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

This study uses data from the United States Household Pulse Survey to fit predictive models for displacement duration and return after disasters. Three predictive models are proposed, which range in complexity and predictive power. The leading contributors to different duration and return outcomes are also explored. These models can be integrated within disaster risk analyses, as illustrated through a hurricane scenario analysis for Atlantic City, NJ.

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. This study benchmarks this conventional approach against officially reported impacts and data-driven estimates using mobile location data for three recent earthquakes in Haiti, Japan, and Nepal.

This review paper 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.