London is densifying unevenly. The main issue is often not height itself, but how new development begins to function once people move in. In planning documents, density is expressed through dry metrics: the number of units, square metres and massing. In the urban environment, it behaves as flow, concentrating at pinch points: lobby exits, station approaches, crossings and local services.
The Elizabeth line illustrates the scale of this challenge. Since 2015, around 71,000 new homes have been built within one kilometre of its stations. [1] On paper, this is development around transport infrastructure. On the ground, it means additional pressure on very specific pavements, crossings, station entrances and local amenities.
A tower may remain within the boundaries of its plot on a plan. In daily life, its impact immediately moves beyond the site.
Piecemeal development that fails to account for behavioural scenarios can create inactive frontages and underused pockets within otherwise well-designed districts.
The City of London is a clear example. Retail vacancy there has reached 22%, partly because much of the surrounding commercial infrastructure was calibrated for an older office-footfall pattern. [2] Hybrid working has changed daily routines, spending patterns and the timing of movement through the area.
Nine Elms shows the issue from another angle. The scale of new development, including around 20,000 homes, has been accompanied by major road works, wider pavements and the complex integration of pedestrian flows with new transport infrastructure. [3]
Location Intelligence, and geomarketing in a broader sense, can help forecast these pressures before they become visible on the street. It can show which crossings are likely to come under pressure, which station approaches will carry the heaviest flows, and which ground-floor units risk being left outside stable pedestrian movement.
Statutory planning tools in the UK, including NTEM and TRICS, have historically been built around the measurement of vehicles rather than people. This reflects a broader bias in trip-generation practice: one international review found that 97% of trip-generation studies estimated vehicle trips, while only 37% measured walking trips. [4]
For London, this is a critical gap. In 2024/25, residents made an average of 6.2 million fully walked trips a day, representing 38% of all trips made by London residents. [5] Yet traditional planning models can still underestimate real pedestrian movement. As a result, decisions about density may be based on road capacity rather than the lived reality of pavements, station entrances and everyday walking routes.
This matters because the success of a dense urban district is not determined only by how many homes are delivered. It also depends on whether people can move through the area comfortably, whether ground floors receive real footfall, and whether local services match daily demand.
Modern location analysis adds a dynamic behavioural layer to planning. It allows a district to be tested in a scenario model before construction begins. In practice, the process has four main stages.
Baseline diagnosis.
The first step is to build a digital profile of the existing place using aggregated mobile data and other privacy-safe signals. Mobile phone data is increasingly used in transport analysis to understand movement patterns at scale, provided it is processed with appropriate safeguards and aggregation. [6] The model captures who moves through the area, when they move, and which routes they use. More advanced systems can analyse movement at a fine-grained spatial level, distinguishing between residents, workers and through-visitors. This helps reveal the real attractors and gaps that are often invisible on conventional maps.
Synthetic density modelling.
The proposed development is then added to the digital model. Flats, offices and mixed-use blocks are translated into behavioural patterns. The question is no longer simply how many square metres are being delivered, but how many people are likely to leave a lobby during the morning peak, which side of the street they may choose, and which station entrance they are likely to use.
What-if scenario modelling.
The model can then test resilience. Digital twins and related simulation approaches are increasingly discussed as tools for testing urban scenarios, not merely as static 3D representations. [7] What happens if 30% of residents work from home several days a week? How does that change daytime demand for cafes, gyms, pharmacies or local services? Where might footfall be cannibalised between existing high street retail and new ground-floor units? Which public spaces will be active throughout the day, and which will only work during narrow time windows?
Project adjustment before construction.
The point is not to produce another report. The point is to change the project while change is still possible. The findings can influence the position of entrances, the permeability of pedestrian routes, the design of active frontages, the retail strategy and the tenant mix. They can also strengthen the evidence base for developer obligations, including S106 contributions, by linking infrastructure requirements to measurable future demand. [8]
In this sense, Location Intelligence is not a replacement for architecture, transport assessment or public consultation. It is an additional layer that makes those processes more precise.
London is already moving toward a more spatially intelligent planning process. In this context, a digital twin should not be treated as a branded 3D viewer or a glossy visualisation layer. Its value is strongest when it combines spatial geometry, land-use data and scenario testing to make the consequences of development easier to inspect before construction. [7] [9]
But a 3D model alone is not enough. It can show what a project looks like. It does not automatically show how people will use it.
That is where pedestrian modelling and Location Intelligence become essential. London already has established walking-analysis and pedestrian-comfort guidance for assessing whether walking networks, crossings and pavements work for real users. [10] The next step is to connect that logic to district-scale digital models: not just whether a street looks generous in a 3D scene, but whether its capacity, frontage, amenities and desire lines match predicted demand.
The practical value lies in connecting the model to decisions. If the forecast shows that movement will be squeezed into one corridor, the project can be adjusted before construction. If part of the public realm falls outside real pedestrian routes, it can be repositioned or reprogrammed. If ground-floor retail is unlikely to benefit from stable pedestrian flow, the retail strategy should be revised before vacancies appear.
This is especially important in mixed-use districts. A mistake rarely stays isolated. A poorly located entrance changes the route. A weak route affects the ground floor. Empty ground floors weaken the street. A weak street reduces the quality of the wider district.
Digital twins show form and context. Location Intelligence shows use and demand. Together, they allow developers, planners and investors to discuss not only architectural form, but the future life of the district: where movement will concentrate, where people will dwell, where demand will appear, where overload may occur, and where emptiness is a risk.
The risk of fragmented densification is that a new building is often not properly stitched into the existing urban fabric. It adds homes, offices or commercial space, but its future flows are not always tested against the daily mechanics of the neighbourhood.
Location Intelligence can strengthen the evidence base for developer obligations and make infrastructure decisions more grounded. [8] It can also help developers avoid avoidable mistakes in access, public realm, ground-floor uses and local service provision.
If a district is tested in a model first, new density has a better chance of becoming a resource for urban growth rather than an additional burden on already strained streets.
The district should be modelled before the tower becomes concrete.
[1] Transport for London, Elizabeth line post-opening evaluation: Full Report, May 2025. https://tfl.gov.uk/cdn/static/cms/documents/elizabeth-line-post-opening-evaluation-full-report.pdf
[2] City of London Corporation, Retail Needs Assessment Study, October 2023. https://www.cityoflondon.gov.uk/assets/Services-Environment/city-of-london-retail-needs-assessment-october-2023.pdf
[3] Transport for London, Nine Elms project page. https://tfl.gov.uk/travel-information/improvements-and-projects/nine-elms
[4] Chris De Gruyter, Alexa Rose, Dhirendra Singh and Graham Currie, "Multimodal Trip Generation from Land Use Developments: International Synthesis and Future Directions", Transportation Research Record, 2019. https://doi.org/10.1177/0361198119833967
[5] Transport for London, Travel in London 2025: Active travel trends, 2025. https://content.tfl.gov.uk/travel-in-london-2025-active-travel-trends-acc.pdf
[6] United Nations Economic Commission for Europe, Use of Mobile Phone Data in Transportation, 2023. https://unece.org/sites/default/files/2023-05/ECE-TRANS-WP6-2023-Inf-1%20%28MPD%20Handbook%29.pdf
[7] Michael Batty, Digital Twins in City Planning, CASA Working Paper 237, University College London, 2023. https://www.ucl.ac.uk/bartlett/sites/bartlett/files/casa_working_paper_237.pdf
[8] GOV.UK, Planning obligations, including Section 106 agreements. https://www.gov.uk/guidance/planning-obligations
[9] The Institution of Engineering and Technology, Digital twins for the built environment. https://www.theiet.org/media/8762/digital-twins-for-the-built-environment.pdf
[10] Transport for London, Streets toolkit: Walking toolkit and pedestrian comfort guidance. https://tfl.gov.uk/corporate/publications-and-reports/streets-toolkit
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