Data quality audits are a necessary precursor to quantitative analysis of human activity patterns using
primary data collected using automated sensors. This paper reports detailed exploration of the sources
of measurement errors that potentially impact upon the quality of the footfall data collected as part of
the Consumer Data Research Centre SmartStreetSensor project. Depiction and analysis of activity
patterns is integral to numerous applications in urban management, retail and transport planning, and
emergency management, yet most analysis to date has remained focused upon data pertaining to nighttime
residence as from the Census of Population and daytime estimates through sample surveys or
traffic counts. Here we investigate how Wi-Fi signals from mobile devices can be used to estimate
levels of human activity at different times and locations and argue about the opportunities and issues
arising when using them for estimating footfall
