Analysis of U.S. National Parks Visitor Patterns and
Experiences
Habeeb Ajibola, Ifeoluwa Kayode, Charles Wibonele, & Tochukwu Nto
Mbah
Project Overview
Objective: Analyze visitor patterns and experiences
in U.S. National Parks
Data Source: National Park Service API
Focus Areas:
Factors influencing park popularity
Visitor satisfaction and park amenities
Importance: Supports sustainable tourism and
resource allocation in parks
Problem Description
Goal: Understand the relationships between park
amenities, visitation rates, and visitor experiences
Relevance: Insights can help improve visitor
experience, optimize resources, and guide conservation efforts
Scope: Patterns related to high visitation periods,
amenity influence, and visitor satisfaction indicators
Analytics Plan
Data Collection: Using httr and
jsonlite packages in R to request and parse API data
Data Wrangling: Clean and transform datasets for
analysis
Exploratory Analysis: Identify data distributions
and key patterns
Predictive
Modeling: Use H2O to build models analyzing visitation
influences
Evaluation Plan
Metrics:
Model accuracy and F1-score
Cross-validation for robustness
Data Visualizations: Highlight relationships and
comparisons across predictors (e.g., amenities, visitor
demographics)
Outcome: Insightful data to support strategies for
park management and sustainable tourism
Update: Unable to implement aspects of our
evaluation plan due to technical difficulties when running our
model.
Summary
Expected Impact:
Aid National Park Service in understanding visitor trends
Enhance park management and visitor satisfaction strategies
Real Impact -Unable to get proper modeling due to
errors and technical difficulties. -Also had issues with the API
Upon further reflection, there are many ways we could have improved
upon the project by cleaning the data more thoroughly and seeing if we
could resolve the errors in our models.