How social-media and mobility data are emerging as new signals for urban planning, tourism and investment
What if the next map of a city is drawn not only from its roads and buildings, but also from the digital traces people leave behind?
Researchers in Riyadh have tested that proposition at city scale.
Their system, SnapScope, collected 515,364 unique public Snapchat posts over 23 days using 2,740 query points arranged on a one-kilometre grid across the Saudi capital. The objective was not primarily to study Snapchat, but to determine whether publicly visible, geotagged content could be systematically collected and explored as a source of urban data.
The experiment also exposed the limitations of that approach.
Across a 21-run saturation test, 94.8 percent of returned observations were duplicates of records already collected. More importantly, the researchers could not establish what proportion of all eligible public Snapmap content their system had captured. Snapchat provides no documented public API for this purpose, while the interfaces on which such collection depends can change.
SnapScope is therefore not a complete map of Riyadh. Its significance lies elsewhere: it demonstrates how digital behaviour could provide a supplementary urban signal, while showing why such signals must be treated cautiously.
For businesses and governments, the experiment raises a potentially valuable question: can digital activity identify changes in how a city is being used before they become visible in conventional statistics?
From Digital Behaviour to Urban Intelligence
Conventional urban datasets can show planners where people live, where roads run and where buildings are located. They are often less effective at showing how patterns of urban activity change between surveys or official reporting periods.
Geotagged social-media activity, aggregated mobile-location information and other location-based datasets can reveal where activity concentrates and how those patterns change over time. Used carefully, such information can supplement conventional measures of mobility, public-space use and emerging destinations.
Alexandria provides a useful example.
Researchers developed an agent-based mobility model for the Egyptian city using Facebook’s Movement Range Maps alongside OpenStreetMap and other inputs to compensate for fragmented or inaccessible conventional mobility data. The methodology used modelling and randomisation techniques to estimate missing information needed for transport simulation.
The resulting model showed a notable correlation between estimated transport demand and traffic demand derived from Google Maps, suggesting that social-media-derived movement data can strengthen mobility analysis in data-scarce environments.
The lesson is not that social-media data can replace conventional transport information. It is that alternative digital signals can make an incomplete urban picture more informative.
When a Digital Footprint Becomes an Investment Signal
That proposition extends beyond transport.
Developers want to know whether a new district is attracting people. Retailers need to understand where activity is concentrating. Tourism authorities increasingly need to know where visitors spend their time, not simply how many arrive.
Digital behaviour could provide an additional — and potentially faster — signal.
A sustained concentration of location-based activity may indicate an emerging commercial, entertainment or tourism destination. Changes in that activity could help measure how people respond to a new attraction, event, transport connection or real-estate development.
For investors, the attraction is timeliness. Official statistics, property transactions and demographic surveys can arrive with significant lags. Some digital indicators can signal behavioural changes earlier.
For a retailer choosing between locations, a hotel developer assessing an emerging destination or a transport authority deciding where additional capacity is needed, such data could provide an earlier indication of changing activity — provided it is tested against conventional evidence.
A digital signal is not an economic verdict.
A heavily photographed destination does not necessarily generate high visitor spending. A social-media hotspot does not prove purchasing power. Conversely, a digitally quiet industrial or residential district may support considerable economic activity.
The investment value therefore lies in triangulation: combining digital behaviour with transport flows, demographics, land use, property data and economic indicators.
Only then can a digital footprint move from an interesting visualisation towards a credible decision-support tool.
The People Missing From the Map
The largest weakness is representation.
Social-media users are not a representative sample of an urban population. Age, income, connectivity, platform adoption, visitor behaviour and privacy preferences all affect who becomes digitally visible.
Two neighbourhoods with similar populations can therefore generate radically different digital footprints.
If planners interpret that difference as evidence of underlying demand, the bias can migrate from the dataset into the physical city: capital and services may flow towards neighbourhoods generating more observable data while less digitally visible communities receive less attention.
The risk is therefore not simply biased data. It is biased data shaping real-world investment and resource allocation.
Privacy creates another boundary. Unlike a conventional road sensor, a social-media trace originates from an individual. SnapScope addresses part of this concern by releasing aggregated information rather than user identities or social-graph data.
As governments and businesses gain access to richer behavioural datasets, harder questions follow: what should be collected, how should it be protected, and which decisions should it influence?
Egypt Enters the Same Data Conversation
Those questions are increasingly relevant in Egypt.
Egypt formally unveiled its National Smart Cities Strategy in September 2025, initially focusing implementation on new cities. The framework connects digital transformation and infrastructure with smarter transport, services, resource management and sustainable urban development.
For investors, the significance lies less in the technology itself than in whether better urban information can reduce uncertainty over where people travel, congregate and live — and, when combined with economic data, where infrastructure and private capital might follow.
Alexandria already provides an early illustration. Its mobility research did not treat Facebook-derived movement information as a complete representation of the city. Instead, researchers combined it with mapping, modelling and other inputs to construct a broader picture.
That may offer the more credible model for smart cities across the region:
One dataset provides a signal. Several independent datasets provide evidence.
The New Urban Data Test
That is ultimately what the Riyadh experiment puts on the table.
Its 515,364 unique public snaps demonstrate the scale of urban information that can potentially be extracted from a platform never designed as a planning system.
The 94.8 percent duplication recorded during the saturation test shows that repeated collection can produce sharply diminishing amounts of new information. It does not mean that 94.8 percent of Snapchat activity itself is duplicated.
More fundamentally, the inability to measure overall coverage exposes the methodological limitation at the heart of such systems: a digital map represents only the behaviour visible through its particular platform, collection method and population.
For cities across the Middle East — and for Egypt as it develops its smart-city agenda — the strategic question is therefore shifting.
It is no longer simply: How much urban data can we collect?
The more important question is: Is the data representative, reliable and economically meaningful enough to influence where we build, move, invest and allocate resources?
The answer will determine whether digital footprints remain compelling maps on a screen or become part of the infrastructure of urban decision-making.
Because the most consequential neighbourhood on tomorrow’s digital map may not be the one generating the most activity.
It may be the one the data barely sees.
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