34:["$","title",null,{"children":"Mini-Project 1: Geographic Financial Aid (Due Oct. 29th) | Xq007 | 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:T691,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:T8e13,const aidD3 = await import("https://cdn.jsdelivr.net/npm/d3@7.9.0/+esm"); const aidTopo = await import("https://cdn.jsdelivr.net/npm/topojson-client@3.1.0/+esm"); const aidRaw = aidD3.csvParse(await FileAttachment("aid.csv").text()); const aidWorld = await aidD3.json("https://cdn.jsdelivr.net/npm/world-atlas@2.0.2/countries-50m.json"); // Everything below is reproducible JavaScript preprocessing in this notebook. // Preserve the one negative amount as a signed adjustment; its meaning is unknown. const aidRows = aidRaw.map(d => ({ id: d.aiddata_id, year: +d.year, donor: d.donor.trim(), recipient: d.recipient.trim(), amount: d.commitment_amount_usd_constant.trim() === "" ? NaN : +d.commitment_amount_usd_constant, code: d.coalesced_purpose_code.trim(), purpose: d.coalesced_purpose_name.trim() })).filter(d => Number.isFinite(d.amount) && Number.isFinite(d.year) && d.donor && d.recipient); const aidDonated = aidD3.rollup(aidRows, v => aidD3.sum(v, d => d.amount), d => d.donor); const aidReceived = aidD3.rollup(aidRows, v => aidD3.sum(v, d => d.amount), d => d.recipient); const aidCountries = Array.from(aidD3.union(aidDonated.keys(), aidReceived.keys()), name => { const donated = aidDonated.get(name) ?? 0, received = aidReceived.get(name) ?? 0; return {name, donated, received, net: donated - received, balance: donated + received > 0 ? (donated - received) / (donated + received) : null}; }); const aidByName = new Map(aidCountries.map(d => [d.name, d])); // Keep original dataset labels in the charts. Korea → South Korea is an explicit // geographic matching assumption, since this simplified CSV has no ISO codes. const aidAliases = new Map([ ["United States of America", "United States"], ["South Korea", "Korea"], ["Slovakia", "Slovak Republic"], ["Czechia", "Czech Republic"] ]); const aidFeatures = aidTopo.feature(aidWorld, aidWorld.objects.countries).features .filter(d => d.properties.name !== "Antarctica"); const aidMapName = f => aidAliases.get(f.properties.name) ?? f.properties.name; const aidMapped = new Set(aidFeatures.map(aidMapName)); const aidUnmatched = aidCountries.filter(d => !aidMapped.has(d.name)).map(d => d.name); const aidPurposes = aidD3.rollups(aidRows.filter(d => d.code), v => ({ count: v.length, amount: aidD3.sum(v, d => d.amount), name: v[0].purpose }), d => d.code).map(([code, d]) => ({code, ...d})) .sort((a, b) => aidD3.descending(a.count, b.count) || aidD3.ascending(a.code, b.code)); const aidTop5 = aidPurposes.slice(0, 5); const aidPurposeByCountry = aidD3.rollup(aidRows, v => ({ count: v.length, amount: aidD3.sum(v, d => d.amount) }), d => d.code, d => d.recipient); const aidUSD = n => "$" + aidD3.format(",.0f")(n); const aidShort = n => n === 0 ? "$0" : "$" + aidD3.format(".3~s")(n).replace("G", "B"); const aidPercent = aidD3.format(".1%"); const aidBalanceColor = aidD3.scaleDiverging([-1, 0, 1], aidD3.interpolateBrBG); const aidSeqColor = aidD3.scaleSequential([0, 0.7], aidD3.interpolateBlues).clamp(true); const aidNeutral = "#d9dce1"; const aidPanel = () => aidD3.create("div").style("font-family", "system-ui, sans-serif") .style("color", "#1f2937").style("max-width", "1000px").style("line-height", "1.5"); const aidText = (panel, text) => panel.append("p").style("font-size", "14px").text(text); const aidSVG = (panel, w, h) => panel.append("svg").attr("viewBox", `0 0 ${w} ${h}`) .style("width", "100%").style("height", "auto").style("display", "block") .attr("font-family", "system-ui, sans-serif").attr("font-size", 12); const aidLegend = (panel, color, domain, label, format, noDataLabel = "No recipient data") => { const svg = aidSVG(panel, 700, 74), x = aidD3.scaleLinear().domain(domain).range([20, 470]); svg.append("text").attr("x", 20).attr("y", 14).attr("font-weight", 600).text(label); svg.selectAll("rect").data(aidD3.range(160)).join("rect") .attr("x", d => 20 + d * 450 / 160).attr("y", 25).attr("width", 450 / 160 + 0.2) .attr("height", 12).attr("fill", d => color(domain[0] + (domain[1] - domain[0]) * d / 159)); svg.append("g").attr("transform", "translate(0,37)") .call(aidD3.axisBottom(x).ticks(5).tickFormat(format)).call(g => g.select(".domain").remove()); svg.append("rect").attr("x", 510).attr("y", 25).attr("width", 14).attr("height", 12).attr("fill", aidNeutral); svg.append("text").attr("x", 530).attr("y", 35).text(noDataLabel); }; const aidTable = (panel, headers, rows) => { const scroll = panel.append("div").style("overflow-x", "auto"); const table = scroll.append("table").style("border-collapse", "collapse").style("font-size", "13px").style("width", "100%"); table.append("thead").append("tr").selectAll("th").data(headers).join("th") .text(d => d).style("text-align", "left").style("padding", "9px").style("border-bottom", "2px solid #cbd5e1"); table.append("tbody").selectAll("tr").data(rows).join("tr").selectAll("td").data(d => d).join("td") .text(d => d).style("padding", "7px 9px").style("border-bottom", "1px solid #e5e7eb"); return table; }; const aidAudit = aidPanel(); aidAudit.append("h2").text("Data, scope, and reproducibility"); aidText(aidAudit, `${aidD3.format(",")(aidRows.length)} records; ${aidD3.min(aidRows, d => d.year)}–${aidD3.max(aidRows, d => d.year)}; ${aidCountries.length} countries; ${aidUSD(aidD3.sum(aidRows, d => d.amount))} in signed constant-USD commitments. All years are pooled.`); aidText(aidAudit, `Invalid rows excluded: ${aidRaw.length - aidRows.length}. Duplicate aiddata_id values: ${aidRows.length - new Set(aidRows.map(d => d.id)).size}. Negative records retained: ${aidRows.filter(d => d.amount < 0).length}. Unmatched geographic labels: ${aidUnmatched.join(", ") || "none"}.`); aidText(aidAudit, "The negative record is Australia → Thailand, social/welfare services, 2009, −$55,619. I retain its signed value without inferring why it is negative. Totals are not disbursements, per-capita measures, or a complete account of global aid. Zero means no recorded amount in this reduced dataset."); aidAudit.append("div").html("

I use the assignment's simplified AidData spreadsheet, attached to this notebook as aid.csv. This snapshot contains 98,540 records covering 1973–2013 and 47 distinct donor/recipient countries. These are the observed limits of the reduced file, rather than the coverage of the full AidData database.

All filtering, numeric conversion, grouping, and aggregation are performed in the notebook in JavaScript. I trim country names, convert years and amounts to numbers, and check for invalid rows and repeated aiddata_id values. This snapshot has neither invalid rows under these checks nor duplicate IDs. I retain the one negative amount: Australia to Thailand, social/welfare services, 2009, −$55,619. I treat it as a signed adjustment without assuming its cause. The signed total is $366,804,150,566. Donated and received totals both reconcile to that amount.

For country c, D(c) sums amounts where c is the donor, and R(c) sums amounts where c is the recipient. I pool all available years. The field measures commitments in constant USD, even though Question 3 uses the word “disbursements”; this file cannot establish actual cash disbursement. The base year of the constant-dollar series is not specified in the simplified file.

Map boundaries come from world-atlas 2.0.2 / Natural Earth. I match four boundary labels to dataset labels: United States of America → United States; Slovakia → Slovak Republic; Czechia → Czech Republic; and South Korea → Korea. The last is an explicit geographic matching assumption because the simplified CSV lacks ISO identifiers. All 47 dataset labels match a map feature after these mappings. Modern boundaries provide a reference for historical totals; they do not reconstruct historical borders.

"); display(aidAudit.node()); const aidQ1 = aidPanel(); aidQ1.append("h2").text("1 · Who gives, and who receives?"); aidText(aidQ1, "Each dot is one country. Right = more donated; up = more received. The dashed diagonal marks equal totals. Brown = mostly receiving; teal = mostly donating. Hover or choose a country for exact amounts. Both axes use the same symlog scale (constant = $1 million): zero is shown and large differences remain readable, but distances are not linear dollar differences."); const aidSelect = aidQ1.append("label").text("Highlight country: ").append("select").attr("aria-label", "Highlight country") .style("padding", "5px").style("font", "inherit"); aidSelect.selectAll("option").data(["All countries", ...aidCountries.map(d => d.name).sort()]) .join("option").text(d => d).attr("value", d => d); const aidDetail1 = aidText(aidQ1, "Choose or hover over a country to inspect its totals."); const aidS1 = aidSVG(aidQ1, 900, 650); const aidX1 = aidD3.scaleSymlog().constant(1e6).domain([0, 150e9]).range([90, 845]); const aidY1 = aidD3.scaleSymlog().constant(1e6).domain([0, 150e9]).range([555, 45]); const aidTicks1 = [0, 1e6, 1e7, 1e8, 1e9, 1e10, 1e11]; aidS1.append("g").attr("transform", "translate(0,555)") .call(aidD3.axisBottom(aidX1).tickValues(aidTicks1).tickFormat(aidShort)); aidS1.append("g").attr("transform", "translate(90,0)") .call(aidD3.axisLeft(aidY1).tickValues(aidTicks1).tickFormat(aidShort)); aidS1.append("g").attr("stroke", "#e5e7eb").selectAll("line").data(aidTicks1.slice(1)).join("line") .attr("x1", d => aidX1(d)).attr("x2", d => aidX1(d)).attr("y1", 45).attr("y2", 555); aidS1.append("g").attr("stroke", "#e5e7eb").selectAll("line").data(aidTicks1.slice(1)).join("line") .attr("x1", 90).attr("x2", 845).attr("y1", d => aidY1(d)).attr("y2", d => aidY1(d)); aidS1.append("line").attr("x1", aidX1(0)).attr("y1", aidY1(0)) .attr("x2", aidX1(150e9)).attr("y2", aidY1(150e9)).attr("stroke", "#64748b").attr("stroke-dasharray", "5,5"); aidS1.append("text").attr("x", 550).attr("y", 85).attr("fill", "#7c4a08").text("Mostly receiving (R > D)"); aidS1.append("text").attr("x", 580).attr("y", 520).attr("fill", "#006a65").text("Mostly donating (D > R)"); aidS1.append("text").attr("x", 465).attr("y", 610).attr("text-anchor", "middle").attr("font-weight", 600) .text("Total donated · constant USD · symlog scale"); aidS1.append("text").attr("transform", "translate(20,300) rotate(-90)").attr("text-anchor", "middle").attr("font-weight", 600) .text("Total received · constant USD · symlog scale"); const aidDescribe1 = d => `${d.name}: donated ${aidUSD(d.donated)}; received ${aidUSD(d.received)}; donated − received ${aidUSD(d.net)}; balance index ${aidD3.format("+.3f")(d.balance)}.`; const aidDots1 = aidS1.append("g").selectAll("circle").data(aidCountries).join("circle") .attr("cx", d => aidX1(d.donated)).attr("cy", d => aidY1(d.received)).attr("r", 5.5) .attr("fill", d => aidBalanceColor(d.balance)).attr("stroke", "#273444").attr("stroke-width", 0.7) .attr("tabindex", 0).attr("aria-label", aidDescribe1) .on("mouseenter focus", function(event, d) { aidDetail1.text(aidDescribe1(d)); aidD3.select(this).attr("r", 9); }) .on("mouseleave blur", function() { aidD3.select(this).attr("r", 5.5); }); aidDots1.append("title").text(aidDescribe1); // A small set of labels is positioned explicitly to avoid collisions. const aidLabels1 = [ {name:"India",dx:9,dy:-9}, {name:"United States",dx:-9,dy:16,end:true}, {name:"Japan",dx:-9,dy:-10,end:true}, {name:"Germany",dx:-10,dy:-10,end:true}, {name:"Thailand",dx:9,dy:3}, {name:"Brazil",dx:9,dy:14}, {name:"Colombia",dx:9,dy:25}, {name:"Korea",dx:8,dy:-10}, {name:"Austria",dx:8,dy:18}, {name:"Netherlands",dx:-8,dy:-12,end:true} ]; aidS1.append("g").selectAll("text").data(aidLabels1).join("text") .attr("x", d => aidX1(aidByName.get(d.name).donated) + d.dx) .attr("y", d => aidY1(aidByName.get(d.name).received) + d.dy) .attr("text-anchor", d => d.end ? "end" : "start").attr("font-size", 11) .attr("paint-order", "stroke").attr("stroke", "white").attr("stroke-width", 3).attr("fill", "#1f2937").text(d => d.name); const aidHighlight1 = aidS1.append("text").attr("font-weight", 700).attr("font-size", 13) .attr("paint-order", "stroke").attr("stroke", "white").attr("stroke-width", 4).attr("fill", "#111827"); aidSelect.on("change", function() { const chosen = this.value, d = aidByName.get(chosen); aidDots1.attr("opacity", p => !d || p.name === chosen ? 1 : 0.2) .attr("r", p => p.name === chosen ? 10 : 5.5); aidDetail1.text(d ? aidDescribe1(d) : "Hover over a country to inspect its totals."); aidHighlight1.text(d ? d.name : "").attr("x", d ? Math.min(760, aidX1(d.donated) + 10) : 0) .attr("y", d ? aidY1(d.received) - 16 : 0); }); const aidDetails1 = aidQ1.append("details"); aidDetails1.append("summary").text("Exact totals for all 47 countries (sorted by donated − received)"); aidTable(aidDetails1, ["Country", "Donated", "Received", "D − R"], [...aidCountries].sort((a,b) => b.net-a.net).map(d => [d.name,aidUSD(d.donated),aidUSD(d.received),aidUSD(d.net)])); aidQ1.append("div").html("

Analysis

The United States and Japan dominate recorded donations. The United States donates approximately $116.18 billion and receives $104.52 million; Japan donates $114.18 billion and receives $2.08 billion. Their donated-minus-received totals are approximately +$116.07 billion and +$112.10 billion, respectively. Germany is another strongly donation-oriented country, donating $47.22 billion while receiving only $60,164 in this reduced dataset.

India is the largest recorded recipient, receiving $133.98 billion with no recorded donations. Thailand receives $49.04 billion, compared with donations of just $25,461. Brazil receives $36.61 billion, compared with approximately $1.13 million donated. These countries lie well above the equal-totals line, whereas the United States, Japan, and Germany lie below it.

Countries need not be exclusively donors or recipients. Austria is nearly balanced: approximately $784.07 million donated and $770.27 million received. Canada records substantial amounts in both directions, approximately $7.47 billion donated and $4.07 billion received. Australia is slightly receipt-oriented over the pooled period, approximately $2.45 billion donated and $2.57 billion received. This is a description of the supplied file, not a claim about these countries' present-day roles.

Design rationale

I use a scatterplot with donated amount on the horizontal axis and received amount on the vertical axis. This allows both quantities to be compared simultaneously. Identical axis domains and transforms make the diagonal an explicit reference for D = R. Brown identifies receipt-oriented countries and teal identifies donation-oriented countries; position also conveys the comparison, so interpretation does not depend on color alone.

A linear scale would compress many countries near zero because amounts span several orders of magnitude. I therefore use the same symlog scale with a $1 million constant on both axes. It accommodates zeros, unlike an ordinary logarithmic scale. Its cost is that equal visual distances do not represent equal dollar differences, so the axes and caption explicitly state the transform. Hover details, a country selector, and a complete table provide exact amounts. Selected direct labels make the principal examples visible without labeling every dot.

"); display(aidQ1.node()); const aidQ2 = aidPanel(); aidQ2.append("h2").text("2 · Where are donors and recipients located?"); aidText(aidQ2, "Country color encodes B = (donated − received) / (donated + received). Brown (−1) = only receiving in this dataset; teal (+1) = only donating; a pale midpoint = balanced. Gray = no country record. This relative index describes role, not dollar volume. Hover to see both."); const aidDetail2 = aidText(aidQ2, "Hover over the world map or Europe detail to inspect a country."); const aidS2 = aidSVG(aidQ2, 960, 490); const aidProjection2 = aidD3.geoEqualEarth().fitExtent([[8,8],[952,478]], {type:"FeatureCollection",features:aidFeatures}); const aidPath2 = aidD3.geoPath(aidProjection2); aidS2.append("path").datum({type:"Sphere"}).attr("d", aidPath2).attr("fill", "#f8fafc").attr("stroke", "#cbd5e1"); const aidPaintMap2 = (svg, path) => { const marks = svg.append("g").selectAll("path").data(aidFeatures).join("path").attr("d", path) .attr("fill", f => aidByName.has(aidMapName(f)) ? aidBalanceColor(aidByName.get(aidMapName(f)).balance) : aidNeutral) .attr("stroke", "white").attr("stroke-width", 0.5) .on("mouseenter", function(event, f) { const d = aidByName.get(aidMapName(f)); aidDetail2.text(d ? `${aidDescribe1(d)}${f.properties.name === "South Korea" ? " Korea is mapped to South Korea by assumption." : ""}` : `${f.properties.name}: no record in the reduced dataset.`); aidD3.select(this).attr("stroke", "#111827").attr("stroke-width", 1.8); }).on("mouseleave", function() { aidD3.select(this).attr("stroke", "white").attr("stroke-width", 0.5); }); marks.append("title").text(f => { const d = aidByName.get(aidMapName(f)); return d ? aidDescribe1(d) : `${f.properties.name}: no record`; }); }; aidPaintMap2(aidS2, aidPath2); // Separate detail view preserves visibility of small neighboring European countries. aidQ2.append("h3").text("Europe detail · same colors and index"); const aidEurope2 = aidSVG(aidQ2, 840, 430).style("background", "#f8fafc").style("overflow", "hidden"); const aidEuropeProjection2 = aidD3.geoMercator().center([12,50]).scale(700).translate([420,210]).clipExtent([[0,0],[840,430]]); const aidEuropePath2 = aidD3.geoPath(aidEuropeProjection2); aidPaintMap2(aidEurope2, aidEuropePath2); const aidEuropeLabels2 = ["Germany","Poland","Austria","Hungary","Spain","Portugal","France","Italy"]; aidEurope2.append("g").selectAll("text").data(aidFeatures.filter(f => aidEuropeLabels2.includes(aidMapName(f)))).join("text") .attr("x", f => aidEuropePath2.centroid(f)[0]).attr("y", f => aidEuropePath2.centroid(f)[1]) .attr("text-anchor", "middle").attr("font-size", 11).attr("pointer-events", "none") .attr("paint-order", "stroke").attr("stroke", "white").attr("stroke-width", 3).attr("fill", "#111827").text(aidMapName); aidLegend(aidQ2, aidBalanceColor, [-1,1], "Balance index · receiving ← 0 → donating", aidD3.format("+.1f"), "No country record"); // Compute contrasts only between actual shared-border neighbors in the topology. const aidGeometries2 = aidWorld.objects.countries.geometries; const aidNeighbors2 = aidTopo.neighbors(aidGeometries2); const aidNeighborPairs2 = []; aidNeighbors2.forEach((neighbors,i) => neighbors.forEach(j => { if (j <= i) return; const a = aidByName.get(aidMapName(aidGeometries2[i])), b = aidByName.get(aidMapName(aidGeometries2[j])); if (a && b) aidNeighborPairs2.push({a,b,gap:Math.abs(a.balance-b.balance)}); })); aidNeighborPairs2.sort((a,b) => b.gap-a.gap); aidQ2.append("h3").text("Largest contrasts among recorded shared-border neighbors"); aidTable(aidQ2, ["Neighbor A", "B index", "Neighbor B", "B index", "Absolute gap"], aidNeighborPairs2.slice(0,6).map(({a,b,gap})=>[a.name,aidD3.format("+.3f")(a.balance),b.name,aidD3.format("+.3f")(b.balance),aidD3.format(".3f")(gap)])); aidText(aidQ2, "Boundaries: world-atlas 2.0.2 / Natural Earth, 1:50m; modern borders are used for pooled historical records. The France–Brazil border in the table is through French Guiana, included in the France geometry. Korea → South Korea is a stated matching assumption. These are visual geographic patterns, not a statistical clustering test."); aidQ2.append("div").html("

Analysis

There is a visible concentration of donation-oriented countries in western and northern Europe, including Germany, France, the Netherlands, Switzerland, Sweden, Denmark, and Norway. The United States and Canada are also net donors in the recorded North American subset, and Japan is a donation-oriented country in East Asia.

Several recorded countries in central/eastern Europe—including Poland, the Czech Republic, Hungary, Slovakia, Romania, and the Baltic states—are strongly receipt-oriented. India and Thailand are receipt-oriented in Asia, as are the recorded South American countries Brazil, Colombia, and Chile. However, the Asian examples also include donor-oriented Japan, and the European pattern includes receipt-oriented Ireland and Portugal. The geographic division is therefore not uniform.

Germany and Poland provide a particularly strong neighboring contrast. Germany's balance index is approximately +0.999997, while Poland's is approximately −0.999967, a gap close to the index's maximum possible difference of 2. Germany donates about $47.22 billion, while Poland receives about $24.52 billion. Germany and the Czech Republic also contrast sharply: the Czech Republic's index is about −0.992. Another bordering example is Spain (+0.564) and Portugal (−0.436), which differ in direction despite their shared location on the Iberian Peninsula.

The displayed neighbor table is calculated from shared boundary arcs, rather than assuming nearby countries are neighbors. Its France–Brazil pair refers to the border through French Guiana, which is included in the France geometry. These results describe visible geographic groupings; they do not establish statistically significant spatial clustering or its causes. In particular, South Africa is the only African country represented in this file, so a continent-wide conclusion about Africa would be unsupported.

Design rationale

I map the normalized balance B = (D − R) / (D + R). A value of −1 means only receiving is recorded; +1 means only donating is recorded; zero means equal recorded amounts. This distinguishes direction and relative imbalance without allowing the largest economies' dollar totals to determine the color scale. The index does not encode absolute volume, so hover text reports D, R, and D − R as well.

A diverging brown-to-teal scale, centered at zero, makes opposite roles easy to identify. Gray indicates an absent country record, and is distinct from the pale balanced midpoint. The world map uses an Equal Earth projection. A larger Europe detail makes small countries and shared-border contrasts easier to inspect. A map is effective for regional patterns and adjacency, while Question 1's scatterplot is better for comparing exact magnitudes; these views answer complementary questions.

"); display(aidQ2.node()); const aidQ3 = aidPanel(); aidQ3.append("h2").text("3 · Five frequent purposes, different recipient geographies"); aidText(aidQ3, "The five purposes are selected globally by RECORD COUNT, not by dollars. Each map shows the country's share of that purpose's signed commitment amount. A shared 0–70% color scale supports comparison across maps; gray = no recipient record; the lightest blue = recipient present, zero for this purpose. Hover for dollars, count, and both percentage denominators."); aidTable(aidQ3, ["Rank", "Purpose", "Code", "Records", "Constant USD"], aidTop5.map((p,i)=>[i+1,p.name,p.code,aidD3.format(",")(p.count),aidUSD(p.amount)])); const aidDetail3 = aidText(aidQ3, "Hover over any country or heatmap cell for exact values."); const aidDescribe3 = (p,name) => { const v = aidPurposeByCountry.get(p.code)?.get(name) ?? {amount:0,count:0}; const received = aidReceived.get(name) ?? 0; return `${name} · ${p.name}: ${aidUSD(v.amount)}, ${aidD3.format(",")(v.count)} records; ${aidPercent(v.amount/p.amount)} of this purpose's dataset-wide amount; ${received>0 ? aidPercent(v.amount/received) : "N/A"} of this country's total received amount.`; }; const aidMaps3 = aidQ3.append("div").style("display", "grid") .style("grid-template-columns", "repeat(auto-fit,minmax(min(100%,360px),1fr))").style("gap", "18px"); for (const p of aidTop5) { const panel = aidMaps3.append("div").style("border", "1px solid #e2e8f0").style("border-radius", "8px").style("padding", "10px"); panel.append("h4").style("margin", "2px 0 4px").text(`${p.name} · ${p.code}`); const leader = [...(aidPurposeByCountry.get(p.code) ?? new Map())].sort((a,b)=>b[1].amount-a[1].amount)[0]; panel.append("p").style("font-size", "12px").style("margin", "0 0 4px").text(`Largest share: ${leader[0]}, ${aidPercent(leader[1].amount/p.amount)}`); const svg = aidSVG(panel, 450, 255); const projection = aidD3.geoEqualEarth().fitExtent([[3,5],[447,248]],{type:"FeatureCollection",features:aidFeatures}); const path = aidD3.geoPath(projection); svg.append("path").datum({type:"Sphere"}).attr("d",path).attr("fill","#f8fafc"); const marks = svg.append("g").selectAll("path").data(aidFeatures).join("path").attr("d",path) .attr("fill",f=> { const name=aidMapName(f),v=aidPurposeByCountry.get(p.code)?.get(name); return (aidReceived.get(name)??0)>0 ? aidSeqColor((v?.amount??0)/p.amount) : aidNeutral; }).attr("stroke","white").attr("stroke-width",0.35) .on("mouseenter",function(event,f){ const name=aidMapName(f); aidDetail3.text((aidReceived.get(name)??0)>0 ? aidDescribe3(p,name) : `${name}: no recipient record in this dataset.`); aidD3.select(this).attr("stroke","#111827").attr("stroke-width",1.2); }).on("mouseleave",function(){aidD3.select(this).attr("stroke","white").attr("stroke-width",0.35);}); marks.append("title").text(f=>(aidReceived.get(aidMapName(f))??0)>0 ? aidDescribe3(p,aidMapName(f)) : `${aidMapName(f)}: no recipient record`); } aidLegend(aidQ3,aidSeqColor,[0,0.7],"Country share of each purpose's dataset-wide commitment amount",aidD3.format(".0%")); // Complementary denominator: purpose amount / ALL-purpose amount received by country. aidQ3.append("h3").text("Recipient portfolios · top 15 recipients by total received amount"); aidText(aidQ3,"Here each cell divides by that country's ALL-purpose received amount. These five columns need not sum to 100%, since other purposes remain in the denominator. The heatmap distinguishes a large country receiving many dollars from a country whose aid portfolio emphasizes a particular purpose."); const aidHeatRows3 = [...aidCountries].filter(d=>d.received>0).sort((a,b)=>b.received-a.received).slice(0,15); const aidHeat3=aidSVG(aidQ3,940,645); const aidHeatX3=aidD3.scaleBand(aidTop5.map(d=>d.code),[195,930]).padding(0.07); const aidHeatY3=aidD3.scaleBand(aidHeatRows3.map(d=>d.name),[140,615]).padding(0.08); const aidHeatColor3=aidD3.scaleSequential([0,0.3],aidD3.interpolatePurples).clamp(true); const aidHeatShort3=["Unspecified","Social / welfare","Civil society","Higher education","Multisector"]; aidTop5.forEach((p,i)=>{ aidHeat3.append("text").attr("x",aidHeatX3(p.code)+aidHeatX3.bandwidth()/2).attr("y",111) .attr("text-anchor","middle").attr("font-size",12).attr("font-weight",600).text(aidHeatShort3[i]); aidHeat3.append("text").attr("x",aidHeatX3(p.code)+aidHeatX3.bandwidth()/2).attr("y",129) .attr("text-anchor","middle").attr("font-size",11).text(p.code); }); const aidHeatCells3=aidHeatRows3.flatMap(d=>aidTop5.map(p=>({d,p,share:(aidPurposeByCountry.get(p.code)?.get(d.name)?.amount??0)/d.received}))); aidHeat3.selectAll("rect.cell").data(aidHeatCells3).join("rect").attr("class","cell") .attr("x",v=>aidHeatX3(v.p.code)).attr("y",v=>aidHeatY3(v.d.name)).attr("width",aidHeatX3.bandwidth()).attr("height",aidHeatY3.bandwidth()) .attr("fill",v=>aidHeatColor3(v.share)).on("mouseenter",(event,v)=>aidDetail3.text(aidDescribe3(v.p,v.d.name))) .append("title").text(v=>aidDescribe3(v.p,v.d.name)); aidHeat3.selectAll("text.row").data(aidHeatRows3).join("text").attr("class","row") .attr("x",182).attr("y",d=>aidHeatY3(d.name)+aidHeatY3.bandwidth()/2+4).attr("text-anchor","end").text(d=>d.name); aidHeat3.selectAll("text.value").data(aidHeatCells3).join("text").attr("class","value") .attr("x",v=>aidHeatX3(v.p.code)+aidHeatX3.bandwidth()/2).attr("y",v=>aidHeatY3(v.d.name)+aidHeatY3.bandwidth()/2+4) .attr("text-anchor","middle").attr("fill",v=>v.share>0.16?"white":"#312e50").attr("pointer-events","none").text(v=>aidPercent(v.share)); aidHeat3.append("text").attr("x",195).attr("y",22).attr("font-weight",600).text("Purpose share of all commitments received by each country"); aidHeat3.selectAll("rect.key").data(aidD3.range(120)).join("rect").attr("class","key") .attr("x",i=>195+i*3).attr("y",35).attr("width",3.2).attr("height",12).attr("fill",i=>aidHeatColor3(i/119*0.3)); const aidHeatKeyX3=aidD3.scaleLinear([0,0.3],[195,555]); aidHeat3.append("g").attr("transform","translate(0,47)").call(aidD3.axisBottom(aidHeatKeyX3).ticks(3).tickFormat(aidD3.format(".0%"))) .call(g=>g.select(".domain").remove()); aidHeat3.append("text").attr("x",575).attr("y",46).attr("font-size",11).text("Shared scale: 0–30% (exact values printed)"); aidQ3.append("div").html("

Selecting the five purposes

I rank purpose codes by the number of records across the entire supplied dataset, before examining individual recipient countries. The ranking is:

\n\n\n\n\n\n\n

Rank	Purpose	Code	Records
1	Sectors not specified	99810	8,140
2	Social/ welfare services	16010	6,407
3	Strengthening civil society	15150	5,805
4	Higher education	11420	5,262
5	Multisector aid	43010	3,028

“Sectors not specified” remains in the ranking because it is an actual recorded category and the most frequent one. I do not substitute a more interpretable category. Its unspecified content limits substantive interpretation. Frequency counts records, not necessarily distinct projects or amounts of money.

Analysis

The five purposes have different concentrations, even though several large recipients recur:

Sectors not specified: India receives approximately $5.07 billion, or 59.3% of this category's amount in the dataset. South Africa receives about 12.0%. This is a geographically concentrated category, but its label does not identify a specific substantive sector.
Social/ welfare services: Colombia receives approximately $6.45 billion, or 67.7% of this category's amount. The concentration is therefore strongest in the Colombian part of South America, rather than in India, the largest recipient overall.
Strengthening civil society: South Africa receives approximately $771.28 million (31.1%), Colombia $625.82 million (25.2%), and India $562.99 million (22.7%). This category is distributed across southern Africa, South America, and South Asia in the represented countries.
Higher education: India leads with approximately $900.68 million (19.4%), followed by Brazil (13.2%) and Thailand (12.1%). Its largest recipient share is substantially lower than for the other four purposes, making the distribution less concentrated in one leading country. Korea and Poland also have visible shares, approximately 7.8% and 7.6%.
Multisector aid: India receives approximately $1.96 billion (40.9%) and Thailand $1.06 billion (22.1%). These two Asian recipients together account for approximately 63.0% of the category's recorded amount.

The recipient-portfolio heatmap addresses whether certain countries receive relatively more of particular types. Social/welfare services represent approximately 25.3% of all aid received by Colombia, compared with about 0.7% for India and 1.8% for Brazil. Civil-society support represents about 4.1% of South Africa's receipts, compared with 0.4% for India. Higher education represents about 7.3% of Romania's receipts and 5.1% of Hungary's, compared with 0.7% for India. Thus, leading in absolute dollars and emphasizing a purpose within one's received-aid portfolio are different properties. These are funding patterns, not evidence of a recipient's preferences or needs.

Design rationale

Five small-multiple maps use the same projection, dimensions, and 0–70% sequential color scale. For a given purpose, a country's map value is its received amount for that purpose divided by the amount for that purpose across all recipients in this dataset. The common scale makes relative concentration comparable without giving every category's leading country an independently rescaled darkest color. The highest observed share is below 70%, so no map value is clipped. Hover text includes absolute amounts and record counts.

The complementary heatmap uses a different denominator: purpose amount divided by that country's all-purpose received amount. It displays the 15 largest recipients by total received amount to keep labels readable; map hover details provide the same portfolio percentage for every represented recipient. The five displayed purposes need not add to 100% because other purposes remain in the denominator. Its shared 0–30% scale also contains all displayed values without clipping, and printed percentages support precise comparison. Maps retain geographic context, while the heatmap gives small countries equal visual space and makes portfolio comparisons easier. Its limitation is the absence of geographic arrangement.

"); display(aidQ3.node()); const aidSources = aidPanel(); aidSources.html("

Limitations and attribution

This is a reduced historical sample. A country absent from it cannot be classified as having no aid in the real world. Pooling 1973–2013 conceals changes over time and can combine different periods of a country's donor and recipient activity. Totals are not normalized by population, income, or length of participation. The visualizations show commitments, and cannot establish causes, actual disbursements, aid effectiveness, or present-day policy.

Sources and software:

Assignment's reduced AidData spreadsheet, supplied as the unmodified aid.csv attachment.
AidData Core Research Release, Level 1, version 3.1, the source dataset identified by the assignment. The analysis uses the assignment's reduced file.
D3 7.9.0, used for all charts, scales, grouping, geographic projections, and DOM/SVG construction.
world-atlas 2.0.2, countries-50m.json, geographic boundaries derived from Natural Earth.
topojson-client 3.1.0, used to decode geographic features and find shared-border neighbors; it is not a separate visualization library.

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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":"$3e","pinned":true,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W3f","stdlib":"2"}} 33:null