The Impact of Local Demographics on Retail Centre Health in England and Wales

Spatial Interaction Models are a widely accepted means of linking retail centres to their local customer catchments. However, without real consumer data to validate the findings, such models remain as mere estimations based on geographic population and store data. Using a spatial interaction model which accounts for both the residential and local workplace populations, the following study seeks to evaluate the extent of which demographic characteristics of estimated retail catchments are an effective predictor of retail health for retail centres in England and Wales. Overall, this case study explores the value of demographics from estimated centre catchments to retail planners

Towards a comprehensive temporal classification of footfall patterns in the cities of Great Britain

The temporal fluctuations of footfall in the urban areas have long been a neglected research problem, and this mainly has to do with the past technological limitations and inability to consistently collect large volumes of data at fine intra-day temporal resolutions. This paper makes use of the extensive set of footfall measurements acquired by the Wi-Fi sensors installed in the retail units across the British town centres, shopping centres and retail parks. We present the methodology for classifying the diurnal temporal signatures of human activity at the urban microsite locations and identify characteristic profiles which make them distinctive regarding when people visit them. We conclude that there exist significant differences regarding the time when different locations are the busiest during the day, and this undoubtedly has a substantial impact on how retailers should plan where and how their businesses operate

Understanding sources of measurement error in the Wi-Fi sensor data in the Smart City

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

Nanoherding: Plasma-chemical synthesis and electric-charge-driven self organization of SiO2 nanodots

We report on the chemical synthesis of the arrays of silicon oxide nanodots and their self-organization on the surface via physical processes triggered by surface charges. The method based on chemically active oxygen plasma leads to the rearrangement of nanostructures and eventually to the formation of groups of nanodots. This behavior is explained in terms of the effect of electric field on the kinetics of surface processes. The direct measurements of the electric charges on the surface demonstrate that the charge correlates with the density and arrangement of nanodots within the array. Extensive numerical simulations support the proposed mechanism and prove a critical role of the electric charges in the self-organization. This simple and environment-friendly self-guided process could be used in the chemical synthesis of large arrays of nanodots on semiconducting surfaces for a variety of applications in catalysis, energy conversion and storage, photochemistry, environmental and biosensing, and several others