#  Measuring Flood Displacement with Digital Trace Data: Evidence from the West Sumatra Floods 

 



####  calendar\_today Date and Time 

 **November 23, 2026** 

 11:00AM - 12:00PM EST 

####  pin\_drop Location 

 **Room K354 (3rd floor) in the CGIS Knafel Building, 1737 Cambridge St. Cambridge, MA.**  



 

 



 

**Presentation by Dr. Martina Pardy**

**Abstract**:  
Floods are the most common climate-related hazard worldwide, affecting an estimated 40 million people in 2024 alone. Their impacts are expected to intensify as climate change and rapid urbanisation increase flood exposure and concentrate populations in vulnerable areas. Understanding how households respond to flooding is central to assessing the residential consequences of climate risk. Yet this response is precisely what conventional data capture least well: the displacement events that matter most are sudden, spatially concentrated, and often unfolding faster than surveys or registration systems can record. This paper uses anonymised device-level mobile phone data to measure short-term residential displacement following the March 2024 flash floods in West Sumatra, Indonesia, a rapid-onset event that struck with almost no warning. Using a difference-in-differences design, we estimate the causal effect of flooding-induced displacement at daily resolution and sub-district granularity. By generating high-frequency, spatially detailed evidence on climate-induced residential displacement, the paper demonstrates how novel data sources can inform the short-term response to flood risk.

**Speaker Bio**:

Martina Pardy is a Postdoctoral Researcher at the University of Liverpool’s Geographic Data Science Lab and a Visiting Fellow at the Center for Geographic Analysis at Harvard University. She completed her PhD in Economic Geography at the London School of Economics and Political Science.

Her research examines the intersection of economic inequality, climate change, and local economic development. Working with the Imago Data Service for Imagery, she develops high-resolution spatial datasets capturing local climate vulnerability, environmental exposure, and the green and built environment. She combines satellite imagery and mobile phone data with causal inference and machine learning.

   ![photo of Martina Pardy](/sites/g/files/omnuum9996/files/styles/hwp_1_1__360x360_scale/public/2026-10/martina.jpg?itok=ziIfwvAB) 

 





 

 



 

 

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