Vue结合leaflet实现克里金插值
更新时间：2023年06月26日 15:27:58 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>到此这篇关于Vue结合leaflet实现克里金插值的文章就介绍到这了,更多相关Vue leaflet克里金插值内容请搜索脚本之家以前的文章或继续浏览下面的相关文章希望大家以后多多支持脚本之家！
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