Quantitative methods I: Reproducible research and quantitative geography

被引:42
作者
Brunsdon, Chris [1 ]
机构
[1] Maynooth Univ, Natl Ctr Geocomputat, Maynooth, Kildare, Ireland
关键词
Big Data; computational paradigm; geocomputation; programming; reproducibility; PARADIGM;
D O I
10.1177/0309132515599625
中图分类号
P9 [自然地理学]; K9 [地理];
学科分类号
0705 ; 070501 ;
摘要
Reproducible quantitative research is research that has been documented sufficiently rigorously that a third party can replicate any quantitative results that arise. It is argued here that such a goal is desirable for quantitative human geography, particularly as trends in this area suggest a turn towards the creation of algorithms and codes for simulation and the analysis of Big Data. A number of examples of good practice in this area are considered, spanning a time period from the late 1970s to the present day. Following this, practical aspects such as tools that enable research to be made reproducible are discussed, and some beneficial side effects of adopting the practice are identified. The paper concludes by considering some of the challenges faced by quantitative geographers aspiring to publish reproducible research.
引用
收藏
页码:687 / 696
页数:10
相关论文
共 39 条
  • [1] Anderson C., 2008, Wired, DOI DOI 10.1180/MINMAG.2008.072.1.7
  • [2] [Anonymous], 2013, Dynamic Documents with R and knitr
  • [3] [Anonymous], 2012, Agent-based models of geographical systems
  • [4] [Anonymous], 2013, ERRATA GROWTH TIME D
  • [5] [Anonymous], 2015, RADIANT NEWS
  • [6] SimBritain: A spatial microsimulation approach to population dynamics
    Ballas, D
    Clarke, G
    Dorling, D
    Eyre, H
    Thomas, B
    Rossiter, D
    [J]. POPULATION SPACE AND PLACE, 2005, 11 (01) : 13 - 34
  • [7] Barni M, 2007, P IEEE INT C AC SPEE
  • [8] Bound by Chains of Carbon: Ecological-Economic Geographies of Globalization
    Bergmann, Luke
    [J]. ANNALS OF THE ASSOCIATION OF AMERICAN GEOGRAPHERS, 2013, 103 (06) : 1348 - 1370
  • [9] Brundson C., 2015, Geocomputation A Practical Primer, V1st, P254
  • [10] Brunsdon C., 2019, An introduction to R for spatial analysis and mapping, V2nd