Enabling vulnerable communities to build back safer
Suggested elective courses:
- Land change modelling
- Spatio-temporal analytics and modelling
Unsafe houses and natural hazards are increasingly common threats for citizens of low-income countries. Failed housing structures caused by disasters affect the lives of 210 million people each year and are responsible for 70% of total disaster damage costs (202 billion USD). So, to fight disaster risks, resistant housing is crucial! However, too often, essential construction techniques are not used by the most vulnerable communities, despite humanitarian or governmental assistance. It would be great if we can find effective ways to help people rebuild.
Currently, choices made during reconstruction - where and how to construct - are still insufficiently understood in disaster science. Why aren’t households protecting themselves sufficiently? And how can they be made to rebuild safer housing?
This study aims to explore reconstruction choices to create effective targeted assistance, and to enhance resilience of houses reconstructed after disasters. The objective of this project is to develop a detailed behavioural simulation model to understand, explain and assess decision-making and support effective recovery assistance. Such assistance must enhance the resilience of reconstructed housing, while respecting priorities and limitations of poor households.
We have a large and very promising dataset from Nepal to find answers! Our large data collection includes 1600 household surveys and structural housing assessments, 65 focus group discussions, and 120 key stakeholder interviews in 25 communities. We have studied several aspects of the dataset but we would like to explore a new angle to understand what parameters are most important for people to build back safer houses. We would like to use an agent based model to see which parameters have been most important for the safety of reconstructed houses after the 2015 earthquake in Nepal. This approach has never been used in this way and the study would be very exploratory and could lead to valuable output for humanitarian and governmental organisations.
We suggest to start with understand the dataset that we have, designing and implementing an agent based model and see what changes in the model influence housing safety. However, we can also discuss alternative statistical approaches.
If you wish, you can present the results in Nepal to relevant stakeholders and discuss what it means for them! There are also opportunities to collaborate in data-collection after your thesis project based on where your findings might lead us.
- Hendriks, E. and Opdyke, A. (2022) ‘The influence of technical assistance and funding on perceptions of post-disaster housing safety after the 2015 Gorkha earthquakes in Nepal’, International Journal of Disaster Risk Reduction, 73. doi: 10.1016/j.ijdrr.2022.102906.
- Hendriks, E. and Opdyke, A. (2021) ‘Adoption of seismic-resistant techniques in reconstructed housing in the aftermath of Nepal’s 2015 Gorkha earthquake:’, Earthquake Spectra, 37(4), pp. 2662–2686. doi: 10.1177/87552930211009530.
- Hendriks, E., Schep, B. and van Leersum, A. (2020) ‘The influence of technical assistance in the adoption of safer construction practices in Nepal’, in Martins, N. et al. (eds) Enhancing disaster preparedness. Elsevier.
- Hendriks, E. and Stokmans, M. (2020) ‘Drivers and barriers for the adoption of hazard-resistant construction knowledge in Nepal: Applying the motivation, ability, opportunity (MAO) theory’, International Journal of Disaster Risk Reduction, 51, p. 101778. doi: 10.1016/j.ijdrr.2020.101778.