When reviewing quantitative content in the geography curriculum, amongst other things it is important to review developments in data analysis outside of the discipline of geography. In this response to the paper by Johnston et al. (2014), a number of such developments are considered. In particular, the issues of big data, data journalism, reproducibility and statistical inference are discussed. In conclusion, it is argued that all of these would make some kind of positive contribution to the geography curriculum, providing in the words of Johnston et al. (2014) ‘an important role in the formation of an informed citizenry in data-driven, evidence-based-policy societies’.
AnselinL (1995) Local indicators of spatial association – LISA. Geographical Analysis27: 93–115.
2.
BrunsdonCComberL (2012) Assessing the changing flowering date of the common lilac in North America: a random coefficient model approach. GeoInformatica16: 1–16.
3.
BrunsdonCFotheringhamASCharltonM (1996) Geographically weighted regression: a method for exploring spatial non-stationarity. Geographical Analysis28: 281–298.
4.
ClaerboutJ (1992) Electronic documents give reproducible research a new meaning. In: Proc. 62nd Ann. Int. Meeting of the Soc. of Exploration Geophysics, pp. 601–604.
5.
CresswellT (2013) Geographical Thought: A Critical Introduction. Chichester, UK: Wiley/Blackwell.
6.
GehlkeCEBiehlK (1934) Certain effects of grouping upon the size of the correlation coefficient in census tract material. Journal of the American Statistical Association29: 169–170.
7.
GelmanAUnwinA (2013) Infovis and statistical graphics: different goals, different looks. Journal of Computational and Graphical Statistics22: 2–28.
8.
GrayJChambersLBounegruL (2012) The Data Journalism Handbook: How Journalists Can Use Data to Improve the News. New York, NY: O'Reilly Media.
9.
GreenlandSRobinsJ (1994) Invited commentary: ecologic studies—biases, misconceptions, and counterexamples. American Journal of Epidemiology139: 747–760.
10.
JohnstonRHarrisRJonesJ. (2014) Mutual mis-understanding and avoidance, mis-representations, and disciplinary politics: spatial science and quantitative analysis in (UK) geographical curricula. Dialogues in Human Geography4(1): 3–25.
11.
KingG (1995) Replication, replication. PS: Political Science & Politics28: 444–452.
12.
KnuthDE (1984) Literate programming. The Computer Journal27: 97–111.
13.
MarewskiJNOlssonH (2009) Beyond the null ritual: formal modeling of psychological processes. Journal of Psychology217: 49–60.
14.
Mayer-SchonbergerVCukierK (2013) Big Data: A Revolution That Will Transform How We Live, Work and Think. Boston, MA: Houghton Mifflin.
15.
NesterMR (1996) An applied statistician's creed. Applied Statistics45: 401–410.
16.
OpenshawS (1983) CATMOG 38: The Modifiable Areal Unit Problem. Norwich, UK: Geo Books.
17.
RussellM (2010) The Independent Climate Change E-mails Review. East Anglia: University of East Anglia.
18.
SalsburgDS (1985) The religion of statistics as practiced in medical journals. The American Statistician39: 220–223.
19.
StiglerSM (1992) A historical view of statistical concepts in psychology and educational research. American Journal of Education101: 60–70.
20.
Universities UK (2013) Universities UK submission to the 2013 Spending Round, p. 25.
21.
WagnerCH (1982) Simpson's paradox in real life. The American Statistician36: 46–48.
22.
WallMM (2004) A close look at the spatial correlation structure implied by the CAR and SAR models. Journal of Statistical Planning and Inference121: 311–324.
23.
WylyE (2014) The new quantitative revolution. Dialogues in Human Geography4(1): 26–38.
