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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 data analysis and visualisation in the course of their dissertation research. They will also be exposed to common datasets and methods for fusing them. 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 analysis technique for a given scientific analysis or inquiry.
Course description 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 ArcGIS may be required for data preprocessing and preparation.

Outline syllabus:
Weeks 1-4: Weekly 3-hour labs in GIS and remote sensing (EO)
Week 5: Assessment 1: GIS-EO, with 3 hour surgery
Weeks 7-10: Weekly 3-hr labs in Python
Week 11: Assessment 2: Python, leveraging GIS, with 3 hour surgery

Detailed Syllabus
Week 1: Intro to remote sensing: What does a satellite measure, what is a raster, vector and region of interest, quality of data. Intro to using GIS to visualise and assess spatial data.
Week 2: Basic remote sensing product generation using GIS.
Week 3: Dealing with data from diverse sources and formats. File types and geodetic projections. Combining raster and vector data.
Week 4: Manipulating and querying data. Using raster calculators to join and merge different rasters. Spatial statistics: querying data using rasters and vectors.
Week 5: Surgery for GIS Assessment.

Part 2) Python and data analysis
Week 7: Introduction to basic programming concepts. Variables and types of variables. Basic programs and user input. Reading data from files and plotting 1D data.
Week 8: Basic descriptive statistics in Python. Generating and formatting plots of distributions. Concepts of code sustainability and control flow through generalisation to different data sets
Week 9: Two-variable and multivariate statistics: correlation and regression. Reading multivariate data from files; Arrays, for-loops and subplots.
Week 10: Different types of data interpolation. Reading and plotting raster files, introduction to PyGDAL.
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.
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Co-requisites
Prohibited Combinations Other requirements None
Course Delivery Information
Academic year 2020/21, 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) 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. . For example: Investigating marine exposure to climate change. Separate from an appendix containing the annotated code, the written report will be no more than 1500 word equivalent.

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 1500 word equivalent.

Assessment deadlines:
Assessment 1 deadline - Week 7
Assessment 2 deadline - early Dec
Feedback Formative feedback will be provided in the course via practicals in Weeks 3-10. This will be provided by a combination of academic staff and postgraduate tutors. 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 understanding of basic concepts of computer programming and GIS analysis (Python and ArcGIS techniques).
  2. Gain appreciation of basic descriptive and inferential statistics.
  3. Become familiar with some common spatial datasets, including remote sensing, climate and social datasets.
  4. Develop an understanding of a range of data processing/analysis techniques and the ability to determine suitable data analysis approaches to test hypotheses.
  5. Attain solid grounding to enable self-learning of additional data analysis techniques beyond those taught in the course.
Reading List
GIS Resources:
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.

Suggested reading;

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 ( 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. 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.

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 Carry Arnold
Tel: (0131 6)50 9847
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