44:["$","title",null,{"children":"Climate Report (Final Submission) | FHOÖ Information Visualization | Observable"}] 4c:T437,In this report, we discuss and showcase the effects of climate change on the world. Thus, we use a multitude of different datasets, which combined, not only confirm the statistics and claims of scientists over the past years, but also try to clarify if and how much the countries can be held responsible for the natural disasters, that occurred on their land over the past 30 years. We provide three distinct and complex visualizations, which combine different datasets, to show these effects and gain awareness. These visualizations are also accompanied by smaller and simpler ones, which provide insight into some of the data, we used for the calculations. With this report, we set out to try and answer three distinct questions about climate change with the available data: 1. What natural disasters did occur and did the amount change over time? 2. How much CO2 did the countries emit, and how did that change over time? 3. Which countries produce the most green house gases and which countries suffer the most (based on the number of disasters)? Is there a correlation?4d:Td11,country_chart = { // Specify the chart’s dimensions. const width = 1200; const height = 400; const marginTop = 20; const marginRight = 20; const marginBottom = 30; const marginLeft = 30; // Create the positional scales. const x = d3.scaleUtc() .domain(d3.extent(countries_Temp , d => d.Year)) .range([marginLeft, width - marginRight]); const y = d3.scaleLinear() .domain([-10, 30]) .range([height - marginBottom, marginTop]); // Create the SVG container. const svg = d3.create("svg") .attr("width", width) .attr("height", height) .attr("viewBox", [0, 0, width, height]) .attr("style", "max-width: 100%; height: auto; overflow: visible; font: 10px sans-serif;"); // Add the horizontal axis. svg.append("g") .attr("transform", `translate(0,${height - marginBottom})`) .call(d3.axisBottom(x).tickFormat(d3.format("d")).ticks(width / 80).tickSizeOuter(0)); // Add the vertical axis. svg.append("g") .attr("transform", `translate(${marginLeft},0)`) .call(d3.axisLeft(y)) .call(g => g.select(".domain").remove()) .call(g => g.selectAll(".tick line").clone() .attr("x2", width - marginLeft - marginRight) .attr("stroke-opacity", 0.1)) .call(g => g.append("text") .attr("x", -marginLeft) .attr("y", 10) .attr("fill", "currentColor") .attr("text-anchor", "start") .text("Temperature")); // Compute the points in pixel space as [x, y, z], where z is the name of the series. const points = countries_Temp .map((d) => [x(d.Year), y(d.Value), d.Country]); // Group the points by series. const groups = d3.rollup(points, v => Object.assign(v, {z: v[0][2]}), d => d[2]); // Draw the lines. const line = d3.line(); const path = svg.append("g") .attr("fill", "none") .attr("stroke", "Teal") .attr("stroke-width", 1.5) .attr("stroke-linejoin", "round") .attr("stroke-linecap", "round") .selectAll("path") .data(groups.values()) .join("path") .style("mix-blend-mode", "multiply") .attr("d", line); // Add an invisible layer for the interactive tip. const dot = svg.append("g") .attr("display", "none"); dot.append("circle") .attr("r", 2.5); dot.append("text") .attr("text-anchor", "middle") .attr("y", -8); svg .on("pointerenter", pointerentered) .on("pointermove", pointermoved) .on("pointerleave", pointerleft) .on("touchstart", event => event.preventDefault()); return svg.node(); // When the pointer moves, find the closest point, update the interactive tip, and highlight // the corresponding line. function pointermoved(event) { const [xm, ym] = d3.pointer(event); const i = d3.leastIndex(points, ([x, y]) => Math.hypot(x - xm, y - ym)); const [x, y, k] = points[i]; path.style("stroke", ({z}) => z === k ? null : "#ddd").filter(({z}) => z === k).raise(); dot.attr("transform", `translate(${x},${y})`); dot.select("text").text(k); } function pointerentered() { path.style("mix-blend-mode", null).style("stroke", "#ddd"); dot.attr("display", null); } function pointerleft() { path.style("mix-blend-mode", "multiply").style("stroke", null); dot.attr("display", "none"); } } 4e:T768,avg_tmp_chart = { // Declare the chart dimensions and margins. const width = 1200; const height = 600; const marginTop = 20; const marginRight = 30; const marginBottom = 30; const marginLeft = 60; // Declare the x (horizontal position) scale. const x = d3.scaleLinear(d3.extent(avg_Temp, d => d.Year), [marginLeft, width - marginRight]); // Declare the y (vertical position) scale. const y = d3.scaleLinear([0, d3.max(avg_Temp, d => d.Global_Average)], [height - marginBottom, marginTop]).domain([18.2, 20]); // Declare the line generator. const line = d3.line() .x(d => x(d.Year)) .y(d => y(d.Global_Average)); // Create the SVG container. const svg = d3.create("svg") .attr("width", width) .attr("height", height) .attr("viewBox", [0, 0, width, height]) .attr("style", "max-width: 100%; height: auto; height: intrinsic;"); // Add the x-axis. svg.append("g") .attr("transform", `translate(0,${height - marginBottom})`) .call(d3.axisBottom(x).tickFormat(d3.format("d")).ticks(width / 80).tickSizeOuter(0)); // Add the y-axis, remove the domain line, add grid lines and a label. svg.append("g") .attr("transform", `translate(${marginLeft},0)`) .call(d3.axisLeft(y).ticks(height / 40)) .call(g => g.select(".domain").remove()) .call(g => g.selectAll(".tick line").clone() .attr("x2", width - marginLeft - marginRight) .attr("stroke-opacity", 0.1)) .call(g => g.append("text") .attr("x", -marginLeft) .attr("y", 10) .attr("fill", "currentColor") .attr("text-anchor", "start") .text("Temperature")); // Append a path for the line. svg.append("path") .attr("fill", "none") .attr("stroke", "steelblue") .attr("stroke-width", 1.5) .attr("d", line(avg_Temp)); return svg.node(); }4f:T793,disasters_filtered_chart = { // Declare the chart dimensions and margins. const width = 1200; const height = 400; const marginTop = 20; const marginRight = 30; const marginBottom = 30; const marginLeft = 60; // Declare the x (horizontal position) scale. const x = d3.scaleLinear(d3.extent(disasters_filtered_count, d => d.Year), [marginLeft, width - marginRight]); // Declare the y (vertical position) scale. const y = d3.scaleLinear([0, d3.max(disasters_filtered_count, d => d.Count)], [height - marginBottom, marginTop]).domain([0, 405]); // Declare the line generator. const line = d3.line() .x(d => x(d.Year)) .y(d => y(d.Count)); // Create the SVG container. const svg = d3.create("svg") .attr("width", width) .attr("height", height) .attr("viewBox", [0, 0, width, height]) .attr("style", "max-width: 100%; height: auto; height: intrinsic;"); // Add the x-axis. svg.append("g") .attr("transform", `translate(0,${height - marginBottom})`) .call(d3.axisBottom(x).tickFormat(d3.format("d")).ticks(width / 80).tickSizeOuter(0)); // Add the y-axis, remove the domain line, add grid lines and a label. svg.append("g") .attr("transform", `translate(${marginLeft},0)`) .call(d3.axisLeft(y).ticks(height / 40)) .call(g => g.select(".domain").remove()) .call(g => g.selectAll(".tick line").clone() .attr("x2", width - marginLeft - marginRight) .attr("stroke-opacity", 0.1)) .call(g => g.append("text") .attr("x", -marginLeft) .attr("y", 10) .attr("fill", "currentColor") .attr("text-anchor", "start") .text("Disasters per year")); // Append a path for the line. svg.append("path") .attr("fill", "none") .attr("stroke", "Tomato") .attr("stroke-width", 1.5) .attr("d", line(disasters_filtered_count)); return svg.node(); }50:T10e6,disaster_norm_chart = { // Specify the chart’s dimensions. const width = 1200; const height = 400; const marginTop = 20; const marginRight = 20; const marginBottom = 30; const marginLeft = 30; // Create the positional scales. const x = d3.scaleUtc() .domain(d3.extent(global_temp_disasters_normalized , d => d.Year)) .range([marginLeft, width - marginRight]); const y = d3.scaleLinear() .domain([0, 1]) .range([height - marginBottom, marginTop]); // Create the SVG container. const svg = d3.create("svg") .attr("width", width) .attr("height", height) .attr("viewBox", [0, 0, width, height]) .attr("style", "max-width: 100%; height: auto; overflow: visible; font: 10px sans-serif;"); // Add the horizontal axis. svg.append("g") .attr("transform", `translate(0,${height - marginBottom})`) .call(d3.axisBottom(x).tickFormat(d3.format("d")).ticks(width / 80).tickSizeOuter(0)); // Add the vertical axis. svg.append("g") .attr("transform", `translate(${marginLeft},0)`) .call(d3.axisLeft(y)) .call(g => g.select(".domain").remove()) .call(g => g.selectAll(".tick line").clone() .attr("x2", width - marginLeft - marginRight) .attr("stroke-opacity", 0.1)) .call(g => g.append("text") .attr("x", -marginLeft) .attr("y", 10) .attr("fill", "currentColor") .attr("text-anchor", "start") .text("Temperature")); // Compute the points in pixel space as [x, y, z], where z is the name of the series. const points = global_temp_disasters_normalized .map((d) => [x(d.Year), y(d.Value), d.Category]); // Group the points by series. const groups = d3.rollup(points, v => Object.assign(v, {z: v[0][2]}), d => d[2]); // Draw the lines. const line = d3.line(); // Define a color scale for the categories const colorScale = d3.scaleOrdinal() .domain(groups.keys()) // Assuming groups is a Map or similar containing category names .range(d3.schemeCategory10); // Using a categorical color scheme // Draw the lines with individual colors const path = svg.append("g") .attr("fill", "none") .attr("stroke-width", 1.5) .attr("stroke-linejoin", "round") .attr("stroke-linecap", "round") .selectAll("path") .data(groups.values()) .join("path") .attr("stroke", d => colorScale(d[0][2])) // Assign color based on category .attr("d", line); // Add a legend const legend = svg.append("g") .attr("class", "legend") .attr("transform", `translate(${width - 100}, ${height - 200})`); // Add rectangles and text for each category in the legend legend.selectAll("rect") .data(groups.keys()) .enter().append("rect") .attr("x", 0) .attr("y", (d, i) => i * 20) .attr("width", 10) .attr("height", 10) .attr("fill", d => colorScale(d)); legend.selectAll("text") .data(groups.keys()) .enter().append("text") .attr("x", 20) .attr("y", (d, i) => i * 20 + 9) .attr("dy", ".35em") .style("font-size", "12px") .text(d => d); // Add an invisible layer for the interactive tip. const dot = svg.append("g") .attr("display", "none"); dot.append("circle") .attr("r", 2.5); dot.append("text") .attr("text-anchor", "middle") .attr("y", -8); svg .on("pointerenter", pointerentered) .on("pointermove", pointermoved) .on("pointerleave", pointerleft) .on("touchstart", event => event.preventDefault()); return svg.node(); // When the pointer moves, find the closest point, update the interactive tip, and highlight // the corresponding line. function pointermoved(event) { const [xm, ym] = d3.pointer(event); const i = d3.leastIndex(points, ([x, y]) => Math.hypot(x - xm, y - ym)); const [x, y, k] = points[i]; path.style("stroke", ({z}) => z === k ? null : "#ddd").filter(({z}) => z === k).raise(); dot.attr("transform", `translate(${x},${y})`); dot.select("text").text(k); } function pointerentered() { path.style("mix-blend-mode", null).style("stroke", "#ddd"); dot.attr("display", null); } function pointerleft() { path.style("mix-blend-mode", "multiply").style("stroke", null); dot.attr("display", "none"); } } 51:Tae0,individPie = { const gasdata = [ {category: 'CH4', value: 1.62 , order: 3}, {category: 'CO2', value: 7.38 , order: 4}, {category: 'F-Gas', value: 0.1260, order: 2}, {category: 'N2O', value: 0.5362, order: 1} ]; const sumgasdata = [ {category: 'Sum Emissions', value: 9.674 , order: 1} ]; const gascolors = { domain: ['CO2', 'CH4', 'F-Gas', 'N2O', 'Sum Emissions'], range: [ '#8C0304', '#F04640', '#424172', '#83AFDB', '#220901'] }; const chart1 = vl.markArc({outerRadius: 150},{tooltip:true}) .data(sumgasdata) .encode( vl.theta().fieldQ('value').stack(true), vl.color().fieldN('category').title(null).scale(gascolors).legend({orient: 'right'}), vl.order().fieldO('order') ) .width(400) .height(300); const chart2 = vl.markArc({outerRadius: 150},{tooltip:true}) .data(gasdata) .encode( vl.theta().fieldQ('value').stack(true), vl.color().fieldN('category').title(null).scale(gascolors).legend({orient: 'right'}), vl.order().fieldO('order') ) .width(300) .height(300); const text = vl.markText({dy: -7, fontWeight: "bold", fontSize: 24 , color:"white"}) .data([{text: '9.674 Tera tonnes GHG'}]) // Text to display .encode( vl.text().fieldN('text'), vl.x().value(200), // X position at the center vl.y().value(150), // Y position at the center ); const text2 = vl.markText({dy: -7, fontWeight: "bold", fontSize: 22 , color:"white"}) .data([{text: '7.38 Tt'}]) // Text to display .encode( vl.text().fieldN('text'), vl.x().value(100), // X position at the center vl.y().value(220), // Y position at the center ); const text3 = vl.markText({dy: -7, fontWeight: "bold", fontSize: 22 , color:"white"}) .data([{text: '1.62 Tt'}]) // Text to display .encode( vl.text().fieldN('text'), vl.x().value(240), // X position at the center vl.y().value(100), // Y position at the center ); const text4 = vl.markText({dy: -7, fontWeight: "bold", fontSize: 12 , color:"black"}) .data([{text: '0.12 Tt'}]) // Text to display .encode( vl.text().fieldN('text'), vl.x().value(172), // X position at the center vl.y().value(30), // Y position at the center ); const text5 = vl.markText({dy: -7, fontWeight: "bold", fontSize: 11 , color:"black"}) .data([{text: '0.53 Tt'}]) // Text to display .encode( vl.text().fieldN('text'), vl.x().value(215), // X position at the center vl.y().value(10), // Y position at the center ); const layer1 = vl.layer(chart1, text) const layer2 = vl.layer(chart2, text2, text3, text4, text5) return vl.hconcat(layer1, layer2).config({ view: { stroke: null } }).render(); }52:T68c,In the below bubble chart the emissions in MtCO2 are shown for each country between 1990 and 2020. Each region is marked by a different color. By clicking on the region of a legend, one or more regions can be marked with their color, while the other regions are grayed out. As visible, most countries with higher emissions are from North America, Asia and Europe. Regions like South America (apart from Brazil), Africa and Oceania have lower emissions, but mostly also fewer inhabitants. The further right on the x axis and lower on the y axis, the more emissions a country has per inhabitants. The size of each country circle is relative to its contribution to the total amount of emitted MtCO2 of that year but also relative to the zoom factor. Unfortunately, even after six hours of trying, the zoom wasn't constrainable in this chart (no zooming out further than the inital position and no zooming beyond -1 on the x axis and 0 on the y axis). However, if you find yourself lost, just click on the 'Reset Zoom' button to zoom to the initial position again.
The year can be changed via the time slider. Additionally, in every year and every zoom factor, the emission type can be changed between excluding and including LUCF. In 2020, the countries with the highest emissions were China, USA, India and Russia. However, it is necessary to take into account that while e.g. India has higher emissions than Russia, India also has more than three times of Russia's population. Some small islands like Palau and Tuvalu have some of the lowset emissions, but are also very small in population. The Fiji islands even have negative emissions in 2020 when LUCF is included.53:T45c,With the help of multiple different datasets and a lot of researched formulas, we calculated the global carbon budget for each year in the time frame of 1990 to 2020 and for each individual country. For this, we used the officially calculated carbon budget and interpolated it with the help of the global emission of the last years. With this approach, we calculated a maximum emission value for the world, which would have been acceptable for each year. This value is an estimation. It is interpolated using a lot of different data calculations, as there are no official values prior to the year of 2013. We then also broke this value down even further and divided it by the population of the world to get a maximum emission value, which each individual on the globe would have been allowed to emit. This value was then again multiplied by the population of each individual country, to get a country wise budget. As a last step we then divided the actual emissions of each country, by what would have been acceptable to get an overshoot factor, which describes, how much a specific country was over their limits.54:T4d0,We now took these differences of each year and divided them by the population. This leaves us with an amount of CO2, an average individual would have emitted. Because of the differences in the regions and countries of the world, some richer and especially the western countries take up way more than the average human would. We now multiplied this average value with the population of each country and divided this by their actual emissions, to get a factor. This factor now tells us how much more a certain country has emitted than it should have in comparison to all the other states of the world. A factor of 1 tells us, that a country actually emits as much as they were eligible to. This factor gives a good overview of how unfair the situation around the world is and which countries use the most or might be to blame the most. However, even a factor of 1 is too much for the climate crisis. The best values should be somewhere in the low zeros, as the globe is approaching the limits of the carbon budget with big steps. Calculation: * Average Eligible Human Emission = Carbon Budget / World Population * Eligible Country Emissions = Average Emission x Country Population * Factor = Country Emissions / Eligible Emissions55:T6ff,In this visualization, we interpret the amount of natural disasters with the factor of how much different countries emit over their base eligible emissions. In this graphic, you can see multiple rings. The rings are in pairs of two and structured in an inner and outer ring and appear 3 times in total. The most inner rings represent the continents of the world. The middle rings are the regions of the world, and the most outer rings are representing the individual countries. The size of the individual beans is linked to the amount of disasters to a specific year. The number in the middle also displays this number, for easier visibility. The inner beans are color indicators of how much over the budget these continents / regions / countries were individually. The buttons at the bottom allow to toggle between two color codes. The first one visualized the continents in a distinct color and the second one also gives a color to the beans based on their severity, the amount of disasters in that area. The slider at the bottom allows the users to scroll through the time of 1990 and 2020 to see how the disasters and the emissions factor has changed over time. By clicking on the outer beans, the view will be zoomed in to the corresponding region, which enables the viewer to have a better view of the region and the countries in there. With a click in the middle of the rings, you get one step back again. With this visualization, it is actually possible to see which countries have the biggest problems with natural disasters, while also looking at their emissions and decide if the harm is to some extent self-inflicted. Because of the timeline feature, we can actually see if the amount of disasters increases in some areas and if the countries try to do something about it. 56:T7e4,In this report, we set out to answer three distinct climate related questions based on the data we had to begin with. ### What natural disasters did occur and did the amount change over time? With the interactive world map, we can very clearly describe where and what types of natural disasters occurred in the past 30 years. This gives the user a good and playful feedback to explore the world and the data in an easy and enjoyable way. ### How much CO2 did the countries emit, and how did that change over time? With the bubble chart, we were able to visually represent the timeline of how the emissions of the individual countries in correlation with the population increased over the past years. Through the interaction with tooltips, the users have easy access to the data and can get a good understanding of how the data changes over time. ### Which countries produce the most green house gases and which countries suffer the most (based on the number of disasters)? Is there a correlation? The zoomable sunburst visualization is not only fascinating looking and visually appealing, but also gives a lot of information in just a glimpse as soon as the user understands the different parts. Even though it gives a good overview of how the emission factor of each country and the amount of disasters is correlated, it still lacks a bit in terms of showing everything as all information would overflow the graph and make it harder to read. ## Conclusion We are really pleased with the outcomes of the visualizations. It also was a fun experience to see how the data would behave in different scenarios and how we would have to prepare and showcase the data so it makes the most sense. The implementation was sometimes a bit tricky and frustrating due to the behavior and performance of observable and the sometimes lacking information sources online. We hope this graphics and this notebook give you a good overview of the current climate crisis situation and maybe even made some thins more understandable. 57:T70a,function Legend(color, { title, tickSize = 12, width = 900, height = 44 + tickSize, marginTop = 18, marginRight = 0, marginBottom = 16 + tickSize, marginLeft = 0, ticks = width / 64, tickFormat, tickValues } = {}) { const svg = d3.create("svg") .attr("width", width) .attr("height", height) .attr("viewBox", [0, 0, width, height]) .style("overflow", "visible") .style("display", "block"); let tickAdjust = g => g.selectAll(".tick line").attr("y1", marginTop + marginBottom - height); let x; x = d3.scaleBand() .domain(domainValues) .rangeRound([marginLeft, width - marginRight]); svg.append("g") .selectAll("rect") .data(domainValues) .join("rect") .attr("x", (d, i) => x(d)) .attr("y", marginTop) .attr("width", (d, i) => Math.max(0, x.bandwidth() - 1)) .attr("height", height - marginTop - marginBottom) .attr("fill", d => color(d)); tickAdjust = () => {}; svg.append("g") .attr("transform", `translate(0,${height - marginBottom})`) .call(d3.axisBottom(x) .ticks(ticks, typeof tickFormat === "string" ? tickFormat : undefined) .tickSize(tickSize) .tickValues(tickValues)) .call(tickAdjust) .call(g => g.select(".domain").remove()) .call(g => g.selectAll(".tick line") .attr("x1", +8) .attr("x2", +8)) .call(g => g.selectAll(".tick text") .attr("x", +12)) .call(g => g.append("text") .attr("x", marginLeft) .attr("y", marginTop + marginBottom - height - 6) .attr("fill", "currentColor") .attr("text-anchor", "start") .attr("font-weight", "bold") .attr("class", "title") .text(title)); return svg.node(); 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48:{"id":"3906820406097d46","slug":"climate-report","title":"Climate Report (Final Submission)","thumbnail":"f8ba720cca9b0404ee1aa6349e28b6f5492c20a32cd03fe0d0c46b5fd0abc281","access_level":"public","create_time":"$D2024-05-03T20:02:53.530Z","update_time":"$D2024-05-20T12:38:36.207Z","view_time":null,"first_public_time":"$D2024-05-07T11:53:05.022Z","has_unpublished_edits":null,"fork":false,"stars":1,"starred":false,"owner":{"type":"team","id":"4fbb25fe821f18db","login":"fhooe-infovis","name":"FHOÖ Information Visualization","avatar_url":"https://avatars-next.observableusercontent.com/ed8c4512a4de73fcd51f2a548db547555258287f869eea03112d4859ccbac5c3","tier":"education_2026","bio":"","followed_by_you":false,"follows_you":false},"version":1301,"publish_version":null,"role":null,"authored":false,"authors":[{"type":"individual","id":"3892395cb498efee","login":"lukas-schmalzer","name":"Lukas 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Visualization"}},"type":"notebook","body":{"titling":"auto","title":"Climate Report (Final Submission)","cells":[{"id":0,"value":"# Climate Report (Final Submission)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":197,"value":"##### by Hanna Rodler, Luca Geiger, Lukas Schmalzer","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":187,"value":"pexelsPixabay51951 = FileAttachment(\"HeadBanner@1.jpg\").image()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1141,"value":"*** For the best experience we recommend using the fullscreen mode! ***","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":825,"value":"fullscreen({\n //breakLayoutAtCell: 12,\n hideAfter: 57,\n hideBefore: 5,\n left: \"10%\",\n right: \"90%\",\n button: html``\n});","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1268,"value":"toc(\"h2, h3, h4\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":173,"value":"$4c","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":170,"value":"## A warming world","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":181,"value":"We always hear about the world getting warmer. This is of course not a local effect, but can be witnessed on the whole world. Especially countries with a low average temperature are affected by this rise, which can be seen in the following graph. \n\nThis line chart, showcases the average temperature of all countries over the last 120 years from 1901 to 2022.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":210,"value":"#### Average temperature per country (1901 - 2022)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":126,"value":"$4d","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":183,"value":"As impressive as it may look, the data of the whole world is quite overwhelming and does not really convey the warming process like it should. If we now calculate an average for each year over all countries, we can observe this effect way better.\n\nIn this graph, we can see that the average temperature of the world has been increasing since the first measurement of this dataset in 1901. The biggest change in temperature can be seen in the last 30–40 years, where most of our data for the analysis will be located. ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":213,"value":"#### Average global temperature (1901 - 2022)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":114,"value":"$4e","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":204,"value":"## Reoccuring disasters","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":258,"value":"Not only the temperature around the world has been rising over the last decade. We can also observe a rise in natural disasters like droughts, floods, storms and wildfires. These natural disasters are directly influenced by the weather, which itself is heavily influenced by the climate. Not all the disasters are deadly for humans, but in many cases huge landmasses become uninhabitable, infrastructure is destroyed or even human lives are taken by the catastrophes.\n\nHere is a look at the last 50 years of data, where we can see a steady increase.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":265,"value":"#### Global Natural Disasters (1970 - 2021)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":216,"value":"$4f","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":262,"value":"We now took these values and normalized them (in a range between 0 and 1). We did the same for the temperature rise from 1970 to 2021, to showcase how closely these two values are related to each other.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":269,"value":"#### Natural Disasters vs Temperature increase (1970 - 2021)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":237,"value":"$50","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":310,"value":"## Natural Disaster World Map\n\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":640,"value":"In order to showcase the severity of natural catastrophes over the last 30 years, we created an interactive world map. This map can be navigated by simply scrolling around or selecting Continents/Regions/Countries to single the selection out. Each individual dot represents a disaster in the world. The color refers to the category or type of the incident, which can be found in the legend. The user also has the ability to change the view from showcasing all the disasters at once, to show each individual year from 1990 to 2020. This setting has to be enabled with the toggle and the slider at the bottom of the visualization.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":743,"value":"","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":325,"value":"## Not all emissions are CO2","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":533,"value":"In the news, we mostly hear about the produced CO2-emissions, which keep increasing the green house effect, but emissions are not only CO2! There are a lot of other green house gases (GHG) which also contribute to this behavior. In our dataset, we showcase the recorded emission from 1990 until 2020. \n\nIn sum over the past 30 years, the world emitted over 9.6 Tera tonnes of green house gases. The biggest part of which is CO2. \nYou can find the exact values and splits below.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":558,"value":"#### Sum of Green House Gases vs individual GHG Split (1990 - 2020)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":359,"value":"$51","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":644,"value":"In the obtained dataset about the emissions for each country, we not only got a list of the different green house gases. We were also given all the values with a so called LUCF (Land Use Change Factor) value. This factor is an indicator of how GHG were affected by the effective change of land use. This effect can be positive for the environment in cases, like afforestation and reforestation but can also have several negative influences on the numbers, when forests are cut down. \n\nAs we can see in the chart below, if we include the LUCF in the analysis, the emissions are higher, but the difference committed by Land change is narrowing in recent years. ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":654,"value":"#### Green House Gases including vs excluding LUCF (1990 - 2020)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":566,"value":"Plot.plot({\n width: 1250,\n x: {axis: null},\n y: {tickFormat: \"s\", grid: true},\n color: {scheme: \"Spectral\", legend: true},\n marks: [\n Plot.barY(gas_calc, {\n x: \"Sector\",\n y: \"Value\",\n fill: \"Sector\",\n fx: \"Year\",\n sort: {x: null, color: null, fx: {value: \"y\", reduce: \"sum\"}},\n }),\n Plot.ruleY([0])\n ]\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":635,"value":"## Emission History Bubble Chart","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":646,"value":"$52","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":749,"value":"","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":838,"value":"## Running Out Of Time","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1017,"value":"","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":847,"value":"https://www.mcc-berlin.net/en/research/co2-budget.html","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":849,"value":"Many researchers have been calculating how much more CO2, we as humans, can still release into the atmosphere until it is too late, to change the outcome of a 1.5°C scenario. This scenario describes a situation, where the world would be in a state of constant warming, without being able to revert the damage (melted poles) that has been done and also no current way of sinking the temperature again.\n\nThis amount of CO2 is in many papers and articles referred to as the Carbon Budget we have left. The first official numbers of this budget were released by the IPCC in 2013.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":851,"value":"### Calculating the emission overshoot","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":858,"value":"$53","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":981,"value":"In the next chart you can see how the CO2 budget of the whole world, for the 1.5°C goal, is steadily decreasing over time while the yearly emissions have been increasing the last couple of years.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":976,"value":"#### CO2 Budget vs Emissions (1990 - 2020)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":864,"value":"viewof line = vl.markLine()\n .data(budget)\n .encode(\n vl.x().fieldT(\"Year\").timeUnit(\"year\"), // date parsing!\n vl.y().fieldQ(\"Value\").scale({domain: [0, 1333650293773]}).title(\"CO2 in Gt\"),\n vl.color().fieldN(\"Condition\") // .sort(vl.max(\"market cap\").order(\"descending\"))\n )\n.width(\"750\")\n.height(450)\n .render()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1068,"value":"If we now take a look at how the Carbon Budget changed overtime, we can subtract each year, with the previous one. This leaves us with the difference, which indicates if and how the global emissions have increased in context to the budget. \n\nIn this chart, we can really see, that there was a big increase over the last 30 years.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1066,"value":"#### Increase in emissions from year to year (1990 - 2020)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":992,"value":"// about viewof https://observablehq.com/@observablehq/a-brief-introduction-to-viewof\nviewof simpleBar = vl.markBar({clip:true}) // Make a bar chart\n .data(factor_global) // Using the alphabet data\n .encode(\n vl.x().fieldO(\"Year\"), // .sort(vl.fieldQ(\"frequency\").order(\"descending\")), // Letters are ordinal on the x-axis\n vl.y().fieldQ(\"Value\").scale({domain: [0, 40000000000]}) // Frequency on the y-axis, formatted as percent\n )\n .width(800)\n .height(400)\n .render()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1076,"value":"#### Emissions per individual","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1078,"value":"$54","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1072,"value":"## Zoomable Sunburst","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1084,"value":"$55","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":692,"value":"","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1086,"value":"### Outliers and Problems","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1089,"value":"This visualization simply aims to show the factor based on the amount of the disasters of each year. We have observed, that there are some countries often time completely missing of this list, as they did not have any natural disasters in a specific year. This is something we know and decided on. We also would have had the option to show all countries, even with 0 disasters, but this approach is not visually appealing and essentially harder to read than the current implementation.\n\nBut we are aware that this puts the negative focus on a lot of countries which makes them seem to be the worst ones, even though there are statistically even worse countries.\n\nFor this purpose we also want to provide this visualization, which like the very first visualization shows all countries and how their emissions changed over time.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1166,"value":"Legend(emissionsFactorScaleObject, {\n title: \"Emission Factor\",\n tickSize: 0\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1150,"value":"viewof heatmap = vl.markRect({tooltip: {\"content\": \"data\"}, clip: true})\n .data(factor_country)\n .encode(\n vl.x().fieldO(\"Year\").timeUnit(\"year\"),\n vl.y().fieldO(\"Country\"),\n vl.color().average(\"Value\").scale({ scheme: \"redyellowblue\", reverse: true }).legend(null),\n )\n .width(890)\n .height(2000)\n .render()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1108,"value":"# Summary","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1111,"value":"$56","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1122,"value":"

","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":162,"value":"## Code","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1271,"value":"import { toc } from \"@nebrius/indented-toc\" // This component generates the Table of Contents","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":763,"value":"import { fullscreen } from \"@pierreleripoll/vegasync\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1223,"value":"domainValues = Array.from({ length: 34 }, (_, i) => (i - 9).toString());","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1216,"value":"reverseColorScale = d3.quantize(d3.interpolateRdYlBu, 33).reverse()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1227,"value":"emissionsFactorScaleObject = d3.scaleQuantile()\n .domain(domainValues)\n .range(reverseColorScale)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1232,"value":"$57","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":321,"value":"#### Pie Chart","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":316,"value":"d3Legend = require('d3-svg-legend')","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":318,"value":"d3 = require(\"d3@7\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":331,"value":"#### Multi Line diagram","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":137,"value":"viewof voronoi = Inputs.toggle({label: \"Show voronoi\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":220,"value":"### Data import","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":80,"value":"avg_Temp = FileAttachment(\"average_1901-2020.csv\").csv()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":59,"value":"countries_Temp = FileAttachment(\"melted_countries_1901-2022@1.csv\").csv()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":218,"value":"disasters_filtered_count = FileAttachment(\"disasters_filtered_count.csv\").csv()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":234,"value":"disasters_filtered_normalized = FileAttachment(\"disasters_filtered_normalized@1.csv\").csv()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":229,"value":"global_temp_disasters_normalized = FileAttachment(\"global-temp_disasters_normalized@1.csv\").csv()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":568,"value":"gas_calc = FileAttachment(\"summed_gases_in_ex.csv\").csv()\n .then(data => {\n // Reverse the order of the data\n data.reverse();\n return data;\n });","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":862,"value":"budget = FileAttachment(\"max_CO2_budget@1.csv\").csv()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":990,"value":"factor_global = FileAttachment(\"Emission_Factor_global_decrease.csv\").csv()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1093,"value":"factor_country = FileAttachment(\"Factor_Country.csv\").csv()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W58","stdlib":"1"}} 24:null