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Illustration: Tactical Urbanism

Tactical Urbanism

Tactical interventions are light, quick and cheap - but if deployed using a complexity lens, could they be a generative learning tool that helps make our cities more fit?

Tactical Urbanism is a branch of urban thinking that tries to understand the role of grassroots, bottom-up initiatives in creating meaningful urban space. While not associating itself directly with complexity theory, many of the tools it employs -particularly its way of 'learning by doing' - ties in with adaptive and emergent concepts from complexity.


Tactical Urbanism is an approach to urban intervention which removes the need for prediction: rather than attempting to forecast what might work in a given environment, tactical strategies instead simply enact various small short-term interventions. Examples might include: putting temporary barricades up on a street to allow for a festival; temporarily allowing a traffic lane to become a bike lane; shifting parking stalls to be pocket parks or outdoor cafe tables; etc. With many of these kinds of interventions beginning to crop up in cities around the world, the term "Tactical Urbanism" was introduced by Mike Lydon & Anthony Garcia, to capture these kinds of activities. 

These kinds of short-term tactics can enliven public space, while avoiding the red-tape of more permanent interventions. They are thus easier to implement given their quick and temporary scope. They often are the result of grass-roots community activism, and are typically described in the context of community empowerment.

At the same time, these kinds of interventions can be related to complexity thinking if they are conceived not as "one -offs", but instead as strategic tests that serve as a kind of environmental probe.  The nature of such interventions are that they are "light, quick, and cheap", meaning that they are also Safe to Fail. Because of their temporary and "light" nature, they can quickly be mobilized on different sites, on different days. This means that they have the inherent ability to provide quick and adaptive Iterations that can support urban 'learning'. 

How the City Learns

In what way might a city learn? Urban Designers often depict renderings of lovely civic interventions: bike paths filled with happy cyclists; amphitheaters enlivened by performers and audiences; sidewalk cafes brimming with smiling people. But are these projections accurate? Too often once spaces are built, they fail to perform in the ways anticipated - but at that point it is too late. Too much capital has been sunk into the project to rip it up and start over again, so we are left with dis-functioning environments.

We can therefore think about tactical approaches as a way to increase the number of functional Variables a particular urban environment can explore. One iteration might involve populating a street with a market, another might be about partially closing it for a bike path, another might test turning sections into pop-up parks. Each of these can be considered to be potentially viable urban functions that are seeking the right "fit" within a given context - ones looking for a supportive niche. It is therefore possible to see tactical interventions as "fitness" probes used to explore the Fitness Landscape of an urban environments. Given that different urban environments are subject to different underlying dynamics (or Driving Flows ) the success of a particular test probe can tell us something about what are suitable niches for longer term interventions.

Example: Play me I'm Yours

Play me I'm Yours began in 2008 as an artist installation by Luke Jerram, by placing pianos in various locations in a city. The project gained international traction and has since been replicated globally. Musicians find pianos in unexpected locations and are able to animate the surrounding environment by playing music. While the project is compelling in and of itself, it also interesting to position it not merely as an artistic intervention, but also as an experiment in probing the city for viable music locations. Each piano, in a sense, could be thought of as a sensor, monitoring how often it is activated by players. Together all pianos thereby gain data about the underlying capacity or propensity for music performance in a section of the city. If we think of each piano as an agent in a complex system, and we think of "being played" as a measure of that agent's fitness, then the pianos can, in a sense, monitor which positions best serve to gather their relevant input (piano playing individuals). Here, the civic environment carries these driving resource flows in differential ways (with some locations being richer in flows than others). These are thereby more "fit" locations.

While this example has its limits, it can be extended to imagine other, similar kinds of civic systems. For example, imagine that we create a temporary pop-up playground set,  capable of being easily dismantled and assembled, and then deployed to different vacant lots in the city. We could then imagine equipping this set with sensors, to determine where and when it is activated and used. This would not involve the top-down monitoring of individual kids (a risk often associated with big data collection), but instead would simply involve the monitoring of the equipment itself: do the swings swing, are the slides being slidden upon, etc.  We can think of each of these activities being a measure of 'fitness' for the playground equipment. A slide, for example, as an agent within this complex system aims to fufill its 'destiny' by being used for sliding: sensors monitoring the frequency of its use can then be used as a measure of its fitness. The various pop-up locations are different niches, each of which provide the slides with differential flows of a particular resource - in this case the energy of sliding children - that the slides are hungry to gather. The deployments of the playground equipment can then be seen as explorations of the fitness landscape, Iterations through which the slide gathers Feedback about locational success. 

It should be apparent that this is a system capable of learning, with each tactical mutation of {{variable}} serving as a test of fit strategies. Furthermore, the system can be thought of as made up of Nested Orders of components, so we have the fitness of the playground as a whole that can be assessed, but we can also examine the fitness of the different sub-elements making up the park: how much a sandbox or a swing-set, or a slide are each activated as part of that whole.

Tactical Strategies as a Method of Deploying Complexity on the Ground

Tactical strategies are most typically lauded as a way to gain grass-roots advocacy, but they are presented here in relationship to complexity, as tangible,  operational way to employ complexity thinking in real-world situations. These strategies, alongside the idea of Urban Datascapes, are a way of gathering meaningful data about the differential needs and functional requirements of the city. This information gathering can either be done using high-tech sensors (leveraging the power of the Internet of Things), simple observation strategies (does a pop-up market look busy or dead), or by figuring out how success can leave an environmental trace Stigmergy.

In the case of stigmergic signals, we need to think about how the environment is structured in ways where it is capable of collecting signals. For example, if we wish to take a tactical approach to placing pathways in a park, rather than setting these in stone, we might instead simply plant grass. Grass, as a medium, is capable of collecting traces of differential flows of footsteps - recording the Driving Flows where routes converge. In this way, what are known as 'desire lines' manifest on the grass as an emergent phenomena, revealing bottom-up flows rather than imposed flows. If the "fitness" of a sidewalk paving stone, pertains to where it best gathers footfalls, then desire lines reveal the optimum location to place these stones.

We can, of course, force these flows into other regimes that will become well-trodden: if there is only one way to go then people will go that way, but just because we have locked-in people to a given behavior by forcing them into this conformance, does not mean that it is best. We can think of the QWERTY keyboard as imposing a limit on more effective ways of typing, but just because lots of people use this keyboard does not make it the most fit of all possible keyboards.

Tactical Urbanism can therefore be seen as a useful tool for designers thinking about how they might explore the underlying fitness landscapes of the city - shaped by different flows and potentials. The challenges are in learning how to conceptualize material artifacts in the city - ranging from movable chairs in parks, to movable buses on self-organizing bus routes - in more tactical ways. 

 


Photo Credit and Caption: A temporary “protected intersection” at Open Streets MPLS on June 8 by Alta Planning and Design; Nick Falbo, Flickr

Cite this page:

Wohl, S. (2022, 13 June). Tactical Urbanism. Retrieved from https://kapalicarsi.wittmeyer.io/field/tactical-urbanism

Tactical Urbanism was updated June 13th, 2022.

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Bottom-up evolutionary space

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Tactical Urbanism.

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For a system to adapt, it needs to have variables to adjust.

See also: Requisite Variety

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Related to the idea of Iterations that accumulate over time

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A notion that describes the ability of an intervention to quickly test whether or not it is 'fit', without expending unnecessary energy

Relates to {{tactical-urbanism}} Learn more →

A fitness landscape is a concept that employs the metaphor of a physical landscape to depict more or less 'fit' regions of phase space.

Related Terms and Topics: {{Fitness-Peaks}}, basins-of-attractions, critical-point, Tipping Points, Phase Space Learn more →

Relates to {{Relational-Geography}} and {{Landscape-Urbanism}}

In geography there has been a move away from thinking about space as a "thing" and to instead think about how different places exist due to how they interact with flows. Places that capture more flows, are more geographically relevant

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Complex Adaptive Systems become more 'fit' over time. Depending on the system, Fitness can take many forms,  but all involve states that achieve more while expending less energy.

What do we mean when we speak of Fitness? For ants, fitness might be discovering a source of food that is abundant and easy to reach. For a city, fitness might be moving the maximum number of people in the minimum amount of time. But fitness criteria can also vary - what might be fit for one agent isn't necessarily fit for all.

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Feedback loops occur in system where an environmental input guides system behavior, but the system behavior (the output), in turn alters the environmental context.

This coupling between input affecting output - thereby affecting input - creates unique dynamics and interdependencies between the two.

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There would be some thought experiments here.

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