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) are being applied increasingly to a variety of human and natural problems that are too numerous and too diverse to list. Since spatial factors are central to almost all issues that involve the management and use of land and human occupancy, it is important that you develop a sound grasp of the principles of GIS and the means of applying it. As surveyors (geographers, planners, geologists, etc.), it is essential to understand the end uses of survey data as they are transformed from field collection into information and eventually into new knowledge. There is barely a single area of local and national government internationally that does not use spatial data of some form or another, and through this the spatial data and information, technology industries are among the fastest-growing in the world, with a multi-billion dollar market.
In this paper, you will be introduced to the key concepts of GIS that are central to the uses of survey and other spatial data. These include technical concepts of data structures, use of ground co-ordinates and maps, and the integration of map data and tables of descriptive information linked to maps.
About this paper
|Paper title||Geographic Information Science|
|Teaching period||Semester 1 (On campus)|
|Domestic Tuition Fees ( NZD )||$1,206.20|
|International Tuition Fees||Tuition Fees for international students are elsewhere on this website.|
- 54 points
- SPIN 201, SURV 218
- Schedule C
This paper supports the 200-level courses in the BAppSc GIS Degree, GIS minor and BSurv degree.
- Teaching staff
- Paper Structure
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 map
- Teaching Arrangements
- There are, in general, three lectures per week, supported by a 3-hour practical lab for eight weeks.
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 and surfaces (DEMs)
- 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