Projects

Informatics for Equitable Recovery in Nepal

A collaboration between the Earth Observatory of Singapore, Stanford Urban Resilience Initiative, ETH Zurich, and Kathmandu Living Labs — funded by the World Bank Trust Fund for Statistical Capacity Building. 

Drawing on data collected in the aftermath of the 2015 Gorkha earthquake in Nepal, this project combines data from multiple sources, including remote sensing proxies, field surveys and social scientific data, to better inform post-disaster aid allocation and recovery planning.

VIEW PROJECT

Black Swans, Black Elephants: Advancing Consequence-Driven Risk Analysis

We are developing a new framework that is specifically designed to identify events with the potential for disproportionate catastrophic consequences. The framework characterises the circumstances and probability of extreme events through the use of “counterfactual thought experiments”.

By identifying them and quantifying the network of paths and feedbacks through which a natural hazard event becomes a disaster, we can turn “black swan” events (unforeseen, catastrophic events) into more manageable “grey swan” events, which are very rare but manageable, specifically because they have been foreseen.

VIEW PROJECT

Novel methods for large-scale dynamic probabilistic risk modelling

Current probabilistic disaster risk assessment methods have been limited to static analyses (i.e. based on the current condition of hazard, vulnerability, and exposure) often leading to large underestimation of risk. 

This work focuses on developing an urban/regional risk analysis framework that is intrinsically time-dependent. The framework utilises multi-level agent-based modeling to simulate complex and dynamic urban environments at multiple spatial and temporal scales.

VIEW PROJECT

Sensitivity Analysis for Flood Impact Modelling

Little has been done to understand the uncertainty in exposure modelling and its impacts on risk analyses.
In this project, exposure data will be collected using various methods, ranging from globally available population data, to high resolution satellite imagery, to street-level 360° camera footage. These data will then be used to study how uncertainty in exposure modelling can impact the results of risk analyses.

VIEW PROJECT

Ecosystems Services for Flood Risk Reduction

In collaboration with the Natural Capital Project based in Stanford University, this project looks to quantify the flood regulating ecosystems services using the risk framework. The goal of this project is to allow better comparisons between nature-based solutions with standard engineering-based “grey infrastructures”. 

This project is funded by the Singapore National Research Foundation (NRF) and an Environmental Ventures Project grant from Stanford University,

VIEW PROJECT

Understanding Risk Field Lab 2019

The Understanding Risk (UR) Field Lab is a one-month long, output-driven unconference led by the Co-Risk Labs. This event, which took place in Chiang Mai, brought together an international and multi-disciplinary pool of people to work on the problem of urban flooding. Throughout the field lab, participants collaborated, proposed and completed  different projects driven by various themes. 

WEBSITE

Evaluation of Natural Catastrophe Impact in Megacities

This research focuses on disaster loss assessment in megacities, especially through future projections. A cellular automata-based urban growth model us used for the prediction of expansion in built-up area of megacities to estimate the loss in macro-level. 

Risk and Resilience DAT/Artathon

This three-week virtual workshop for data visualizers will be a collaborative and constructive experience to learn from each other, create disaster data art, and co-create criteria for visualizing risk data. Apply now to join us July 20-August 7, 2020! 

LEARN MORE

Towards Ethical Research in Post-Disaster Settings

By creating a set of processes to inform and guide researcher behavior, this project tackles the unique ethical issues that emerge during fieldwork and data collection in complex, post-disaster settings.

VIEW PROJECT