34:["$","title",null,{"children":"Mini-Project 1: Geographic Financial Aid (Due Oct. 29th) | Jorge Morales | Observable"}] 3c:Ta99,

[NOTE]

Please make sure to Read ALL instructions carefully for each part of this assignment!

1. Please **fork this notebook** for this mini-project. Ensure that the notebook is made either _public_ or _unlisted_ in the settings. 2. Complete each question to the best of your ability, clearly marking which visualization and/or written answer corresponds to which question asked. 3. Your coded solutions **must use D3**. Other visualization libraries (.e.g Vega-Lite) or other code languages (e.g. Python) are not allowed. - You are allowed to use functions and modules created by other creators. For example, you can use Mike Bostock's Color Legend notebook to create your legends. 4. You are free to modify any datasets mentioned in this project, or introduce new datasets if it helps you with any explanations or visualizations. For datasets originating from this dataset, you are free to modify them in any way as long as 1) the preprocessing is performed here in JavaScript, or 2) you preprocessed the dataset elsewhere (e.g. a Python notebook or Google Colab notebook via Python). - If you modified the dataset elsewhere, **please make sure to explicitly mention in your write-up where and how to access the code; preprocessing the dataset without a way for us to verify will lead to penalties on your score**. - If you are introducing a new dataset from elsewhere, **please make sure to explicitly mention how to access the dataset**. 5. Please adhere to our AI policy for the class, which is provided on our course syllabus: > _... you can use generative AI tools to do the assignments in this class. If you use an AI to guide you in completing an assignment, you must **disclose which parts were generated by the AI**. Remember that you are responsible for any errors or inaccuracies in AI-generated content, and you must be able to explain all submitted work._ 6. Submit the URL of this notebook on Brightspace by the listed due date. ### Submission Details This assignment is due **Oct. 29th @ 11:59pm**. This submission expects a URL to your notebook that is either public or unlisted. Please be aware that we will grade notebooks based on the version of the notebook we receive at the submission deadline. This means that if you decide to modify your notebooks after the deadline, your changes may not be considered while grading. This policy also applies to late submissions as well.3d:T698,Your goal is to create **3 independent visualizations of the same dataset**, each one with the intent of answering the questions stated below. For each numbered question, you should be able to 1) create a data visualization that answers **all** of the questions specified, 2) provide a written analysis that extrapolates from your visualization and answers **all** of the questions specified, and 3) provide a written description of your thought process and why you think your solution is *appropriate* and *effective*. These are the 3 visualizations you should create: - **Visualization #1 [25pts]:** How do the countries compare in terms of how much they receive and donate? Are there countries that donate much more than they receive or receive much more than they donate? - **Visualization #2 [25pts]:** Do the countries that mostly receive or mostly donate tend to cluster around specific geographical areas of the world? Are there neighboring countries that have radically different patterns in terms of how much they receive vs. how much they donate? - **Visualization #3 [25pts]:** Are there any major differences in how the top 5 most frequent purposes of disbursements (across all countries) distribute geographically in terms of countries that receive donations? Are there countries that tend to receive more of certain types of donations than others?

[NOTE]

It is okay to submit more than one solution, plot, etc. for a given visualization problem if you think there are some competing solutions that work well. When you do that, make sure to compare the solutions by commenting on their advantages and disadvantages.

3e:T115b,const scatter_svg = (() => { const w = 600; const h = 500; const m = {top: 50, right: 200, bottom: 60, left: 90}; const formatShort = (num) => { const formatted = d3.format("$.1s")(num); return formatted .replace("G", "B") }; const formatLong = d3.format("$,.2f"); const max_val = d3.max(difference_data, d => Math.max(d.donated, d.received)); const max_abs = d3.max(difference_data, d => Math.abs(d.net)); const x_scale = d3.scaleSymlog().constant(1000000).domain([0, max_val]).range([0, w]); const y_scale = d3.scaleSymlog().constant(1000000).domain([0, max_val]).range([h, 0]); const color_scale = d3.scaleDivergingSymlog(d3.interpolatePiYG).constant(10000000).domain([-max_abs, 0, max_abs]); const svg = d3.create("svg") .attr("width", w + m.left + m.right) .attr("height", h + m.top + m.bottom); const chart = svg.append("g") .attr("width", w) .attr("height", h) .attr("transform", `translate(${m.left}, ${m.top})`); const tick_vals = [0, 1e6, 1e7, 1e8, 1e9, 1e10, 1e11]; chart.append("g") .attr("transform", `translate(0, ${h})`) .call(d3.axisBottom(x_scale).tickValues(tick_vals).tickFormat(formatShort)); chart.append("g") .call(d3.axisLeft(y_scale).tickValues(tick_vals).tickFormat(formatShort)); chart.append("line") .attr("x1", x_scale(0)).attr("y1", y_scale(0)) .attr("x2", x_scale(max_val)).attr("y2", y_scale(max_val)) .attr("stroke", "#999") .attr("stroke-dasharray", "4 4"); chart.append("text") .attr("x", x_scale(max_val) - 25) .attr("y", y_scale(max_val) + 16) .attr("text-anchor", "end") .attr("font-size", 11) .text("Donated = Received"); chart.selectAll("circle.country") .data(difference_data) .enter() .append("circle") .attr("class", "country") .attr("cx", d => x_scale(d.donated)) .attr("cy", d => y_scale(d.received)) .attr("r", 5) .attr("fill", d => color_scale(d.net)) .attr("stroke", "#555") .attr("stroke-width", 0.75) .append("title") .text(d => `${d.country}\nDonated: ${formatLong(d.donated)}\nReceived: ${formatLong(d.received)}\nNet: ${formatLong(d.net)}`); const label_nudge = {"Colombia": -6, "Australia": -6, "Belgium": -6, "Monaco": -6, "Netherlands": -6, "Germany": -3, "Slovenia": 3, "Romania": -3}; chart.selectAll("text.country-label") .data(difference_data) .enter() .append("text") .attr("class", "country-label") .attr("x", d => x_scale(d.donated) + 7) .attr("y", d => y_scale(d.received) + (label_nudge[d.country] ?? 0)) .attr("dominant-baseline", "middle") .attr("font-size", 7) .text(d => d.country); // Titles svg.append("text") .attr("x", m.left + w / 2).attr("y", 25) .attr("text-anchor", "middle").attr("font-weight", "bold").attr("font-size", 16) .text("Total Aid Donated vs. Received by Country (1947–2013)"); svg.append("text") .attr("x", m.left + w / 2).attr("y", m.top + h + 45) .attr("text-anchor", "middle") .text("Total Donated (USD, symlog scale)"); svg.append("text") .attr("transform", "rotate(-90)") .attr("x", -(m.top + h / 2)).attr("y", 25) .attr("text-anchor", "middle") .text("Total Received (USD, symlog scale)"); //Legend const legend_w = 160; const legend = svg.append("g") .attr("transform", `translate(${m.left + w + 25}, ${m.top + 20})`); const gradient = svg.append("defs") .append("linearGradient") .attr("id", "scatter-net-gradient"); d3.range(0, 1.01, 0.1).forEach(t => { gradient.append("stop") .attr("offset", `${t * 100}%`) .attr("stop-color", d3.interpolatePiYG(t)); }); legend.append("text") .attr("font-weight", "bold").attr("font-size", 12) .text("Net (donated − received)"); legend.append("rect") .attr("y", 10) .attr("width", legend_w).attr("height", 12) .attr("fill", "url(#scatter-net-gradient)") .attr("stroke", "#999").attr("stroke-width", 0.5); legend.append("text").attr("x", 0).attr("y", 36) .attr("font-size", 10).text(formatShort(-max_abs)); legend.append("text").attr("x", legend_w / 2).attr("y", 36) .attr("font-size", 10).attr("text-anchor", "middle").text("$0"); legend.append("text").attr("x", legend_w).attr("y", 36) .attr("font-size", 10).attr("text-anchor", "end").text(formatShort(max_abs)); return svg; })(); display(scatter_svg.node());3f:T553,From this scatter plot, we can see that there are very clear differences in net aid per country. Countries such as the US, Japan, and Germany give far more than they receive, and others such as India, Thailand, and Brazil, among others, receive far more than they give. There is also a group of countries that sit close to the balanced dashed line on the graph that represents equal amount of aid given and received, some of which are Australia, Austria, and Italy. In terms of design, I chose a scatter plot so that it would be clear and easy to see each countries’ net aid at a glance based on how far away from the center line they are. To help with this, I used a diverging color scale so that balanced countries would be close to white while unbalanced ones would be more saturated in color. For the scale, I used a Symlog scale, which is a combination of linear at lower values and logarithmic past a threshold, for which I chose 10,000,000. This scale managed to properly differentiate lower values while compressing higher orders of magnitude, to keep the plot legible. Both axes use the same scale so the “donated = received” line is a true diagonal, and the color scale matches the map in Visualization #2 so each country has the same color in both charts. Every point is labeled, and hovering shows the exact donated, received, and net amounts. 40:Td79,const net_map_svg = (() => { const map_w = 960; const map_h = 500; const formatShort = (num) => { const formatted = d3.format("$.1s")(num); return formatted .replace("G", "B") }; const map_projection = d3.geoNaturalEarth1().fitExtent([[0, 40], [map_w, map_h]], world_map); const map_path = d3.geoPath(map_projection); const net_by_country = new Map( difference_data .map(d => [name_fixes[d.country] ?? d.country, d.net]) ); const map_max_abs = d3.max(difference_data, d => Math.abs(d.net)); const map_color = d3.scaleDivergingSymlog(d3.interpolatePiYG) .constant(10000000) .domain([-map_max_abs, 0, map_max_abs]); const map_svg = d3.create("svg") .attr("width", map_w) .attr("height", map_h) map_svg.selectAll("path.country") .data(world_map.features) .enter() .append("path") .attr("class", "country") .attr("d", map_path) .attr("stroke", "white") .attr("stroke-width", 0.5) .attr("fill", d => { const net = net_by_country.get(d.properties.admin); return net == null ? "#ddd" : map_color(net); }) .append("title") .text(d => { const net = net_by_country.get(d.properties.admin); return net == null ? `${d.properties.admin}\nNot in dataset` : `${d.properties.admin}\nNet: ${d3.format("$,.0f")(net)}`; }); // Title map_svg.append("text") .attr("x", map_w / 2).attr("y", 24) .attr("text-anchor", "middle").attr("font-weight", "bold").attr("font-size", 16) .text("Net Aid Balance by Country (Donated − Received), 1947–2013"); // Legend const legend_w = 160; const map_legend = map_svg.append("g") .attr("transform", `translate(20, ${map_h - 95})`); const map_gradient = map_svg.append("defs") .append("linearGradient") .attr("id", "map-net-gradient"); d3.range(0, 1.01, 0.1).forEach(t => { map_gradient.append("stop") .attr("offset", `${t * 100}%`) .attr("stop-color", d3.interpolatePiYG(t)); }); map_legend.append("text") .attr("font-weight", "bold").attr("font-size", 12) .text("Net (donated − received)"); map_legend.append("rect") .attr("y", 10) .attr("width", legend_w).attr("height", 12) .attr("fill", "url(#map-net-gradient)") .attr("stroke", "#999").attr("stroke-width", 0.5); map_legend.append("text").attr("x", 0).attr("y", 36) .attr("font-size", 10).text(formatShort(-map_max_abs)); map_legend.append("text").attr("x", legend_w / 2).attr("y", 36) .attr("font-size", 10).attr("text-anchor", "middle").text("$0"); map_legend.append("text").attr("x", legend_w).attr("y", 36) .attr("font-size", 10).attr("text-anchor", "end").text(formatShort(map_max_abs)); map_legend.append("text").attr("x", 0).attr("y", 50) .attr("font-size", 10).attr("fill", "#c51b7d").text("Net recipient"); map_legend.append("text").attr("x", legend_w).attr("y", 50) .attr("font-size", 10).attr("text-anchor", "end").attr("fill", "#4d9221").text("Net donor"); map_legend.append("rect") .attr("y", 60).attr("width", 12).attr("height", 12) .attr("fill", "#ddd").attr("stroke", "#999").attr("stroke-width", 0.5); map_legend.append("text") .attr("x", 18).attr("y", 66) .attr("dominant-baseline", "middle").attr("font-size", 10) .text("Not in dataset"); return map_svg; })(); display(net_map_svg.node());41:T8e2,This choropleth map shows the net aid balance by country, and we can see some clear geographic clustering in the visualization. North America, Western and Northern Europe, and Japan stand out as net donors, while South America, South and Southeast Asia, and Eastern Europe emerge as net recipients. Generally, the Northern Hemisphere contains all of the net donors in the dataset while the countries in Southern Hemisphere in the dataset are all net recipients. The dataset does not contain every country, so some regions are represented by only one or two countries, such as South Africa in Africa, so a wider dataset could show whether these countries reflect regional patterns or are outliers. The biggest contrasts between neighbors in the dataset appear in Europe and in East Asia. Germany, one of the biggest net donors, shares a border with Poland, one of the biggest net recipients. The country density in this region shows a significant number of sharp differences, like Luxembourg as a net recipient being surrounded by net donors, or Spain and Portugal having another notable contrast in net aid. In East Asia, Japan and Korea show a similar contrast, even though they do not share a land border, they are in close proximity and have a big difference in net aid, with Japan being a big donor and Korea being a net recipient. It’s also worth mentioning that Alaska in North America and French Guiana in South America share aid information with the US and France respectively, so geographical comparisons for these regions could be misleading. For design, I decided to use a choropleth so that it would be easy to see at a glance where clusters of net aid were located and where dramatic differences over shared borders appear. I reutilized the same diverging color scalae as Q1, also utilizing the Symlog scale. Countries not in the dataset are gray, so they are not mistaken for countries with a net balance of $0. Hovering over each country gives the net exact value as well. One limitation of illustrating this with a choropleth is that large countries dominate visually, and it’s very difficult to make out small countries such as Luxembourg, but since this question relates specifically to geographical relationships, I believe it is worth the tradeoff.42:Tcf8,const purpose_svg = (() => { const w = 960; const h = 520; const formatLong = d3.format("$,.0f"); const by_country = new Map(); for (const purpose of top5_purposes) { const country_amounts = recipient_by_purpose.get(purpose) ?? new Map(); for (const [country, amount] of country_amounts) { const map_name = name_fixes[country] ?? country; if (!by_country.has(map_name)) by_country.set(map_name, {}); by_country.get(map_name)[purpose] = amount; } } const dominant = new Map(); for (const [country, amounts] of by_country) { const entries = Object.entries(amounts).sort((a, b) => b[1] - a[1]); const total = d3.sum(entries, e => e[1]); dominant.set(country, { purpose: entries[0][0], breakdown: entries }); } const projection = d3.geoNaturalEarth1().fitExtent([[0, 40], [w, h]], world_map); const path = d3.geoPath(projection); const color_Scale = d3.scaleOrdinal() .domain(top5_purposes) .range(d3.schemeCategory10); const no_data = "#e0e0e0"; const svg = d3.create("svg") .attr("width", w) .attr("height", h) svg.append("text") .attr("x", w / 2).attr("y", 26) .attr("text-anchor", "middle").attr("font-weight", "bold").attr("font-size", 16) .text("Largest Aid Purpose Received by Each Country (Top 5 Purposes, 1947–2013)"); svg.selectAll("path.country") .data(world_map.features) .enter() .append("path") .attr("class", "country") .attr("d", path) .attr("stroke", "white") .attr("stroke-width", 0.5) .attr("fill", d => { const info = dominant.get(d.properties.admin); return info == null ? no_data : color_Scale(info.purpose); }) .append("title") .text(d => { const info = dominant.get(d.properties.admin); if (info == null) return `${d.properties.admin}\nNo aid received for these purposes / Not in dataset`; const lines = info.breakdown.map(([p, amt]) => ` ${p}: ${formatLong(amt)}`); return `${d.properties.admin}\nLargest: ${info.purpose} \n${lines.join("\n")}`; }); // Legend const legend = svg.append("g") .attr("transform", `translate(20, ${h - 150})`); legend.append("text") .attr("font-weight", "bold").attr("font-size", 12) .text("Largest purpose received"); legend.selectAll("rect.swatch") .data(top5_purposes) .enter() .append("rect") .attr("class", "swatch") .attr("x", 0) .attr("y", (d, i) => 12 + i * 20) .attr("width", 12).attr("height", 12) .attr("fill", d => color_Scale(d)); legend.selectAll("text.label") .data(top5_purposes) .enter() .append("text") .attr("class", "label") .attr("x", 18) .attr("y", (d, i) => 18 + i * 20) .attr("dominant-baseline", "middle") .attr("font-size", 11) .text(d => d); const gray_y = 12 + top5_purposes.length * 20; legend.append("rect") .attr("y", gray_y).attr("width", 12).attr("height", 12) .attr("fill", no_data).attr("stroke", "#999").attr("stroke-width", 0.5); legend.append("text") .attr("x", 18).attr("y", gray_y + 6) .attr("dominant-baseline", "middle").attr("font-size", 11) .text("None of these purposes / not in dataset"); return svg; })(); display(purpose_svg.node());43:T685,This visualization shows some regional patterns when it comes to the top 5 most frequent disbursement purposes. First off, the Americas are almost exclusively dominated by the Higher Education purpose, with the lone outlier being Colombia, which lists Social Welfare services as its highest purpose, but it still has Higher Education within its breakdown. Higher education is also scattered in the East, with Saudi Arabia, Korea, and Taiwan showing it as its top purpose. South and Southeast Asia show purpose clustering as well, as both of its countries list Multisector Aid as its highest purpose. Europe as a region shows less homogenous clustering, and it contains countries with Social/Welfare services, Multisector aid, and Material relief assistance and services as top purposes with no particular clustering. Also interesting to note that although Strengthening civil society is one of the top 5 most frequent purposes, South Africa is the only country that has it as its top purpose. For this visualization, I also chose a choropleth since the question also dealt with geographical distribution. The top five purposes were chosen by number of commitments, and I omitted ‘Sectors not specified’ since it isn’t a real category. Each countries’ top purpose by dollar amount is shown as its category while the tooltip gives the full breakdown within each country. This choropleth has similar limitations as the one in Q2, where it is hard to locate small countries and bigger countries have a larger visual impact. Also, there is no differentiation between real purpose amount between countries at a glance, but the tooltip does give you that information.44:[{"id":"09040dddbda03374a466600047ccbc70c46e6853884eb725db2141c566ca1b78fb7cc935096e8d5468c21e8df215e73fc49cdaaf7e2f1f89139793da25be02b2","href":"https://static.observableusercontent.com/files/09040dddbda03374a466600047ccbc70c46e6853884eb725db2141c566ca1b78fb7cc935096e8d5468c21e8df215e73fc49cdaaf7e2f1f89139793da25be02b2","name":"aiddata-countries-only - aiddata-countries-only.csv","type":"text/csv","lastModified":1792692393000,"size":8760387},{"id":"47649e87b281bf44e79d68fb41ba4236a85999dcd5cd7339562e22ccc3e409072787869fa00436b5d86f3d8c11e0865c0de2bacb33d6cb23622a512b74dc8118","href":"https://static.observableusercontent.com/files/47649e87b281bf44e79d68fb41ba4236a85999dcd5cd7339562e22ccc3e409072787869fa00436b5d86f3d8c11e0865c0de2bacb33d6cb23622a512b74dc8118","name":"custom.geo.json","type":"application/json","lastModified":1792695414000,"size":987825}] 38:{"id":"c4c1a21d54209be7","slug":"mini-project-1-geographic-financial-aid-due-oct-29th","title":"Mini-Project 1: Geographic Financial Aid (Due Oct. 29th)","thumbnail":"3926ee5b7c736474f1201e8b877cebd63d745ea9ed43fbb38e3bd45c97c547ea","access_level":"public","create_time":"$D2026-10-20T20:28:37.871Z","update_time":"$D2026-10-30T00:28:49.164Z","view_time":null,"first_public_time":"$D2026-10-30T00:30:04.295Z","has_unpublished_edits":null,"fork":{"id":"e6fcea972bfc4543","slug":"2026-infovis-cse_project-1","title":"Mini-Project 1: Geographic Financial Aid (Due Oct. 29th)","owner":{"id":"3d6c0f5acd6a20a4","login":"rk2546","name":"rk2546"}},"stars":0,"starred":false,"owner":{"type":"team","id":"1aeababb50a2cd58","login":"jorge-morales-pico","name":"Jorge Morales","avatar_url":null,"tier":"starter_2024","bio":"jam940","followed_by_you":false,"follows_you":false},"version":557,"publish_version":557,"role":null,"authored":false,"authors":[{"type":"individual","id":"25470ad802e830b8","login":"jorge-morales-pico","name":"Jorge Morales","avatar_url":null,"tier":null,"bio":"jam940","followed_by_you":false,"follows_you":false}],"members":[{"type":"team","id":"1aeababb50a2cd58","login":"jorge-morales-pico","name":"Jorge Morales","avatar_url":null,"tier":"starter_2024","role":"editor"}],"comments":[],"custom_thumbnail":false,"trashed":false,"lapsed":null,"subscribed":null,"forks":0,"collection":null,"type":"notebook","body":{"titling":"auto","title":"Mini-Project 1: Geographic Financial Aid (Due Oct. 29th)","cells":[{"id":0,"value":"# Mini-Project 1: Geographic Financial Aid (Due Oct. 29th)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":27,"value":"import { css } from \"@rk2546/infovis-cse-backend\";","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":34,"value":"css","pinned":true,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":62,"value":"---\n## Submission Instructions","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":32,"value":"$3c","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":6,"value":"---\n## About the Dataset","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":21,"value":"This spreadsheet is a simplified and reduced version of the AidData dataset. This dataset describes commitment information for over 1.5 million development finance activities funded between 1947 and 2013. The simplified dataset contains the following columns:\n\n- **`aiddata_id`**: unique ID of the commitment\n- **`aiddata_2_id`**: secondary unique ID\n- **`year`**: year of the commitment\n- **`donor`**: country providing the financial resource\n- **`recipient`**: country receiving the financial resource\n- **`commitment_amount_usd_constant`**: the total amount of financial resources provided\n- **`coalesced_purpose_code`**: categorization ID for the stated purpose of the commitment\n- **`coalesced_purpose_name`**: the purpose of the commitment","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":58,"value":"---\n## Questions","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":12,"value":"$3d","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":60,"value":"---\n## Grading Criteria","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":55,"value":"Your answers to each question are graded on the following qualities, with each question totaling up to **25 points**:\n\n- **[10pts]**: Does the visualization answer the questions posed in the exercise?\n- **[10pts]**: Does the written answer & discussion the questions posed in the exercise?\n- **[5pts]**: Is the visual representation clear? Does it have good aesthetics? If needed, you can review this notebook on clarity for reference.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":15,"value":"The questions above are a very useful tool for you to verify whether you are producing a good solution or not. As you go about completing this project, you can use the questions above as a self-evaluation tool in two main ways:\n\n1. Verify that you can indeed answer the questions with your visualization. If you can’t fully answer the questions, it means your solution has some problems.\n2. Compare multiple solutions to the same problem and ask yourself: “which one makes it easier for me to answer these questions?”, where easier may mean, more accurately, with less effort or faster.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":65,"value":"\n\n---","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":64,"value":"## Students' Workspace\n\n_From this point onward, you are free to modify the notebook in any way you see fit._","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":56,"value":"const aid_data = FileAttachment(\"aiddata-countries-only - aiddata-countries-only.csv\").csv({ typed: true })","pinned":true,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":67,"value":"display(Inputs.table(aid_data.slice(0, 10)))","pinned":true,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":71,"value":"const totals = new Map();\n\nfor (const d of aid_data) {\n const amount = d.commitment_amount_usd_constant\n\n if (!totals.has(d.donor)) totals.set(d.donor, {country: d.donor, donated: 0, received: 0});\n if (!totals.has(d.recipient)) totals.set(d.recipient, {country: d.recipient, donated: 0, received: 0});\n\n totals.get(d.donor).donated += amount;\n totals.get(d.recipient).received += amount;\n}\n\nconst difference_data = Array.from(totals.values(), d => ({ ...d, net: d.donated - d.received }));\n\ndisplay(Inputs.table(difference_data));","pinned":true,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":78,"value":"$3e","pinned":true,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":88,"value":"$3f","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":72,"value":"const world_map = FileAttachment(\"custom.geo.json\").json()","pinned":true,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":77,"value":"display(Object.keys(world_map.features[0].properties))","pinned":true,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":85,"value":"const name_fixes = {\n \"United States\": \"United States of America\",\n \"Korea\": \"South Korea\",\n \"Czech Republic\": \"Czechia\",\n \"Slovak Republic\": \"Slovakia\"\n};","pinned":true,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":74,"value":"$40","pinned":true,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":89,"value":"$41","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":81,"value":"const purpose_totals = new Map();\n\nfor (const d of aid_data) {\n const purpose = d.coalesced_purpose_name;\n if (!purpose_totals.has(purpose)) {purpose_totals.set(purpose, {purpose: purpose, count: 0, amount: 0});}\n const entry = purpose_totals.get(purpose);\n entry.count += 1;\n entry.amount += d.commitment_amount_usd_constant;\n}\n\nconst purpose_ranking = Array.from(purpose_totals.values())\n .sort((a, b) => b.count - a.count);\ndisplay(Inputs.table(purpose_ranking));","pinned":true,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":82,"value":"const top5_purposes = purpose_ranking\n .filter(d => d.purpose !== \"Sectors not specified\")\n .slice(0, 5)\n .map(d => d.purpose);\n\nconst recipient_by_purpose = d3.rollup(\n aid_data.filter(d => top5_purposes.includes(d.coalesced_purpose_name)),\n v => d3.sum(v, d => d.commitment_amount_usd_constant),\n d => d.coalesced_purpose_name,\n d => d.recipient\n);\n\ndisplay(top5_purposes);\ndisplay(recipient_by_purpose);","pinned":true,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":83,"value":"$42","pinned":true,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":90,"value":"$43","pinned":true,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W44","stdlib":"2"}} 33:null