  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.
