How to Characterise a 1914 Architect using Big Data


  • Adrian Humphris Wellington City Archives
  • Geoff Mew



Architectural practice, Research – Methodology, Big data, Architects, Aotearoa, New Zealand, History, Architecture, 20th Century


"Big data" analytics is a means that can be used for examining large and varied data sets to uncover hidden patterns, unknown correlations, market trends, customer preferences and other potentially useful information. The analysis of large data sets has become possible, and much more relevant, as such data has come increasingly accessible, and computing technology has advanced considerably. In the study reported here we have taken the first steps in using this technique to investigate whether big data can tell us more about the characteristics of New Zealand architects and their work. Part of the research for our book Raupo to Deco resulted in a large data set of newspaper tenders. Tender notices enabled us to identify architects, the time frames they worked, the types and numbers of buildings they designed, and the connections and relationships between individuals and practices. In addition, we used genealogy techniques to work out birth and death dates, to study obituaries and to track movement patterns. We have continued to build this data set, adding in architects from all over New Zealand and relevant data about them; currently we have around 20,000 individual building tenders for buildings across the country between 1840 and 1940.

This paper analyses our data to give a view of "an architect" in 1914, the year architects in New Zealand were first required to be registered. How many were there? How old were they and how long had they been practicing? And what else can we find out? We also discuss the advantages and pitfalls of dealing with a large data set, and explore how we can ensure the validity and accuracy of the results.


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How to Cite

Humphris, A., & Mew, G. (2022). How to Characterise a 1914 Architect using Big Data. Architectural History Aotearoa, 14, 31–38.

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