Bayesian modelling and cities

Chris Brunsdon

Chapter 3 in Handbook on Big Data, Artificial Intelligence and Cities, 2025, pp 16-34 from Edward Elgar Publishing

Abstract: In addition to ‘traditional’ sources of urban data, such as census data, a great deal more data is currently available. In this chapter, data provided by the London Fire Brigade (LFB) on incident callouts is analysed. This data provides detailed information on individual callouts during a period spanning over a decade. However, some geographical problems emerge, such as areas in a city with unexpectedly high levels of fire callouts. Here, it is proposed that a powerful tool is the combination of Bayesian spatial modelling with a simulation-based approach. However, certain computational challenges need to be considered to enable these techniques to be applied to urban Big Data. In this chapter, methods will be outlined, and examples of Bayesian data analysis will be given using openly available urban data to demonstrate computational approaches, producing estimates of indicators, hypotheses evaluations, and map-based outputs to aid in the visualisation of patterns.

Keywords: Bayesian methods; Model comparison; Spatial modelling; Big data; Quantitative urban models; Bayesian Information Criterion (search for similar items in EconPapers)
Date: 2025
ISBN: 9781803928043
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