For years, international agencies have been effusing the benefits of big data for sustainable development. Emerging technology–such as crowdsourcing, satellite imagery, and machine learning–have the power to better inform decision-making, especially those that support the 17 Sustainable Development Goals. When a disaster occurs, overwhelming amounts of big data from emerging technology are produced with the intention to support disaster responders. We are seeing this now with the recent earthquakes in Turkey and Syria: space agencies are processing satellite imagery to map faults and building damage or digital humanitarians are crowdsourcing baseline data like roads and buildings.
Eight years ago, the Nepal 2015 earthquake was no exception–emergency managers received maps of shaking or crowdsourced maps of affected people’s needs from diverse sources. A year later, I began research with a team of folks involved during the response to the earthquake, and I was determined to understand how big data produced after disasters were connected to the long-term effects of the earthquake. Our research team found that a lot of data that was used to guide the recovery focused on building damage, which was often viewed as a proxy for population needs. While building damage information is useful, it does not capture the full array of social, environmental, and physical factors that will lead to disparities in long-term recovery. I assumed information would have been available immediately after the earthquake that was aimed at supporting vulnerable populations. However, as I spent time in Nepal during the years after the 2015 earthquake, speaking with government officials and nongovernmental organizations involved in the response and recovery, I found they lacked key information about the needs of the most vulnerable households–those who would face the greatest obstacles during the recovery from the earthquake. While governmental and nongovernmental actors prioritized the needs of vulnerable households as best as possible with the information available, I was inspired to pursue research that could provide better information more quickly after an earthquake, to inform recovery efforts.
In our paper published in Communications Earth and Environment [link], we develop a data-driven approach to rapidly estimate which areas are likely to fall behind during recovery due to physical, environmental, and social obstacles. This approach leverages survey data on recovery progress combined with geospatial datasets that would be readily available after an event that represent factors expected to impede recovery. To identify communities with disproportionate needs long after a disaster, we propose focusing on those who fall behind in recovery over time, or non-recovery. We focus on non-recovery since it places attention on those who do not recover rather than delineating the characteristics of successful recovery. In addition, in speaking to several groups in Nepal involved in the recovery, they understood vulnerability–a concept that is place-based and can change over time–as those who would not be able to recover due to the earthquake.
What we found is that many ongoing social and environmental factors of vulnerability and risk were predictive of slowed reconstruction progress in Nepal. For example, ongoing risks from landslides and food insecurity were important concerns before the 2015 earthquake and were also important predictors of non-recovery after the earthquake in Nepal. However, these risks were not fully considered early on in Nepal’s post-earthquake housing recovery program, partly because the least resilient households were physically disconnected, exposed to higher hazards, and were the least visible in post-earthquake datasets. The non-recovery estimate, instead, demonstrates how these risks are indeed associated with recovery capacity, which is not currently captured in rapidly available post-earthquake data.
Practically speaking, the estimate of non-recovery could be used to inform initial situational awareness and to frame early recovery plans. For example, the National Reconstruction Authority (NRA) was initiated by the Government of Nepal months after the earthquake to lead and manage the recovery process in Nepal. In addition, the Housing Recovery and Reconstruction Platform (HRRP) was created to coordinate the multiple governmental and nongovernmental organizations involved with reconstruction throughout the 14 most affected districts from the earthquake. Early estimates of non-recovery can identify which factors may be associated with lower recovery rates and which areas should be investigated further. Such information can inform recovery efforts such as those carried out by the NRA and HRRP for the Nepal earthquake. As with any model, early post-disaster estimates should not replace (and will likely never replace) household surveys (like those organized by Kathmandu Living Labs, our partner in this project). However, early post-disaster estimates are useful to reframe how the recovery process is approached.
The approach inherently captures both broadly-applicable and context-specific factors affecting recovery potential. While we demonstrate this approach in Nepal, it can be expanded for other contexts and hazards. Broadly, non-recovery is one crucial aspect on which we can focus our attention when quantifying post-disaster metrics, in order to prioritize the recovery of those most vulnerable.
About the research team
This project was part of a larger project on studying the recovery process in Nepal to develop improved informatics for equitable recovery. Our research process was extremely collaborative, with a team with multiple disciplinary and cultural backgrounds. In addition, we spoke with many recovery organizations in Nepal to frame the needs for our research.
A transdisciplinary approach was not only advantageous but necessary to conduct this work. Key to our team’s success was planning for in-person time together, holding bi-weekly remote meetings, and most importantly, building a dynamic where all team members could reflect on our process and goals. We are extremely grateful to have conducted this research together and to the many collaborators who supported this research.
We thank The Asia Foundation and supporting field researchers, including Lena Michaels, Pranaya Sthapit, and Carolyn O'Donnell, for providing survey data and feedback on the importance of the predictors included in this model. We also thank Mhairi O'Hara, Robert Soden, Lisa Modifica, Sabin Ninglekhu, Ritika Singh, Jasna Budhathoki, and the rest of the Kathmandu Living Labs team who have all contributed to this project. Finally, we thank the households, community leaders, and other residents of Nepal who were generous with their time, energy, and trust to support this work. This project is supported by the World Bank’s Trust Fund for Statistical Capacity Building (TFSCB) with financing from the UK DFID, the government of Korea, and the Dept. of Foreign Affairs and Trade of Ireland. The Disaster Analytics for Society Lab is funded by the Singapore National Research Foundation under the NRF-NRFF2018-06 award, and the Earth Observatory of Singapore. Sabine Loos was partially funded by the Stanford Urban Resilience Initiative, the John A. Blume Earthquake Engineering Center, and the National Science Foundation Graduate Research Fellowship.
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