MTHM609 | 2024/5 | University of Exeter

MODULE TITLE	Environmental Intelligence: Data	CREDIT VALUE	15
MODULE CODE	MTHM609	MODULE CONVENER	Unknown

DURATION: TERM	1	2	3
DURATION: WEEKS	11	0	0

Number of Students Taking Module (anticipated)	30

DESCRIPTION - summary of the module content

In this module you will learn how to extract information from data to support evidence-based decision making. You will learn about the different types of environmental data that are available, including data from monitoring, remote sensing satellites, surveys and numerical models and gain first-hand experience of data wrangling and will understand the need for different data formats, e.g. with big data, and how to manipulate them to produce datasets that are in a form suitable for the application of sophisticated statistical, machine learning and artificial intelligence techniques. You will learn about the power of spatial data and the use of geographical information systems (GIS) in overlaying spatial datasets and in producing maps of data and model outputs.

Co-requisite modules: MTHM610

AIMS - intentions of the module

The aim of this module is to equip you with the skills you will need to clean, manipulate, analyse, visualise, model and interpret spatial data appropriately. An important part of this will be the ability to merge information from multiple sources as well as deal with changes in support of the data, in order to answer questions and gain extra insight. Learning these skills will be based on a combination of taught material and ‘hands-on’ sessions using R/RStudio. Assessment will be based on a series of practical examples using real-world data examples that aim to demonstrate the full range of skills require to make effective use of data.

INTENDED LEARNING OUTCOMES (ILOs) (see assessment section below for how ILOs will be assessed)

On successful completion of this module you should be able to:

Module Specific Skills and Knowledge

1. Demonstrate an understanding of how the source of data, and how it was collected, can influence subsequent analyses.

2. Demonstrate the ability to import, manipulate and summarise spatial data, including an understanding of the relative merits of different methods of formatting.

3. Demonstrate effective spatial data wrangling, modelling and visualisation.

Discipline Specific Skills and Knowledge

4. Demonstrate an understanding of data analyses.

5. Demonstrate an understanding of the important and practical use of the graphical representation of summaries of, and patterns in, spatial data.

Personal and Key Transferable / Employment Skills and Knowledge

6. Perform spatial data analysis.

7. Use R/RStudio (or other software) to implement methods in data science, machine learning, artificial intelligence and statistics.

8. Use learning resources effectively.

9. Communicate the results of data analysis clearly and accurately, both in writing and verbally.

SYLLABUS PLAN - summary of the structure and academic content of the module

Whilst the module’s precise content may vary from year to year, an example of an overall structure is as follows:

Types of data (monitoring, surveys, remote sensing, numerical models)
Data collection mechanisms (biases, errors and uncertainties, measurement error)
Data formats (points, shapefiles, grids, similarities and differences, GIS, projections)
Visualisation (effective map drawing)
Data wrangling (change of support, working between projections, overlaying)
Spatial data analysis and modelling
Challenges of working with big data

Other suitable topics may also be offered.

LEARNING AND TEACHING

LEARNING ACTIVITIES AND TEACHING METHODS (given in hours of study time)

Scheduled Learning & Teaching Activities	33	Guided Independent Study	117	Placement / Study Abroad	0

DETAILS OF LEARNING ACTIVITIES AND TEACHING METHODS

Category	Hours of study time	Description
Scheduled Learning and Teaching Activities	22	Lectures
Scheduled Learning and Teaching Activities	11	Hands-on practical sessions
Guided Independent Study	47	Self-study and background reading
Guided Independent Study	70	Assessed data analyses, report writing

ASSESSMENT

FORMATIVE ASSESSMENT - for feedback and development purposes; does not count towards module grade

Form of Assessment	Size of Assessment (e.g. duration/length)	ILOs Assessed	Feedback Method
Feedback on unassessed practical session activities, problem sheets or data analyses	10 x 1 hour	1-9	Oral, in practical sessions

SUMMATIVE ASSESSMENT (% of credit)

Coursework	100	Written Exams	0	Practical Exams	0

DETAILS OF SUMMATIVE ASSESSMENT

Form of Assessment	% of Credit	Size of Assessment (e.g. duration/length)	ILOs Assessed	Feedback Method
Coursework – extended data analysis involving data collection, analysis and reporting	100	Max 10 pages (plus appendices)	1-9	Written and oral

DETAILS OF RE-ASSESSMENT (where required by referral or deferral)

Original Form of Assessment	Form of Re-assessment	ILOs Re-assessed	Time Scale for Re-assessment
Coursework – extended data analysis involving data collection, analysis and reporting	Coursework – extended data analysis involving data collection, analysis and reporting (100%)	1-9	Ref/def period

RE-ASSESSMENT NOTES

Please refer to the TQA section on Referral/Deferral: https://as.exeter.ac.uk/academic-policy-standards/tqa-manual/aph/consequenceoffailure/

RESOURCES

INDICATIVE LEARNING RESOURCES - The following list is offered as an indication of the type & level of
information that you are expected to consult. Further guidance will be provided by the Module Convener

Reading list for this module:

Type	Author	Title	Edition	Publisher	Year
Set	Bivand, Roger S., et al.	Applied Spatial Data Analysis with R	Vol. 747248717	Springer	2008
Set	Blangiardo, Marta and Cameletti, Michela	Spatial and Spatio-temporal Bayesian Models with R-INLA		John Wiley & Sons	2015
Set	Moraga, Paula	Geospatial Health Data: Modeling and Visualization with R-INLA and Shiny		CRC Press	2019
Set	Lawson, Andrew B., et al., eds.	Handbook of Spatial Epidemiology		CRC Press	2016

CREDIT VALUE	15	ECTS VALUE	7.5

PRE-REQUISITE MODULES	None
CO-REQUISITE MODULES	None

NQF LEVEL (FHEQ)	7	AVAILABLE AS DISTANCE LEARNING	No
ORIGIN DATE	Wednesday 12th January 2022	LAST REVISION DATE	Tuesday 17th January 2023

KEY WORDS SEARCH	Environmental data; visualisation; spatial data analysis; data formatting

Environmental Intelligence: Data - 2024 entry