RevUrb: Understanding Urban Activity and Human Dynamics through Point Process Modelling of Telecoms Data

Abstract

Vibrant public spaces which consistently attract dense and diverse populations while hosting continuous social activity are an integral part of any successful urban district. The design of such places has motivated the efforts of urban planners and decision makers for centuries. In recent decades, data-driven and analytical approaches to evaluate the performance of public spaces have begun to emerge. In particular, the recent emergence of Location-Based Services data from mobile phones has created opportunities for detailed modelling of urban behavior. Previous applications of such data to urbanism have been exploratory and have occasionally lacked rigorous statistical modelling. In this paper, the relationship between physical and functional characteristics of urban environments and the propensity for dense clusters of activity to form is examined. Activity clusters are identified using a density based clustering algorithm. These clusters are modelled as the realization of an Inhomogeneous Poisson Process (IPP), where the density of the process varies in space in association with the urban features. The IPP intensity was found to be positively associated with amenities such as shopping and entertainment, the availability of parking and bus stops and the presence of natural water features. The intensity was negatively associated with the distance from the city centre and streets and with presence of hotels. Ultimately, this study suggests a methodology of ‘reversed urbanism’, where statistical relationships can offer an evidence-based approach to urban design, planning and decision making.

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