ENHANCE Cities

This section includes the methodologies to produce the indicators on the ENHANCE Cities Mapping Tool. Data download links are included where available.

Travel Sustainability Index 2021

The Travel Sustainability Index measures sustainable travel behaviour and car dependence using car ownership and journey to work data from the 2021 Census. More sustainable locations have higher proportions of zero car households, and of public transport and active travel. The formula used to create the index between 0-100 is-

Where ZeroCarHH% is the percentage of households who do not own a car. ActiveTravelJTW% is the percentage of commuters walking and cycling. PublicTransJTW% is the percentage of commuters taking the bus, train and metro. CarJTW% is percentage of commuters taking a car, either as driver or passenger. Motorcyclists and taxi trips were included as car trips. Note that homeworkers were excluded from the total commuters, so each journey-to-work variable percentage is a percentage of those commuters who travelled to work excluding home workers. Note also the car ownership variable is double weighted, as this is a more influential variable in travel behaviour.

Department for Transport Connectivity Metrics 2025

The Department for Transport Connectivity Metrics measure location accessibility by analysing travel times to employment, education, health, shopping, community, leisure and population opportunities by Public Transport, Car, Walking and Cycling modes. The outputs are normalised on scale of 0 (least connected) to 100 (most connected). The Connectivity Metrics are derived using a sophisticated methodology based on analysing average travel times to key destinations, including factors such as public transport services, congestion and variations by time of day. Impendence functions are used based on the National Travel Survey. The full methodology is available on the DfT website, and the DfT also have a Connectivity Mapping Tool.

The DfT metrics include multiple modes of transport. The accessibilty approach - which is based on travel time - generally is more effective for public transport and walking trips, where travel time is the dominant factor in trip feasibility. Accessibility by car is constrained by ownership and parking restrictions rather than travel time only, while cycling accessibility metrics arguably need to also measure the quality of cycle infrastructure as well as travel time.

Local Accessibility by Public Transport & Walking

The Local Accessibility by Public Transport & Walking measure summarises local accessibility to 40 different types of services and amenities using walking and public transport modes, based on the concept of the 15 or X Minute City. Travel time to the nearest destination of each type on a weekday off-peak is used. Destinations are classified into 7 groups: Education, Health, Community, Cafe/Pub, Local Retail, Leisure/Play and Public Transport.

The travel times are calculated using R5R and public transport timetable data from the DfT BODS data and National Rail data. The times are based on the median time between 11:30am and 12:30pm on a weekday in March 2026. The destination location data uses the Ordnance Survey Point of Interest data, downloaded in March 2026. The park location data uses OS Open Greenspace data for entrances to parks over 500 square metres in area.

The category scores are calculated by converting the travel times using an impedance function. This function follows the DfT method of using a sigmoid/logistic function and calibrating the impedance function against National Travel Survey data for walking and bus modes (these modes were chosen to represent local trips). Each destination type is weighted by how frequently the destinations are visited (note some destination types did not correspond with survey classificaitons and estimates were used). The final overall Local Accessibility Score is an unweighted average of score for the seven different groups.

Cycle Infrastructure and Level of Traffic Stress

There are two related indicators provided on cycling provision. The Level of Traffic Stress indicator assesses general road condictions for cycling, including road type, traffic speed and cycle lane provision. The second Cycle Infrastructure indicator measures the extent and quality of cycle infrastructure, favouring protected cycle lanes segregated from road traffic. The indicators are derived from OpenStreetMap data, with the methodology described in this blogpost and this working paper.

New Build Housing EPC Data

The New Build Housing data layer uses Energy Performance Certificate (EPC) data, published by MCHLG, to identify all new build properties built between 2012 and 2026. All properties must register an EPC before sale or renting.

The EPC data includes an identifier to select new build properties, and is recognised as a reliable method of quantifying new build (see Greater London Authority, 2022). The EPC data is at property address level. There are some limitations with the EPC data. It measures total new build rather than net additional dwellings. A second limitation is that the EPC data does not provide a classification of new build housing by tenure, and additional data is needed to assess affordable housing completions.

House Prices Per Square Metre

Median prices per square metre for properties sold in 2019, 2022 and 2024. The dataset was created through a complicated joining process between the Land Registry Price Paid dataset and the Energy Performance Certificate floorspace data. Dataset produced by Chi et al. (2025) UCL. Dataset: House Prices per Square Metre at London Datastore.

Brownfield Site Data MHCLG

The Ministry for Housing Communities and Local Government have been working to integrate and harmonise Brownfield Site Data for England, including estimated numbers of dwellings for each site, ownership and current planning permission status.

This data is still undergoing development, and there were missing Local Authorities (e.g. Liverpool, City of London) in the dataset when this was mapped for the ENHANCE site (July 2026).

Green Belt Land Near Rail Stations

This dataset maps Green Belt sites within an 800 metre buffer of rail stations, as a proxy of accessible Green Belt sites. Land that has specific environmental protections (SSSIs, nature reserves) have been excluded, as have sites of high flood risk, identified using Environment Agency Flood Risk data.

Note that proximity to rail stations is a basic indicator and does not ensure suitability for development. Many of the sites identified have existing uses. The original Green Belt spatial data can be downloaded from MHCLG.