Introduction: Sources: Earthquake:https://www.kaggle.com/datasets/syedanwarafridi/earthquake-events-worldwide-2023 Poverty: https://ourworldindata.org/poverty
For this project I will by trying my hand at using geographic data and exploring Earthquakes Worldwide in 2023. After looking over this dataset Ive recognized a few different methods I can use to exemplify what I have learned throughout the semester.
Objective:
I want to try and push my knowledge and comfort around new types of data programs and software. So for this final project I will try and use an online DB, I have signed up for an AWS account and intend to use this as well as a CSV likely in GitHub for the other source. In my AWS account I have opened up an Aurora PostgreSQL server that I will be using for data storage.
Methodology:
EDA and data cleaning will be very important for the project, so I will ensure that I spend a large amount of time cleaning and formatting the data. I would also like to try and map poverty data and see if there might be any overlapping trends between the two.
Questions: What does the distribution of major earthquakes look like globally? Is there any overlap between this and global poverty distribution?
pov_data <- read.csv("https://raw.githubusercontent.com/owid/poverty-data/main/datasets/pip_dataset.csv")