2b:null 2c:["$","title",null,{"children":"Project Report | LilyLi | Observable"}] 31:T42c,### Intro Since COVID-19 pandemic, there has been waves of layoffs in the whole world. Many businesses have had to shut down or reduce their operations, leaving millions of workers without jobs. Recent tech layoffs shocked everyone in Bay Area and brought the anxiety to the next level. The problem is people are prepared for this massive layoff and sudden increase in unemployment. If we can foresee what might happen in the near future, we can prepare ourselves for the worst situation.
The unemployment rate is the key indicator of the health of an economy. When the rate is high, it can have devastating effects on people's lives. There is a complex relationship between debt, GDP, and unemployment rate, with each factor affecting the others in different ways. The ripple effects of high unemployment can also extend to the broader community, with decreased spending and decreased economic activity. Thus, it's important to learn more about debt, GDP and unemployment rate for ensuring the well-being of individuals and the stability of society.
32:Ta43,gdp = { const us_unemployment_gdp_chart = () => { const line_unemployment = vl.markLine().data(dataSet.filter(d => d.country == 'US' && d.type =='unemployment')).encode( vl.x().fieldT("date").timeUnit('yearmonth'), vl.y().fieldQ("value").title('Unemployment Percentage Rate'), vl.color().fieldN("type"), ).width(550) .height(250); const line_income = vl.markLine().data(dataSet.filter(d => d.country == 'US' && d.type =='gdp')).encode( vl.x().fieldT("date").timeUnit('yearmonth'), vl.y().fieldQ("value").title('US GDP'), vl.color().fieldN("type"), ).width(550) .height(250); const line = vl.layer(line_unemployment, line_income).resolve({scale: {y: 'independent'}}) return line } const canada_unemployment_gdp_chart = () => { const line_unemployment = vl.markLine() .data(dataSet.filter(d => d.country == 'Canada' && d.type =='unemployment')).width(width - 250).encode( vl.x().fieldT("date").timeUnit('yearmonth'), vl.y().fieldQ("value").title('Unemployment Percentage Rate'), vl.color().fieldN("type"), ).width(550) .height(250); const line_income = vl.markLine().data(dataSet.filter(d => d.country == 'Canada' && d.type =='gdp')).width(width - 250).encode( vl.x().fieldT("date").timeUnit('yearmonth'), vl.y().fieldQ("value").title('Canada GDP'), vl.color().fieldN("type"), ).width(550) .height(250); const line = vl.layer(line_unemployment, line_income).resolve({scale: {y: 'independent'}}) return line } const japan_unemployment_gdp_chart = () => { const line_unemployment = vl.markLine() .data(dataSet.filter(d => d.country == 'Japan' && d.type =='unemployment')) .width(width - 250) .encode( vl.x().fieldT("date").timeUnit('yearmonth'), vl.y().fieldQ("value").title('Unemployment Percentage Rate'), vl.color().fieldN("type"), ).width(550) .height(250); const line_income = vl.markLine() .data(dataSet.filter(d => d.country == 'Japan' && d.type=='gdp')) .width(width - 250).encode( vl.x().fieldT("date").timeUnit('yearmonth'), vl.y().fieldQ("value").title('Japan GDP'), vl.color().fieldN("type"), ).width(550) .height(250); const line = vl.layer(line_unemployment, line_income).resolve({scale: {y: 'independent'}}) return line } return vl .concat(us_unemployment_gdp_chart(),canada_unemployment_gdp_chart(),japan_unemployment_gdp_chart()) .columns(1) .render() }33:T4f0,h1b_chart = { const onmouseover = vl .selectSingle() .on("mouseover") .encodings("x") .nearest(true) .empty({ created: [] }); const data = dataSet.filter(d => d.country == 'US' && d.type=='unemployment' && d.date.getFullYear() >= 2009) // h1b.forEach(d => { // if (d.type == 'approval rate'){ // data.push(d) // } // }) const line_h1b = vl .markLine() .data(h1b.filter(d => d.type != 'approval rate')) .encode( vl.x().fieldT("date").title("Year").timeUnit('year'), vl.y().fieldQ('value').title("H1b Numbers"), vl.color().fieldN('type'), ).width(width - 250) const line_unemployment = vl.markLine().data(data).width(width - 250).encode( vl.x().fieldT("date").timeUnit('year'), vl.y().mean("value").title('Unemployment Percentage Rate'), vl.color().fieldN("type"), ) const line = vl .layer(line_unemployment, line_h1b) .resolve({scale: {y: 'independent'}}) const points = line_h1b .markPoint({ tooltip: true, size: 1 }) .select(onmouseover) .encode( vl .color() .if(onmouseover, vl.color().value("firebrick")) .value("steelblue") ); return line.render() // return vl.layer(points, line).render(); }34:Td64,### Evaluation Plan I will be running an usability study for [Idiom 3](https://observablehq.com/d/b3f60f8225ad01e0#cell-273), I'll be evaluating tasks... - I1_MT: Summarize the correlation between the GDP and unemployment rate changes in Japan, US and Canada. (**Summarize -> Features**, Main Task by Lily) - I1_ST1: Identify how GDP changed when unemployment rate increased. (**Identify -> Features**, Sub Task by Lily) - I1_ST2: Compare the overall GDP trends in Japan, US and Canada.(**Compare -> trends**, Sub Task by Lily) __Script__ Welcome to the study. I am Lily and I did a visualization project about unemployment rate. Thank you for helping me with this project. Everything you tell me would be helpful. Everything we do will be recorded. Is this ok for you? (waiting for the answer). Great, I will send you a URL of our application, could you please share your screen? I am evaluating the tools not the participant. So there is no way of failing for the participant. Don't be nervous. Just share with me what you are thinking. __Tasks to test__

Please tell me what you think this is for and everything went through your brain.
Please identify the years when unemployment rate went up by a lot for Japan, US and Canada.
Please identify the years when GDP went down for Japan, US and Canada.
Do you think the unemployment rate in different countries tends to have similar trend?
Why do you think so?
Do you think GDP can be one of the good indicators for the changes of unemployment rate?
Why do you think so?
What do you think the correlation is between the GDP and unemployment rate?
How do you think it could be of use? Anything you see will call your attention?
Why you think it is useful?
1 to 10, How useful you think this would be?
Do you have any suggestion ?

__Expected answers__

This is for analyzing the relationship between debt, GDP and unemployment rate and how unemployment rate affects out life.
For Japan, the years are 2002, 2009 and 2020; For US, the years are 2002, 2009 and 2020; For Canada, the years are 2002, 2009 and 2020.
For Japan, the years are 2009 and 2020; For US, the years are 2009 and 2020; For Canada, the years are 2009 and 2020.
Yes I do.
Because Japan, US and Canada's unemployment rate went up in the same year. But I think it will be better to more countries' unemployment rate to make sure.
Yes I think so.
Because in Japan, US and Canada, when GDP went down, unemployment rate all went up a lot. Although I think GDP is not the only one that affects unemployment rate because when GDP went up, unemployment rate can still went up.
I think GDP and unemployment rate have negative correlation. When GDP goes down, there is a high chance that unemployment rate might go up.
Government can take use of it to analyze the health of economy. People can use it to have a better understanding of current market. The global debt to GDP ratio got my attention the most.
Because there are some insights that can actually help with predicting the incoming unemployment rate increase.
I think I will give it 8. Because there are some meaningful insights.
I think it would be helpful to include more countries.

35:T433,h1b_dataset = { const h1bData = [h1b_2009, h1b_2010, h1b_2011, h1b_2012, h1b_2013, h1b_2014, h1b_2015, h1b_2016, h1b_2017, h1b_2018, h1b_2019, h1b_2020, h1b_2021, h1b_2022] const res = {} for (let i = 2009; i<=2022; i++){ res[i] = { approvals: 0, denials: 0 } } for (let h of h1bData){ h.forEach(d => { if (Object.keys(d).includes('Initial Approvals')){ res[d['Fiscal Year']]['approvals'] += isNaN(d['Initial Approvals']) ? 0 : d['Initial Approvals'] }else{ res[d['Fiscal Year']]['approvals'] += isNaN(d['Initial Approval']) ? 0 : d['Initial Approval'] } if (Object.keys(d).includes('Initial Denials')){ res[d['Fiscal Year']]['denials'] += isNaN(d['Initial Denials']) ? 0 : d['Initial Denials'] }else{ res[d['Fiscal Year']]['denials'] += isNaN(d['Initial Denial']) ? 0 : d['Initial Denial'] } }) } for (let i = 2009; i<=2022; i++){ res[i]['approval rate'] = 100*res[i]['approvals']/(res[i]['denials'] + res[i]['approvals']) } return res 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Report","cells":[{"id":0,"value":"# Project Report\n\n- CS7295 Final project progress report created by [Jiangyan Li](https://observablehq.com/@lilyneu?tab=profile)
\n- Class: [Data Visualization / Information Visualization](https://johnguerra.co/classes/data_visualization_spring_2023/)
\n- Team Member: Fengrui Gan, Jiangyan Li\n- [Website](https://jiangyanlineu.github.io/CS7295_Final_Project/)\n- [Presentation Video](https://youtu.be/Ud1G61_w6SA)\n- [Slides](https://docs.google.com/presentation/d/1pTMAqsAzvrNwX9il-lMneN8NX6wIjLPC2p_tGvOMRxI/edit#slide=id.g22a785a6857_0_347)\n- [Twitter](https://twitter.com/neu7295/status/1651317006044639233)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":69,"value":"

","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":12,"value":"$31","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":67,"value":"

","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":881,"value":"### Target Users / Audiences:\n -\tNew Graduates\n -\tPeople who got laid off \n -\tJob marketing analyst\n -\tGovernment Economy Department","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":879,"value":"

","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":265,"value":"## 1. Unemployment rate trends, debt to GDP ratio and GDP in Japan, Canada and US\n\nLet's take a look at the big picture. Unemployment rates have fluctuated significantly over time in various countries due to a variety of factors such as economic policies, technological advancements, and global events. For instance, countries like the United States and Canada have experienced periods of low unemployment in the past, but have also seen an increase in recent years. On the other hand, some countriy like japan have a stable unemployment rates. Overall, understanding the trends of unemployment changes is crucial for policymakers to implement effective strategies to reduce joblessness and promote economic growth.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":941,"value":"**Insights 1.1**: \n - the unemployment rate of Japan, Canada and US have similar trend since 2000, where the unemployment rate went up in 2009 and 2020. Canada and US hit the highest unemployment rates in 2020, and Japan had the highest unemployment rate in 2009.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1044,"value":"**Data Abstraction**\n- Unemployment rate datasets\n - [US Unemployment rate](https://fred.stlouisfed.org/series/UNRATE)\n - [Canada Unemployment rate](https://fred.stlouisfed.org/series/LRUNTTTTCAM156S)\n - [Japan Unemployment rate](https://fred.stlouisfed.org/series/LRUN64TTJPM156S)\n- Dataset Type (for all datasets:\n - Table, Temporal(one attribute is date);\n- Data availability:\n - Static\n- Attributes\n - country: Categorical;\n - date: Ordinal, Sequential (temporal);\n - value: Quantitative, Sequential;\n - type: Categorical;\n- Derived Attributes: None","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":312,"value":"**Tasks (why)**\n- I1_MT: Compare the trends of unemployment rate of three countries. (**Compare -> Trends**, Main Task by Lily)\n- I1_ST1: Identify the peaks of unemployment rate in three countries. (**Identify -> Features**, Sub Task by Lily)\n- I1_ST2: Summarize the common changes in unemployment rate of three countries. (**Summarize -> Features**, Sub Task by Lily)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":316,"value":" **How**\n- Marks: Line\n- Channels:\n - Vertical Position: unemployment rate, Express\n - Horizontal Position: Date, Express\n - Color hue: Different Countries","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":339,"value":"**Considerations**\n\nTo derive this insight, I built a line chart, where we can see the trends of unemployment rate changes in three countries since 2000. This idiom can help me complete all the tasks.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":951,"value":"### Unemployment Rate in Japan, US and Canada Visualization","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":344,"value":"unemployment_chart = {\n // const onmouseover = vl\n // .selectSingle()\n // .on(\"mouseover\")\n // .encodings(\"x\")\n // .nearest(true)\n // .empty({ created: [] });\n\n const line = vl.markLine().data(dataSet.filter(d => d.type=='unemployment')).width(width - 250)\n .encode(\n vl.x().fieldT(\"date\").timeUnit('yearmonth'),\n vl.y().fieldQ(\"value\").title('Unemployment Percentage Rate'),\n vl.color().fieldN(\"country\"),\n )\n // const points = line\n // .markPoint({ tooltip: true, size: 1 })\n // .select(onmouseover)\n // .encode(\n // vl\n // .color()\n // .if(onmouseover, vl.color().value(\"firebrick\"))\n // .value(\"steelblue\")\n // );\n return line.render()\n // return vl.layer(points, line).render();\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":328,"value":"

","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":947,"value":"**Insights 1.2**: \n - the debt to GDP ratio of Japan, US and Canada are very similar since 2000, and Debt to GDP ratio all went up when unemployment rate increased (from 2009 to 2010 and from 2019 to 2020).","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1060,"value":"**Data Abstraction**\n- Global Debt to GDP ratio\n - [Global Debt to GDP ratio](https://www.imf.org/external/datamapper/GG_DEBT_GDP@GDD/SWE)\n- Dataset Type (for all datasets:\n - Table, Temporal(one attribute is date);\n- Data availability:\n - Static\n- Attributes\n - country: Categorical;\n - date: Ordinal, Sequential (temporal);\n - value: Quantitative, Sequential;\n - type: Categorical;\n- Derived Attributes: None","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":365,"value":"**Tasks (why)**\n- I1_MT: Compare the trends of debt to GDP ratio especially for Japan, US and Canada. (**Compare -> Trends**, Main Task by Lily)\n- I1_ST1: Identify which country among Japan, US and Canada has higher debt to GDP ratio. (**Identify -> Features**, Sub Task by Lily)\n- I1_ST2: Summarize the common changes in debt to GDP ratio when unemployment rate increased. (**Summarize -> Features**, Sub Task by Lily)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":369,"value":" **How**\n \n- Marks: Line\n- Channels:\n - Vertical Position: Debt to GDP ratio, Express\n - Horizontal Position: Date, Express\n - Color hue: Different Countries","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":429,"value":"**Consideration**\n\nTo derive this insight, I built a multiseries line chart, where we can see the trends of Debt to GDP ratio changes in all countries since 2000. This idiom can help me complete all the tasks.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":965,"value":"**Global Debt to GDP Ration Visualization**","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":375,"value":"viewof selected_country = Inputs.select(\n global_countries, { label: \"Choose another country: \", value: 'Japan' }\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":373,"value":"global_chart = {\n const x = vl.x().field(\"date\");\n\n const onmouseover = vl\n .selectSingle()\n .on(\"mouseover\")\n .encodings(\"x\")\n .nearest(true)\n .empty({ created: [] });\n\n const line = vl\n\n .markLine()\n .data(\n debt_to_gdp_ratio_global\n )\n .encode(\n // x.timeUnit('year'), \n vl.x().field(\"date\").title(\"Year\"),\n vl.y().fieldQ('value').title(\"Debt to GDP ratio\"),\n vl.color().fieldN('country'),\n vl.opacity()\n .if(\n `datum.country == \"${selected_country}\" || datum.country == 'Japan' || datum.country == 'Canada' || datum.country == 'United States'` \n , vl.opacity().value(1.5)).value(0.1)\n \n )\n .width(width - 250);\n\n const points = line\n .markPoint({ tooltip: true, size: 1 })\n .select(onmouseover)\n .encode(\n vl\n .color()\n .if(onmouseover, vl.color().value(\"firebrick\"))\n .value(\"steelblue\")\n );\n\n return vl.layer(points, line).render();\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":371,"value":"

","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":971,"value":"**Insights 1.3**\n- For Canada, US and Japan, when GDP dropped in 2009 and 2020, the unemployment rate rapidly increased.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1066,"value":"**Data Abstraction**\n- GDP datasets\n - [US GDP](https://fred.stlouisfed.org/series/GDP)\n - [Canada GDP](https://fred.stlouisfed.org/series/NGDPRSAXDCCAQ)\n - [Japan GDP](https://fred.stlouisfed.org/series/JPNNGDP)\n- Dataset Type (for all datasets:\n - Table, Temporal(one attribute is date);\n- Data availability:\n - Static\n- Attributes\n - country: Categorical;\n - date: Ordinal, Sequential (temporal);\n - value: Quantitative, Sequential;\n - type: Categorical;\n- Derived Attributes: None","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":460,"value":"**Tasks (why)**\n\n- I1_MT: Summarize the correlation between the GDP and unemployment rate chagnes in Japan, US and Canada. (**Summarize -> Features**, Main Task by Lily)\n- I1_ST1: Identify how GDP changed when unemployment rate increased. (**Identify -> Features**, Sub Task by Lily)\n- I1_ST2: Compare the overall GDP trends in Japan, US and Canada.(**Compare -> trends**, Sub Task by Lily)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":462,"value":" **How**\n \n- Marks: Line\n- Channels:\n - Vertical Position: GDP, Express\n - Horizontal Position: Year, Express\n - Color hue: different type (GDP or unemployment rate)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":443,"value":"**Consideration**\n\nTo derive this insight, I built a dual Y axis multiline chart, where we can compare the trends of GDP and unemployment rate in Japan, US and Canada since 2000. This idiom can help me complete all the tasks.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":974,"value":"**GDP and Unemployment rate Visualization**","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":467,"value":"$32","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":439,"value":"

","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":977,"value":"## 2. Unemployment Rate And Entertainment Market\n\nThe relationship between video sales and unemployment rate is not straightforward. In times of economic hardship, consumers may be more likely to stay at home and watch movies, leading to an increase in video sales. However, high levels of unemployment can also mean reduced disposable income and less spending on non-essential goods like entertainment, which can ultimately lead to lower video sales. Ultimately, the impact of unemployment on video sales depends on a variety of factors, including consumer behavior and market conditions.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":981,"value":"**Insights 2.1**\n\nJapan and North America area video sales dropped when unemployment rate peaked in 2019, which suggests that video games are a discretionary spending item for many households, and during times of economic uncertainty, entertainment is not the top priority.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1070,"value":"**Data Abstraction**\n- Japan and North America Video Sales datasets\n - [Video Games Sales](https://www.kaggle.com/datasets/sidtwr/videogames-sales-dataset)\n- Dataset Type (for all datasets:\n - Table, Temporal(one attribute is date);\n- Data availability:\n - Static\n- Attributes\n - year: Ordinal, Sequential (temporal);\n - sales: Quantitative, Sequential;\n - type: Categorical;\n- Derived Attributes: None","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":484,"value":"**Tasks (why)**\n- I1_MT: Discover the correlation between the unemployment rate and video game sales (**Discover -> correlation**, Main Task by Lily)\n- I1_ST1: Identify the year when video game market had the highest sales (**Identify -> Features**, Sub Task by Lily)\n- I1_ST2: Compare the trends of different video game types.(**Compare -> trends**, Sub Task by Lily)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":480,"value":" **How**\n- Marks: Area\n- Channels:\n - Vertical Position: video game sales, Express\n - Horizontal Position: Year, Express\n - Color hue: different type of video games","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":445,"value":"**Consideration**\n\nTo derive this insight, I built a stacked bar chart, where we can see the changes of video sales in Japan and North America Area.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":984,"value":"**Japan and North American Video Game Sales Visualization**","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":491,"value":"bar = {return vl.markBar()\n .data(videoGameData).encode(\n vl.y().aggregate('sum').title('sales').fieldQ('sales'),\n vl.x().timeUnit('year').field('year').title('Year'),\n vl.color().field('type')\n).render()}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":441,"value":"

","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":990,"value":"## 3. Unemployment Rate And H1b Lottery\n\nH1B visas are non-immigrant visas that allow US employers to temporarily employ foreign workers in specialty occupations. The relationship between H1B visas and the unemployment rate is complex. Some argue that H1B workers may displace US workers, particularly in fields like IT. However, others contend that H1B workers help fill gaps in the labor market and stimulate economic growth, ultimately leading to job creation and lower unemployment rates. The impact of H1B visas on the US labor market remains a topic of debate among policymakers and economists.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":993,"value":"**Insights 3.1**\n\nThe H1B denials number is negatively correlated with the unemployment rate, while the H1B approvals number has no significant correlation with the unemployment rate.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1076,"value":"**Data Abstraction**\n- US H1B Lottery Result Dataset\n - [US H1b dataset](https://www.uscis.gov/tools/reports-and-studies/h-1b-employer-data-hub/h-1b-employer-data-hub-files)\n- Dataset Type (for all datasets:\n - Table, Temporal(one attribute is date);\n- Data availability:\n - Static\n- Attributes\n - date: Ordinal, Sequential (temporal);\n - value: Quantitative, Sequential;\n - type: Categorical;\n- Derived Attributes: None","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":498,"value":"**Tasks (why)**\n- I1_MT: Discover the correlation between the unemployment rate and h1b approvals/denials (**Discover -> correlation**, Main Task by Lily)\n- I1_ST1: Identify how h1b approvals and h1b denials changed when unemployment rate increased. (**Identify -> Features**, Sub Task by Lily)\n- I1_ST2: Compare the trends of unemployment rate and h1b approvals/denials.(**Compare -> trends**, Sub Task by Lily)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":494,"value":" **How**\n- Marks: Line\n- Channels:\n - Vertical Position: Unemployment rate, H1b numbers, Express\n - Horizontal Position: Year, Express\n - Color hue: different type of data(approvals, denials, unemployment)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":447,"value":"**Consideration**\n\nTo derive this insight, I built a multiseries line chart, where we can see the changes of unemployment rate, h1b approvals and h1b denials. This idiom can help me complete all the tasks.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":996,"value":"**H1b Lottery Visualization**","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":502,"value":"$33","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":263,"value":"

","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1009,"value":"## Usability Study\n- [Study Report](https://docs.google.com/document/d/1X7kbGvtOYtA2n-esyPzcb2TASOV70tFstg43byANW5k/edit#heading=h.3889grurbl14)\n- [Post study questionnaire](https://docs.google.com/forms/d/142Xikt0w3SzgmdqSccHNK5w_M1ZUugqAI-szlHjdtZQ/prefill)\n- Videos:","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1011,"value":"

\n \n \n \n

","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":527,"value":"$34","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1007,"value":"---","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":84,"value":"### Data Processing","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":119,"value":"**Global debt to gdp ratio dataset**","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":86,"value":"- Get all the years in global_debt_gdp dataset","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":88,"value":"global_keys = Object.keys(global_debt_gdp[1])","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":379,"value":"- Get all the countries in this dataset","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":377,"value":"global_countries = {\n const res = new Set()\n debt_to_gdp_ratio_global.forEach(d => res.add(d.country))\n const ret = []\n for (let r of res){ret.push(r)}\n return ret\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":97,"value":"- format the global_debt_gdp_ration dataset","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":93,"value":"debt_to_gdp_ratio_global = {\n const res = []\n const years = global_keys.slice(0, global_keys.length-1)\n global_debt_gdp.forEach(data => {\n const country = data['General Government Debt (Percent of GDP)']\n \n if (country!=\"\"){\n for (let year of years){\n data[year]!== NaN && res.push({\n country: country,\n date: parseInt(year),\n type:'debt',\n value: parseInt(data[year])\n })\n }\n } \n })\n return res.filter(d => d.date >= 2000)\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":121,"value":"**US/Canada/Japan Unemployment rate and GDP dataset**","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":134,"value":"- Combine US unemployment dataset and US GDP dataset","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":123,"value":"usData = {\n const data = []\n usunemployment.forEach(d => {\n const time = new Date(d.DATE)\n const yearMonth = new Date(time.getFullYear(), time.getMonth())\n data.push({date: yearMonth, type:'unemployment', value: d.UNRATE, country: 'US'})\n })\n usgdp.forEach(d => {\n const time = new Date(d.DATE)\n const yearMonth = new Date(time.getFullYear(), time.getMonth())\n data.push({date: yearMonth, type:'gdp', value: d.GDP, country: 'US'})\n })\n return data\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":143,"value":"- Combine Canada unemployment dataset and Canada GDP dataset","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":145,"value":"canadaData = {\n const data = []\n canadaunemployment.forEach(d => {\n const time = new Date(d.DATE)\n const yearMonth = new Date(time.getFullYear(), time.getMonth())\n data.push({date: yearMonth, type: 'unemployment', value: d.LRUNTTTTCAM156S, country:'Canada'})\n })\n canadagdp.forEach(d => {\n const time = new Date(d.DATE)\n const yearMonth = new Date(time.getFullYear(), time.getMonth())\n data.push({date: yearMonth, type: 'gdp', value: ((d.NGDPRSAXDCCAQ)/1000)*0.73, country:'Canada'})\n })\n return data\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":149,"value":"- Combine Japan unemployment dataset and Japan GDP dataset","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":151,"value":"japanData = {\n const res = []\n japanunemployment.forEach(d => {\n res.push({\n date: new Date(d.DATE),\n type:'unemployment',\n value: d.LRUN64TTJPM156S,\n country: 'Japan'\n })\n })\n\n japangdp.forEach(d => {\n res.push({\n date: new Date(d.DATE),\n type:'gdp',\n value: d.GDP,\n country: 'Japan'\n })\n })\n return res\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":158,"value":"- Put US, Canada and Japan datasets to one dataset","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":156,"value":"dataSet = {\n const res = []\n // eu_unemployment_rate.forEach(d => res.push(d))\n usData.forEach(d => res.push(d))\n canadaData.forEach(d => res.push(d))\n japanData.forEach(d => res.push(d))\n // ukData.forEach(d => res.push(d))\n return res\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":182,"value":"- **Video Game Sales dataset**","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":187,"value":"- Format the video game dataset","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":184,"value":"videoGameData = {\n const res = []\n const temp = {}\n const years = new Set()\n const types = new Set()\n \n videogame.forEach(d => {\n years.add(d.Year_of_Release)\n types.add(d.Genre)\n })\n for (let y of years){\n temp[y] = {}\n for (let t of types){\n temp[y][t] = 0\n }\n }\n\n videogame.forEach(d => {\n temp[d.Year_of_Release][d.Genre] += d.NA_Sales\n })\n for (let i of Object.entries(temp)){\n const year = new Date(i[0])\n for (let j of Object.entries(temp[i[0]])){\n const type = j[0]\n res.push({\n year: year,\n type: type,\n sales:temp[i[0]][type]\n })\n }\n \n }\n return res\n \n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":234,"value":"- **US H1b Dataset**","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":237,"value":"- The website only provide h1b dataset from 2009 to 2022","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":232,"value":"h1b_year = {\n const res = []\n for (let i = 2009; i<=2022; i++){\n res.push(i)\n }\n return res\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":243,"value":"- Get total number of approvals and denials of each year","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":240,"value":"$35","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":248,"value":"- Create a dataset to combine all years' h1b datasets","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":245,"value":"h1b = {\n const res = []\n for (let year of h1b_year){\n res.push({\n date: new Date(year, 1),\n type: 'approvals',\n value: h1b_dataset[year]['approvals']\n })\n res.push({\n date: new Date(year, 1),\n type: 'denials',\n value: h1b_dataset[year]['denials']\n })\n res.push({\n date: new Date(year, 1),\n type: 'approval rate',\n value: h1b_dataset[year]['approval rate']\n })\n }\n return res\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":24,"value":"

","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":21,"value":"chkb = () => htl.html``","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":348,"value":"import {vl} from \"@vega/vega-lite-api\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":74,"value":"import {navio} from \"@john-guerra/navio\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":80,"value":"global_debt_gdp = await FileAttachment(\"Global_debt_gdp.csv\").csv({typed: \"auto\"})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"global_debt_gdp","database":"$undefined"},{"id":113,"value":"canadagdp = await FileAttachment(\"canadagdp.csv\").csv({typed: \"auto\"})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"canadagdp","database":"$undefined"},{"id":116,"value":"japangdp = await FileAttachment(\"japanGDP.csv\").csv({typed: \"auto\"})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"japangdp","database":"$undefined"},{"id":118,"value":"usgdp = await FileAttachment(\"usgdp.csv\").csv({typed: \"auto\"})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"usgdp","database":"$undefined"},{"id":125,"value":"usunemployment = await FileAttachment(\"usunemployment.csv\").csv({typed: \"auto\"})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"usunemployment","database":"$undefined"},{"id":142,"value":"canadaunemployment = await FileAttachment(\"canadaunemployment.csv\").csv({typed: \"auto\"})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"canadaunemployment","database":"$undefined"},{"id":148,"value":"japanunemployment = await FileAttachment(\"japanUnemployment.csv\").csv({typed: \"auto\"})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"japanunemployment","database":"$undefined"},{"id":178,"value":"videogame = await FileAttachment(\"videoGame.csv\").csv({typed: \"auto\"})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"videogame","database":"$undefined"},{"id":203,"value":"h1b_2009 = await FileAttachment(\"h1b_datahubexport-2009.csv\").csv({typed: \"auto\"})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"h1b_2009","database":"$undefined"},{"id":206,"value":"h1b_2010 = await FileAttachment(\"h1b_datahubexport-2010.csv\").csv({typed: \"auto\"})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"h1b_2010","database":"$undefined"},{"id":207,"value":"h1b_2012 = await FileAttachment(\"h1b_datahubexport-2012.csv\").csv({typed: \"auto\"})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"h1b_2012","database":"$undefined"},{"id":208,"value":"h1b_2014 = await FileAttachment(\"h1b_datahubexport-2014.csv\").csv({typed: \"auto\"})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"h1b_2014","database":"$undefined"},{"id":209,"value":"h1b_2016 = await FileAttachment(\"h1b_datahubexport-2016.csv\").csv({typed: \"auto\"})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"h1b_2016","database":"$undefined"},{"id":210,"value":"h1b_2018 = await FileAttachment(\"h1b_datahubexport-2018.csv\").csv({typed: \"auto\"})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"h1b_2018","database":"$undefined"},{"id":211,"value":"h1b_2019 = await FileAttachment(\"h1b_datahubexport-2019.csv\").csv({typed: \"auto\"})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"h1b_2019","database":"$undefined"},{"id":212,"value":"h1b_2020 = await FileAttachment(\"h1b_datahubexport-2020.csv\").csv({typed: \"auto\"})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"h1b_2020","database":"$undefined"},{"id":213,"value":"h1b_2021 = await FileAttachment(\"h1b_datahubexport-2021.csv\").csv({typed: \"auto\"})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"h1b_2021","database":"$undefined"},{"id":214,"value":"h1b_2017 = await FileAttachment(\"h1b_datahubexport-2017.csv\").csv({typed: \"auto\"})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"h1b_2017","database":"$undefined"},{"id":215,"value":"h1b_2015 = await FileAttachment(\"h1b_datahubexport-2015.csv\").csv({typed: \"auto\"})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"h1b_2015","database":"$undefined"},{"id":216,"value":"h1b_2011 = await FileAttachment(\"h1b_datahubexport-2011.csv\").csv({typed: \"auto\"})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"h1b_2011","database":"$undefined"},{"id":217,"value":"h1b_2013 = await FileAttachment(\"h1b_datahubexport-2013.csv\").csv({typed: \"auto\"})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"h1b_2013","database":"$undefined"},{"id":218,"value":"h1b_2022 = await FileAttachment(\"h1b_datahubexport-2022.csv\").csv({typed: \"auto\"})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"h1b_2022","database":"$undefined"}],"files":"$W36","stdlib":"1","titling":"auto"}} 30:"$2f:body"