City Resilience through Data Analytics: A Human-centric Approach. (2015)

• Record Type: Journal Article
• Authors: Falco, Gregory J.
• Is Part Of: Procedia engineering. Volume 118(2015)
• Page Start: 1008
• Page End: 1014
• Publication Date: 2015
• Subjects: resilience -- resiliency -- planning -- data analytics -- risk management -- cities -- emergency management; Engineering -- Congresses; Engineering -- Periodicals; Engineering; Conference proceedings; Periodicals; 620.005
• DOI: 10.1016/j.proeng.2015.08.542
• Languages: English
• ISSNs: 1877-7058

Abstract

Abstract: Our cities are being redefined daily based on social, political and environmental factors. This creates substantial challenges for those that attempt to develop resilience strategies for cities. Resilience planning requires a set of assumptions often based on data; however, the dynamic nature of our growing urban environments has impeded our ability to rely on these suppositions. To account for the unpredictable ebb and flow of changes in our cities we have become heavily dependent on data modeling and analytics. The ability to collect and store data from a variety of systems in a cloud infrastructure has enabled the potential for resilience planning to be based on historical scenarios and societal context – prioritizing risks and issues based on multiple factors. As our infrastructure becomes "smarter" with the ability to capture more data and make decisions through machine learning algorithms, resilience plans may become less in touch with the citizens for whom the resilience strategies exist. Thusly, an emergent risk to the inhabitants of cities is the imbalance of qualitative versus quantitative feedback that is leveraged to develop and improve a city's resilience strategy. Cities are living organisms that cannot be purely defined through machine data. A modern way to establish policies and plans for major urban centers is to leverage machine data collected through various "smart" technology programs. Such data-aggregation mechanisms feed into analytics tools that often fail to account for historical context or citizens' perspectives. Without leveraging this information, a resilience plan cannot be complete as it will not address the city as a system, but only a component thereof. This paper proposes a new model for developing a city's comprehensive resilience strategy that integrates machine data, historical context and societal effects. The risk of not pursuing a three-pronged model would be that future resilience strategies would lack a human-centric approach.