25:["$","title",null,{"children":"Impact environnemental du secteur agricole par départements | penguinos | Observable"}] 49:Tc5f,Les données des parcelles sont fournies par le site du gouvernement Français [geoservices.ign.fr/](https://geoservices.ign.fr/) tandis que les données de l'impact environnemental de chaque culture sont fournies par le site [ourworldindata.org](https://ourworldindata.org). Les données des parcelles de la France métropolitaine (données de l'année 2021) ont été téléchargées au format GPKG (GeoPackage) et occupent un volume d'à peu près 7GB. En passant ces données au format JSON, nous avons obtenu un fichier d'un volume de 10GB. Un traitement des données GPKG était ainsi nécessaire avant de les sauvegarder sous un format final (CSV semble à priori le plus approprié pour sa compression). Avec Python, la librairie GeoPandas a été utilisée pour lire le fichier GPKG sous forme d'un DataFrame sur lequel on peut exécuter les requêtes classiques de Pandas. Ce DataFrame contient presque 10 millions de ligne et chaque ligne correspond à une parcelle. Les colonnes quand à elles concernent les parcelles (le code de la culture principale de la parcelle, les cordonnées des points du polygone qui la définit, sa superficie etc.). Pour notre application, il nous faut les données géographiques qui sont stockées comme (longitude, latitude) des points d'un objet MultiPolygon, desquelles les tuples sont directement extrayables grâce à la librairie Shapely. Nous avons ainsi pu obtenir pour chaque parcelle, sa superficie, la culture principale qui est cultivée et les coordonnées du polygone qui la définit. Cependant un problème subsistait toujours : notre RAM n'est pas suffisante pour afficher tous les polygones. Le choix a donc été fait de calculer les centroïdes de chaque parcelle et de ne garder que cela comme donnée, les polygones étant des formes beaucoup trop lourdes à stocker. Cela n'étant toujours pas suffisant, une sélection des cultures a été réalisées et les types de culture dont l'impact écologique global était très minoritaires ont été retirés. Entre cela et le retrait des parcelles non agricoles dans la base de données, le nombre de parcelles est passé de 10 millions à 3.5 millions. La compression est encore meilleure en réduisant le nombre de chiffres significatifs et en changeant l'encodage (float32 au lieu de float64 par exemple). Cela conduit à un fichier CSV final contenant 3.5 millions de parcelles et occupant un volume de 132MB, avec comme données stockées les coordonnées (longitude, latitude) des centroïdes des parcelles, leur impact (qui est égal à la superficie de la parcelle multipliée par le rendement annuel de la culture principale qui y est cultivée multipliée par l'émission CO2 de cette culture, et donne donc pour chaque parcelle son impact en kg de CO2 émis). Pour finir, l'agrégation par départements a été réalisée en prétraitement pour faciliter les calculs du front sur cette quantité de données restant importante. Cette agrégation permet de comparer les différents départements de France en terme de kg de CO2 émis par hectare de surface agricole, et de voir les types de cultures les plus émissifs par département.4a:T100b,projectionData = ({ "Ain": [[4.902211, 46.107346], 4500], "Aisne": [[3.466667, 49.5], 19000], "Allier": [[3.202778, 46.083333], 9000], "Alpes-de-Haute-Provence": [[6.116667, 44.1], 12000], "Hautes-Alpes": [[6.166667, 44.683333], 11000], "Alpes-Maritimes": [[7.166667, 43.933333], 6000], "Ardèche": [[4.3, 44.583333], 9000], "Ardennes": [[4.7, 49.6], 16000], "Ariège": [[1.6, 42.966667], 11000], "Aube": [[4.083333, 48.333333], 13000], "Aude": [[2.25, 43.166667], 10000], "Aveyron": [[2.583333, 44.333333], 8000], "Bouches-du-Rhône": [[5.166667, 43.4], 7000], "Calvados": [[-0.333333, 49.166667], 16000], "Cantal": [[2.75, 45.166667], 10000], "Charente": [[0.25, 45.75], 10000], "Charente-Maritime": [[-0.75, 45.833333], 8000], "Cher": [[2.25, 47.166667], 13000], "Corrèze": [[1.75, 45.333333], 10000], "Corse-du-Sud": [[8.916667, 41.833333], 6000], "Haute-Corse": [[9.166667, 42.416667], 6000], "Côte-d'Or": [[4.833333, 47.333333], 10000], "Côtes-d'Armor": [[-2.75, 48.5], 10000], "Creuse": [[2.166667, 46.5], 12000], "Dordogne": [[0.666667, 45.166667], 8000], "Doubs": [[6.25, 47.166667], 11000], "Drôme": [[5.166667, 44.75], 8000], "Eure": [[0.916667, 49.083333], 11000], "Eure-et-Loir": [[1.416667, 48.333333], 12000], "Finistère": [[-4.166667, 48.333333], 7000], "Gard": [[4.083333, 43.833333], 9000], "Haute-Garonne": [[1.416667, 43.5], 8000], "Gers": [[0.583333, 43.666667], 12000], "Gironde": [[-0.5, 44.833333], 7000], "Hérault": [[3.25, 43.5], 8000], "Ille-et-Vilaine": [[-1.5, 48.166667], 10000], "Indre": [[1.5, 46.666667], 12000], "Indre-et-Loire": [[0.666667, 47.166667], 12000], "Isère": [[5.5, 45.166667], 7000], "Jura": [[5.833333, 46.833333], 11000], "Landes": [[-0.75, 43.916667], 10000], "Loir-et-Cher": [[1.333333, 47.5], 12000], "Loire": [[4.166667, 45.75], 8000], "Haute-Loire": [[3.833333, 45.166667], 11000], "Loire-Atlantique": [[-1.5, 47.25], 8000], "Loiret": [[2, 47.833333], 12000], "Lot": [[1.5, 44.5], 12000], "Lot-et-Garonne": [[0.5, 44.333333], 11000], "Lozère": [[3.416667, 44.333333], 13000], "Maine-et-Loire": [[-0.5, 47.333333], 10000], "Manche": [[-1.25, 49], 12000], "Marne": [[4.166667, 49], 11000], "Haute-Marne": [[5.25, 47.833333], 11000], "Mayenne": [[-0.583333, 48.25], 12000], "Meurthe-et-Moselle": [[6.166667, 48.5], 11000], "Meuse": [[5.25, 49], 12000], "Morbihan": [[-2.75, 47.666667], 10000], "Moselle": [[6.75, 49.166667], 8000], "Nièvre": [[3.5, 47], 12000], "Nord": [[3.166667, 50.5], 5000], "Oise": [[2.5, 49.5], 10000], "Orne": [[-0.166667, 48.583333], 12000], "Pas-de-Calais": [[2.5, 50.5], 6000], "Puy-de-Dôme": [[3.25, 45.75], 8000], "Pyrénées-Atlantiques": [[-0.666667, 43.25], 9000], "Hautes-Pyrénées": [[0.166667, 43.166667], 12000], "Pyrénées-Orientales": [[2.5, 42.5], 10000], "Bas-Rhin": [[7.5, 48.5], 8000], "Haut-Rhin": [[7.333333, 47.666667], 8000], "Rhône": [[4.75, 45.75], 9000], "Haute-Saône": [[6.166667, 47.5], 12000], "Saône-et-Loire": [[4.5, 46.666667], 10000], "Sarthe": [[0.333333, 47.75], 12000], "Savoie": [[6.25, 45.583333], 10000], "Haute-Savoie": [[6.333333, 46], 10000], "Paris": [[2.333333, 48.833333], 35000], "Seine-Maritime": [[1, 49.5], 9000], "Seine-et-Marne": [[2.666667, 48.666667], 9000], "Yvelines": [[1.833333, 48.75], 11000], "Deux-Sèvres": [[-0.416667, 46.5], 12000], "Somme": [[2, 50], 11000], "Tarn": [[2, 44], 12000], "Tarn-et-Garonne": [[1, 44], 12000], "Var": [[6.166667, 43.416667], 8000], "Vaucluse": [[5, 44], 10000], "Vendée": [[-1.166667, 46.666667], 10000], "Vienne": [[0.5, 46.416667], 12000], "Haute-Vienne": [[1.5, 45.833333], 10000], "Vosges": [[6.666667, 48.333333], 9000], "Yonne": [[3.5, 47.833333], 10000], "Territoire de Belfort": [[6.833333, 47.666667], 18000], "Essonne": [[2.25, 48.5], 12000], "Hauts-de-Seine": [[2.25, 48.833333], 16000], "Seine-Saint-Denis": [[2.5, 48.916667], 16000], "Val-de-Marne": [[2.5, 48.666667], 16000], "Val-d'Oise": [[2, 49.083333], 12000] }) 4b:T3268,{ const width = 700 const height = 580 const svg = d3.create("svg") var last_region_selected = "" svg.attr("width", width) .attr("height", height) var tooltip = d3.select('body').append('div') .attr('class', 'hidden tooltip'); const g = svg.append("g") var projection = d3.geoConicConformal() .center([2.454071, 46.279229]) .scale(2800) var path = d3.geoPath().projection(projection) const dataArray = Object.entries(json_aggregated); const minValueObject = d3.min(dataArray, d => d[1][0]); const maxValueObject = d3.max(dataArray, d => d[1][0]); /* var color_aggregated = d3 .scaleLinear() .range(["#edf8e9", "#006d2c"]) .domain([minValueObject, maxValueObject]) .clamp(true); */ var color_aggregated = d3 .scaleLinear() .range(["#FFD267", "#FF7D67"]) .domain([minValueObject, maxValueObject]) .clamp(true); function format_emission(emission) { return emission + " kg de CO2/ha" } function getSelectValues(select) { var result = []; var options = select && select.options; var opt; for (var i=0, iLen=options.length; i d.region == region_name && codes_selected.includes(mapping_code_culture[d.code_culture])); var color = d3 .scaleLinear() .range(["#edf8e9", "#006d2c"]) .domain([ d3.min(data, function (d) { return d.value; }), d3.max(data, function (d) { return d.value; }) ]) .clamp(true); // Legend var legend_colors_range = d3.scaleLinear() .domain(color.domain()) .nice() .ticks(5); const points_legend = d3.selectAll(".points-legend") var legendRects = points_legend.selectAll(".legend-rects") .data(legend_colors_range) .enter() .append("rect") .attr("x", 10) .attr("y", function(d, i) { return i * 30 + 10; }) .attr("width", 20) .attr("height", 20) .style("fill", function(d) { return color(d); }); var legendLabels = points_legend.selectAll("text") .data(legend_colors_range) .enter() .append("text") .attr("x", 40) .attr("y", function(d, i) { return i * 30 + 25; }) .text(function(d) { return d + "kg de CO2"; }); // Déterminer la taille de chaque sous-tableau var batchSize = Math.ceil(filtered_data.length / 100); var numBatches = Math.ceil(filtered_data.length / batchSize); // Diviser le tableau de données en sous-tableaux à l'aide de d3.group() var nestedData = Array.from(d3.group(filtered_data, (d, i) => Math.floor(i / batchSize)).values()); for (let dataBatchIndice in nestedData) { g_circles.selectAll(".production-plots-" + dataBatchIndice) .data(nestedData[dataBatchIndice]) .join("circle") .attr("cx", d => region_projection([d.lon, d.lat])[0]) .attr("cy", d => region_projection([d.lon, d.lat])[1]) .attr("r", d => { // var delta_lat = Math.sqrt((d.surface / Math.PI)) / 1105.74; // var center = projection(d.lat, d.lon) // var side = projection(d.lat - delta_lat, d.lon) // return Math.abs(center[0] - side[0]) return 0.5; }) .style("fill", function (d) { //on prend la valeur recupere plus haut var value = d.value; if (value) { return color(value); } else { // si pas de valeur alors en gris return "#eee"; } }) .style("stroke", function (d) { //on prend la valeur recupere plus haut var value = d.value; if (value) { return color(value); } else { // si pas de valeur alors en gris return "#eee"; } }) } } //On fusionne les donnees avec le GeoJSON for (let region in json_aggregated) { //Valeur associee a l'etat var dataValue = parseFloat(json_aggregated[region][0]); var top_3 = json_aggregated[region][1]; //Recherche de l'etat dans le GeoJSON for (var j = 0; j < json.features.length; j++) { var jsonState = json.features[j].properties.nom; if (region == jsonState) { //On injecte la valeur de l'Etat dans le json json.features[j].properties.value = dataValue; json.features[j].properties.ranks = top_3; var ranks_html = ""; for (let rank of top_3) { ranks_html += '

' + mapping_code_culture[rank[0]] + '

' + format_emission(rank[1]) + '

'; } json.features[j].properties.ranks_html = '

' + ranks_html + '

'; //Pas besoin de chercher plus loin break; } } } g.selectAll(".map") .data(json.features) .join("path") .attr("d", path) .style("fill", function (d) { //on prend la valeur recupere plus haut var value = d.properties.value; return color_aggregated(value); }) .style("stroke", "#ccc") .attr("class", "plot-region") .on('mouseenter', function(e, d) { if (!last_region_selected) { // Hover var hoveredPath = d3.select(this); var otherPaths = d3.selectAll("path").filter(function() { return this !== hoveredPath.node(); }); otherPaths.attr('opacity', 0.3) } // Hist const svg_2 = d3.create("svg") var width_2 = 400 var height_2 = 300 svg_2.attr("width", width_2) .attr("height", height_2) const margin = ({top: 20, right: 20, bottom: 50, left: 100}) const y = d3.scaleLinear() .domain([0, d3.max(d.properties.ranks.map(d => d[1]))]) .nice() .range([height_2 - margin.bottom, margin.top]) const x = d3.scaleBand() .domain(d.properties.ranks.map(d => d[0])) .range([margin.left, width_2 - margin.right]) svg_2.selectAll("rect") .data(d.properties.ranks) .enter() .append("rect") .attr("x", (d, i) => x(d[0]) + 15) .attr("y", (d, i) => y(d[1])) // data[i] .attr("width", x.bandwidth() - 30) .attr("height", (d, i) => height_2 - margin.bottom - y(d[1])) .attr("fill", "#8B4513") const xAxis = g => g .attr("transform", `translate(0, ${height_2 - margin.bottom})`) .call(d3.axisBottom(x)) svg_2.append("text") .attr("font-size", '12px') .attr("text-anchor", "end") .attr("x", width_2 - margin.right) .attr("y", height_2 - 1) .text("Culture"); const yAxis = g => g .attr("transform", `translate(${margin.left}, 0)`) .call(d3.axisLeft(y)) svg_2.append("text") .attr("font-size", '12px') .attr("text-anchor", "end") .attr("y", 6) .attr("dy", "0.3em") .attr("dx", "-2em") .attr("transform", "rotate(-90)") .text("kg de CO2 émis"); svg_2.append("g") .call(xAxis); svg_2.append("g") .call(yAxis); tooltip.selectAll("*").remove(); // Supprime tous les enfants actuels tooltip.append("h1").text(d.properties.nom); tooltip.append("p").text("Total : " + format_emission(d.properties.value)); tooltip.node().appendChild(svg_2.node()); // Ajoute l'élément SVG à l'intérieur }) .on('mousemove', function(e, d) { // on recupere la position de la souris, // e est l'object event d var mousePosition = [e.x, e.y]; d3.select(this).style("cursor", "pointer"); // on affiche le toolip tooltip.classed('hidden', false) // on positionne le tooltip en fonction // de la position de la souris .attr('style', 'left:' + (mousePosition[0] + 15) + 'px; top:' + (mousePosition[1] - 35) + 'px') // on recupere le nom de l'etat //.html("

" + d.properties.nom + "

" + '

' + format_emission(d.properties.value) + '

' + d.properties.ranks_html) }) .on('mouseout', function() { var hoveredPath = d3.select(this); var otherPaths = d3.selectAll("path").filter(function() { return this !== hoveredPath.node(); }); otherPaths.attr('opacity', 1) // on cache le toolip et la légende des couleurs aggregées d3.select(this).style("cursor", "default"); tooltip.classed('hidden', true); }) .on('click', function (e, d) { var element = document.getElementById("g_circles"); if(element) { // Department selected element.remove(); } if (d.properties.nom != last_region_selected) { drawPoints(g, full_data, d.properties.nom, getSelectValues(select), projectionData); last_region_selected = d.properties.nom; // Zoom on department var projection = d3.geoConicConformal() .center(projectionData[d.properties.nom][0]) .scale(projectionData[d.properties.nom][1]) // remove aggregation legend d3.select(".aggregation-legend").classed('hidden', true) d3.select(".points-legend").classed('hidden', false); } else { last_region_selected = ""; // Unzoom var projection = d3.geoConicConformal() .center([2.454071, 46.279229]) .scale(2800); // Aggregation data legend d3.select(".aggregation-legend").classed('hidden', false); d3.select(".points-legend").classed('hidden', true); } path = d3.geoPath().projection(projection) g.selectAll("path") .attr("d", path); }); select.addEventListener("input", function() { var element = document.getElementById("g_circles"); if(element) { element.remove(); } drawPoints(g, full_data, last_region_selected, getSelectValues(select), projectionData); }); // Legends const legend = svg.append("g").attr("class", "aggregation-legend") var legend_colors_range = d3.scaleLinear() .domain(color_aggregated.domain()) .nice() .ticks(5); var legendRects = legend.selectAll("rect") .data(legend_colors_range) .enter() .append("rect") .attr("x", 10) .attr("y", function(d, i) { return i * 30 + 10; }) .attr("width", 20) .attr("height", 20) .style("fill", function(d) { return color_aggregated(d); }); var legendLabels = legend.selectAll("text") .data(legend_colors_range) .enter() .append("text") .attr("x", 40) .attr("y", function(d, i) { return i * 30 + 25; }) .text(function(d) { return format_emission(d); }); const pointsLegend = svg.append("g").attr("class", "points-legend hidden") pointsLegend.append("rect") .attr("x", 5) .attr("y", 5) .attr("width", 170) .attr("height", 185) .style("fill", "white"); return svg.node() 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