Inferring data through Mapping

This chapter introduces some recent analytic and predictive approaches developed by the authors at different scales. Through different projects we are developing and testing a methodology that allows to infer new information to describe urban phenomena where datapoints are scarce or non-existent.
The chapter includes a series of case studies where different methods and data types have been used, applied to different urban contexts and scales, from mapping access to public transport in Milton Keynes to resilience of urban communities in Copenhagen, and from tracking air quality in St Albans (UK) to decoding the current urban development in Bucharest and the housing phenomena in UK.
We will present and discuss these projects within the larger theoretical context of mapping and representation, with a special focus on key technical aspects that allow new knowledge to be produced through mapping.
The chapter concludes with a reflection of the analysis and understanding of the complexity underpinning contemporary cities within the context of new technologies, AI and data-driven approaches and the potentials and risks behind the generation of new urban knowledge.