national-parks-agglomerate 0.1.0

NPS data acquisition and analysis package

National Parks: An Agglomerate

This project uses the National Park Service (NPS) API to build a curated dataset of U.S. National Park Service sites. The goal is to combine multiple API endpoints into a single, clean dataset that supports analysis and visualization.

---

Live App

Explore the interactive Streamlit app here:
https://national-parks-agglomerategit-fx4mzpepe8eaqgridzjnzr.streamlit.app/

---

Project Goal

This project investigates how park amenities (activities and campgrounds) relate to operational complexity (alerts).

Rather than using a pre-existing dataset, this project builds one from scratch by:

• Collecting data from the NPS API
• Cleaning and transforming raw data
• Engineering useful features
• Merging multiple data sources into one dataset

---

Data Sources

This project uses the official National Park Service API:

• Parks endpoint
• Alerts endpoint
• Campgrounds endpoint

To re-run the data collection process, you will need a free API key from: https://www.nps.gov/subjects/developer/get-started.htm

Create a .env file in the root directory:

NPS_API_KEY=your_key_here

---

Installation

Clone the repository and install the package:

git clone https://github.com/rylion9-lgtm/national-parks-agglomerate
cd national-parks-agglomerate
python -m pip install -e .

To run the Streamlit app, install Streamlit:

python -m pip install streamlit

---

Running the App

streamlit run app.py

---

Example Usage (Package)

import pandas as pd
from national_parks import summarize_parks

df = pd.read_csv("data/processed/parks_final.csv")
summary = summarize_parks(df)

print(summary)

---

Final Dataset

The final dataset is located at:

data/processed/parks_final.csv

It contains:

• 474 rows
• 9 columns

Variables

• fullName: Full name of the park unit
• parkCode: Unique park identifier
• states: State abbreviation(s)
• latitude: Latitude
• longitude: Longitude
• description_length: Length of park description
• num_activities: Count of activities
• num_alerts: Number of alerts
• num_campgrounds: Number of campgrounds

---

Key Insight

Most parks have relatively few alerts regardless of activity level, suggesting only a weak relationship between amenities and alerts. However, parks with more activities tend to show slightly higher alert counts, indicating increased operational complexity.

---

Project Structure

national-parks-agglomerate/
├── README.md
├── requirements.txt
├── pyproject.toml
├── .gitignore
├── app.py
│
├── data/
│   ├── raw/
│   └── processed/
│
├── src/
│   ├── get_parks.py
│   ├── clean_parks.py
│   ├── get_alerts.py
│   ├── merge_alerts.py
│   ├── get_campgrounds.py
│   └── merge_campgrounds.py
│
└── national_parks/
    ├── __init__.py
    ├── data.py
    ├── clean.py
    └── analyze.py

---

Notes and Limitations

• Data represents a snapshot in time (not live-updating)
• Alerts and campgrounds were limited to 500 records
• num_activities is an engineered approximation
• Park units vary widely in size and type

---

Why This Project Matters

This project demonstrates:

• API data collection
• Data cleaning and transformation
• Feature engineering
• Multi-source data integration
• Building an installable Python package
• Deploying an interactive Streamlit app

It reflects a real-world data science workflow from raw data to deployed application.