32:Tf05,viewof selectedCrimes = { const form = html`

Filter by Crime Type (${allCrimes.length} types available)

`; const checkboxContainer = form.querySelector("#checkboxes"); const selectAllBtn = form.querySelector("#selectAll"); const deselectAllBtn = form.querySelector("#deselectAll"); const onlyDrugsBtn = form.querySelector("#onlyDrugs"); const selected = new Set(allCrimes); // Start with all selected // Create checkboxes allCrimes.forEach(crime => { const label = html` ${crimeTranslationMap.get(crime) || crime} `; const checkbox = label.querySelector("input"); checkbox.addEventListener("change", () => { if (checkbox.checked) { selected.add(crime); } else { selected.delete(crime); } form.value = Array.from(selected); form.dispatchEvent(new Event("input")); }); checkboxContainer.appendChild(label); }); // Button handlers selectAllBtn.onclick = () => { checkboxContainer.querySelectorAll("input").forEach(cb => { cb.checked = true; selected.add(cb.value); }); form.value = Array.from(selected); form.dispatchEvent(new Event("input")); }; deselectAllBtn.onclick = () => { checkboxContainer.querySelectorAll("input").forEach(cb => { cb.checked = false; selected.delete(cb.value); }); form.value = Array.from(selected); form.dispatchEvent(new Event("input")); }; onlyDrugsBtn.onclick = () => { checkboxContainer.querySelectorAll("input").forEach(cb => { const shouldCheck = cb.value === "NARCOTRÁFICO"; cb.checked = shouldCheck; if (shouldCheck) { selected.add(cb.value); } else { selected.delete(cb.value); } }); form.value = Array.from(selected); form.dispatchEvent(new Event("input")); }; form.value = Array.from(selected); return form; }33:T502,

Selected Crimes Statistics

${crimeStats.map(stat => html` `)}

Crime	Male	Female	Total	% Male	% Female
${crimeTranslationMap.get(stat.crime) \|\| stat.crime}	${stat.male.toLocaleString()}	${stat.female.toLocaleString()}	${stat.total.toLocaleString()}	${stat.malePct}%	${stat.femalePct}%

34:T1f94,## Gender-Specific Crime Patterns - Visualization Guide ### Visual Structure ``` OUTER RING (Larger, 90% opacity) ╔════════════════════════════════╗ ║ 🔵 Male arrests ║ ║ Top 4 crimes by men ║ ║ ╔══════════════════╗ ║ ║ ║ INNER RING ║ ║ ║ ║ (55% size) ║ ║ ║ ║ 🟣 Female ║ ║ ║ ║ arrests ║ ║ ║ ║ Top 4 crimes ║ ║ ║ ║ by women ║ ║ ║ ╚══════════════════╝ ║ ╚════════════════════════════════╝ ``` ## 📊 Data Structure ```json { "id": "ES3", "name": "Comunidad de Madrid", "total": 57328, "totalMale": 48799, // 85.1% "totalFemale": 8529, // 14.9% "maleData": [ // OUTER RING {"name": "NARCOTRÁFICO", "value": 12768}, {"name": "ROBO / HURTO", "value": 4181}, {"name": "HOMICIDIO / TENTATIVA DE", "value": 1935} ], "femaleData": [ // INNER RING {"name": "NARCOTRÁFICO", "value": 3551}, {"name": "ROBO / HURTO", "value": 346}, {"name": "DELITO MIGRATORIO", "value": 338} ] } ``` ## 🔍 Key Insights Revealed ### 1. Madrid (ES3) - Largest Colombian Community **MALE ARRESTS (48,799 - 85%)** ``` 1. Drug Trafficking: 12,768 (26%) 🔵 // #5DA5DA Light blue 2. Robbery/Theft: 4,181 (9%) 🟠 // #FAA43A Light orange 3. Homicide: 1,935 (4%) 🔴 // #F17CB0 Rose pink 4. Immigration: 1,181 (2%) 🟤 // #B2912F Brass ``` **FEMALE ARRESTS (8,529 - 15%)** ``` 1. Drug Trafficking: 3,551 (42%) 🔵 // #5DA5DA Light blue 2. Robbery/Theft: 346 (4%) 🟠 // #FAA43A Light orange 3. Immigration: 338 (4%) 🟤 // #B2912F Brass 4. Fraud: 256 (3%) 🟢 // #60BD68 Soft green ``` **🚨 KEY FINDING:** Women show a **HIGHER percentage** (42% vs 26%) in drug trafficking! This suggests women are disproportionately used as drug couriers. --- ### 2. Vienna (AT1) - Interesting Reversal **MALE ARRESTS (238 - 71%)** ``` 1. Robbery/Theft: 130 (55%) 🟠 // #FAA43A Light orange 2. Homicide: 36 (15%) 🔴 // #F17CB0 Rose pink 3. Immigration: 19 (8%) 🟤 // #B2912F Brass 4. Document Forgery: 14 (6%) 🔴 // #F15854 Soft red ``` **FEMALE ARRESTS (97 - 29%)** ``` 1. Drug Trafficking: 40 (41%) 🔵 // #5DA5DA Light blue 2. Robbery/Theft: 27 (28%) 🟠 // #FAA43A Light orange 3. Other: 23 (24%) 🟣 // #B276B2 Lilac 4. Document Forgery: 4 (4%) 🔴 // #F15854 Soft red ``` **🚨 KEY FINDING:** Complete reversal! Men arrested for theft, women for drugs. --- ### 3. Switzerland (CH0) - Gender Outlier **MALE ARRESTS (270 - 100%)** ``` 1. Robbery/Theft: 149 (55%) 🟠 // #FAA43A Light orange 2. Drug Trafficking: 46 (17%) 🔵 // #5DA5DA Light blue 3. Sexual Crimes: 36 (13%) ⚫ // #4D4D4D Charcoal 4. Other: 36 (13%) 🟣 // #B276B2 Lilac ``` **FEMALE ARRESTS (0 - 0%)** ``` No female arrests recorded ``` **🚨 KEY FINDING:** Unique case - only male arrests. May indicate: - Data collection issue - Very small Colombian female population - Different migration patterns --- ### 4. London (UKI) - High Drug Focus **MALE ARRESTS (3,839 - 85%)** ``` 1. Drug Trafficking: 2,132 (56%) 🔵 // #5DA5DA Light blue 2. Other: 707 (18%) 🟣 // #B276B2 Lilac 3. Robbery: 240 (6%) 🟠 // #FAA43A Light orange 4. Fraud: 204 (5%) 🟢 // #60BD68 Soft green ``` **FEMALE ARRESTS (660 - 15%)** ``` 1. Drug Trafficking: 446 (68%) 🔵 // #5DA5DA Light blue 2. Other: 116 (18%) 🟣 // #B276B2 Lilac 3. Robbery: 69 (10%) 🟠 // #FAA43A Light orange 4. Fraud: 38 (6%) 🟢 // #60BD68 Soft green ``` **🚨 KEY FINDING:** Even higher female drug % (68%) than Madrid! UK's Colombian female arrests are heavily drug-focused. --- ## 📈 Overall Gender Patterns (All Zones) ### TOP 5 CRIMES - MALE (Total: ~90,000) ``` 1. Drug Trafficking: 24,167 (27%) 🔵 // #5DA5DA Light blue 2. Robbery/Theft: 10,317 (11%) 🟠 // #FAA43A Light orange 3. Homicide: 4,271 (5%) 🔴 // #F17CB0 Rose pink 4. Other: 2,395 (3%) 🟣 // #B276B2 Lilac 5. Immigration: 1,519 (2%) 🟤 // #B2912F Brass ``` ### TOP 5 CRIMES - FEMALE (Total: ~17,000) ``` 1. Drug Trafficking: 5,850 (34%) 🔵 ← HIGHER % 2. Robbery/Theft: 1,252 (7%) 🟠 // #FAA43A Light orange 3. Immigration: 466 (3%) 🟤 // #B2912F Brass 4. Other: 318 (2%) 🟣 // #B276B2 Lilac 5. Homicide: 228 (1%) 🔴 // #F17CB0 Rose pink ``` --- ## Emoji to Color Mapping (Minimalist Palette) | Crime Type | Emoji | Actual Color (HEX) | Note | |------------|-------|-------------------|------| | Drug Trafficking | 🔵 | `#5DA5DA` Light blue | Perfect match | | Robbery/Theft | 🟠 | `#FAA43A` Light orange | Perfect match | | Homicide | 🔴 | `#F17CB0` Rose pink | Red emoji for pink color | | Immigration | 🟤 | `#B2912F` Brass | Perfect match | | Fraud | 🟢 | `#60BD68` Soft green | Perfect match | | Other | 🟣 | `#B276B2` Lilac | Perfect match | | Document Forgery | 🔴 | `#F15854` Soft red | Perfect match | | Sexual Crimes | ⚫ | `#4D4D4D` Charcoal | Perfect match | ## 💡 Sociological Insights ### 1. Drug Mule Pattern **Observation:** Women disproportionately arrested for drug trafficking **Analysis:** - 34% of female arrests vs 27% of male arrests - Known vulnerability: Women recruited as couriers - Often single mothers, economic desperation - Lower sentences incentivize recruiters ### 2. Violent Crime Gender Gap **Observation:** Homicide is 5% male, 1% female **Analysis:** - Consistent with global patterns - Male violence significantly higher - Women's crimes more economic in nature ### 3. Immigration Offenses **Observation:** Similar rates (2-3%) for both genders **Analysis:** - Administrative crimes affect both equally - Reflects irregular migration challenges - Not gender-specific vulnerability ### 4. Geographic Variations **Observation:** Crime patterns vary by country **Examples:** - Austria: Men do theft, women do drugs - UK: Extreme drug focus for women (68%) - Spain: More balanced patterns **Analysis:** - Local drug routes matter - Economic opportunities differ - Law enforcement priorities vary ## 🗺️ Map Reading Guide ### What to Look For: **1. Circle Size** - Larger = more arrests - Madrid dominates (57k arrests) **2. Outer Ring Colors** - What crimes do men commit here? - Is it drug-heavy or property crime-heavy? **3. Inner Ring Colors** - What crimes do women commit here? - Compare to outer: Different patterns? **4. Ring Proportions** - Thick outer, thin inner = Few female arrests - More balanced = Higher female participation ### Example Reading: **Madrid's Chart Says:** ``` "This is a large community (big circle). Men are arrested mainly for drugs and theft (outer ring). Women are arrested almost exclusively for drugs (inner ring magenta). Women show higher drug proportion than men - vulnerability pattern" ``` ### Narrative possibilities This visualization tells multiple stories: 1. **The Numbers Story**: Who gets arrested where 2. **The Gender Story**: Different crime patterns by gender 3. **The Vulnerability Story**: Women as drug mules 4. **The Geography Story**: Patterns vary by location ### Discussion - Why are women more involved in drug trafficking? - What support do female prisoners need? - How do economic factors drive crime patterns? - What role does migration policy play? 35:T4f5,generateBasemap = function () { // Create SVG let svg = d3 .select(DOM.svg(width, height)) .attr("viewbox", "0 0 100 100") .style("background-color", "#f8f9fa"); // Slightly off-white for softer look // Add country borders (background) svg .append("g") .attr("class", "countries") .selectAll("path") .data(countryRegions.features) .enter() .append("path") .attr("fill", "#e8e8e8") // Light gray for countries .attr("stroke", "#999") .attr("stroke-width", "0.3") .attr("d", svgPath); // Add NUTS regions svg .append("g") .attr("class", "nuts-regions") .selectAll("path") .data(nutsRegions.features) .enter() .append("path") .attr("fill", "white") .attr("stroke", "#ccc") .attr("stroke-width", "0.2") .attr("d", svgPath) .attr("opacity", 0.7); // Slight transparency // Add EU borders (emphasis) svg .append("g") .attr("class", "boundaries") .selectAll("path") .data(nutsBoundaries.features) .enter() .append("path") .attr("fill", "none") .attr("stroke-width", d => d.properties.lvl == 0 ? "0.6" : "0.3") .attr("stroke", d => d.properties.lvl == 0 ? "#333" : "#999") .attr("d", svgPath); return svg; } 36:Tb23,addPieChartsToMap = function (map) { filteredData.forEach((region) => { if (region.geometry && region.geometry.coordinates[0] !== null) { let x = projection(region.geometry.coordinates)[0]; let y = projection(region.geometry.coordinates)[1]; // Skip if no data if (region.maleData.length === 0 && region.femaleData.length === 0) return; let outerRadius = radiusFunction(region.total); let innerRadius = outerRadius * 0.55; // Inner ring is 55% of outer // Create group for this region let g = map .append("g") .attr("transform", `translate(${x}, ${y})`) .attr("class", "pie-chart-group") .attr("data-zone", region.id); // ======================================== // OUTER RING: Male crimes // ======================================== if (region.maleData.length > 0) { let maleArcs = pie(region.maleData); g.selectAll("path.male-ring") .data(maleArcs) .join("path") .attr("class", "male-ring") .attr("fill", d => sectorColor(d.data.name)) .attr("stroke", "white") .attr("stroke-width", 1.2) // Increased from 0.8 .attr("opacity", 0.9) .attr("d", d3.arc() .innerRadius(innerRadius) .outerRadius(outerRadius) ) .append("title") .text(d => { let pct = (d.data.value / region.totalMale * 100).toFixed(1); return `MALE: ${d.data.name}\n${d.data.value.toLocaleString()} arrests (${pct}%)`; }); } // ======================================== // INNER RING: Female crimes // ======================================== if (region.femaleData.length > 0) { let femaleArcs = pie(region.femaleData); g.selectAll("path.female-ring") .data(femaleArcs) .join("path") .attr("class", "female-ring") .attr("fill", d => sectorColor(d.data.name)) .attr("stroke", "white") .attr("stroke-width", 1.2) // Increased from 0.8 .attr("opacity", 0.75) // Slightly more transparent to distinguish .attr("d", d3.arc() .innerRadius(0) .outerRadius(innerRadius) ) .append("title") .text(d => { let pct = (d.data.value / region.totalFemale * 100).toFixed(1); return `FEMALE: ${d.data.name}\n${d.data.value.toLocaleString()} arrests (${pct}%)`; }); } // Optional: Add subtle border around entire pie chart g.append("circle") .attr("r", outerRadius) .attr("fill", "none") .attr("stroke", "#333") .attr("stroke-width", 0.3) .attr("opacity", 0.3); } }); }37:T44f,sectorColor = function(value) { if (value == "NARCOTRÁFICO") return "#5DA5DA"; // Light blue if (value == "ROBO / HURTO") return "#FAA43A"; // Light orange if (value == "HOMICIDIO / TENTATIVA DE") return "#F17CB0"; // Rose pink if (value == "DELITO MIGRATORIO") return "#B2912F"; // Brass if (value == "FRAUDE / ESTAFA") return "#60BD68"; // Soft green if (value == "OTROS") return "#B276B2"; // Lilac if (value == "LESIONES PERSONALES") return "#DECF3F"; // Soft yellow if (value == "FALSEDAD EN DOCUMENTO") return "#F15854"; // Soft red if (value == "DELITOS SEXUALES") return "#4D4D4D"; // Charcoal if (value == "VIOLENCIA INTRAFAMILIAR") return "#B0B0B0"; // Light gray if (value == "CRIMEN ORGANIZADO") return "#8C564B"; // Taupe if (value == "PORTE ILEGAL DE ARMAS") return "#7F7F7F"; // Medium gray if (value == "SECUESTRO") return "#9467BD"; // Muted purple if (value == "NO REPORTA – CONFIDENCIALIDAD ESTATAL") return "#D0D0D0"; // Very light gray if (value == "DESCONOCIDO") return "#E8E8E8"; // Near white return "#AAAAAA"; // Default gray }38:Te70,addLegendsToMap = function(map) { let g = map .append("g") .attr("transform", `translate(${legendX}, 35)`) .attr("text-anchor", "end"); // Title g.append("text") .text("Colombians in European Prisons") .attr("font-weight", "bold") .attr("font-size", "18px"); g.append("text") .text("Gender-Specific Crime Patterns") .attr("transform", "translate(0, 22)") .attr("font-size", "13px") .attr("fill", "#666"); let circleX = 0; // Explanation - using colors from the minimalist palette g.append("text") .text("Outer Ring: Male arrests") .attr("transform", "translate(0, 50)") .attr("font-weight", "bold") .attr("font-size", "12px") .attr("fill", "#5DA5DA"); // Light blue (from Drug Trafficking) g.append("text") .text("Inner Ring: Female arrests") .attr("transform", "translate(0, 68)") .attr("font-weight", "bold") .attr("font-size", "12px") .attr("fill", "#F17CB0"); // Rose pink (from Homicide) // Crime colors g.append("text") .text("Crime Types") .attr("transform", "translate(0, 95)") .attr("font-weight", "bold") .attr("font-size", "12px"); const crimeData = [ { name: "Drug Trafficking", color: "#5DA5DA", y: 118 }, { name: "Robbery/Theft", color: "#FAA43A", y: 142 }, { name: "Homicide", color: "#F17CB0", y: 166 }, { name: "Immigration", color: "#B2912F", y: 190 }, { name: "Fraud", color: "#60BD68", y: 214 }, { name: "Other", color: "#B276B2", y: 238 } ]; crimeData.forEach(item => { g.append("circle") .attr("r", 6) .attr("fill", item.color) .attr("stroke", "#fff") .attr("stroke-width", 1) .attr("transform", `translate(${circleX}, ${item.y})`); g.append("text") .text(item.name) .attr("transform", `translate(${circleX - 12}, ${item.y + 5})`) .attr("font-size", "12px"); }); // Size legend - MORE SPACING g.append("text") .text("Total Arrests per Zone") .attr("transform", `translate(${circleX}, 280)`) // More space from crimes .attr("font-weight", "bold") .attr("font-size", "12px"); let sizeLegend = g .append("g") .attr("fill", "#444") .attr("transform", `translate(${circleX - 45}, 365)`) // More space below title .attr("text-anchor", "middle") .selectAll("g") .data([57000, 5000]) .join("g"); sizeLegend .append("circle") .attr("fill", "none") .attr("stroke", "#444") .attr("stroke-width", 1.5) .attr("cy", d => -radiusFunction(d)) .attr("r", radiusFunction); sizeLegend .append("text") .attr("y", d => -2 * radiusFunction(d) - 5) .attr("x", 0) .text(d => d.toLocaleString()) .attr("font-size", "11px") .attr("text-anchor", "middle"); // Key insights - MORE SPACING g.append("text") .text("Key Patterns") .attr("transform", "translate(0, 420)") // More space from size legend .attr("font-weight", "bold") .attr("font-size", "12px"); g.append("text") .text("Total: 115,306 arrests") .attr("transform", "translate(0, 442)") .attr("font-size", "11px"); g.append("text") .text("Male: 85% | Female: 15%") .attr("transform", "translate(0, 462)") .attr("font-size", "11px"); g.append("text") .text("Top male crime: Drug trafficking") .attr("transform", "translate(0, 482)") .attr("font-size", "11px"); g.append("text") .text("Top female crime: Drug trafficking") .attr("transform", "translate(0, 502)") .attr("font-size", "11px"); g.append("text") .text("Data: 2018-2024") .attr("transform", "translate(0, 530)") .attr("font-size", "10px") .attr("fill", "#888"); } 39:T5a0,crimeTranslationMap = new Map([ ["CRIMEN ORGANIZADO", "Organized Crime (crimen organizado)"], ["DELITO MIGRATORIO", "Migration Offense (delito migratorio)"], ["DELITOS SEXUALES", "Sexual Crimes (delitos sexuales)"], ["DESACATO A LA AUTORIDAD", "Contempt of Authority (desacato a la autoridad)"], ["FALSEDAD EN DOCUMENTO", "Document Forgery (falsedad en documento)"], ["FRAUDE / ESTAFA", "Fraud / Scam (fraude / estafa)"], ["HOMICIDIO / TENTATIVA DE", "Homicide / Attempted Homicide (homicidio / tentativa de)"], ["LAVADO DE ACTIVOS", "Money Laundering (lavado de activos)"], ["LESIONES PERSONALES", "Personal Injury (lesiones personales)"], ["NARCOTRÁFICO", "Drug Trafficking (narcotráfico)"], ["NO REPORTA � CONFIDENCIALIDAD ESTATAL", "Not Reported / State Confidentiality (no reporta / confidencialidad estatal)"], ["OTROS", "Other (otros)"], ["ROBO / HURTO", "Theft / Robbery (robo / hurto)"], ["TRATA DE PERSONAS", "Human Trafficking (trata de personas)"], ["VIOLENCIA INTRAFAMILIAR", "Domestic Violence (violencia intrafamiliar)"], ["VIOLENCIA DE GENERO", "Gender-based Violence (violencia de género)"], ["CONDUCCIÓN TEMERARIA", "Reckless Driving (conducción temeraria)"], ["SECUESTRO", "Kidnapping (secuestro)"], ["PORTE ILEGAL DE ARMAS", "Illegal Possession of Weapons (porte ilegal de armas)"], ["EXTORSIÓN", "Extortion (extorsión)"], ["DESCONOCIDO", "Unknown (desconocido)"], ]); 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Colombians in European Prisons: Mapping the Incarcerated Diaspora (2018-2024)

","pinned":false,"hidden":false,"mode":"html"},{"id":659,"value":"## Gender specific Crime patterns","pinned":false,"hidden":false,"mode":"md"},{"id":41,"value":"map = {\n let basemap = generateBasemap();\n addPieChartsToMap(basemap);\n addLegendsToMap(basemap);\n return basemap.node();\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":678,"value":"$32","pinned":false,"hidden":false,"mode":"ojs"},{"id":690,"value":"$33","pinned":false,"hidden":false,"mode":"html"},{"id":687,"value":"crimeStats = {\n const stats = selectedCrimes.map(crime => {\n let maleTotal = 0;\n let femaleTotal = 0;\n \n filteredData.forEach(zone => {\n zone.maleData.forEach(c => {\n if (c.name === crime) maleTotal += c.value;\n });\n zone.femaleData.forEach(c => {\n if (c.name === crime) femaleTotal += c.value;\n });\n });\n \n return {\n crime: crime,\n male: maleTotal,\n female: femaleTotal,\n total: maleTotal + femaleTotal,\n malePct: (maleTotal / (maleTotal + femaleTotal) * 100).toFixed(1),\n femalePct: (femaleTotal / (maleTotal + femaleTotal) * 100).toFixed(1)\n };\n });\n \n return stats.sort((a, b) => b.total - a.total);\n}\n\n// Display statistics table\n","pinned":false,"hidden":false,"mode":"ojs"},{"id":650,"value":"$34","pinned":false,"hidden":false,"mode":"md"},{"id":6,"value":"$35","pinned":false,"hidden":false,"mode":"ojs"},{"id":208,"value":"svgPath = d3.geoPath().projection(projection)","pinned":false,"hidden":false,"mode":"ojs"},{"id":185,"value":"projection = d3\n .geoIdentity()\n .reflectY(true)\n .fitSize([width, height], nutsRegions)","pinned":false,"hidden":false,"mode":"ojs"},{"id":34,"value":"height = width / 1.2","pinned":false,"hidden":false,"mode":"ojs"},{"id":104,"value":"md`## Pie charts`","pinned":false,"hidden":false,"mode":"ojs"},{"id":320,"value":"totalExtent = d3.extent(colombian_arrests_with_gender.map(r => r.total))","pinned":false,"hidden":false,"mode":"ojs"},{"id":310,"value":"radiusFunction = d3\n .scaleSqrt()\n .domain(totalExtent)\n .range([12, 30])","pinned":false,"hidden":false,"mode":"ojs"},{"id":252,"value":"radius = 12","pinned":false,"hidden":false,"mode":"ojs"},{"id":107,"value":"$36","pinned":false,"hidden":false,"mode":"ojs"},{"id":109,"value":"arc = d3\n .arc()\n .innerRadius(0)\n .outerRadius(radius)","pinned":false,"hidden":false,"mode":"ojs"},{"id":111,"value":"pie = d3\n .pie()\n .sort(null)\n .value(d => d.value)","pinned":false,"hidden":false,"mode":"ojs"},{"id":114,"value":"$37","pinned":false,"hidden":false,"mode":"ojs"},{"id":606,"value":"genderColor = function(gender) {\n if (gender == \"MASCULINO\") {\n return \"#1E88E5\"; // Blue for male\n } else if (gender == \"FEMENINO\") {\n return \"#E91E63\"; // Pink for female\n } else {\n return \"#9E9E9E\"; // Gray for unknown\n }\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":258,"value":"md`## Legends`","pinned":false,"hidden":false,"mode":"ojs"},{"id":390,"value":"legendX = width - 20","pinned":false,"hidden":false,"mode":"ojs"},{"id":262,"value":"$38","pinned":false,"hidden":false,"mode":"ojs"},{"id":31,"value":"md`## Data`","pinned":false,"hidden":false,"mode":"ojs"},{"id":524,"value":"colombianos_en_europa1 = await FileAttachment(\"Colombianos_en_Europa (1).csv\").csv({typed: \"auto\"})","pinned":true,"hidden":false,"mode":"ojs"},{"id":600,"value":"extracted from https://www.datos.gov.co/Estad-sticas-Nacionales/Colombianos-detenidos-en-el-exterior/e97j-vuf7/about_data on 12.10.2025","pinned":false,"hidden":false,"mode":"md"},{"id":675,"value":"$39","pinned":false,"hidden":false,"mode":"ojs"},{"id":708,"value":"allCrimes = {\n const crimeSet = new Set();\n \n colombian_arrests_final.forEach(zone => {\n // Add all crimes\n zone.data.forEach(crime => {\n if (crime.name !== \"NO REPORTA – CONFIDENCIALIDAD ESTATAL\" && crime.name !== \"DESCONOCIDO\" && crime.name !== \"CONDUCCIÓN TEMERARIA\") {\n return crimeSet.add(crime.name)\n }\n })\n });\n \n return Array.from(crimeSet).sort();\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":682,"value":"filteredData = {\n if (selectedCrimes.length === 0) {\n return []; // Show all if nothing selected\n }\n \n return colombian_arrests_gender_crimes.map(zone => {\n // Filter male crimes\n const filteredMaleData = zone.maleData.filter(crime => \n selectedCrimes.includes(crime.name)\n );\n \n // Filter female crimes\n const filteredFemaleData = zone.femaleData.filter(crime => \n selectedCrimes.includes(crime.name)\n );\n \n // Recalculate totals\n const totalMale = filteredMaleData.reduce((sum, crime) => sum + crime.value, 0);\n const totalFemale = filteredFemaleData.reduce((sum, crime) => sum + crime.value, 0);\n const total = totalMale + totalFemale;\n \n return {\n ...zone,\n maleData: filteredMaleData,\n femaleData: filteredFemaleData,\n totalMale: totalMale,\n totalFemale: totalFemale,\n total: total\n };\n }).filter(zone => zone.total > 0); // Remove zones with no data\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":46,"value":"md `### Geographic`","pinned":false,"hidden":false,"mode":"ojs"},{"id":409,"value":"md`The NUTS geometries and centroids are retrieved using the [Nuts2json API](https://github.com/eurostat/Nuts2json)`","pinned":false,"hidden":false,"mode":"ojs"},{"id":151,"value":"centroidsData = (\n await fetch(\n `https://raw.githubusercontent.com/eurostat/Nuts2json/master/pub/v2/2016/3035/nutspt_1.json`\n )\n).json()","pinned":false,"hidden":false,"mode":"ojs"},{"id":176,"value":"md`add id to root object to facilitate merging with other datasets`","pinned":false,"hidden":false,"mode":"ojs"},{"id":173,"value":"centroids = centroidsData.features.map(c => {\n c.id = c.properties.id;\n return c;\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":12,"value":"nuts = await d3.json(\n \"https://raw.githubusercontent.com/eurostat/Nuts2json/master/pub/v2/2021/3035/20M/1.json\"\n)","pinned":false,"hidden":false,"mode":"ojs"},{"id":22,"value":"countryRegions = topojson.feature(nuts, nuts.objects.cntrg)","pinned":false,"hidden":false,"mode":"ojs"},{"id":20,"value":"countryBoundaries = topojson.feature(nuts, nuts.objects.cntbn)","pinned":false,"hidden":false,"mode":"ojs"},{"id":18,"value":"nutsRegions = topojson.feature(nuts,nuts.objects.nutsrg);","pinned":false,"hidden":false,"mode":"ojs"},{"id":17,"value":"nutsBoundaries = topojson.feature(nuts,nuts.objects.nutsbn);","pinned":false,"hidden":false,"mode":"ojs"},{"id":24,"value":"graticule = topojson.feature(nuts, nuts.objects.gra)","pinned":false,"hidden":false,"mode":"ojs"},{"id":28,"value":"md`## Dependencies`","pinned":false,"hidden":false,"mode":"ojs"},{"id":26,"value":"topojson = require(\"topojson\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":14,"value":"d3 = require(\"d3\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":538,"value":"colombian_arrests_final = FileAttachment(\"colombian_arrests_final.json\").json()","pinned":false,"hidden":false,"mode":"ojs"},{"id":615,"value":"colombian_arrests_with_gender = FileAttachment(\"colombian_arrests_gender_crimes_COMPLETE.json\").json()","pinned":false,"hidden":false,"mode":"ojs"},{"id":617,"value":"colombian_arrests_gender_crimes = FileAttachment(\"colombian_arrests_gender_crimes_COMPLETE@1.json\").json()","pinned":false,"hidden":false,"mode":"ojs"}],"files":"$W3a","stdlib":"1","titling":"auto"}