The context of this challenge revolves around the alarming decline of butterfly populations, particularly monarch butterflies, which poses a significant threat to environmental health. This decline raises pressing questions about its potential causes and the subsequent effects on ecosystems and human well-being.
During this weekend's datathon, I focused on uncovering possible correlations between pesticide usage and the decline in monarch populations. Given the substantial impact of pollinator loss on food production and biodiversity, understanding this relationship is crucial. I aimed to analyze the connection between the most frequently used pesticides in each state and monarch sightings, including adults, eggs, and larvae. This approach stemmed from my belief that there could be a significant correlation between pesticide application and butterfly populations.
To conduct my analysis, I utilized Python frameworks for data manipulation and gathering. Specifically, I employed Pandas for data analysis and Beautiful Soup for web scraping to collect relevant data on monarch sightings and pesticide usage. Additionally, I used ChatGPT to assist in writing the necessary code, which streamlined my process.
Throughout the project, I was able to complete all three tasks outlined in the beginner track. I successfully mapped the states with the highest pesticide usage and tracked butterfly sightings in each state. However, despite my efforts, I was unable to establish a clear correlation between pesticide use and monarch populations, indicating the need for further exploration.
Looking ahead, if I had more time, I would go deeper into analyzing data across different years to identify trends over time. This longitudinal analysis could provide valuable insights into how pesticide usage and butterfly populations interact, ultimately aiding in the development of more effective conservation strategies.
Built With
- codap
- python
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