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US National Parks Analysis using Python, Pandas library, Matplotlib.

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US-National-Park-Suitability-Analysis-With-Python

Analyzing National Park Suitability Data pulled out from US National Park Service and US Census Bureau website were analyzed to locate trails within parks and calculate population within that radius.

Assumptions

  1. The radius around the park for trail selection will be 2 times the circular surface area of the park.
  2. Any linkage with census should use a zip code radius of 60 miles (possible for a day trip)

Talking in Code Python, Pandas, Matplotlib, API and Jupyter notebook were used to explore and process the data.

GitHub Link: https://github.com/theaddies/bootcamp_project_1.git

Data Collection: API Sources Hikingproject.com (trail information) National Park Service US Census Bureau Wikipedia (National Park list check)

Methodology: The methodology consists on: Radius of park zip code set to 60 miles Locate trails within parks Calculate population within that radius Radius for trail search is 2x circular radius of park surface area

Retrieving & Cleaning Jupyter notebook (Python) were used to retrieve and clean data Zip-Code Coding! Pinpointing Park Locations & Census Data Pinpointing Park Locations & Census Data Total Pop. Per National Park Zip Code

Results Different maps and statistical analysis were done:

  • Visitor Count Per Park
  • Population Within 60 Miles Radius of Parks
  • Visitor Count Per Park Radius
  • Park Visitors vs. Surrounding Population
  • Number of Trails Per National Park
  • Number of Trails Per National Park

The statistical analysis show. No correlations found for:

  • Trail number and park size
  • Visitor number and population density 495 National Park installations
  • Only 60 “National Park” properties
  • Excluded monuments, trails, rivers, reserves

Coolness Factor:

  • Popularity due to park characteristics → visitor ratings vs. surrounding population

Future Directions

If we had more time:

  1. GEOJSON
  2. Categorize trails by specific use
  3. Prices
  4. Park attributes
  5. Trail attributes
  6. Weather information
  7. Visitor demographics
  8. Visitor count and wildlife habitat Applications:
  9. No API (of which we are aware) allowing users to search exclusively on National Park trails
  10. Expand to a website or application allowing users to look for specific trails across parks

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