Open data is not enough to facilitate solutions that can make cities more responsive to resident problems and more prepared for inevitable setbacks like environmental or economic crises.
This story was originally published by Data-Smart City Solutions.
How do leaders create resilient cities — those that are able to adapt to changes and disturbances, whether they be natural disasters, economic downturns, technological advancements or any other large-scale agitations? According to the authors of Urban Resilience and Planning Support Systems: The Need for Sentience, developing resilience requires careful urban planning. Going forward, resilient cities will be those that develop systems to monitor various interdependent systems, cultivate processes for adapting to changes and govern these systems in inclusive ways.
Planning support systems (PSSs), geo-information instruments that allow planners to explore and manage their activities, are critical tools for achieving these goals. However, current PSSs are lacking in their ability to gather data in real time and deliver it to residents in an accessible format, capabilities that are integral for detecting disturbances. The authors call for the creation of a new wave of sentient planning support systems that have more awareness of application context and user needs, can learn and adapt based on user behavior, and provide accessible and interactive platforms.
Open data is not enough to facilitate solutions that can make cities more responsive to resident problems and more prepared for inevitable setbacks like environmental or economic crises. As the authors argue, “Data on its own lacks meaning and usefulness. Like binary code, it has no meaning unless someone or something manipulates it into something useful, that can be understood and garner a response” (Deal et al, 41).
Many practitioners and researchers have already pursued efforts to make data more useful. Tools like interactive data visualizations, themed open data releases and story maps provide residents and policymakers alike with accessible information that can direct better-educated decisions.
However, the authors argue that contemporary PSSs need to move beyond these tools in order to extract bigger troves of useful data faster and make them accessible to all potential users. The next wave of PSSs must possess the dynamic capacity to update underlying datasets in real time as well search other data repositories for potentially useful data.
Moreover, these PSSs should not merely be designed to appeal to a certain community of data users, but rather should be tailored to each individual user based on preferences and level of knowledge. The authors foresee a PSS that uses artificial intelligence (AI) and machine learning to adapt to user behavior. For example, if a user saves a certain data set, the system may store that data set as well as search for and store other similar sets for ease of access down the road.
And to ensure usability by a wide range of users, these systems should offer a variety of user interfaces that also adapt based on the behavior of users. This means not only visualizations, but also verbal representations for users who are not spatially oriented. And, as users interact with data, the system should analyze which interfaces they prefer, then adjust options based on their needs.
However, the usefulness of these prescriptions hinges on the availability of technology — can cities realistically create such responsive PSSs? The authors point to Building Information Modeling (BIM) technology — an intelligent 3-D model-based process that allows architects, engineers and construction professionals to visualize the outcomes of design tweaks — as evidence that the capabilities for such systems exist. Recent BIM research has produced models that build design based on dynamic frameworks, involving a series of design refinements and simulations of environmental and energy impacts, habitability and social performance measures. These models integrate real-time contextual data that allows designers to better predict the impact of changes. Other technologies like sentient buildings have the desired capacity to adapt to user preferences: For example, if users consistently override temperature settings, the system adjusts future settings to reflect the desired change. Often these sentient buildings also send real-time building performance data to a dashboard for building managers to monitor activity.
According to the authors, the next wave of PSSs will allow planners to visualize and integrate critical data in real time, while simultaneously making data more accessible to all. The result will be not only better understanding of urban systems that will facilitate more resilient design, but also more transparency and accessibility achieved by open data. The technology is there — it’s just a matter of using it to build these tools.
Cities looking to make their data more accessible and encourage users to create tools that contribute to a resilient urban ecosystem should consider integrating artificial intelligence and adaptable design into future planning technologies. In order to begin designing these systems, cities can collaborate with creators of BIM technologies, such as GRAPHISOFT or Autodesk. And, while this article sits behind a paywall, readers can freely access additional information on developing innovative PSSs in the resources below.
Discerning and Addressing Environmental Failures in Policy Scenarios Using Planning Support System (PSS) Technologies by Brian Deal and Haozhi Pan, Sustainability, 2017
What-Ifs, If-Whats and Maybes: Sketch of Ubiquitous Collaborative Decision Support Technologyby Soora Rasouli and Harry Timmermans, Lecture Notes in Geoinformation and Cartography, 2013
Ecosystem Services, Green Infrastructure and the Role of Planning Support Systemsby Brian Deal, Varkki Pallathucheril and Tom Heavisides, Lecture Notes in Geoinformation and Cartography, 2013
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