34:["$","title",null,{"children":"How Does the Weather Affect U.S. Suicide Rate? | Fuller, Flynn | Observable"}] 3c:T463,When you picture a scenario of suicide, it's difficult to not imagine a dreary day. The weather unfortunately matches the person-in-question's demeanor - cold, dark, dreary, maybe teary, or possibly icy if things fall into some misconstrued place. In this project, I wanted to test this linkage between the world's tears and those of its subjects in the boundary of the United States. I propose this hypothesis: - ***There exists a positive correlation between drearier weather and suicide rates by US state.*** *(Note: This project infers "dreary weather" to fall under three categorizations: **Higher Cloud Cover**, **Higher Levels of Precipitation**, and **Colder Temperatures**. These three labels will represent the three sets of data I intend to hypothesize under.)* The data used in this project will be from 2017 to 2021 over the lower 48 states, as those bounds were all that was shared by both my suicide data and my raster data without having too much data to process. The data for suicide rate can be changed from year to year by the reader, while the weather rasters are averages over the above time period.3d:T40e,Plot.plot({ title: "US States by Suicide Rate (per 100,000 people) vs Cloud Cover Percentage", width: 975, height: 610, projection: "albers-usa", color: { scheme: "reds", domain: [0, 35], label: "# of Suicides (per 100,000 people)", legend: true, ticks: 8 }, marks: [ Plot.geo(states, { fill: d => suicideMap.get(d.properties.name)?.pct, fillOpacity: opacityChecker, channels: { State: (d) => d.properties.name, "Deaths (Raw)": (d) => suicideMap.get(d.properties.name)?.["Deaths"] }, tip: true }), Plot.geo(statemesh, {stroke: "white", strokeWidth: .5}), Plot.raster(cloudrasterdata[rasterFilter], { width: 3589, height: 859, x1: cloudcoverBoundingBox[0], y1: cloudcoverBoundingBox[3], x2: cloudcoverBoundingBox[2], y2: cloudcoverBoundingBox[1], interpolate: "nearest", fill: d => (isNaN(d) ? null : d3.interpolateBlues(d / 100)), fillOpacity: rasterOpacity }), ] })3e:T420,Plot.plot({ title: "US States by Suicide Rate (per 100,000 people) vs Amount of Precipitation", width: 975, height: 610, projection: "albers-usa", color: { scheme: "reds", domain: [0, 35], label: "# of Suicides (per 100,000 people)", legend: true, ticks: 8 }, marks: [ Plot.geo(states, { fill: d => suicideMap2.get(d.properties.name)?.pct, fillOpacity: opacityChecker2, channels: { State: (d) => d.properties.name, "Deaths (Raw)": (d) => suicideMap2.get(d.properties.name)?.["Deaths"] }, tip: true }), Plot.geo(statemesh, {stroke: "white", strokeWidth: .5}), Plot.raster(precrasterdata[rasterFilter2], { width: 3589, height: 859, x1: precipitationBoundingBox[0], y1: precipitationBoundingBox[3], x2: precipitationBoundingBox[2], y2: precipitationBoundingBox[1], interpolate: "nearest", fill: d => (isNaN(d) ? null : d3.interpolateBlues(d / 100)), fillOpacity: rasterOpacity2 }) ] }) 3f:T425,Plot.plot({ title: "US States by Suicide Rate (per 100,000 people) vs Average Temperature", width: 975, height: 610, projection: "albers-usa", color: { scheme: "reds", domain: [0, 35], label: "# of Suicides (per 100,000 people)", legend: true, ticks: 8 }, marks: [ Plot.geo(states, { fill: d => suicideMap3.get(d.properties.name)?.pct, fillOpacity: opacityChecker3, channels: { State: (d) => d.properties.name, "Deaths (Raw)": (d) => suicideMap3.get(d.properties.name)?.["Deaths"] }, tip: true }), Plot.geo(statemesh, {stroke: "white", strokeWidth: .5}), Plot.raster(temprasterdata[rasterFilter3], { width: 3589, height: 859, x1: temperatureBoundingBox[0], y1: temperatureBoundingBox[3], x2: temperatureBoundingBox[2], y2: temperatureBoundingBox[1], interpolate: "nearest", fill: d => (isNaN(d) ? null : d3.interpolateBlues(1 - (d - 243) / (301-243))), fillOpacity: rasterOpacity3 }) ] })40:T40d,Plot.plot({ title: "US States by Suicide Rate (per 100,000 people) vs Cloud Cover Percentage", width: 975, height: 610, projection: "albers-usa", color: { scheme: "reds", domain: [0, 35], label: "# of Suicides (per 100,000 people)", legend: true, ticks: 8 }, marks: [ Plot.geo(states, { fill: d => suicideMap5.get(d.properties.name)?.pct, fillOpacity: opacityChecker4, channels: { State: (d) => d.properties.name, "Deaths (Raw)": (d) => suicideMap5.get(d.properties.name)?.["Deaths"] }, tip: true }), Plot.geo(statemesh, {stroke: "white", strokeWidth: .5}), Plot.raster(cloudValues2017, { width: cloudImage2017.getWidth(), height: cloudImage2017.getHeight(), x1: cloudcoverBoundingBox[0], y1: cloudcoverBoundingBox[3], x2: cloudcoverBoundingBox[2], y2: cloudcoverBoundingBox[1], interpolate: "nearest", fill: d => (isNaN(d) ? null : d3.interpolateBlues(d / 100)) }), ] })41:T404,Plot.plot({ title: "US States by Suicide Rate (per 100,000 people) vs Cloud Cover Percentage", width: 975, height: 610, projection: "albers-usa", color: { scheme: "reds", domain: [0, 35], label: "# of Suicides (per 100,000 people)", legend: true, ticks: 8 }, marks: [ Plot.geo(states, { fill: d => suicideMap6.get(d.properties.name)?.pct, fillOpacity: opacityChecker5, channels: { State: (d) => d.properties.name, "Deaths (Raw)": (d) => suicideMap6.get(d.properties.name)?.["Deaths"] }, tip: true }), Plot.geo(statemesh, {stroke: "white", strokeWidth: .5}), Plot.raster(cloudValues2017, { width: 3589, height: 859, x1: cloudcoverBoundingBox[0], y1: cloudcoverBoundingBox[3], x2: cloudcoverBoundingBox[2], y2: cloudcoverBoundingBox[1], interpolate: "nearest", fill: d => (isNaN(d) ? null : d3.interpolateBlues(d / 100)), fillOpacity: rasterOpacity4 }), ] 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38:{"id":"af2716ea14e2ad8b","slug":"how-does-the-weather-affect-u-s-suicide-rate","title":"How Does the Weather Affect U.S. Suicide Rate?","thumbnail":null,"access_level":"public","create_time":"$D2026-10-18T03:35:34.696Z","update_time":"$D2026-10-26T02:35:24.572Z","view_time":null,"first_public_time":"$D2026-10-26T02:36:35.437Z","has_unpublished_edits":null,"fork":false,"stars":0,"starred":false,"owner":{"type":"team","id":"c1840ee67d00e58d","login":"fuller-flynn","name":"Fuller, Flynn","avatar_url":"https://avatars-next.observableusercontent.com/e828411dff52a8988d00f6820779f412c6bf4ea1d1f1d01f016c00205c8ef67b","tier":"starter_2024","bio":"","followed_by_you":false,"follows_you":false},"version":836,"publish_version":836,"role":null,"authored":false,"authors":[{"type":"individual","id":"9a587d59cba6991c","login":"flynn-fuller","name":"Fuller, Flynn","avatar_url":"https://avatars-next.observableusercontent.com/e828411dff52a8988d00f6820779f412c6bf4ea1d1f1d01f016c00205c8ef67b","tier":null,"bio":"","followed_by_you":false,"follows_you":false}],"members":[{"type":"team","id":"c1840ee67d00e58d","login":"fuller-flynn","name":"Fuller, Flynn","avatar_url":"https://avatars-next.observableusercontent.com/e828411dff52a8988d00f6820779f412c6bf4ea1d1f1d01f016c00205c8ef67b","tier":"starter_2024","role":"editor"}],"comments":[],"custom_thumbnail":false,"trashed":false,"lapsed":null,"subscribed":null,"forks":0,"collection":null,"type":"notebook","body":{"titling":"auto","title":"How Does the Weather Affect U.S. Suicide Rate?","cells":[{"id":1,"value":"# How Does the Weather Affect U.S. Suicide Rate?","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":11,"value":"$3c","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":24,"value":"# Graphs","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":159,"value":"## Graph #1","pinned":true,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":27,"value":"This first graph compares Suicide Rate by US State to Cloud Cover Percentage, testing how much blocking of the sun can affect peoples' overall moods.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":53,"value":"viewof yearFilter = Inputs.select(\n yearGroup,\n { value: yearGroup.get(\"2021\"), sort: true, label: \"Year of Choropleth Data\" }\n)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":116,"value":"viewof rasterFilter = Inputs.select(\n rasteryears,\n { value: \"2021\", sort: true, label: \"Year of Raster Data\" }\n)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":58,"value":"viewof opacityChecker = Inputs.range([0, 1], {\n value: 1,\n step: 0.05,\n label: \"Choropleth Opacity\"\n})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":68,"value":"viewof rasterOpacity = Inputs.range([0, 1], {\n value: 0.75,\n step: 0.05,\n label: \"Raster Opacity\"\n})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":123,"value":"

\n \n

\n Cloud Cover Percentage (Low to High)\n

","pinned":true,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":25,"value":"$3d","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":158,"value":"The above graph definitely seems to suggest some kind of correlation - states with higher suicide rates like Montana, Wyoming, and Alaska have a lot of dark blue regions, while a bluish hue seems to envelop the entire right half of the map, with states like Vermont, Maine, West Virginia having higher suicide rates alongside quite a lot of spikes in cloud cover, and states like those in the Deep South (Georgia, Mississippi, and Alabama) having suicide rates similar in reach to their middling bluish hues. Even though there's a high chance cloud cover could play a part, states like New York, Maryland, Illinois, Massachusetts, and California provide amble dissonance.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":160,"value":"## Graph #2","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":29,"value":"Let's try a different metric. Graph #2 compares Suicide Rate by US State to Precipitation Levels. Rain and snow are typically associated with poorer weather, so I wanted to test how much that played a role in this discussion as well.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":55,"value":"viewof yearFilter2 = Inputs.select(\n yearGroup,\n { value: yearGroup.get(\"2021\"), sort: true, label: \"Year of Data\" }\n)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":117,"value":"viewof rasterFilter2 = Inputs.select(\n rasteryears,\n { value: \"2021\", sort: true, label: \"Year of Raster Data\" }\n)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":59,"value":"viewof opacityChecker2 = Inputs.range([0, 1], {\n value: 1,\n step: 0.05,\n label: \"Choropleth Opacity\"\n})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":66,"value":"viewof rasterOpacity2 = Inputs.range([0, 1], {\n value: 0.5,\n step: 0.05,\n label: \"Raster Opacity\"\n})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":143,"value":"

\n \n

\n Precipitation (Low to High)\n

","pinned":true,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":30,"value":"$3e","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":162,"value":"Right off the bat, you can notice a clear correlation - the right half of the map is a consistent high blue, states along the Rockies have a lot of dark blue spikes, and Alaska is completely covered in blue. Similar states, like Alaska, Maine, and West Virginia have lots of blue cover, suggesting there's a correlation to be found with precipitation as well, but states like New Jersey and Illinois are still outliers in that right half of the map in terms of their small suicide rates.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":161,"value":"## Graph #3","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":31,"value":"Let's do one last test. This third and final graph compared Suicide Rate by US State to Temperature, testing if simple temperature disparity could be a causation.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":56,"value":"viewof yearFilter3 = Inputs.select(\n yearGroup,\n { value: yearGroup.get(\"2021\"), sort: true, label: \"Year of Data\" }\n)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":118,"value":"viewof rasterFilter3 = Inputs.select(\n rasteryears,\n { value: \"2021\", sort: true, label: \"Year of Raster Data\" }\n)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":63,"value":"viewof opacityChecker3 = Inputs.range([0, 1], {\n value: 1,\n step: 0.05,\n label: \"Choropleth Opacity\"\n})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":67,"value":"viewof rasterOpacity3 = Inputs.range([0, 1], {\n value: 0.75,\n step: 0.05,\n label: \"Raster Opacity\"\n})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":155,"value":"

\n \n

\n Average Temperature, in Kelvin (High to Low)\n

","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":33,"value":"$3f","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":163,"value":"There's something more concrete there. The bluish hue on the right half of the map is gone, and it's clear to see that mountainous regions seem to play the biggest role - the darkest regions are Alaska, the Rockies, and the Appalachians. In particular, Alaska is a sea of dark blue, and the left half of the map (bar California) all have suicide rates that coincide with a theory of low temperatures being a cause.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":164,"value":"## Conclusion","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":157,"value":"Based on the above graphics, I assert that my hypothesis was mostly correct. Despite outliers in all three observed data points, there were consistent states that shared dreary characteristics and high suicide rates, like Montana, Wyoming, Vermont, and especially Alaska. While temperature was the most damning data point, and seems like the clearest correlation between dreary weather and suicide rate, cloud cover and precipitation were also relatively solid despite their greater quantities of outliers.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":12,"value":"# Revisions","pinned":true,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":13,"value":"## Revision #1\n\nEarly on in my project, I got my map choropleth to work properly. I decided to change its color scheme from blues to reds, as this project requires the color scheme of each graph to coincide with red-green color blindness. With the base choropleth in differing shades of red, I just needed to avoid choosing a green color scheme and focus on making the choropleth and raster visually appealing with each other.","pinned":true,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":35,"value":"Plot.plot({\n title: \"US States by Suicide Rate (per 100,000 people) vs Cloud Cover Percentage\",\n width: 975,\n height: 610,\n projection: \"albers-usa\",\n color: {\n scheme: \"blues\",\n domain: [0, 35],\n label: \"# of Suicides (per 100,000 people)\",\n legend: true,\n ticks: 8\n },\n marks: [\n Plot.geo(states, {\n fill: d => suicideMap.get(d.properties.name)?.pct,\n channels: {\n State: (d) => d.properties.name,\n \"Deaths (Raw)\": (d) => suicideMap.get(d.properties.name)?.[\"Deaths\"]\n },\n tip: true\n }),\n Plot.geo(statemesh, {stroke: \"white\", strokeWidth: .5})\n ]\n})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":14,"value":"## Revision #2\n\nNow that I had an appealing color scheme for my choropleths, I needed to give the data some life. Before the revision I made, it seemed as if the choropleth was accumulating all the data given into one point for each state. I added a year dropdown to allow for easy changing of the year of data, so that each year's data would be displayed properly.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":49,"value":"Plot.plot({\n title: \"US States by Suicide Rate (per 100,000 people) vs Cloud Cover Percentage\",\n width: 975,\n height: 610,\n projection: \"albers-usa\",\n color: {\n scheme: \"reds\",\n domain: [0, 35],\n label: \"# of Suicides (per 100,000 people)\",\n legend: true,\n ticks: 8\n },\n marks: [\n Plot.geo(states, {\n fill: d => suicideMap.get(d.properties.name)?.pct,\n channels: {\n State: (d) => d.properties.name,\n \"Deaths (Raw)\": (d) => suicideMap.get(d.properties.name)?.[\"Deaths\"]\n },\n tip: true\n }),\n Plot.geo(statemesh, {stroke: \"white\", strokeWidth: .5})\n ]\n})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":15,"value":"## Revision #3\n\nWith a year dropdown obtained, I felt my graph still needed more user customization as there would be two maps overlaid on each other. For this reason, I added an opacity meter for each choropleth, so that the user could decide for themselves as to how much each individual choropleth would be visible.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":64,"value":"viewof yearFilter4 = Inputs.select(\n yearGroup,\n { value: yearGroup.get(\"2021\"), sort: true, label: \"Year of Data\" }\n)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":65,"value":"Plot.plot({\n title: \"US States by Suicide Rate (per 100,000 people) vs Cloud Cover Percentage\",\n width: 975,\n height: 610,\n projection: \"albers-usa\",\n color: {\n scheme: \"reds\",\n domain: [0, 35],\n label: \"# of Suicides (per 100,000 people)\",\n legend: true,\n ticks: 8\n },\n marks: [\n Plot.geo(states, {\n fill: d => suicideMap4.get(d.properties.name)?.pct,\n channels: {\n State: (d) => d.properties.name,\n \"Deaths (Raw)\": (d) => suicideMap4.get(d.properties.name)?.[\"Deaths\"]\n },\n tip: true\n }),\n Plot.geo(statemesh, {stroke: \"white\", strokeWidth: .5})\n ]\n})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":16,"value":"## Revision #4\n\nWith a lot of trial and error with QGIS and some assistance from my class's TA, I was able to get my rasters implanted into my project. As the rasters blocked out my choropleth, I added an opacity meter to allow for better data analysis and differentiation.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":126,"value":"viewof yearFilter5 = Inputs.select(\n yearGroup,\n { value: yearGroup.get(\"2021\"), sort: true, label: \"Year of Data\" }\n)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":128,"value":"viewof opacityChecker4 = Inputs.range([0, 1], {\n value: 1,\n step: 0.05,\n label: \"Choropleth Opacity\"\n})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":104,"value":"$40","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":17,"value":"## Revision #5\n\nAs a final change to my project, I wanted to allow for the user to flick between each year of raster data covered in this project. As such, a year filter for rasters was added.\n\nI attempted to combine this year filter to cover both rasters and the choropleth at the same time, but I was not able to do so, so this is what I've decided to settle with.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":127,"value":"viewof yearFilter6 = Inputs.select(\n yearGroup,\n { value: yearGroup.get(\"2021\"), sort: true, label: \"Year of Data\" }\n)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":130,"value":"viewof opacityChecker5 = Inputs.range([0, 1], {\n value: 1,\n step: 0.05,\n label: \"Choropleth Opacity\"\n})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":129,"value":"viewof rasterOpacity4 = Inputs.range([0, 1], {\n value: 0.75,\n step: 0.05,\n label: \"Raster Opacity\"\n})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":125,"value":"$41","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":9,"value":"# Sources\n- CHELSA Weather Data in Rasters\n- Suicide Rate by State","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":52,"value":"# Data Processing Section","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":23,"value":"suiciderates = FileAttachment(\"Suicide Mortality by State.csv\").csv()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":20,"value":"us = FileAttachment(\"us-counties-10m.json\").json()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":21,"value":"states = topojson.feature(us, us.objects.states).features","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":22,"value":"statemesh = topojson.mesh(us, us.objects.states)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":26,"value":"const suicideMap = new Map(yearFilter.map(d => [d.Location, {\n ...d,\n pct: Number(d[\"Death Rate\"])\n}]))\n\nconst suicideMap2 = new Map(yearFilter2.map(d => [d.Location, {\n ...d,\n pct: Number(d[\"Death Rate\"])\n}]))\n\nconst suicideMap3 = new Map(yearFilter3.map(d => [d.Location, {\n ...d,\n pct: Number(d[\"Death Rate\"])\n}]))\n\nconst suicideMap4 = new Map(yearFilter4.map(d => [d.Location, {\n ...d,\n pct: Number(d[\"Death Rate\"])\n}]))\n\nconst suicideMap5 = new Map(yearFilter5.map(d => [d.Location, {\n ...d,\n pct: Number(d[\"Death Rate\"])\n}]))\n\nconst suicideMap6 = new Map(yearFilter6.map(d => [d.Location, {\n ...d,\n pct: Number(d[\"Death Rate\"])\n}]))","pinned":true,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":37,"value":"yearGroup = d3.group(suiciderates, (d) => d.YEAR.toString())","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":70,"value":"cloudcover2017 = FileAttachment(\"resizedcloudcover2017.tif\").arrayBuffer().then((buffer) => GeoTIFF.fromArrayBuffer(buffer))","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":92,"value":"cloudImage2017 = cloudcover2017.getImage()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":93,"value":"cloudValues2017 = cloudImage2017.readRasters({interleave: true})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":72,"value":"cloudcover2018 = FileAttachment(\"resizedcloudcover2018.tif\").arrayBuffer().then((buffer) => GeoTIFF.fromArrayBuffer(buffer))","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":108,"value":"cloudImage2018 = cloudcover2018.getImage()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":109,"value":"cloudValues2018 = cloudImage2018.readRasters({interleave: true})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":73,"value":"cloudcover2019 = FileAttachment(\"resizedcloudcover2019.tif\").arrayBuffer().then((buffer) => GeoTIFF.fromArrayBuffer(buffer))","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":110,"value":"cloudImage2019 = cloudcover2019.getImage()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":111,"value":"cloudValues2019 = cloudImage2019.readRasters({interleave: true})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":74,"value":"cloudcover2020 = FileAttachment(\"resizedcloudcover2020.tif\").arrayBuffer().then((buffer) => GeoTIFF.fromArrayBuffer(buffer))","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":113,"value":"cloudImage2020 = cloudcover2020.getImage()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":115,"value":"cloudValues2020 = cloudImage2020.readRasters({interleave: true})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":75,"value":"cloudcover2021 = FileAttachment(\"resizedcloudcover2021.tif\").arrayBuffer().then((buffer) => GeoTIFF.fromArrayBuffer(buffer))","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":119,"value":"cloudImage2021 = cloudcover2021.getImage()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":120,"value":"cloudValues2021 = cloudImage2021.readRasters({interleave: true})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":91,"value":"cloudcoverBoundingBox = cloudcover2017.getImage().then((d) => d.getBoundingBox())","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":76,"value":"precipitation2017 = FileAttachment(\"resizedprecipitation2017.tif\").arrayBuffer().then((buffer) => GeoTIFF.fromArrayBuffer(buffer))","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":131,"value":"precImage2017 = precipitation2017.getImage()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":132,"value":"precValues2017 = precImage2017.readRasters({interleave: true})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":77,"value":"precipitation2018 = FileAttachment(\"resizedprecipitation2018.tif\").arrayBuffer().then((buffer) => GeoTIFF.fromArrayBuffer(buffer))","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":133,"value":"precImage2018 = precipitation2018.getImage()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":134,"value":"precValues2018 = precImage2018.readRasters({interleave: true})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":78,"value":"precipitation2019 = FileAttachment(\"resizedprecipitation2019.tif\").arrayBuffer().then((buffer) => GeoTIFF.fromArrayBuffer(buffer))","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":135,"value":"precImage2019 = precipitation2019.getImage()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":136,"value":"precValues2019 = precImage2019.readRasters({interleave: true})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":79,"value":"precipitation2020 = FileAttachment(\"resizedprecipitation2020.tif\").arrayBuffer().then((buffer) => GeoTIFF.fromArrayBuffer(buffer))","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":137,"value":"precImage2020 = precipitation2020.getImage()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":138,"value":"precValues2020 = precImage2020.readRasters({interleave: true})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":80,"value":"precipitation2021 = FileAttachment(\"resizedprecipitation2021.tif\").arrayBuffer().then((buffer) => GeoTIFF.fromArrayBuffer(buffer))","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":139,"value":"precImage2021 = precipitation2021.getImage()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":140,"value":"precValues2021 = precImage2021.readRasters({interleave: true})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":99,"value":"precipitationBoundingBox = precipitation2017.getImage().then((d) => d.getBoundingBox())","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":156,"value":"temperature2017 = FileAttachment(\"resizedtemperature2017.tif\").arrayBuffer().then((buffer) => GeoTIFF.fromArrayBuffer(buffer))","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":144,"value":"tempImage2017 = temperature2017.getImage()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":145,"value":"tempValues2017 = tempImage2017.readRasters({interleave: true})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":87,"value":"temperature2018 = FileAttachment(\"resizedtemperature2018.tif\").arrayBuffer().then((buffer) => GeoTIFF.fromArrayBuffer(buffer))","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":146,"value":"tempImage2018 = temperature2018.getImage()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":147,"value":"tempValues2018 = tempImage2018.readRasters({interleave: true})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":88,"value":"temperature2019 = FileAttachment(\"resizedtemperature2019.tif\").arrayBuffer().then((buffer) => GeoTIFF.fromArrayBuffer(buffer))","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":149,"value":"tempImage2019 = temperature2019.getImage()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":150,"value":"tempValues2019 = tempImage2019.readRasters({interleave: true})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":89,"value":"temperature2020 = FileAttachment(\"resizedtemperature2020.tif\").arrayBuffer().then((buffer) => GeoTIFF.fromArrayBuffer(buffer))","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":151,"value":"tempImage2020 = temperature2020.getImage()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":152,"value":"tempValues2020 = tempImage2020.readRasters({interleave: true})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":90,"value":"temperature2021 = FileAttachment(\"resizedtemperature2021.tif\").arrayBuffer().then((buffer) => GeoTIFF.fromArrayBuffer(buffer))","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":153,"value":"tempImage2021 = temperature2021.getImage()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":154,"value":"tempValues2021 = tempImage2021.readRasters({interleave: true})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":102,"value":"temperatureBoundingBox = temperature2017.getImage().then((d) => d.getBoundingBox())","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":106,"value":"cloudrasterdata = ({\n 2017: cloudValues2017,\n 2018: cloudValues2018,\n 2019: cloudValues2019,\n 2020: cloudValues2020,\n 2021: cloudValues2021\n})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":141,"value":"precrasterdata = ({\n 2017: precValues2017,\n 2018: precValues2018,\n 2019: precValues2019,\n 2020: precValues2020,\n 2021: precValues2021\n})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":142,"value":"temprasterdata = ({\n 2017: tempValues2017,\n 2018: tempValues2018,\n 2019: tempValues2019,\n 2020: tempValues2020,\n 2021: tempValues2021\n})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":121,"value":"rasteryears = [\"2017\", \"2018\", \"2019\", \"2020\", \"2021\"]","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":71,"value":"GeoTIFF = 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