【Disaster Research: Infograph】1985 mexico city earthquake

The infographic of the 1985 Mexico City Earthquake, mainly focusing on the social factors with earthquake characteristics, is shown as an infographic: http://disasters.weblike.jp/mexico%20infogr.html

The distance impact reminded me of the situation in Bangkok when an earthquake occurred in Myanmar in April 2025.

【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：Infograph】Myanmar’s Education Sector Impact & Recovery

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.

【Disaster Research：Excel】Regression Analysis

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:

  1. Click on File > Options.
  2. Select Add-Ins.
  3. In the Manage box at the bottom, select Excel Add-ins and click Go.
  4. Check Analysis ToolPak and click OK.

3. Visualize the Data

Before running the regression, it’s helpful to visualize the relationship:

  1. Select the columns Earthquake_Magnitude and Economic_Loss (excluding the header if desired).
  2. Go to the Insert tab.
  3. Choose Scatter from the Charts group and select the basic scatter plot.

This chart helps you see if there’s a linear trend between the two variables.

4. Conduct the Regression Analysis

  1. Go to the Data tab and click Data Analysis (in the Analysis group).
  2. In the Data Analysis dialog, select Regression and click OK.
  3. Input Y Range:
    • Select the range for the dependent variable (Economic_Loss). For example, if Economic_Loss is in column C from row 2 to row 16, enter C2:C16.
  4. Input X Range:
    • Select the range for the independent variable (Earthquake_Magnitude). For example, B2:B16.
  5. If your data has headers, check the Labels box.
  6. Choose an Output Range where you want the results to appear (or select a New Worksheet Ply).
  7. Click OK.

5. Interpret the Regression Output

Excel will generate a regression output that includes several key pieces of information:

  • Coefficients:
    • Intercept: The expected value of Economic_Loss when Earthquake_Magnitude is zero.
    • X Variable 1 (Slope): The change in Economic_Loss for each one-unit increase in Earthquake_Magnitude.
  • R-squared:
    • Indicates how much of the variance in Economic_Loss is explained by Earthquake_Magnitude. A value closer to 1 indicates a better fit.
  • p-Value:
    • Helps determine the statistical significance of the model. A low p-value (typically less than 0.05) suggests that the relationship is significant.

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>

  1. Regression Statistics

Multiple R (Correlation Coefficient):

  • Value: 0.964775
  • Meaning: This measures how strongly two variables (in this case, Earthquake_Magnitude and Economic_Loss) are related. A value close to 1 indicates a very strong linear relationship.

R Square (Coefficient of Determination):

  • Value: 0.930791
  • Meaning: About 93% of the variation in the Economic_Loss can be explained by the Earthquake_Magnitude. This is considered a high value, indicating the model fits the data well.

Adjusted R Square:

  • Value: 0.925467
  • Meaning: This is a slightly adjusted version of R Square that takes into account the number of explanatory variables and the sample size. Because this is a simple linear regression with only one explanatory variable, the Adjusted R Square is still very close to the R Square value.

Standard Error:

  • Value: 9.409751
  • Meaning: On average, the model’s predictions of Economic_Loss deviate from the actual observed values by about 9.41 units (likely millions of dollars if your data is in that unit). The lower this number, the more precise the model’s predictions tend to be.

Observations:

  • Value: 15
  • Meaning: The total number of data points (earthquake events) used in the analysis.
  1. ANOVA (Analysis of Variance) Table

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):

  • Regression df: 1 (one explanatory variable)
  • Residual df: 13 (the remainder)
  • Total df: 14 (because 15 data points minus 1)

SS (Sum of Squares):

  • Regression SS: 15480.54
  • Residual SS: 1151.064
  • Total SS: 16631.6
  • Meaning: The total SS (16631.6) is split between the portion explained by the model (15480.54) and the unexplained portion (1151.064). Since the regression SS is much larger than the residual SS, the model explains most of the variation.

MS (Mean Square):

  • Regression MS: 15480.54 (because the Regression SS is divided by 1, the df for regression)
  • Residual MS: 88.5432 (because 1151.064 is divided by 13)

F and Significance F (p-value for the overall model):

  • F: 174.835
  • Significance F: 6.44E-07 (which is 0.000000644)
  • Meaning: A very low p-value indicates that the overall regression model is statistically significant. In other words, Earthquake_Magnitude has a statistically significant effect on Economic_Loss.
  1. Coefficients Table

This table provides information about the intercept and the slope of your regression line.

Intercept (Coefficient):

  • Value: -152.261
  • Standard Error: 14.936
  • t Stat: -10.193
  • P-value: 1.47E-07
  • Lower 95%: -184.529
  • Upper 95%: -119.994
  • Meaning:
      • The intercept is the predicted Economic_Loss when Earthquake_Magnitude is 0. Mathematically, it’s part of the best-fit line. Although a negative intercept doesn’t make real-world sense for something like “loss” (you can’t have negative loss), it’s a valid outcome in a simple linear model.
      • The very small p-value (< 0.05) indicates the intercept is statistically different from zero.

Earthquake_Magnitude (Coefficient):

  • Value: 28.6965
  • Standard Error: 2.123497
  • t Stat: 13.513
  • P-value: 6.44E-07
  • Lower 95%: 23.865
  • Upper 95%: 33.528
  • Meaning:
      • For each 1-unit increase in Earthquake_Magnitude, the model predicts an increase of about 28.70 in Economic_Loss (again, presumably in millions of dollars).
      • The very small p-value (< 0.05) shows that Earthquake_Magnitude is a statistically significant predictor of Economic_Loss.
      • The 95% confidence interval (23.865 to 33.528) means we are 95% confident the true slope lies between these values.
  1. Putting It All Together

Regression Equation:

Predicted Economic Loss=−152.261+(28.6965×Earthquake Magnitude)

  1. Interpretation of R Square (0.930791):
    • About 93% of the variation in Economic_Loss is explained by the variation in Earthquake_Magnitude. This suggests a strong linear relationship.
  2. Model Significance (Significance F and p-values):
    • The overall model is highly significant (p < 0.001).
    • Earthquake_Magnitude is a very strong predictor (p < 0.001).
  3. Practical Meaning:
    • As the earthquake magnitude increases, expected economic losses rise substantially. Even though the intercept is negative (which is not realistic in a real-world scenario), the main takeaway is the slope: larger earthquakes lead to significantly higher losses.
  4. Model Limitations:
  • This is a simple linear model using only one predictor (Earthquake_Magnitude). Real-world economic loss is influenced by many factors (e.g., population density, building codes, depth of the quake, location, etc.).
  • The model’s negative intercept highlights that while it fits the data well for the range of magnitudes observed, it may not be meaningful for magnitudes far outside that range.
  1. Final Tips for Beginners
  • Always Plot Your Data: A scatter plot of Earthquake_Magnitude vs. Economic_Loss can confirm if a linear trend is reasonable.
  • Check Residuals: Look at how well the model performs across all data points. If there’s a clear pattern in the residuals, the linear model might not be appropriate.
  • Real-World Context: Negative intercepts can appear in purely statistical models but might not have a direct real-world meaning. Always interpret results carefully.
  • Add More Variables: If you suspect other factors affect Economic_Loss, consider multiple regression in the future to improve your model’s accuracy.

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.

【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.