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Curricular information is subject to change
This module is as a core skills module and aims to deliver the following learning outcomes:
• Familiarize yourself with different programming languages commonly used in geospatial data analysis, and
how to use these technologies to expand upon existing GIS software functionality.
• Demonstrate an understanding of programming concepts, methods, and approaches such as debugging,
error checking, and documentation.
• Conduct advanced geospatial statistics with R.
• Program small-scale GIS-based models in Python and integrated within QGIS to automate geoprocessing
tasks.
• Critically evaluate different methodologies for developing applications in GIS.
• Conceptualize, plan, implement, and write up the results of an original GIS programming application,
customization, automation and/or extension.
Week 1 Introduction to R & QGIS for spatial data analysis. Lab: Data engineering & Summary Statistics in R.
Week 2 Hypothesis Testing & Autocorrelation. Lab: Hot Spots in R.
Week 3 Clustering & Aggregating Data. Lab: Point Pattern Analysis in R.
Week 4 Spatial Regression Models, Extrapolation & Forecasting. Lab: Spatial Regression in R and Raster Time Series Forecasting.
Week 5 Spatial Indexing SQL & Indexing in QGIS. Lab: Creating indexes from regression models in QGIS.
Week 6 Network Analysis. Lab: Network models.
Week 7 Introduction to Python for GIS analysis. Lab: PyQGIS Fundamentals.
Week 8 & 9 Fieldtrip weeks - No lectures or labs.
Week 10 Python Programming. Lab: Scraping Geo Data with Python
Week 11 GIS Data Access and Manipulation with Python. Lab: Working with Raster Data in Python.
Week 12 Practical Python for the GIS Analyst. Lab: Writing Geometries in Python.
Week 13 Advanced GIS with Python. Lab: Advanced GIS methods in Python.
Week 14 Final Project Presentations.
| Student Effort Type | Hours |
|---|---|
| Lectures | 12 |
| Computer Aided Lab | 12 |
| Autonomous Student Learning | 80 |
| Total | 104 |
Not applicable to this module.
| Description | Timing | Component Scale | % of Final Grade | ||
|---|---|---|---|---|---|
| Assignment: Final project. | Coursework (End of Trimester) | n/a | Graded | Yes | 40 |
| Continuous Assessment: Weekly lab. | Throughout the Trimester | n/a | Graded | Yes | 55 |
| Presentation: Final Project presentation | Week 12 | n/a | Pass/Fail Grade Scale | No | 5 |
| Remediation Type | Remediation Timing |
|---|---|
| In-Module Resit | Prior to relevant Programme Exam Board |
• Feedback individually to students, post-assessment
• Group/class feedback, post-assessment
- Group feedback on common issues arising from the written assignments will be provided on brightspace. - Timely individualised feedback will be provided on written assignments. - Students are welcome to meet with the module coordinator during office hours (virutally) if more detailed feedback / further clarification is required. Please note: The UCD standard for feedback is within 20 working days, i.e. 5 weeks. We will endeavour to return assignments that are completed on time within 2-3 weeks, and where possible before the next assignment is due. Assignments handed in late will be subject to University timescales. This may mean that if you hand in your assignment late, another assignment will need to be completed, before the original one is marked. This will also be the case with the final project report, i.e. if you complete the assignments late, you may not have feedback in time to use it for your final project report.
| Name | Role |
|---|---|
| Dr Srikanta Sannigrahi | Lecturer / Co-Lecturer |