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.
An infographic, “Thailand’s Demographic Transformation,” was created.https://disasters.weblike.jp/Thai%20demographic%20change.html
You can click the link to use: https://disasters.weblike.jp/text%20comvert.html
An infographic, “Japan’s Demographic Transformation,” was created.https://disasters.weblike.jp/Jap%20population%20v2.html
An infographic, “Myanmar Earthquake 2025 Educational Sector Impact & Recovery Roadmap,” was created. Myanmar Earthquake 2025 Educational Sector Impact & Recovery Roadmap
*Please note that these are my research results, for my memo.
Step-by-Step Guide for Excel Regression Analysis
1. Prepare Your Dataset in Excel
Dataset Overview:
Create an Excel file (e.g., DisasterData.xlsx) with the following columns and sample data:
Objective:
Use the earthquake magnitude (independent variable) to predict economic loss (dependent variable: MN USD) through simple linear regression.
2. Enable the Analysis ToolPak
Excel’s Data Analysis ToolPak is required for regression analysis. If it’s not already enabled:
3. Visualize the Data
Before running the regression, it’s helpful to visualize the relationship:
This chart helps you see if there’s a linear trend between the two variables.
4. Conduct the Regression Analysis
5. Interpret the Regression Output
Excel will generate a regression output that includes several key pieces of information:
6. Use the Regression Model for Predictions
Once you have the coefficient and intercept from the output, you can create a prediction formula:
Y=(slope)X＋(intercept) Y:Economic_Loss X:Earthquake_Magnitude
<Interpretation for beginners>
Multiple R (Correlation Coefficient):
R Square (Coefficient of Determination):
Adjusted R Square:
Standard Error:
Observations:
The ANOVA table helps you see how much of the total variation in Economic_Loss is explained by the regression (model) versus how much is left unexplained (residual).
df (Degrees of Freedom):
SS (Sum of Squares):
MS (Mean Square):
F and Significance F (p-value for the overall model):
This table provides information about the intercept and the slope of your regression line.
Intercept (Coefficient):
Earthquake_Magnitude (Coefficient):
Regression Equation:
Predicted Economic Loss=−152.261+(28.6965×Earthquake Magnitude)
In summary, these regression results show a strong linear relationship between Earthquake_Magnitude and Economic_Loss. The model explains about 93% of the variation in economic losses, and both the intercept and the slope are statistically significant. However, as with any statistical model, interpret the results with caution and consider real-world factors that may affect the outcome beyond just magnitude.
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.
From Tragedy to Leadership: The Birth of ADRC
The Asian Disaster Reduction Center (ADRC) was established in 1998 following the devastating Great Hanshin-Awaji Earthquake (commonly known as the Kobe Earthquake) that struck Japan in 1995. This catastrophic event became a catalyst for change, transforming how Japan—and later Asia—approached disaster management and resilience.
Kobe’s Remarkable Recovery Journey
Kobe’s recovery story stands as a powerful testament to resilience and strategic rebuilding. Within just 9 years after the earthquake, Kobe’s population returned to pre-disaster levels—an extraordinary achievement considering the scale of destruction. This recovery wasn’t merely about rebuilding structures but reimagining the city’s future role.
HAT Kobe: A Hub for Disaster Reduction Excellence
Today, Kobe has reinvented itself as a global center for disaster reduction policies and activities. The area known as HAT Kobe hosts numerous disaster-related organizations, including ADRC. The name “HAT” carries dual significance:
This wordplay perfectly captures Kobe’s transformation from a disaster-struck city to a knowledge hub that helps others prepare for and respond to unexpected disasters.
Learning From Kobe: A Model for Disaster Recovery
Kobe’s recovery process offers valuable lessons for communities worldwide facing similar challenges. The city demonstrates how effective post-disaster planning can transform tragedy into opportunity, creating not just infrastructure but institutional knowledge that benefits others.
ADRC’s Mission Across Asia
ADRC plays a vital role in sharing disaster reduction expertise with its member countries throughout Asia. The organization:
Resources for Disaster Management Professionals
ADRC maintains comprehensive resources that disaster management professionals can access:
These resources provide valuable insights into regional disaster management systems, country-specific approaches, and up-to-date information on current disaster situations across Asia.
Building Regional Resilience Together
Through organizations like ADRC and the example set by Kobe, Asian countries are developing stronger collaborative approaches to disaster risk reduction. By learning from past experiences and sharing knowledge, communities across the region are better prepared to face future challenges with resilience and determination.
Today, I gonna explain how to use a pivot table to conduct disaster research using dummy data.
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.
dummy dataset
The sum of Aid by Disaster Type
Sorted Table
Key Insights from Disaster Research Pivot Tables
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
Imagine 3
Did you know natural disasters drained over $400 billion from the global economy last year? Your wallet might be next.
2024 was the hottest year since 1850, with catastrophic consequences. We faced 21 separate billion-dollar disasters worldwide, with insurers covering only $154 billion of the total $417 billion in damages.
Hurricanes Helene and Milton were the costliest events, each causing about $20 billion in insured losses alone. Meanwhile, severe thunderstorms and hail contributed a staggering $64 billion to the insurance bill.
As a disaster management expert, I’ve watched climate patterns shift dramatically in recent years. What’s truly alarming is how wildfire and storm seasons are becoming increasingly unpredictable – like the January fires we saw in Los Angeles, completely outside the traditional season.
Our old preparedness playbooks are becoming obsolete. In my experience, communities that adapt now with flexible emergency plans will save both lives and money when – not if – disasters strike.
Source： Wall Street Journal
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