25:["$","title",null,{"children":"How does interest rate affect employment market? | Evan Liu | Observable"}] 49:T62b,## Overview Higher interest rates tend to dampen economic activity, potentially leading to reduced employment. When interest rates are high, borrowing becomes more expensive, and businesses and individuals are less likely to invest in new projects, make large purchases and hire additional workers. However, the relationship between interest rates and employment is not always straightforward. For example, if interest rates are lowered too low and inflation starts to rise, the Fed may raise interest rates to curb inflation, which may cause a temporary decline in employment. There are still challenges in the U.S. job market. We chose this topic as our exploration topic to observe how interest rate cause the U.S. job market to fluctuate. ## Scope This project will help people understand how interest rates affect our employment market. ## Audience 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.4a:T9d9,{ 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:[-2,15]}) ) .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:[-2,15]}) ) .width(180) .height(150) const inflationChart = vl.markArea({color:'gray'}) .data(dataMap) .title('Inflation rate') .transform( vl.filter(brush) ) .encode( vl.x().fieldT("year"), vl.y().fieldQ("Inflation_consumer_prices").title("Inflation").scale({domain:[-2,15]}) ) .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(680) .height(400) return vl.vconcat(brushChart, vl.hconcat(interestChart,unemploymentChart, inflationChart)) .render() }4b:Teee,{ //defining the selection for the map const select = vl.selectPoint("Each") .fields('Month') // limit selection to the month field .init(Months[0]) // use first month entry as initial value .bind(vl.menu(Months)); // bind to a menu of unique genre values const brushy = vl.selectInterval().encodings('x'); //resolve all selections to consist of the intersection of all brushes (only points that reside within all brushes will be selected) // const click = vl.selectPoint().encodings('color'); const show = vl.and(select, brushy); // combine selections const interestChart = vl.markCircle({color:"brown"}) .data(detailMap) .params(select,brushy) .encode( vl.x().fieldT("Year"), vl.y().fieldQ("Real_interest_rate").title("interest rate"), vl.tooltip([{field: "Real_interest_rate", title: "Interest Rate"}]), vl.size().value(80), vl.color().if(brushy).value('grey') ) .height(150) .width(800) .title("Hover over and brush the circle to see the Interest Rate") //Asian group const Chart = vl.markCircle() .data(detailMap) .params(select,brushy) .encode( vl.x().fieldT("Year"), vl.y().fieldQ("Asian").title("The Asian group's unemployment rate"), vl.tooltip([{field: "Asian", title: "Unemployment Rate of Asian"},{field: "Month", title: "The month of the year"}]), vl.color().if(show, vl.color().fieldN('Month').scale({scheme: 'tableau10'})).value('grey'), vl.opacity().if(show, vl.value(1.0)).value(0.1) ) .height(220) .width(400) .title("Hover over and brush the circle to see details") //White group const Chart2 = vl.markCircle() .data(detailMap) .params(select,brushy) .encode( vl.x().fieldT("Year"), vl.y().fieldQ("White").title("The White group's unemployment rate"), vl.tooltip([{field: "White", title: "Unemployment Rate of White"},{field: "Month", title: "The month of the year"}]), vl.color().if(show, vl.color().fieldN('Month').scale({scheme: 'tableau10'})).value('grey'), vl.opacity().if(show, vl.value(1.0)).value(0.1) ) .height(220) .width(400) .title("Hover over and brush the circle to see details") //Black group const Chart3 = vl.markCircle() .data(detailMap) .params(select,brushy) .encode( vl.x().fieldT("Year"), vl.y().fieldQ("Black").title("The Black group's unemployment rate"), vl.tooltip([{field: "White", title: "Unemployment Rate of Black"},{field: "Month", title: "The month of the year"}]), vl.color().if(show, vl.color().fieldN('Month').scale({scheme: 'tableau10'})).value('grey'), vl.opacity().if(show, vl.value(1.0)).value(0.1) ) .height(220) .width(400) .title("Hover over and brush the circle to see details") //White group const Chart4 = vl.markCircle() .data(detailMap) .params(select,brushy) .encode( vl.x().fieldT("Year"), vl.y().fieldQ("Hispanic").title("The Hispanic group's unemployment rate"), vl.tooltip([{field: "Hispanic", title: "Unemployment Rate of Hispanic"},{field: "Month", title: "The month of the year"}]), vl.color().if(show, vl.color().fieldN('Month').scale({scheme: 'tableau10'})).value('grey'), vl.opacity().if(show, vl.value(1.0)).value(0.1) ) .height(220) .width(400) .title("Hover over and brush the circle to see details") const filteredInterestChart = interestChart.transform( vl.filter(select,brushy) ); const filteredChart = Chart.transform( vl.filter(select,brushy) ); const filteredChart2 = Chart2.transform( vl.filter(select,brushy) ); const filteredChart3 = Chart3.transform( vl.filter(select,brushy) ); const filteredChart4 = Chart4.transform( vl.filter(select,brushy) ); return vl.vconcat( interestChart, vl.hconcat( vl.vconcat(Chart, Chart2), vl.vconcat(Chart3, Chart4) ) ).render(); } 4c:Tbd4,{ 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(350) .height(260) 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(350) .height(260) 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(600) .height(320) return vl.vconcat(mapChart, vl.hconcat(interestChart, countyChart),employChart) .render() }4d:T6a5, { //resolve all selections to a single global instance const brush = vl.selectInterval().resolve('union').encodings('x'); //interest rate detail chart const interestChart = vl.markBar({color:'lightblue'}) .data(industrialMap) .title('Fed interest rate') .params(brush) .encode( vl.x().fieldT("Year").timeUnit('year'), vl.y().fieldQ("Real_interest_rate").title("Interest Rate"), vl.color().if(brush, vl.color()).value('grey') //the incomelevel only have the high level ) .width(800) .height(150) const point = vl .data(industrialMap) .encode( vl.x().fieldT('Year').timeUnit('year'), //repeat the chart with the fields below vl.y().fieldQ(vl.repeat('repeat')) ) .layer( vl.markPoint() //Layer 1: Background color .params(brush) .title("Industries'employment") .encode( vl.y().fieldQ(vl.repeat('repeat')), vl.x().fieldT('Year'), vl.color().if(brush, vl.color()).value('grey'), ), vl.markPoint({color: 'green'}) //Layer 2: Foreground color .transform( vl.filter(brush) ) .encode( vl.y().fieldQ(vl.repeat('repeat')), vl.x().fieldT('Year') ) ) .height(150) .width(180) .repeat(['Health_care_and_social_assistance', 'Educational_services','Finance_and_insurance','Private_nonfarm_employmen','Accommodation_and_food_services','Utilities','Arts_entertainment_and_recreation','Wholesale_trade']) //The repeated columns .columns(4) return vl.vconcat(point,interestChart) .render() }4e:T4d9,## Unimplemented Features We intend to create a map chart using Leaflet for question three, but we were unable to find data in the geoJson format. Instead, we opted to use a d3 Geoshape chart as a substitute. We also planned to implement a slider, allowing users to select the year for the charts. However, after several attempts, the slider failed to work, so we employed alternative interaction methods, such as touch selection and the brush tool, to achieve a similar function. There were a few aspects of our project that did not work as intended. Firstly, in question three, users can select the year for the county data, but the Geoshape map chart remains unchanged due to our inability to find a method for selecting the year. Another issue we faced was in question two, where we intended to display income levels in relation to employment. Unfortunately, due to the data structure, we could not isolate the income level data from the dataset to display it on the chart. We are confident that we will have the opportunity to improve this project in the future. We aim to find and generate more suitable data and explore new types of charts, such as scatter plot charts, to provide users with clearer and more comparative visuals. 4f:[{"id":"783cf2bf259e16d662d92c9f59ec97e564c50841c5984f2bb6f65a6d31f8c1b80846bffb65cb654dc2b587ac96f0007ab68c24bacce33655fd785e46020aff74","href":"https://static.observableusercontent.com/files/783cf2bf259e16d662d92c9f59ec97e564c50841c5984f2bb6f65a6d31f8c1b80846bffb65cb654dc2b587ac96f0007ab68c24bacce33655fd785e46020aff74","name":"us-counties-10m.json","type":"application/json","lastModified":1680989898025.11,"size":842177}] 29:{"id":"b6e7f55b18cf029c","slug":null,"title":"How does interest rate affect employment market?","thumbnail":"2a02d20dd06164332d23dd44c6c2fd4022e4effdeefbac89b5494221ffa14a07","access_level":"public","create_time":"$D2023-04-08T20:26:18.582Z","update_time":"$D2023-04-18T07:30:13.966Z","view_time":null,"first_public_time":"$D2023-04-08T20:26:18.582Z","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":412,"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":"5dabb9c4eaedb3d8","login":"brenda992","name":"Brenda Chen","avatar_url":"https://avatars-next.observableusercontent.com/65c43ee695a5e3c1b6c73e0ae2fb276ce22c2d00ab5d6742db163ebac7cfaac0","tier":null,"role":"editor"},{"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":2,"collection":null,"type":"notebook","body":{"titling":"auto","title":"How does interest rate affect employment market?","cells":[{"id":0,"value":"# How does interest rate affect employment market?\n\n#### by Evan Liu & Xutong Chen\n#### IAT 355 - Spring 2023","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":6,"value":"$49","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":227,"value":"## Top level Questions\n- Understanding the pattern of interest rates and the employment market\n- How do changes in interest rates differentially impact unemployment rates across various demographics, regions, and industries?\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":11,"value":"## Questions\n- Is there a relationship between the unemployment rate, interest rate and inflation rate?\n- How do interest rate adjustments impact the unemployment rate among different ethnic?\n- Are there certain counties's employment that are more affected by the change in interest rate?\n- What kind of industries' employment was affected more with the interest rate, and which were affected less?","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":411,"value":"### Demo of the Video\nhttps://youtu.be/vZ-XMgQPefU","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":403,"value":"","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":13,"value":"## Question 1 - Is there a relationship between the unemployment rate, interest rate and inflation rate?\n\n#### By Evan Liu","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":15,"value":"### Data Dimension and Data Transformations","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":17,"value":"The data source comes from Kaggle.com.\n\nThis Data contain 196 countries' interest, inflation and unemployment rate overtime.\n\nWe use the map function to filter the United States' dataset from the original and form a new array and we also transfer all quantitative data dimensions from string format to double format.\n\n\nThe data dimensions used in this visualization include the following:\n- Year from 1970-2021 (Temporal)\n- Fed interest rate (Quantitative)\n- Unemployment rate (Quantitative)\n- Inflation rate (Quantitative)\n- Inflation GDP deflator (Quantitative)\n\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":25,"value":"### Idiom and Encoding","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":27,"value":"The Line chart uses three-layer to show interest, inflation, and unemployment rate changes over time. Since we have data from 1970 to 2020, a line chart is an excellent choice to show the trend of each data. Besides, it’s also easier for users to compare and contrast these three rates.\n\nThe three area charts below aim to show each rate’s trend over time. So that the user can find patterns and relations between these three rates.\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":118,"value":"### User manual","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":120,"value":"There is a brush tool in the line chart on the x-axis. The user can select a period of time and the three area charts below will show the data in that period. This brush tool aims to show detailed rate trends in the selected range of the year to help users find the pattern and relation of these rates.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":122,"value":"## The green line is the Unemployment rate, The orange line is the interest rate, The gray line is the inflation rate","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":125,"value":"$4a","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":136,"value":"## Question 2 - How do interest rate adjustments impact the unemployment rate among different ethnic in different months?\n\n#### By Xutong Chen","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":138,"value":"### Data Dimension and Data Transformations\nThe data source comes from Kaggle.com.\n\nThis Data contains the US' interest rate and unemployment rate over time.\n\nWe use the map function to filter the United States' dataset from the original and form a new array and we also transfer all quantitative data dimensions from string format to double format.\n\nThe data dimensions used in this visualization include:\n\n1. Year from 2010-2020 (Temporal)\n2. Fed interest rate (Quantitative)\n3. Ethnic's unemplyment rate(Quantitative)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":142,"value":"### Visualization Idiom and Encoding\nIn the visualization below I want to show the relationship between interest rates and unemployment rates for different race groups. So I used 5 different dot charts\n\nUsing a dot plot is a handy way to show the trend of interest rates over the period 2010-2020. By encoding each data point, we can clearly see the actual interest rate value at different time points, and can quickly observe the overall trend of interest rate changes. \n\nUsing a dot chart to present data for different race groups in each different month. Because each race has its own unique data characteristics and trends, presenting each race group separately allows us to gain a deeper understanding of the data and better explore differences and similarities between races.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":242,"value":"### User manual\nI use detailed on demand and the brush tool along x-asix to realize this function, Meanwhile, a menu under the chart for selecting the different months. For example, when you hover over the interest rate and other ethnic groups in 2013, it will show you detailed data on 2013. And when you use the brush tool, you can select the year you want to see, and it will show the specific year that you select. For the menu below, when you select a month, it will show the specific data of the month you select.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":144,"value":"$4b","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":149,"value":"## Question 3 - Are there certain counties's employment that are more affected by the change in interest rate?\n\n#### By Evan Liu","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":151,"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":153,"value":"Data source come from an observable notebook https://observablehq.com/@observablehq/sources-for-outlier-2022-notebooks\n\nThis data contain all the United states counties' employment information, such as employed and unemployed population; employment and unemployment rate.\n\nWe did clean the dataset because there are many counties has the same name across states and that affect user to select county on the map chart. So we use a for loop to add the state's name in front of the county name to solve the problem.\n\n\nThe 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":156,"value":"### Idiom and Encoding","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":158,"value":"The GeoShape chart showed the counties’ unemployment rate in 2006. The color shows the level of the unemployment rate, the county with red color has the highest unemployment rate, and the county with dark blue has the lowest unemployment rate.\n\nThe line chart on the left shows the interest between 2000 to 2020. And the line chart on the right shows the county’s unemployment rate between 2000 and 2020. We make these charts to let a user compare the county’s unemployment rate with interest over time.\n\nThe bar chart below shows the population of the employed and unemployed of the county selected by the user. This chart aims to provide detailed population data to the user for a better understanding of the situation of the employment market.\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":160,"value":"### User manual","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":162,"value":"By clicking on the map chart user can select the county. The county selected will show the unemployment rate over time on the line chart below and the employed population on the bar chart. Users can also use the brush tool on the bar chart to select period of time, and the unemployment line chart will correspond to it.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":164,"value":"$4c","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":167,"value":"## Question 4 - What kind of industries' employment was affected more with the interest rate, and which were affected less?\n\n#### By Xutong Chen","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":169,"value":"### The data dimensions used in this visualization include:\nThe data source comes from Kaggle.com.\n\nThis Data contains the US' interest rate and industries' employment rate over time.\n\nWe use the map function to filter the United States' dataset from the original and form a new array and we also transfer all quantitative data dimensions from string format to double format.\n\n1. Year from 2001-2019 (Temporal)\n2. Fed interest rate (Quantitative)\n3. Number of different industries' employment (Quantitative)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":173,"value":"### Visualization Idiom and Encoding\nIn the visualization below I want to show the impact between interest rates and employment in each industry.\n\nUsing a bar chart to represent the interest rate can help us observe the trend of the interest rate between 2001-2019 in more detail because the histogram can display the specific value at each point in time, helping us analyze and compare the data of different years more accurately. \n\nIn addition, it is also a common data visualization method to use each different point plot to show different industry employment rates. Used eight point graphs to represent the employment rates of 8 different industries, because each employment rate is an individual so the separate display can be more convenient for observation. One point corresponds to one year of data.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":238,"value":"### User manual\nThe function of brush is used to show the selection of different years, the above 8 small pictures will filtering the quantity position of a specific year by brushing. At the same time, selecting the number of employment rates of different industries in different years will also display the corresponding annual interest rates in the interest rate chart.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":181,"value":"$4d","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":266,"value":"$4e","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":379,"value":"## Conclusion\nThis interactive notebook offers valuable insights into how interest rates impact the employment market in the United States. It presents multi-faceted comparisons of interest rates with the national unemployment rate, unemployment rates among various ethnic groups, and employment data across different industries. This comprehensive analysis enables users to better understand the effects of interest rates on the job market.\n\nAfter exploring this notebook, users will recognize that rising interest rates can lead to recessions and depressions in the employment market. They will also gain an understanding of which counties or states are relatively less affected by interest rate fluctuations. In addition, users will discover which industries are more susceptible to interest rate changes. Armed with this information, they will be better equipped to make informed decisions regarding their career and life choices.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":29,"value":"## Data Import and Cleaning","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":35,"value":"d3 = require('d3@7') //Importing D3","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":37,"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":"$undefined"},{"id":41,"value":"vegaLite = require('vega-lite@2.0.0-beta.2/build/vega-lite.min.js')","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":43,"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":"$undefined"},{"id":45,"value":"import{uniqueValid} from '@uwdata/data-utilities'","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":62,"value":"import {grid, hexGrid} from '@bryangingechen/grid-inputs'","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":31,"value":"#### Interest, inflation and unemployment rate data for question 1","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":52,"value":"rawdata = d3.csv(\"https://gist.githubusercontent.com/EvanLiu66/ccc213d27c5fd20bc760cf399d1801d9/raw/a9a24794b2f60a28070085f5f08102b91a83cf90/inflation%2520interest%2520rate%2520unemployment\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":54,"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":"$undefined"},{"id":58,"value":"interestdata = {//seleted data\n let data1 = new Array();\n for(let i = 1; i\"1999\"){\n data1.push(dataMap[i]);\n }\n }\n return data1;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":66,"value":"#### Industry dataset and Different ethnic dataset for Question 2 and 4","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":69,"value":"detaildata = d3.csv(\"https://gist.githubusercontent.com/Brenda992/daea42b14892405c69c2d7bf6578166d/raw/3b12267a8a190c78a580f08ad43ae51133e3c3e0/unemployment_data_us\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":71,"value":"detailMap = detaildata.map(d=>{\n var a=d;\n a.Year = d.Year;\n a.Date = d.Date;\n a.High_School = +d.High_School;\n a.Associates_Degree = +d.Associates_Degree;\n a.Professional_Degree = +d.Professional_Degree;\n a.White = +d.White;\n a.Black = +d.Black;\n a.Asian = +d.Asian;\n a.Hispanic = +d.Hispanic;\n a.Men = +d.Men;\n a.Women = +d.Women;\n a.Real_interest_rate = +d.Real_interest_rate;\n return a;\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":73,"value":"markerColors = [\"pink\", \"cornflowerblue\", \"paleturquoise\", 'lightcoral', \"gold\",\"brown\",\"yellow\",\"lightblue\",\"green\",\"orange\",\"purple\",\"black\"]\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":349,"value":"monthColor =[\"#a6cee3\",\"#1f78b4\",\"#b2df8a\",\"#33a02c\",\"#fb9a99\",\"#e31a1c\",\"#fdbf6f\",\"#ff7f00\",\"#cab2d6\",\"#6a3d9a\",\"#ffff99\",\"#b15928\"]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":175,"value":"industryRawdata = d3.csv(\"https://gist.githubusercontent.com/Brenda992/6a29fdb08fb0a92724bfc352cc47ea52/raw/984c3ccca9c0d1ed0c30bea1e61416bedd772814/industry_data_us\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":177,"value":" industrialMap = industryRawdata.map(d=>{\n var a=d;\n a.Year = d.Year;\n a.incomeLevel = d.incomeLevel;\n a.Health_care_and_social_assistance = +d.Health_care_and_social_assistance;\n a.Educational_services = +d.Educational_services;\n a.Finance_and_insurance = +d.Finance_and_insurance;\n a.Private_nonfarm_employmen = +d.Private_nonfarm_employmen;\n a.Utilities = +d.Utilities;\n a.Arts_entertainment_and_recreation = +d.Arts_entertainment_and_recreation;\n a.Wholesale_trade = +d.Wholesale_trade;\n a.Accommodation_and_food_services = +d.Accommodation_and_food_services;\n a.Real_interest_rate = +d.Real_interest_rate;\n \n return a;\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":300,"value":"Months = uniqueValid(detaildata, d=>d.Month)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":76,"value":"#### Counties Dataset for Question 3","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":79,"value":"rawStateData = d3.csv(\"https://gist.githubusercontent.com/EvanLiu66/81aa731ed370faeec96a02b31695c9fd/raw/5ad7f016612080fdc9213ec7246edb2f4fae626b/unemployment%2520rate%2520per%2520state\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":81,"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":"$undefined"},{"id":83,"value":"cleandata = {//seleted data\n let cleandata = new Array();\n for(let i = 1; id.State_Area)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":87,"value":"Years = uniqueValid(unemployment2000to2020, d=>d.year)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":89,"value":"import {unemployment2000to2020, us, collectionPreviews, header} from \"@observablehq/sources-for-outlier-2022-notebooks\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":93,"value":"unemployment2000to2020","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":95,"value":"usa = FileAttachment(\"us-counties-10m.json\").json()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":97,"value":"Counties = uniqueValid(unemployment2000to2020, d=>d.county)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":187,"value":"cleanUnemployment2000to2020 = {\n let data = new Array();\n for(let i=1; i