Project Title:

Fire the Visualisation World: Australia Wildfire Distribution

Project Experience Summary: Data Visualisation of Australia Wildfire Distribution

Project Title: Fire the Visualisation World: Australia Wildfire Distribution

Role: Data Analyst and Visualisation Specialist

Project Description: This project aimed to create an informative and accessible visualisation of wildfire data in Australia, highlighting the distribution and intensity of wildfires using data from NASA's FIRMS interface. The visualisation was designed to be used as a material for news, providing clear and accurate information to the public.

Key Responsibilities:

  1. Data Collection and Preparation:

    • Collected wildfire data from NASA’s FIRMS interface, focusing on latitude, longitude, acquisition date, and brightness.

    • Cleaned and formatted the data to ensure accuracy, including correcting date formats and sampling to increase processing speed.

  2. Coding and Visualisation:

    • R Programming:

      • Utilised R for data preparation and visualisation, employing the viridis, maps, and mapdata packages.

      • Created a distribution map of wildfire locations in Australia, using colour to represent different levels of brightness and highlighting high-alert areas with brightness above 500.

      • Developed a scatter plot to show the distribution of brightness over time, illustrating trends and extreme values.

      • Example code snippet for creating the visualisation:

      • library(viridis)

        library(maps)

        library(mapdata)

        # Load and prepare the data

        fia <- read.csv("fire_nrt_M6_96062.csv", sep = ",")

        fia$acq_date <- as.Date(fia$acq_date, format = "%d/%m/%Y")

        set.seed(123)

        NumberOfSamples <- 1000

        fia_sample <- fia[sample(1:nrow(fia), NumberOfSamples), ]

        # Create new window and layout

        dev.new()

        layout(matrix(c(1, 2), nrow = 1), widths = c(2, 1))

        # Map with wildfire points

        color_map <- viridis(10)

        color_index <- cut(fia_sample$brightness, breaks = 10, labels = FALSE)

        point_colors <- color_map[color_index]

        par(mar = c(5, 4, 4, 2))

        map("world", regions = "Australia", col = "lightblue", fill = TRUE)

        points(fia_sample$longitude, fia_sample$latitude, col = point_colors, pch = 19, cex = 0.2)

        points(fia$longitude[fia$brightness > 500], fia$latitude[fia$brightness > 500], pch = 3, col = "red")

        title(main = "Australia Wild Fire Distribution", adj = 0.5, line = -20, cex.main = 1.5)

        legend("bottom", legend = "High Alert: brightness>500", col = "red", pch = 3, bty = "n", xjust = 0.5, yjust = 2)

        grid()

        axis(1, col.ticks="purple")

        axis(2, col.ticks="purple")

        axis(3, col.ticks="purple")

        axis(4, col.ticks="purple")

        box()

        # Scatter plot

        par(mar = c(5, 2, 4, 2))

        plot(fia_sample$acq_date, fia_sample$brightness, xlab = "Date", ylab = "Brightness", col = point_colors, pch = 19, cex = 0.5)

        title(main = "Brightness Distribution", cex.main = 1.5)

Visual Design Choices:

Selected a colour palette using the viridis package to ensure clear visual differentiation of brightness levels.

Highlighted areas with extreme brightness to indicate high-risk zones, facilitating quick identification of critical regions.

Used a combination of map and scatter plot to provide both spatial and temporal perspectives on wildfire data​​.

  • Validation and Testing:

    • Ensured the accuracy and effectiveness of visualisations by testing different layouts and colour schemes.

    • Focused on creating an intuitive and easily interpretable visualisation for non-expert audiences, particularly for use in news reporting.

  • Project Outcomes:

    • Created an accessible and informative visualisation of wildfire distribution in Australia, enhancing public understanding of wildfire risks and patterns.

    • Demonstrated the importance of using clear and accurate data visualisations in news reporting to avoid misinformation and enhance public awareness.

    • Contributed to the discourse on the boundaries between visualisation and reality, stressing the need for accurate and objective data representation in media.

  • Skills and Tools:

    • Data Collection and Analysis: R

    • Data Visualisation: ggplot2, viridis, maps, mapdata

    • Visual Design: Emphasis on accessibility and clarity