Welcome

status License: CC BY-SA 4.0

Hello CASA0005 course and a warm welcome to the CASA0005 Geographic Information Systems and Science online practical handbook.

The buzzword ‘Big data’ and associated roles of data scientists have become common additions to academia, industry and government organisations seeking to optimise workflows and base decisions on factual evidence (Donoho 2017; Li et al. 2016).In response, the demand for data scientists has significantly grown, with two major recruitment websites LinkedIn and Glassdoor reporting a respective 56% and 44% year on year increase in advertised roles along with classification as the most promising career based on salary, growth, promotion opportunities and satisfaction within the USA in 2019 (Glassdoor 2020; Pattabiraman 2019). In a similar theme data from the UK’s Universities and Colleges Admissions Service (UCAS) shows that the number of postgraduate level data science courses within the UK has grown from 104 across 51 institutions to 195 across 85 institutions between the academic years commencing in 2019 and 2020 (The Universities and Colleges Admissions Service 2020).

Whilst materials such as the R for Data Science book provide a foundational and essential resource for learning the R data science programming language, consideration of spatial data is extremely limited (Grolemund and Wickham 2017). The welcome addition of Geocomputation with R specifically addressed this resource gap, but often used basic examples that are unrepresentative of the complex spatial data wrangling, transformation and manipulation skills required by masters level graduates (Lovelace, Nowosad, and Muenchow 2019). Consequently, this content presents an academic teaching resource using applied real world government (e.g. (Cosh 2020)) and academic examples (e.g. (Guha et al. 2018)) manipulating complex and large datasets in creating reproducible and actionable outputs, being made possible through delivery as a taught masters level module.

Acknowledgement

Thanks to the following people who have either contributed directly or provided code in repositories that have helped me style this book:


The R package and analysis artwork used within this book has been produced by allison_horst, whilst artwork used in information boxes has been produced by Desirée De Leon. You can find Allison’s images on the stats illustration GitHub repository and Desirée’s on the rstudio4edu GitHub repository.

I’ve certainly learnt a lot from their open code repositories!

Citation

To cite this resource, please use:

MacLachlan et al., (2022). An Applied Geographic Information Systems and Science Course in R. Journal of Open Source Education, 5(50), 141, https://doi.org/10.21105/jose.00141 or copy the Bibtex citation string.

References

Cosh, Georgie. 2020. “GLA Housing and Land Short-term and holiday letting in London.”
Donoho, David. 2017. “50 Years of Data Science.” Journal of Computational and Graphical Statistics 26 (4): 745–66. https://doi.org/10.1080/10618600.2017.1384734.
Glassdoor. 2020. “50 Best Jobs in America for 2019.” https://www.glassdoor.co.uk/List/Best-Jobs-in-America-LST_KQ0,20.htm.
Grolemund, Garrett., and Hadley. Wickham. 2017. R for Data Science. O’Reilly.
Guha, Subhanil, Himanshu Govil, Anindita Dey, and Neetu Gill. 2018. “Analytical study of land surface temperature with NDVI and NDBI using Landsat 8 OLI and TIRS data in Florence and Naples city, Italy.” European Journal of Remote Sensing 51 (1): 667–78. https://doi.org/10.1080/22797254.2018.1474494.
Li, Songnian, Suzana Dragicevic, Francesc Antón Castro, Monika Sester, Stephan Winter, Arzu Coltekin, Christopher Pettit, et al. 2016. “Geospatial big data handling theory and methods: A review and research challenges.” ISPRS Journal of Photogrammetry and Remote Sensing 115: 119–33. https://doi.org/10.1016/j.isprsjprs.2015.10.012.
Lovelace, Robin., Jakub. Nowosad, and Jannes. Muenchow. 2019. Geocomputation with R. CRC Press. https://doi.org/10.1201/9780203730058.
Pattabiraman, Kumaresh. 2019. “LinkedIn’s Most Promising Jobs of 2019.” https://blog.linkedin.com/2019/january/10/linkedins-most-promising-jobs-of-2019.
The Universities and Colleges Admissions Service. 2020. “UCAS Postgraduate courses.” https://digital.ucas.com/coursedisplay/results/providers?studyYear=2019{\&}destination=Postgraduate{\&}postcodeDistanceSystem=imperial{\&}pageNumber=1{\&}sort=MostRelevant{\&}searchTerm=data science.

  1. The Bartlett Centre for Advanced Spatial Analysis, https://www.andymac.uk/↩︎

  2. The Bartlett Centre for Advanced Spatial Analysis, https://adamdennett.co.uk/↩︎

  3. The Bartlett Centre for Advanced Spatial Analysis, https://profiles.ucl.ac.uk/98496-claire-dooley/about↩︎