Geospatial and Temporal Patterns of Natural and Man-Made (Technological) Disasters (1900–2024): Insights from Different Socio-Economic and Demographic Perspectives

🌍 New Publication: Geospatial and Temporal Patterns of Natural and Man-Made (Technological) Disasters (1900–2024): Insights from Different Socio-Economic and Demographic Perspectives 📢

Dear colleagues, We are excited to share that our latest article, “Geospatial and Temporal Patterns of Natural and Man-Made (Technological) Disasters (1900–2024): Insights from Different Socio-Economic and Demographic Perspectives”, has been published in Applied Sciences and is now available online! 📄🔗

🔗 Read the full article here: https://www.mdpi.com/2076-3417/14/18/8129

This research analyzes disaster data over a century, examining the geospatial and temporal distribution of natural and technological disasters across different socio-economic contexts. Our findings shed light on the frequency, location, and severity of disasters worldwide, offering a comparative perspective between high- and low-income countries.

Key insights:

🌍 The results showed a dramatic escalation in both natural and man-made (technological) disasters over the decades, with notable surges in the 1991–2000 and 2001–2010 periods.

📊A total of 25,836 disasters were recorded worldwide, of which 69.41% were natural disasters (16,567) and 30.59% were man-made (technological) disasters (9269).

📊The most significant increase in natural disasters occurred from 1961–1970, while man-made (technological) disasters surged substantially from 1981–1990.

📊Seasonal trends reveal that floods peak in January and July, while storms are most frequent in June and October.

📊Droughts and floods are the most devastating in terms of human lives, while storms and earthquakes cause the highest economic losses.

📊The most substantial economic losses were reported during the 2001–2010 period, driven by catastrophic natural disasters in Asia and North America.

📊Also, Asia was highlighted by our research as the most disaster-prone continent, accounting for 41.75% of global events, with 61.89% of these events being natural disasters.

📊 The Pearson correlation analysis underscores that socio-economic factors, particularly population density and urbanization rate, play a significant role in influencing the distribution and consequences of disasters, including the number of deaths, injuries, and natural disasters. Higher population density is linked to a greater number of individuals affected, while higher urbanization rates and better governance are associated with a reduction in the overall number of disasters, deaths, and injuries. These insights emphasize the importance of considering socio-economic contexts in disaster risk management, highlighting the need for tailored strategies to enhance community resilience.

We hope this study contributes to the ongoing discussions around improving global disaster risk management strategies and socio-economic resilience.

#DisasterRiskReduction #GeospatialAnalysis #NaturalDisasters #TechnologicalDisasters #Research #AppliedSciences

Abstract

This pioneering study explores the geospatial and temporal patterns of natural and human-induced disasters from 1900 to 2024, providing essential insights into their global distribution and impacts. Significant trends and disparities in disaster occurrences and their widespread consequences are revealed through the utilization of the comprehensive international EM-DAT database. The results showed a dramatic escalation in both natural and man-made (technological) disasters over the decades, with notable surges in the 1991–2000 and 2001–2010 periods. A total of 25,836 disasters were recorded worldwide, of which 69.41% were natural disasters (16,567) and 30.59% were man-made (technological) disasters (9269). The most significant increase in natural disasters occurred from 1961–1970, while man-made (technological) disasters surged substantially from 1981–1990. Seasonal trends reveal that floods peak in January and July, while storms are most frequent in June and October. Droughts and floods are the most devastating in terms of human lives, while storms and earthquakes cause the highest economic losses. The most substantial economic losses were reported during the 2001–2010 period, driven by catastrophic natural disasters in Asia and North America. Also, Asia was highlighted by our research as the most disaster-prone continent, accounting for 41.75% of global events, with 61.89% of these events being natural disasters. Oceania, despite experiencing fewer total disasters, shows a remarkable 91.51% of these as natural disasters. Africa is notable for its high incidence of man-made (technological) disasters, which constitute 43.79% of the continent’s disaster events. Europe, representing 11.96% of total disasters, exhibits a balanced distribution but tends towards natural disasters at 64.54%. Examining specific countries, China, India, and the United States emerged as the countries most frequently affected by both types of disasters. The impact of these disasters has been immense, with economic losses reaching their highest during the decade of 2010–2020, largely due to natural disasters. The human toll has been equally significant, with Asia recording the most fatalities and Africa the most injuries. Pearson’s correlation analysis identified statistically significant links between socioeconomic factors and the effects of disasters. It shows that nations with higher GDP per capita and better governance quality tend to experience fewer disasters and less severe negative consequences. These insights highlight the urgent need for tailored disaster risk management strategies that address the distinct challenges and impacts in various regions. By understanding historical disaster patterns, policymakers and stakeholders can better anticipate and manage future risks, ultimately safeguarding lives and economies.

Keywords:

hazards; emergencies; natural disasters; man-made (technological) disasters; disaster risk management; socio-economic and human impact; EM-DAT database; geospatial; temporal analysis