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The overwhelming majority of earthquakes transpire within the Ring of Fire, a series of volcanoes and tectonic activities surrounding the Pacific Ocean coastlines. However, when an earthquake occurs, the regions that endure the most intense shaking are not always the ones that experience the most significant destruction.

Consider the significant Chi-Chi earthquake, which inflicted considerable destruction in Taiwan during the autumn of 1999, claiming over 2,400 lives. The damage was distributed in an uneven manner: Fatalities from the earthquake were predominantly found not in densely populated urban centers, but rather in the suburbs and outer regions of those cities. A similar trend has been observed after earthquakes in China, Chile, and Nepal.

Over two decades later, researchers from the University of Washington have pinpointed a concealed element behind what they term ‘suburban syndrome’ — migration. Individuals from small, rural communities frequently relocate to the outskirts of cities, which present more substantial economic prospects but often feature subpar housing that is predisposed to suffering more damage during an earthquake. The danger escalates even further when migrants originate from low-income or tribal settlements.

The results, published in December in the journal Natural Hazards and Earth System Sciences, indicate that disaster management agencies should prioritize migration and the quality of housing when formulating disaster preparedness and response strategies.

UW News engaged with co-lead author Tzu-Hsin Karen Chen, an assistant professor specializing in environmental & occupational health sciences and urban planning, to delve into ‘suburban syndrome,’ the ways migration can exacerbate inequalities in a disaster’s effects, and lessons U.S. officials might derive from a Taiwanese catastrophe.

Your research on this study expands upon an existing model that evaluates earthquake risk by taking into account migration trends and the movement of at-risk populations. What aspects does the current model overlook, and why is it crucial to address these omissions?  

Tzu-Hsin Karen Chen: This risk assessment framework has been utilized by various organizations globally and in the United States. For instance, FEMA employs a comparable risk model to gauge populations vulnerable to hazards, their susceptibilities, and potential disaster consequences. Typically, they perform a thorough risk evaluation geographically within states and counties, identify zones with potential higher impacts, and then develop a preparedness strategy.

In the United States, temporary domestic migrants and unauthorized immigrants do not always officially register within governmental systems. A common reason for this is the fear of deportation and other legal consequences. Therefore, when governmental bodies like FEMA allocate resources for disaster readiness or recovery, depending on registered demographic data can result in an underrepresentation of the support needed in specific locales.

In Taiwan, our case study, many migrant workers transitioning from rural to urban areas do not update their registered addresses. They maintain their registration in their hometown, such as in a tribal region. It is often impractical to re-register, as they may hold multiple jobs throughout a single year across different locations. To reduce costs, some individuals seek the lowest rental prices, and their rented accommodations may not be officially documented either. These could consist of informal housing setups, like a metal platform placed atop a concrete structure, which do not adhere to safety regulations. While this informality can reduce living expenses, it may also increase their exposure to disasters.

What inspired your journey into this research? 

TKC: I’ll share my personal narrative, but I also wish to recognize my co-authors for their extensive contributions to risk assessments. My journey began in 2010 when I volunteered in a tribal region of Taiwan imparting computer skills. This experience taught me more than any knowledge I could have passed on. I discovered that adolescents often migrate from their tribal locales downhill to nearby towns to obtain construction employment during the off-crop periods. These jobs offer higher wages compared to agricultural work but are also physically taxing and frequently lack protections like job stability and health insurance. Observing this planted a seed in my mind. 

While pursuing my master’s degree, a group from the National Earthquake Center and Academia Sinica in Taiwan was conducting a risk assessment for the Chi-Chi earthquake utilizing the framework of exposure, vulnerability, and hazard. They had already published a foundational risk assessment and reached out to me to develop a more extensive study by integrating spatial statistics. That collaboration eventually evolved into the current study discussed in this paper. 

The COVID-19 pandemic also influenced this research. I stumbled upon information regarding how migrant workers were stranded in the fringes of urban areas in India. Due to the lockdown, they were unable to continue working, and their overcrowded living conditions increased their vulnerability during the pandemic. I began to ponder: How can we transition from a purely statistical model to something more substantial? How can we center migration in the conversation? 

The final impetus arose from the work of my colleagues at the UW. I noticed initiatives aimed at undocumented students and research projects surrounding environmental justice and health equality. For example, my co-author Diana Ceballos’s research focused on the health of migrant workers was especially inspiring. We corresponded to refine and enhance the framing and discussions in this project.
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How did you integrate migration data into a broader earthquake risk framework, and what insights did you discover?

TKC: During the Chi-Chi earthquake of the late 1990s, we lacked comprehensive migration data. Presently, recent studies utilize mobile phone signals for tracking populations, but such information was not accessible at that time. Consequently, we modified the radiation model— a model extensively employed to forecast human migration — to approximate migration routes and leveraged it to estimate the flow of migrants from low-income and indigenous regions. This introduced new variables for integration into the extensive risk assessment model. 

Most of our conclusions corroborate prior studies, revealing a logical connection: stronger ground motion typically correlates with higher fatality rates. This straightforward perspective offers insight into how disasters unfold. However, the narrative extends beyond physical phenomena. We also found that areas with lower income levels experience a greater number of fatalities. Income levels serve as a recognized risk factor within vulnerability theory. What distinguishes this study is our exploration of whether increased migration flows correlate with heightened fatality numbers, and our findings validate this theory. 

Could you explain the migration model? What exactly is it estimating? 

TKC: We employed the radiation model and customized it to evaluate different migration demographics. The core concept of the radiation model originates from a simplified representation known as the gravity model. In this scenario, the term ‘gravity’ implies that populations of larger size exert a stronger attraction on individuals in adjacent communities. The model posits that for any location, the number of individuals wishing to migrate to nearby urban centers is contingent upon the population size of those centers. Generally, larger cities draw more individuals. 

If the distance to a location is excessive, the travel expenses become prohibitive, resulting in predictions of fewer migrants. Conversely, if a city is closer, or even at a distance yet possesses a substantial population, it turns into a more enticing destination, which consequently leads to increased migration flow. 

The radiation model expands on these concepts and introduces an additional dimension. It takes into account competing options en route. In essence, migration flows may also be swayed by alternative cities or opportunities located between the departure point and the target destination. 

At first glance, it seems evident that an increase in migration would result in a greater number of fatalities in a specific region, simply due to the larger population present during a disaster. Is that the main cause, or do other elements also contribute? 

TKC: Rationally, with a larger populace, if the fatality rate remains constant, we would expect more individuals to perish in a particular event. However, our findings indicate that the narrative is not solely about sheer population numbers. Two additional factors emerge: When migrant workers originate from lower-income regions or tribal communities, these elements notably enhance the risk of fatalities in their destination areas. 

Our hypothesis focuses on housing safety. Migrant workers typically relocate to urban areas, and when urban housing is expensive, wealthier workers often secure accommodations that provide better safety against disasters. In contrast, workers from tribal or low-income backgrounds usually settle in urban peripheries where affordable housing may not adhere to safety regulations, rendering them more susceptible to earthquakes.

What prompted you to investigate this specific earthquake from 1999?  

TKC: The research group that invited me to collaborate on this initiative expressed a keen interest in the Chi-Chi earthquake, primarily because it ranks among the most catastrophic events in Taiwan’s history. Even after 20 years, there remains a conference concentrated on the Chi-Chi earthquake, drawing researchers both domestically and internationally to discuss it.

How broadly applicable are your findings? Could they assist in enhancing our comprehension of hazards in other earthquake-prone regions, such as the Pacific Northwest?  

TKC: It is crucial to perceive this risk evaluation as a tool for preparedness for upcoming hazards. When the next seismic event occurs, migrant communities will likely experience heightened impacts if housing safety measures do not advance.

I contend that the migration element holds universal significance, extending beyond Taiwan’s borders. There exists a persistent paradox, a structural challenge in disaster governance: migrants often remain invisible, resulting in minimal support. However, highlighting their presence can sometimes lead to exclusion and discrimination. This model conceptualizes migrants geographically, rather than identifying every individual through governmental monitoring, which could mitigate this issue. By ensuring anonymity while still recognizing migrant populations, the model may facilitate the consideration of their needs in housing safety and resource distribution.

Co-authors of this research comprise Diana Ceballos from the UW Department of Environmental & Occupational Health Sciences; Kuan-Hui Elaine Lin from National Taiwan Normal University; Thung-Hong Lin from Academia Sinica in Taiwan; and Gee-Yu Liu and Chin-Hsun Yeh from the National Center for Research on Earthquake Engineering in Taiwan.

For additional information, get in touch with Chen at [email protected].

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