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Quantifying the ambient population

As cities and urban areas continue to grow and develop into economic and social hubs, the ability to enumerate the ambient population of these areas is becoming increasingly important. The ambient population is defined as the number of persons within an outdoor geographical area, at a given point in time, excluding those located on modes of transport or at their place of residence. It is valuable resource to both the private and public sector for the management of civil emergencies, city planning and monitoring exposure to air pollution.

This project critically reviews potential data sources and analyses counts from Wi-Fi sensors in the town of Otley, West Yorkshire.

Data Sources

  • Census
  • Wi-Fi sensors
  • Footfall cameras
  • Mobile phone data
  • Remote sensing
  • Social media posts

Exploration of Wi-Fi sensor data for Otley, West Yorkshire

Data from Wi-Fi sensors in the market town of Otley were explored in order to investigate the utility of the data. The spatio-temporal analysis revealed that the data was able to exhibit temporal trends in the town which would be useful for town and event planning.

The temporal analysis highlighted that sensors in some locations were impacted upon by vehicular traffic near the sensors. The only way in which this could be avoided, as it cannot be quantified, is to relocate the cameras to pedestrian areas; however this limits the number of people being captured by the sensors.

The literature review deemed footfall cameras and Wi-Fi sensors the most valuable data sources which did not compromise geo-privacy

Opportunity for further research

There is much work still to be done in order to successfully work towards quantifying the ambient population. The potential of available datasets needs to be explored in more detail in order to quantify their utility. Enumerating the ambient population is an extremely valuable tool and is useful for a vast range of applications, especially within smart cities and urban dynamics research.

Limitations of the data:

  • Geo-privacy of mobile phone and social media data
  • Unknown accuracy of devices due to private sector
  • Resource location
  • Validation
  • Limited resources
  • Influence of passers-by

Research Team

Annabel Whipp – PhD Student, CDT for Data Analytics and Society, University of Leeds
Nick Malleson – CDRC and School of Geography, University of Leeds