Taka Yabe: Addressing Disaster Recovery for Businesses with Mobility Data

by Placekey

Taka Yabe is a PhD candidate at Lyles School of Civil Engineering at Purdue University. He is currently a Data Science Consultant for the Global Facility for Disaster Reduction and Recovery (GFDRR) and Transport Global Practice at The World Bank, where he studies mobility patterns to help with urban development, transportation modeling, and disaster risk management.

Taka Yabe’s recent research

Check out some of the really cool things Taka Yabe has been working on lately:

Quantifying the economic impact of disasters on businesses using human mobility data: a Bayesian causal inference approach

2020, EPJ Data Science

Previously, cities and businesses relied on survey data, which are high-cost and take a long time to implement, review, and integrate into your strategy. Human mobility data, provided by SafeGraph, empowers us to observe and analyze human mobility patterns with greater spatio-temporal granularity than ever before, drastically increasing the takeaways and allowing for improved planning and preparation for disaster relief.

Pystan - Causal inference using Bayesian Structural Time Series

2021, Blog

Taka Yabe outlines how to use Pystan implementation of Bayesian Structural Time Series (BSTS). This paper explores - and offers examples - of how to do this so that you can implement this yourself. It also covers examples and leads into how this was used in their research paper above.

Understanding Post-Disaster Population Recovery Patterns

2020, Journal of The Royal Society

To understand how communities recover after extreme disasters, this study uses mobility pattern data to understand how people behave before, during, and after five major disasters. They can then analyze recovery trends alongside this data and gain insights into how communities are impacted, displaced, and ultimately recover.

To check out all his research, find Taka Yabe on ResearchGate!

What Taka Yabe has been doing with Placekey & SafeGraph data

Yabe’s research on Quantifying the economic impact of disasters on businesses using human mobility data: a Bayesian causal inference approach relied heavily on SafeGraph mobility data and the Placekey universal identifier for physical locations.

Rather than relying on survey data, that is time-consuming and costly to collect and analyze, mobility data can give them access to more accurate, precise mobility data. With this data at their disposal, they were able to compare patterns to previous levels and see how recovery takes form.

With this much granular information, teams can predict the impact of natural disasters on businesses, and adequately plan their recovery efforts, reducing the time to a full recovery and streamlining the process.

See the seminar summarizing their findings below:

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