Baselight

Resilient To Disasters

but investment is needed to save more lives progress doesn’t reverse

@kaggle.willianoliveiragibin_resilient_to_disasters

Loading...
Loading...

About this Dataset

Resilient To Disasters

this graph was created in OurDataWorld:



In 1970, more than 300,000 people died when a strong cyclone hit the coast of Bangladesh.1 In 1985, another storm caused 15,000 deaths. Just six years later, another killed 140,000.

Fast-forward to 2020. Bangladesh was hit by cyclone Amphan, one of the strongest storms on record in the Bay of Bengal. The death toll was 26 — barely visible on the chart below, compared to these very deadly disasters.

That’s 26 too many deaths, and the cyclone also caused huge amounts of damage: millions of people were displaced, and there were large economic losses. But tens — possibly hundreds — of thousands of lives were saved through early warnings, evacuations, and increased resilience. People in Bangladesh are much better protected from disasters than they were a few decades ago.

This development is part of a longer-term and widespread success in reducing humanity’s vulnerability to storms, floods, earthquakes, and other hazards.

Bangladesh is not an isolated example. We can observe long-term improvements in the world's resilience.

Here, I will look at data published by the International Disaster Database, EM-DAT, which stretches back to 1900. In the chart below, I’ve shown the number of deaths from disasters, given as the decadal average. This is helpful as there is a lot of volatility in disasters from year to year.2 You can also explore this data annually.

The number of people killed in disasters has fallen a lot over the last century. That’s despite there being four times as many people. That means the decline in death rates has been even more dramatic.

Tables

Natural Disasters New

@kaggle.willianoliveiragibin_resilient_to_disasters.natural_disasters_new
  • 22.96 kB
  • 3,068 rows
  • 10 columns
Loading...
CREATE TABLE natural_disasters_new (
  "country_name" VARCHAR,
  "year" BIGINT,
  "number_of_deaths_from_drought" DOUBLE,
  "number_of_people_injured_from_drought" DOUBLE,
  "number_of_people_affected_from_drought" DOUBLE,
  "number_of_people_left_homeless_from_drought" DOUBLE,
  "number_of_total_people_affected_by_drought" DOUBLE,
  "reconstruction_costs_from_drought" BIGINT,
  "insured_damages_against_drought" BIGINT,
  "total_economic_damages_from_drought" DOUBLE
);

Share link

Anyone who has the link will be able to view this.