论文标题
开场实践：支持可重复性和关键的空间数据科学
Opening practice: supporting Reproducibility and Critical spatial data science
论文作者
论文摘要
本文反映了近年来对地理和空间数据科学采用更开放和可重复的方法的许多趋势。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。它确定了学术界的转变为编码为核心分析工具，并从没有透露分析的内部工作的“黑匣子”的专有软件工具中脱颖而出。有人认为，这种封闭的表单软件是有问题的，并且考虑了多种方法，即在使用封闭工具时可以忽略空间数据分析（例如MAUP）中确定的问题，从而导致解释问题以及可能基于这些的不当行动和政策。此外，本文并考虑了可再现和开放的空间科学可能在这种方法中发挥的作用，考虑到提出的问题。它突出了无法说明数据的地理特性的危险，因为所有数据都是空间（它们是在某个地方收集的），这是对数据科学中所有观察结果的渴望的问题，并且确定了对关键方法的需求。这是一种开放性，透明度，共享和可重复性为可辩护和强大的空间数据科学提供咒语。
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 on spatial data 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 and 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.
