25:["$","title",null,{"children":"Xuanming Liu A5 - US Employment Market and Fed Interest Rate | Evan Liu | Observable"}] 49:T55f,For the charts below, I want to show the relation between unemployment, interest, and inflation rates. So I mapped one line chart and two area chart. The line chart aims to show all three data on the chart for comparison and contrast. I encoded the year dimension on the x-axis and applied the brush tool to the x-axis. I encoded the percentage of the rate on the y-axis. I used three layers in this chart. In the first layer, I encoded color with field unemployment rate with the value green and opacity 0.7. In the second layer, I encoded color with field interest rate with value orange and opacity 0.7. In the third layer, I encoded color with field inflation rate with value grey and opacity 0.5. In this chart, I want to hierarchy the unemployment rate and interest rate, so I set inflation opacity to 0.5. The area chart for interest rates aims to show the detailed value by the year selected. I encoded the year dimension on the x-axis, and the value of it comes from the brush tool that collects data from the line chart. I encoded the interest rate (%) dimension on the y-axis. The area chart for the unemployment rate aims to show the detailed value by the year selected. I encoded the year dimension on the x-axis, and the value of it comes from the brush tool, which collects data from the line chart. I encoded the unemployment rate dimension on the y-axis. 4a:T882,{ const brush = vl.selectInterval().encodings('x'); //interest rate detail chart const interestChart = vl.markArea({color:'orange'}) .data(dataMap) .title('Fed interest rate') .transform( vl.filter(brush) //link to brush result ) .encode( vl.x().fieldT("year"), vl.y().fieldQ("Real_interest_rate").title("interest rate").scale({domain:[0,10]}) ) .width(180) .height(150) //unemployment rate detail chart const unemploymentChart = vl.markArea({color:'green'}) .data(dataMap) .title('Unemployment rate') .transform( vl.filter(brush) ) .encode( vl.x().fieldT("year"), vl.y().fieldQ("Unemployment").title("unemployment").scale({domain:[0,10]}) ) .width(180) .height(150) const brushChart = vl.data(dataMap) .title("Interest rate, inflation, and Unemployment rate Combined chart") .encode( vl.x().fieldT("year"), vl.y().fieldQ('Inflation_GDP_deflator') ) .layer( vl.markLine({point:true},{tooltip:{"content":"encoding"},clip:true}) .params(brush) .encode( vl.y().fieldQ("Inflation_consumer_prices"), vl.color().value('gray'), vl.opacity().value(0.3), vl.tooltip([{field:"year", type:"Temporal"},{field:"Inflation_consumer_prices", type:"quantitative"}]) ), vl.markLine({point:true},{tooltip:{"content":"encoding"},clip:true}) .encode( vl.y().fieldQ("Real_interest_rate"), vl.color().value('orange'), vl.opacity().value(0.7), vl.tooltip([{field:"year", type:"Temporal"},{field:"Real_interest_rate", type:"quantitative"}]) ), vl.markLine({point:true},{tooltip:{"content":"encoding"}}) .encode( vl.y().fieldQ("Unemployment"), vl.color().value('green'), vl.opacity().value(0.7), vl.tooltip([{field:"year", type:"Temporal"},{field:"Unemployment", type:"quantitative"}]) ) ) .width(550) .height(400) return vl.vconcat(brushChart, vl.hconcat(interestChart,unemploymentChart)) .render() }4b:T5db,For the chart below, I want to show each County's unemployment and employment rate from 2000 to 2020. I mapped one Geoshape chart, two line chart and one bar chart. The GeoShape chart aim to show unemployment rate for each county on the map. In this chart, I encoded color for fields rate to show the difference of unemployment rate between each county. I also applied selection function by clicking and set the field to county. So users can select county on the map chart. The line chart of unemployment rate aim to show unemployment rate for specific county which selected by users between 2000-2020. I encoded nominal field year on x-axis and unemployment rate on y-axis. So user can easily see the unemployment changes of certain county in these 20 years. The line chart of interest rate aim to show interest rate and form a contrast with the unemployment rate line chart. I encoded year on x-axis and interest rate on y-axis which is similar to the unemployment rate line chart. So that they can form a connection with each other, and users can compare them easily. The bar chart aim to show the employed and unemployed population along these 20 years. I encoded fields year on x-axis and population on y-axis. Then I encoded color orange for unemployed population on layer 1 and green for employed population on layer 2. I also encoded brush tool on x-axis which is fields year. Users can use brush tool to select year range for unemployment rate line chart to see the detail information. 4c:Tbd9,{ const selection = vl.selectPoint() .on("click") .fields("county") .bind("legend") .clear("dblclick") // double click to clear selection .toggle(true); const brush = vl.selectInterval().encodings('x'); const mapChart = vl.markGeoshape({stroke:'#aaa', strokeWidth: 0.25}) .data(vl.topojson(usa).feature('counties')) .transform( vl.lookup('id').from(vl.data(cleanUnemployment2000to2020).key('id').fields('state','county','year','labor_force','employed','unemployed','rate')) ) .params(selection) .encode( vl.color().if(selection, vl.color().fieldQ('rate').scale({scheme:'turbo'},{domain:[1,16],clamp:true})).value('grey'), vl.tooltip([{field:"state"},{field:"county"},{field:"rate"},{field:"year"}]) ) .title('US unemployment rate by County 2006, Unit %') .project(vl.projection('albersUsa')) .width(890).height(500) .config({view: {stroke: null}}) const countyChart = vl.markLine({tooltip:{"content":"encoding"},clip:true}) .data(cleanUnemployment2000to2020) .transform( vl.filter(selection), vl.filter(brush) ) .title("County's unemployment rate between 2000-2020") .encode( vl.x().fieldN("year"), vl.y().fieldQ("rate").title("Unemployment rate %") //vl.color().fieldN('county').scale({scheme:'turbo'}) // vl.color().fieldN('county') ) .width(445) .height(320) const interestChart = vl .markLine() .data(interestdata) .title("Interest rate by year") .encode( vl.x().fieldT("year"), vl.y().fieldQ("Real_interest_rate").title("interest rate %").scale({domain:[0,7]}), vl.tooltip([{field:"year", type:"Temporal"},{field:"Unemployment", type:"quantitative"}]) ) .width(445) .height(320) const employChart = vl.data(cleanUnemployment2000to2020) .title("Employed and unemployed population, green for employed, orange for unemployed") .encode( vl.x().fieldN("year"), vl.y().fieldQ('labor_force') ) .layer( vl.markBar({point:true},{tooltip:{"content":"encoding"},clip:true}) .params(brush) .transform( vl.filter(selection), ) .encode( vl.y().fieldQ("employed"), vl.color().value('green'), vl.opacity().value(0.7), vl.tooltip([{field:"year", type:"Nominal"},{field:"employed", type:"quantitative"}]) ), vl.markBar({point:true},{tooltip:{"content":"encoding"},clip:true}) .transform( vl.filter(selection), ) .encode( vl.y().fieldQ("unemployed"), vl.color().value('orange'), vl.opacity().value(0.7), vl.tooltip([{field:"year", type:"Nominal"},{field:"unemployed", type:"quantitative"}]) ), ) .width(800) .height(420) return vl.vconcat(mapChart, vl.hconcat(interestChart, countyChart),employChart) .render() }4d:Ta0f,// { // const selectState = vl // .selectPoint("SelectionMenu") // .fields("State_Area") // .init(States[0]) // .bind(vl.menu(States).name("State: ")); // // const filterRate = vl // // .param("RateSlider") // // .value(0) // // .bind(vl.slider(0,18,0.01).name("Unemployment Rate selector")); // //{stroke:'black', strokeWidth:1,opacity:.75} // // const show = vl.and(selectState, "datum.Percent_of_Labor_Force_Unemployed_in_Area >= RateSlider"); // const stateChart = vl // .markCircle() // .data(stateDataMap) // .params(selectState) // .title("Unemployment rate and employment rate per state") // .encode( // vl.x().fieldQ("Percent_of_Labor_Force_Employed").scale({domain:[45,75]}), // vl.y().fieldQ("Percent_of_Labor_Force_Unemployed_in_Area").scale({domain:[0,18]}), // vl.color().if(selectState, vl.fieldN("State_Area")), // vl.opacity().if(selectState, vl.value(0.8)).value(0.1), // vl.size().if(selectState, vl.fieldQ("Percent_of_Labor_Force_Unemployed_in_Area")), // vl.stroke().if(selectState,vl.value('black')), // vl.strokeWidth().if(selectState,vl.value('2')), // vl.tooltip([{field:"Year", type:"Temporal"},{field:"Percent_of_Labor_Force_Unemployed_in_Area", type:"quantitative"},{field:"Percent_of_Labor_Force_Employed",type:"quantitative"}]) // ) // .width(500) // .height(500) // const stateDetailChart = vl // .markLine() // .data(stateDataMap) // .params(selectState) // .title("State's unemployment rate by year") // .encode( // vl.x().fieldT("Year"), // vl.y().fieldQ("Percent_of_Labor_Force_Unemployed_in_Area").scale({domain:[0,18]}).title("Unemployment rate"), // vl.color().if(selectState,vl.fieldN("State_Area")), // vl.opacity().if(selectState,vl.value(0.8)).value(0.02), // vl.tooltip([{field:"Year", type:"Temporal"},{field:"Percent_of_Labor_Force_Unemployed_in_Area", type:"quantitative"}]) // ) // .width(200) // .height(160) // const interestDetailChart = vl // .markLine() // .data(dataMap) // .title("Interest rate by year") // .encode( // vl.x().fieldT("year"), // vl.y().fieldQ("Real_interest_rate").title("interest rate").scale({domain:[0,10]}), // vl.tooltip([{field:"year", type:"Temporal"},{field:"Unemployment", type:"quantitative"}]) // ) // .width(200) // .height(160) // return vl.vconcat(stateChart, vl.hconcat(interestDetailChart,stateDetailChart)) // .render() // }4e:[{"id":"783cf2bf259e16d662d92c9f59ec97e564c50841c5984f2bb6f65a6d31f8c1b80846bffb65cb654dc2b587ac96f0007ab68c24bacce33655fd785e46020aff74","href":"https://static.observableusercontent.com/files/783cf2bf259e16d662d92c9f59ec97e564c50841c5984f2bb6f65a6d31f8c1b80846bffb65cb654dc2b587ac96f0007ab68c24bacce33655fd785e46020aff74","name":"us-counties-10m.json","type":"application/json","lastModified":1680483989811.125,"size":842177}] 29:{"id":"14154276ed91c1ba","slug":null,"title":"Xuanming Liu A5 - US Employment Market and Fed Interest Rate","thumbnail":"1bfd0eeff82b0bb37a23a9bdc952254baf13be17c9cdfc2bd762ca1960b12004","access_level":"public","create_time":"$D2023-03-19T19:57:34.836Z","update_time":"$D2023-04-09T02:59:07.473Z","view_time":null,"first_public_time":"$D2023-03-19T19:57:34.836Z","has_unpublished_edits":null,"fork":false,"stars":0,"starred":false,"owner":{"type":"team","id":"a2328f0bf45baa34","login":"evanliu66","name":"Evan Liu","avatar_url":"https://avatars-next.observableusercontent.com/2bf41248caa58bbcfb58ce1f1ccd2c801130380605b7c72db21ac7a852f3b13f","tier":"starter_2024","bio":"","followed_by_you":false,"follows_you":false},"version":558,"publish_version":null,"role":null,"authored":false,"authors":[{"type":"individual","id":"f7c778280d4d9d4f","login":"evanliu66","name":"Evan Liu","avatar_url":"https://avatars-next.observableusercontent.com/2bf41248caa58bbcfb58ce1f1ccd2c801130380605b7c72db21ac7a852f3b13f","tier":null,"bio":"","followed_by_you":false,"follows_you":false}],"members":[{"type":"individual","id":"f7c778280d4d9d4f","login":"evanliu66","name":"Evan Liu","avatar_url":"https://avatars-next.observableusercontent.com/2bf41248caa58bbcfb58ce1f1ccd2c801130380605b7c72db21ac7a852f3b13f","tier":null,"role":"editor"}],"comments":[],"custom_thumbnail":false,"trashed":false,"lapsed":null,"subscribed":null,"forks":0,"collection":null,"type":"notebook","body":{"titling":"auto","title":"Xuanming Liu A5 - US Employment Market and Fed Interest Rate","cells":[{"id":0,"value":"# Xuanming Liu A5 - US Employment Market and Fed Interest Rate","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":5,"value":"**Imports**","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":13,"value":"d3 = require('d3@7') //Importing D3","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":15,"value":"vegalite = require(\"@observablehq/vega-lite@0.1\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":21,"value":"vegaLite = require('vega-lite@2.0.0-beta.2/build/vega-lite.min.js')","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":23,"value":"L = require('leaflet@1.2.0')//import leaflet","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":25,"value":"Import data","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":27,"value":"rawdata = d3.csv(\"https://gist.githubusercontent.com/EvanLiu66/ccc213d27c5fd20bc760cf399d1801d9/raw/a9a24794b2f60a28070085f5f08102b91a83cf90/inflation%2520interest%2520rate%2520unemployment\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":29,"value":"data = {//seleted data\n let data = new Array();\n for(let i = 1; i{\n var a=d;\n a.country = d.country;\n a.year = d.year;\n a.Inflation_consumer_prices = +d.Inflation_consumer_prices;\n a.Inflation_GDP_deflator = +d.Inflation_GDP_deflator;\n a.Real_interest_rate = +d.Real_interest_rate;\n a.Deposit_interest_rate = +d.Deposit_interest_rate;\n a.Lending_interest_rate = +d.Lending_interest_rate;\n a.Unemployment = +d.Unemployment;\n a.Unemployment_total = +d.Unemployment_total;\n return a;\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":394,"value":"interestdata = {//seleted data\n let data1 = new Array();\n for(let i = 1; i\"1999\"){\n data1.push(dataMap[i]);\n }\n }\n return data1;\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":287,"value":"## Analytic Scope","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":290,"value":"#### Top level questions:\n\n 1. How do changes in the Federal Reserve's interest rates impact the employment market in the short and long term?\n\n 2. How do different sectors within the employment market respond to changes in FED interest rates, and are there any patterns or trends that can be observed?\n \n\n#### Specific Questions:\n\n 1. How do FED interest rate adjustments impact the unemployment rate among different age groups and different ethnic?\n\n 2. Is there a relationship between the unemployment rate, Fed interest rate and inflation?\n\n 3. Are there certain county that are more sensitive to the change in interest rate?\n\n 4. Which industries' employment was affected more, and which were affected less?\n\n \n \n ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":296,"value":"## Audience","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":299,"value":"Our target user/user group is people interested in the employment market and interest rate trends. There are two key groups: new graduate students and people who do not have working experience. The problem for them is that they lack knowledge about the employment market and the factors that influence the employment market. So, they may make the wrong decision in finding a job and career planning. On the other hand, people already have a job and people with working experience. They want to switch a job to get more salary and welfare, so it is essential for them to understand whether it is a good time for them to change jobs. Because if the employment market is not well, they will get a bad job or even loss the job.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":165,"value":"## 1. Is there a relationship between the unemployment rate, Fed interest rate and inflation?","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":168,"value":"### Data Dimension and Data Transformations","pinned":true,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":172,"value":"The data dimensions used in this visualization include:\n 1. Year from 1970-2021 (Temporal)\n 2. Fed interest rate (Quantitative)\n 3. Unemployment rate (Quantitative)\n 4. Inflation rate (Quantitative)\n 5. Inflation GDP deflator (Quantitative)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":303,"value":"### Visual Feature - Data Mapping","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":309,"value":"$49","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":311,"value":"### Visualization Idiom","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":313,"value":"I chose a line chart because I have data from 1970 to 2020, and I wish to see the change in unemployment, inflation and interest rates by the year change. I used three layers to show them all on the line chart because I wish users could compare them and find out the pattern of these three data. I also applied a tool brush for the user to select specific periods and see the details of unemployment and interest rate changes in the two area charts below.\n\nI chose two area charts to show detailed information on unemployment and interest rates because I want to let users easier see the trend of these two data changed by year. So they can easier find out the relation and pattern of the dataset.\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":152,"value":"## The green line is the Unemployment rate, The orange line is the interest rate, The gray line is the inflation rate","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":97,"value":"$4a","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":181,"value":"rawStateData = d3.csv(\"https://gist.githubusercontent.com/EvanLiu66/81aa731ed370faeec96a02b31695c9fd/raw/5ad7f016612080fdc9213ec7246edb2f4fae626b/unemployment%2520rate%2520per%2520state\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":185,"value":"stateDataMap = cleandata.map(d=>{\n var a=d;\n a.State_Area = d.State_Area;\n a.Year = d.Year;\n d.Month = d.Month;\n \n a.Total_Civilian_Labor_Force = +d.Total_Civilian_Labor_Force;\n a.Percent_of_Area_Population = +d.Percent_of_Area_Population;\n a.Total_Employment_in_Area = +d.Total_Employment_in_Area;\n a.Percent_of_Labor_Force_Employed = +d.Percent_of_Labor_Force_Employed;\n a.Total_Unemployment = +d.Total_Unemployment;\n a.Percent_of_Labor_Force_Unemployed_in_Area = +d.Percent_of_Labor_Force_Unemployed_in_Area;\n return a;\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":222,"value":"cleandata = {//seleted data\n let cleandata = new Array();\n for(let i = 1; id.State_Area)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":480,"value":"Years = uniqueValid(unemployment2000to2020, d=>d.year)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":278,"value":"## 2. Are there certain county that are more sensitive to the change in interest rate?","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":280,"value":"#### Data Dimension and Data Transformations","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":282,"value":"The data dimention used in this visualization include:\n\n 1. State Name (Nominal)\n 2. County Name (Nominal)\n 3. Geo id (Quantitative)\n 4. employed population (Quantitative)\n 5. Unemployment population (Quantitative)\n 6. Unemployment rate (Quantitative)\n 7. Fed interest rate (Quantitative)\n 8. Year 2000-2020 (Nominal)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":318,"value":"### Visual Feature - Data Mapping","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":320,"value":"$4b","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":322,"value":"### Visualization Idiom","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":324,"value":"I choose geoshape map chart because, in this dataset, I have each county's data and there are hundred of county, so it is better to show them on map. I also link the map chart to the unemployment line chart. User can select a county on the map chart and the selection will also filtered and showed on the line chart. The line chart clearly show the trend of unemployment rate between 2000 to 2020 for user selected county.\n\nI choose bar chart because for population change in years it's more clear to use a bar chart to represent. I create two layer to compare and contrast the employed population and unemployed population on the bar chart. The bar chart also link to the unemployment line chart. I create a brush tool allow user to select certain year range and line chart will also involved.\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":397,"value":"$4c","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":332,"value":"import {unemployment2000to2020, us, collectionPreviews, header} from \"@observablehq/sources-for-outlier-2022-notebooks\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":336,"value":"unemployment2000to2020","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":544,"value":"cleanUnemployment2000to2020 = {\n let data = new Array();\n \n \n for(let i=1; id.county)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":245,"value":"$4d","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W4e","stdlib":"1"}} 24:null