Simulating Behaviour

Data science, predictive analytics

• Arribas-Bel, D. & Reades, J. (2018)Dani Arribas-Bel and Jon Reades, “Geography and Computers: Past, Present, and Future,” Geography Compass 12, no. 10 (2018): e12403, doi:10.1111/gec3.12403.
  
  “Geography and computers: Past, present and future”.

• Lazer, D. et al. (2014)David Lazer et al., “The Parable of Google Flu: Traps in Big Data Analysis,” Science 343, no. 6176 (2014): 1203–5, doi:10.1126/science.1248506.
  
  “The Parable of Google Flu: Traps in Big Data Analysis”.

• Singleton, A. & Arribas-Bel, D. (2019)Alex Singleton and Dani Arribas-Bel, “Geographic Data Science,” Geographical Analysis, 2019, doi:10.1111/gean.12194.
  
  “Geographic Data Science”.

Spatial Microsimulation

General

• Birkin M. et al. (2016) Using Census Data in Microsimulation Modelling, chapter in Stillwell, J. (ed)John Stillwell, The Routledge Handbook of Census Resources, Methods and Applications (London, UK: Routledge, 2016), https://www.routledge.com/The-Routledge-Handbook-of-Census-Resources-Methods-and-Applications-Unlocking/Stillwell/p/book/9781472475886.
  
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• Whitworth et al. (2017)A Whitworth et al., “Estimating Uncertainty in Spatial Microsimulation Approaches to Small Area Estimation: A New Approach to Solving an Old Problem,” Computers, Environment and Urban Systems 63, no. 63 (2017): 50–57, doi:10.1016/j.compenvurbsys.2016.06.004.
  
  “Estimating uncertainty in spatial microsimulation approaches to small area estimation: A new approach to solving an old problem”. Interesting paper exploring uncertainty in spatial microsimulation approaches.

With R

• Lovelace, R. and Dumont, M. (2016)Robin Lovelace and M Dumont, Spatial Microsimulation with R (London, UK: CRC Press, 2016), https://spatial-microsim-book.robinlovelace.net/.
  
  Spatial microsimulation with R. London: Taylor and Francis. Comprehensively discusses Spatial Microsimulation and procedures for generating micsrosimulated datasets in R.

R primers

• Hadley Wickham and Garrett Grolemund’sHadley Wickham and Garrett Grolemund, R for Data Science: Import, Tidy, Transform, Visualize, and Model Data (Sebastopol, California: O’Reilly Media, 2017), http://r4ds.had.co.nz/.
  
  R for Data Science provides everything you need to know when starting with data analysis in R.
• Kieran Healy’sKieran Healy, Data Visualization: A Practical Introduction (Princeton: Princeton University Press, 2018), http://socviz.co/.
  
  recent book Data Visualization is perhaps the best I’ve seen on Data Visualization in R.