From Smart City to Quantified Community: A New Approach to Urban Science
Constantine E. Kontokosta (NYU CUSP & NYU Tandon)
Urban planning as a profession shifted radically after World War II. A result of the military development of systems engineering and optimization processes for radar and missile control, planners attempted to apply complex systems models and new decision-making algorithms to create optimized solutions to dynamic problems. The reliance on quantitative analysis and the ascendance of social scientific and technical solutions to planning problems led to the acceptance of a rational comprehensive model of decision-making at the expense of political and contextual realities. This pivot left more significant activities, such as agenda-setting and goal-formation, to those government officials or special interests who wielded political or financial capital.
Today, the convergence of two phenomena – the ability to collect, store, and process an expanding volume of data and the increasing level of global urbanization – presents the opportunity and need to use large-scale datasets and analytics to address fundamental problems and challenges of city operations, policy, and planning. Unfortunately, the techno-centric marketing rhetoric around “Smart Cities” has been replete with unfulfilled promises, and the persistent use (and mis-use) of the term Big Data has generated confusion and distrust about potential applications of technology in cities. Despite this, the reality remains that disruptive shifts in ubiquitous data collection (including mobile devices, GPS, social media, and synoptic video) and its analysis will have a profound effect on urban policy and planning and our collective understanding of urban life.
There is an opportunity now to learn from the mistakes of the past and to use new data streams and computing capabilities not in a singular quest for optimal solutions, but rather to enhance and support how communities identify, define, and collectively try to address their most pressing challenges. Problems vary by neighborhood, time, and demographics. Needs are defined by personal expectations, feelings, and values. Practitioners in the emerging field of urban informatics should recognize the importance of difference and develop a grounded appreciation of the social and behavioral dynamics of place.
At NYU’s Center for Urban Science and Progress (CUSP), I am leading work on a major research initiative called the Quantified Community (QC), which will soon expand as it becomes a founding partner of NYC’s Neighborhood Innovation Labs initiative. The intent is to use new methods to collect, fuse, and analyze data to enable improved neighborhood planning and urban design, and, ultimately, positively impact quality-of-life for those who live in cities by addressing persistent questions on how the built environment shapes individual and collective outcomes. This goal is grounded in the need to engage the local community and let residents better understand and ultimately define problems and needs, and to use data analytics to advance potential ways to reduce or eliminate these challenges. It is an experiment in every sense, as many of the “what, why, and how” questions of community data science still remain to be answered, although we are making progress.
We have initially launched the QC in three very distinct neighborhoods: at Hudson Yards, a ground-up “city-within-a-city” on the far west side of Manhattan; in Lower Manhattan, a mixed-use neighborhood that attracts residents, workers, and visitors; and, most recently, in Red Hook, Brooklyn, an economically-distressed community facing significant development and demographic changes. In each of these communities, we are working with different constituents to define problems and build an “informatics infrastructure” to support community planning and local decision-making. At Hudson Yards, which is still a construction site, our partner is the developer who is designing and building the project. In Lower Manhattan, we are partnering with the local non-profit Business Improvement District, whose goals are to improve quality-of-life in the area to increase the neighborhood’s attractiveness to residents, workers, and tourists. And in Red Hook, we are collaborating with a community organization that provides social service support and workforce training for neighborhood residents.
Our work in Red Hook is perhaps the most compelling opportunity to test to the potential of data analytics and internet-of-things (IoT) technology to actually enhance well-being in a traditionally under-served community. The case is not clear, and the outcome not assured. But already the partnership has proven to demonstrate that technology – when used transparently, guided by community problem-solving, and translated in a way that can be understood by a range of stakeholders – can be both a direct source of economic opportunity and a means to re-think how we guide and evaluate urban planning decisions and the role of citizen engagement in that process.