AbstractThis 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
fornn = allobservations
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.