The analysis of data from a medium-scale ecological restoration project in Colombia. Available at https://github.com/Slid61/Plant-Restoration-Data
The jupyter notebook contains detailed comments explaining what I've done, including a full analysis of the results at the end.
Check out the medium post that this produced: https://medium.com/@f.vasquez.tavera/data-science-and-ecological-restoration-4-steps-to-action-with-a-real-life-case-study-e5003578b082
The motivation for this project was a combination of wanting to improve my python and data science skills, as well as to provide actionable analysis on a growing pile of data from my job at the time. While this started as an academic exercise, management became interested and the scope of the project increased. The branch "Spanish" contains the full extent of the project, while the branch "main" contains the much more polished highlights for an english-speaking audience unfamiliar with ecological restoration.
Project done entirely in Python. Libraries used:
- Numpy
- Pandas
- Matplotlib
- Seaborn
Files: plant-restoration-data/ ├─ .ipynb_checkpoints/ ├─ Figures/ │ ├─ Fig01 Aggregate Biomass Indicators over Time.png │ ├─ Fig02 Aggregate Health Indicators Over Time.png │ ├─ Fig03 Guinea Pig Lunch.png │ ├─ Fig03 Guinea Pig Lunch (Adjusted).png │ ├─ Fig04 Geographic Distribution of Guinea Pig Attacks.png │ ├─ Fig05 Geographic Distribution of Guinea Pig Attacks Over Time.png │ ├─ Fig06 Geographic Distribution of Guinea Pig Attacks Over Time, by Time of Planting.png │ ├─ Fig07 Geographic Distribution of Biomass Metrics.png │ ├─ Fig08 Geographic Distribution of Survival and Health Metrics.png │ ├─ Fig09 Geographic Distribution of the Most Successful Plants.png │ ├─ Fig10 Geographic Distribution of the Least Successful Plants.png │ ├─ Fig11 Satellite Image of Planting Area.png │ ├─ Fig12 Geographic Distribution of Competition.png │ ├─ Img01 GuineaPig.jpg ├─ Original Data/ │ ├─ Coordinates.xlsx │ ├─ Reserve.xlsx ├─ .gitignore ├─ Medium Blog Draft v2.docx ├─ README.md ├─ Reserve.xlsx ├─ Udacity Final Data Submission.ipynb
Acknowledgements: Many thanks to the team I work with at Fundacion Natura, especially to Oriana for showing interest and allowing me to use this data, and to Vivi for all of the useful feedback. Without them this would be a boring boilerplate data project with nothing applicable to show for it.
Also thanks to the hardworking people that collected the data painstakingly by hand: Martha, Wilmer, Brian, Johanna, Vivi, Jessica, Nestor, Edinson, myself, and additional helpers.