Project data description
The theme of our project is about global temperature and mass disasters, be they natural (e.g., epidemics, droughts, earthquakes, storms, etc.) or technological (e.g., radiation, fire, chemical and oil spills, etc.). We want to explore some deep insights between natural disasters and other associated patterns.
Our main dataset is from Kaggle and The International Disaster Database. Other indicators about economies and health from World Bank (e.g., GDP per capita) are added to enrich our dimensions. Besides that, we care much more about data of mass disaster.Following this logic, we get the following data. The climate dataset contains 577 462 data points in total and we will use at least 2 dimensions. The mass disaster dataset contains 24845 data points and we will use at least 6 dimensions.
Furthermore, the following research questions are proposed to construct the design.
- What are the most prominent indicators influencing climate change?
The climate dataset is from 1743 to 2021 and thus covers both the first and the second industrial revolutions. Industrial revolutions changed agrarian and handicraft economy to industrial and machine manufactural economy. These also lead to more CO2 emissions which accelerate the global warming and climate change. By combining historical facts and other development indicators, we want to find possible patterns behind the climate changes and its prominent indicators.
2. Which relations can be found between mass natural disaster occurrence and climate change? And what are the discrepancies between emission rate of different countries/continents and the occurrence of natural disaster?
Our mass disaster dataset is from 1900 to 2021 and contains 15 828 data points. Combing temperature-changing data we expect that the increasing of average temperature and mass natural disaster occurrence are positively correlated. Also, we want to explore whether the regions with maximum temperature change are more likely to be attacked by natural disasters and thus which countries are more likely to be affected by mass natural disasters. We would also like to focus more specifically on country/region level to seek possible pattern behind countries and certain natural disaster type.
For example, from the following picture, we can easily see that Asia and Americas are more easily suffered by Hydrological and Meteorological disasters, also Africa much more disturbed by Biological disasters. We want to get deeper insights on both time and space dimensions of mass disaster occurrence.
