intelligence through neural network and fuzzy logic examples
- Visual analytics and multivariate mapping
- Digital and physical interfaces to geographic data
- Spatial cognition and usability testing of maps and interfaces
- Abstracted visualisation through diagrams and spatialisations
- Art, cartography and the analysis of artefacts
- The Geographical Information Science discipline
- Teaching Arrangements
In general there are two or three lectures per week supported by a two-hour practical.
- Textbooks
There are no textbooks covering the entire paper, though the following support the geostatistics and cartography / visualisation parts of the paper.
Stein A., van der Meer F., and Gorte B., Spatial Statistics for Remote Sensing, Springer, Netherlands, 1999, 284pp. (online access).
Slocum, T, McMaster, R, Kessler, F and Howard, H. 2023. Thematic Cartography and Geovisualization, 4th edition. Prentice Hall, NJ.
Otherwise, reading will be distributed via the learning management system.
- Graduate Attributes Emphasised
Interdisciplinary perspective, Lifelong learning, Scholarship, Communication, Critical thinking, Environmental literacy, Information literacy, Research, Ethics, Global Perspective, Self-motivation.
View more information about Otago's graduate attributes- Learning Outcomes
Through successful completion of this paper, students will be able to:
- Understand and apply spatial analysis to an advanced level through geostatistics, including error modelling, spatial autocorrelation and kriging interpolation topics.
- Understand and apply spatial Principal Component Analysis from a mathematical and algorithmic basis.
- Understand the nature and scope of spatial Artificial Intelligence techniques, including neural networks for remote sensing classification and fuzzy logic to extract natural features.
- Understand and apply visual techniques of geographic exploration, including multivariate mapping and geovisual analytics.
- Understand and apply cognitive principles to maps and interfaces in order to assess their usability and to foster better design.
- Understand advanced visual representation through maps and interfaces, including sound and touch maps, spatialisations, graphs, game-based and artistic approaches.
- Develop and apply practical research project experience in the design, development, demonstration, evaluation and reporting of a spatial analysis, modelling and/or visualisation procedure or application.