Assignment 5

Introduction

library(tidyverse)
library(plotly)
library(leaflet)
library(readr)
parks_data <- read_csv("A:/R/National_Parks_Filtered.csv")

This report explores 2024 visitation trends across all 63 U.S. National Parks using interactive visualizations designed to highlight both spatial and non-spatial patterns. The dataset includes each park’s name, state, region, latitude, longitude, and the number of recreational visits recorded in 2024. It was compiled from an open dataset published by Responsible Datasets in Context and supplemented with coordinate data from the National Park Service.

The goal of this analysis is to examine how visitation varies across geographic regions and to identify key factors that may influence national park popularity. To achieve this, one interactive map and one non-map visualization are used in combination to provide a broader perspective on how and where people interacted with national parks during the 2024 season.

Non-Map Interactive Analysis

Visualization

top_parks <- parks_data |>
  arrange(desc(RecreationVisits)) |>
  slice(1:15)

plot_ly(
  top_parks,
  x = ~reorder(ParkName, RecreationVisits),
  y = ~RecreationVisits,
  type = "bar",
  text = ~paste("State:", State, "<br>Region:", Region),
  hoverinfo = "text+y",
  marker = list(color = "steelblue")
) |>
  layout(
    title = "Top 15 Most Visited U.S. National Parks (2024)",
    xaxis = list(title = "", tickangle = -45),
    yaxis = list(title = "Recreation Visits"),
    margin = list(b = 100)
  )

Analysis & Reflection

The interactive bar chart shows that parks such as Great Smoky Mountains NP, Zion NP, and Yellowstone NP had the highest number of recreational visits in 2024. These parks stand out for their popularity, which can be attributed to factors like ease of access, large visitor centers, or consistent media attention. On the other hand, parks in more remote areas, such as Gates of the Arctic NP or Kobuk Valley NP, received far fewer visits.

A bar chart was chosen because it allows for quick visual comparison across categories. It makes it easy to see which parks received the most attention from visitors. The interactive hover tool adds depth by showing the park’s state and region, helping viewers further understand how a park’s location relates to its visitor numbers.

This type of visualization works well for identifying high-level patterns in the data. However, it does not explain the reasons behind park popularity. For that, the chart needs to be paired with a map to show geographic context. Together, these tools support a clearer understanding of national park performance in 2024.

Interactive Map Analysis

Visualization

leaflet(parks_data) |>
  addTiles() |>
  setView(lng = -98.5795, lat = 39.8283, zoom = 4) |>
  addCircleMarkers(
    lng = ~Longitude,
    lat = ~Latitude,
    label = ~ParkName,
    radius = ~scales::rescale(RecreationVisits, to = c(4, 12)),
    popup = ~paste0(
      "<b>", ParkName, "</b><br>",
      "Visits: ", format(RecreationVisits, big.mark = ","), "<br>",
      "Region: ", Region, "<br>",
      "State: ", State
    ),
    color = "darkgreen",
    stroke = FALSE,
    fillOpacity = 0.7
  )

Analysis & Reflection

The interactive map helps illustrate how national park visitation in 2024 varied by geographic region. Parks in the western U.S., such as Zion, Grand Canyon, and Rocky Mountain, form a visible cluster of high visitation. These parks tend to be close to urban centers or major highways, which may explain the higher traffic. Meanwhile, many of the least visited parks are concentrated in Alaska or island territories, which are harder to reach and more expensive to visit.

This map uses proportional circle markers to represent the number of visits, with hover labels and popups providing detailed information. The design allows viewers to quickly compare park size and location while interacting with the data in a more immersive way.

The visualization is effective because it connects visitation data to real-world geography. It shows not just which parks are popular, but also where they are located. One limitation is that circles can overlap in dense areas, making it harder to isolate smaller parks. However, the use of popups and zoom features helps offset this issue.

Self-Assessment: 8/10:** **I’d say this map tells a strong visual story and complements the non-map chart well. It’s clear, interactive, and informative. With more time, clustering or regional filters could be added to make it even more dynamic.

Conclusion

Together, the two interactive visualizations present a well-rounded view of national park visitation in 2024. The bar chart highlights which parks received the most visitors, allowing for easy ranking and comparison. The map adds geographic context by showing where these parks are located and how accessibility or region may influence visitation patterns. Used together, the two views provide a clearer understanding of both numerical trends and spatial distribution.

This analysis also raises questions about how external factors affect visitation. For example, to what extent do factors like park infrastructure or seasonal climate impact visitor numbers? While the current analysis covers overall trends, more work could be done to explore monthly patterns, demographic data, or historical comparisons over time.

One technical challenge was preparing a fully clean dataset with consistent naming and coordinate values. Another was working within software limitations while attempting to render all 63 parks. If I were comfortable with more tools, it would be valuable to add filtering by region or clustering markers on the map to improve usability. These enhancements could make the visualizations more adaptable for different audiences or use cases.

Data Sources: https://www.responsible-datasets-in-context.com/posts/np-data/

https://www.nps.gov/findapark/index.htm