Home » News » Understanding and Comparing Mobility Data – 4th Feb 2021

Understanding and Comparing Mobility Data – 4th Feb 2021

Through the ABC (Accelerating Business Collaboration) Research Programme, funded by ESRC & UBEL, PhD candidate James Todd worked with Geolytix to validate the representativeness of mobile mobility data from Unacast. Geolytix were interested in gaining a deeper understanding of how comparable their (Unacast) data is to alternative mobility data sources as well as insights into the factors that influence the number of devices that are found within small geographical areas.

Overall, the analysis within this project finds that Unacast mobility data is a comparable to many alternative mobility data sources, observing a 70-100% decline in activity by the start of April 2020 across the vast majority of mobility data sources.

This research project composed of 2 main methods. Firstly, a descriptive analysis of mobility trends in London were assessed by comparing Unacast mobility data to a large number of open mobility data sources (Google, Apple, Purple, Open Table, Transport for London, City Mapper, Santander Bike Sharing). Using this method, it was possible to visually compare multiple mobility data sources within the context of Covid-19 lockdown restrictions.

DatasetDescriptionSource (link)
UnacastMobile mobility dataGeolytix (private)
GoogleCategorised mobility dataGoogle (open source)
AppleCategorised mobility dataApple (open source)
SSSWifi footfall dataCDRC (private)
PurpleWifi footfall dataPurple (open source)
Open TableRestaurant reservation dataOpen Table (open source)
TfLTransport use dataTfL (open source)
City MapperMobility index dataCity Mapper (open source)
Santander Bike SharingBikeshare activity dataCDRC (open source)
Open Street MapGeographical features dataOSM (open source)
Table 1. Sources of Mobility Data used in this analysis

To enable a deeper understanding of the representativeness of Unacast data, statistical regression analysis was conducted. A fixed-effect regression was conducted to find the representativeness of Unacast mobile devices in relation to the Local Data Company’s (LDC) Smart Street Sensor (SSS) footfall data. In addition to this, a linear regression was conducted to find the relationship between Unacast mobility data to local geographic features taken from Open Street Map (OSM).

Geolytix were very happy with the project. Blair Freebairn (CEO Geolytix Ltd), said “The work is valuable to us in and of itself, but also as it has sparked additional areas of interest. In particular the comparisons to other broad brush indicators of human movement has provided context and reassurance as to the high-level appropriateness of mobility data. The micro correlations at site level are well elucidated and have shed new light on the nature of mobility data.”

James Todd, PhD candidate, said “This experience has been extremely valuable as it has given me insights into the private sector’s area of interest in the context of mobility data, which I have been working on within my PhD. This has given me many ideas on how I would like to adapt my PhD to include similar analysis as part of an empirical chapter.”

Written by Dr Nick Bearman, Project Delivery Manager