Lambeth Council recognised as a trailblazer in post-COVID adaption with Unacast’s uniquely granular, comparative location data

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By disrupting our usual routines, COVID-19 has created fresh data patterns that must be captured so that the world can adjust accordingly.

Using Unacast’s datasets and consultation, Lambeth Council was one of the first London borough councils to leverage comparative, granular location data and human mobility insights to measure the pandemic’s impact on all corners of its district, develop new strategies, and predict recovery timelines.

The challenge

Lambeth Council, a government agency for a borough in south London, was tasked with rapidly adapting to a post-COVID reality to continue providing optimised support for businesses and individuals in the local community. To begin developing a strategy that was localised and relevant to the ‘unprecedented’ era, and start building a picture of how the pandemic’s impact might evolve over time, the Council needed to acquire a thorough understanding of new patterns in movement, transportation, retail and schooling.

Unfortunately, in terms of seeking a solution, Lambeth Council came up against a dearth in organizations providing sufficiently granular or comparative data. Financial services companies, telecoms agencies and data providers were all beginning to track movements in foot traffic as they pertained to COVID-19 lockdowns and closures, but most were focused on the broad administrative area and neglected to provide pre-COVID-19 data for comparison.

Commented Tom Brown, Spatial Information Manager of Lambeth Council, “When you can easily look at your window and see shops are empty and/or closed, it is of little value-add to have this re-iterated to you, unless there is a direct prior comparison from which to draw.”

The solution

Unacast was known to be a trusted international source of readily-available, localised human mobility data. By gaining access to their COVID-19 location toolkit, Lambeth Council was able to work from precise models based on multiple key data streams, including census and purchase data, creating a real-time, accurate view of the activity surrounding different sites, and comparative data to model changes over time.

With the ability to compare current foot traffic to the pre-COVID-19 days, Lambeth Council now has a deep understanding of the commercial and economic impact of the virus across different industries and geographies. Better still, industry- or brand-specific traffic trends have helped identify early signals of a potential recovery.

The granularity and precision of Unacast’s offering allowed the Council to look in-depth at the effects of the pandemic on the borough of Lambeth – not just at an administrative area level, which can be quite large, but at the level of individual shopping precincts.

The outcome

Most workers have not returned fully to on-site positions; many may never. There are no longer specific rush hours and quiet periods creating distinct peaks and troughs of human traffic. Instead, footfall is flatter and more consistent, as is high street demand. Shopping and socialising have also, at least temporarily, become far more localised and outdoor-based as a result of COVID-19-related restrictions.

From such insights, Lambeth Council was able to use Unacast data and insights to understand the nuances and needs of a rapidly changing borough, and subsequently reflect, rebuild and rework new strategies. They produced a series of instructive maps and apps showing COVID-19’s impact through before-and-after comparisons. They also created several slide decks summarising key findings, including the difference in the evolution of human mobility between morning, afternoon and evening time periods. Such findings were used by different departments to empower decision-making in different Lambeth Council remits. For example, the department focused on supporting the night-time economy was able to pull data showing that businesses operating primarily at night were worst hit by COVID-19 – suffering a drop of over 33% during the week and over 66% on a Saturday from pre- to post-lockdown – supporting a case for additional funding and support. Meanwhile, the department focused on the day-time economy used Unacast’s insights to hone in on things like pedestrianization, layering Unacast footfall data with third-party motorised vehicle data, justifying closing off certain roads to expand paving space, particularly in areas where pedestrians are finding it difficult to observe social distancing.

Said Brown, “Most other boroughs and their data providers weren’t comparing the post-pandemic reality with the pre-pandemic one. By only focusing on the present, and not whether specific datapoints were actually a deviation or continuation of prior trends, certain ‘impacts’ were disproportionately ascribed to COVID. For example, retail was already struggling in the fight against Amazon and e-commerce, but many were quick to blame the pandemic for a dying high street. Unacast was able to provide us with the right level of granularity in both pre- and post-COVID data at the right time – enabling us to get on the front foot with adapting our strategies and policies way ahead of most other boroughs.”

Taking inspiration from Lambeth Council, London authorities have since launched a cross-borough, city-wide effort to mine and share more granular, comparative location data.

“We are proud to have been involved in kickstarting a de-siloed, whole-city approach to post-COVID recovery that prioritises leveraging data that is both granular and comparative,” commented Eric Hynes of Unacast. “Life isn’t a petri dish, and the closer we can get as a society to mining data that represents what is happening in the real world, not just in individual sample analyses, the better.”

Lambeth itself is continuing to make longer-term changes to its transport and mobility plans, including creating more COVID-safe routes to and from the borough’s town centres, and investing more in green spaces where many more Lambeth residents are now choosing to spend their time.


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