2d:null 2e:["$","title",null,{"children":"Course Assignment in Asset Management | Fotis Zapantis | Observable"}] 33:T436,After a quick inspection, FederalID is a unique identifier for each row but there are only 1379 unique NIDs indicating that some of the dams have multiple structures attached to them (also see **Number of Associated Structures [1-12]**). When looking at the number of rows some of the associated structures seem to be missing, so I have assumed that these are not dam related: see for example Lake Aloha Dam, where only 2 of the 12 structures appear on the list. Therefore, I made a necessary assumption that UID FederalID is a good enough identifier, with multiple condition assessments for an asset being accounted as discrete structures: see for example Lake Aloha Main (_High_), Lake Aloha Auxiliary (_Low_). Any errors in the totals are most probably a miscounting of the assets in the processed dataset (_caDam Multi-Structures_). Also, most condition assessments for multi-structures seem to be missing. _A1: Missing associated structures are not dams_ _A2: Assets with the different FederalID are accounted for separately_34:T563,--- ### Task 2: Water storage and hazard classification (interactive) In Observable Plot, create an interactive **bubble chart of dam water storage volume (acre-feet) versus surface area (acres)** (the variable names in `caDams` are `StorageAcreFeet` and `SurfaceAreaAcres`), with the **radius dependent on dam drainage area** (`DrainageAreaSqMiles`). Add tooltips that show each of the channels above, **plus the dam name.** Add a radio button Input that lets a user select a HazardPotentialClassification (Low, Significant, or High) such that only damns with the selected hazard classification are highlighted in a meaningful color, and others are in gray. **Task 2 minimum requirements (but feel free to do more!):** - An interactive bubble chart of dam water storage versus surface area, with radius based on drainage area, and with color indicating the hazard potential. From an Input, a user can select different hazard potential classifications, and dams with that classification are highlighted in the chart. - A custom color scale (with a legend in meaningful order, Low > Significant > High) - One (or both) axis updated to a log scale to compress values across orders of magnitudes - An interactive tooltip that shows the dam name, along with other channels you choose Create your Task 2 chart in a cell immediately below.35:T431,Plot.plot({ color: { type: "categorical", domain: ["Low", "Significant", "High"], range: ["green", "red","darkred"], legend: true }, x: { type: "log", label: "Water Storage Volume (acre-feet)", grid: true, domain: [10, 10000000], nice:true}, y: { type: "log", label: "Surface Area (acres)", grid: true, domain: [0.05, 150000], nice:true }, r: { domain: d3.extent(caDams, (d) => d.DrainageAreaSqMiles), range: [1, 45]}, insetTop: 25, insetRight: 25, insetBottom: 0, insetLeft: 0, width, marks: [ Plot.frame(), Plot.dot(caDams, { x: "StorageAcreFeet", y: "SurfaceAreaAcres", r: "DrainageAreaSqMiles", fill: "#ccc", opacity: 0.8, channels: { DamName: "DamName" } }), Plot.dot(caDams.filter((d) => hazardClass.includes(d.HazardPotentialClassification)), { x: "StorageAcreFeet", y: "SurfaceAreaAcres", fill: "HazardPotentialClassification", r: "DrainageAreaSqMiles", tip: true, channels: { DamName: "DamName", year: "YearCompleted" } }) ] })36:T452,Plot.plot({ color: { type: "categorical", domain: ["Low", "Significant", "High"], range: ["green", "red", "darkred"], }, x: { type: "log", label: "Water Storage Volume (acre-feet)", grid: true, domain: [10, 10000000], nice:true}, y: { type: "log", label: "Surface Area (acres)", grid: true, domain: [0.05, 150000], nice:true }, r: { domain: d3.extent(caDams, (d) => d.DrainageAreaSqMiles), range: [3, 45]}, insetTop: 25, insetRight: 25, insetBottom: 0, insetLeft: 0, width, marks: [ Plot.frame(), Plot.dot(caDams, { x: "StorageAcreFeet", y: "SurfaceAreaAcres", r: "DrainageAreaSqMiles", fill: "#ccc", opacity: 0.8, channels: { DamName: "DamName" } }), Plot.dot(caDams, { filter: (d) => d.PrimaryPurpose == selectPrimaryPurpose && d.YearCompleted <= selectYearRange[1] && d.YearCompleted >= selectYearRange [0], x: "StorageAcreFeet", y: "SurfaceAreaAcres", fill: "HazardPotentialClassification", r: "DrainageAreaSqMiles", tip: true, channels: { DamName: "DamName" } }) ] })37:T470,Plot.plot({ color: { type: "categorical", domain: ["Low", "Significant", "High"], range: ["green", "red","darkred"], }, x: { type: "log", label: "Water Storage Volume (acre-feet)", grid: true, domain: [10, 10000000], nice:true}, y: { type: "log", label: "Surface Area (acres)", grid: true, domain: [0.05, 150000], nice:true }, r: { domain: d3.extent(caDams, (d) => d.DrainageAreaSqMiles), range: [3, 35]}, insetTop: 25, insetRight: 25, insetBottom: 0, insetLeft: 0, width, marks: [ Plot.frame(), Plot.dot(caDams, { x: "StorageAcreFeet", y: "SurfaceAreaAcres", r: "DrainageAreaSqMiles", fill: "#ccc", opacity: 0.8, channels: { DamName: "DamName" } }), Plot.dot(caDams.filter((d) => hazzzardClass.includes(d.HazardPotentialClassification) && pickPrimaryPurposes.includes(d.PrimaryPurpose)), { x: "StorageAcreFeet", y: "SurfaceAreaAcres", fill: "HazardPotentialClassification", r: "DrainageAreaSqMiles", tip: true, fy: "PrimaryPurpose", channels: { DamName: "DamName", year: "YearCompleted" } }) ] })38:T421,Plot.plot({ projection: { type: "mercator", domain: caCounties }, width, color: { type: "categorical", domain: ["Low", "Significant", "High"], range: ["green", "red","darkred"], legend: true }, r: { domain: d3.extent(caDams, (d) => d.DrainageAreaSqMiles), range: [4, 45]}, symbol: { domain: caDams.primaryPurpose, legend: true }, marks: [Plot.geo(caCounties, {fill:"lightgray", stroke: "white", strokeWidth: 1, opacity: 0.8}), Plot.dot(caDams, { filter: (d) => hazzardClass.includes(d.HazardPotentialClassification) && pickPrimaryPurpose.includes(d.PrimaryPurpose) && d.YearCompleted <= selectYearsRange[1] && d.YearCompleted >= selectYearsRange [0], x: "Longitude", type: "log", y: "Latitude", r: "DrainageAreaSqMiles", tip: true, fill: "HazardPotentialClassification", symbol: "PrimaryPurpose", opacity: 0.5, channels: { Dam_Name: "DamName", Year_Completed: "YearCompleted" }}) ] }) 39:[{"id":"b1bbc6ad24ded1e336293e40ec279d5d81d22fba986ccd43f4251e96d5e8c02742de26be597aa0377fed25bb0801fa08d5dfc25d4797caca05e8003da702c781","href":"https://static.observableusercontent.com/files/b1bbc6ad24ded1e336293e40ec279d5d81d22fba986ccd43f4251e96d5e8c02742de26be597aa0377fed25bb0801fa08d5dfc25d4797caca05e8003da702c781","name":"californiaDams.csv","type":"text/csv","lastModified":1698864966619.032,"size":505612},{"id":"5443aa69344628c1768d1610c485534930d91e481db4b16c2a9287259cb7a033959c6c9af3965762f3fe500a0f5c699f7847caa653f32558c5edd3bbb41b3a0f","href":"https://static.observableusercontent.com/files/5443aa69344628c1768d1610c485534930d91e481db4b16c2a9287259cb7a033959c6c9af3965762f3fe500a0f5c699f7847caa653f32558c5edd3bbb41b3a0f","name":"california-counties.geojson","type":"application/geo+json","lastModified":1698864966619.032,"size":416370}] 31:{"id":"4b1bbea071dd11f2","slug":"california-dams-hazard-assessment","title":"Course Assignment in Asset Management","thumbnail":"375b73075c673d35c53037f8c7ac7e719a1fac674a6604ea79e03587cefdded4","access_level":"public","create_time":"$D2023-11-01T18:56:06.619Z","update_time":"$D2025-10-27T19:26:37.032Z","view_time":null,"first_public_time":"$D2023-11-01T18:56:06.619Z","has_unpublished_edits":null,"fork":true,"stars":0,"starred":false,"owner":{"type":"team","id":"1a8df8d954834dc2","login":"fotisz","name":"Fotis Zapantis","avatar_url":"https://avatars-next.observableusercontent.com/901e545cdb3cac06c4e839534bbc428dfeaf42076ff667a94c51a36ffa05a710","tier":"starter_2024"},"version":924,"publish_version":null,"role":null,"authored":false,"authors":[{"type":"individual","id":"acd5a464e612f2ce","login":"fotisz","name":"Fotis Zapantis","avatar_url":"https://avatars-next.observableusercontent.com/15771ec342f0d7ed8668e8ab4d7b9e0ec698cf2903a9002e4ac0c9b5f5178a99","tier":null}],"members":[{"type":"individual","id":"acd5a464e612f2ce","login":"fotisz","name":"Fotis Zapantis","avatar_url":"https://avatars-next.observableusercontent.com/15771ec342f0d7ed8668e8ab4d7b9e0ec698cf2903a9002e4ac0c9b5f5178a99","tier":null,"role":"editor"}],"comments":[],"custom_thumbnail":false,"trashed":false,"lapsed":null,"subscribed":null,"forks":0,"collection":null,"type":"notebook","body":{"title":"Course Assignment in Asset Management","cells":[{"id":3,"value":"# Course Assignment in Asset Management\n## Introduction to data visualization with Observable Plot","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":4,"value":"## Logistics\n\n- **Fork this notebook** to make your own copy. Complete tasks below in your fork.\n- When you are ready to submit, ensure that your fork visibility is **Public unlisted** so that we can see your work. To update the setting, go to the Share button, the under Access choose “Can view (unlisted)” for Public visibility. Make sure to Save changes.\n- Submit assignment by sharing your notebook URL in [this Google Form](https://forms.gle/JEKph2PVvow1cFJK7) by end of day **November 6, 2023**\n- Ask questions in the Observable Community Slack **#introduction-observable-plot** channel.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":5,"value":"## Data: Dams on California waterways","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":6,"value":"For this assignment, you will use Observable Plot to visualize information about dams on California waterways from the [National Inventory of Dams](https://nid.sec.usace.army.mil/#/).\n\nAll the data you'll need is already attached and loaded in this notebook, named **caDams**. The `caDams` data is previewed below:\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":7,"value":"data = caDams","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"data","database":"$undefined"},{"id":726,"value":"// To use copied code replace \"data\" with your own variable\ncaDamsMultiStructures = aq.from(caDams)\t.select('DamName','NID_ID','FederalID','PrimaryOwnerType','NumberOfAssociatedStructures','PrimaryPurpose','County','HazardPotentialClassification','ConditionAssessment')\n\t.filter(d => d[\"NumberOfAssociatedStructures\"] > 1)\n\t// .objects() // Uncomment to return an array of objects","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":732,"value":"caDamsMultiStructures","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":168,"value":"$33","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":8,"value":"---\n## Assignment tasks","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":9,"value":"---\n### Task 1: Visualize condition assessment by decade-built\nThe `ConditionAssessment` variable contains an ordinal descriptor of each dam's condition (No data, Poor, Unsatisfactory, Satisfactory, or Fair). In Observable Plot, create a **bar chart** to visualize the **number of dams in each condition, binned by decade completed** (using the `YearCompleted` variable).\n\nTask 1 minimum requirements (but feel free to do more!): \n- Stacked bar chart of dam counts by decade completed (hint: `interval: 10`), with color indicating the condition\n- A meaningful, custom color scheme indicating the different conditions (e.g., \"Unsatisfactory\" might be red)\n- Updated x- and y-axis labels\n- Below your chart in a separate markdown cell, describe major takeaways from your chart in 1 - 2 sentences.\n\nCreate your Task 1 chart in a cell immediately below.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":44,"value":"Plot.plot({\n color: {\n type: \"categorical\",\n domain: [\"Satisfactory\", \"Fair\", \"Poor\", \"No data\", \"Unsatisfactory\"],\n range: [\"green\", \"orange\", \"red\", \"lightgrey\",\"darkred\"],\n legend: true\n },\n x: {label: \"Decade Built\", type: \"band\", nice: true },\n y: {label: \"Condition Asssessments (#)\", nice: true},\n width,\n marks: [\n Plot.frame(),\n Plot.rectY(\n caDams,\n Plot.binX(\n { y: \"count\" },\n { x: \"YearCompleted\", fill: \"ConditionAssessment\", tip: true, order: [\"No data\", \"Unsatisfactory\", \"Poor\", \"Fair\", \"Satisfactory\"]},\n )\n ),\n Plot.ruleY([0])\n ]\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":126,"value":"### Key Takeaways ###\n- **Period to WW1 (1850-1930): [Exponential Increase]** Build Up period when new dams were being built and construction activity increased exponentially all the way to the Great Recession. Missing condition assessments for those assets are more important because of the age of the assets, along with the existence of poor condition clusters in that age class\n- **Great Depression (1930-1950): [Stable]** Decline in the construction activity during the recession all the way to WW2 with fewer but relatively stable number of assets being built\n- **Period post WW2 (1950-1970): [Increase]** Construction activity returns to pre-war levels, almost half of condition assessments are missing\n- **1970 to today: [Exponential Decrease]** Ramp Down period where exponentially fewer new dams are being built but more condition assessments are available, certifying that recently built assets are - in their majority - in a satisfactory condition as expected","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":11,"value":"$34","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":384,"value":"viewof hazardClass = Inputs.checkbox([\"Low\", \"Significant\", \"High\"], {\n label: \"Hazard Potential Filter:\",\n unique: true,\n value: [\"Low\", \"Significant\", \"High\"],\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":342,"value":"$35","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":442,"value":"viewof selectPrimaryPurpose = Inputs.select(caDams.map(d => d.PrimaryPurpose), {label: \"Select Primary Purpose:\", unique: true})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":581,"value":"viewof selectYearRange = rangeSlider({\n min: 1850,\n max: 2022,\n step: 1,\n value: [1850, 1930],\n title: 'Year Completed',\n description: 'Select periods according to Task1: 1850-1930, 1930-1950, 1950-1970, 1970-2020',\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":447,"value":"$36","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":677,"value":"## Summary View ##","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":767,"value":"viewof hazzzardClass = Inputs.checkbox([\"Low\", \"Significant\", \"High\"], {\n label: \"Hazard Potential Filter:\",\n unique: true,\n value: [\"Low\", \"Significant\", \"High\"],\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":775,"value":"viewof pickPrimaryPurposes = Inputs.checkbox(caDams.map(d => d.PrimaryPurpose).sort(), {label: \"Select Asset Primary Purpose:\", unique: true, value: [\"Other\", \"Recreation\", \"Debris Control\"]})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":552,"value":"$37","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":371,"value":"A straight line in a log-scaled chart indicates exponential growth: the amount of water storage grows exponentially (n-times/slope of log-log plot 1%) with the surface area. And if something grows exponentially it will hit the next magnitude in a constant interval, separating the asset base in equal risk spaces evenly (decade/log10 or every 10 years). \n\nThis also agrees with the insight that most of the lower hazard potential structures are in the lower water storage volumes, since bigger volume of water does indicate more catastrophic consequences in case of failure. Equally, higher water storage volumes indicate higher hazard potential, although high hazard potential assets seem to exist across the portfolio which means that other factors also come into play.\n\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":847,"value":"Primary Purpose | Of Interest | Hazard Potential | Observation\n-------- | -------- | -------- |\nDebris Control | Yes | High for pre 1970s structures | Low Surface area of structures\nFire Protection | No | N/A | Few Data Points\nFish and Wildlife Pond | No | N/A | Few Data Points\nFlood Risk Reduction | Yes | High | Many Assets with big Drainage Area\nHydroelectric | Yes | Balanced | Many Assets with big Drainage Area\nIrrigation | Yes | Highest | Asset with highest Drainage Area\nOther | Yes | Balanced | -10\nRecreation | No | N/A | Very few Data Points\nTailings | No | N/A | Very few Data Points\nWater Supply | Yes | Balanced | Many Assets with high dispersion and average drainage area\n | No | No | Very few Data Points","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":14,"value":"---\n### Task 3: An interactive map of California dams\nIn Observable Plot, create an interactive map of dams in California. The spatial data you need with California county boundaries (as a basemap) is already attached and loaded as `caCounties`, and `caDams` contains variables for Longitude and Latitude. **Hint:** to crop a map to only the area of this spatial data, you can set a domain option within the projection (e.g. `projection: {type: \"mercator\", domain: caCounties}`).\n\nFor this activity, you can choose which variable(s) you want to visualize! \n\n**Task 3 minimum requirements (but feel free to do more):**\n- A basemap of California\n- At least two Inputs (you choose the types) that allow a viewer to focus on different subset of dams (for example by dam type, size/storage, hazard potential, condition, or year completed)\n- A tooltip revealing the dam name (and any other channels you want to include)\n\nCreate your Task 3 map in a cell immediately below.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":640,"value":"viewof pickPrimaryPurpose = Inputs.checkbox(caDams.map(d => d.PrimaryPurpose).sort(), {label: \"Select Asset Primary Purpose:\", unique: true, value:[\"Other\", \"Recreation\", \"Debris Control\"] })","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":646,"value":"viewof selectYearsRange = rangeSlider({\n min: 1850,\n max: 2022,\n step: 1,\n value: [1850, 1930],\n title: 'Year Completed',\n description: 'Select periods according to Task1: 1850-1930, 1930-1950, 1950-1970, 1970-2020',\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":663,"value":"viewof hazzardClass = Inputs.checkbox([\"Low\", \"Significant\", \"High\"], {\n label: \"Hazard Potential Filter:\",\n unique: true,\n value: [\"Low\", \"Significant\", \"High\"],\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":506,"value":"$38","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":897,"value":"### Spatial Distribution ###","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":863,"value":"Primary Purpose | 1850-1930 | 1930-1950 | 1950-1970 | 1970-today\n-------- | -------- | -------- | -------- | --------\nDebris Control | 2 Counties | 3 Counties | 2 Counties (most in LA) | More Counties\nFire Protection | 2 Counties | 3 Counties| 3 Counties | One County \nFish and Wildlife Pond | 2 Counties | - | 2 Counties | One County\nFlood Risk Reduction | 3 Counties (most in LA) | More Counties | More Counties | More Counties\nHydroelectric | More Counties | More Counties | More Counties | More Counties \nIrrigation | More Counties (Lake Tahoe prevalent) | More Counties | More Counties | More Counties\nOther | More Counties | More Counties | More Counties | More Counties \nRecreation | One County | One County | 3 Counties | - \nTailings | - | - | - | 3 Counties\nWater Supply | More Counties | More Counties | More Counties | More Counties\n | - | One County | 3 Counties | One County","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":18,"value":"---\n## Congratulations! Submit your assignment.\n\nOnce you’re done with the assignment, make sure to: \n\n- Update your notebook settings (click the Share button) so that Public access is “View (unlisted)”\n- Submit the URL to your notebook in [this Google form](https://forms.gle/JEKph2PVvow1cFJK7) by midnight on November 6, 2023\n- Keep an eye out for course certificates, which we'll send out via email by November 10, 2023 \n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":19,"value":"_**Thank you** for joining us for the **Introduction to Data Visualization in Observable Plot** course! We look forward to seeing you at our upcoming events, and in our Community Slack!_ ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":20,"value":"---\n## Appendix","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":593,"value":"import {rangeSlider} from '@mootari/range-slider'","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":718,"value":"import {Wrangler, op} from \"@observablehq/data-wrangler\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":21,"value":"dams = FileAttachment(\"californiaDams.csv\").csv({typed: true})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":22,"value":"caDams = dams.map((d) => ({\n ...d,\n ConditionAssessment:\n d[\"ConditionAssessment\"] == \"\" ||\n d[\"ConditionAssessment\"] == \"Not Available\" ||\n d[\"ConditionAssessment\"] == \"Not Rated\" ||\n d[\"ConditionAssessment\"] == null\n ? \"No data\"\n : d[\"ConditionAssessment\"]\n}))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":23,"value":"caCounties = FileAttachment(\"california-counties.geojson\").json()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":716,"value":"californiadamrs = await FileAttachment(\"californiaDams.csv\").csv({typed: \"auto\"})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"californiadamrs","database":"$undefined"}],"files":"$W39","stdlib":"1","titling":"auto"}} 32:"$31:body"