Category Archives: Disaster Reponse

Why Your Culture May Determine Whether You Survive a Disaster

When disaster strikes, we tend to focus on the storm, the flood, or the earthquake itself. But after two decades of fieldwork across Southeast Asia and Japan, I keep coming back to something less visible — and just as powerful: the culture, institutions, and social structures of the communities involved shape survival just as much as the disaster itself.

It’s Not Just About the Disaster

Sociologist Benjamin F. McLuckie compared disaster responses across Japan, Italy, and the United States as far back as the 1970s. His key finding: how centralized a government is — how much decision-making power sits with national versus local authorities — significantly shapes what actually happens on the ground when emergencies unfold.

But culture alone does not explain everything. What really drives outcomes is a three-way mix of culture, institutions, and technology. A community that values collective action still needs neighborhood associations, shelters, and early warning systems to turn that value into real protection. Without those structures, values remain just values.

What I Found in the Field

My work on the 2011 Thailand floods — which disrupted global supply chains and devastated communities around industrial parks — brought this home clearly. Social cohesion and local governance structures were just as predictive of recovery as physical flood barriers. Through the Japan-Thailand SATREPS collaboration, my colleagues and I developed community capacity assessments and social vulnerability indices to help local leaders act before the next disaster, not scramble after it.

What struck me most was this: communities with strong internal networks recovered faster, not because they had more resources, but because they already knew how to work together.

The New Orleans Lesson

After Hurricane Katrina in 2005, researchers noticed that Vietnamese-American communities in New Orleans recovered more quickly than many others. The easy explanation was “culture.” But the real answer was more grounded: strong churches functioning as organizing hubs, dense social networks built through shared migration experience, and established community leadership capable of coordinating a return. Culture mattered — but it worked through concrete institutions. That distinction is important.

Why This Matters Now

Climate change is making disasters more frequent and more severe. Yet many governments still treat disaster response as a purely technical problem — better seawalls, faster alert systems. Those matters. But they miss the human layer that makes those tools actually work.

When we recognize that community trust, family networks, and local governance are all part of the disaster equation, we can design better evacuation plans, more effective early warnings, and recovery programs that genuinely reach the people who need them most.

Every disaster holds up a mirror to the society it strikes. What we see reflected — who gets help quickly, who rebuilds together, who gets left behind — is shaped by culture, institutions, and history working in combination. That is not just a scholarly observation. It is, ultimately, a matter of life and death.

Sources:

McLuckie, B.F. (1977). Italy, Japan, and the United States: Effects of Centralization on Disaster Responses. University of Delaware;

Nakasu, T. et al. (2022). International Journal of Disaster Resilience and Built Environment. https://doi.org/10.1108/IJDRBE-10-2020-0107;

Nakasu, T. (2023). Environmental Development and Sustainability. https://doi.org/10.1007/s10668-023-04305-7

Great Kanto Fire Disaster 1923

The following is my past short essay for the institute after the study session on the 1923 Great Kanto Earthquake at the open office event:

In 1923, an earthquake killed over 105,000 people in the Tokyo area (including Kanagawa, where contributes approx. 30% death toll of the total). But here’s the shocking truth—87% weren’t killed by the shaking. They were killed by fire.

September 1, 1923. The earthquake struck at 11:58 AM—two minutes before noon—when families across Tokyo were cooking lunch over open flames. Within an hour, over 100 fires erupted across a city built almost entirely of wood and paper.

The fires merged into massive firestorms, generating winds so powerful they created fire tornadoes—what survivors called “dragon twists.” At the Honjo Clothing Depot, 40,000 refugees thought they’d found safety in an open field. At 4:00 PM, a fire tornado swept through. Within minutes, 38,000 people perished—over a third of the entire disaster’s death toll, in one location.

What’s tragic is that seismologist Imamura Akitsune had predicted this exact scenario 18 years earlier. He warned that cooking fires would turn an earthquake into an inferno. His senior colleague ridiculed him publicly. Imamura was right.

Japan learned. In 1960, September 1st became Disaster Prevention Day. Every Japanese child now practices earthquake drills. Gas meters have automatic seismic shut-offs. Tokyo’s wide avenues and parks? They were designed as firebreaks. The deadliest disasters aren’t always the ones we expect. Sometimes the real killer comes after.

【The 2011 Chao Phraya River Floods Case Study Content: Nikkei BizRuptors (website)】

Balancing Continuity and Survival: Lessons for Overseas Manufacturers from Thailand’s 2001 Flood

【Updated : Disaster Links Library launched (website)】

Disaster Link Library

【Project launched (website)】Disaster Risk Management in Aging Societies: Bridging Japanese Experience with Thai Policy Needs

Disaster Risk Management in Aging Societies

【Disaster Research: Infograph】AI-Integrated Disaster Preparedness Platforms (Open Access Examples)

The infographic of the AI-Integrated Disaster Preparedness Platforms is shown as an infographic: AI-Integrated Disaster Preparedness Platforms

【Disaster Research: Infograph】The 2004 Tsunami in Thailand

This infographic was presented at RIHN in Japan as part of the Prof. Ito project, as part of the Feasibility Study. The infographic website is: https://disasters.weblike.jp/IOT%20v2.html

The presented numbers should be confirmed. Especially, the foreigner’s death toll and the Thai national death toll, with their proportion, are under reinvestigation.

【Disaster Research】When Nature Meets Human Error: Lessons from History’s Deadliest Volcanic Mudflow 40 Years Ago

Growing up, many of us were taught that natural disasters are inevitable acts of nature beyond human control. This perspective changed dramatically for me when I started working at a research institute. My senior researcher emphatically told me, “The natural disaster is not natural.” This profound statement transformed my approach to disaster research, helping me understand that human decisions often determine whether natural hazards become catastrophic disasters.

The Forgotten Tragedy of Armero

On November 13, 1985, the Nevado del Ruiz volcano in Colombia erupted after 69 years of dormancy. The eruption triggered massive mudflows (lahars) that rushed down the volcano’s slopes, burying the town of Armero and claiming over 23,000 lives. This catastrophe stands as Colombia’s worst natural hazard-induced disaster and the deadliest lahar ever recorded.

What makes this tragedy particularly heartbreaking is its preventability. Scientists had observed warning signs for months, with seismic activity beginning as early as November 1984. By March 1985, a UN seismologist had observed a 150-meter vapor column erupting from the mountain and concluded that a major eruption was likely.

Despite these warnings, effective action to protect the vulnerable population never materialized. The devastation of Armero wasn’t simply the result of volcanic activity but the culmination of multiple human failures in risk communication, historical memory, and emergency response.

When Warning Systems Fail: Communication Breakdown

The Armero disaster epitomizes what disaster researchers call “cascading failures” in warning systems. Scientists had created hazard maps showing the potential danger to Armero in October 1985, just weeks before the eruption. However, these maps suffered from critical design flaws that rendered them ineffective.

One version lacked a clear legend to interpret the colored zones, making it incomprehensible to the general public. Devastatingly, Armero was placed within a green zone on some maps, which many residents misinterpreted as indicating safety rather than danger. According to reports, many survivors later recounted they had never even heard of the hazard maps before the eruption, despite their publication in several major newspapers.

As a disaster researcher, I’ve seen this pattern repeatedly: scientific knowledge fails to translate into public understanding and action. When I conducted fieldwork in flood-prone regions in Thailand, I discovered a similar disconnect between technical risk assessments and public perception. Effective disaster mitigation requires not just accurate information but information that is accessible and actionable for those at risk.

The Cultural Blindspots of Risk Perception

The tragedy of Armero illustrates how cultural and historical factors shape how communities perceive risk. Despite previous eruptions destroying the town in 1595 and 1845, causing approximately 636 and 1,000 deaths respectively, collective memory of these disasters had seemingly faded as the town was rebuilt in the same location.

In the hours before the disaster, when ash began falling around 3:00 PM, local leaders, including the town priest, reportedly advised people to “stay calm” and remain indoors. Some residents recall a priest encouraging them to “enjoy this beautiful show” of ashfall, suggesting it was harmless. These reassurances from trusted community figures likely discouraged self-evacuation that might have saved lives.

My research in disaster-prone communities has consistently shown that risk perception is heavily influenced by cultural factors, including trust in authority figures and historical experience with hazards. In Japan, for instance, the tsunami markers that indicate historic high-water levels serve as constant physical reminders of past disasters, helping to maintain community awareness across generations.

Systemic Failures and Institutional Response

The Armero tragedy wasn’t just a failure of risk communication or cultural blind spots—it revealed systemic weaknesses in disaster governance. Colombia was grappling with significant political instability due to years of civil war, potentially diverting governmental resources from disaster preparedness. Just a week before the eruption, the government was heavily focused on a guerrilla siege at the Palace of Justice in Bogotá.

Reports suggest there was reluctance on the part of the government to bear the potential economic and political repercussions of ordering an evacuation that might have proven unnecessary. This hesitation proved fatal when communication systems failed on the night of the eruption due to a severe storm, preventing warnings from reaching residents even after the lahars were already descending toward the town.

In my research examining large-scale flood disasters, I’ve found that effective disaster governance requires robust institutions that prioritize public safety over short-term economic or political considerations. My 2021 comparative analysis of major flood events demonstrated that preemptive protective actions consistently save more lives than reactive emergency responses, even when accounting for false alarms.

Learning from Tragedy: The Path Forward

The Armero disaster, while devastating, catalyzed significant advancements in volcano monitoring and disaster risk reduction globally. Colombia established specialized disaster management agencies with greater emphasis on proactive preparedness. The

Colombian Geological Service expanded from limited capacity to a network of 600 stations monitoring 23 active volcanoes.

The contrast with the 1991 eruption of Mount Pinatubo in the Philippines demonstrates the impact of these lessons. There, timely forecasts and effective evacuation procedures saved thousands of lives. The memory of Armero remains a powerful reminder of the consequences of inadequate disaster preparedness.

As I’ve emphasized in my own research on disaster resilience in industrial complex areas, building sustainable communities requires integrating technical knowledge with social systems. My work developing social vulnerability indices demonstrates that effective disaster risk reduction must address both physical hazards and social vulnerabilities.

Remember, disasters may be triggered by natural events, but their impact is determined by human decisions. By learning from tragedies like Armero, we can create more resilient communities prepared to face future challenges.

【Disaster Research:Excel】Pivot Tables for Disaster Research: A Step-by-Step Guide

Today, I gonna explain how to use a pivot table to conduct disaster research using dummy data.

What is a Pivot Table?

Imagine you have a big pile of data, and you want to see summaries or patterns quickly. A pivot table lets you rearrange (or “pivot”) that data to show different views, like totals, averages, or counts, without changing the original data.

Sample Disaster Research Dataset:

dummy dataset

Step-by-Step Pivot Table Analysis for Disaster Research

  1. Select Data & Insert Pivot Table
  • Select all the data (Ctrl+A or Cmd+A)
  • Go to “Insert” → “PivotTable” → “OK” (for a new worksheet or you can choose the location in the same sheet)
  1. Total Aid Provided by Disaster Type

The sum of Aid by Disaster Type

  • Drag “Disaster Type” to the “Rows” box
  • Drag “Aid Provided (USD)” to the “Values” box (automatically shows sum of aid)
  • Interpretation: Quickly identify which disaster types received the most total aid
  1. Aid Provided by Organization
  • Remove “Disaster Type” from rows and add “Organization” instead
  • Keep “Aid Provided (USD)” in the “Values” box
  • Interpretation: Visualize which organizations have contributed the most aid overall
  1. Aid Provided by Year
  • Replace “Organization” with “Year” in the “Rows” box
  • Keep “Aid Provided (USD)” in the “Values” box
  • Interpretation: Track annual patterns in aid disbursement over time
  1. Aid Provided by Disaster Type and Year
  • Add “Disaster Type” to the “Rows” box
  • Place “Year” in the “Columns” box
  • Keep “Aid Provided (USD)” in the “Values” box
  • Interpretation: Create a cross-tabulation showing aid distribution across disaster types and years
  1. Average Aid Provided
  • Click on “Sum of Aid Provided (USD)” in the “Values” box
  • Select “Value Field Settings” → “Average” → “OK”
  • Interpretation: Compare the average aid amounts across categories
  1. Filtering by Location
  • Add “Location” to the “Filters” box
  • Use the dropdown to select a specific location (e.g., Nepal)
  • Interpretation: Focus your analysis on specific geographic regions
  1. Counting Disaster Occurrences

Sorted Table

  • Remove “Aid Provided (USD)” from values
  • Add “Disaster Type” to the values box
  • Change the value field setting from sum to count
  • Interpretation: Track the frequency of different disaster types in your dataset

Key Insights from Disaster Research Pivot Tables

  • Aid Distribution Analysis: Identify which disaster types or locations receive the most financial support
  • Organizational Impact Assessment: Understand which relief organizations are most active in different scenarios
  • Temporal Trend Identification: Analyze how aid distribution patterns change over months, quarters, or years
  • Comparative Regional Analysis: Compare aid efforts across different geographic areas and disaster contexts

By experimenting with different field combinations, you can uncover valuable insights from your disaster research data. Pivot tables transform complex datasets into actionable intelligence for disaster management, policy development, and resource allocation.

Content Gap Opportunities

  • A section on advanced pivot table features specifically useful for disaster research
  • Guidance on data visualization options after creating pivot tables
  • Information on combining pivot tables with other analytical tools for comprehensive disaster analysis
  • Tips for presenting pivot table findings to non-technical stakeholders