25:["$","title",null,{"children":"Final Project (Milestone) | DataViz 2023: Group Work | Observable"}] 49:T453,Plot.plot({ marginLeft: 90, marginBottom: 50, color: {domain: ["Low income","Lower middle income","Upper middle income","High income"], legend: true}, fy: {axis: "left", tickRotate: -90, tickPadding: 40, label: null, domain: ["High income","Upper middle income","Lower middle income","Low income"] }, x: {label: "People above the age of 65 (as % of total population) →", labelOffset: 35, grid: true}, y: {label: "↑ Number of deaths per 1000 persons", grid: true}, marks: [ Plot.dot(data_processed_cleanedIncomeGroup, { x: "Pop%_65+", y: "Death_rate", stroke: "Income Group", strokeOpacity: 0.7, fy: "Income Group", title: d=> d["Country Name"] + " " + d["Year"] }), Plot.linearRegressionY(data_processed_cleanedIncomeGroup, { x: "Pop%_65+", y: "Death_rate", fy: "Income Group", stroke: "grey", ci: 0 }), Plot.areaY(data_processed_cleanedIncomeGroup, { x: [0,36], y1: 5, y2: 15, fill: "gray", fillOpacity: 0.1 }) ] });4a:T4d5,The plot demonstrates many insights: 1. High income and upper middle income countries have a clear pattern: 'The higher the proportion of old people, the higher the death rate'. As we process further to lower income countries, this pattern gradually diminishes as the death rates tend to scatter closer to the y-axis. 2. The populations get "older" (on average) as one moves from the low income bracket to the high income bracket, i.e. fewer older people (compared to whole population) in low income countries. This is observed from the distributions of the scatter plots along the x-axis. This pattern may be due to the fact that not many people survived until old age in the low income countries (likely because of inadequate healthcare facilities as associated with low development). 3. If one focusses in the lower old population range (up to 5% old people in the population) along the x-axis, i.e. at the same age structure, high income countries show much better death rates (below 5). This is already convincing to demonstrate that high income countries have overall better death rates, if all else remains equal. 4. Lower middle income countries show much better death rates than other groups (below 10 for most data points).4b:T86d,In both our analyses, we find the population dynamics of a country highly correlated with its economic development levels. In the case of birth rates, the line of reasoning suggests that with increasing development, people's lifestyles and attitudes change which causes unwillingness to have children. This leads to the correlation of dipping birth rates with economic development. The surprising aspect here was the nature of the trend -- it is exponential and the drop in birth rates is most significant in lower income segments until it settles to an overall low birth rate in high income economies. In the case of death rates, GDP per capita does not explain any trends in the death rates on its own. However, when combined with an analysis including the age dynamics, the story becomes clearer. The high income countries, where better healthcare facilities should lead to a low death rate, often have a significantly large old population so death rates are nonetheless high because more people die naturally of old age. This is demonstrated by the regression that shows a clean positive trend between death rates and proportion of people above the age of 65 in upper middle income and high income countries. The high income levels of the countries also explain the large old population -- they must have had better healthcare and living conditions when they were young so more people survive into old age. This is combined with the effect of low birth rates that lead to a "smaller young population". In low income countries however, death rates are quite high even with a young population suggesting younger deaths potentially due to inadequate healthcare development. To sum up, the birth rates and the death rates of a country are highly related to the development economics of the country and the two aspects go hand-in-hand with each other. In the case of birth rates, we regard this as correlation but in the case of death rates, causation is also expected. Traditional income classifications are in both cases quite helpful to group the trends and most countries within the same income classification exhibit very similar population dynamics.4c:[{"id":"c869796663e13c19d3b5d0951a1897fa26425d3e8dc5af1a5a7626ed5fa0028a3cfd1154981eeab114ec6e3576d154aca7c030e5c48597377fbee9e21834d2e7","href":"https://static.observableusercontent.com/files/c869796663e13c19d3b5d0951a1897fa26425d3e8dc5af1a5a7626ed5fa0028a3cfd1154981eeab114ec6e3576d154aca7c030e5c48597377fbee9e21834d2e7","name":"Tableau-Dashboard.twbx","type":"application/zip","lastModified":1684775091880.183,"size":115813},{"id":"081a9f508a408967070c988a72a6ba40be554fb76e8d39266ad8af157bee8c522b4edf2ac04600e39015bebc45fd50219291594a29f3d016d8dd24907e9e5a94","href":"https://static.observableusercontent.com/files/081a9f508a408967070c988a72a6ba40be554fb76e8d39266ad8af157bee8c522b4edf2ac04600e39015bebc45fd50219291594a29f3d016d8dd24907e9e5a94","name":"data_raw.csv","type":"text/csv","lastModified":1684166235599.686,"size":80710},{"id":"0a1b005afe2d1b756b130e04a1d436a93a8c13df144f15186f4e4d1481a3629da19eaff6befa0af7491b56f4903b5fd2c48e6586f91b75ae4b0a1ee59f91e024","href":"https://static.observableusercontent.com/files/0a1b005afe2d1b756b130e04a1d436a93a8c13df144f15186f4e4d1481a3629da19eaff6befa0af7491b56f4903b5fd2c48e6586f91b75ae4b0a1ee59f91e024","name":"country_metadata.csv","type":"text/csv","lastModified":1684166235599.686,"size":146867}] 29:{"id":"9a3bd794696ffe8e","slug":null,"title":"Final Project (Milestone)","thumbnail":"5f7bfcdd264276cd0f748374e29241dc4ebbea443eb95c90b93903315edcc562","access_level":"public","create_time":"$D2023-05-15T15:57:15.599Z","update_time":"$D2023-05-28T17:25:29.576Z","view_time":null,"first_public_time":"$D2023-05-15T15:57:15.599Z","has_unpublished_edits":null,"fork":true,"stars":0,"starred":false,"owner":{"type":"team","id":"47657b1e2783c487","login":"ahi","name":"DataViz 2023: Group Work","avatar_url":"https://avatars-next.observableusercontent.com/041b9b2a2977e31c2c939aabc8eca64e3344ccd7510c62ae0e360917ce633878","tier":"starter_2024","bio":"","followed_by_you":false,"follows_you":false},"version":1972,"publish_version":null,"role":null,"authored":false,"authors":[{"type":"individual","id":"a8a3d8db995c29c0","login":"hxrshu","name":"Harshita Goyal","avatar_url":"https://avatars-next.observableusercontent.com/cff2e80a4a1fb72555549f6f1b1959fdd073d04cec0d54aae8b3d822ad373002","tier":null,"bio":"","followed_by_you":false,"follows_you":false}],"members":[{"type":"team","id":"47657b1e2783c487","login":"ahi","name":"DataViz 2023: Group Work","avatar_url":"https://avatars-next.observableusercontent.com/041b9b2a2977e31c2c939aabc8eca64e3344ccd7510c62ae0e360917ce633878","tier":"starter_2024","role":"editor"},{"type":"individual","id":"a8a3d8db995c29c0","login":"hxrshu","name":"Harshita Goyal","avatar_url":"https://avatars-next.observableusercontent.com/cff2e80a4a1fb72555549f6f1b1959fdd073d04cec0d54aae8b3d822ad373002","tier":null,"role":"editor"}],"comments":[],"custom_thumbnail":false,"trashed":false,"lapsed":null,"subscribed":null,"forks":4,"collection":null,"type":"notebook","body":{"titling":"auto","title":"Final Project (Milestone)","cells":[{"id":1155,"value":"# Final Project (Milestone)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":0,"value":" _\"Economic Development & the Population Dynamics \n
of a country go hand-in-hand with each other\"_ ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":48,"value":"## Introduction","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":456,"value":"In this project, we analyze birth and death rates in 267 countries over a five-year period between 2016 and 2020 alongside economic development indicators (namely GDP per capita and income group classification) to visualize the relations between economic development and population dynamics. This leads to some interesting observations in the form of clear trends visible in the data, formulating close associations between the two aspects explored.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":51,"value":"## Table of Contents","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":537,"value":"toc({headers: 'h2,h3,h4',skip:[\"Introduction\",\"Table of Contents\",\"Appendix\"], title: \"\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":41,"value":"## Data Import & Preparation","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":63,"value":"All data is taken from the [World Bank's World Development Indicators Database](https://databank.worldbank.org/source/world-development-indicators).\n\nWe extract one main dataset which contains information about the following macroeconomic and demographic attributes for 267 countries for 5 years each (period 2016-2021):\n\n1. **GDP per capita**: \tThe gross domestic product divided by midyear population. Data are in constant 2015 U.S. dollars to allow comparability over time.\n2. **Birth rate**: Crude birth rate indicates the number of live births per 1,000 midyear population.\n3. **Death rate**: Crude death rate indicates the number of deaths per 1,000 midyear population.\n4. **Population ages 65+ (% of total population)**: Population ages 65 and above as a percentage of the total population.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":993,"value":"**Preview of main dataset `data_raw`**","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":5,"value":"data_raw = await FileAttachment(\"data_raw.csv\").csv({typed: \"auto\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"data_raw","database":"$undefined"},{"id":89,"value":"A second dataset `country_metadata` contains lots of other information about each country. From this, we would like to include information about the Income Group in our analysis.\n\nThe classification of the income group is based on the countries' GNI per capita in 2021 according to the following limits:","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":779,"value":"Plot.plot({\n width: 1200,\n marginBottom: 50,\n x: {domain: [0, 27000], \n label: \"2021 GNI per capita (US$) →\", \n labelOffset: 40, \n ticks: [0,1085, 4255, 13205]},\n color: {domain: [\"Low income\",\"Lower middle income\",\"Upper middle income\",\"High income\"], \n legend: true},\n marks: [\n Plot.barX(incomeGroupClassificationLimits, {x1: \"LowerLimit\", x2: \"UpperLimit\", fill: \"Classification\"}),\n ]\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":777,"value":"incomeGroupClassificationLimits = [\n {\"LowerLimit\": 0, \"UpperLimit\": 1085, \"Classification\": \"Low income\"},\n {\"LowerLimit\": 1085, \"UpperLimit\": 4255, \"Classification\": \"Lower middle income\"},\n {\"LowerLimit\": 4255, \"UpperLimit\": 13205, \"Classification\": \"Upper middle income\"},\n {\"LowerLimit\": 13205, \"UpperLimit\": 50000, \"Classification\": \"High income\"}\n]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":939,"value":"**Preview of `country_metadata`**","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":6,"value":"country_metadata = await FileAttachment(\"country_metadata.csv\").csv({typed: \"auto\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"country_metadata","database":"$undefined"},{"id":106,"value":"Arquero's Data Wrangler is used to add the column \"Income Group\" to `data_raw` and rename the columns for convenience. The years are formatted to remove the thousands separator.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":18,"value":"data_processed = aq.from(data_raw)\n\t.join_left(aq.from(country_metadata), [\"Country Code\", \"Code\"])\n .derive({'Year': aq.escape(d => d3.format(\".0f\")(d.Time))})\n .rename({'GDP per capita (constant 2015 US$) [NY.GDP.PCAP.KD]':'GDP_per_capita'\n ,'Birth rate, crude (per 1,000 people) [SP.DYN.CBRT.IN]': 'Birth_rate'\n ,'Death rate, crude (per 1,000 people) [SP.DYN.CDRT.IN]': 'Death_rate'\n ,'Population ages 65 and above (% of total population) [SP.POP.65UP.TO.ZS]': 'Pop%_65+'\n })\n\t.select('Year'\n ,'Country Name'\n ,'GDP_per_capita'\n ,'Birth_rate'\n ,'Death_rate'\n ,'Pop%_65+'\n ,'Income Group'\n )\n\t.objects()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":164,"value":"We also clean the data by removing rows that have an empty or undefined `Income Group` for later use.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":750,"value":"data_processed_cleanedIncomeGroup = aq.from(data_processed)\n .filter(d=> d[\"Income Group\"] != null & d[\"Income Group\"] != \"\")\n .objects()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1236,"value":"Inputs.table(data_processed_cleanedIncomeGroup)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1837,"value":"## Data Preview: Countries and their GDPs per capita","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1846,"value":"Before moving on to the main analysis, we try to get an idea about what countries are included in the data, especially with respect to the GDP per capita figures.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1852,"value":"It is best to choose the year to preview this data for and a range slider is built in as below.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1334,"value":"data_processed_cleanedIncomeGroup_forChosenYear \n= data_processed_cleanedIncomeGroup.filter(d => d.Year == Year_chosen)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1258,"value":"viewof Year_chosen = Inputs.range([2016, 2020], {label: \"Year\", step: 1})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1432,"value":"key = Swatches(charts.scales.color)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1411,"value":"charts = BubbleChart(data_processed_cleanedIncomeGroup_forChosenYear,{\n label: d => d.GDP_per_capita > 43000 ? [d[\"Country Name\"], d.GDP_per_capita.toLocaleString(\"en\")].join(\"\\n\") : \"\",\n value: d => d.GDP_per_capita,\n group: d => d[\"Income Group\"],\n title: d => [d[\"Country Name\"], d[\"Income Group\"], d[\"GDP_per_capita\"].toLocaleString(\"en\")].join(\"\\n\"),\n groups: [\"Low income\", \"Lower middle income\", \"Upper middle income\", \"High income\"]\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1860,"value":"**Bubble size: GDP per capita (2015 US$)**","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1855,"value":"The details of each bubble can be previewed upon hovering. Labels are shown for some larger bubbles (GDP per capita above 2015 US$ 43,000) to provide a feel for the scale.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1857,"value":"The chart brings to attention that many small countries like Monaco, Luxembourg, Bermuda and Cayman Islands have really large GDPs per capita which may be outliers in some cases.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1894,"value":"Another observation is that (using the slider functionality) fewer labels appear in 2020 than in the other years. This may indicate effects of the pandemic on the GDP (decrease). Reminder that the GDP per capita figures are computed using 2015 as a base year to allow comparability of the units across years.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1868,"value":"***Note***: *due to the level of customization that was necessary for a clean visualization, the chart could not be imported as it was, but rather the function was imported and the code reused in such a way.*","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":125,"value":"## Analysis of Birth Rates","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":127,"value":"It is heard that people in developed countries are not having enough children and have alarmingly low birth rates. On the other hand, people in underdeveloped economies have high birth rates.\n\nUsing GDP per capita as a proxy for the \"development level\", we investigate the above by plotting the crude birth rates against the GDP per capita.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":195,"value":"Plot.plot({\n width:1000,\n x: {grid: true},\n y: {grid: true},\n marks: [\n Plot.dot(data_processed, {\n x: \"GDP_per_capita\", \n y: \"Birth_rate\",\n title: d => d[\"Country Name\"] + \" \" + d[\"Year\"]}),\n ]\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":275,"value":"Based on the scatter plot above, we see that the birth rates drop very sharply as GDP increases to around 10,000 2015 US$ per capita and then decrease more slowly as the GDP per capita increases further. As this seems like an exponential decline pattern, we further plot the logarithms of the two variables, i.e. log(`Birth_rate`) vs log (`GDP_per_capita`).","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":298,"value":"Plot.plot({\n x: {label: \"log(GDP_per_capita) →\", grid: true},\n y: {label: \"↑ log(Birth_rate)\", grid: true},\n marks: [\n Plot.dot(data_processed, {x: d => op.log10(d[\"GDP_per_capita\"])\n , y: d => op.log10(d[\"Birth_rate\"])\n , title: d => d[\"Country Name\"] + \" \" + d[\"Year\"]\n , strokeOpacity: 0.8\n , stroke: \"gray\"\n }),\n Plot.linearRegressionY(data_processed, {x: d => op.log10(d[\"GDP_per_capita\"]),\n y: d => op.log10(d[\"Birth_rate\"]),\n ci: 0, strokeWidth: 2\n }),\n ]\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":344,"value":"We observe a very clean linear trend which confirms our hypothesis.\n\nThe equation for the trendline is as follows:","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":352,"value":"\\log(\\text{Birth Rate}) = -0.297771 \\cdot \\log(\\text{GDP per capita}) + 2.37417","pinned":false,"hidden":false,"mode":"tex","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":384,"value":"howTo(\n`Number of modeled observations:\t1223\n
Model degrees of freedom:\t2\n
Residual degrees of freedom (DF):\t1221\n
SSE (sum squared error):\t18.0417\n
MSE (mean squared error):\t0.0147762\n
R-Squared:\t0.685557\n
Standard error:\t0.121557\n
p-value (significance):\t< 0.0001\n`,{title: \"Show more details about the trend model\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1954,"value":"***Note***: *These details are obtained from the equivalent visualization in Tableau, workbook in attachments.*","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":561,"value":"Using the equation so obtained we plot the exponential trendline on the original untransformed scatter plot while also adding a color guide based on the income group classifications of the countries.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":334,"value":"Plot.plot({\n width:1000,\n x: {label: \"GDP per capita (2015 US$) →\", grid: true},\n y: {label: \"↑ Number of births per 1000 persons\", grid: true},\n color: {domain: [\"Low income\",\"Lower middle income\",\"Upper middle income\",\"High income\"], \n legend: true},\n marks: [\n Plot.dot(data_processed_cleanedIncomeGroup, {\n x: \"GDP_per_capita\", y: \"Birth_rate\", \n title: d => d[\"Country Name\"] + \" \" + d[\"Year\"],\n stroke: \"Income Group\", strokeOpacity: 0.8\n }),\n Plot.line(data_processed,{\n x:\"GDP_per_capita\", \n y: d=> 10**2.37417 * d.GDP_per_capita**-0.297771, \n sort: \"GDP_per_capita\"\n })\n ]\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1139,"value":"Overall, the scatter plots show evidence of a very strong and clear negative trend between birth rates and GDP per capita. As made even more clear by the color guide, the birth rates drop very sharply within the GDP per capita brackets of low income and lower middle income countries and then decrease more slowly as the GDP per capita increases further and stabilize at relatively low rates in the high income countries.\n\nAs such, the traditional income classifications show a clear demarcation with regards to the rate of birth rate decline, strongly suggesting that the birth rates are influenced by the economic development trends.\n\n_Does the same hold for death rates as well?_","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":408,"value":"## Analysis of Death Rates\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":547,"value":"As a country develops, we expect that the death rate would decrease as people have access to better healthcare and medical treatments against potential causes of death.\n\nContinuing to use GDP per capita as a proxy for development levels, we investigate this claim by plotting the death rates versus GDP per capita. As such, we expect the trend of death rates to be analagous to the birth rate trend.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":57,"value":"###\tDeath Rate vs GDP per capita","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":262,"value":"Plot.plot({\n width:1000,\n x: {grid: true},\n y: {grid: true},\n marks: [\n Plot.dot(data_processed, {\n x: \"GDP_per_capita\", \n y: \"Death_rate\",\n title: d => d[\"Country Name\"] + \" \" + d[\"Year\"]\n }),\n ]\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":523,"value":"The scatter plot does not show the expected trends and compared to our theory, death rates in the developed countries seem to be much higher than expected.\n\nOne reason for this may be that these developed countries have a large old population and the deaths may simply be higher because there are lots of old people dying naturally of old age. This motivates us to plot the death rate against the proportion of people in the age group 65+.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":59,"value":"### Death Rate vs % of Population above the age of 65","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":66,"value":"Plot.plot({\n x: {grid: true},\n y: {grid: true},\n marks: [\n Plot.dot(data_processed, {x: \"Pop%_65+\", y: \"Death_rate\"\n ,title: d=>d[\"Country Name\"] + \" \" + d[\"Year\"]\n ,stroke:\"grey\",strokeOpacity:0.7}),\n Plot.linearRegressionY(data_processed, {\n x: \"Pop%_65+\",\n y: \"Death_rate\", ci: 0\n })\n ]\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":82,"value":"This regression shows better results but it is not convincing enough.\n\nTo dig deeper into this, we include the income of the countries into the visualiation. ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":119,"value":"Plot.plot({\n x: {grid: true},\n y: {grid: true},\n color: {domain: [\"Low income\",\"Lower middle income\",\"Upper middle income\",\"High income\"], \n legend: true},\n marks: [\n Plot.dot(data_processed_cleanedIncomeGroup, {\n x: \"Pop%_65+\", \n y: \"Death_rate\",\n title: d=> d[\"Country Name\"] + \" \" + d[\"Year\"],\n stroke:\"Income Group\",\n strokeOpacity: 0.7\n }),\n Plot.linearRegressionY(data_processed, {\n x: \"Pop%_65+\",\n y: \"Death_rate\",\n ci: 0\n })\n ]\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":129,"value":"From this plot, it is evident that each income group shows a different pattern. So next, we make plots for each income group separately to further anaylse the data.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":733,"value":"### Death Rate vs % of Population above the age of 65 by Income Group","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":659,"value":"$49","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":453,"value":"$4a","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":472,"value":"## Conclusion\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":478,"value":"$4b","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":13,"value":"## Appendix","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":7,"value":"import {Wrangler, op} from \"@observablehq/data-wrangler\"","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":211,"value":"import {Plot} from \"@mkfreeman/plot-tooltip\"","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":380,"value":"import {howTo} from \"@clokman/howto\"","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":533,"value":"import {toc} from \"@nebrius/indented-toc\"","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1409,"value":"import {BubbleChart} from \"@d3/bubble-chart\"","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1430,"value":"import {Swatches} from \"@d3/color-legend\"","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W4c","stdlib":"1"}} 24:null