Opening practice: supporting reproducibility and critical spatial data science
Opening practice: supporting reproducibility and critical spatial data science
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DOI：
10.1007/s10109-020-00334-2
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发表时间：
2020-08-08
影响因子：
2.9
通讯作者：
Comber, Alexis
中科院分区：
文献类型：
--
作者：
Brunsdon, Chris;Comber, Alexis
本文反映了近年来地理和空间数据科学更开放和可复制的方法的一些趋势。特别是，它考虑了大数据的趋势，以及这对空间数据分析和建模的影响。它确定了学术界转向编码作为核心分析工具，远离专有软件工具提供的“黑匣子”，其中分析的内部工作不被揭示。有人认为，这种封闭的形式软件是有问题的，并认为在空间数据分析（如MAUP）中发现的问题可能会被忽视的一些方式与封闭的工具工作时，导致解释和可能不适当的行动和政策的问题。此外，本文认为，可再生的和开放的空间科学可能在这样一种方法中发挥的作用，考虑到所提出的问题。它强调了未能考虑数据的地理属性的危险，现在所有数据都是空间的（它们在某个地方收集），数据科学中需要n =所有观测的问题，并确定了关键方法的必要性。这是一个开放、透明、共享和可复制性为防御性和强大的空间数据科学提供了咒语的时代。
This paper reflects on a number of trends towards a more open and reproducible approach to geographic and spatial data science over recent years. In particular, it considers trends towards Big Data, and the impacts this is having onspatialdata analysis and modelling. It identifies a turn in academia towards coding as a core analytic tool, and away from proprietary software tools offering 'black boxes' where the internal workings of the analysis are not revealed. It is argued that this closed form software is problematic and considers a number of ways in which issues identified in spatial data analysis (such as the MAUP) could be overlooked when working with closed tools, leading to problems of interpretation and possibly inappropriate actions and policies based on these. In addition, this paper considers the role that reproducible and open spatial science may play in such an approach, taking into account the issues raised. It highlights the dangers of failing to account for the geographical properties of data, now that all data are spatial (they are collected somewhere), the problems of a desire for n = all observations in data science and it identifies the need for a critical approach. This is one in which openness, transparency, sharing and reproducibility provide a mantra for defensible and robust spatial data science.