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Move the locations of Viz 4 and 5, modify the text for Viz 5 and 6.
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src/components/Viz4.tsx

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@@ -478,6 +478,18 @@ const Viz4: React.FC = () => {
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return (
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<div className="section" id="section4">
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<div className="viz-container">
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<div className="paragraph">
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<p className="section-badge">/ Visualization 04</p>
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<h1 className="viz-title">Crashed flight routes around the world</h1>
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<p>
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Each line on the globe is a flight that never made it to its destination. Lines connect the
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departure (white) and arrival (cyan) airports as great-circle arcs, and their colour scales
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with the number of fatalities, pale yellow for incidents with no deaths, deep red for the
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deadliest. Drag the globe to rotate it, or let it spin on its own. Brush the year range below
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to focus on a period. Hover any line to reveal a red X at the crash location (precise where
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it's known, dashed and labelled <em>approx.</em> when only city-level data is available).
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</p>
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</div>
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<div className="widget" ref={widgetRef}>
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<button className="fullscreen-btn" aria-label="Toggle Fullscreen" onClick={toggle}>
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<svg viewBox="0 0 24 24" width="18" height="18" fill="none" stroke="currentColor" strokeWidth="1.75" strokeLinecap="round" strokeLinejoin="round">
@@ -528,19 +540,6 @@ const Viz4: React.FC = () => {
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</div>
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</div>
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</div>
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<div className="paragraph">
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<p className="section-badge">/ Visualization 04</p>
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<h1 className="viz-title">Crashed flight routes around the world</h1>
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<p>
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Each line on the globe is a flight that never made it to its destination. Lines connect the
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departure (white) and arrival (cyan) airports as great-circle arcs, and their colour scales
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with the number of fatalities, pale yellow for incidents with no deaths, deep red for the
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deadliest. Drag the globe to rotate it, or let it spin on its own. Brush the year range below
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to focus on a period. Hover any line to reveal a red X at the crash location (precise where
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it's known, dashed and labelled <em>approx.</em> when only city-level data is available).
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</p>
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</div>
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</div>
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</div>
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)

src/components/Viz5.tsx

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@@ -342,18 +342,6 @@ const Viz5: React.FC = () => {
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return (
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<div className="section" id="section5">
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<div className="viz-container">
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<div className="paragraph">
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<p className="section-badge">/ Visualization 05</p>
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<h1 className="viz-title">Origin–destination flows: the most perilous routes</h1>
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<p>
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Each arc segment represents a city, country, or continent. The bright
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portion marks departures; the darker portion marks arrivals. Ribbon
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thickness encodes crash count. Arrowheads point toward the arrival end.
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Hover a node or ribbon for details.
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</p>
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</div>
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<div className="widget" ref={widgetRef}>
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<button className="fullscreen-btn" aria-label="Toggle Fullscreen" onClick={toggle}>
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<svg viewBox="0 0 24 24" width="18" height="18" fill="none" stroke="currentColor" strokeWidth="1.75" strokeLinecap="round" strokeLinejoin="round">
@@ -405,6 +393,19 @@ const Viz5: React.FC = () => {
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</div>
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</div>
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</div>
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<div className="paragraph">
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<p className="section-badge">/ Visualization 05</p>
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<h1 className="viz-title">Origin–destination flows: the most perilous routes</h1>
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<p>
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Deeping a little further into where the crashes happened, the connections between
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departure and arrival locations can be explored through this chord diagram. Ribbon
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thickness encodes crash count. Arrowheads point toward the arrival end. Hover a node or ribbon for details.
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By filtering between cities, countries or continents, explore the most dangerous routes between two locations.
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Connections from one location to the same location needed to be omitted for readability, as they
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represented the vast majority of crashes (e.g. domestic flights within the US).
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</p>
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</div>
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</div>
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</div>
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)

src/components/Viz6.tsx

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@@ -412,11 +412,11 @@ const viz6: React.FC = () => {
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<p className="section-badge">/ Visualization 06</p>
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<h1 className="viz-title">Modelling your flight's crash risk</h1>
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<p>
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Using a Poisson process, we model crashes as a rate λ — the mean
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number of incidents per year for a filtered subset of the data.
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The chart shows the full probability distribution P(X = k): the
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chance of exactly k crashes occurring in any given year. Filter by
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manufacturer, airline, departure city, arrival city, and decade range.
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Assuming crashes are independent events occurring at a constant average rate, we can model them as a Poisson distribution.
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This then gives the probability of experiencing k crashes in a year,
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based on the historical average λ for filtered subset of the data.
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By leveraging the various filters, you can model the probability of a custom
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flight of crashing over a given period of time.
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</p>
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</div>
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</div>

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