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29:null 2a:["$","title",null,{"children":"Final Project | Camille Lust | Observable"}] 2f:T5d6,md`# Introduction - What Social vulnerability is - Every community must prepare for and respond to hazardous events, whether a natural disaster like a tornado or a disease outbreak, or an anthropogenic event such as a harmful chemical spill. The degree to which a community exhibits certain social conditions, including high poverty, low percentage of vehicle access, or crowded households, may affect that community’s ability to prevent human suffering and financial loss in the event of disaster. These factors describe a community’s social vulnerability (CDC SVI Documentation) - Standardization of the data, property loss per capita and not total, why? - This data was standardized by the property loss per capita, or total property loss/population. This was done to allow us to see losses more equally and be able to attribute losss patterns to more than population centers, or counties with areas of high density. While totals would be interesting to look at as well, we feel as though totals may just end up representing counties with higher populations. - Losses adjusted to 2021 - The loss data that we used, from the SHELDUS data base, was loss adjusted to 2021. - Data ranges from 2010-2020 - The data that we used from SHELDUS was over the time span 2010-2020. Because SVI is calculated using census data, we used this ten year range so that we could then use the 2020 SVI to hopefully represent the modeled population and demographic variables for the census. `30:T884, //this whole chunk of code creates your map choroplethflooding = { const svg = d3.create("svg") //this is creating our canvas to put our map on .attr("viewBox", [0, 0, width, height]); //this is bringing in the canvas that we defined above svg.append("g") .attr("transform", "translate(60,20)") //this value 'translate' changes where your legend is positioned based on the x and y values of our previously defined canvas .append(() => legend({ color: colorflood, //inputting the color range and colors that we created before title: dataflood.title, //bringing down the title that we created earlier //you can also put the tile in here manually as well if you want width: 300, //width of the legend tickFormat: ".2f" //number of decimal places within the legend }) ); svg.append("g") //"painting" the map onto our canvas //"g" is grouping our graphics together, in our case polygons .selectAll("path") .data(IowaCounties.features) //what we are going to read for our data, reads from the topojson //you need to pull the features here, not just the counties .join("path") //paths are polygons .attr("stroke", "black") //color of the outlines .attr("stroke-linejoin", "round") //you can change the type of your line .attr("stroke-width", 1.1) //the thickness of your lines // .attr("fill", function(d){ // console.log(color(data.get(d.properties.FIPS)[0])) // return color(data.get(d.properties.FIPS)[0]); // }) .attr("fill", d => colorflood(dataflood.get(d.properties.COUNTY))) //determines the color of each polygon, using the data 'dictionary' that we created //the data also has to match in type, numeric or string .attr("d", path) .append("title") .text(d => "Flooding Losses" + (dataflood.get(d.properties.COUNTY))); //allows you to hover over and see the exact values for each polygon return svg.node(); }31:T88f, //this whole chunk of code creates your map choroplethtornado = { const svg = d3.create("svg") //this is creating our canvas to put our map on .attr("viewBox", [0, 0, width, height]); //this is bringing in the canvas that we defined above svg.append("g") .attr("transform", "translate(60,20)") //this value 'translate' changes where your legend is positioned based on the x and y values of our previously defined canvas .append(() => legend({ color: colortornado, //inputting the color range and colors that we created before title: data_Tornado.title, //bringing down the title that we created earlier //you can also put the tile in here manually as well if you want width: 300, //width of the legend tickFormat: ".2f" //number of decimal places within the legend }) ); svg.append("g") //"painting" the map onto our canvas //"g" is grouping our graphics together, in our case polygons .selectAll("path") .data(IowaCounties.features) //what we are going to read for our data, reads from the topojson //you need to pull the features here, not just the counties .join("path") //paths are polygons .attr("stroke", "black") //color of the outlines .attr("stroke-linejoin", "round") //you can change the type of your line .attr("stroke-width", 1.1) //the thickness of your lines // .attr("fill", function(d){ // console.log(color(data.get(d.properties.FIPS)[0])) // return color(data.get(d.properties.FIPS)[0]); // }) .attr("fill", d => colortornado(data_Tornado.get(d.properties.COUNTY))) //determines the color of each polygon, using the data 'dictionary' that we created //the data also has to match in type, numeric or string .attr("d", path) .append("title") .text(d => "Tornado Losses" + (data_Tornado.get(d.properties.COUNTY))); //allows you to hover over and see the exact values for each polygon return svg.node(); }32:T4e1,md`# Conclusion Throughout this project we have learned that by using different visualization methods we can reveal different patterns. We have also uncovered a pattern in that central Iowa has a higher number of events and lower losses compared to the rest of the state. This means that the number of events is not highly correlated to the per capita property losses. We have also learned more about how looking at multiple variables can help us to better understand underlying patterns or causes for those patterns in loss. From our analysis, we also believe that SVI is correlated to total losses, but not strongly. We think that if you were interested in a specific hazard type that looking more into geographic vulnerability to specific hazards (ex. flooding) would be useful in truly understanding a counties hazard vulnerability. to In the future, we think it would be helpful to also include data on the magnitude of events to see if that brings more understanding to the relationship between the number of events and losses, and if magnitude has an impact on losses. We also think that if a larger data range was used different patterns may be revealed because hazards are not continuous or yearly events, especially at the county level. `33:T465,legendbivar = () => { const k = 24; const arrow = DOM.uid(); return svg` ${d3.cross(d3.range(n), d3.range(n)).map(([i, j]) => svg` ${databivar.title[0]}${labels[j] && ` (${labels[j]})`} ${databivar.title[1]}${labels[i] && ` (${labels[i]})`} `)} ${databivar.title[0]} ${databivar.title[1]} `; 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Lust","avatar_url":"https://avatars-next.observableusercontent.com/4f05fcc8a124015b3715477d74eaa6207ff13dc89bb94e846fca6e6e7739be7c","tier":"starter_2024"},"version":749,"publish_version":null,"role":null,"authored":false,"authors":[{"type":"individual","id":"d5b41e509b598772","login":"camillelust","name":"Camille Lust","avatar_url":"https://avatars-next.observableusercontent.com/4f05fcc8a124015b3715477d74eaa6207ff13dc89bb94e846fca6e6e7739be7c","tier":null}],"members":[{"type":"individual","id":"d77829c4ac534e39","login":"adamdodds3","name":"Adam","avatar_url":"https://avatars-next.observableusercontent.com/bad8c981edcea83c1862670d32628899338c2b33c3f5e91d35eac13b73ed5ed3","tier":null,"role":"editor"},{"type":"individual","id":"d5b41e509b598772","login":"camillelust","name":"Camille Lust","avatar_url":"https://avatars-next.observableusercontent.com/4f05fcc8a124015b3715477d74eaa6207ff13dc89bb94e846fca6e6e7739be7c","tier":null,"role":"editor"}],"comments":[],"custom_thumbnail":false,"trashed":false,"subscribed":null,"forks":0,"collection":null,"type":"notebook","body":{"title":"Final Project","cells":[{"id":0,"value":"md`# Final Project\nFinal Project of Camille Lust and Adam Dodds \n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":110,"value":"md`# Objectives\n- Visualize spatial patterns in hazard losses across the State of Iowa and identify the relationship between Social Vulnerability (SVI) and hazard losses.\n`\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":684,"value":"md`## The Motivation: \n- The motivation behind uncovering these patterns is that we will hopefully be able to better understand if there is a relationship between flood losses and census variables. If we are able to first understand this relationship, we may be able to decrease negative impacts in the future.`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":132,"value":"md`# Uses\n- To provide a framework for mitigation spending based on counties with high loss values and high SVI. This will hopefully increase equity in mitigation spending and promote a shift away from focusing only on Cost Benefit Analysis (CBA) methods in the distribution of funds. \n- An intended audience for this project could be the state government to see what counties are accruing more losses than others and adjust spending or mitigation efforts accordingly. This could also be a starting place for anyone to understand the impacts of social vulnerability variables and how efforts may need to be shifted to better account for these disparities.\n\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":688,"value":"md`## Data Sources\n- Our main data source is SHELDUS data, for our hazard loss and number of events values.\n- We used the CDC SVI data for our SVI values.\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":691,"value":"md`## Limitations to the data\nSome limitations to our data are that the SHELDUS data for hazard losses has a very low spatial resolution with the smallest measurement level being Counties. While this is useful for uncovering general patterns, we do wish that it had a higher spatial resolution - possibly down to the block group level- to better understand how social variables impact losses. Because many of the counties in Iowa are quite large, we do recognize that the patterns we find may lead to over generalization. We also recognize that there is often a lot of variability within a county, and due to the low spatial resolution, we may not be able to gain a full understanding of that variability. `","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":113,"value":"$2f","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":609,"value":"md`## SOMVIS for Number of Hazard Events by event and Total Number of Events and Total Losses`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":616,"value":"somvistotaltotal = FileAttachment(\"SOMVIStotaltotal.png\").image()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":618,"value":"somvistototalsdj = FileAttachment(\"SOMVIStototalsdj.png\").image()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":663,"value":"md`## Explanation of SomVis Tool:\nIn the map above we used the SomVis tool to find clusters of similarities. We used the total number of events for all hazards, the number of each event for each hazard, as well as the total property loss per capita for all the hazards. The lines of similar color represent a cluster of several counties. \n\nBelow you can see each cluster selected to visualize on its own. Whereas counties with similar levels of events and losses will be clustered together. This would indicate the areas of green colors to be at the high end for a number of events as well as high losses.\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":620,"value":"md`### Clusters`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":623,"value":"screenshot20230508122120 = FileAttachment(\"Screenshot 2023-05-08 122120.png\").image()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":697,"value":"md`#### Pink/Purple Cluster\nThis cluster for example, shows counties that have relatively few hazard events overall, but also had relatively high loss values considering how few events they had. Spatially, these counties are clustered along the eastern and western borders of the State.`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":625,"value":"screenshot20230508122214 = FileAttachment(\"Screenshot 2023-05-08 122214.png\").image()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":705,"value":"md`#### Green Cluster\nThis cluster shows counties that had a high number of events and a somewhat low property loss per capita values. Spatially, these counties are clustered in central Iowa.`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":627,"value":"screenshot20230508122308 = FileAttachment(\"Screenshot 2023-05-08 122308.png\").image()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":708,"value":"md`#### Blues Cluster\nThis cluster shows the counties that had a middle number of events and lower property loss per capita values.`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":629,"value":"screenshot20230508122346 = FileAttachment(\"Screenshot 2023-05-08 122346.png\").image()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":711,"value":"md`#### Reds/Oranges Cluster\nThis cluster is mainly related by their high loss values. While the number of events is less correlated between these counties, it seems like they have mainly higher levels of events. Spatially, these counties are not as clustered as some of our previous clusters.`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":631,"value":"screenshot20230508122441 = FileAttachment(\"Screenshot 2023-05-08 122441.png\").image()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":714,"value":"md`#### Pinks Cluster\nThis cluster has both low numbers of events and low loss values. Spatially, these counties are clustered in both the South Eastern and North Western corners of the State.`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":717,"value":"md`### Cluster Analysis\nBased on these clusters and their number of event and loss values, we think that we can see a pattern in lower loss values and lower numbers of events along the outside borders of the state. This may mean that these areas are less hazardous or less prone to higher magnitude events. There is also a pattern of central Iowa having a high number of events and somewhat lower loss values.`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":670,"value":"md`## Use of Choropleth Maps:\nIn the 5 choropleth maps below we used these to look at what counties had high amounts of property loss per capita for each of the selected hazards. We used these initially to look at where these events are happening and to see if we could find any correlation between them, we later used the somvis tool to better identify these areas but still think these are useful to visualize each hazard type and its loss patterns individually.\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":19,"value":"md`# Property Losses from Flooding Per Capita`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":174,"value":"$30","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":31,"value":"md`# Property Losses from Tornadoes Per Capita`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":256,"value":"$31","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":21,"value":"md`# Hail`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":260,"value":"choroplethhail = {\n const svg = d3.create(\"svg\")\n .attr(\"viewBox\", [0, 0, width, height]);\n\n svg.append(\"g\")\n .attr(\"transform\", \"translate(60,20)\")\n .append(() => \n legend({\n color: color_hail,\n title: data_hail.title,\n width: 300,\n tickFormat: \".2f\"\n })\n );\n\n svg.append(\"g\")\n .selectAll(\"path\")\n .data(IowaCounties.features)\n .join(\"path\")\n .attr(\"stroke\", \"black\")\n .attr(\"stroke-linejoin\", \"round\")\n .attr(\"stroke-width\", 1.1)\n .attr(\"fill\", d => color_hail(data_hail.get(d.properties.COUNTY)))\n .attr(\"d\", path)\n .append(\"title\")\n .text(d => \"Hail\" + (data_hail.get(d.properties.COUNTY)));\n\n return svg.node();\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":29,"value":"md`# Severe / Thunder Storm`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":316,"value":"choroplethsevere = {\n const svg = d3.create(\"svg\")\n .attr(\"viewBox\", [0, 0, width, height]);\n\n svg.append(\"g\")\n .attr(\"transform\", \"translate(60,20)\")\n .append(() => \n legend({\n color: color_severe,\n title: data_severe.title,\n width: 300,\n tickFormat: \".2f\"\n })\n );\n\n svg.append(\"g\")\n .selectAll(\"path\")\n .data(IowaCounties.features)\n .join(\"path\")\n .attr(\"stroke\", \"black\")\n .attr(\"stroke-linejoin\", \"round\")\n .attr(\"stroke-width\", 1.1)\n .attr(\"fill\", d => color_severe(data_severe.get(d.properties.COUNTY)))\n .attr(\"d\", path)\n .append(\"title\")\n .text(d => \"Severe Storm\" + (data_severe.get(d.properties.COUNTY)));\n\n return svg.node();\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":35,"value":"md`# Winter Weather`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":349,"value":"choroplethwinter = {\n const svg = d3.create(\"svg\")\n .attr(\"viewBox\", [0, 0, width, height]);\n\n svg.append(\"g\")\n .attr(\"transform\", \"translate(60,20)\")\n .append(() => \n legend({\n color: color_winter,\n title: data_winter.title,\n width: 300,\n tickFormat: \".2f\"\n })\n );\n\n svg.append(\"g\")\n .selectAll(\"path\")\n .data(IowaCounties.features)\n .join(\"path\")\n .attr(\"stroke\", \"black\")\n .attr(\"stroke-linejoin\", \"round\")\n .attr(\"stroke-width\", 1.1)\n .attr(\"fill\", d => color_winter(data_winter.get(d.properties.COUNTY)))\n .attr(\"d\", path)\n .append(\"title\")\n .text(d => \"Winter Weather\" + (data_winter.get(d.properties.COUNTY)));\n\n return svg.node();\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":137,"value":"md`# Why a Social Vulnerability Index (SVI) Needs to be used: \n- As you can see in the maps above, there are counties that have very high loss values per hazard (could mean high property value) but this does not necessarily mean that these counties need the most assistance in mitigiation funding . By visualizing SVI compared to total losses we hope to emphasize the importance of recognizing differing levels of social vulnerability. `","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":78,"value":"md`# SVI vs. Total Losses Bivariate`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":438,"value":"viewof colors = {\n const form = html`

color scheme`;\n form.i.selectedIndex = 1;\n form.oninput = () => form.value = schemes[form.i.selectedIndex].colors;\n form.oninput();\n return form;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":386,"value":"chart = {\n const svg = d3.create(\"svg\")\n .attr(\"viewBox\", [0, 0, width, height]);\n\n svg.append(legendbivar)\n .attr(\"transform\", \"translate(100,400)\");\n\n svg.append(\"g\")\n .selectAll(\"path\")\n .data(topojson.feature(polygons, polygons.objects.propertylossesnew).features)\n .join(\"path\")\n .attr(\"stroke\", \"black\")\n .attr(\"fill\", d => color(databivar.get(d.properties[idAttribute])))\n .attr(\"d\", path)\n .append(\"title\")\n .text(d => `${d.properties[idAttribute]}, ${format(databivar.get(d.properties[idAttribute]))}`); \n\n return svg.node();\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":657,"value":"md`## Explanation of Bivariate:\nIn the bivariate map, we are looking at the relationship between the total property loss per capita and social vulnerability. Areas of high loss and high vulnerability are shown in dark blue, these are areas where it is key to identify why these people are more vulnerable and if that vulnerability is correlating to higher property loss than use the knowledge to adjust mitigation spending accordingly, making equity a focus.`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":605,"value":"md`## SOMVIS for Per Capita Hazard Losses and SVI\nThis SOMVIS map shows the property losses per capita for each event type along with the SVI values. The whole map and its clusters are shown directly below while the individual clusters are shown under \"SVI/Losses SOMVIS Clusters\".`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":640,"value":"screenshot20230508123823 = FileAttachment(\"Screenshot 2023-05-08 123823.png\").image()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":643,"value":"svi = FileAttachment(\"svi.png\").image()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":636,"value":"md`## SVI/Losses SOMVIS Clusters`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":645,"value":"screenshot20230508124044 = FileAttachment(\"Screenshot 2023-05-08 124044.png\").image()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":730,"value":"md`#### Red Cluster\nThis cluster is spatially pretty distributed throughout the state. But this cluster represents counties that have lower values for all of the attributes that we were looking at, both losses per capita by event and SVI.`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":647,"value":"screenshot20230508124108 = FileAttachment(\"Screenshot 2023-05-08 124108.png\").image()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":734,"value":"md`#### Pinks Cluster\nThis cluster is spatially located mainly in central Iowa. It represents the counties that have slightly higher values for both SVI and losses than our Reds cluster. `","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":651,"value":"screenshot20230508124131 = FileAttachment(\"Screenshot 2023-05-08 124131.png\").image()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":737,"value":"md`#### Greens Cluster\nThis cluster is spatially all over the place. But it seems like the variable that they share the most in similarities is SVI. They all have high SVI values but their loss values are not as strongly correlated as we expected.`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":653,"value":"screenshot20230508124209 = FileAttachment(\"Screenshot 2023-05-08 124209.png\").image()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":655,"value":"screenshot20230508124237 = FileAttachment(\"Screenshot 2023-05-08 124237.png\").image()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":743,"value":"md`#### Purples Cluster\nThis cluster is spatially located in Northwest Iowa. It seems like these counties have high property loss values for winter weather but lower loss values and a lower SVI.\n`\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":746,"value":"md`## SOMVIS SVI Analysis\nOverall, when looking at SVI and property losses, it seems like SVI is not highly correlated with SVI. While there may be some patterns, it seems like geographic location is more indictive of losses than SVI. Although it is interesting to see at the county level, we think that SVI and hazard losses at the census tract level may be more revealing about the patterns and impacts of SVI values. But because hazard loss data was not widely available at the census tract level, this was our best visualization method. `","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":722,"value":"$32","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":275,"value":"md`### Background stuff`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":388,"value":"// http://www.joshuastevens.net/cartography/make-a-bivariate-choropleth-map/\nschemes = [\n {\n name: \"RdBu\", \n colors: [\n \"#e8e8e8\", \"#e4acac\", \"#c85a5a\",\n \"#b0d5df\", \"#ad9ea5\", \"#985356\",\n \"#64acbe\", \"#627f8c\", \"#574249\"\n ]\n },\n {\n name: \"BuPu\", \n colors: [\n \"#e8e8e8\", \"#ace4e4\", \"#5ac8c8\",\n \"#dfb0d6\", \"#a5add3\", \"#5698b9\", \n \"#be64ac\", \"#8c62aa\", \"#3b4994\"\n ]\n },\n {\n name: \"GnBu\", \n colors: [\n \"#e8e8e8\", \"#b5c0da\", \"#6c83b5\",\n \"#b8d6be\", \"#90b2b3\", \"#567994\",\n \"#73ae80\", \"#5a9178\", \"#2a5a5b\"\n ]\n },\n {\n name: \"PuOr\", \n colors: [\n \"#e8e8e8\", \"#e4d9ac\", \"#c8b35a\",\n \"#cbb8d7\", \"#c8ada0\", \"#af8e53\",\n \"#9972af\", \"#976b82\", \"#804d36\"\n ]\n }\n]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":46,"value":"topojson = require(\"topojson-client@3\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":390,"value":"labels = [\"low\", \"\", \"high\"]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":51,"value":"IowaCounties = topojson.feature(Counties, Counties.objects.propertylossesnew)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":401,"value":"polygon_features = topojson.feature(polygons, polygons.objects.propertylossesnew)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":48,"value":"Counties = FileAttachment(\"propertylossesnew.json\").json()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":37,"value":"d3 = require(\"d3@5\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":42,"value":"simple = require(\"simple-statistics@7.0.7/dist/simple-statistics.min.js\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":44,"value":"format = d => `${d}%`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":419,"value":"polygons = FileAttachment(\"propertylossesnew.json\").json()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":417,"value":"idAttribute = \"COUNTY\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":39,"value":"import {legend} from \"@d3/color-legend\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":425,"value":"formatNum = d3.format(\".1f\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":403,"value":"databivar = Object.assign(new Map(d3.csvParse(await FileAttachment(\"proplossestable.csv\").text(), ({COUNTY, Grand_Tota\n, RPL_THEMES }) => [COUNTY, [+Grand_Tota, +RPL_THEMES]])), {title: [\"Total Losses\", \"SVI\"]})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":421,"value":"$33","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":398,"value":"pathbivariate = d3.geoPath().projection(projection)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":423,"value":"color = {\n return value => {\n if (!value) return \"#ccc\";\n let [a, b] = value;\n return colors[y(b) + x(a) * n];\n };\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":427,"value":"formatbivar = (value) => {\n if (!value) return \"N/A\";\n let [a, b] = value;\n return `${a} ${databivar.title[0]}${labels[x(a)] && ` (${labels[x(a)]})`}\n${formatNum(b)} ${databivar.title[1]}${labels[y(b)] && ` (${labels[y(b)]})`}`;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":347,"value":"color_winter = d3.scaleQuantile()\n .domain(loss_winter)\n .range(pink_winter)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":343,"value":"loss_winter = Array.from(csv_data_winter.values(), d => d[1])","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":341,"value":"pink_winter = [d3.color(\"#feebe2\"), d3.color(\"#fbb4b9\"), d3.color(\"#f768a1\"), d3.color(\"#ae017e\")]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":336,"value":"csv_data_winter = d3.csvParse(await FileAttachment(\"PropertyLossPerCapWGS84.csv\").text(),({COUNTY, Winter_Weather}) => [COUNTY, +Winter_Weather])","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":339,"value":"data_winter= Object.assign(new Map(csv_data_winter), {title: \"Winter Storms\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":301,"value":"csv_data_severe = d3.csvParse(await FileAttachment(\"PropertyLossPerCapWGS84.csv\").text(),({COUNTY, Severe_Storm_Thunder_Storm}) => [COUNTY, +Severe_Storm_Thunder_Storm])","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":312,"value":"naturalbreakssevere = simple.ckmeans(loss_severe, pink_severe.length).map(v => v.pop())","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":308,"value":"pink_severe = [d3.color(\"#feebe2\"), d3.color(\"#fbb4b9\"), d3.color(\"#f768a1\"), d3.color(\"#ae017e\")]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":314,"value":"color_severe = d3.scaleQuantile()\n .domain(loss_severe)\n .range(pink_severe)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":310,"value":"loss_severe = Array.from(csv_data_severe.values(), d => d[1])","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":305,"value":"data_severe= Object.assign(new Map(csv_data_severe), {title: \"Severe Storms\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":96,"value":"csv_data_hail = d3.csvParse(await FileAttachment(\"PropertyLossPerCapWGS84.csv\").text(),({COUNTY, Hail}) => [COUNTY, +Hail])","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":102,"value":"data_hail= Object.assign(new Map(csv_data_hail), {title: \"Hail\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":146,"value":"pink_hail = [d3.color(\"#feebe2\"), d3.color(\"#fbb4b9\"), d3.color(\"#f768a1\"), d3.color(\"#ae017e\")]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":149,"value":"loss_hail = Array.from(csv_data_hail.values(), d => d[1])","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":153,"value":"naturalbreakshail = simple.ckmeans(loss_hail, pink_hail.length).map(v => v.pop())","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":283,"value":"color_hail = d3.scaleQuantile()\n .domain(loss_hail)\n .range(pink_hail)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":209,"value":"csv_data_Tornado = d3.csvParse(await FileAttachment(\"PropertyLossPerCapWGS84.csv\").text(),({COUNTY, Tornado}) => [COUNTY, +Tornado])","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":211,"value":"data_Tornado= Object.assign(new Map(csv_data_Tornado), {title: \"Tornado Losses Per Capita\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":214,"value":"pink_Tornado = [d3.color(\"#feebe2\"), d3.color(\"#fbb4b9\"), d3.color(\"#f768a1\"), d3.color(\"#ae017e\")]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":216,"value":"loss_Tornado = Array.from(csv_data_Tornado.values(), d => d[1])","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":223,"value":"colortornado = d3.scaleQuantile()\n .domain(loss_Tornado)\n .range(pink_Tornado)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":53,"value":"csv_data_Flood = d3.csvParse(await FileAttachment(\"PropertyLossPerCapWGS84.csv\").text(),({COUNTY, Flooding}) => [COUNTY, +Flooding])","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":60,"value":"dataflood = Object.assign(new Map(csv_data_Flood), {title: \"Flooding Losses Per Capita\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":67,"value":"Pink_Flood = [d3.color(\"#feebe2\"), d3.color(\"#fbb4b9\"), d3.color(\"#f768a1\"), d3.color(\"#ae017e\")]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":72,"value":"Floodloss = Array.from(csv_data_Flood.values(), d => d[1])","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":69,"value":" naturalbreaksflood = simple.ckmeans(Floodloss, Pink_Flood.length).map(v => v.pop())\n//\n//quantile_Flood = d3.scaleQuantile()\n //.domain(0, d3.max(Floodloss))\n // .range(Pink_Flood)\n\n//color = d3.scaleThreshold()\n //.domain(quantile)\n // .range(Pink_Flood)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":182,"value":"//color = d3.scaleThreshold()\n// .domain(natural)\n// .range(Pink_Flood)\ncolorflood = d3.scaleQuantile()\n .domain(Floodloss)\n .range(Pink_Flood)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":394,"value":"x = d3.scaleQuantile(Array.from(databivar.values(), d => d[0]), d3.range(n))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":392,"value":"n = Math.floor(Math.sqrt(colors.length))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":396,"value":"y = d3.scaleQuantile(Array.from(databivar.values(), d => d[1]), d3.range(n))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":162,"value":"width = 975","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":166,"value":"margin = 100","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":164,"value":"height = 610","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":168,"value":"projection = d3.geoTransverseMercator().rotate([95,120]).fitExtent([[80, 80], [width, height]], IowaCounties);","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":172,"value":"path = d3.geoPath().projection(projection);","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"}],"files":"$W34","stdlib":"1","titling":"auto"}} 2e:"$2d:body"