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Infrastructure Resilience Conference 2018

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Urban Resilience: Integrating Disaster Risk Management Networks

According to the UN the 54% of the world population live in urban areas and it is expected that by 2050 this percentage will increase to 66%. Due to the effects of climate change and the unplanned expansion of urban areas, damages and losses from natural disasters events will likely continue to grow[1]. Understanding disaster dynamics in urban areas and how the disruptions propagate through infrastructure, services and affect community well being is important to prioritize public policy to offset the effects of disasters. The purpose of this work is to present a theoretical approach to measure urban community resilience across cities and to identify critical components mixing methodologies form [2,3,4,5]. This proposal takes into consideration a system of system composed by governance/ institutional networks ( government, private sector, academia and non governmental organizations), service networks ( electricity , water , gas, waste ), physical and natural infrastructure ( transportation,safety, public spaces and environmental quality), and social and human capital (population characteristics, such as demographic structure, education, diversity, income, access to financial instruments, poverty and health among others). A Connection Cost model is used to integrate the 4 R´s robustness, rapidity, resourcefulness, and redundancy measures to the cost-benefit function that the central planner want to maximize in each subsystem. This framework incorporates disaster risk management in order to measure an overall performance of each subsystem to calculate the aggregate resilience of an urban system allowing the decision maker see the tradeoffs between policies that impact the 4Rs and cost of each subsystem to identify critical subsystems. Future work will explore the economic impact of varying resilience as a baseline for disaster impact assessment.

References [1] Field, C. B. (Ed.). (2012). Managing the risks of extreme events and disasters to advance climate change adaptation: special report of the intergovernmental panel on climate change. Cambridge University Press.

[2] Ramirez-Marquez, J. E., Rocco, C. M., Moronta, J., & Dessavre, D. G. (2016). Robustness in network community detection under links weights uncertainties. Reliability Engineering & System Safety, 153, 88-95.

[3] Mosleh, M., Ludlow, P., & Heydari, B. (2016, April). Resource allocation through network architecture in systems of systems: A complex networks framework. In Systems Conference (SysCon), 2016 Annual IEEE (pp. 1-5). IEEE.

[4] Keating, A., Campbell, K., Szoenyi, M., McQuistan, C., Nash, D., & Burer, M. (2017). Development and testing of a community flood resilience measurement tool. Natural Hazards and Earth System Sciences, 17(1), 77.

[5] Meerow, S., Newell, J. P., & Stults, M. (2016). Defining urban resilience: A review. Landscape and urban planning, 147, 38-49.

Andrea Garcia Tapia
Stebens Institute of Technology
United States

Jose E Ramirez-Marquez
Stevens Institute of Technology
United States


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