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March 4, 2021
Amid a global pandemic, a swelling social justice movement and the growing threat of climate change, urban designers are responding to renewed calls for shaping urban environments that are more accessible, equitable, healthy, sustainable and resilient. No small task.
Urban design sits at the intersection of design, sociology and science. The physical design of our environment impacts a city’s everyday functioning and the interaction of people, driven by a process that is part art, part science. Recently, urban design strategies including 15-minute cities, eco-districts, innovation districts, smart cities, and regenerative cities have addressed some of these issues, each in different ways.
But even with the proliferation of data and adoption of evidence-based design, urban designers are faced with the challenge of making sense of the ever-growing streams of data now available to us. Nor can we overlook the component most crucial to our work: the people served by the spaces we shape.
GOALS, KEY METRICS, BASELINES
The first stage of the urban design process is to define the goals for the project, the key metrics that will be used to evaluate potential solutions against the project goals and the baseline values for those metrics.
A common initial step to get an understanding of site conditions is mapping. Although mapping is a historical tool, datasets and visualization methods are constantly evolving. Data is changing, not only in the sheer amount available and the increased accessibility due to open source tools, but also in the ability to collect dynamic, real-time data.
For example, there are now tools that use anonymous cell phone data to represent the movement of people (e.g., vehicular, pedestrian or bicycle movement) through an area. Collecting data real-time allows for increased frequency of updates to the dataset to understand patterns more clearly.
In terms of visualization, there is a greater variety of tools that assist in aggregating, visualizing, and interpreting these large datasets. There are platforms that aggregate everything from parcel and environmental information to transportation and demographics for a given geography and help customize the resulting visualizations. These tools help us analyze complex data and distill them into easy-to-understand, actionable insights for our clients.
But given these recent developments in how we collect, visualize and analyze data, it is easier than ever to forget about the most important aspect of our work: the people who will occupy the spaces we design. Ground truthing, through community engagement, is a critical piece of the process. It’s important to cross-correlate the qualitative information (experiences, anecdotes, and thoughts) from users and occupants of the spaces with the quantitative information analyzed. This makes it possible to create a bigger picture of the existing conditions of a site or urban area.
With in-person interaction still limited due to the pandemic, it’s increasingly important to leverage tools and technologies that allow remote community engagement. Online surveys, live polling, and interactive design and documentation platforms for live sketching, Post-it notes and annotation, back-and-forth commenting, and other forms of virtual collaboration, have proven to be effective.
The second stage is to develop potential solutions and to evaluate and compare their metrics. Parametric design can help us quickly understand how changing just one parameter can impact the overall design something that advances in computational design tools have made widely available to designers in recent years.
For example, when designing a new office development, one input parameter may be the parking ratio or open space ratio. By altering either of these ratios, the design output may vary in building massing, lot coverage, or overall square footage. Most importantly, parametric design has made it feasible to explore hundreds of design alternatives at the touch of a button, and identify which alternatives best achieve the project goals.
Alongside parametric design, predictive modeling is another tool to support the evaluation of design solutions and prototypes. Predictive modeling uses existing data to create a trained statistical model that can be used to predict outcomes based on chosen variables. For example, a predictive model can analyze future traffic patterns, crime patterns, or economic trends. If one of the project goals is to reduce car traffic in an area and increase bicycle traffic, applying a traffic pattern predictive model to each design alternative allows you to compare the predicted car and bicycle traffic patterns to the baseline traffic patterns identified in the first design stage. This comparison helps the design team identify which design alternatives best fit the project goals.
Of course, design alternatives need to be tested by users and occupants to gain qualitative, experiential feedback on the design. We are seeing the ways in which we help immerse communities in prototypes is evolving with augmented reality and virtual reality. Augmented reality overlays digital images over your real surroundings, while virtual reality creates a completely digital experience. Although the technology required for each varies, a person could virtually “walk around” or zoom around a model of a new development, experiencing it at different scales.
MONITORING, CONTINUOUS IMPROVEMENT
Designing resilient and adaptive places requires one final stage: continual monitoring and improvement, creating a closed loop system. A continuous feedback loop enables quicker design response to undesirable metric levels and provides a complete picture of how the design is functioning.
We’re starting to see smart technologies integrated into design to do just this such as sensors, cameras, smart meters that monitor energy and water use, traffic patterns, and more creating more dynamic datasets and helping us benchmark results over time. For example, dynamic data collection in traffic and parking has resulted in dynamic congestion pricing and parking pricing. Each are generating an adaptive response to attain an adequate metric level to achieve goals such as lowering CO2 emissions or increasing active transportation methods.
In general, collecting data post-design, whether quantitative or qualitative, gives us an understanding of the success of a project, but also supports innovation for new projects.
Even with new data streams that allow us to ideate faster than ever before, people cannot be lost in this process. Engagement and ground truthing are key parts of the feedback system as well. No matter how much data we have at our disposal, we need the thoughts and feedback of people experiencing the design first-hand to fully measure success and design functionality.
Amy Triscoli is an urban designer with ZGF Architects. Her work is informed by a passion for studying the intersection of design, data and health.