Overview
Foundations of spatial data management, analysis and mapping through Geographic Information Systems (GIS). Built environment spatial analysis, cartography, vector and raster spatial data structures, and data discovery and acquisition emphasised.
Geographic Information Systems (GIS) deal with the acquisition, management, analysis, and visualization of geographic information, called spatial data. They apply concepts drawn from the interdisciplinary field of Geographic Information Science (GIScience), which spans across the disciplines of surveying, geography, ecology, geology, archaeology, public health, social sciences, into the arts and beyond. The GIS helps contextualises raw data, enabling meaningful information and eventually new knowledge. Spatial data are used throughout local and national government, by private businesses and in diverse research fields. GIS and spatial data technology are among the fastest-growing industries in the world, with a multi-billion dollar market.
This course will introduce you to the different aspects of both GIS and GIScience. Topics include spatial data structures and representation, coordinate systems and data transformations, Mobile/Web-GIS and volunteered geographic information, spatial analysis, mapping techniques, cartography and geovisualisation, uncertainty analysis and the social and ethical considerations of spatial data and the GIS.
About this paper
| Paper title | Geographic Information Science |
|---|---|
| Subject | Surveying |
| EFTS | 0.1334 |
| Points | 18 points |
| Teaching period | Semester 1 (On campus) |
| Domestic Tuition Fees ( NZD ) | $1,278.51 |
| International Tuition Fees | Tuition Fees for international students are elsewhere on this website. |
- Prerequisite
- 54 points
- Restriction
- SPIN 201, SURV 208
- Schedule C
- Commerce, Science
- Eligibility
No prior experience is required, however, a 100-level background introductory paper in geospatial science is beneficial.
- Contact
- Teaching staff
Co-ordinator and Lecturer:Aubrey Miller
Lecturers:Associate Professor Tony Moore and Kelly Gragg
- Paper Structure
Lectures are supported by computer lab-based practical assignments.
Paper topics include:
- Spatial data capture and Big Data
- Data acquisition, Metadata and uncertainty
- Data transformation and georeferencing
- GIS and data management
- Data formats and algorithms
- Vector and Raster data structures
- Simple vector analysis
- Network vector analysis and topology
- Cartography, composition and symbolisation
- Thematic maps and generalisation
- Neocartography and other types of mapping
- Teaching Arrangements
There are, in general, two lectures per week, supported by a 2-hour practical lab for nine weeks.
- Textbooks
Geographic Information Systems and Science, 4th Edition (2015): by P. Longley, M. Goodchild, D. Maguire, and D. Rhind, John Wiley and Sons, Toronto (available as eBook or on reserve in the Central Library).
- Graduate Attributes Emphasised
- Interdisciplinary perspective, Lifelong learning, Scholarship, Environmental literacy, Information literacy.
View more information about Otago's graduate attributes. - Learning Outcomes
Students who successfully complete the paper will:
- Be able to distinguish between continuous and discrete geographic phenomena and field and object conceptual models of space
- Demonstrate the capabilities of basic GIS data analysis and visualisation methods
- Know how to apply simple analysis techniques such as database search and retrieval, overlay, buffering and filtering
- Demonstrate knowledge and use of more advanced analytical techniques associated with networks
- Be able to use GIS to create effective maps based on cartographic symbology and composition principles
- Know about geographic visualisation technologies
- Be able to use fundamental GIS analytical techniques to solve a variety of problems
- Know the correct technique to use in the correct situation and practically apply them in a structured way
- Appreciate the massive variety of applications that GIS is used in
- Generating derived spatial data and creating new spatial data, understanding the current context in which this occurs
- Understand the underlying role of map projections and coordinate systems for spatial data
- Know about the sources of spatial data and appreciate their complex nature (including data quality, data that changes through time, and three-dimensional data)
- Appreciate that data can now be volunteered (crowdsourcing) and collected by widely-available devices (e.g. smartphones) and delivered via the web and in a mobile sense