Space Syntax Laboratory Research Seminar: Stephen Law

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Mapping the Intangible: discovering geographical patterns in cities using data science methods

In this seminar, Stephen Law presents two papers on how data science methods can be used to discover geographic patterns in cities at scale. In the first paper, he studies how street network subgraph structures can be used to infer a city’s characteristics. In the second, he uses street and satellite images to predict house prices, and in the third he extracts urban image features using computer vision methods for semi-supervised learning tasks. 

Biography

Stephen Law completed his PhD in Architectural Space & Computation at The Bartlett School of Architecture, studying the effect of spatial configuration on the housing market in the UK.  He has over ten years of consultancy experience with Space Syntax and is currently involved in Likeways, a novel navigation app, with fellow colleagues from The Bartlett. 

Stephen recently began a lectureship in Geographic Data Science at UCL’s Department of Geography and currently serves as a Turing Fellow at the Alan Turing Institute/UCL and as an Honorary Research Fellow at the Centre for Urban Science and Progress (CUSP) in London. His research interests include spatial networks, computer vision and its applications in the housing market. 

Stephen’s work is supported by the Alan Turing Institute under the UK Engineering and Physical Sciences Research Council (EPSRC) grant no. EP/N510129/1.

About this series

This academic seminar series features researchers sharing their findings, discussing their ideas and showing work in progress from The Bartlett's internationally renowned Space Syntax Laboratory. Seminars are moderated by PhD candidate Sepehr Zhand. They are open to the public and attended by Bartlett’s staff and students.

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Image: Made with Mapbox. OpenStreetMap.