TY - JOUR
T1 - Agent-based modelling of post-disaster recovery with remote sensing data
AU - Ghaffarian, S.
AU - Roy, Debraj
AU - Filatova, Tatiana
AU - Kerle, N.
N1 - Funding Information:
The satellite images were provided by the European Space Agency, and Digital Globe Foundation, which was granted for a project at ITC entitled ?Post-disaster recovery assessment using remote sensing data and spatial economic modelling?. The Survey data were provided by the German Institute for Development Evaluation (DEval), Competence Centre for Evaluation Methodologies, Bonn, Germany. This work was partially supported by the European Research Council (ERC) under the European Union's Horizon 2020 Research and Innovation Program (grant agreement number: 758014).
Funding Information:
The satellite images were provided by the European Space Agency, and Digital Globe Foundation, which was granted for a project at ITC entitled “Post-disaster recovery assessment using remote sensing data and spatial economic modelling”. The Survey data were provided by the German Institute for Development Evaluation (DEval), Competence Centre for Evaluation Methodologies, Bonn, Germany. This work was partially supported by the European Research Council ( ERC ) under the European Union's Horizon 2020 Research and Innovation Program (grant agreement number: 758014 ).
Publisher Copyright:
© 2021 The Author(s)
PY - 2021/6/15
Y1 - 2021/6/15
N2 - Disaster risk management, and post-disaster recovery (PDR) in particular, become increasingly important to assure resilient development. Yet, PDR is the most poorly understood phase of the disaster management cycle and can take years or even decades. The physical aspects of the recovery are relatively easy to monitor and evaluate using, e.g. geospatial remote sensing data compared to functional assessments that include social and economic processes. Therefore, there is a need to explore the impacts of different dimensions of the recovery, including individual behaviour and their interactions with socio-economic institutions. In this study, we develop an agent-based model to simulate and explore the PDR process in urban areas of Tacloban, the Philippines devastated by Typhoon Haiyan in 2013. Formal and informal (slum) sector households are differentiated in the model to explore their resilience and different recovery patterns. Machine learning-derived land use maps are extracted from remote sensing images for pre- and post-disaster and are used to provide information on physical recovery. We use the empirical model to evaluate two realistic policy scenarios: the construction of relocation sites after a disaster and the investments in improving employment options. We find that the speed of the recovery of the slum dwellers is higher than formal sector households due to the quick reconstruction of slums and the availability of low-income jobs in the first months after the disaster. Finally, the results reveal that the households' commuting distance to their workplaces is one of the critical factors in households’ decision to relocate after a disaster.
AB - Disaster risk management, and post-disaster recovery (PDR) in particular, become increasingly important to assure resilient development. Yet, PDR is the most poorly understood phase of the disaster management cycle and can take years or even decades. The physical aspects of the recovery are relatively easy to monitor and evaluate using, e.g. geospatial remote sensing data compared to functional assessments that include social and economic processes. Therefore, there is a need to explore the impacts of different dimensions of the recovery, including individual behaviour and their interactions with socio-economic institutions. In this study, we develop an agent-based model to simulate and explore the PDR process in urban areas of Tacloban, the Philippines devastated by Typhoon Haiyan in 2013. Formal and informal (slum) sector households are differentiated in the model to explore their resilience and different recovery patterns. Machine learning-derived land use maps are extracted from remote sensing images for pre- and post-disaster and are used to provide information on physical recovery. We use the empirical model to evaluate two realistic policy scenarios: the construction of relocation sites after a disaster and the investments in improving employment options. We find that the speed of the recovery of the slum dwellers is higher than formal sector households due to the quick reconstruction of slums and the availability of low-income jobs in the first months after the disaster. Finally, the results reveal that the households' commuting distance to their workplaces is one of the critical factors in households’ decision to relocate after a disaster.
KW - Post-disaster recovery
KW - Resilience
KW - agent-based model
KW - Remote sensing
KW - Philippines
KW - Machine learning
KW - ITC-ISI-JOURNAL-ARTICLE
KW - ITC-HYBRID
KW - UT-Hybrid-D
UR - https://ezproxy2.utwente.nl/login?url=https://library.itc.utwente.nl/login/2021/isi/kerle_age.pdf
U2 - 10.1016/j.ijdrr.2021.102285
DO - 10.1016/j.ijdrr.2021.102285
M3 - Article
VL - 60
SP - 1
EP - 15
JO - International journal of disaster risk reduction
JF - International journal of disaster risk reduction
SN - 2212-4209
M1 - 102285
ER -