Defining Cultural Data Science

Since 2019 a new term called “cultural data science” has emerged as a result of the increased utilisation of data within the fields of art and humanities, cultural studies and the built environment. It differs from a similar term called “cultural analytics” in that the latter is principally concerned with the “analysis and visualization of massive cultural data sets and flows” (Manovich, 2007). Cultural data science, on the other hand, can be thought of as an extension of cultural analytics, urban studies and computational social science with a particular focus on applying advanced computational methods to various forms of cultural data to test hypotheses and devise new frameworks for policy, planning and market action within the creative and cultural industries. At present there is no clear definition of cultural data science, therefore, this paper presents the first definition of the field. Adapted from Irizarry (2020), cultural data science is defined as the umbrella term used to describe the entire complex and multistep process used to extract value from cultural data with real-world implications to influence policy, placemaking and market dynamics within the creative and cultural industries. This new work is a unification of several fields and hopes to respect and honour the ideas, methodologies and tools developed by these adjacent fields. Its intention is to assist purpose-driven collaboration between public and private organisations and has the potential to become increasingly important for stakeholders in the built environment and policy makers who want to apply novel frameworks and evidence-based methods to influence the artistic and cultural vibrancy of place, space, societies and our local economies