Teaching R as a GIS: problems, solutions and lessons learned

67b1027cca3877a76a9024425519ddde?s=47 Robin
July 15, 2015

Teaching R as a GIS: problems, solutions and lessons learned

Slides presented by myself and Rachel Oldroyd at the FOSS4G conference in Como, Italy. See http://europe.foss4g.org/2015/ for more details of the conference.

67b1027cca3877a76a9024425519ddde?s=128

Robin

July 15, 2015
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  1. Teaching R as a GIS: problems, solutions and lessons learned

    Robin Lovelace and Rachel Oldroyd University of Leeds
  2. • Statistical programming language and data environment • Based on

    the statistical language S • Can rival proprietary GIS's due to the emergence of advanced spatial packages Background
  3. Advantages • Reproducibility • Batch Processing • Extensible environment –

    packages / libraries • Fast • Advanced Graphics
  4. None
  5. Slow uptake amongst social scientists – command line interface (CLI)

    – the popularity of traditional GIS (QGIS, ArcMap, MapInfo) – Pre-conceptions, steep learning curve – Failure to see usefulness – Absence on UG /PG curriculum Barriers to Learning
  6. Aims and Objectives • Raise awareness of R's spatial capabilities

    • Teach new methods to social scientists – TALISMAN project (2010-2015) – Consumer Data Research Centre (2015 - ) • Promote the use of Open Source Software
  7. The Teaching Activity • 1 day practical workshop • Variety

    of audiences • Over an 18 month period • Formal and informal Likert feedback collected • 'Experiential learning'
  8. Difficulties • Variety of participant prior knowledge – 3 'types

    of participant' • Material Covered – Originally 2 tutorials – 'I was acting more like a typewriter' – 'I was tying code without understanding what I was doing'
  9. Difficulties • Technical Difficulties – Permissions • Finishing early

  10. Solutions I • Quality not quantity of materials – Focus

    on 1 tutorial • Extension tasks – 'The course strikes a good balance...for an R novice and interesting for more experienced R users' – 'The pace was just enough and there were enough extra bits of information to keep me interested'
  11. None
  12. Solutions II • Supportive learning environment – High number of

    demonstrators – Interactive • Use of IDE - RStudio – Autocompletion – Help – Plots – Data Environment
  13. Solutions II • Use of traditional GIS – QGIS –

    For those not familiar with geographical datatypes – Visualisation of attribute and geometry data
  14. Results

  15. Lessons Learned • Start at the beginning • Evolving, dynamic

    materials • Communication = vital
  16. Lessons Learned • Dissemination – CRAN, Twitter, Conferences • Clearly

    define target audience – Not suitable for some participants – Clearly identify prerequisites beforehand
  17. Reference Lovelace, Robin, and James Cheshire. "Introduction to visualising spatial

    data in R." (2014) http://robinlovelace.net/r/2014/01/30/spatial- data-with-R-tutorial.html