1. Anderson, C. (2008). The end of theory: The data deluge makes the scientific method obsolete. Wired Magazine. Updated 6/23/2008), Available at: http://www. wired. com/science/discoveries/magazine/16-07/pb_theory.
  2. Angrist, J. D., & Pischke, J.-S. (2014). Mastering’metrics: The Path from Cause to Effect. Princeton University Press.
  3. Angrist, J. D., & Pischke, J.-S. (2008). Mostly harmless econometrics: An empiricist’s companion. Princeton university press.
  4. Anselin, L. (2002). Under the hood Issues in the specification and interpretation of spatial regression models. Agricultural Economics, 27(3), 247–267.
  5. Anselin, L. (1996). The Moran scatterplot as an ESDA tool to assess local instability in spatial association. Spatial Analytical Perspectives on GIS, 111, 111–125.
  6. Anselin, L., & Rey, S. J. (2014). Modern Spatial Econometrics in Practice: A Guide to GeoDa, GeoDaSpace and PySAL. Chicago, IL: GeoDa Press LLC.
  7. Arribas-Bel, D. (2014). Accidental, open and everywhere: Emerging data sources for the understanding of cities . Applied Geography , 49, 45–53. The New Urban World .
  8. Brunsdon, C., & Singleton, A. (2015). Geocomputation: A Practical Primer. SAGE.
  9. C. Brunsdon, L. C. (2015). An Introduction to R for Spatial Analysis and Mapping. SAGE Publications Ltd.
  10. Downey, A. (2012). Think Python - How to Think Like a Computer Scientist. Green Tea Press.
  11. Duque, J. C., Ramos, R., & Suriñach, J. (2007). Supervised regionalization methods: A survey. International Regional Science Review, 30(3), 195–220.
  12. Gelman, A., & Hill, J. (2006). Data analysis using regression and multilevel/hierarchical models. Cambridge University Press.
  13. Goodchild, M. F. (2007). Citizens as sensors: the world of volunteered geography. GeoJournal, 69(4), 211–221.
  14. Haining, R. (2014). Spatial Data and Statistical Methods: A Chronological Overview. In Handbook of Regional Science (pp. 1277–1294). Springer.
  15. Kitchin, R. (2014). The data revolution: Big data, open data, data infrastructures and their consequences. Sage.
  16. Lazer, D., Kennedy, R., King, G., & Vespignani, A. (2014). The Parable of Google Flu: Traps in Big Data Analysis. Science, 343(6176), 1203–1205.
  17. McKinney, W. (2012). Python for data analysis: Data wrangling with Pandas, NumPy, and IPython. O’Reilly Media, Inc.
  18. Openshaw, S., & Openshaw, S. (1984). The modifiable areal unit problem. Geo Abstracts University of East Anglia.
  19. Rey, S. (2015). Geovisualization. In GPH471: Geographic Information Analysis. Lecture slides from course taught at Arizona State University.
  20. Rey, S. (2015). Point Pattern Basics. In GPH471: Geographic Information Analysis. Lecture slides from course taught at Arizona State University.
  21. Schutt, R., & O’Neil, C. (2013). Doing data science: Straight talk from the frontline. “ O’Reilly Media, Inc.”
  22. Symanzik, J. (2014). Exploratory Spatial Data Analysis. In Handbook of Regional Science (pp. 1295–1310). Springer.
  23. Tufte, E. R. (1983). The visual display of quantitative information. Graphics press Cheshire, CT.
  24. Wickham, H. (2014). Tidy Data. Journal Of Statistical Software, 59(10), ??–?? Retrieved from