Introduction

This project introduces the January rainfall data of Ireland using an interactive map. The visualization showcases rainfall distribution across various weather stations, grouping them into distinct intervals for better comprehension.


Explanation of the Data

This analysis uses two datasets to create a map visualizing rainfall patterns in Ireland:

1. Rainfall Data (rain)

Description: This dataset contains monthly rainfall measurements for weather stations across Ireland.

Purpose: To calculate the median January rainfall for each weather station.

Key Columns:

  • Year: Year of the measurement.
  • Month: Month of the measurement.
  • Rainfall: Rainfall measurement (in millimeters).
  • Station: The station where the rainfall was recorded.
2. Station Metadata (stations)

Description: This dataset provides metadata about the weather stations.

Purpose: To provide geographical and location-specific information for mapping.

Key Columns:

  • Station: Name of the station.
  • Lat and Long: Latitude and longitude coordinates of the station.
  • County: The county in which the station is located.

Save the stations and rain data as CSV files for further processing:

# Load the .RData file
load("rainfall.RData")

# Save the 'stations' data to a CSV file
write.csv(stations, "stations.csv", row.names = FALSE)

# Save the 'rain' data to a CSV file
write.csv(rain, "rain.csv", row.names = FALSE)


Code to Create the Map

The following R code outlines the steps to process the data and generate the map:

# Load necessary libraries
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(leaflet)
## Warning: package 'leaflet' was built under R version 4.4.2
stations <- read.csv("stations.csv")
rain <- read.csv("rain.csv")

# Calculate median rainfall for January at each station
median_rainfall <- rain %>%
  filter(Month == "Jan") %>%
  group_by(Station) %>%
  summarize(MedianRainfall = median(Rainfall, na.rm = TRUE))

# Merge median rainfall with station data
stations_with_rainfall <- stations %>%
  inner_join(median_rainfall, by = "Station")

# Define rainfall intervals and assign bright colors
stations_with_rainfall <- stations_with_rainfall %>%
  mutate(
    RainfallInterval = cut(
      MedianRainfall,
      breaks = c(0, 80, 100, 120, 140, 160, 180),
      labels = c("0-80", "80-100", "100-120", "120-140", "140-160", "160-180"),
      right = FALSE
    )
  )

# Define a manual bright color palette
color_palette <- c(
  "0-80" = "#FF0000",    # Bright red
  "80-100" = "#FFFF00",  # Bright yellow
  "100-120" = "#00FF00", # Bright green
  "120-140" = "#0000FF", # Bright blue
  "140-160" = "#FF00FF", # Bright purple
  "160-180" = "#ff8000"  # Bright orange
)

# Add color column based on the rainfall interval
stations_with_rainfall$Color <- color_palette[as.character(stations_with_rainfall$RainfallInterval)]

# Create an interactive map using leaflet
leaflet(data = stations_with_rainfall) %>%
  addTiles() %>%  # Add base map tiles
  addCircleMarkers(
    lng = ~Long,
    lat = ~Lat,
    color = ~Color,
    radius = ~sqrt(MedianRainfall),  # Adjust radius based on rainfall level
    stroke = TRUE,
    fillOpacity = 0.9,
    popup = ~paste0(
      "<b>Station: </b>", Station, "<br>",
      "<b>County: </b>", County, "<br>",
      "<b>Median Rainfall (Jan): </b>", MedianRainfall, " mm"
    )
  ) %>%
  addLegend(
    colors = unname(color_palette),
    labels = names(color_palette),
    title = "Rainfall Intervals (mm)",
    position = "bottomright"
  )


Embedded Map

The map uses circle markers to represent each station. Each circle is color-coded based on the rainfall interval, with the color legend clearly showing the associated ranges.


Discussion of Patterns

Each station is represented by a colored circle, with the color corresponding to specific rainfall intervals (0-80, 80-100, 100-120, 120-140, 140-160, 160-180). Here’s a detailed discussion of the patterns visible in the map:

1. Higher Rainfall in Western Regions
  • Stations in western coastal regions, particularly near the Atlantic Ocean, exhibit higher rainfall levels, predominantly in the 160-180 mm range (orange circles).
  • These areas are directly influenced by Atlantic weather systems, which bring frequent and heavy rainfall.
2. Lower Rainfall in Eastern Regions
  • Stations in eastern and southeastern regions, particularly around counties such as Dublin, have much lower rainfall levels, falling in the 0-80 mm and 80-100 mm ranges (red and yellow circles).
  • These areas are relatively shielded from Atlantic weather systems, resulting in drier conditions.
3. Rainfall Distribution in the Midlands
  • The central region of Ireland (Midlands) shows intermediate rainfall levels, with stations typically in the 100-120 mm or 120-140 mm range (green and blue circles).
  • This suggests a transition zone between the wetter western regions and the drier eastern regions.
4. Influence of Geography
  • Proximity to the Atlantic Ocean: Western stations receive more rainfall due to their closeness to the ocean, which acts as a source of moisture.
  • Elevation: Stations located at higher elevations, particularly in the west, are more prone to orographic rainfall, contributing to their higher rainfall levels.


Conclusion

This project demonstrates the power of data visualization in uncovering climate trends. By leveraging R and leaflet, we created an interactive map that not only illustrates rainfall distribution but also highlights how geographical factors influence these patterns. Such insights are invaluable for policymakers, environmentalists, and researchers aiming to address climate-related challenges in Ireland.

The map highlights a clear gradient in rainfall distribution across Ireland:

This analysis provides valuable insights for weather-related planning, agriculture, and infrastructure development in Ireland.