We believe there are 3 key components to this journey: vehicles (demand), infrastructure (support) and air quality (impact). We believe each constituency can only achieve a positive impact on air quality, if there is sufficient effort and attention in driving demand and support those who have made the transition. Particularly, we are interested in how well-equipped constituencies are in catering to both drivers that have home charging and those that rely on public infrastructure more regularly.
In this post, we break down the methodology behind the Constituency EV Map, detailing what constituencies are, how they are linked to other data sources such as charging infrastructure, EV registrations and air quality data.
Constituencies in the UK are geographically defined areas that are delimited based on population size, with each constituency aiming to have a relatively equal number of residents. Boundary Commission organisations across the UK review and adjust boundaries periodically to ensure constituencies remain representative of their residents.
Constituencies and local authorities share the goal of representing the interests and needs of residents, but they operate at different levels of government. A single local authority may encompass multiple constituencies or only part of a constituency, depending on the population distribution.
Each constituency is made up of many postcodes and it’s this information that helps us connect different sources of data at a constituency-level. We use the Office for National Statistics’ Postcode Directory (ONSPD) to retrieve all postcodes for a given constituency.
Given that boundaries are subject to change, it’s highly likely that postcodes can move between constituencies. This is important to consider when tracking progress of a particular constituency over several years. There is a planned boundary shift which is due to take effect at the next general election. At this stage, we would retrospectively update our data to reflect this change in the Constituency EV Map and help us accurately track EV readiness progress.
The boundaries do not necessarily align with postcode districts so it’s indeed possible that a postcode could lie between two constituencies. The way this is handled with the ONPSD dataset is that the postcode is assigned to the area based on where the majority lie.
Zapmap is an incredibly useful resource that catalogues the UK’s charging infrastructure. Typically used for finding a place to charge or planning a route, Zapmap lists details on charge point location, the charger type, availability, the type of location and includes reviews from the EV driver community. Using the Zapmap data, we have created a table showing the number of chargers by constituency.
This categorisation is important to be able assess a constituency’s EV Readiness on how well they are catering to various EV driver profiles.
A thousand lamp post chargers are no use to someone who needs to drive to Scotland and equally pricey ultra-rapid chargers are not helpful for someone who needs to charge their car overnight on a regular basis.
Thus we have distinguished between the high powered chargers on one-hand and the low powered chargers on the other. Within the high powered chargers we have pulled out Tesla Superchargers separately. Even though some Tesla Superchargers are now open to non-Tesla electric vehicles, it is only a minority (25 out of around 111 locations) and the majority just service Tesla drivers.
Within the low powered chargers sub 25kW, we have split the categorisation into on-street, which are typically used to support those drivers with no off-street parking, and off-street typically in car parks or destinations which are primarily used as destination chargers where a driver is topping up when they are parked up for a few hours.
These tables provide data to be able to assess the EV Readiness of various constituencies, so we can see who is well-served and who has more work to do.
This data has been kindly provided by New Automotive and has required more number crunching. In this section, I’ll give an overview of the initial data source and further processing.
Department for Transport provide Vehicle Licensing Statistics to the public free of charge. There are several different data tables available for different purposes. The data table used for this work is VEH0132 which the number of ULEVs by:
This information is populated whenever a car is registered (either for the first time or whenever it’s sold). Each car will have a registered address and the type of keepership. Using the address, the upper and lower tier local authority is labelled. We’re interested in understanding where vehicles are located within the UK which may not correspond with the registered address.
In many cases, the registered keeper is usually the person driving the car and the address is where the car is usually kept overnight. This is true for privately owned vehicles and those bought on finance Hire Purchase or Personal Contract Purchase. However, vehicles that are leased or rented are typically registered at the company’s address. New Automotive have an approximation for this which models how “EV friendly” a city is. We believe they have done an excellent job with their approximation, however it is hard to quantify the accuracy or validate this method. We hope to share more details soon.
New Automotive ensures that the BEVs within a given local authority are accurately distributed among multiple constituencies based on their population. This assumes there’s a correlation between the number of people and the number of vehicles registered.
By applying a weight-based population, we preserve the total number of vehicles and avoid distorting the general picture while also attempting to distribute vehicles across constituencies.
It’s important to remember that this is just an approximation of BEVs in each constituency and actual numbers may differ. There are several assumptions:
The Constituency EV Map is an ongoing piece of work, and we endeavour to improve this methodology where possible.
To provide a more complete picture, we wanted to look at air quality data.
This involved integrating with OpenWeatherMap’s Air Pollution API. It’s worth noting that the UK has a wide list of measuring sites that are located across the country. However, there’s no guarantee that there’s a measuring site near each constituency so this API offers an approximation based on GPS coordinates. The coordinates used are each Constituency’s central latitude and longitude as provided by the ONPSD dataset.
Besides basic Air Quality Index, the API returns data about polluting gases, such as Carbon monoxide (CO), Nitrogen monoxide (NO), Nitrogen dioxide (NO2), Ozone (O3), Sulphur dioxide (SO2), Ammonia (NH3), and particulates (PM2.5 and PM10).
When looking at our data we found that the particulate matter pollutants correlated strongest with the change in BEVs. However, we would be extremely cautious with taking this at face value since we only have data comparing 2 sets of time periods. To add depth to our analysis, we cross-reference the air quality data from OpenWeatherMap with reports from the government on major sources of pollutants. These reports delineate various sources of pollutants, helping us understand the root causes of air pollution across the UK.
One significant finding from these reports is the substantial contribution of the transport sector to nitrogen oxide (NOx) emissions. There are two gases that are typically referred together as NOx, these are nitrogen monoxide or nitric oxide (NO) and nitrogen dioxide (NO2). Nitrogen dioxide (NO₂) is a primary component of NOx and is closely associated with vehicular emissions, especially those from conventional internal combustion engine vehicles (source). Of note is that it’s “estimated that on average 70 per cent of the NOx concentrations at the roadside originate as NOx emissions from road transport”.
Particulate Matter (PM) is of interest since road transport is a major contributor of PM emissions (13% PM10 and 12% PM2.5 in 2021). However, non-exhaust road emissions (e.g. brake, tyre and road wear) represent most of these emissions (10% of PM10 and PM2.5) in 2021 and we expect this to be relatively unchanged with the introduction of electric vehicles (source).
As well as approximating geographic pollution, another limitation with this dataset is that the pollutants are not broken down based on specific sources (e.g. roadside). As an alternative, there is the DEFRA dataset which does differentiate roadside emissions with other sources, but this has not been updated since 2021. In the future, this is something we would look into to derive more meaningful correlations.
While we currently lack sufficient data to draw precise correlations, we view this as an ongoing process with immense potential. By continuously monitoring air quality and cross-referencing it with data on transportation trends.
As we gather more data over time, we anticipate uncovering valuable insights that can inform our strategies for promoting EV adoption and addressing the environmental challenges posed by transportation-related emissions. Our commitment to data-driven analysis and sustainability drives us to explore these connections further, with the ultimate goal of fostering a cleaner, more sustainable future.
As well as the total number of chargers and electric vehicles, we have provided a list of “readiness” metrics. We feel it would be unfair to compare the total number of chargers in an area with 10 EVs against an area with 1000. Therefore, we have included a ratio of the number of electric vehicles to charger types.
We believe it’s important to quantify how suitable a constituency’s infrastructure is relative to the number of EVs in the area. To compare constituencies we provide:
When looking at these metrics, if a constituency has a checkered pattern instead of colour, this means there are no chargers of that type.
The Department for Transport’s Vehicle Statistics dataset is only updated quarterly for 1 quarter before (latest is March 2023). This makes it difficult to achieve updates more often than 6 months, but this could be longer. The cadence at which we update the Constituency EV Map depends heavily on our commitments and the commitments of our partners at the time.
We hope breaking down the methodology behind the Constituency EV Map was useful. The Constituency EV Map is an evolving piece of work and we welcome any feedback and comments. It has been worked on by our very own Maz Shar.