Analyzing Tree Data

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This is a lab performed by Owen Cruz-Abrams, Class of 2026 at Aspen High School. The lab aims to determine the effect of elevation on tree height, and uses a large enough dataset that control variables are largely unnecessary.

Purpose

To determine the effect of elevation on tree height, and thus the effect of elevation on the ability of trees to grow. The actual research question states:

How does the elevation of a tree (2500-3100m) affect its average height (meters)?

Data Collection

We collected data from the following source:

• Tree Data
• City Elevation Data

Data Exploration

See notebooks/eda.ipynb for the code used to explore the data.

Meaning of Data Columns

See the following table, taken directly from the dataset. We are specifically looking at the tree height in meters to average for each city, etc.

column_name	definition
city_ID	Unique identifier given to the tree by the city
tree_ID	our unique identifier for each tree
planted_date	date tree was planted
most_recent_observation	most recent observation date of tree (among dates such as condition_date, edit_date, inspect_date)
retired_date	date tree was retired.
most_recent_observation_type	what is the most recent observation? condition_date, edit_date, etc.
common_name	plain english common name of the tree species
scientific_name	biological name of the tree species (Quercus rubrus)
greater_metro	greater metro area in which the city is found, which will match the city name in the filename
city	city name, as it is properly spelled (Las Vegas)
state	state name (as it is properly spelled, not abbreviation)
longitude_coordinate	exact location of tree species (longitude)
latitude_coordinate	exact location of tree species (latitude)
location_type	where the tree is located, whether in a park or an urban area (green_space, built_environment, no_info)
zipcode	zipcode of the location
address	address where the data was collected
neighborhood	neighborhood of the location of the tree
location_name	If the location is named without being an address, such as Smith Cemetary or Route 11 Median
ward	city ward
district	the district tree is located
overhead_utility	Is there an overhead utility (yes, no, conflicting)?
diameter_breast_height_CM	trunk diameter in cm at breast height
condition	tree condition as coded by the city-specific protocol converted to standardized conditions
height_M	height of tree in meters
native	Is the tree native to the state (naturally_occurring), not native (introduced), or of unknown status
height_binned_M	range of heights into which the tree falls, converted from feet.
diameter_breast_height_binned_CM	range of diameters into which the tree falls, converted from inches often

Analysis

1. We needed a dataset of all the city names. I produced this dataset (see data/city_elevation.csv) by using the National Map's API to search for all cities in the tree dataset. See notebooks/get_city_elevation.ipynb for the code used to generate this dataset.
2. We then used the pandas library to merge the tree dataset with the city dataset. This allowed us to calculate the average tree length for each city. See scripts/analyze_tree_data.py for the code used to merge the datasets and calculate the average tree length for each city.
3. We saved this new dataset, with elevation for each tree, to data/tree_data_with_elevation.csv.
4. From there, we analyzed the data in a massive spreadsheet, and calculated the average tree height for each elevation. We did not publish this spreadsheet, but we did publish the code used to analyze the data. See notebooks/analyze_tree_data.ipynb for the code used to analyze the data.

Reproducibility

To reproduce the analysis, follow these steps:

1. Install necessary tools to clone and work with this repository.
   • git (see git-scm.com for installation instructions)
   • python (see python.org for installation instructions)
   • homebrew (see brew.sh for installation instructions)
   • git-lfs (after installing homebrew, run brew install git-lfs)
2. Clone the repository and navigate to the repository directory.
   • Hit Command + Space and type terminal and hit Enter.
   • In the terminal, type git clone https://github.com/MrDevel0per/bio-data-analysis.git and hit Enter.
   • Then, type cd bio-data-analysis and hit Enter.
3. Install the necessary Python packages.
   • In the terminal, type python3 -m venv venv and hit Enter.
   • Then, type source venv/bin/activate and hit Enter.
   • Finally, type pip install -r requirements.txt and hit Enter.
4. Download the data from https://www.kaggle.com/datasets/mexwell/5m-trees-dataset
5. Run the following script in the terminal to properly format the data:
   • sh scripts/data.sh, and type in the path to the downloaded .zip file.
     • For example, if the file is in your Downloads folder, you would type sh scripts/data.sh ~/Downloads/5m-trees-dataset.zip.
6. Run the Jupyter Notebooks in notebooks to analyze your now-formatted data. Project Jupyter should have been installed in the requirements.txt step, but if not, see Installing Jupyter. If you want a Jupyter server to work in, run python3 -m jupyter notebook.