Critical infrastructure and public utility systems are often severely damaged by natural disasters like hurricanes. Based on a framework of household disaster resilience, this paper focuses on the role of utility disruption on household-level recovery in the context of Hurricane Sandy. Using data collected through a two-stage household survey, it first confirms that the sample selection bias is not present, thus the responses can be estimated sequentially. Second, it quantitatively examines factors contributing to hurricane-induced property damages and household-level recovery. The finding suggests that respondents who suffered from a longer period of utility disruptions (e.g., electricity, water, gas, phone/cell phone, public transportation) are more likely to incur monetary losses and have more difficulty in recovering. Effective preparedness activities (e.g., installing window protections, having an electric generator) can have positive results in reducing adverse shocks. Respondents with past hurricane experiences and higher educational attainments are found to be more resilient compared to others. Finally, the paper discusses the implications of the findings on effective preparation and mitigation strategies for future disasters.
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Household Evacuation Planning and Preparation for Future Hurricanes: Role of Utility Service Disruptions
We analyzed data from a survey administered to 1,212 respondents living in superstorm Hurricane Sandy-affected areas. We estimated the effect of having experienced hurricane-induced disruptions to utility services, such as electricity, water, gas, phone service, and public transportation, on having an evacuation plan. Around 39% of respondents reported having an evacuation plan in case a hurricane affects their neighborhood this year. Respondents who had experienced disruptions to electricity supply had an approximately 11 percentage-point higher likelihood of having an evacuation plan than those who had experienced no such disruptions. Respondents who had experienced monetary losses from Hurricane Sandy had around a five percentage-point higher likelihood of having an evacuation plan compared with those who had not. Among control variables, prior evacuation, distance to the coastline, residence in a flood zone, concern about the impacts of future natural disaster events, had window protection, and household members being disabled, each had an association with residents’ future evacuation planning and hurricane preparedness. In light of these findings, we discuss the policy implications of our findings for improving disaster management in hurricane-prone areas.
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- Award ID(s):
- 1832693
- NSF-PAR ID:
- 10313246
- Date Published:
- Journal Name:
- Transportation Research Record: Journal of the Transportation Research Board
- Volume:
- 2675
- Issue:
- 10
- ISSN:
- 0361-1981
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Critical infrastructures are ubiquitous and their interdependencies have become more complex leading to their uncertain behaviors in the aftermath of disasters. The article develops an integrated economic input–output model that incorporates household‐level survey data from Hurricane Sandy, which made its landfall in 2012. In this survey, 427 respondents who were living in the state of New Jersey during Hurricane Sandy were used in the study. The integration of their responses allowed us to show the probability and duration of various types of critical infrastructure failures due to a catastrophic hurricane event and estimate the economic losses across different sectors. The percentage of disruption and recovery period for various infrastructure systems were extracted from the survey, which were then utilized in the economic input–output model comprising of 71 economic sectors. Sectors were then ranked according to: (i) inoperability, the percentage in which a sector is disrupted relative to its ideal level, and (ii) economic loss, the monetary worth of business interruption caused by the disaster. With the combined infrastructure disruptions in the state of New Jersey, the model estimated an economic loss of $36 billion, which is consistent with published estimates. Results from this article can provide insights for future disaster preparedness and resilience planning.
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Extreme weather events have significant economic and social impacts, disrupting essential public services like electricity, phone communication, and transportation. This study seeks to understand the performance and resilience of critical infrastructure systems in Houston, Texas, using Hurricane Harvey (2017) as a case study. We surveyed 500 Houston Metropolitan Statistical Area residents after Hurricane Harvey’s landfall about disruption experience in electricity, water, phone/cellphone, internet, public transportation, workplace, and grocery stores. Our household survey data revealed the proportion and duration of disruption in each system. Approximately 70% of respondents reported experiencing electricity outages, while half (51%) had no access to water for up to six days. Two-thirds of surveyed households lacked internet access, and 50% had their phone services disconnected. Additionally, around 71% of respondents were unable to commute to work, and 73% were unable to purchase groceries for their families during this period. We incorporated the household survey responses into the Dynamic Inoperability Input-Output Model (DIIM) to estimate inoperability and economic losses across interconnected sectors. The projected economic loss was estimated to be in the range of $6.7- $9.7 billion when sensitivity analysis is performed with respect to the number of working days. Understanding the resilience of each sector and the inherent interdependencies among them can provide beneficial insight to policymakers for disaster risk management, notably preparedness and recovery planning for future events.more » « less
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Abstract How evacuations are managed can substantially impact the risks faced by affected communities. Having a better understanding of the mobility patterns of evacuees can improve the planning and management of these evacuations. Although mobility patterns during evacuations have traditionally been studied through surveys, mobile phone location data can be used to capture these movements for a greater number of evacuees over a larger geographic area. Several approaches have been used to identify hurricane evacuation patterns from location data; however, each approach relies on researcher judgment to first determine the areas from which evacuations occurred and then identify evacuations by determining when an individual spends a specified number of nights away from home. This approach runs the risk of detecting non‐evacuation behaviors (e.g., work trips, vacations, etc.) and incorrectly labeling them as evacuations where none occurred. In this article, we developed a data‐driven method to determine which areas experienced evacuations. With this approach, we inferred home locations of mobile phone users, calculated their departure times, and determined if an evacuation may have occurred by comparing the number of departures around the time of the hurricane against historical trends. As a case study, we applied this method to location data from Hurricanes Matthew and Irma to identify areas that experienced evacuations and illustrate how this method can be used to detect changes in departure behavior leading up to and following a hurricane. We validated and examined the inferred homes for representativeness and validated observed evacuation trends against past studies.
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1177/03611981211014529
