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DRPS : Course Catalogue : School of Geosciences : Geography

Undergraduate Course: Key Methods in Physical Geography (GEGR09018)

Course Outline
SchoolSchool of Geosciences CollegeCollege of Science and Engineering
Credit level (Normal year taken)SCQF Level 9 (Year 3 Undergraduate) AvailabilityNot available to visiting students
SCQF Credits20 ECTS Credits10
SummaryThis 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.

This course is compulsory for 3rd year students on the BSc Geography Degree programme. The course is open to other university students, however priority will be given to BSc Geography students.
Course description The overall aim of the course is to show how geoscientists can use data to guide decision making. The course will provide 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. Through 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 analysis of real geographical data drawn from freely available 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 simpler for data pre-processing and preparation.

Outline Syllabus:
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

Detailed Syllabus:
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 for decision making. SH
Week 3: Accuracy assessments. How useful is your map? SH
Week 4: Upscaling land surface property mapping in time and space and using it to inform society SH

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: 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.

This course is compulsory for 3rd year students on the BSc Geography Degree programme. The course is open to other university students, however priority will be given to BSc Geography students.
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Co-requisites
Prohibited Combinations Other requirements No pre-requisites.

This course is compulsory for 3rd year students on the BSc Geography Degree programme. The course is open to other university students, however priority will be given to BSc Geography students.
Course Delivery Information
Academic year 2023/24, Not available to visiting students (SS1) Quota:  None
Course Start Semester 1
Timetable Timetable
Learning and Teaching activities (Further Info) Total Hours: 200 ( Programme Level Learning and Teaching Hours 4, Directed Learning and Independent Learning Hours 196 )
Assessment (Further Info) Written Exam 0 %, Coursework 100 %, Practical Exam 0 %
Additional Information (Assessment) 100% Coursework

1) GIS Assessment (50%). Report generated making use of remote sensing data to investigate an environmental phenomenon of societal importance. 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 pre-processing). 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: To give practice before the GIS assessment, there is a formative assessment at the end of week 4. This will be a write up of the week 3 practical and will touch on the skills required for the GIS assessment. Due in week 4.

Assessment deadlines

Assessment 1 deadline: week 6.
Assessment 2 deadline: week 11
Formative assessment deadline: week 4

Feedback 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
Learning Outcomes
On completion of this course, the student will be able to:
  1. Gain an understanding of basic concepts of computer programming and GIS analysis (Python, QGIS and ArcGIS techniques)
  2. Become familiar with some common spatial datasets, including remote sensing, climate and social datasets.
  3. Develop an understanding of a range of data processing/analysis techniques and the ability to determine suitable data analysis approaches to test hypotheses.
  4. Gain appreciation of basic descriptive and inferential statistics and their use in interpreting data.
  5. Attain solid grounding to enable self-learning of additional data analysis techniques beyond those taught in the course
Reading List
Remote sensing and image interpretation / Thomas M. Lillesand, Ralph W. Kiefer, Jonathan W. Chipman. Lillesand, Thomas M. ; Kiefer, Ralph W. ; Chipman, Jonathan W. Hoboken, NJ : Wiley & Sons ; 2015

Python Beginners Guide:

Python Quick Start (Beginner) and Python Data Analysis (Advanced) on LinkedinLearning (access through myed)
Additional Information
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.
KeywordsQuantitative techniques,statistical analysis,programming,GIS,remote sensing
Course organiserDr Steven Hancock
Tel: (01316)51 7112
Course secretaryMiss Leigh Corstorphine
Tel: (01316) 502572
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