Undergraduate Course: Key Methods in Physical Geography (GEGR09018)
|School||School of Geosciences
||College||College of Science and Engineering
|Credit level (Normal year taken)||SCQF Level 9 (Year 3 Undergraduate)
||Availability||Not available to visiting students
|Summary||This course is designed to provide Physical Geography students with the key computational and GIS techniques needed for geospatial data analysis and visualisation; skills that can be used during dissertation research and beyond. Students will be exposed to common geospatial datasets, how they are derived, where they can be found, and methods for fusing them with other physical and human geography datasets to inform decisions. The students will be trained in data analysis using Python and ArcGIS through a series of taught practicals and two assessed coursework projects. Emphasis will be placed on transferable skills and on identifying the most appropriate technique for a given scientific analysis or inquiry.
The overall aim of the course is to provide the BSc students competency in visualising and statistically analysing large data sets from a range of subdisciplines across physical geography, and in a range of data formats commonly used in geoscientific data analysis (e.g., geotiff and NetCDF). Methods taught will centre around 2 commonly-used data analysis tools: GIS software, and the Python programming language. Though this lens the students will be taught basic statistical and numerical analysis techniques (e.g. correlation, regression and statistical significance), and sequential and spatial analysis techniques (e.g. interpolation, mosaicking and smoothing). The course will be delivered as a series of practicals, devised to take the students through the core skills and concepts while at the same time engaging them with data analysis of real geographical data drawn from online sources. Demonstrators will provide support but course staff will play a proactive role via demonstrations and discussion.
Emphasis throughout the course will be in delivering key academic and transferrable skills training which students can tailor to an area of geography of their choice, and which will act as crucial preparation for undertaking independent research in Years 3 to 4 of a Geography degree. Additionally, emphasis will be placed on identifying the most appropriate computational/analysis technique for a given scientific investigation. For instance, regression may be more easily carried out in Python but GIS may be required for data pre-processing and preparation.
Weeks 1-4: Weekly 3-hour labs in GIS and remote sensing (EO)
Weeks 5-10: Weekly 3-hour labs in Python (Assessment 1 in week 6)
Week 11: Assessment 2: Python, leveraging GIS, with optional 3 hour python surgery
Part 1) GIS and Remote Sensing
Week 1: Intro to geospatial analysis and remote sensing: Common forms of geospatial data (raster, vector and shapefile), what does a satellite measure and how to extract information from it. SH
Week 2: Mapping land surface properties with GIS and remote sensing. SH
Week 3: Accuracy assessment of remote sensing products to inform decision making. SH
Week 4: Using remotely sensed and modelled data to inform society; adding human data. GW
Part 2) Python and data analysis
Week 5: Introduction to basic programming concepts. Variables and types of variables. Basic programs and user input. DG
Week 6: Understanding modules, lists and arrays in Python. Simple plotting and plot formatting; Reading geographical data in Python; basic Batch Processing. DG
Week 7: More with Python arrays, loading data from text files. Working with shapefiles. Descriptive statistics of large datasets. DG
Week 8: Sequential Analysis. Different types of interpolation. Trend analysis. Statistics of time series data. User defined functions. DG
Week 9: Spatial Analysis. Reading and plotting raster data: working with geotiffs. Comparing vector and raster data (ground-truth methods). Raster generation. Slope analysis. Plotting NetCDF data. DG
Week 10: Bringing GIS and Python together: using Python to analyse snow data DG and SH
Week 6: Optional GIS surgery and GIS assessment deadline
Week 11: Surgery for Python assessment and Python assessment deadline
For a 20 credit 200hr option, the student time will be 30hrs contact, 3 hrs computer surgeries and 167hrs of Independent study, completion of work associated with practicals, and completion of assignments. For 2021 the practicals will be performed online due to restrictions in room capacity during COVID. Links will be provided through LEARN.
Entry Requirements (not applicable to Visiting Students)
||Other requirements|| None
Course Delivery Information
|Academic year 2021/22, Not available to visiting students (SS1)
|Learning and Teaching activities (Further Info)
Programme Level Learning and Teaching Hours 4,
Directed Learning and Independent Learning Hours
|Assessment (Further Info)
|Additional Information (Assessment)
||1) GIS Assessment (50%). Report generated making use of remote sensing data to investigate an environmental phenomenon of societal importance. For example: Investigating marine exposure to climate change. Separate from an appendix containing the annotated code, the written report will be no more than 2,000 word equivalent. Due in week 6.
2) Python Assessment, leveraging GIS skills (50%). Students will be asked to carry out modelling and interpretation with multisource environmental data using python programming, but also will be required to engage with GIS methods where appropriate (e.g. visualisation and preprocessing). Example: tidewater glacier response to large-scale atmospheric and oceanic anomalies. Separate from an appendix containing the annotated code, the written report will be no more than 1,500 word equivalent. Due in week 11
||Formative feedback will be provided in the course via practicals in Weeks 1-10. This will be provided by a combination of academic staff and postgraduate demonstrators. Summative feedback will be provided via written comments from the coursework assessments.
|No Exam Information
On completion of this course, the student will be able to:
- Gain understanding of basic concepts of computer programming and GIS analysis (Python, QGIS and ArcGIS techniques)
- Become familiar with some common spatial datasets, including remote sensing, climate and social datasets.
- Develop an understanding of a range of data processing/analysis techniques and the ability to determine suitable data analysis approaches to test hypotheses.
- Gain appreciation of basic descriptive and inferential statistics and their use in interpreting data.
- Attain solid grounding to enable self-learning of additional data analysis techniques beyond those taught in the course
Computer lab with access to the University¿s windows system. Practicals will make use of ArcGIS and excel software (installed as standard). Access to around 50 Gbytes of input data will be required, to be used throughout the course.
Lillesand, T., Kiefer, R.W. and Chipman, J., 2015. Remote sensing and image interpretation. John Wiley & Sons.
Heywood, I., Cornelius, S. and Carver, S. (2011) An Introduction to Geographical Information Systems. Prentice Hall, Fourth Edition.
In addition, each week¿s work will be illustrated by research papers and reports, links to which will be provided through LEARN.
Python Programming Resources:
There are extensive resources for learning how to program with Python, ranging from beginner to very in-depth. Two popular texts are
Python for Beginners: The Ultimate Beginners Guide to Python Programming With Step by Step Guidance and Hands-On Examples by Denny Novikov (Amazon Digital Services)
Python For Dummies by Stef Maruch (Wiley).
However, the Python Software foundation (https://www.python.org/about/gettingstarted/) has a number of links to helpful sites and online video courses. Also, the university subscribes to LinkedIn Learning, a website with thousands of online courses on various topics ¿ including introductory videos for Python. You can access LinkedIn Learning via MyEd, and we highly encourage you to use this resource.
Wikipedia is helpful for your own reference but should not be used in your bibliography. Similarly a number of online resources (e.g. https://stackoverflow.com/) provide informal solutions to common problems. A commonly-used approach is simply to enter a python error message into Google. However, these websites again should not be used in a bibliography.
Still, the best initial source is the material provided on Learn as it distills out the concepts on which the course focuses.
|Graduate Attributes and Skills
||Students will be familiar with the capabilities of ArcGIS and Python and be able to use them to analyse geospatial data. They will understand the fundamentals of remote sensing data, its abilities and limitations and how to use it to measure and model socially relevant processes.
|Keywords||Quantitative techniques,statistical analysis,programming,GIS,remote sensing
|Course organiser||Dr Steven Hancock
Tel: (01316)51 7112
|Course secretary||Miss Carry Arnold
Tel: (0131 6)50 9847