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Discovering patterns in retail related pedestrian activity using WiFi based sensors
The accurate measurement and estimation of human activity is one of the first steps towards understanding the structure of the urban environment. Human activities are highly granular and dynamic in both the spatial and temporal dimensions and estimating them with confidence is crucial for decision-making in numerous applications such as urban management, retail, transport planning and emergency management. Traditionally insights into the distributions of such activities were gathered by studying the data available on night-time residence through population censuses and daytime estimates through various sample surveys such as traffic counts. The data generated by censuses, while being comprehensive, are only updated once a decade in countries such as the UK. In contrast, sample surveys and traffic counts get updated more frequently but are usually very specific. The key challenge has always been to be able to capture and understand these dynamic and complex phenomena in detail by spending minimal resources and without compromising the privacy of those involved. This research focuses on WiFi based technologies and explores the possibility of using mobile phone probe requests collected by a national network of “SmartStreetSensors” as a proxy for measuring the spatio-temporal patterns of human activity in urban environments.