This talk will introduce the Urban Grammar, a project using data science and machine learning to build a detailed, consistent, and scalable characterization of urban form and function in Britain. How we spatially arrange cities matters. Their building blocks include the built and natural environments of cities (form), but also the purpose they serve (function). Urban form and function are relevant because, on the one hand, their fabric encodes the socio-economic history, technology and values of the society that has built them; and, on the other, once in place, form and function have direct implications for a wide range of outcomes, from productivity to social inclusion, energy consumption or carbon emissions. We will introduce a new data product -the spatial signatures- that provides granular descriptions of urban form and function in Britain. We will cover its design, implementation, and some of the most interesting things it can teach us about the British urban landscape.
Daniel Arribas-Bel is interested in computers, cities, and data. He is Professor in Geographic Data Science at the the University of Liverpool, and Deputy Programme Director for Urban Analytics at the Alan Turing Institute, where he is also an ESRC Fellow. Prior to arriving at Liverpool in 2015, Prof. Arribas-Bel held positions at the University of Birmingham (UK), the VU University in Amsterdam (Netherlands), Arizona State University (US), and Universidad de Zaragoza (Spain). He holds honorary positions at the University of Chicago's Center for Spatial Data Science, and the Center for Open Geographical Science of San Diego State University. Prof. Arribas-Bels research combines modern computation with new forms of data to shed light on the spatial structure of cities.