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Analysis of Environmental Data

Module titleAnalysis of Environmental Data
Module codeGEO1406B
Academic year2020/1
Module staff

Dr Jo Browse ()

Duration: Term123
Duration: Weeks


Number students taking module (anticipated)


Description - summary of the module content

Module description

This module will give you practical insights into how scientists address fundamental questions and hypotheses using data. We start with simple toolkits to describe data and move on to more advanced ways of comparing data and describing data trends. Once you finish the module you will be competent in managing data and handling data in software programmes (e.g. MS Excel and R), and you will know how to critique different methods commonly used in scientific data analysis.

This module uses a combination of lectures, supervised practical classes, online (ELE) teaching resources, help sessions, and private study time to provide you with the support necessary for achieving the learning objectives. Weekly lectures provide a synoptic overview of the techniques covered in each week’s practical class, and in addition, include discussion of the broader aspects of application of such techniques. Practical classes follow the lecture module, and provide a practical insight into implementation of key techniques. Each practical class is led by lecturing staff, and support staff. Staff are also accessible through an online (ELE) discussion forum to answer your queries. The emphasis is placed upon learning how to apply statistical techniques to solve environmental problems using numerical data of various forms. As such, geographical data from a range of environmental applications are provided in the practical classes for analysis. Weekly summative assessment, completed online provides you with the opportunity to evaluate your progress, since these tests cover the same techniques (different data) to those learned in the lectures and practical sessions. Practical classes also focus on developing essential IT skills where MS Excel and R are used as tools for data manipulation, allowing you to learn key transferable skills in data entry, manipulation and analysis.

Module aims - intentions of the module

This module aims to introduce you to the use of data-centred quantitative analysis techniques in geographical research. The module will establish the purpose and scope of statistical analysis methods, focusing on analytical tests and their execution.  We follow the ‘scientific method’ through from first principles (hypothesis development, distribution testing) to hypothesis testing. We ask you to think about the underlying principles of data collection, sampling and hypothesis-driven research. We use computers to assist us in the aggregation, analysis and presentation of data.

Through lectures and assisted practical classes, you will be encouraged to evaluate and critique statistical methods as one of a suite of analytical techniques available to geographers and scientists. Assisted practical classes complement the lecture series and will provide you with key transferable skills in data handling which will increase your future employability. You will undertake an independent research project during which you will quantitatively explore an unseen dataset using the skills acquired during the module. These skills are relevant for a range of different careers from environmental management and assessment through to energy policy, and so they will help to underpin your future career.

Relevant examples from research being carried out within the Centre for Geography, Environment and Society (CGES) will be showcased to demonstrate how the methods you are learning are used in current research. These examples will include, but are not limited to, shellfish aquaculture, climate studies and the use of drones and satellite observations in environmental research.

Intended Learning Outcomes (ILOs)

ILO: Module-specific skills

On successfully completing the module you will be able to...

  • 1. Describe the range of approaches to collecting geographical data
  • 2. Correctly identify different forms of data, and identify different scales of measurement
  • 3. Calculate and understand basic descriptive statistics including the mean, median, mode, standard deviation and coefficient of variation
  • 4. Discuss the limitations associated with each type of descriptive statistic
  • 5. Apply appropriate techniques to determine whether geographical data are normally distributed (histograms, z-scores, cumulative probability functions)
  • 6. Explain the difference between parametric and non-parametric tests. Those introduced in this module will include: student’s t-test, difference of means (t) test, t-test for tied samples, one-way analysis of variance (ANOVA), Mann-Whitney U test, Wilcoxon’s test for matched samples, Chi Squared.
  • 7. Choose the correct statistical test for determining whether there is a difference in the means and/or variances of two or more samples
  • 8. Use computer software with guidance, to apply the appropriate statistical test in order to accurately and correctly solve predefined geographically related problems
  • 9. Effectively interpret the outcomes of statistical tests using critical values taken from look-up tables

ILO: Discipline-specific skills

On successfully completing the module you will be able to...

  • 10. Describe essential facts and theory across a sub-discipline of the environmental sciences
  • 11. Identify critical questions from the literature and synthesise research-informed examples into written work
  • 12. Identify and implement, with some guidance, appropriate methodologies and theories for addressing a specific research problem in environmental sciences
  • 13. With guidance, deploy established techniques of analysis, practical investigation, and enquiry within environmental sciences
  • 14. Describe and begin to evaluate approaches to our understanding of environmental sciences with reference to primary literature, reviews and research articles

ILO: Personal and key skills

On successfully completing the module you will be able to...

  • 15. Develop, with guidance, a logical and reasoned argument with sound conclusions
  • 16. Communicate ideas, principles and theories using a variety of formats in a manner appropriate to the intended audience
  • 17. Collect and interpret appropriate data and undertake straightforward research tasks with guidance
  • 18. Evaluate own strengths and weaknesses in relation to professional and practical skills identified by others
  • 19. Reflect on learning experiences and summarise personal achievements

Syllabus plan

Syllabus plan

There will be several key themes covered in this module as follows:

  • Lecture: Introduction to module and overview of subject
  • Lecture: Introduction to research design; descriptive statistics; central tendency and dispersion.
  • Practical: Calculate and display descriptive statistics showing key data attributes (e.g. in Excel)
  • Lecture: Theoretical frequency distributions.
  • Practical: Exploring frequency distributions using computer software
  • Lecture: Parametric inferential statistics
  • Practical: Parametric hypothesis testing
  • Lecture: Counts and frequencies, non-parametric techniques
  • Practical: Non-parametric hypothesis testing
  • Lecture: Correlation analysis
  • Practical: Exploring correlation
  • Lecture: Linear regression
  • Practical: Modeling data trends
  • Lecture: Transforming data and alternative distributions

Learning and teaching

Learning activities and teaching methods (given in hours of study time)

Scheduled Learning and Teaching ActivitiesGuided independent studyPlacement / study abroad

Details of learning activities and teaching methods

CategoryHours of study timeDescription
Scheduled Learning and Teaching10Lectures
Scheduled Learning and Teaching20Practicals
Scheduled Learning and Teaching8Optional support sessions
Scheduled Learning and Teaching10You answer questions that are made available to you through the ELE portal. You have a week to prepare for these tests and they are ‘open book’.
Guided independent study102Additional research, reading and preparation for module assessments


Formative assessment

Form of assessmentSize of the assessment (eg length / duration)ILOs assessedFeedback method
Short answer questions during lectures and practical sessionsOngoing throughout the module1-16, 18-19Oral

Summative assessment (% of credit)

CourseworkWritten examsPractical exams

Details of summative assessment

Form of assessment% of creditSize of the assessment (eg length / duration)ILOs assessedFeedback method
Weekly tests30Not applicable1-17Model answers
Statistics project701000 words1-17Written


Details of re-assessment (where required by referral or deferral)

Original form of assessmentForm of re-assessmentILOs re-assessedTimescale for re-assessment
Weekly testsNot applicableNot applicableNot applicable
Statistics projectStatistics project1-17August Assessment Period

Re-assessment notes

Deferral – if you miss an assessment for certificated reasons judged acceptable by the Mitigation Committee, you will normally be either deferred in the assessment or an extension may be granted. The weekly tests are not deferrable because of their cumulative and practical nature. The mark given for a re-assessment taken as a result of deferral will not be capped and will be treated as it would be if it were your first attempt at the assessment.

Referral – if you have failed the module overall (i.e. a final overall module mark of less than 40%) you will be required to complete a further statistics project. The mark given for a re-assessment taken as a result of referral will count for 100% of the final mark and will be capped at 40%.


Indicative learning resources - Basic reading

  • Fowler, J. and Cohen, L. (1999) Practical statistics for field biology, (Chichester, John Wiley and Sons)
  • Clegg, F. (1997) Simple Statistics, (Cambridge, Cambridge University Press).
  • Silk, J. (1979) Statistical Concepts in Geography.
  • Ebdon, D. (1985) Statistics in Geography, (Oxford, Blackwell).
  • Robinson, G. (1999) Methods and Techniques in Human Geography, (London, Wiley).
  • Kitchen, R. and Tate, N.J. (1999) Conducting Research in Human Geography, (London, Prentice Hall).
  • Campbell, J.B.(2002) Introduction to Remote Sensing, (3rd edition), (Taylor and Francis).
  • Burrough, P.A. (1998) Principles of Geographic Information Systems, (2nd edition), (Clarendon Press)

Indicative learning resources - Web based and electronic resources

Module has an active ELE page

Key words search

Data, analysis, statistics, hypothesis testing, scientific method

Credit value15
Module ECTS


Module pre-requisites


Module co-requisites


NQF level (module)


Available as distance learning?


Origin date


Last revision date