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Unit information: Mapping and Modelling Geographic Data in R in 2020/21

Unit name Mapping and Modelling Geographic Data in R
Unit code GEOGM0046
Credit points 20
Level of study M/7
Teaching block(s) Teaching Block 1 (weeks 1 - 12)
Unit director Dr. Tranos
Open unit status Not open
Pre-requisites

Participants should have an understanding of statistics, including descriptive and inferential statistics, and linear regression. Experience in command-line coding will be beneficial but is not essential.

Co-requisites

One of:

Advanced Quantitative Methods for Social and Policy Research (POLIM0021);

Advanced Interdisciplinary Research Design (GEOGM0015);

Research Design Methods & Skills in the Social Sciences (GEOGM0035 or the equivalent unit at Bath or Exeter University)

School/department School of Geographical Sciences
Faculty Faculty of Science

Description

This unit introduces students to principles of Geographic Data Science in R, looking at the theory and practice of how geographical information is analysed and modelled in R, a popular open source statistical and computing environment that offers both GIS and spatial statistical functionality suitable for research and other applications.

The unit focuses on visualisation – especially geographic visualisation (maps) – methods of GIS and their implementation in R, and on spatial analysis: geometric operation, measures of spatial dependency, spatial regression and geographically weighted statistics.

The aims of the unit are:

  • To provide an introduction to coding in R
  • To use R as a tool for visualisation and the mapping of geographic data
  • To show how common GIS operations are undertaken in R
  • To teach what is meant by a spatial weights matrix and how it can be specified in R
  • To show how the spatial weights matrix is used in methods of spatial analysis and regression.

Intended learning outcomes

Upon successful completion of this unit, students will:

1) Have experience of using R to map and model geographic data

2) Understand why the presence of geography can disrupt the assumptions of classic statistical analysis

3) Be able to employ methods of spatial analysis to detect, to allow for and to model patterns of geographical clustering

4) Know the differences between global and local approaches

5) To understand the centrality of a spatial weights matrix to most spatial analysis

6) Have an appreciation of R as a software environment for geographic data science

Teaching details

The unit will be taught through a blended combination of online and, if possible, in-person teaching, including

  • online resources
  • synchronous group workshops, seminars, tutorials and/or office hours
  • asynchronous individual activities and guided reading for students to work through at their own pace
  • computer practical work; students who either begin or continue their studies in an online mode may be required to complete practical work, or alternative activities, in person, either during the academic year 2020/21 or subsequently, in order to meet the intended learning outcomes for the unit, prepare them for subsequent units or to satisfy accreditation requirements

Assessment Details

An individual data analysis project and report of approximately 3000 words, written using R Markdown (100%) [ILOs 1-6]

Reading and References

There is no core textbook for the unit; however, the following inform the class material:

Brunsdon C & Comber L (2018) An Introduction to R for Spatial Analysis and Mapping (2nd edition). London: Sage.

Lovelace R, Nowosad J & Muenchow J (2019) Geocomputation with R. Boca Raton FL: CRC Press. Available online at https://geocompr.robinlovelace.net/

Wickham H & Grolemund G (2017) R for Data Science. Sebastopol, CA: O’Reiley. Available online at https://r4ds.had.co.nz/

Directed reading will be provided through the course.

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