Focussing on outcomes to better understand footfall data

The way we use town centres and High Streets has changed. Even before the pandemic, High Streets have suffered significant decline as people have moved online or changed the way they shop. Add the Covid-19 pandemic and our once bustling town centres have been hit hard. 

We know citizen behaviour has become more digitally orientated in terms of work, socialising, entertainment, and communication but that doesn’t necessarily mean the death of the High Street. It just means we need to find out what people want, to understand how we bring them back. 

Whilst local authorities have always had an interest in understanding the flow of people and spend across their areas, these high street headwinds have led to councils wanting to understand more about the recovery of their jurisdictions and how Local Authorities can drive economic recovery, keep citizens safe and aid future decision making around funding and investment. 

Currently the data makes interesting reading in the current context. The Centre for Cities think tank’s high streets recovery tracker shows that larger cities across the UK such as London, Manchester, Glasgow and Birmingham as well as major commuter belt towns and cities such as Slough and Reading are the areas that are still struggling to recover to footfall and spend levels pre-pandemic. Whilst some areas are struggling, others are thriving. Popular UK tourist spots are seeing booms in footfall and spending with Blackpool seeing the biggest relative increases in both footfall and spending, with seemingly more citizens spending time and money within larger towns and cities within their regions rather than further afield or even abroad. 

With high street investment and funding more critical than ever and the ongoing risks and fluctuations that come with potential new COVID variants, better understanding and baselining footfall data is more important than ever for councils. 

We take a look at how councils are currently trying to establish this data, what they should be considering when taking a data-led approach and how IoT is positioned to provide greater clarity and intelligence to aid the recovery of town and city centres in the UK. 


What data can you collect and how does it help? 

It seems straightforward in terms of what data you collect when looking at footfall, but whilst the number of people that are in an area is helpful to know, that in of itself is fairly limiting in terms of providing any real meaningful context. 

Pairing footfall monitoring data with other indicators like dwell time, returning visitor data, new visitors etc. can help you better understand behavioural trends in certain areas alongside just pure visitor numbers and add extra context. When you start to also pair this data with things such as weather, traffic counting, parking capacity, cellular and purchasing data you can start to understand any relationships between datasets that you feel could impact on footfall or other use cases and start to identify leading and lag indicators across your datasets. 


How accurate are current methods? 

It’s been commented before how notoriously inaccurate some forms of large-scale people counting solutions can be, that’s not to say that some methods aren’t helpful but more to say that there needs to be awareness of how much you can trust one source of information used on its own as different methods come different pros and cons and it’s not a one size fits all approach. 

Most commonly, from what¬†we‚Äôve¬†seen at Connexin, is that local authorities will use solutions that take large scale mobile data or¬†purchasing¬†data¬†that‚Äôs¬†geo-located¬†within their regions or will often rely solely on¬†WiFi¬†data if they have a public network¬†that‚Äôs¬†accessible to citizens. These often rely on mac addresses of mobile devices to ‚Äúestimate‚ÄĚ the number of users within the vicinity.¬†

Whilst these types of solutions do provide good amounts of data, its accuracy can be challenged due to several factors including some mobiles devices using mac address randomisation to improve privacy, blind spots in networks or devices being in range of multiple access points, thus increasing counts. Similar issues occur from huge mobile datasets too where there are external factors affecting the accuracy of standard footfall monitoring data. 

Some councils implement camera systems which in recent years have become much more affordable in terms of rolling out city-wide solutions. These often work by taking live camera images and using an Edge processor to work out the number of people in the image without storing the footage (maintaining privacy of citizens), this footfall data is then stored in the cloud or a central storage location for the council to use and analyse. 

Cameras with image processing are usually more accurate in providing standard footfall data but are expensive and difficult to use for the purposes of identifying other datapoints such as dwell time, repeat visits and so on, especially if you are not storing images for privacy reasons. 

So, whilst both have their advantages and disadvantages, we have seen that the combination of these technologies can help fill in the gaps and improve the value of this data by understanding how they can be used together. For example, when working with the East Riding of Yorkshire Council we used cameras and edge processing to capture footfall data, and then existing public WiFi access points to gain indications of dwell times, visitors vs passers-by’s, and visitor loyalty (how often they returned). These two solutions together meant that we could use them both in tandem to ensure that the most accurate data could be generated through the full solution. 


Why should councils take an IoT led approach? 

The Internet of Things for local authorities is ultimately an enabler to help make better, more informed decisions. By utilising different network technologies and solutions, data can be captured from existing data sources through APIs and live sensor data, then used together to increase understanding across different use cases. 

In the case of footfall monitoring, taking an approach where data is captured primarily through various sources not only helps with accuracy as mentioned but allows that data to be aggregated with other datasets through an agnostic IoT platform. This reduces time and saves resource in terms of analysing that data but can also further this using artificial intelligence and machine learning to work out patterns in the data. This could then alert councils when thresholds are met, or unusual instances occur and even start to identify lead and lag indicators. 


Focus on the outcomes 

Ultimately, councils need to take an outcome focussed approach when it comes to data-led projects such as footfall monitoring too. Different use cases may require different solutions and it is not always a one size fits all solution for every situation. 

Working back from the outcome allows you to identify the data that helps you make decisions, the solution that helps you get that data within your budget, deploy the right infrastructure for the solution(s) and work out how you view and use that data to inform decisions. 

Proofs of concepts are incredibly valuable in doing this by setting clear outcomes and testing solutions within a localised area to ensure the technology will work for you within your setting and deliver exactly what you need. 

This is how we achieved success alongside East Riding of Yorkshire Council. 


How East Riding use footfall monitoring to support funding bids and COVID response 

The East Riding of Yorkshire Council (ERYC) is a large and diverse area of over 1,000sq miles. This contains 16 towns, from suburbs of large cities, rural market towns which serve residents, tourists, and large agricultural hinterlands alike. The area also includes coastal towns with seasonal economies and some industrial towns seeking to reinvent and reimagine the role of their town centres.   

With such a diverse and large area, with areas of both affluence and deprivation, it was important for them to understand the differences in their towns and to support each location based on its unique trends and patterns. Whilst they had a plethora of data from the ONS and stakeholders they still had gaps in their knowledge of what footfall truly looked like across their area. 

Through the Reopening High Streets Safely Fund, ERYC were able to commission Connexin and invest in digital footfall monitoring across their largest town centres. Utilising access points from public WiFi to provide indicative data the dwell time and loyalty of citizens they also used a camera-based solution to take more accurate footfall measurements. 

This¬†contributed¬†to a more detailed understanding of how their town centres were being used and allowed the council to view and visualise data through Connexin’s¬†ConnexinOS¬†platform as well as export it for other use.¬†

Joe Russell, part of the Welcome Back Fund team at East Riding of Yorkshire Council said: 

‚ÄúWe will be able to use this data for engaging with local stakeholders and businesses, as well as¬†assisting¬†senior managers in understanding the trends¬†impacting¬†town centre sustainability and recovery following Covid. Additionally, the evidence will support and evidence future funding bids to maximise the prosperity of our town centres. Furthermore, the¬†ConnexinOS¬†platform is highly adaptable and will allow future investment in smart counties technologies to optimise Council services and support both our urban and rural economies.‚Ä̬†