Presented by

  • Claire Daniel

    Claire Daniel

    Claire is an urban planner and programmer and are currently undertaking a PhD in the intersection of these two fields. They find themselves writing scripts frequently during their research to help them record and make sense of the large amounts of qualitative data necessary to answer questions about the adoption of digital technology and the future of urban planning work. Outside of their PhD Claire works with others in the urban planning profession to advocate for the use of open technology and standards to ensure good governance of our cities and regions.


Conceptually simple, but sometimes computationally intense - Citation Network Analysis (CNA) provides a robust method to examine the overall structure of a research field. In citation network analysis, it is assumed that a cited document is related to the citing document as perceived by its author and each citation forms a link in a network which represents the collective view of the authors within the field. Properties of this network can then be directly measured using various statistical techniques quantifying the relative number of connections between different papers or authors. Traditionally a labour intensive process, CNA techniques are rapidly becoming more accessible through global citation databases and freely available software. Existing software will always have its limits for researchers and so this talk will provide a crash course in how to perform a CNA from scratch using Python, including the main methodical considerations and metrics. It will also provide an overview of currently available tools and challenges with proprietary databases.