cas/taxonomy/field.php (field)
Illustration: Urban Datascapes

Urban Datascapes

Increasingly, data is guiding how cities are built and managed. 'Datascapes' are both derived from our actions but then can also steer them. How do humans and data interact in complex ways?

More and more, the proliferation of data is leading to new opportunities in how we inhabit space. How might a data-steered environment operate as a complex system?

In the long history of urbanization, infrastructural elements have been critical in defining the nature of settlement. Be it the river-routes that formed trade channels constraining settlements, the rail-lines defining where frontier towns would be situated, or the freeways marking a shift from urbanization to sub-urbanization, different infrastructural regimes have played a key role in determining where and how we live. Further infrastructural layers made new modes of life possible: the power-grid shifted daily rhythms so as to extend the workday into the night hours; telecommunication lines enabled physically distant transactions to occur with ease; highway and sewage infrastructures helped spur massive suburban expansion. These infrastructures - carrying people, goods, and ultimately ideas - have formed the skeletal framework upon which lifestyles and livelihoods are anchored.

As we move into an age increasingly mediated by digital infrastructures and the flows they channel, we ask the question:  what kind of worlds will these new regimes make possible, and how will these be steered to ensure ‘fit’ urban practices? What does ‘fit’ even mean within this context? Whether through driverless cars, the internet of things, or digitally enabled access economies, cities are poised to afford new kinds of behaviors and lifestyle options.

From Bell Curves to Power Laws

To date, individuals have been expected to live their civic lives in ways that cater largely to the average needs of population, rather than particular, exceptional requirements. Cities, meet standards. This, despite the fact that needs differ, and may differ both across individuals, and for the same individual across time. Nonetheless, we tend to relegate our urban systems to support a narrow range of options that remain relatively fixed. Historically, this has made sense, because individuated needs that shift or differ from norms are too variable and have, until now, been difficult if not impossible to track and accommodate.

While norms remain important (and if assumed to be governed by a power-law distribution, would align with the small number of urban offerings (20%) that meet the greatest proportion of urban needs (80%)), this leaves 80% of the more particular and finely tuned needs unharnessed.

Chris Anderson (2004) describe this full breadth of differential offerings - the non-impactful 80% - as ‘the long tail’:  the huge scope of ongoing (but small) demand that is not part of the "fat head" of the power law distribution.  Anderson argues that highly tuned niche offerings in this long tail are viable but, until now, have not been fully tapped due to the difficulties in pinpointing where and when they exist.

Today, new information technologies are changing all this, providing detailed access to the long tail of highly tuned offerings that may appeal only to the very few or for a very brief time, but would nonetheless be viable if there were a way to match needs to offerings. Anderson writes that, ‘many of our assumptions about popular taste are actually artifacts of poor supply-and-demand matching — a market response to inefficient distribution’.  Mass supply of standard urban environments or infrastructures may appeal to the norm but, in the end, no one is actually getting precisely what they want, when they want it. Instead, they are getting what the market has the capacity to supply with its coarse information availability.

Furthermore, they are getting what would seem to be viable given notions of "economy of scale". But these perspectives can shift when information coordination becomes more  efficient: instead of economies of scale, we can begin to activate access economies, which enable the pooling of diverse resources which can be accessed by individuals on an as-needed basis. Economies of Scale suggest Mass Transit Systems; Access Economies suggest Uber. One is fine tuned to individual needs, the other is not.

Fine Tuning: An Example

Considering the rise of Airbnb. Big hotel chains are based on a model that offers accommodations appealing to the widest possible demographics within certain price point. Accordingly, when making comparisons within a given price category, rooms offered by large chains appear generic and interchangeable. Airbnb changed this (and dramatically altered the accommodation industry) by providing a platform able to match highly specified needs with highly specified offerings. If I am looking for a vegan and pet-friendly one-bedroom apartment with a bicycle in the 16th arrondissement in Paris, I am now able to identify this niche with surprising speed and accuracy.  The capacity for Airbnb to offer highly specific information, tuned to individual preferences, that is also deemed reliable (because of reviews), allows individuals to stay in accommodation tailored to their personal requirements rather than generic ones.

Airbnb's success is based, in part, on how it is able to illuminate this broad array of atypical and variable niches – the long tail. This long tail shifts.  Accordingly, when I travel I may wish to stay in the normative Holiday Inn 50% of the time, a quaint bed and breakfast 49% of the time and a vegan glamping yurt only 1% of the time.  Until now, it has been very difficult to enact the behaviors desired only 1% of the time. But these niches, if made visible and accessible are in fact viable.

Today's data technologies now illuminate these.

From the Standard to the Particular

Airbnb is a classic example of how information technologies are making previously invisible urban assets more tangible and accessible for people. But such technologies  are also changing how we perceive the urban environments around us.  If hotel locations could previously be mapped and located according to their proximity to normative assets (for example major highway interchanges, major business centers, or major entertainment facilities), then today's data of occupied Airbnb sites might reveal a host of other locational preferences - ones that are irrelevant at the macro scale, but or interest to individuals at the micro-scale. We can imagine a new kind of mapping of these urban niches as having a more nuanced and variegated quality - ones capturing and relaying multiple kinds of urban flows and revealing latent flows not previously channeled.

Consider a host of other urban assets: when do people use particular roads, or trains, or bike routes? What routes are the fastest at a given hour of the day? Or perhaps speed is not important - what routes then are the quietest? Or the prettiest?

Or, consider the new potentials of the Access Economy. Here, it becomes less important that I have constant, physical possession of an urban asset (a car for example), and more important that I have easy, on-demand, and customized access to this asset (any make of car I want in a given instant; any video I want to watch on Netflix). The Access economy does not mean that all cars (from a car-sharing service) nor all videos (from a streaming service) will be  accessed in identical ways: certain cars and videos will be part of the fat head of the power law. But the long-tail is now on offer as well.

If previous city planning strategies only had the power to attune to normative needs (the fastest road), today we can construct civic Datascapes tuned to individuated desires. In a sense, data allows us to increase the city's Degrees of Freedom. Thus, if a standardized bus route was, at one point, the most effective way to transport people along "common" routes from A to B, then Uber offers a way for individuals to construct their own specified routes from E to Z. We can think of this shift as being one that moves us from mass-standardization to mass-customization, all of which is discovered and made tangible through individual data: our preferences when we call an Uber, or stay in an AirBnB.  At the same time data-scapes emerge on the other side of this: pleasant bike routes that are crowd-sourced and then promoted; quirky accommodation options rise to star status; pop-up events are made visible through social media posts.

This is a different kind of city: one viewed primarily through intensities of data, that can be curated so as to be viewed and filtered according to individual needs. Accordingly, my teenage daughter's view of the city is informed and highlighted by pathways, infrastructures and gathering places all of which constitute data points that are most salient to her: my tech colleague's perspective of the city will have its own matrix of data points. Neither will ride the same bus, nor stay in the same hotel, nor gather in the same meet-up spots. The "central square" will no longer be centralized. But there will be niches of localized interests and intensities that emerge, over time.


This is what we mean when we introduce the idea of "data-scapes". The term is used here to capture a range of interests which are still in nascent form - not quite yet emerged as a clear line of urban enquiry -  but which is "in the air" in various ways. Some of the Smart City discourses touch upon it, but the emphasis is more on big-data collection for optimization. Speculations around the Internet of Things relate to this area, as do investigations around the Access Economy.

What binds these research themes is a common awareness that information is now able to help steer how we experience and data-scape of the city, with material conditions being supplemented by informational conditions that alter the ways in which we engage with the material world. Apps on cell phones become the tools we use to navigate these scapes, which the city no longer something that is seen primarily as fixed pattern, but rather as something that can be activated and drawn from in unique ways.

Complexity How?


One of the ways in which these dynamics of civic activation and appropriation differ from current models is that the ways in which common needs or goods come to the forefront need no longer rest be driven from the top down. There are far greater opportunities for special niches to emerge from the collective actions of Bottom-up Agents, with novel and surprising features gaining prominence. In a civic data-scape, a particular club might gain prominence on social media on a particular evening - going 'viral' in the same way that a cat video might, and thereby gaining prominence in the shared Datascape of club-goers.

Contingency and Non-Linearity:

We see as well from the club example that some of the dynamics that generates points of prominence in data-scapes may in fact be caused by initial random-fluctuations, that gradually self-perpetuate,  (as is seen in systems phenomena governed by growth and Preferential Attachment. For example,  in the data-scape of accommodation, or restaurants, small changes in initial conditions may have a disproportionate impact on system performance: with certain sites gaining prominence in the Datascape even though are not inherently superior to others.

Driven by Flows

We often think of civic form as coming first - that we put in a road and then the road directs flows. Traffic engineers might look at a city plan and make decisions about location not because of existing flows, but instead because of existing cheap real-estate upon which to build a corridor.  Datascapes flip this relationship, by first determining flows and then allowing these Driving Flows to direct civic infrastructure. The simplest example of this is comparing 20 Uber passengers with 20 passengers bus passengers. The bus forces people to conform to its pre-determined course of navigation, whereas the flow of Ubers are instead driven by their desires. What is of interest is that, once this relationship is flipped we may observe new patterns of flows that are consistent and coherent, but previously invisible. This is also why the phrase 'data-scape' is invoked, because what emerges in tracing the pathways of 1000 Uber rides (in contrast to a 1000 bus rides) is a new kind of mapping about cities not evident before.

Thought Experiment:

For more insights into how IoT technologies might combine with complexity principles to reveal data-scapes of fit urban conditions, check out the "Urban Lemna" student project in the InDepth "Resources" tab to the right.

Sections of this text were extracted and modified from an earlier paper by S Wohl and R Revariah: Fluid Urbanism : How Information Steered Architecture Might Reshape the Dynamics of Civic Dwelling, published 2018 in The Plan Journal. See also "Sensing the City: Legibility in the Context of Mediated Spatial Terrains, published in 2018 in Space and Culture.


Photo Credit and Caption: Image Credit: curtis-macnewton-vVIwtmqsIuk-unsplash

Cite this page:

Wohl, S. (2022, 9 June). Urban Datascapes. Retrieved from

Urban Datascapes was updated June 9th, 2022.

Nothing over here yet

In Depth: Urban Datascapes

This is the feed, a series of related links and resources. Add a link to the feed →

Reinventing Society in the Digital Age - Dirk Helbing

This talk took place on May 9, 2017 as part of the Complexity Science Hub Vienna workshop series "RE-INVENTING SOCIETY IN THE DIGITAL AGE". The program for the event can be found at

Masters of Networks 3: online communities

Network scientists and online community managers come together to investigate the secrets of online collaboration

This is a list of People that Urban Datascapes is related to.

This is a list of Terms that Urban Datascapes is related to.

This is a collection of books, websites, and videos related to Urban Datascapes

A thought experiment project on self-organizing bridges

Project developed by Chunyao Liu

Learn more →

This is a list of Urban Fields that Urban Datascapes is related to.

This is a list of Key Concepts that Urban Datascapes is related to.

There would be some thought experiments here.

Navigating Complexity © 2015-2024 Sharon Wohl, all rights reserved. Developed by Sean Wittmeyer
Sign In (SSO) | Sign In

Test Data
Related (this page): Emergence (24), Driving Flows (25), Bottom-up Agents (22), TUTORIAL: Algorithms & Differentials (61), Tipping Points (218), Power Laws (66), Information (73), Fitness (59), Bifurcations (81), Attractor States (72), 
Section: fields
Related (same section): Urban Modeling (11, fields), Resilient Urbanism (14, fields), Relational Geography (19, fields), Landscape Urbanism (15, fields), Evolutionary Geography (12, fields), Communicative Planning (18, fields), Assemblage Geography (20, fields), 
Related (all): Tipping Points (218, concepts), Path Dependency (93, concepts), Far From Equilibrium (212, concepts), 
Nested Orders
Related (same section): Urban Modeling (11, fields), Urban Informalities (16, fields), Resilient Urbanism (14, fields), 
Related (all): Self-Organized Criticality (64, concepts), Scale-Free (217, concepts), Power Laws (66, concepts), 
Related (same section): Urban Modeling (11, fields), Urban Informalities (16, fields), Urban Datascapes (28, fields), Incremental Urbanism (13, fields), Evolutionary Geography (12, fields), Communicative Planning (18, fields), Assemblage Geography (20, fields), 
Related (all): Self-Organization (214, concepts), Fitness (59, concepts), Attractor States (72, concepts), 
Driving Flows
Related (same section): Urban Datascapes (28, fields), Tactical Urbanism (17, fields), Relational Geography (19, fields), Parametric Urbanism (10, fields), Landscape Urbanism (15, fields), Evolutionary Geography (12, fields), Communicative Planning (18, fields), Assemblage Geography (20, fields), 
Related (all): Open / Dissipative (84, concepts), Networks (75, concepts), Information (73, concepts), 
Bottom-up Agents
Related (same section): Urban Modeling (11, fields), Urban Informalities (16, fields), Resilient Urbanism (14, fields), Parametric Urbanism (10, fields), Incremental Urbanism (13, fields), Evolutionary Geography (12, fields), Communicative Planning (18, fields), 
Related (all): Rules (213, concepts), Iterations (56, concepts), 
Adaptive Capacity
Related (same section): Urban Modeling (11, fields), Urban Informalities (16, fields), Tactical Urbanism (17, fields), Parametric Urbanism (10, fields), Landscape Urbanism (15, fields), Incremental Urbanism (13, fields), Evolutionary Geography (12, fields), 
Related (all): Feedback (88, concepts), Degrees of Freedom (78, concepts),