Sociology, Normative Pluralism, and Disasters: A Framework for Evaluating Post-Disaster Recovery Projects in the Philippines

2017 ◽  
Author(s):  
Vivencio Ballano
Keyword(s):  
Disaster Recovery ◽  
The Philippines ◽  
Normative Pluralism ◽  
Post Disaster

Agency-driven post-disaster recovery: A comparative study of three Typhoon Washi resettlement communities in the Philippines

2018 ◽  
Vol 27 ◽  
pp. 480-489 ◽  
Author(s):  
J. Sedfrey S. Santiago ◽  
Wilfred S. Manuela ◽  
Marion Lara L. Tan ◽  
Siegfried Kiel B. Saňez ◽  
Aldo Zelig U. Tong
Keyword(s):  
Comparative Study ◽  
Disaster Recovery ◽  
The Philippines ◽  
Post Disaster

scholarly journals Post-Disaster Recovery Monitoring with Google Earth Engine

2020 ◽  
Vol 10 (13) ◽  
pp. 4574 ◽  
Author(s):  
Saman Ghaffarian ◽  
Ali Rezaie Farhadabad ◽  
Norman Kerle
Keyword(s):  
Land Cover ◽  
Disaster Recovery ◽  
Recovery Process ◽  
The Philippines ◽  
Google Earth ◽  
Free Access ◽  
Change Analysis ◽  
Time Step ◽  
Google Earth Engine ◽  
Post Disaster

Post-disaster recovery is a complex process in terms of measuring its progress after a disaster and understanding its components and influencing factors. During this process, disaster planners and governments need reliable information to make decisions towards building the affected region back to normal (pre-disaster), or even improved, conditions. Hence, it is essential to use methods to understand the dynamics/variables of the post-disaster recovery process, and rapid and cost-effective data and tools to monitor the process. Google Earth Engine (GEE) provides free access to vast amounts of remote sensing (RS) data and a powerful computing environment in a cloud platform, making it an attractive tool to analyze earth surface data. In this study we assessed the suitability of GEE to analyze and track recovery. To do so, we employed GEE to assess the recovery process over a three-year period after Typhoon Haiyan, which struck Leyte island, in the Philippines, in 2013. We developed an approach to (i) generate cloud and shadow-free image composites from Landsat 7 and 8 satellite imagery and produce land cover classification data using the Random Forest method, and (ii) generate damage and recovery maps based on post-classification change analysis. The method produced land cover maps with accuracies >88%. We used the model to produce damage and three time-step recovery maps for 62 municipalities on Leyte island. The results showed that most of the municipalities had recovered after three years in terms of returning to the pre-disaster situation based on the selected land cover change analysis. However, more analysis (e.g., functional assessment) based on detailed data (e.g., land use maps) is needed to evaluate the more complex and subtle socio-economic aspects of the recovery. The study showed that GEE has good potential for monitoring the recovery process for extensive regions. However, the most important limitation is the lack of very-high-resolution RS data that are critical to assess the process in detail, in particular in complex urban environments.

scholarly journals A Conceptual Framework Integrating “Building Back Better” and Post-Earthquake Needs for Recovery and Reconstruction

2021 ◽  
Vol 13 (10) ◽  
pp. 5608
Author(s):  
Manjiang Shi ◽  
Qi Cao ◽  
Baisong Ran ◽  
Lanyan Wei
Keyword(s):  
Disaster Recovery ◽  
Social Process ◽  
Principal Component ◽  
Assessment Method ◽  
International Consensus ◽  
Long Term Stability ◽  
The People ◽  
Policy Guidance ◽  
The Impact ◽  
Post Disaster

Global disasters due to earthquakes have become more frequent and intense. Consequently, post-disaster recovery and reconstruction has become the new normal in the social process. Through post-disaster reconstruction, risks can be effectively reduced, resilience can be improved, and long-term stability can be achieved. However, there is a gap between the impact of post-earthquake reconstruction and the needs of the people in the disaster area. Based on the international consensus of “building back better” (BBB) and a post-disaster needs assessment method, this paper proposes a new (N-BBB) conceptual model to empirically analyze recovery after the Changning Ms 6.0 earthquake in Sichuan Province, China. The reliability of the model was verified through factor analysis. The main observations were as follows. People’s needs focus on short-term life and production recovery during post-earthquake recovery and reconstruction. Because of disparities in families, occupations, and communities, differences are observed in the reconstruction time sequence and communities. Through principal component analysis, we found that the N-BBB model constructed in this study could provide strong policy guidance in post-disaster recovery and reconstruction after the Changning Ms 6.0 earthquake, effectively coordinate the “top-down” and “bottom-up” models, and meet the diversified needs of such recovery and reconstruction.

Chain tourism in post-disaster recovery

2020 ◽  
Vol 20 (4) ◽  
pp. 429-449
Author(s):  
Elyse Zavar ◽  
Brendan L Lavy ◽  
Ronald R Hagelman
Keyword(s):  
Social Networks ◽  
Place Attachment ◽  
Disaster Recovery ◽  
Business Owners ◽  
Local Scale ◽  
Disaster Research ◽  
Family Recreation ◽  
Post Disaster

Post-disaster research relating to tourism tends to focus on broad economic measures that can miss local-scale actors and contemporaneous impressions by tourists and tourism-based business owners in places undergoing recovery from a disaster. Hurricane Harvey, a Category 4 storm, swept across coastal Texas in August 2017. Many of the communities affected by Harvey have economies largely based on family recreation. Interviews in Rockport–Fulton, Texas, with tourism-oriented business owners, staff, and tourists during the Independence holiday provide qualitatively robust accounts of the community’s first major summer event following Harvey and highlight the importance of social networks and place attachment to bringing tourists to the recovering area. Furthermore, we discuss the chain tourist’s role in the recovery of affected locations and consider strategies to draw on these social networks to increase the number of tourists visiting the recovering communities.

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