Studying at York
Spatial Analysis in GIS - ARC00083M
- Department: Archaeology
- Credit value: 5 credits
- Credit level: M
- Academic year of delivery: 2022-23
Module summary
This module is aimed at deepening students’ basic GIS skills by developing their understanding of analysis and model building in archaeology. This module will introduce methods beyond standard curatorial GIS practice in archaeology, and introduce skills that will allow innovative archaeological research, as well as enable wider application in cognate disciplines. Students choose this module particularly if they plan to incorporate GIS in their dissertation research.
Related modules
This module follows on from ARC00049M Geographic Information Systems. If you have not completed this course, you may be allowed to attend if you can demonstrate competence in ArcGIS. Contact Helen Goodchild (helen.goodchild@york.ac.uk) well in advance to establish whether you have the requisite skills.
Module will run
| Occurrence | Teaching period |
|---|---|
| A | Spring Term 2022-23 |
Module aims
By engaging with spatial analytical methods in GIS, students will:
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demonstrate understanding of the different analysis techniques that can be used with archaeological data, and the inherent problems in these datasets
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carry out problem-solving activities with spatial data
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have experience of advanced methods such as predictive modelling, geomorphometry and spatial statistics, and be aware of their potential within archaeology
Module learning outcomes
By the end of the module the students should be able to:
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apply their understanding of the use of GIS in complex, spatially-based archaeological investigation and analysis
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demonstrate expertise in navigating and analysing with the ArcGIS software
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create layouts at publication standard
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apply appropriate spatial analytical techniques to data in order to solve archaeological problems
Module content
The course is taught via a series of lectures and practical computing workshops. We cover basic image processing techniques for LiDAR and satellite imagery, and geomorphometric analysis of landscapes to enable the modelling of terrain, beyond the standard techniques usually employed. Students will look at how to analyze point distributions using spatial statistical techniques. In the latter half of the course, they apply these techniques to a final project of their own choosing, which forms the bulk of their assessment. Many students choose to use this opportunity to carry out pilot projects for their dissertation analyses, but there are a number of pre-set projects that may also be chosen from.
Indicative assessment
| Task | % of module mark |
|---|---|
| Essay/coursework | 100 |
Special assessment rules
Pass/fail
Additional assessment information
This is a 5 credit vocational learning module, it aims to equip students with competence in the technical/ specialist skills learned and is Pass / Fail, assessed according to whether the student demonstrates a satisfactory competence or not. The form of assessment cannot be exactly replicated for a student who fails and needs to resubmit, or for a student who is absent for the session in which the assessment occurs. The written alternative assessment is designed specifically to test the same skill learning outcomes through a critique.
Indicative reassessment
| Task | % of module mark |
|---|---|
| Essay/coursework | 100 |
Module feedback
Feedback will be available within 4 weeks.
Indicative reading
Historic England (2018) Using Airborne Lidar in Archaeological survey
https://historicengland.org.uk/images-books/publications/using-airborne-lidar-in-archaeological-survey/heag179-using-airborne-lidar-in-archaeological-survey/ [Online pdf guidance]
Stavrova, T., Borel, A., Daujeard, C., & Vettese, D. (2019) “A GIS based approach to long bone breakage patterns derived from marrow extraction”, PLoS ONE 15(5): e0216733
https://doi.org/10.1371/journal.pone.0216733
Verhagen, P. (2017) “Spatial Analysis in Archaeology: Moving into New Territories”. In C. Siart, M. Forbriger & O. Bubenzer (eds) Digital Geoarchaeology; p11-25.
https://link.springer.com/chapter/10.1007/978-3-319-25316-9_2