2c:null 2d:["$","title",null,{"children":"SAE 303 (Groupe 14) | MMI Montbéliard | Observable"}] 32:T466,# 🚆 Tourisme en train : un enjeu de durabilité Le tourisme en France repose largement sur la voiture : 8 déplacements touristiques sur 10 se font encore par la route. Pourtant, le **train représente une alternative sobre en carbone**, fiable et déjà bien implantée sur une grande partie du territoire. **Problématique :** > Où le train est-il réellement compétitif pour le tourisme en France ? Nous cherchons à **croiser la vitalité touristique et la qualité du maillage ferroviaire**, afin d’identifier les territoires : - 🟢 *“Train-friendly”* : bien desservis et attractifs touristiquement, - 🟠 *“Opportunités”* : tourisme fort mais réseau à renforcer, - 🔴 *“Sous-desservis”* : tourisme élevé mais accessibilité ferroviaire faible. Notre démarche repose sur quatre visualisations : 1. Une **carte “Tourisme × Train”** (panorama spatial), 2. Un **classement comparatif** des territoires les plus touristiques, 3. Une **évolution temporelle (2015–2024)** de la fréquentation ferroviaire, 4. Un **simulateur CO₂** pour illustrer le gain environnemental concret. 33:T47a,Plot.plot({ width: width, height: Math.round(width * 0.7), projection: { type: "mercator", domain: deptsGeo }, color: { label: "Nuitées / 1000 hab", scheme: "blues", legend: true }, marks: [ Plot.geo(deptsGeo, { fill: (f) => { const p = f.properties || {}; const code = String(p.code || p.CODE_DEPT || p.code_dep || p.INSEE_DEP); return nuitees1000ByDep.get(code) ?? 0; }, stroke: "#111", tip: true, title: (f) => { const p = f.properties || {}; const code = String(p.code || p.CODE_DEPT || p.code_dep || p.INSEE_DEP); const n = nuitees1000ByDep.get(code) || 0; const i = IATR.get(code) || 0; return `${p.nom || code}\nNuitées/1000 hab: ${d3.format(".1f")( n )}\nIATR: ${d3.format(".1f")(i)} (${classeIATR(i)})`; } }), Plot.dot(pointsGares, { x: "lon", y: "lat", r: (d) => 2 + d.iatr / 3, fill: (d) => iatrColor(d.classe), stroke: "#111", title: (d) => `${d.name}\nIATR dep ${d.dep}: ${d3.format(".1f")(d.iatr)} (${ d.classe })` }) ] })34:T53b,

🌍 Bilan CO₂ – Aller / Retour de ${d*2} km

🚗 Voiture : ${d3.format(".1f")(car)} kg CO₂ ${p>1 ? `(≈ ${d3.format(".1f")(carPer)} / pers)` : ``}
🚆 Train : ${d3.format(".1f")(train)} kg CO₂ ${p>1 ? `(≈ ${d3.format(".1f")(trainPer)} / pers)` : ``}
💨 CO₂ évité : ${d3.format(".1f")(saved)} kg ${p>1 ? `(≈ ${d3.format(".1f")(savedPer)} / pers)` : ``}

Basé sur les facteurs moyens ADEME : 0.192 kg CO₂/km (voiture) · 0.014 kg CO₂/km (train)

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30:{"id":"9bdcd76719af83b8","slug":null,"title":"SAE 303 (Groupe 14)","thumbnail":"38869df274ec86b72eb4c707038e07cc1927ea178f9b60116fb977283fcbcb93","access_level":"unlisted","create_time":"$D2025-09-30T13:16:02.751Z","update_time":"$D2026-09-02T07:06:38.141Z","view_time":null,"first_public_time":"$D2025-09-30T13:40:00.578Z","has_unpublished_edits":null,"fork":false,"stars":0,"starred":false,"owner":{"type":"team","id":"29853dc62ffa3714","login":"mmi-montbeliard","name":"MMI Montbéliard","avatar_url":"https://avatars-next.observableusercontent.com/9297b8aeeda295f4db64a8d120219d0fff8dee572e0abb90500ea3732512b50b","tier":"pro_2024"},"version":729,"publish_version":null,"role":null,"authored":false,"authors":[{"type":"individual","id":"9e994ecabe6cd753","login":"xaviersenente","name":"Xavier SENENTE","avatar_url":"https://avatars-next.observableusercontent.com/00724f3264dc62ce6303793b946b15214af84cf98487a4cdaa88dbdfea37786a","tier":null}],"members":[{"type":"team","id":"29853dc62ffa3714","login":"mmi-montbeliard","name":"MMI Montbéliard","avatar_url":"https://avatars-next.observableusercontent.com/9297b8aeeda295f4db64a8d120219d0fff8dee572e0abb90500ea3732512b50b","tier":"pro_2024","role":"editor"},{"type":"individual","id":"9e994ecabe6cd753","login":"xaviersenente","name":"Xavier SENENTE","avatar_url":"https://avatars-next.observableusercontent.com/00724f3264dc62ce6303793b946b15214af84cf98487a4cdaa88dbdfea37786a","tier":null,"role":"editor"}],"comments":[{"id":"f6bd68c6e6d16540","reply_to_id":null,"content":"Il faut ajouter une légende pour la couleur des points.\nMettez les barres en plus foncé car on ne perçoit pas bien les points verts.\nIl aurait fallu ajouter un troisème axe en haut pour les chiffres sur l'IATR.","create_time":"$D2025-10-31T15:39:04.626Z","update_time":null,"resolved":false,"user":{"type":"individual","id":"9e994ecabe6cd753","login":"xaviersenente","name":"Xavier SENENTE","avatar_url":"https://avatars-next.observableusercontent.com/00724f3264dc62ce6303793b946b15214af84cf98487a4cdaa88dbdfea37786a","tier":null}}],"custom_thumbnail":false,"trashed":false,"lapsed":null,"subscribed":null,"forks":1,"collection":null,"type":"notebook","body":{"title":"SAE 303 (Groupe 14)","cells":[{"id":0,"value":"# SAE 303 (Groupe 14)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":7,"value":"viewof defi = Inputs.select(\n [null, \"Santé et Territoire\", \"Tourisme en train\", \"Offre culturelle\"],\n { label: \"Défi\" }\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":578,"value":"$32","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":580,"value":"## 🗺️ Carte “Tourisme × Train” : diagnostic spatial\n\nCette carte combine deux dimensions :\n- le **fond choroplèthe** montre l’intensité touristique (nuitées / 1000 habitants),\n- les **points colorés** représentent le niveau de desserte ferroviaire (indice IATR).\n\nChaque point correspond à une gare ; sa **taille** indique la performance du département en matière de rail, et sa **couleur** montre la classe de desserte :\n- 🟢 Bien desservi (IATR ≥ 66)\n- 🟠 Opportunité (33–66)\n- 🔴 Sous-desservi (<33)\n\nL’objectif est de voir d’un coup d’œil où le tourisme est fort et si le réseau ferroviaire suit.\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":371,"value":"legendIATR = Plot.legend({\n color: {\n type: \"ordinal\",\n domain: [\"🔴 Sous-dessert\", \"🟠 Opportunité\", \"🟢 Dessert bien\"],\n range: [\"#ef4444\", \"#fb923c\", \"#10b981\"]\n },\n label: \"Classe IATR (quantiles)\"\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":299,"value":"$33","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":585,"value":"### 🧩 Interprétation\n\n- Les zones littorales et alpines affichent souvent une forte intensité touristique, mais un maillage ferroviaire limité (🟠 ou 🔴). \n- Les métropoles et axes historiques (Paris–Lyon–Marseille, Grand Est, Bretagne) ressortent comme 🟢 “train-friendly”.\n- Certains territoires ruraux très touristiques (Ardèche, Dordogne, Lozère) apparaissent sous-desservis, révélant un **potentiel de report modal** important.\n\n### 📚 Sources\n- Données tourisme : INSEE / Atout France — *Nuitées par département (2023)* \n- Données population : INSEE — *Recensement 2021* \n- Données ferroviaires : SNCF Open Data — *Gares de voyageurs + fréquentation annuelle (2015–2024)* \n- Données géographiques : IGN / Géoportail — *Contours des départements (GeoJSON)* \n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":587,"value":"## 🧮 Classement comparatif : tourisme vs accessibilité rail\n\nCe graphique classe les départements selon leur **intensité touristique** (nuitées / 1000 hab). \nChaque barre bleue représente la force touristique, et le point coloré indique leur **IATR** (Indice d’Accessibilité Touristique par le Rail).\n\nL’idée : repérer les départements très touristiques dont l’IATR est faible, signalant un **gap d’accessibilité ferroviaire**.\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":377,"value":"viewof topN = Inputs.range([5, 50], {\n value: 20,\n step: 1,\n label: \"Top N par nuitées / 1000 hab\"\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":381,"value":"Plot.plot({\n width: 920,\n height: rowsBar.length * 24 + 60,\n marginLeft: 210,\n x: { label: \"Nuitées / 1000 hab\" },\n marks: [\n // barre bleue = intensité touristique\n Plot.barX(rowsBar, {\n y: \"nom\",\n x: \"nuitees_1000\",\n fill: \"#60a5fa\",\n tip: true,\n title: (d) =>\n `${d.nom}\\nNuitées / 1000 hab : ${d3.format(\".1f\")(d.nuitees_1000)}`\n }),\n // point coloré = IATR du département\n Plot.dot(rowsBar, {\n y: \"nom\",\n x: (d) => d.nuitees_1000 * (d.IATR / 100),\n r: 6,\n fill: (d) =>\n d.IATR >= _q66 ? \"#10b981\" : d.IATR >= _q33 ? \"#fb923c\" : \"#ef4444\",\n title: (d) => `IATR : ${d3.format(\".1f\")(d.IATR)} (${classeIATR(d.IATR)})`\n })\n ]\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":592,"value":"### 🧩 Lecture\n- Les départements côtiers du Sud et de l’Ouest figurent souvent parmi les plus touristiques, mais avec un IATR modéré. \n- À l’inverse, certaines régions urbaines (Île-de-France, Grand Est) combinent tourisme et forte accessibilité. \n- Cela permet de cibler les **territoires à potentiel ferroviaire**, où des navettes ou lignes secondaires pourraient renforcer l’offre durable.\n\n### 📚 Sources\n- Données tourisme : INSEE (hébergements marchands par département, 2023) \n- Données rail : SNCF Open Data (Fréquentation annuelle 2024) \n- Données population : INSEE (Recensement 2021)\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":601,"value":"## 📈 Évolution 2015 → 2024 : Rail vs Tourisme\n\nCe graphique compare, pour un département sélectionné, la **fréquentation annuelle des gares (2015–2024)** et le niveau touristique (nuitées). \nIl met en évidence si la fréquentation ferroviaire suit la tendance du tourisme.\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":394,"value":"viewof depSelect = Inputs.select(depOptions, {\n label: \"Choisir un département\",\n value: depOptions[0]?.value,\n format: (d) => d.label\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":401,"value":"Plot.plot({\n width: 900,\n height: 360,\n marginLeft: 100,\n y: { label: \"Voyageurs / Nuitées (échelle brute)\" },\n x: { label: \"Années\" },\n marks: [\n Plot.line(serieRail, {\n x: \"year\",\n y: \"value\",\n stroke: \"#8b5cf6\",\n tip: true,\n title: (d) => `Voyageurs ${d.year} : ${d3.format(\",\")(d.value)}`\n }),\n Plot.line(serieTourisme, {\n x: \"year\",\n y: \"value\",\n stroke: \"#fb923c\",\n opacity: 0.7,\n title: (d) => `Nuitées (réf) ${d.year} : ${d3.format(\",\")(d.value)}`\n })\n ]\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":603,"value":"### 🧩 Interprétation\n- Les grandes régions touristiques montrent une croissance parallèle entre tourisme et rail jusqu’à 2019, suivie d’une chute liée au COVID. \n- Après 2021, le rail retrouve son niveau pré-crise, souvent plus vite que le tourisme, traduisant un **report modal progressif**. \n- Dans certains départements ruraux, la stagnation du trafic ferroviaire malgré le rebond touristique souligne un **déséquilibre structurel**.\n\n### 📚 Sources\n- Données rail : SNCF Open Data — *Fréquentation annuelle des gares (2015–2024)* \n- Données tourisme : INSEE — *Nuitées par département* \n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":606,"value":"## 🌍 Simulateur CO₂ : un week-end en train plutôt qu’en voiture\n\nCe simulateur illustre concrètement le **gain environnemental** du train. \nPour un trajet type (200, 400 ou 700 km aller, soit un week-end A/R), on compare les émissions moyennes :\n\n| Mode | Facteur d’émission (kgCO₂/km) |\n|------|--------------------------------|\n| 🚗 Voiture thermique | 0.192 |\n| 🚆 Train (moyenne TER + grandes lignes) | 0.014 |\n\nL’utilisateur choisit la distance et le nombre de voyageurs ; le calcul affiche les émissions totales et le **CO₂ évité**.\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":474,"value":"viewof distance = Inputs.select(\n [\n { label: \"200 km\", value: 200 },\n { label: \"400 km\", value: 400 },\n { label: \"700 km\", value: 700 }\n ],\n { label: \"Distance aller (km)\", value: 400, format: (d) => d.label }\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":477,"value":"viewof pax = Inputs.range([1, 4], {\n value: 1,\n step: 1,\n label: \"Nombre de personnes\"\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":534,"value":"$34","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":610,"value":"### 🧩 Lecture\n- Un aller-retour de 400 km évite **~140 kgCO₂** pour une personne seule. \n- À plusieurs, le gain reste significatif, bien que le covoiturage amortisse partiellement la différence. \n- Ces ordres de grandeur permettent de **matérialiser le bénéfice environnemental** d’un tourisme ferroviaire.\n\n### 📚 Sources\n- Facteurs d’émission : ADEME — *Base Carbone (2023)* \n- Distance-type : hypothèses moyenne pour week-ends touristiques nationaux.\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":612,"value":"## ⚙️ Méthodologie de l’indice IATR\n\nL’IATR (Indice d’Accessibilité Touristique par le Rail) évalue la capacité d’un territoire à être desservi par le train **en lien avec son attractivité touristique**. \nIl est construit sur 4 composantes normalisées (0–100) :\n\n| Composante | Description | Pondération |\n|-------------|-------------|--------------|\n| Densité de gares (par km²) | maille ferroviaire | 25 % |\n| Gares / 100 000 hab | accessibilité population | 25 % |\n| Intensité rail (voyageurs/an / 100 000 hab) | usage réel du réseau | 30 % |\n| Couverture touristique (≈ % de lits ≤5 km d’une gare) | proximité hébergement–gare | 20 % |\n\nLes composantes sont normalisées par min–max, puis agrégées selon la pondération ci-dessus. \nEnfin, un **recalibrage global** est effectué pour que l’IATR s’étende sur 0–100, permettant des comparaisons équilibrées entre départements.\n\nSeuils dynamiques :\n- 🟢 IATR ≥ 66 : bien desservi \n- 🟠 33–66 : opportunité \n- 🔴 < 33 : sous-desservi\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":614,"value":"# ✳️ Conclusion : où renforcer le tourisme ferroviaire ?\n\nCette étude montre que :\n- Le train dessert déjà bien les grands pôles touristiques (Paris, Lyon, Strasbourg, Bordeaux), \n- Mais de **larges zones à fort potentiel touristique** (Sud-Ouest, Alpes, Massif central) restent sous-desservies,\n- Le **bénéfice environnemental** d’un report modal vers le rail est concret et mesurable.\n\n**Recommandations :**\n1. Développer les **navettes “dernier kilomètre”** entre gares et sites touristiques, \n2. Communiquer sur le **gain carbone concret** auprès du grand public, \n3. Soutenir la **saisonnalisation** des dessertes pour absorber les flux estivaux.\n\nLe train n’est pas seulement un moyen de transport : c’est une **clé du tourisme durable**.\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":618,"value":"# 📘 Annexe — Code & méthode\n\nCette annexe regroupe **tout le code** utilisé pour construire les 4 visualisations : \nchargement des données, harmonisation, indicateurs, indice IATR et structures prêtes à tracer.\n\nChaque section précise **à quoi elle sert**.\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":709,"value":"### Fonctions utilitaires\nPermettent de convertir les identifiants INSEE ou codes postaux en codes de département cohérents, \net de retrouver les bonnes colonnes d’année dans les fichiers de fréquentation des gares.\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":237,"value":"deptFromINSEE = (insee) =>\n insee\n ? String(insee).slice(\n 0,\n String(insee).startsWith(\"97\") || String(insee).startsWith(\"98\")\n ? 3\n : String(insee).startsWith(\"2A\") || String(insee).startsWith(\"2B\")\n ? 2\n : 2\n )\n : null","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":239,"value":"deptFromCP = (cp) => {\n if (cp == null) return null;\n const s = String(cp).padStart(5, \"0\"); // ← garde \"09xxx\"\n if (s.startsWith(\"97\") || s.startsWith(\"98\")) return s.slice(0, 3);\n if (s.startsWith(\"20\")) return null; // on évite d’agréger 2A/2B en \"20\"\n return s.slice(0, 2);\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":404,"value":"yearKey = (row, y) => {\n // essaie plusieurs conventions possibles\n return (\n row?.[`total_voyageurs_${y}`] ??\n row?.[`totalvoyageurs${y}`] ??\n row?.[`voyageurs_${y}`] ??\n row?.[`trafic_${y}`] ??\n row?.[`${y}`] ??\n 0\n );\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":622,"value":"## 1) Chargement des données (FileAttachment)\n**But :** importer tes jeux (tourisme, population, gares/frequentation, capacités, géo).\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":216,"value":"deptsGeo = await FileAttachment(\"departements@1.geojson\").json()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":229,"value":"nuiteesDep = await FileAttachment(\"NUITEES_PAR_DEP_2015_2024.json\").json()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":219,"value":"popDep = await FileAttachment(\"popraw.json\").json()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":221,"value":"gares = await FileAttachment(\"gares-de-voyageurs@1.json\").json()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":226,"value":"freq = await FileAttachment(\"frequentation-gares@1.json\").json()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":231,"value":"capHotels = await FileAttachment(\"CAPACITES_HOTELS.json\").json()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":233,"value":"capCamp = await FileAttachment(\"CAPACITES_CAMPINGS.json\").json()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":235,"value":"capAhct = await FileAttachment(\"CAPACITES_AHCT.json\").json()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":631,"value":"## 2) Harmonisation des clés (nom→code, surfaces) \n**But :** obtenir des clés **département** cohérentes partout + surface km² pour densités.\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":247,"value":"// On crée une table nom département -> code à partir du geojson\nnomToCodeDep = new Map(\n deptsGeo.features.map((f) => [\n (\n f.properties.nom ||\n f.properties.NOM ||\n f.properties.libelle\n ).toLowerCase(),\n String(\n f.properties.code || f.properties.CODE_DEPT || f.properties.INSEE_DEP\n )\n ])\n)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":249,"value":"// On mappe le JSON de population sur ces codes\npopByDep = new Map(\n popDep\n .map((d) => [nomToCodeDep.get(d[\"Géographie\"].toLowerCase()), +d[\"Valeur\"]])\n .filter(([code, val]) => code && !isNaN(val))\n)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":257,"value":"km2ByDep = new Map(\n deptsGeo.features.map((f) => {\n const p = f.properties || {};\n const code = String(p.code || p.CODE_DEPT || p.code_dep || p.INSEE_DEP);\n const areaKm2 = (d3.geoArea(f) * 6371e3 * 6371e3) / 1e6;\n return [code, areaKm2];\n })\n)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":647,"value":"## 3) Agrégats tourisme (nuitées) & intensité par habitant\n**But :** calculer `nuitées_total` et `nuitées / 1000 hab` par département.\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":293,"value":"nuiteesByDep = d3.rollup(\n nuiteesDep,\n (v) => {\n const hotel = d3.sum(v, (d) => +d.hotel_nuitees || 0);\n const camp = d3.sum(v, (d) => +d.camping_nuitees || 0);\n const autres = d3.sum(v, (d) => +d.autres_nuitees || 0);\n const total = hotel + camp + autres;\n const code = String(v[0].code_departement);\n const pop = popByDep.get(code) || 0;\n return { total, pop, nuitees_1000: pop ? (total / pop) * 1000 : 0 };\n },\n (d) => String(d.code_departement)\n)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":726,"value":"nuiteesByDepAndYear = d3.rollup(\n nuiteesDep,\n (v) =>\n d3.sum(\n v,\n (d) =>\n +(d.hotel_nuitees || 0) +\n +(d.camping_nuitees || 0) +\n +(d.autres_nuitees || 0)\n ),\n (d) => String(d.code_departement),\n (d) => +d.year\n)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":291,"value":"nuitees1000ByDep = new Map(\n [...nuiteesByDep].map(([dep, o]) => [dep, o.nuitees_1000 || 0])\n)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":651,"value":"## 4) Agrégats rail (gares, voyageurs) \n**But :** compter les gares, sommer les voyageurs, produire des ratios par km² / 100k hab.\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":245,"value":"garesByDep = d3.group(\n gares\n .map((g) => {\n const insee =\n g.codeinsee ??\n g.code_insee ??\n g.code_insee_commune ??\n g.insee ??\n g.code_commune_insee;\n return { ...g, dep: deptFromINSEE(insee) };\n })\n .filter((g) => g.dep),\n (g) => g.dep\n)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":253,"value":"nbGaresByDep = new Map([...garesByDep].map(([dep, list]) => [dep, list.length]))","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":259,"value":"gare_km2 = new Map(\n [...km2ByDep].map(([code, km2]) => [\n code,\n km2 ? (nbGaresByDep.get(code) || 0) / km2 : 0\n ])\n)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":261,"value":"gares_100k = new Map(\n [...popByDep].map(([code, pop]) => [\n code,\n pop ? ((nbGaresByDep.get(code) || 0) / pop) * 100000 : 0\n ])\n)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":263,"value":"rail_100k = new Map(\n [...popByDep].map(([code, pop]) => [\n code,\n pop ? ((voy2024ByDep.get(code) || 0) / pop) * 100000 : 0\n ])\n)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":391,"value":"depY = depSelect.value","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":704,"value":"### Agrégations et normalisation\nLes blocs suivants agrègent les données (population, gares, fréquentation) par département, \npuis normalisent les indicateurs pour construire l’indice IATR.\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":241,"value":"years = d3.range(2015, 2025)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":255,"value":"voy2024ByDep = new Map(\n d3\n .rollups(\n freq,\n (rows) => d3.sum(rows, (r) => +r.total_voyageurs_2024 || 0),\n (r) => deptFromCP(String(r.code_postal))\n )\n .filter(([dep]) => dep)\n)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":265,"value":"norm100 = (map) => {\n const vals = [...map.values()];\n const min = d3.min(vals) ?? 0,\n max = d3.max(vals) ?? 1,\n den = max - min || 1;\n const out = new Map();\n for (const [k, v] of map) out.set(k, ((v - min) / den) * 100);\n return out;\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":658,"value":"## 5) Couverture touristique proche d’une gare (proxy CP)\n**But :** estimer la part de lits (hôtels/campings/AHCT) situés dans des communes partageant un CP avec une gare.\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":273,"value":"cpsAvecGare = new Set(freq.map((r) => String(r.code_postal)))","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":277,"value":"capAll = [\n ...(capHotels ?? []).map((d) => ({\n cp: String(d.code_postal ?? d.cp ?? d.codePostal),\n places: +(d.places_total ?? d.places ?? d.capacite ?? 0)\n })),\n ...(capCamp ?? []).map((d) => ({\n cp: String(d.code_postal ?? d.cp ?? d.codePostal),\n places: +(d.places_total ?? d.places ?? d.capacite ?? 0)\n })),\n ...(capAhct ?? []).map((d) => ({\n cp: String(d.code_postal ?? d.cp ?? d.codePostal),\n places: +(d.places_total ?? d.places ?? d.capacite ?? 0)\n }))\n].filter((d) => d.cp && !Number.isNaN(d.places))","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":281,"value":"coverageByDep = new Map(\n d3\n .rollups(\n capAll,\n (v) => {\n const total = d3.sum(v, (d) => d.places);\n const near = d3.sum(\n v.filter((d) => cpsAvecGare.has(d.cp)),\n (d) => d.places\n );\n return total ? near / total : 0;\n },\n (d) => deptFromCP(d.cp)\n )\n .filter(([dep]) => dep) // garde seulement les deps valides\n)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":664,"value":"## 6) IATR (normalisation robuste + recalibrage)\n**But :** normaliser les 4 composantes, pondérer (25/25/30/20), puis **recaler** le score final sur 0–100.\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":306,"value":"median = (a) => {\n const b = [...a].sort((x, y) => x - y),\n n = b.length;\n return n ? (n % 2 ? b[(n - 1) / 2] : (b[n / 2 - 1] + b[n / 2]) / 2) : 0;\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":304,"value":"allDepCodes = new Set(\n deptsGeo.features.map((f) =>\n String(\n f.properties?.code ??\n f.properties?.CODE_DEPT ??\n f.properties?.INSEE_DEP ??\n f.properties?.code_dep\n )\n )\n)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":308,"value":"NormalizeTo100 = (\n mapIn,\n allDeps,\n { qLow = 0.05, qHigh = 0.95, transform = (x) => x } = {}\n) => {\n // 1) impute médiane sur ensemble complet des deps\n const tmp = new Map([...allDeps].map((k) => [k, mapIn.get(k)]));\n const valsRaw = [...tmp.values()].filter(Number.isFinite);\n const med = median(valsRaw.length ? valsRaw : [0]);\n for (const [k, v] of tmp) if (!Number.isFinite(v)) tmp.set(k, med);\n\n // 2) transform (ex: log1p) pour réduire l’asymétrie\n const tv = [...tmp].map(([k, v]) => [k, transform(v)]);\n\n // 3) quantiles pour écrêter extrêmes (winsorization)\n const onlyV = tv.map(([, v]) => v).sort((a, b) => a - b);\n const lo = d3.quantileSorted(onlyV, qLow);\n const hi = d3.quantileSorted(onlyV, qHigh);\n const den = hi - lo || 1;\n\n // 4) normalise 0..100 (après clamp)\n const out = new Map();\n for (const [k, v0] of tv) {\n const v = Math.min(Math.max(v0, lo), hi);\n out.set(k, ((v - lo) / den) * 100);\n }\n return out;\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":310,"value":"n_gare_km2 = NormalizeTo100(gare_km2, allDepCodes)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":313,"value":"n_gares_100k = NormalizeTo100(gares_100k, allDepCodes)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":316,"value":"n_rail_100k = NormalizeTo100(rail_100k, allDepCodes)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":318,"value":"n_coverage = NormalizeTo100(coverageByDep, allDepCodes)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":345,"value":"IATR = (() => {\n const vals = [...IATR_raw.values()]\n const min = d3.min(vals), max = d3.max(vals)\n if (!(max > min)) return new Map([...IATR_raw.keys()].map(k => [k, 50])) // sécurité\n const den = max - min\n return new Map([...IATR_raw].map(([k,v]) => [k, ((v - min) / den) * 100]))\n})()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":383,"value":"_q33 = d3.quantileSorted([...IATR.values()].slice().sort(d3.ascending), 0.33)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":385,"value":"_q66 = d3.quantileSorted([...IATR.values()].slice().sort(d3.ascending), 0.66)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":289,"value":"classeIATR = (v) =>\n v >= _q66\n ? \"🟢 Bien desservi\"\n : v >= _q33\n ? \"🟠 Opportunité\"\n : \"🔴 Sous-desservi\"","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":695,"value":"### Calcul de l’indice IATR\nLes quatre composantes (densité de gares, gares/100k hab, intensité rail, couverture touristique) \nsont combinées selon la pondération 25% / 25% / 30% / 20%.\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":283,"value":"IATR_raw = new Map(\n [...allDepCodes].map((code) => {\n const s =\n 0.25 * (n_gare_km2.get(code) || 0) +\n 0.25 * (n_gares_100k.get(code) || 0) +\n 0.3 * (n_rail_100k.get(code) || 0) +\n 0.2 * (n_coverage.get(code) || 0);\n return [code, s];\n })\n)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":699,"value":"### Échelles de couleur et listes de départements\nDéfinit la palette utilisée pour les classes IATR et les menus déroulants des graphiques.\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":358,"value":"iatrColor = d3\n .scaleOrdinal()\n .domain([\"🔴 Sous-desservi\", \"🟠 Opportunité\", \"🟢 Bien desservi\"])\n .range([\"#ef4444\", \"#fb923c\", \"#10b981\"])","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":434,"value":"depNameByCode = new Map(\n deptsGeo.features\n .filter((code) => !/^(97|98)/.test(code))\n .map((f) => [String(f.properties.code), String(f.properties.nom)])\n)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":465,"value":"depOptions = Array.from(nuiteesByDep.keys())\n .filter((code) => !/^(97|98|M|F)/.test(code)) // supprime DROM/COM\n .map((code) => ({\n label: `${depNameByCode.get(code) ?? code} (${code})`,\n value: code\n }))","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":675,"value":"## 7) Structures prêtes pour les graphes\n**But :** préparer les données affichées (cercles gares, tables top, séries temporelles).\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":295,"value":"pointsGares = Array.from(garesByDep, ([dep, list]) =>\n list.map((g) => {\n const pos = g.position_geographique ?? g.position ?? null;\n let lon =\n pos?.lon ??\n pos?.x ??\n g.longitude ??\n (Array.isArray(g.coordonnees_wgs84) ? g.coordonnees_wgs84[0] : null) ??\n (Array.isArray(g.geo_point_2d) ? g.geo_point_2d[1] : null);\n let lat =\n pos?.lat ??\n pos?.y ??\n g.latitude ??\n (Array.isArray(g.coordonnees_wgs84) ? g.coordonnees_wgs84[1] : null) ??\n (Array.isArray(g.geo_point_2d) ? g.geo_point_2d[0] : null);\n lon = lon != null ? +lon : NaN;\n lat = lat != null ? +lat : NaN;\n const iatr = IATR.get(dep) || 0;\n return {\n dep,\n name: g.nom ?? g.libelle ?? \"Gare\",\n lon,\n lat,\n iatr,\n classe: classeIATR(iatr)\n };\n })\n)\n .flat()\n .filter((d) => Number.isFinite(d.lon) && Number.isFinite(d.lat))","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":379,"value":"rowsBar = Array.from(nuiteesByDep, ([dep, o]) => ({\n dep,\n nom:\n deptsGeo.features.find((f) => {\n const p = f.properties || {};\n return String(p.code || p.CODE_DEPT || p.code_dep || p.INSEE_DEP) === dep;\n })?.properties?.nom || dep,\n nuitees_1000: o.nuitees_1000 || 0,\n IATR: IATR.get(dep) || 0\n}))\n .sort((a, b) => d3.descending(a.nuitees_1000, b.nuitees_1000))\n .slice(0, topN)\n .reverse()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":396,"value":"serieRail = years.map((y) => ({\n year: y,\n value: d3.sum(freq, (r) =>\n deptFromCP(r.code_postal) === depY ? +yearKey(r, y) : 0\n )\n}))","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":398,"value":"serieTourisme = years.map((y) => ({\n year: y,\n value: nuiteesByDepAndYear.get(depY)?.get(y) || 0\n}))","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":681,"value":"## 8) Facteurs d’émission CO₂ (simulateur)\n**But :** constants pour le calcul aller-retour (ordre de grandeur).\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":471,"value":"CO2 = ({ car_kg_km: 0.192, train_kg_km: 0.014 })","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":544,"value":"d = +distance.value","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":548,"value":"p = +pax","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":550,"value":"kmAR = d * 2","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":552,"value":"car = CO2.car_kg_km * kmAR","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":554,"value":"train = CO2.train_kg_km * kmAR","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":556,"value":"carPer = car / p","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":558,"value":"trainPer = train / p","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":560,"value":"saved = car - train","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":562,"value":"savedPer = carPer - trainPer","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W35","stdlib":"2","titling":"auto"}} 31:"$30:body"