2021 information for papers will be published in early September.
Spatial data structures and algorithms, underpinned by spatial databases and programming, including customisation of GIS software.
Geographic Information Systems (GIS) have gained increasing acceptance as important
tools for the organisation, analysis and visualisation of data pertinent to a host
of land/resource management and planning activities. This paper teaches students
to expand their effective use of a GIS by looking "under the hood" of this technology
and science. Firstly, an in-depth grounding in the major spatial data structures (raster
and vector) is provided. In this way, the major aspects of GIS (analysis, storage,
visualisation) are explored from a programmatic (Python) and algorithmic point of
An important aspect to be introduced in this paper is the customisation of ArcGIS (i.e. adding extra functions) using programming - along with scripting, these are skills that can be adapted to make the most of the expanding open-source GIS world (e.g. QGIS). Programming for the web and geostatistical, 3D model and dynamic model scripting will also be examined. More specifically, the paper will cover spatial databases being used to store and manipulate different types of spatial data (e.g. point, line, polygon, grid cell features), as well as an algorithmic look at the some of the simpler spatial analysis and visualisation. This computational approach to GIS is essential to support and underpin proficiency in utilising GIS and is cemented by a major final project in the paper assessment.
|Paper title||Spatial Algorithms and Programming|
|Teaching period||Second Semester|
|Domestic Tuition Fees (NZD)||$1,233.91|
|International Tuition Fees (NZD)||$5,150.69|
- SURV 102 and (SURV 208 or SURV 218 or SPIN 201)
- SURV 319
- Recommended Preparation
- COMP 150
- This paper supports the 500-level courses in the BAppSc(Hons) GIS degree, PGDipAppSci in GIS, MAppSc in GIS and MSc in GIS.
- Teaching staff
- Paper Structure
- The paper covers topics on:
- Raster and vector spatial data models and structures
- Spatial algorithms for data analysis, visualisation and processing
- Dynamic modelling techniques based on raster and vector structures
- Programming, scripting and customisation in commercial GIS, open-source GIS, web, geostatistical, 3D modelling and dynamic modelling environments
- Teaching Arrangements
- In general there will be two lectures, one programming tutorial and a 3-hour lab per week, supporting the assessment schedule (consisting of spatial programming labs and two self-defined projects).
- M. Worboys and M. Duckham (2005) GIS: A Computing Perspective, 2nd Edition
- Graduate Attributes Emphasised
- 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
- The objectives of this paper are to:
- To establish foundational knowledge of the spatial data models (raster and vector), structures and algorithms that underpin GIS, practically reinforced by activities in the Python programming language with a leading GIS
- Use the same approach to understand and implement simple spatial analysis and visualisation techniques
- Introduce the process of customisation of a GIS, interacting with the GIS with Python code to implement a procedure that currently does not specifically exist in the GIS
- Provide a strong foundation of knowledge in the nature of spatial data as a special, unique and complex resource and its manipulation using programming
- Introduce other geospatial programming and scripting contexts, such as open-source GIS, programming for the web and scripting for geostatistics (e.g. R, Matlab), 3D modelling (e.g. Unity) and dynamic modelling (e.g. NetLogo)
- Provide practical project experience in the development, documentation and demonstration of a spatial processing, analysis or visualisation procedure (which may use collected data) in a commercial GIS (e.g. using Python in conjunction with ArcGIS or open-source GIS), web, geostatistical, 3D model or dynamic model context