Vue结合leaflet实现克里金插值
作者:努力搬砖的giser
本文主要介绍了Vue结合leaflet实现克里金插值,文中通过示例代码介绍的非常详细,对大家的学习或者工作具有一定的参考学习价值,需要的朋友们下面随着小编来一起学习学习吧
本文介绍了Web端使用 Leaflet
开发库进行克里金插值的三种方法 (底图来源:天地图),分别结合了kriging、kriging-contour组件库实现克里金插值功能,效果如下图所示。
开发环境
- Vue开发库:3.2.37 & Leaflet开发库:1.9.3
- Leaflet主要插件:
turf
、kriging.js
、kriging-contour
代码简介
组件库简介
kriging.js
是一个开源的克里金插值算法组件库,核心方法如下:
// 使用gaussian、exponential或spherical模型对数据集进行训练,返回的是一个variogram对象; kriging.train(t, x, y, model, sigma2, alpha) // 使用variogram对象使polygons范围描述的地理位置内的格网元素具备不一样的预测值,width是插值格点精度大小 kriging.grid(polygons,variogram,width) // 将得到的格网grid渲染至canvas上。 kriging.plot(canvas,grid,xlim,ylim,colors):
kriging-contour
组件库是一个基于克里金插值算法,根据离散点位置及其权重,生成等值面矢量数据(GeoJSON格式)和栅格数据(Canvas绘制图片),这些数据在任何WebGIS客户端上都可通用展示。
安装依赖库
# 安装克里金插值所需的插件 npm i @turf/turf # 方法一 npm i @sakitam-gis/kriging # 方法二 npm i kriging-contour # 引入kriging.js import kriging from '@sakitam-gis/kriging'; # 引入kriging-contour import { getVectorContour, drawCanvasContour } from 'kriging-contour/dist/kriging-contour.js'
实现思路
首先划定一个区域,利用turfJS随机一部分数据(也可使用真实点状数据集),然后插件渲染空间插值至canvas或生成面状geojson,然后添加到地图容器中进行展示。
turf生成随机数据
// 随机点的边界(折线的最大包围盒坐标) const boundaries = turf.lineString([[110, 32], [118, 40], [120, 35]]); // 随机50个点状要素数据 let positionData = turf.randomPoint(50, { bbox: turf.bbox(boundaries) }); // 再生成些随机数做属性 turf.featureEach(positionData, function (currentFeature, featureIndex) { currentFeature.properties = { value: (Math.random() * 100).toFixed(2) }; });
方法一:kriging库插值
使用体验:在大量数据进行插值时,处理速度较慢;色带颜色如果较少,格网间颜色差异较大;插值的范围要按照特定的格式填入,否则无法加载。
核心代码
效果预览
方法二:kriging-contour插值(矢量)
使用体验:插值效果较为平滑;可以对插值效果进行裁剪,仅保留研究区的范围;颜色样式定制更方便一些。
核心代码
效果预览
方法三:kriging-contour插值(栅格)
使用体验:相比kriging库使用起来更简单,需要设置的参数更少,更容易上手;海量数据插值时,处理速度较慢;
核心代码
效果预览
详细源码
<template> <div class="app-contain"> <!-- leaflet 地图容器 --> <canvas id="canvasMap" style="display: none;"></canvas> <div id="myMap"></div> <div class="controls"> <el-button color="#626aef" @click="startKriging('kriging')">普通克里金</el-button> <el-button color="#626aef" @click="startKriging('Vector')">克里金矢量</el-button> <el-button color="#626aef" @click="startKriging('Image')">克里金图像</el-button> <el-button color="#626aef" @click="clearKriging()">清空</el-button> </div> </div> </template> <script setup> // 引入样式 import L from 'leaflet'; import 'leaflet/dist/leaflet.css' // 引入turf import * as turf from '@turf/turf' // 引入kriging.js import kriging from '@sakitam-gis/kriging'; // 引入kriging-contour import { getVectorContour, drawCanvasContour } from 'kriging-contour/dist/kriging-contour.js' // VUE组件 import { onMounted, reactive, ref } from "vue" // 天地图TK let tdtKey = 'YOURS_TK' let map = null; let featureLayerGroup = null; let imageLayerGroup = null; const initMap = () => { // 矢量地图 const tiandituMap = new L.TileLayer(`http://t0.tianditu.gov.cn/cva_c/wmts?layer=cva&style=default&tilematrixset=c&Service=WMTS&Request=GetTile&Version=1.0.0&Format=tiles&TileMatrix={z}&TileCol={x}&TileRow={y}&tk=${tdtKey}`, { tileSize: 512, noWrap: true, bounds: [[-90, -180], [90, 180]] }) // 文字注记 const tiandituText = new L.TileLayer(`http://t0.tianditu.com/vec_c/wmts?layer=vec&style=default&tilematrixset=c&Service=WMTS&Request=GetTile&Version=1.0.0&Format=tiles&TileMatrix={z}&TileCol={x}&TileRow={y}&tk=${tdtKey}`, { tileSize: 512, noWrap: true, bounds: [[-90, -180], [90, 180]] }) const layers = L.layerGroup([tiandituText, tiandituMap]) map = L.map('myMap', { //需绑定地图容器div的id center: [39.56, 116.20], //初始地图中心 crs: L.CRS.EPSG4326, zoom: 5, //初始缩放等级 maxZoom: 18, //最大缩放等级 minZoom: 0, //最小缩放等级 zoomControl: true, //缩放组件 attributionControl: false, //去掉右下角logol scrollWheelZoom: true, //默认开启鼠标滚轮缩放 // 限制显示地理范围 maxBounds: L.latLngBounds(L.latLng(-90, -180), L.latLng(90, 180)), layers: [layers] // 图层 }) // 矢量图层组 featureLayerGroup = new L.FeatureGroup().addTo(map).bringToFront() // 图像图层组 imageLayerGroup = new L.FeatureGroup().addTo(map).bringToFront() } const startKriging = (krigingType) => { // 随机点的边界(折线的最大包围盒坐标) const boundaries = turf.lineString([[110, 32], [118, 40], [120, 35]]); // 随机50个点状要素数据 let positionData = turf.randomPoint(50, { bbox: turf.bbox(boundaries) }); // 再生成些随机数做属性 turf.featureEach(positionData, function (currentFeature, featureIndex) { currentFeature.properties = { value: (Math.random() * 100).toFixed(2) }; }); if ('Vector' == krigingType) { showKrigingVector(boundaries, positionData); } else if ('Image' == krigingType) { showKrigingImage(boundaries, positionData) } else if ('kriging' == krigingType) { showKriging(boundaries, positionData) } } const showKriging = (boundaries, positionData) => { // 清空图层 clearKriging(); // 完全透明 let scope = L.geoJSON(boundaries, { style: function () { return { fillColor: '6666ff', color: 'red', weight: 2, opacity: 0, fillOpacity: 0, }; } }).addTo(imageLayerGroup); map.fitBounds(scope.getBounds()); //根据scope边界线,生成范围信息 let xlim = [scope.getBounds()._southWest.lng, scope.getBounds()._northEast.lng]; let ylim = [scope.getBounds()._southWest.lat, scope.getBounds()._northEast.lat]; function loadkriging(points) { let canvas = document.getElementById("canvasMap"); canvas.width = 2000; canvas.height = 1000; // 数量 let pointLength = points.features.length; let t = [];// 数值 let x = [];// 经度 let y = [];// 纬度 // 加载点数过多的话,会出现卡顿 for (let i = 0; i < pointLength; i++) { x.push(points.features[i].geometry.coordinates[0]); y.push(points.features[i].geometry.coordinates[1]); t.push(points.features[i].properties.value); // 将插值点展示到地图中,并添加提示文字(可删除,非核心代码) L.circle([y[i], x[i]], { radius: 1 }) .addTo(imageLayerGroup) .bindTooltip(points.features[i].properties.value, { permanent: true, //是永久打开还是悬停打开 direction: 'top' //方向 }).openTooltip(); } // 克里金插值参数 const params = { krigingModel: 'exponential',//model还可选'gaussian','spherical' krigingSigma2: 0, krigingAlpha: 100, canvasAlpha: 0.8,//canvas图层透明度-0.75 colors: ["#00A600", "#01A600", "#03A700", "#04A700", "#05A800", "#07A800", "#08A900", "#09A900", "#0BAA00", "#0CAA00", "#0DAB00", "#0FAB00", "#10AC00", "#12AC00", "#13AD00", "#14AD00", "#16AE00", "#17AE00", "#19AF00", "#1AAF00", "#1CB000", "#1DB000", "#1FB100", "#20B100", "#22B200", "#23B200", "#25B300", "#26B300", "#28B400", "#29B400", "#2BB500", "#2CB500", "#2EB600", "#2FB600", "#31B700", "#33B700", "#34B800", "#36B800", "#37B900", "#39B900", "#3BBA00", "#3CBA00", "#3EBB00", "#3FBB00", "#41BC00", "#43BC00", "#44BD00", "#46BD00", "#48BE00", "#49BE00", "#4BBF00", "#4DBF00", "#4FC000", "#50C000", "#52C100", "#54C100", "#55C200", "#57C200", "#59C300", "#5BC300", "#5DC400", "#5EC400", "#60C500", "#62C500", "#64C600", "#66C600", "#67C700", "#69C700", "#6BC800", "#6DC800", "#6FC900", "#71C900", "#72CA00", "#74CA00", "#76CB00", "#78CB00", "#7ACC00", "#7CCC00", "#7ECD00", "#80CD00", "#82CE00", "#84CE00", "#86CF00", "#88CF00", "#8AD000", "#8BD000", "#8DD100", "#8FD100", "#91D200", "#93D200", "#95D300", "#97D300", "#9AD400", "#9CD400", "#9ED500", "#A0D500", "#A2D600", "#A4D600", "#A6D700", "#A8D700", "#AAD800", "#ACD800", "#AED900", "#B0D900", "#B2DA00", "#B5DA00", "#B7DB00", "#B9DB00", "#BBDC00", "#BDDC00", "#BFDD00", "#C2DD00", "#C4DE00", "#C6DE00", "#C8DF00", "#CADF00", "#CDE000", "#CFE000", "#D1E100", "#D3E100", "#D6E200", "#D8E200", "#DAE300", "#DCE300", "#DFE400", "#E1E400", "#E3E500", "#E6E600", "#E6E402", "#E6E204", "#E6E105", "#E6DF07", "#E6DD09", "#E6DC0B", "#E6DA0D", "#E6D90E", "#E6D710", "#E6D612", "#E7D414", "#E7D316", "#E7D217", "#E7D019", "#E7CF1B", "#E7CE1D", "#E7CD1F", "#E7CB21", "#E7CA22", "#E7C924", "#E8C826", "#E8C728", "#E8C62A", "#E8C52B", "#E8C42D", "#E8C32F", "#E8C231", "#E8C133", "#E8C035", "#E8BF36", "#E9BE38", "#E9BD3A", "#E9BC3C", "#E9BB3E", "#E9BB40", "#E9BA42", "#E9B943", "#E9B945", "#E9B847", "#E9B749", "#EAB74B", "#EAB64D", "#EAB64F", "#EAB550", "#EAB552", "#EAB454", "#EAB456", "#EAB358", "#EAB35A", "#EAB35C", "#EBB25D", "#EBB25F", "#EBB261", "#EBB263", "#EBB165", "#EBB167", "#EBB169", "#EBB16B", "#EBB16C", "#EBB16E", "#ECB170", "#ECB172", "#ECB174", "#ECB176", "#ECB178", "#ECB17A", "#ECB17C", "#ECB17E", "#ECB27F", "#ECB281", "#EDB283", "#EDB285", "#EDB387", "#EDB389", "#EDB38B", "#EDB48D", "#EDB48F", "#EDB591", "#EDB593", "#EDB694", "#EEB696", "#EEB798", "#EEB89A", "#EEB89C", "#EEB99E", "#EEBAA0", "#EEBAA2", "#EEBBA4", "#EEBCA6", "#EEBDA8", "#EFBEAA", "#EFBEAC", "#EFBFAD", "#EFC0AF", "#EFC1B1", "#EFC2B3", "#EFC3B5", "#EFC4B7", "#EFC5B9", "#EFC7BB", "#F0C8BD", "#F0C9BF", "#F0CAC1", "#F0CBC3", "#F0CDC5", "#F0CEC7", "#F0CFC9", "#F0D1CB", "#F0D2CD", "#F0D3CF", "#F1D5D1", "#F1D6D3", "#F1D8D5", "#F1D9D7", "#F1DBD8", "#F1DDDA", "#F1DEDC", "#F1E0DE", "#F1E2E0", "#F1E3E2", "#F2E5E4", "#F2E7E6", "#F2E9E8", "#F2EBEA", "#F2ECEC", "#F2EEEE", "#F2F0F0", "#F2F2F2" ] } // 对数据集进行训练 let variogram = kriging.train(t, x, y, params.krigingModel, params.krigingSigma2, params.krigingAlpha); // 将插值范围封装成特定格式 let bbox = turf.bbox(boundaries); // 外包矩形范围 // 根据外包矩形范围生成外包矩形面Polygon let bboxPolygon = turf.bboxPolygon(bbox); let positions = []; bboxPolygon.geometry.coordinates[0].forEach((v) => { positions.push([v[0], v[1]]) }) // 将边界封装成特定的格式 let range = [positions] // 使用variogram对象使polygons描述的地理位置内的格网元素具备不一样的预测值,最后一个参数,是插值格点精度大小 let grid = kriging.grid(range, variogram, 0.05); // 将得到的格网grid渲染至canvas上 kriging.plot(canvas, grid, [xlim[0], xlim[1]], [ylim[0], ylim[1]], params.colors); } //将canvas对象转换成image的URL function returnImgae() { let mycanvas = document.getElementById("canvasMap"); return mycanvas.toDataURL("image/png"); } // 执行克里金插值函数 loadkriging(positionData); let imageBounds = [[ylim[0], xlim[0]], [ylim[1], xlim[1]]]; L.imageOverlay(returnImgae(), imageBounds, { opacity: 0.8 }).addTo(imageLayerGroup); } // 生成矢量等值面并渲染 const showKrigingVector = (boundaries, positionData) => { // 清空图层 clearKriging(); // 展点(可删除) L.geoJSON(positionData, { pointToLayer: function (feature, latlng) { return L.circleMarker(latlng, { radius: 5, fillColor: '#6666ff', fillOpacity: 1, color: "#fff", weight: 2, }); }, onEachFeature(feature, layer) { // 显示文字 let content = feature.properties.value // marker的icon文字 let myIcon = L.divIcon({ html: `<div style="white-space: nowrap;color:#6666ff;">${content}</div>`, iconAnchor: [0, 0], className: 'my-div-icon', iconSize: 120 }); let featureCenter = L.latLng(feature.geometry.coordinates[1], feature.geometry.coordinates[0]); featureLayerGroup.addLayer(L.marker(featureCenter, { icon: myIcon })); } }).addTo(featureLayerGroup) // 颜色色带 let colors = [{ fill: "#ffdc84" }, { fill: "#ffd782" }, { fill: "#ffd281" }, { fill: "#ffcd7f" }, { fill: "#ffc87e" }, { fill: "#ffc37c" }, { fill: "#ffbe7a" }, {fill: "#ffb979"}, {fill: "#feb477"},{fill: "#feaf76"}, {fill: "#feaa74"}, {fill: "#fea573"}, {fill: "#fea071"}, {fill: "#fe9b6f"}, {fill: "#fe966e"}, {fill: "#fe906c"}, {fill: "#fe8b6b"}, {fill: "#fe8669"}, {fill: "#fe8167"}, {fill: "#fe7c66"}, {fill: "#fe7764"}, {fill: "#fe7263"}, {fill: "#fd6d61"}, {fill: "#fd6860"}, {fill: "#fd635e"}, {fill: "#fd5e5c"}, {fill: "#fd595b"}, {fill: "#fd5459"}, {fill: "#fd4f58"}, {fill: "#fd4a56"}] // 等级分级 let levelV = [0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 210, 220, 250, 260, 270, 280, 290, 300, 400]; let kriging_contours = getVectorContour(positionData, 'value', { model: 'exponential', sigma2: 0, alpha: 100 }, levelV, boundaries); // 展示生成的矢量等值面 L.geoJSON(kriging_contours, { style: function (feature) { return { fillColor: hotColor(feature.properties.value), weight: 0, fillOpacity: 0.3, }; } }).addTo(featureLayerGroup); // 根据值来配色 function hotColor(d) { let index = levelV.findIndex((item) => item >= d); if (index > -1) { return colors[index].fill } else { return colors[colors.length - 1].fill } } } // 生成图像等值面并渲染 const showKrigingImage = (boundaries, positionData) => { // 清空图层 clearKriging(); // 完全透明 let scope = L.geoJSON(boundaries, { style: function () { return { fillColor: '6666ff', color: 'red', weight: 2, opacity: 0, fillOpacity: 0, }; } }).addTo(imageLayerGroup); map.fitBounds(scope.getBounds()); //根据scope边界线,生成范围信息 let xlim = [scope.getBounds()._southWest.lng, scope.getBounds()._northEast.lng]; let ylim = [scope.getBounds()._southWest.lat, scope.getBounds()._northEast.lat]; // 色带 let colors = ["#006837", "#1a9850", "#66bd63", "#a6d96a", "#d9ef8b", "#ffffbf", "#fee08b", "#fdae61", "#f46d43", "#d73027", "#a50026"] // 画布 let canvas = document.getElementById("canvasMap"); canvas.width = 1000; canvas.height = 1000; let kriging_contours = drawCanvasContour(positionData, 'value', { model: 'exponential', sigma2: 0, alpha: 100 }, canvas, [xlim[0], xlim[1]], [ylim[0], ylim[1]], colors); //将canvas对象转换成image的URL function returnImgae() { let mycanvas = document.getElementById("canvasMap"); return mycanvas.toDataURL("image/png"); } let imageBounds = [[ylim[0], xlim[0]], [ylim[1], xlim[1]]]; L.imageOverlay(returnImgae(), imageBounds, { opacity: 0.9 }).addTo(imageLayerGroup); } // 清空图层 const clearKriging = () => { imageLayerGroup.clearLayers(); featureLayerGroup.clearLayers(); } onMounted(() => { initMap(); }) </script> <style scoped> #myMap { width: 96vw; height: 96vh; } .controls { position: absolute; top: 0px; left: 200px; padding: 15px; z-index: 1000; } </style>
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