31:T647,md`A wildfire is an uncontrolled fire that burns on wild vegetation. Wildfires can burn in forests, grasslands, savannas, and other ecosystems and have been doing so for millions of years. Wildfires are not unique to a particular environment and they are truly a universal phenomenon. Around 60,000 wildfires occur in the US alone every year and a staggering 3 million acres of land have been burning each year since 1999. **This is a case study of wildfires in California** as the world is increasingly affected by climate change. California is part of an extremely fire prone Western United States and Canada. The state of California has consistently had the most wildfires in the US -- there were 8,112 fires in 2020 and this is a huge increase from 5,687 fires recorded in 2019 [2]. Similar trends are occurring in Russia, Indonesia, Brazil and several other countries. Most recently in July 2021, the Jakarta province in Indonesia was declared a state of emergency after identifying more than 700 fires damaging 1.6 million hectares of forest land [3]. This problem is not going away anytime soon and the purpose of our project is to analyze the interdependence of following focal factors of fires in California: **Location, Time, Severity and Cause**. We are curious to discover if understanding two or more core factors can help predict the fourth. For instance, if location, time and severity of fires can be used to speculate the cause. The takeaway is to examine these factors and provide detailed graphs and trends that convey the cyclical relationship of the aforementioned four entities. `32:T53f,viewof selectYear = { const rowWidth = 100; const rowHeight = 15; const fontSize = 11; const svg = d3.create('svg') .attr('width', rowWidth * years.length) .attr('height', rowHeight); const main = svg.append('g') .attr('transform', `translate(2)`); const cols = main.selectAll('g') .data(years) .join('g') .attr('transform', (d, i) => `translate(${i * 52}, 1)`); cols.append('rect') .attr('width', fontSize+1) .attr('height', fontSize+1) .attr('stroke-width', 2) .attr('stroke', d => colorYears(d)) .attr('fill', d => colorYears(d)) .on('click', onclick); cols.append('text') .attr('font-size', fontSize) .attr('font-family', 'sans-serif') .attr('x', rowHeight + 2) .attr('y', (fontSize + 2) / 2) .attr('dominant-baseline', 'middle') .text(d => d); // Track which Years are selected const selected = new Map(years.map(d => [d, true])); function onclick(event, d) { const isSelected = selected.get(d); // select square and toggle it const square = d3.select(this); square.attr('fill', d => isSelected ? 'white' : colorYears(d)); selected.set(d, !isSelected); svg.property('value', selected).dispatch('input'); } svg.property('value', selected).dispatch('input'); return svg.node(); }33:T6b8,{ // Setup const visHeight = 400; const visWidth = 800; const svg = d3.create('svg') .attr('width', visWidth + margin.left + margin.right) .attr('height', visHeight + margin.top + margin.bottom); const g = svg.append('g') .attr('transform', `translate(${margin.left}, ${margin.top})`); // Axes const x = d3.scaleLinear() .domain([1, 12]) .range([0, visWidth]); const y = d3.scaleLinear() .domain([0, maxSingleYearWildfires]).nice() .range([visHeight, 0]); const xAxis = d3.axisBottom(xAxisAll.range([0, visWidth])); const yAxis = d3.axisLeft(y).tickFormat(d3.format('~s')); g.append('g') .attr('transform', `translate(0,${visHeight})`) .call(xAxis) .call(g => g.select('.domain').remove()); g.append('g') .call(yAxis) .call(g => g.select('.domain').remove()) .append('text') .attr('font-family', 'sans-serif') .attr('font-size', 12) .attr('fill', 'black') .attr('text-anchor', 'start') .attr('font-family', 'sans-serif') .attr('font-size', 12) .attr('dominant-baseline', 'hanging') .attr('font-weight', 'bold') .attr('y', -margin.top + 5) .attr('x', -margin.left) .text('Wildfires by Month, 2014-2018') // Line selectYear.get(d.origin) const line = d3.line() .x(d => x(d.date)) .y(d => y(d.wildfires)); const linesGroup = g.append('g'); linesGroup .selectAll('path') .data(wildfiresByMonth.filter(d => selectYear.get(d.year))) .join('path') .attr('stroke', d => colorYears(d.year)) .attr('fill', 'none') .attr('stroke-width', 2) .attr('d', d => line(d.counts)); return svg.node(); }34:T540,viewof selectYear2 = { const rowWidth = 100; const rowHeight = 15; const fontSize = 11; const svg = d3.create('svg') .attr('width', rowWidth * years.length) .attr('height', rowHeight); const main = svg.append('g') .attr('transform', `translate(2)`); const cols = main.selectAll('g') .data(years) .join('g') .attr('transform', (d, i) => `translate(${i * 52}, 1)`); cols.append('rect') .attr('width', fontSize+1) .attr('height', fontSize+1) .attr('stroke-width', 2) .attr('stroke', d => colorYears(d)) .attr('fill', d => colorYears(d)) .on('click', onclick); cols.append('text') .attr('font-size', fontSize) .attr('font-family', 'sans-serif') .attr('x', rowHeight + 2) .attr('y', (fontSize + 2) / 2) .attr('dominant-baseline', 'middle') .text(d => d); // Track which Years are selected const selected = new Map(years.map(d => [d, true])); function onclick(event, d) { const isSelected = selected.get(d); // select square and toggle it const square = d3.select(this); square.attr('fill', d => isSelected ? 'white' : colorYears(d)); selected.set(d, !isSelected); svg.property('value', selected).dispatch('input'); } svg.property('value', selected).dispatch('input'); return svg.node(); }35:T6c0,{ // Setup const visHeight = 400; const visWidth = 800; const svg = d3.create('svg') .attr('width', visWidth + margin.left + margin.right) .attr('height', visHeight + margin.top + margin.bottom); const g = svg.append('g') .attr('transform', `translate(${margin.left}, ${margin.top})`); // Axes const x = d3.scaleLinear() .domain([1, 12]) .range([0, visWidth]); const y = d3.scaleLinear() .domain([0, maxSingleYearAcresBurned]).nice() .range([visHeight, 0]); const xAxis = d3.axisBottom(xAxisAll.range([0, visWidth])); const yAxis = d3.axisLeft(y).tickFormat(d3.format('~s')); g.append('g') .attr('transform', `translate(0,${visHeight})`) .call(xAxis) .call(g => g.select('.domain').remove()); g.append('g') .call(yAxis) .call(g => g.select('.domain').remove()) .append('text') .attr('font-family', 'sans-serif') .attr('font-size', 12) .attr('fill', 'black') .attr('text-anchor', 'start') .attr('font-family', 'sans-serif') .attr('font-size', 12) .attr('dominant-baseline', 'hanging') .attr('font-weight', 'bold') .attr('y', -margin.top + 5) .attr('x', -margin.left) .text('Acres burned by Month, 2014-2018') // Line selectYear.get(d.origin) const line = d3.line() .x(d => x(d.date)) .y(d => y(d.wildfires)); const linesGroup = g.append('g'); linesGroup .selectAll('path') .data(acresBurnedByMonth.filter(d => selectYear2.get(d.year))) .join('path') .attr('stroke', d => colorYears(d.year)) .attr('fill', 'none') .attr('stroke-width', 2) .attr('d', d => line(d.counts)); return svg.node(); }36:T427,md`The number of fires along with acres burned by fires are factors that have been correlated with the above charts. The third factor we wanted to analyze was the Severity of these uncontrolled fires. **Do the number of hours taken to contain fires vary across the months? How do these trends compare across the years? ** From the matrix chart, we can infer that the relationship between the number of fires, acres burned by fires, and the number of hours taken to contain them is directly proportional. As expected, the hours taken to control fires is distinctly the most in July across all years. 2016 seems to be the exception year having the least hours taken to control fires overall. Additionally, we noticed anomalies in 2015. First, the fires in July 2015 took more hours to control than July 2017 or July 2018. This is despite July 2017 and July 2018 experiencing more fires than July 2015. Second, the March 2015 bubble is unexpectedly large as the number of fires was only around 200 and it was April 2015 actually that had a larger number of fires.`37:T846,{ // Setup const refScaleHeight = 35; const refScaleWidth = 300; const list = [TTCmax / 4, TTCmax/2, TTCmax*3/4, TTCmax]; // Axes const x = d3.scalePoint() .domain(list) .range([0, refScaleWidth]); const r = d3.scaleSqrt() .domain([0, TTCmax]) .range([0, 30]); // SVG const svg = d3.create('svg') .attr('width', refScaleWidth * 2 + margin.left + margin.right) .attr('height', refScaleHeight + margin.top + margin.bottom); const swatch = svg.append('g') .attr('transform', `translate(2)`); const cols = swatch.selectAll('g') .data(years) .join('g') .attr('transform', (d, i) => `translate(${i * 52}, ${margin.top + 6})`); cols.append('rect') .attr('width', 12) .attr('height', 12) .attr('stroke-width', 2) .attr('stroke', d => colorYears(d)) .attr('fill', d => colorYears(d)); cols.append('text') .attr('font-size', 11) .attr('font-family', 'sans-serif') .attr('x', 15 + 2) .attr('y', (11 + 2) / 2) .attr('dominant-baseline', 'middle') .text(d => d); const main = svg.append('g') .attr('transform', `translate(${refScaleWidth})`); main.selectAll('circle') .data(list) .join('circle') .attr('transform', `translate(${margin.left})`) .attr('cy', (margin.top + margin.bottom + refScaleHeight) / 2) .attr('cx', d => x(d)) .attr('r', d => r(d)) .attr('fill', lightgray); main.selectAll('text') .data(list) .join('text') .text(d => Math.round(d)) .attr('transform', `translate(${margin.left})`) .attr('x', d => x(d)) .attr('y', (margin.top + margin.bottom + refScaleHeight) / 2) .attr('dominant-baseline', 'middle') .attr('text-anchor', 'middle'); main.append('text') .attr('fill', 'black') .attr('text-anchor', 'start') .attr('dominant-baseline', 'hanging') .attr('text-anchor', 'start') .attr('font-weight', 'bold') .attr('font-family', 'sans-serif') .attr('font-size', 12) .text('Average number of hours to contain fires'); return svg.node(); }38:T6b8,{ const visHeight = 322; const visWidth = 800; // Axes const x = d3.scalePoint() .domain(months) .range([0, visWidth]) .padding(0.4); const xAxis = d3.axisBottom(x); const y = d3.scalePoint() .domain(years.reverse()) .range([visHeight, 0]) .padding(0.4); const yAxis = d3.axisLeft(y); const r = d3.scaleSqrt() .domain([0, TTCmax]) .range([0, 30]); // SVG const svg = d3.create('svg') .attr('width', visWidth + margin.left + margin.right) .attr('height', visHeight + margin.top + margin.bottom); const g = svg.append('g') .attr('transform', `translate(${margin.left}, ${margin.top})`); const circles = g.selectAll('g') .data(timeToContain) .join('g') .attr('transform', ([yr, nest]) => `translate(0, ${y(yr)})`) .attr('fill', ([yr, nest]) => colorYears(yr)); circles.selectAll('circle') .data(([cause, nest]) => nest) .join('circle') .attr('cx', ([mon, count]) => x(mon)) .attr('r', ([mon, count]) => r(count)); g.append('g') .attr('class', 'x-axis') .call(xAxis) .attr('transform', `translate(0, ${visHeight})`) .call(g => g.select('.domain').remove()); g.append('g') .attr('class', 'y-axis') .call(yAxis) .call(g => g.select('.domain').remove()) .append('text') .attr('fill', 'black') .attr('text-anchor', 'start') .attr('dominant-baseline', 'hanging') .attr('font-weight', 'bold') .attr('font-family', 'sans-serif') .attr('font-size', 12) .attr('y', -margin.top + 5) .attr('x', -margin.left) .text('Average number of hours to contain fires by Month and Year, 2014-2018'); return svg.node(); }39:T4c8,md`After finding the aforementioned exception and anomaly, we had to understand if there were several such exceptions to the directly proportional relationship of Severity with other factors. So we had to analyze the **association between Average number of hours to contain fires and Total acres burned (per month) across all years**. From the scatter plot, we can read that there is predominantly a linear correlation between the average hours to contain fires and total acres burned for each month. There are also quite a few outliers and they occur in 2015 and 2018. **Facts!** On researching, we found that the Rough fire in July 2015 destroyed around 150k acres, 4 buildings -- making it the largest fire of that year. This explains the 2015 anomaly in the Matrix chart above and the outlier in the Scatter plot (orange dot) below [5]. We also found that the Camp fire in November 2018 killed 85 people and destroyed around 150k acres, 18k buildings -- making it the deadliest fire in California's history [6]. There were also the Mendocino Complex and Carr fires in July 2018 that destroyed around 600k acres, 2884 buildings together [7][8]. This explains the 2018 outliers in the Scatter plot (purple dots) below.`3a:T8f2,{ const visHeight = 420; const visWidth = 420; const x = d3.scaleSqrt() .domain([0, d3.max(ttcAb, d => d.acresBurned)]) .range([0, visWidth]); const y = d3.scaleSqrt() .domain([0, d3.max(ttcAb, d => d.hrCont)]) .range([visHeight, 0]); const svg = d3.create('svg') .attr('width', visWidth + margin.left + margin.right) .attr('height', visHeight + margin.left + margin.right); const main = svg.append('g') .attr('transform', `translate(${margin.left}, ${margin.top})`); const gs = main.selectAll('circle') .data(ttcAb) .join('circle') .attr('cx', d => x(d.acresBurned)) .attr('cy', d => y(d.hrCont)) .attr('fill', d => colorYears(d.year)) .attr('r', 6); main.append('g') .call(d3.axisLeft(y)) //.call(g => g.select('.domain').remove()) .append('text') .attr('fill', 'black') .attr('font-family', 'sans-serif') .attr('x', visWidth / 2) .attr('y', visHeight + 40) .text("Total acres burned") main.append('g') .call(d3.axisBottom(x).tickFormat(d3.format('~s'))) .attr('transform', `translate(0, ${visHeight})`) //.call(g => g.select('.domain').remove()) .append('text') .attr('fill', 'black') .attr('font-family', 'sans-serif') .attr("transform", `translate(${-40}, ${-200}) rotate(-90)`) .text("Average number of hours to contain fires") main.append('text') .attr('fill', 'black') .attr('text-anchor', 'start') .attr('dominant-baseline', 'hanging') .attr('font-weight', 'bold') .attr('font-family', 'sans-serif') .attr('font-size', 12) .attr('y', -margin.top) .attr('x', -margin.left) .text('Association Analysis (dot = month):') main.append('text') .attr('fill', 'black') .attr('text-anchor', 'start') .attr('dominant-baseline', 'hanging') //.attr('font-weight', 'bold') .attr('font-family', 'sans-serif') .attr('font-size', 10) .attr('y', -margin.top+12) .attr('x', -margin.left) .text('Average number of hours to contain fires & Total acres burned, 2014-2018 ') return svg.node(); }3b:T4ec,{ const svg = d3.create('svg') .attr('width', visWidth + margin.left + margin.right) .attr('height', visHeight + margin.top + margin.bottom); const g = svg.append("g") .attr('transform', `translate(${margin.left}, ${margin.top})`); const xAxisCause = d3.axisBottom(causeScale) const yAxisCause = d3.axisLeft(acreScale).tickFormat(d3.format('~s')) g.selectAll('rect') .data(causesMap) .join('rect') .attr('x', d => causeScale(d[0])) .attr('width', d => causeScale.bandwidth()) .attr('y', d => acreScale(d[1])) .attr('height', d => visHeight - acreScale(d[1])) .attr('fill', 'steelblue'); g.append('g') .call(yAxisCause) .call(g => g.select('.domain').remove()) .append('text') .attr('fill', 'black') .attr('text-anchor', 'start') .attr('dominant-baseline', 'hanging') .attr('font-weight', 'bold') .attr('font-family', 'sans-serif') .attr('font-size', 12) .attr('y', -margin.top + 5) .attr('x', -margin.left) .text('Top 6 Causes of fires, 2014-2018'); g.append('g') .attr('transform', `translate(0, ${visHeight})`) .call(xAxisCause) .call(g => g.select('.domain').remove()) return svg.node(); }3c:T546,viewof selectYear4 = { const rowWidth = 250; const rowHeight = 20; const fontSize = 11; const svg = d3.create('svg') .attr('width', rowWidth) .attr('height', rowHeight* causes.length); const main = svg.append('g') .attr('transform', `translate(2)`); const cols = main.selectAll('g') .data(causes) .join('g') .attr('transform', (d, i) => `translate(2, ${i * 20 + 2})`); cols.append('rect') .attr('width', fontSize+1) .attr('height', fontSize+1) .attr('stroke-width', 2) .attr('stroke', d => causeColor(d)) .attr('fill', d => causeColor(d)) .on('click', onclick); cols.append('text') .attr('font-size', fontSize) .attr('font-family', 'sans-serif') .attr('x', rowHeight + 2) .attr('y', (fontSize + 2) / 2) .attr('dominant-baseline', 'middle') .text(d => d); // Track which Years are selected const selected = new Map(causes.map(d => [d, true])); function onclick(event, d) { const isSelected = selected.get(d); // select square and toggle it const square = d3.select(this); square.attr('fill', d => isSelected ? 'white' : causeColor(d)); selected.set(d, !isSelected); svg.property('value', selected).dispatch('input'); } svg.property('value', selected).dispatch('input'); return svg.node(); }3d:T521,{ const visWidth = 400; const visHeight = 650; const row = d3.scaleBand() .domain([...new Set(rowCol.map(d => d.Row))].sort((a, b) => d3.ascending(a, b))) .range([0, visHeight]) .padding(0.1); const col = d3.scaleBand() .domain([...new Set(rowCol.map(d => d.Col))].sort()) .range([0, visWidth]) .padding(0.1); const pie = d3.pie() .value(d => d[1]); const arc = d3.arc() .innerRadius(0) .outerRadius(col.bandwidth() / 3); const svg = d3.create('svg') .attr('width', visWidth + margin.left + margin.right) .attr('height', visHeight + margin.top + margin.bottom); const main = svg.append('g') .attr('transform', `translate(${margin.left}, ${margin.top})`); const gs = main.selectAll('g') .data(ctyCausePieChart) .join('g') .attr('transform', d => `translate(${col(d.Col)}, ${row(d.Row)})`); gs.selectAll('path') .data(d => pie(d.causes)) .join('path') .attr('d', arc) .attr('fill', d => causeColor(d.data[0])); gs.append('text') .text(d => d.Abbr) .attr('y', col.bandwidth() / 2) .attr('text-anchor', 'middle') .attr('font-size', 11); return svg.node(); }3e:T7ef,{ const visHeight = 350; //const visWidth = visWidth; // Setup const svg = d3.create('svg') .attr('width', visWidth + margin.left + margin.right) .attr('height', visHeight + margin.top + margin.bottom); const g = svg.append('g') .attr('transform', `translate(${margin.left}, ${margin.top})`); // this scale will be used to position the groups for each cause const group = d3.scaleBand() .domain(years.reverse()) // .range([0, visWidth]) .padding(0.2); const y = d3.scaleSqrt() .domain([0, maxSingleYearAcres2]).nice() // .range([visHeight, 0]); // this scale will be used to position the bars within a group const x = d3.scaleBand() .domain(causes) // .range([0, group.bandwidth()]) .padding(0.2); // Axes const xAxis = d3.axisBottom(group); const yAxis = d3.axisLeft(y).tickFormat(d3.format('~s')); g.append('g') .attr('transform', `translate(0,${visHeight})`) .call(xAxis) .call(g => g.selectAll('.domain').remove()); g.append("g") .call(yAxis) .call(g => g.selectAll('.domain').remove()) .append('text') .attr('font-family', 'sans-serif') .attr('font-size', 12) .attr('fill', 'black') .attr('text-anchor', 'start') .attr('dominant-baseline', 'hanging') .attr('font-weight', 'bold') .attr('y', -margin.top + 5) .attr('x', -margin.left) .text('Acres burned by Years, 2014-2018') // create and position one group for each cause const groups = g.append('g') .selectAll('g') .data(acresByYear) // .join('g') .attr('transform', d => `translate(${group(d.year)},0)`); // // add the bars to each group groups.selectAll('rect') .data(d => d.counts) // .join('rect') .attr('fill', d => colorCauses(d.cause)) .attr('y', d => y(d.acres)) .attr('height', d => visHeight - y(d.acres)) .attr('x', d => x(d.cause)) .attr('width', x.bandwidth()); return svg.node(); }3f:T67a,md` California wildfires' overall severity has primarily been on a steady increase from 2014 to 2018. The three severity factors on comparison were determined to be directly proportional i.e. number of fires, acres burned by fires, and the average number of hours taken to control them (with exceptions detailed out in the Severity section). But when Location is tied in, a counter-intuitive observation was made. Counties with a larger number of fires don't experience more acres of land burned by fires. On analyzing further, we were able to detect a difference in two broad Causes of fires i.e. natural and man-made. natural caused fires have the expected directly proportional relationship of number of fires with acres burned by fires. While man-made caused fires don't have the expected directly proportional relationship. Other important trends include a. the severity of fires peaking in summer (July) and b. the causes of fires being more man-made versus natural over the passage of time. We can infer that man-made caused fires have not only increased the severity of fires over the years but also resulted in unpredictable patterns of destruction. An example of an unpredictable pattern being, the lack of a relationship between the number of fires and acres of land burned by fires for a man-made caused fire. In conclusion, while Time of the year and Severity can be correlated by themselves -- it's impossible to correlate Time, Severity with Location due to the arbitrary destruction patterns of man-made caused fires. As for Cause, we can safely say that recent fires have been caused more due to humans over naturally occurring changes.`40:T526,md`--- ## References **Introduction** [1] [This is Fine meme](https://knowyourmeme.com/memes/this-is-fine) [2] [California fires](https://www.fire.ca.gov/stats-events/) [3] [Indonesia fires](https://www.nytimes.com/2019/09/17/world/asia/indonesia-fires-photos.html) **Dataset** - Short, Karen C. 2021. Spatial wildfire occurrence data for the United States, 1992-2018 [FPA_FOD_20210617]. 5th Edition. Fort Collins, CO: Forest Service Research Data Archive. https://doi.org/10.2737/RDS-2013-0009.5 [4] [1.88 million US Wildfires](https://www.kaggle.com/rtatman/188-million-us-wildfires) **Location** - Map data from the [US Census Bureau](https://www.census.gov/geographies/mapping-files/time-series/geo/tiger-line-file.html) **Severity** [5] [Rough, July 2015](https://en.wikipedia.org/wiki/Rough_Fire) [6] [Camp, Nov 2018](https://en.wikipedia.org/wiki/Camp_Fire_(2018)) [7] [Mendocino Complex, July 2018](https://en.wikipedia.org/wiki/Mendocino_Complex_Fire) [8] [Carr, July 2018](https://en.wikipedia.org/wiki/Carr_Fire) **Cause** [9] [Thomas 2017](https://www.cnbc.com/2017/12/13/southern-california-utilities-shut-off-power-to-prevent-wildfires.html) [10] [Camp 2018](https://www.cnbc.com/2019/05/15/officials-camp-fire-deadliest-in-california-history-was-caused-by-pge-electrical-transmission-lines.html) `41:[{"id":"9fcc1f9a4d1b6e0ce2735fe5508fc34daa25738568c378d3283d0cbded7c69d012a38d6b8118f98416b1fe8ee57839ac0a3ebb76f84a1e03c57ef371dd761243","href":"https://static.observableusercontent.com/files/9fcc1f9a4d1b6e0ce2735fe5508fc34daa25738568c378d3283d0cbded7c69d012a38d6b8118f98416b1fe8ee57839ac0a3ebb76f84a1e03c57ef371dd761243","name":"CaliforniaCountyMap.json","type":"application/json","lastModified":1633567272272.4,"size":6717426},{"id":"7232de4975fa24d538f7abdef35868221a66b0f5845a761e80c90d0f61f99981021b85a110a42ddc2826c69fd7c2ae64e3409d0ae60882e0d3eaa7a95d97992c","href":"https://static.observableusercontent.com/files/7232de4975fa24d538f7abdef35868221a66b0f5845a761e80c90d0f61f99981021b85a110a42ddc2826c69fd7c2ae64e3409d0ae60882e0d3eaa7a95d97992c","name":"ctyToAbb.json","type":"application/json","lastModified":1633567272272.4,"size":2994},{"id":"4b4352652c90e269068423e4b0b1ee1798ed8cbb1d8392a44bf89d6232f5ea870475790da06e1897a67449e86f9706323e112568a3a63da3bbd5b7a1be5abe06","href":"https://static.observableusercontent.com/files/4b4352652c90e269068423e4b0b1ee1798ed8cbb1d8392a44bf89d6232f5ea870475790da06e1897a67449e86f9706323e112568a3a63da3bbd5b7a1be5abe06","name":"Factors.png","type":"image/png","lastModified":1633567272272.4,"size":198807}] 2f:{"title":"Wildfires -- Interdependence of Humans and Nature","cells":[{"id":0,"value":"md`# Wildfires -- Interdependence of Humans and Nature\n## Eehita Parameswaran and Aria Diamond\n![Dog sitting in a room on fire, drinking coffee](https://i.kym-cdn.com/entries/icons/original/000/018/012/this_is_fine.jpeg)[1]\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":4661,"value":"md`## Introduction`","pinned":false,"hidden":false,"mode":"ojs"},{"id":111,"value":"$31","pinned":false,"hidden":false,"mode":"ojs"},{"id":4620,"value":"html ` $\"drawing\"$ `","pinned":false,"hidden":false,"mode":"ojs"},{"id":55,"value":"md`---\n# 0. Dataset`","pinned":false,"hidden":false,"mode":"ojs"},{"id":119,"value":"md`The database by Karen Short is sourced from systems of federal, state, and local fire organizations that contain information on wildfires that occurred in the US from 1992 to 2018. Our project will be focused solely on California wildfires that took place for **5 years (2014 to 2018)**. CA accounts for 10% of the dataset i.e. 235,229 out of 2,166,753 fires. Essentially, we are working with 2% of the dataset as we are focussing on 5 years [4].\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":58,"value":"md`---\n# 1. Location: Hey, there's a lot of Fires!`","pinned":false,"hidden":false,"mode":"ojs"},{"id":2700,"value":"md`The first factor we wanted to analyze was Location. **What is the distribution of fires across the counties? How do the acres of land burned by fires vary across the counties?**\n\nFrom the Number of fires map, we can see that Central and Southern CA counties experienced a higher number of fires. However, the Acres burned map shows that Northern CA counties had more acres burned by fires. For instance, Riverside (Southern CA county) had the most number of fires but the acres burned by fires are on the lower end of the spectrum. \n\nThis is counter-intuitive as we would expect that number of fires would be directly proportional to the acres burned by fires. \n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":2786,"value":"viewof acresNumberLocation = Inputs.radio([\"Number of fires\", \"Acres burned\"], {label: \"Number of Fires vs Acres Burned\", value: \"Number of fires\"})","pinned":false,"hidden":false,"mode":"ojs"},{"id":3722,"value":"legend({color: d3.scaleSequential()\n .domain([0, d3.max(countyToCount.values())])\n .interpolator(d3.interpolateReds), title: `${acresNumberLocation} per County, 2014-2018`})","pinned":false,"hidden":false,"mode":"ojs"},{"id":3718,"value":"{\n const maxVal = d3.max(countyToCount.values());\n // Axes\n const intensityScale = d3.scaleSequential()\n .domain([0, maxVal])\n .interpolator(d3.interpolateReds);\n const radius = d3.scaleSqrt()\n .domain([0, maxVal])\n .range([0, maxRadius]);\n \n // Svg\n const svg = d3.create('svg')\n .attr('width', mapWidth + margin.left + margin.right)\n .attr('height', mapHeight + margin.top + margin.bottom);\n const main = svg.append('g')\n .attr('transform', `translate(${margin.left}, ${margin.top})`);\n \n main.selectAll('path')\n .data(caCountyMap.features)\n .join('path')\n .attr('d', caPath)\n .attr('fill', d => intensityScale(countyToCount.get(d.properties.NAMELSAD10)))\n .attr('stroke', 'white');\n \n return svg.node();\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1952,"value":"md`\n

Rationale

\n\n

We wanted to see the number of fires for each county to understand the distribution of fires throughout the state. We used a choropleth map as it allows for quick recognition of predetermined geographic areas. We used a red color intensity scale to map the number of fires.

\n\n

We also wanted to see the acres burned for each county to understand the severity of fires throughout the state. We again used a choropleth map with the a similar red color intensity scale as it allows to maintain consistency while switching between the two filter options.

\n\n

The filter interaction helps transition and compare the number of fires and acres burned for each county easily.

`","pinned":false,"hidden":false,"mode":"ojs"},{"id":83,"value":"md`---\n# 2. Time: Oh no! It's getting bad`","pinned":false,"hidden":false,"mode":"ojs"},{"id":2609,"value":"md `The second factor we wanted to analyze was Time. **What is the distribution of fires across the months? What are the acres of land burned by fires across the months? How do these trends compare across the years?**\n\nFrom the Wildfires line chart, we can see that there is a distinct increase in the number of fires during summer (July) across all years. The total number of fires also generally increased with the progression of the years. `","pinned":false,"hidden":false,"mode":"ojs"},{"id":2635,"value":"md`*Click on a colored box to de-select/select a particular year*`","pinned":false,"hidden":false,"mode":"ojs"},{"id":2367,"value":"$32","pinned":false,"hidden":false,"mode":"ojs"},{"id":585,"value":"$33","pinned":false,"hidden":false,"mode":"ojs"},{"id":1957,"value":"md`\n

Rationale

\n\n

For this visualization, we wanted to see the number of fires for each month to interpret seasonal and year-over-year trends. We used a line chart as it allows to identify small changes in fires over time.

\n\n

We chose a sequential multi-hue color scale to elucidate years instead of months. The same color scheme has been used to reference years in several following visualizations to maintain consistency.

\n\n

The legend selection interaction helps focus on individual years and thus understand the wildfires trend better.

`","pinned":false,"hidden":false,"mode":"ojs"},{"id":4649,"value":"md`On comparing the Wildfires line chart with the Acres burned line chart, we can clearly see that there is a correlation between the number of fires and acres burned. Like the number of fires, the acres burned peaked in July across all years. The acres burned also increased across the years with 2014 having the least acres burned and 2018 having the most acres burned.`","pinned":false,"hidden":false,"mode":"ojs"},{"id":4040,"value":"md`*Click on a colored box to de-select/select a particular year*`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1497,"value":"$34","pinned":false,"hidden":false,"mode":"ojs"},{"id":3803,"value":"$35","pinned":false,"hidden":false,"mode":"ojs"},{"id":1959,"value":"html`\n

Rationale

\n\n

For this visualization, we wanted to see the acres burned for each month to interpret seasonal and year-over-year trends. We used a line chart again to maintain consistency across the Time section visualizations.

\n\n

The legend selection interaction helps focus on individual years and thus understand the acres burned trend better.

`","pinned":false,"hidden":false,"mode":"ojs"},{"id":4651,"value":"md`The seasonal trend along with strong correspondence of the number of fires and acres burned by fires reflects the importance of having more measures in place to contain them during summer months in California.`","pinned":false,"hidden":false,"mode":"ojs"},{"id":85,"value":"md`---\n# 3. Severity: Oh NO!`\n","pinned":false,"hidden":false,"mode":"ojs"},{"id":2625,"value":"$36","pinned":false,"hidden":false,"mode":"ojs"},{"id":1425,"value":"$37","pinned":false,"hidden":false,"mode":"ojs"},{"id":500,"value":"$38","pinned":false,"hidden":false,"mode":"ojs"},{"id":1961,"value":"html`\n

Rationale

\n\n

For this visualization, we wanted to see the average number of hours taken to contain fires for each month and year combination. We used a matrix chart as it allows to depict the relationship across several groups of data (months and years in this case). This chart also enables the visual representation of two ordinal attributes to be keys to a quantitative one.

\n\n

We used the average number of hours to contain fires as a metric of severity over something more traditional like damage costs, injuries, or deaths as we do not have that information.

\n\n

`","pinned":false,"hidden":false,"mode":"ojs"},{"id":4121,"value":"$39","pinned":false,"hidden":false,"mode":"ojs"},{"id":4382,"value":"swatches({color: colorYears})","pinned":false,"hidden":false,"mode":"ojs"},{"id":3847,"value":"$3a","pinned":false,"hidden":false,"mode":"ojs"},{"id":4065,"value":"html`\n

Rationale

\n\n

For this visualization, we wanted to analyze the relationship between average number of hours taken to contain fires and total acres burned (per month). We used a scatter plot as it allows each dot/month to represent values for two different numeric variables. Each data point enables visual observation between the average number of hours taken to contain fires and total acres burned (per month).

\n\n

`","pinned":false,"hidden":false,"mode":"ojs"},{"id":89,"value":"md`\n# 4. Cause: How do we fix it?`\n","pinned":false,"hidden":false,"mode":"ojs"},{"id":2668,"value":"md`The last factor we wanted to focus on was Cause. **How do the causes correlate to the acres of land burned by fires?**\n\nFrom the bar chart, we can see that the top 6 causes with the most acres burned by fires are mainly man-made with a single natural cause.`","pinned":false,"hidden":false,"mode":"ojs"},{"id":3931,"value":"$3b","pinned":false,"hidden":false,"mode":"ojs"},{"id":2871,"value":"html`\n

Rationale

For this visualization, we wanted to see the top causes for the acres burned by fires. We used a bar chart as it displays the top causes with the largest amount of acres burned. Thus, this chart shows the most relevant groups over time.

`","pinned":false,"hidden":false,"mode":"ojs"},{"id":4485,"value":"md`To understand the top 6 causes better, we wanted to see **what is the distribution of causes for each county across the state?**\n\nFrom the cartogram with pie charts, we realized that the relationship of the causes with the counties is actually quite complex. Natural caused fires are more in Northern and El Dorado (ED) to Southern counties (forested areas). Arson caused fires don't have a set geographical clustering (mainly rural areas with San Francisco (SF), Alameda (ALA), and Sacramento (SAC) being outliers to this observation). Equipment and vehicle caused fires are undoubtedly quite widespread throughout the state. \n\n**Facts!**\nInterestingly, we were able to confirm information about two major fires with this chart. Thomas fire took place in Santa Barbara(SB) in 2017 and Camp fire took place in Butte(BUT) in 2018. Both fires resulted in a lot of destruction and were caused due to Power generation failures [9][10].`","pinned":false,"hidden":false,"mode":"ojs"},{"id":4435,"value":"md`*Click on a colored box to de-select/select a particular cause*`","pinned":false,"hidden":false,"mode":"ojs"},{"id":4439,"value":"$3c","pinned":false,"hidden":false,"mode":"ojs"},{"id":4442,"value":"$3d","pinned":false,"hidden":false,"mode":"ojs"},{"id":4446,"value":"html`\n

Rationale

\n\n

For this visualization, we wanted to see the distribution of causes throughout the state for each county. We used a cartogram to place the counties as it allows to avoid the bias that can be observed while using a choropleth map. We also used pie charts for the distribution of causes for each county as it allows to depict the proportions of a whole.

\n\n

The legend selection interaction helps focus on a couple of causes and thus compare the distribution across the state better.

`","pinned":false,"hidden":false,"mode":"ojs"},{"id":2852,"value":"md`Due to the complex relation of causes with location, we wanted to correlate causes with a severity factor identified before i.e. acres burned. **How do acres of land burned by fires compare across the years for each cause?**\n\nFrom the nested bar chart, we noticed two interesting trends: acres burned due to 'power generation/transmission/distribution' had consistently increased across all years and that 'equipment and vehicle use' was unusually high in 2018. \n\nA major takeaway of the visualization is that with the progression of years, the *severity of California fires has mostly increased. While natural caused severity of fires has declined recently, man-made causes like equipment use, power generation, firearms use have caused a gradual increase in the severity of fires. \n\n**severity = number of fires, acres burned by fires, average number of hours to contain them*`","pinned":false,"hidden":false,"mode":"ojs"},{"id":4004,"value":"swatches({color: colorCauses})","pinned":false,"hidden":false,"mode":"ojs"},{"id":3985,"value":"$3e","pinned":false,"hidden":false,"mode":"ojs"},{"id":1963,"value":"html`\n

Rationale

\n\n

For this visualization, we wanted to see the acres burned for each cause and year combination. We used a nested bar chart as it allows to depict the relationship across several groups of data (causes and years in this case). This chart also enables the visual representation of two ordinal attributes to be keys to a quantitative one.

\n\n

We used the six causes with the most acres burned as it allows to focus on the main causes without letting other minor causes clutter the visualization.

`","pinned":false,"hidden":false,"mode":"ojs"},{"id":3317,"value":"md` To dig deeper into the severity of natural versus man-made caused fires and to inspect the counter-intuitive inversely proportional relationship of number of fires with acres burned by fires, we had to tie in Location again i.e. counties with these two severity factors. **How do the number of fires and acres of land burned by fires distribute across counties for each cause?**\n\nOn comparing the choropleth maps for the number of fires and acres burned by fires for each cause, an interesting trend can be spotted. For natural caused fires, the counties experiencing a higher number of fires have more acres burned by fires (directly proportional). However, for the rest of the causes i.e. man-made caused fires, the counties experiencing a higher number of fires don't have more acres burned by fires. \n\nThis visualization, in fact, solidified our previous observation about the difference of the effect of natural and man-made caused fires. \n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":2251,"value":"viewof category = Inputs.radio(causes, {value: \"Natural\", label: \"Choose a Cause\"})","pinned":false,"hidden":false,"mode":"ojs"},{"id":2297,"value":"viewof acresNumberCause = Inputs.radio([\"Number of fires\", \"Acres Burned\", ], {value: \"Number of fires\", label: \"Number of fires or Acres Burned\"})","pinned":false,"hidden":false,"mode":"ojs"},{"id":4280,"value":"legend({color: colorSingleCauseCty, title: `${acresNumberCause} by ${category}, 2014-2018`})","pinned":false,"hidden":false,"mode":"ojs"},{"id":1980,"value":"{\n /* SVG */\n const svg = d3.create('svg')\n .attr('width', mapWidth + margin.left + margin.right)\n .attr('height', mapHeight + margin.top + margin.bottom);\n\n const main = svg.append('g')\n .attr('transform', `translate(${margin.left}, ${margin.top})`);\n\n const mapG = main.append('g')\n .attr('transform', `translate(0, 20)`);\n mapG.selectAll('path')\n .data(caCountyMap.features)\n .join('path')\n .attr('d', caPath)\n .attr('stroke', 'white')\n .attr('fill', d => colorSingleCauseCty(singleCauseCounties.get(d.properties.NAMELSAD10)));\n \n return svg.node();\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":2005,"value":"html`\n

Rationale

For this visualization, we wanted to see the distribution of the number of fires and acres burned throughout the state for each cause to understand the relationship between *severity metrics, cause, and location. As in the Location section, we used choropleth maps for the number of fires and acres burned. We also used interactions to filter between the six causes and two severity metrics to achieve a seamless transition.

\n\n*severity = number of fires, acres burned by fires\n

`","pinned":false,"hidden":false,"mode":"ojs"},{"id":3351,"value":"md`## Conclusion`\n","pinned":false,"hidden":false,"mode":"ojs"},{"id":3354,"value":"$3f","pinned":false,"hidden":false,"mode":"ojs"},{"id":33,"value":"$40","pinned":false,"hidden":false,"mode":"ojs"},{"id":40,"value":"md`---\n## Appendix\n---\n### Globals`","pinned":false,"hidden":false,"mode":"ojs"},{"id":43,"value":"d3 = require(\"d3@7\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":779,"value":"import {swatches, legend} from \"@d3/color-legend\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":1516,"value":"import { lightgray } from \"@nyuvis/fundamental-graphs\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":212,"value":"margin = ({top: 30, right: 30, bottom: 20, left: 50})","pinned":false,"hidden":false,"mode":"ojs"},{"id":224,"value":"n = 10","pinned":false,"hidden":false,"mode":"ojs"},{"id":623,"value":"visWidth = width - margin.left - margin.right","pinned":false,"hidden":false,"mode":"ojs"},{"id":626,"value":"visHeight = 400 - margin.top - margin.bottom","pinned":false,"hidden":false,"mode":"ojs"},{"id":1506,"value":"xAxisAll = d3.scalePoint()\n .domain(months)\n .range([0, 1000])","pinned":false,"hidden":false,"mode":"ojs"},{"id":4031,"value":"colorYears = d3.scaleOrdinal()\n .domain(years)\n .range(d3.quantize(d3.interpolatePlasma, 7).slice(1, 6).reverse())","pinned":false,"hidden":false,"mode":"ojs"},{"id":2234,"value":"months = [\"January\", \"February\", \"March\", \"April\", \"May\", \"June\", \"July\", \"August\", \"September\", \"October\", \"November\", \"December\"]//.map(d => d3.timeFormat('%b')(d3.timeParse('%B')(d)))","pinned":false,"hidden":false,"mode":"ojs"},{"id":2238,"value":"years = [2014, 2015, 2016, 2017, 2018]","pinned":false,"hidden":false,"mode":"ojs"},{"id":1020,"value":"md`---\n### Dataset`","pinned":false,"hidden":false,"mode":"ojs"},{"id":27,"value":"url = \"https://gist.githubusercontent.com/ariadiamond/b74804435a80070f9b9274bd993a2311/raw/dfd2cf40fead08b278c3ecef83fcffe4a1c94f49/gistfile1.txt\"\n// url = 'https://gist.githubusercontent.com/ariadiamond/7258c382427561f5d012c57101763265/raw/aa8e83f7c950dc33a73073ea8bbc2cb100883f0f/fires10K.csv' 10,000 records\n\n// below is the full 2018 dataset, but it makes things slow\n//'https://gist.githubusercontent.com/ariadiamond/ce387db7d0fcab98db548db42b4e7f0e/raw/5e7aadad205d4c09e63319e98f2e85e1ce9baa5e/fires2018.csv'\n// below is the original dataset\n//'https://gist.githubusercontent.com/ariadiamond/94b29ef3680fe3e7388b7c080bf37d47/raw/0851e6487923ee74dd724113b41483f27e0e779d/fires.csv'","pinned":false,"hidden":false,"mode":"ojs"},{"id":31,"value":"data = d3.csv(url, d => ({\n name: d.FIRE_NAME,\n year: +d.FIRE_YEAR,\n disc_date: d3.timeParse('%Y %j %H:%M:%S')(d.FIRE_YEAR + ' ' + d.DISCOVERY_DOY + ' ' + d.DISCOVERY_TIME),\n cont_date: d3.timeParse('%Y %j %H:%M:%S')(d.FIRE_YEAR + ' ' + d.CONT_DOY + ' ' + d.CONT_TIME),\n cause: d.NWCG_GENERAL_CAUSE, //STAT_CAUSE_DESCR\n size: +d.FIRE_SIZE,\n latitude: +d.LATITUDE,\n longitude: +d.LONGITUDE,\n county: d.FIPS_NAME\n}))","pinned":false,"hidden":false,"mode":"ojs"},{"id":992,"value":"md`\n---\n### Location`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1233,"value":"maxRadius = 10","pinned":false,"hidden":false,"mode":"ojs"},{"id":1226,"value":"mapWidth = 500","pinned":false,"hidden":false,"mode":"ojs"},{"id":1229,"value":"mapHeight = 500","pinned":false,"hidden":false,"mode":"ojs"},{"id":1204,"value":"caCountyMap = FileAttachment(\"CaliforniaCountyMap.json\").json()","pinned":false,"hidden":false,"mode":"ojs"},{"id":1207,"value":"projection = d3.geoAlbers().fitSize([mapWidth, mapHeight], caCountyMap)","pinned":false,"hidden":false,"mode":"ojs"},{"id":1210,"value":"caPath = d3.geoPath().projection(projection)","pinned":false,"hidden":false,"mode":"ojs"},{"id":1858,"value":"countyToCount = d3.rollup(\n data,\n group => acresNumberLocation === \"Acres burned\" ? d3.sum(group, d => d.size) : group.length,\n d => d.county\n)","pinned":false,"hidden":false,"mode":"ojs"},{"id":1639,"value":"countyToAcresBurned = Object.fromEntries(d3.rollup(data,\n group => d3.sum(group, d => d.size),\n d => d.county))","pinned":false,"hidden":false,"mode":"ojs"},{"id":1643,"value":"maxAcresBurnedForCounty = d3.max(Object.values(countyToAcresBurned))","pinned":false,"hidden":false,"mode":"ojs"},{"id":999,"value":"md`---\n### Time`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1305,"value":"wildfiresByMonth = d3.rollups(\n data,\n group => group.length,\n d => d.year,\n d => d.disc_date.getMonth()+1)\n\n .map(([year, months]) => ({\n year: year,\n counts: months.map(([date, wildfires]) => ({date: date, wildfires}))\n .sort((a, b) => d3.ascending(a.date, b.date))\n }))","pinned":false,"hidden":false,"mode":"ojs"},{"id":1333,"value":"maxSingleYearWildfires = d3.max(wildfiresByMonth, d => d3.max(d.counts, e => e.wildfires))","pinned":false,"hidden":false,"mode":"ojs"},{"id":1371,"value":"acresBurnedByMonth = d3.rollups(\n data,\n group => d3.sum(group, d => d.size),\n d => d.year,\n d => d.disc_date.getMonth()+1)\n\n .map(([year, months]) => ({\n year: year,\n counts: months.map(([date, wildfires]) => ({date: date, wildfires}))\n .sort((a, b) => d3.ascending(a.date, b.date))\n }))","pinned":false,"hidden":false,"mode":"ojs"},{"id":1378,"value":"maxSingleYearAcresBurned = d3.max(acresBurnedByMonth, d => d3.max(d.counts, e => e.wildfires))","pinned":false,"hidden":false,"mode":"ojs"},{"id":1007,"value":"md`---\n### Severity`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1580,"value":"timeToContain = d3.rollups(\n data.filter(d => d.disc_date !== null && d.cont_date !== null),\n group => d3.mean(group, d => (d.cont_date.getTime() - d.disc_date.getTime()) / (1000 * 3600)),\n d => d.disc_date.getYear() + 1900, d => d3.timeFormat(\"%B\")(d3.timeParse(\"%m\")(d.disc_date.getMonth() + 1))\n)","pinned":false,"hidden":false,"mode":"ojs"},{"id":465,"value":"TTCmax = d3.max(timeToContain, ([yr, nest]) => d3.max(nest, ([mon, amt]) => amt))","pinned":false,"hidden":false,"mode":"ojs"},{"id":3849,"value":"ttcAb = d3.flatRollup(\n data.filter(d => d.disc_date != null && d.cont_date != null),\n group => ({hrCont: d3.mean(group, d => (d.cont_date.getTime() - d.disc_date.getTime()) / (1000 * 3600)),\n acresBurned: d3.sum(group, d => d.size),\n numFires: group.length}),\n d => d.disc_date.getYear() + 1900,\n d => d3.timeFormat(\"%B\")(d3.timeParse(\"%m\")(d.disc_date.getMonth() + 1))\n)\n .map(([yr, mon, obj]) => {\n obj.month = mon;\n obj.year = yr;\n return obj;\n })","pinned":false,"hidden":false,"mode":"ojs"},{"id":1010,"value":"md`---\n### Cause\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1889,"value":"causes = d3.groupSort(\n data,\n group => d3.sum(group, d => d.size),\n d => d.cause)\n .reverse()\n .filter(cause => cause !== 'Missing data/not specified/undetermined' && cause !== 'Other causes')\n .slice(0,6)","pinned":false,"hidden":false,"mode":"ojs"},{"id":3969,"value":"acreScale = d3.scaleLinear()\n .domain([0, d3.max(causesMap, d => d[1])]).nice() \n .range([visHeight, 0])","pinned":false,"hidden":false,"mode":"ojs"},{"id":3972,"value":"causeScale = d3.scaleBand()\n .domain(causes)\n .range([0, visWidth])\n .padding(0.2)","pinned":false,"hidden":false,"mode":"ojs"},{"id":3975,"value":"causesMap = d3.rollups(\n data,\n group => d3.sum(group, d => d.size),\n d => d.cause\n)\n .filter(d => causes.includes(d[0]))\n .sort((a,b) => d3.descending(a[1], b[1]))","pinned":false,"hidden":false,"mode":"ojs"},{"id":1898,"value":"acresByYear = d3.rollups(\n data.filter(d => causes.includes(d.cause)),\n group => d3.sum(group, d => d.size),\n d => d.disc_date.getYear()+1900,\n d => d.cause\n)\n .map(([year, cause]) => ({\n year: year,\n counts: cause.map(([cause, acres]) => ({cause, acres}))\n }))\n","pinned":false,"hidden":false,"mode":"ojs"},{"id":1901,"value":"maxSingleYearAcres2 = d3.max(acresByYear, d => d3.max(d.counts, e => e.acres))","pinned":false,"hidden":false,"mode":"ojs"},{"id":4330,"value":"rowCol = FileAttachment(\"ctyToAbb.json\").json()","pinned":false,"hidden":false,"mode":"ojs"},{"id":4334,"value":"ctyCausePieChart = {\n const map = new Map(d3.rollups(\n data,\n group => d3.sum(group, d => d.size),\n d => d.county, d => d.cause\n ));\n var arr = [];\n const selectCause = Array.from(selectYear4).filter(d => d[1]).map(d => d[0]);\n // Index Nested Loop Join\n for (var obj of rowCol) {\n const causeCount = map.get(obj.County + \" County\").filter(d => selectCause.includes(d[0])); \n obj.causes = causeCount;\n arr.push(obj);\n }\n return arr;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":4338,"value":"causeColor = d3.scaleOrdinal()\n .domain(causes)\n .range(d3.schemeTableau10);","pinned":false,"hidden":false,"mode":"ojs"},{"id":3998,"value":"colorCauses = d3.scaleOrdinal()\n .domain(causes)\n .range(d3.schemeTableau10)","pinned":false,"hidden":false,"mode":"ojs"},{"id":2280,"value":"singleCauseCounties = d3.rollup(\n data.filter(d => d.cause === category),\n group => {\n if (acresNumberCause !== \"Number of fires\") {\n return d3.sum(group, d => d.size);\n }\n return group.length;\n },\n d => d.county\n );","pinned":false,"hidden":false,"mode":"ojs"},{"id":2283,"value":"maxSingleCauseCty = \n d3.max(d3.rollups(\n data,\n group => acresNumberCause === \"Acres Burned\" ? d3.sum(group, d => d.size) : group.length,\n d => d.cause, d => d.county\n ).filter(([cause, amt]) => causes.includes(cause)),\n ([cause, nest]) => d3.max(nest, ([cty, amt]) => amt))\n","pinned":false,"hidden":false,"mode":"ojs"},{"id":2286,"value":"colorSingleCauseCty = d3.scaleSequential()\n .domain([0, maxSingleCauseCty])\n .interpolator(d3.interpolateReds)\n .unknown('#eeeeee')","pinned":false,"hidden":false,"mode":"ojs"}],"files":"$W41","stdlib":"1","titling":"auto"}