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SURV511 Advanced Spatial Analysis and Modelling

Spatial analysis, including geostatistics, error propagation, geographically weighted regression; environmental modelling and AI-based spatial modelling, including cellular automata / agents & 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.

Paper title Advanced Spatial Analysis and Modelling
Paper code SURV511
Subject Surveying
EFTS 0.1667
Points 20 points
Teaching period Second Semester
Domestic Tuition Fees (NZD) $1,307.93
International Tuition Fees (NZD) $5,151.03

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Prerequisite
216 points (including SURV 208 or SURV 218 or SPIN 201)
Restriction
SURV 411, SPIN 402
Recommended Preparation
One of SURV 310, SURV 319, SURV 508, SURV 519
Eligibility
This paper supports the 400/500-level courses in the BAppSc (GIS) degree, PGDipAppSci in GIS and MAppSc in GIS.
Contact
pascal.sirguey@otago.ac.nz
Teaching staff
Convenor: Dr Pascal Sirguey
Lecturers: Dr Pascal Sirguey, Dr Tony Moore and Professor Christina Hulbe
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 4 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 (eg Matlab).
Textbooks
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, 2nd edition, 2005 (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

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Timetable

Second Semester

Location
Dunedin
Teaching method
This paper is taught On Campus
Learning management system
Blackboard

Lecture

Stream Days Times Weeks
Attend
L1 Wednesday 10:00-11:50 28-34, 36-41

Practical

Stream Days Times Weeks
Attend
P1 Thursday 09:00-10:50 28-34, 36-41

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.

Paper title Spatial Analysis and Modelling
Paper code SURV511
Subject Surveying
EFTS 0.1667
Points 20 points
Teaching period Second Semester
Domestic Tuition Fees (NZD) $1,334.10
International Tuition Fees (NZD) $5,357.07

^ Top of page

Prerequisite
216 points (including SURV 208 or SURV 218 or SPIN 201)
Restriction
SURV 310, SURV 411, SURV 508, SPIN 402
Recommended Preparation
One of SURV 319, SURV 519
Eligibility
This paper supports the 400-/500-level courses in the BAppSc (GIS) degree, PGDipAppSci in GIS and MAppSc in GIS.
Contact
pascal.sirguey@otago.ac.nz
Teaching staff
Convenor: Dr Pascal Sirguey
Lecturers: Dr Pascal Sirguey, Dr Tony Moore and Professor Christina Hulbe
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.
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.
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 (eg Matlab).
Textbooks
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, 2nd edition, 2005 (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)
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

^ Top of page

Timetable

Second Semester

Location
Dunedin
Teaching method
This paper is taught On Campus
Learning management system
Blackboard

Lecture

Stream Days Times Weeks
Attend
L1 Wednesday 10:00-11:50 28-34, 36-41

Practical

Stream Days Times Weeks
Attend
P1 Thursday 09:00-10:50 28-34, 36-41