Opening practice: supporting Reproducibility and Critical spatial data science

TL;DR

This paper advocates for open, reproducible spatial data science practices to enhance transparency, address biases like the MAUP, and improve the robustness of geographic analysis amidst Big Data trends.

Contribution

It emphasizes the importance of open coding and reproducibility in spatial data analysis to mitigate issues caused by proprietary tools and closed analysis methods.

Findings

Open practices improve transparency and reproducibility.

Closed tools risk overlooking spatial biases like MAUP.

Reproducible spatial science supports more robust policy decisions.

Abstract

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.