FFX County Biodiversity Dashboard

// Example of D3.js temporal pattern visualization implementation
function createTemporalPatternChart(data, container) {
    // Configure visualization dimensions and scales
    const margin = {top: 40, right: 30, bottom: 50, left: 60};
    const width = 800 - margin.left - margin.right;
    const height = 400 - margin.top - margin.bottom;
    
    // Process temporal data for analysis visualization
    const processedData = processTemporalData(data);
    
    // Create SVG container with margins
    const svg = d3.select(container)
        .append("svg")
        .attr("width", width + margin.left + margin.right)
        .attr("height", height + margin.top + margin.bottom)
        .append("g")
        .attr("transform", `translate(${margin.left},${margin.top})`);
        
    // Define scales for data dimensions
    const xScale = d3.scaleTime()
        .domain(d3.extent(processedData, d => d.date))
        .range([0, width]);
        
    const yScale = d3.scaleLinear()
        .domain([0, d3.max(processedData, d => d.count) * 1.1])
        .range([height, 0]);
        
    // Create visualization components
    
    // 1. Add seasonal pattern areas
    const seasonColors = {
        "Winter": "#a8ddb5",
        "Spring": "#7bccc4",
        "Summer": "#43a2ca",
        "Fall": "#0868ac"
    };
    
    // Seasonal bands to highlight patterns
    Object.keys(seasonColors).forEach(season => {
        const seasonData = processedData.filter(d => d.season === season);
        
        svg.append("path")
            .datum(seasonData)
            .attr("fill", seasonColors[season])
            .attr("fill-opacity", 0.2)
            .attr("stroke", "none")
            .attr("d", d3.area()
                .x(d => xScale(d.date))
                .y0(height)
                .y1(d => yScale(d.count))
            );
    });
    
    // 2. Draw trend line
    const trendLine = svg.append("path")
        .datum(processedData)
        .attr("fill", "none")
        .attr("stroke", "#0868ac")
        .attr("stroke-width", 2)
        .attr("d", d3.line()
            .x(d => xScale(d.date))
            .y(d => yScale(d.count))
            .curve(d3.curveBasis) // Smoothed line for trend visualization
        );
        
    // 3. Add moving average for pattern detection
    const movingAvgData = calculateMovingAverage(processedData, 30); // 30-day moving average
    
    svg.append("path")
        .datum(movingAvgData)
        .attr("fill", "none")
        .attr("stroke", "#e31a1c")
        .attr("stroke-width", 2)
        .attr("stroke-dasharray", "5,5")
        .attr("d", d3.line()
            .x(d => xScale(d.date))
            .y(d => yScale(d.avg))
        );
        
    // Add statistical annotations for significant patterns
    const significantPatterns = detectSignificantPatterns(processedData);
    
    significantPatterns.forEach(pattern => {
        svg.append("circle")
            .attr("cx", xScale(pattern.date))
            .attr("cy", yScale(pattern.value))
            .attr("r", 5)
            .attr("fill", "#fd8d3c")
            .attr("stroke", "#fff");
            
        svg.append("text")
            .attr("x", xScale(pattern.date) + 10)
            .attr("y", yScale(pattern.value) - 10)
            .text(pattern.description)
            .attr("font-size", "12px");
    });
    
    // Add axes and labels
    // ... additional visualization components and interactivity
}