Supervised by Prof. Carmine Galasso and Prof. Jack Baker

Supervised by Prof. Vitor Silva at the GEM Foundation

Supervised by Ibrahim (Ibbi) Almufti at Arup

In the aftermath of an earthquake, the number of residents whose housing was destroyed is often used to approximate the number of people displaced (i.e., rendered homeless) after the event. While this metric can provide rapid situational awareness regarding potential long-term housing needs, more recent research highlights the importance of additional factors beyond housing damage within the scope of household displacement and return (e.g., utility disruption, tenure, place attachment). This study benchmarks population displacement estimates using this simplified conventional approach that considers only housing destruction through three scenario models for recent earthquakes in Haiti, Japan, and Nepal. These model predictions are compared with officially reported values and data-driven estimates using mobile location data. The results highlight the promise of scenario models to realistically estimate population displacement and potential long-term housing needs after earthquakes, but also highlight a large range of uncertainty in the predicted values. Furthermore, purely basing displacement estimates on housing damage offers no view on how the displaced population counts vary with time as compared to more comprehensive models that include other factors influencing population return or alternative approaches, such as using mobile location data.

Household displacement following disasters has become endemic in many areas worldwide, affecting at least 265 million people between 2008 and 2018. Although this figure includes short-term and potentially life-saving evacuations, there is ample evidence that not all households return after the emergency phase. Protracted displacement is associated with particularly negative consequences for the affected households and community. Yet, existing data on displacement duration are limited, and only a few disaster recovery models incorporate the multitude of factors beyond housing damage that are known to influence household return. This review synthesizes the current literature on disaster-induced displacement, including key terminology and context, the determinants of household return decisions, existing model-based approaches, and opportunities for future research.

Apps, services, etc. that I’ve found useful for research.

A simple and free to low-cost way to create a personal academic website for those with some basic comfort using code and Git.

Easily query population and housing statistics from the ACS using Python. Code snippets and examples provided.