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The Transport and Accessibility Explorer maps how travel options vary across Great Britain: public transport frequency back to 2004, vehicle ownership and electric vehicle uptake, and how easily residents can reach shops and services. For a full explanation of the tool please see the manual. You can also access sections of the manual via the help buttons ().
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Click on the population-weighted centroid of an LSOA to show 15-minute isochrones for different modes of travel
Walking
Bicycle
Walk + Transit
Bike + Transit
Travel time isochrones have been calculated based on Weekday AM peak-time timetables.
Bike + Transit assumes you can board the transit service with your bike and then use it
at the other end of your journey.
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Zone Summary: XXXXX
The 2022 Census in Scotland significantly changed Data Zone boundaries, historic data has been adjusted to the new boundaries
This LSOA had a complex border change between 2011 and 2021, historical data uses closest matching LSOA
This LSOA was formed in 2021 by merging two LSOAs, historical data has been merged
This LSOA was formed in 2021 by splitting two LSOAs, historical data has been split
At some point this LSOA has had zero residents
In some years this LSOA has an unusually large number of company cars
Private vehicles by body type and licence
This chart shows the number of private vehicles in the LSOA by body type and licence status. Licensed vehicles can be legally driven on public roads, while those with a Statutory Off Road Notification (SORN) are not registered for road use. Three types of vehicles are shown: cars, motorbikes, and other vehicles (mostly vans).
Vehicle numbers, not just engine technology, drive transport's footprint: parking demand, congestion, and embodied manufacturing emissions all scale with the size of the fleet. Councils can influence ownership through parking policy and controlled parking zones, support for car clubs, and ensuring new developments are genuinely usable without a car. Around a fifth of households have no car at all (National Travel Survey), so planning around universal car ownership underserves them.
Vehicle data comes from DfT/DVLA vehicle licensing statistics, which count vehicles by the registered keeper's address at each quarter. Key limitations: company vehicles are registered to office addresses, inflating counts in commercial areas and undercounting where the vehicles are actually used or kept; SORN vehicles are off-road by declaration but still registered; and registration address can lag house moves. See the manual for details.
Private vehicles fuel type and licence
This chart shows the number of private vehicles in the LSOA by fuel type and licence status. It highlights the uptake of Battery Electric Vehicles (BEVs), which have zero exhaust emissions but still contribute emissions through electricity generation and manufacture. Ultra Low Emission Vehicles (ULEVs) are defined as vehicles producing less than 75g/km of CO2 at the exhaust. For some vehicles, notably plug-in hybrids, the official definition assumes usage patterns that may not reflect real world use.
Accelerating EV uptake needs charging infrastructure matched to local housing: on-street and lamp-post charging where homes lack driveways, and rapid hubs on main roads. Uptake is strongly income-linked, so equitable policy also supports car clubs, e-bikes and public transport rather than assuming universal EV ownership.
Vehicle data comes from DfT/DVLA vehicle licensing statistics, which count vehicles by the registered keeper's address at each quarter. Key limitations: company vehicles are registered to office addresses, inflating counts in commercial areas and undercounting where the vehicles are actually used or kept; SORN vehicles are off-road by declaration but still registered; and registration address can lag house moves. See the manual for details. Fuel categories follow DfT definitions; ULEV includes some efficient petrol/diesel and plug-in hybrids whose official ratings assume charging behaviour that may not reflect real-world use.
Private vehicles ownership rate
This chart shows three measures of the ownership rate of private vehicles, per person, per adult, and per household. Each measure tells a different story about how vehicle ownership interacts with the demographics and needs of the population.
Ownership rates reveal car dependency, but the same number means different things in different places. High ownership where alternatives are good suggests habit and abundant parking, which pricing and street design can address; high ownership in rural areas reflects genuine need, where electric vehicle support and demand-responsive transport matter more. Falling per-household ownership among younger urban households (National Travel Survey) shows that change is possible where the alternatives exist.
Vehicle data comes from DfT/DVLA vehicle licensing statistics, which count vehicles by the registered keeper's address at each quarter. Key limitations: company vehicles are registered to office addresses, inflating counts in commercial areas and undercounting where the vehicles are actually used or kept; SORN vehicles are off-road by declaration but still registered; and registration address can lag house moves. See the manual for details. Rates divide registered private vehicles by ONS mid-year population, adult population, and modelled household counts respectively; each denominator has its own uncertainty, so the trends matter more than exact values.
Company vehicles by body type and licence
This chart shows the number of company vehicles in the LSOA by body type and licence status. Company vehicles are those registered to a business or organisation rather than an individual.
Company fleets turn over quickly, typically every three to four years, and respond strongly to tax incentives, which makes them the fastest lever for electrifying the national fleet: today's company electric cars become tomorrow's second-hand electric cars. Benefit-in-kind rates that favour zero emission vehicles and salary sacrifice schemes have proven highly effective at shifting fleet purchases (company car tax rules).
Vehicle data comes from DfT/DVLA vehicle licensing statistics, which count vehicles by the registered keeper's address at each quarter. Key limitations: company vehicles are registered to office addresses, inflating counts in commercial areas and undercounting where the vehicles are actually used or kept; SORN vehicles are off-road by declaration but still registered; and registration address can lag house moves. See the manual for details. Company registration at office addresses means these counts reflect where fleets are registered, not where vehicles operate; some neighbourhoods show implausibly large fleets for this reason.
Company vehicles fuel type and licence
This chart shows the number of company vehicles in the LSOA by fuel type and licence status.
Company fleet fuel mix leads the private market by several years thanks to benefit-in-kind incentives for zero-emission vehicles. Monitoring it locally shows the pipeline of EVs that will reach the second-hand market, and where workplace charging investment is needed.
Vehicle data comes from DfT/DVLA vehicle licensing statistics, which count vehicles by the registered keeper's address at each quarter. Key limitations: company vehicles are registered to office addresses, inflating counts in commercial areas and undercounting where the vehicles are actually used or kept; SORN vehicles are off-road by declaration but still registered; and registration address can lag house moves. See the manual for details. Fuel categories follow DfT definitions, as for private vehicles.
An Accessibility-Proximity analysis attempts to capture whether a neighbourhood is under or over-provided with services. One of the core principles of sustainable transport is that people's needs should be near where they live. Nearby services reduce travel distances and reduce energy consumption and emissions. They also make it more likely that people can use healthy and sustainable modes of travel like walking and cycling. Short travel distances also save people time and money.
Ideally, everything you need would be nearby, but that is not always practical. Especially as the number of services we need varies. For example, we have far more hairdressers than speech therapists because many people regularly go to the hairdressers, and very few people need a speech therapist. So, we look at the ratio between the number of people and the number of services. For example, if there are 1,500 people within a 15-minute walk of your home, that could support around two hairdressers. But if only one hairdresser were within that distance, we would consider your neighbourhood underserved.
We can do this calculation for all kinds of services and produce a score between -3 and +3, where 0 means your neighbourhood has services in about the same proportion as the national average. A positive score means you have more of that kind of service than average, and a negative score means you have less than average. The chart and table below summarise the scores for 385 types of service listed in the Ordnance Survey Points of Interest
The scale shows how to interpret the Access and Proximity scores
We look at different time and distance bands because some services are common (like hairdressers) while others are rare (like speech therapists). We measure travel time by walking and/or public transport for 15, 30, 45, and 60 minutes (Accessibility) and straight line distance (Proximity) for 0.75, 1.5, 2.25, and 3 miles. For example, you may have no plasterers within 0.75 miles of your home, but plenty of plasterers within 1.5 miles of your home, and that is probably fine. But if your nearest postbox were 1.5 miles away, you would probably consider that too far.
The chart and table below present the same information in two different formats. This will help you explore how service provision varies from place to place.
This data is not available for Scotland.
Accessibility & Proximity Summary Table
This chart shows the 30 minute / 1.5 mile data from the table below. Each dot is a type of service (shops, schools, GPs, and so on): dots towards the top right are well provided both nearby and by public transport, while dots towards the bottom left are hard to reach, meaning longer, more car-dependent journeys for everyday needs.
Proximity is a planning outcome: local plans decide whether homes are built near services or far from them. Councils can use under-provision scores to target new services, protect existing local centres, require mixed-use development, and prioritise public transport where accessibility (not proximity) is the gap. This underpins "15-minute neighbourhood" policies adopted by many UK cities.
Services come from the Ordnance Survey Points of Interest (385 types). For each neighbourhood we compute the population-to-service ratio within walking/public-transport travel-time bands (15/30/45/60 minutes, from timetable-based routing) and straight-line distance bands (0.75-3 miles), and express it as a z-score relative to the national distribution (-3 to +3). Limitations: POI data has classification errors and lags reality; travel times use timetables, not live services; scores are relative, so a national under-supply of a service still scores 0; and Scotland is not covered. See the manual.
Accessibility & Proximity Summary Table
Public Transport Frequency
The chart shows the frequency of public transport (trips per hour) passing through or near the selected neighbourhood, for each year from 2004 to 2023, in five periods of the day: morning peak (6-10am), midday (10am-3pm), afternoon peak (3-6pm), evening (6-10pm), and night (10pm-6am). Frequency is what makes public transport usable without consulting a timetable, a "turn up and go" service. You can select the type of public transport and the day of the week from the drop-down menus. Note that 2012 and 2013 are missing due to a lack of data.
Bus service levels are a policy choice: since deregulation outside London, frequencies are set commercially unless authorities intervene. Franchising (as adopted in Greater Manchester), enhanced partnerships, bus priority lanes, and tendered evening and weekend services can all restore frequency. The long decline visible in many areas outside London reflects cuts to supported services documented in the official bus statistics; where frequency fell, ridership and access to jobs fell with it.
Frequencies are computed from archived public transport timetables (2004-2023), counting scheduled trips stopping in or near each neighbourhood per hour within each period. Limitations: 2012-2013 timetable archives are unavailable; timetables record scheduled, not actual, services (cancellations and reliability are invisible); "near" uses a fixed catchment around each neighbourhood; and early years have patchier coverage for some operators. See the manual for details.
