Weather Data Analysis for Tourism

1. Introduction

The purpose of this report is to analyze current weather conditions in selected cities to provide insights for optimizing tourism activities. The analysis focuses on temperature, humidity, wind speed, and precipitation levels across five locations: Birmingham, AL; Nashville, TN; Orlando, FL; Pensacola, FL; and Savannah, GA.

2. Data Collection

The weather data was collected using the OpenWeatherMap API, which provided real-time weather conditions for each city. The collected parameters include: - Temperature (°F) - Humidity (%) - Wind Speed (mph) - Precipitation (inches) - Sunrise and Sunset Times

library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

library(ggplot2)

# Load the cleaned dataset
weather_data <- read.csv("current_weather_data.csv")

# Extract key insights dynamically
hottest_city <- weather_data$City[which.max(weather_data$Temperature_F)]
coldest_city <- weather_data$City[which.min(weather_data$Temperature_F)]
rainiest_city <- weather_data$City[which.max(weather_data$Precipitation_in)]
windiest_city <- weather_data$City[which.max(weather_data$Wind_Speed_mph)]

3. Data Visualization

3.1 Temperature Trends

ggplot(weather_data, aes(x = City, y = Temperature_F, fill = City)) +
  geom_bar(stat = "identity") +
  labs(
    title = "Current Temperature by City",
    subtitle = "Comparing temperature across tourism locations",
    x = "City",
    y = "Temperature (°F)",
    fill = "City"
  ) +
  theme_minimal()

## 3.2 Humidity Levels

ggplot(weather_data, aes(x = City, y = Humidity, fill = City)) +
  geom_bar(stat = "identity") +
  labs(
    title = "Current Humidity Levels by City",
    subtitle = "Comparing humidity levels across tourism locations",
    x = "City",
    y = "Humidity (%)",
    fill = "City"
  ) +
  theme_minimal()

## 3.3 Wind Speed Comparison

ggplot(weather_data, aes(x = City, y = Wind_Speed_mph, fill = City)) +
  geom_bar(stat = "identity") +
  labs(
    title = "Current Wind Speed by City",
    subtitle = "Comparing wind speeds across tourism locations",
    x = "City",
    y = "Wind Speed (mph)",
    fill = "City"
  ) +
  theme_minimal()

## 3.4 Precipitation Analysis

ggplot(weather_data, aes(x = City, y = Precipitation_in, fill = City)) +
  geom_bar(stat = "identity") +
  labs(
    title = "Current Precipitation by City",
    subtitle = "Comparing rainfall across tourism locations",
    x = "City",
    y = "Precipitation (inches)",
    fill = "City"
  ) +
  theme_minimal()

4. Insights & Recommendations

4.1 Key Findings

• The city with the highest temperature is Orlando, making it ideal for warm-weather tourism.
• The lowest temperature was observed in Nashville, which may be better for cooler-weather activities.
• Precipitation is highest in Nashville, meaning tourists might need alternative indoor activities.
• Wind speeds are strongest in Birmingham, which could impact outdoor experiences such as beach visits and guided tours.

4.2 Recommendations for Tourism Activities

A. Best Locations for Outdoor Tourism

• Cities with mild temperatures and low precipitation (e.g., Orlando) are ideal for hiking, city tours, and open-air attractions.
• Cities with warm weather but moderate humidity (e.g., Nashville) are best for beach and water-based activities.

B. Indoor & Alternative Tourism Options

• Cities with high rainfall (e.g., Nashville) should promote museums, cultural centers, and indoor attractions.
• High humidity locations should focus on early morning or evening tours to avoid peak heat.

C. Marketing & Operational Strategies

• Dynamic pricing: Offer discounts during extreme weather conditions (high heat, heavy rain) to keep bookings steady.
• Real-time weather updates: Notify tourists about sudden weather changes via SMS or app updates.
• Seasonal planning: Recommend best months to visit each location based on weather data.

5. Conclusion

This analysis provides actionable insights for the tourism company to optimize customer experiences based on real-time weather data. By adjusting tour schedules, pricing, and marketing strategies based on weather conditions, the company can enhance visitor satisfaction and improve business operations.