openlayers6结合echarts4实现迁徙图

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效果图如下:
技术图片

参考GitHub来实现的,更详细的源码以及参数说明见:GitHub

  • 本篇文章的html源码:
<!DOCTYPE html>
<html>
<head>
<title>openlayers6结合echarts4实现迁徙图</title>
<link rel="stylesheet" href="lib/ol.css">
<script src="lib/ol.js"></script>
<script src="lib/echarts.js"></script>
<script src="lib/ol-echarts.js"></script>
<!--<script src="https://cdn.jsdelivr.net/npm/echarts/dist/echarts.js"></script>
<script src="https://unpkg.com/ol-echarts/dist/ol-echarts.js"></script>-->
<style>
html, body, #map {
height: 100%;
margin: 0;
padding: 0;
}
</style>
</head>
<body>
<div id="map"></div>
<script>
/**
* 地图创建初始化
*/
var map = new ol.Map({
target: ‘map‘,
layers: [
new ol.layer.Tile({
source: new ol.source.XYZ({
url: ‘http://cache1.arcgisonline.cn/arcgis/rest/services/ChinaOnline‘ +
‘StreetPurplishBlue/MapServer/tile/{z}/{y}/{x}‘
})
})
],
view: new ol.View({
center: [116.55406673632812, 39.94828066015626],
projection: ‘EPSG:4326‘,
zoom: 10
})
});
 
//迁徙图图层初始化
var echartslayer = new EChartsLayer(getOption());
echartslayer.appendTo(map)
function getOption () {
var geoCoordMap = {
‘上海‘: [121.4648, 31.2891],
‘东莞‘: [113.8953, 22.901],
‘东营‘: [118.7073, 37.5513],
‘中山‘: [113.4229, 22.478],
‘临汾‘: [111.4783, 36.1615],
‘临沂‘: [118.3118, 35.2936],
‘丹东‘: [124.541, 40.4242],
‘丽水‘: [119.5642, 28.1854],
‘乌鲁木齐‘: [87.9236, 43.5883],
‘佛山‘: [112.8955, 23.1097],
‘保定‘: [115.0488, 39.0948],
‘兰州‘: [103.5901, 36.3043],
‘包头‘: [110.3467, 41.4899],
‘北京‘: [116.4551, 40.2539],
‘北海‘: [109.314, 21.6211],
‘南京‘: [118.8062, 31.9208],
‘南宁‘: [108.479, 23.1152],
‘南昌‘: [116.0046, 28.6633],
‘南通‘: [121.1023, 32.1625],
‘厦门‘: [118.1689, 24.6478],
‘台州‘: [121.1353, 28.6688],
‘合肥‘: [117.29, 32.0581],
‘呼和浩特‘: [111.4124, 40.4901],
‘咸阳‘: [108.4131, 34.8706],
‘哈尔滨‘: [127.9688, 45.368],
‘唐山‘: [118.4766, 39.6826],
‘嘉兴‘: [120.9155, 30.6354],
‘大同‘: [113.7854, 39.8035],
‘大连‘: [122.2229, 39.4409],
‘天津‘: [117.4219, 39.4189],
‘太原‘: [112.3352, 37.9413],
‘威海‘: [121.9482, 37.1393],
‘宁波‘: [121.5967, 29.6466],
‘宝鸡‘: [107.1826, 34.3433],
‘宿迁‘: [118.5535, 33.7775],
‘常州‘: [119.4543, 31.5582],
‘广州‘: [113.5107, 23.2196],
‘廊坊‘: [116.521, 39.0509],
‘延安‘: [109.1052, 36.4252],
‘张家口‘: [115.1477, 40.8527],
‘徐州‘: [117.5208, 34.3268],
‘德州‘: [116.6858, 37.2107],
‘惠州‘: [114.6204, 23.1647],
‘成都‘: [103.9526, 30.7617],
‘扬州‘: [119.4653, 32.8162],
‘承德‘: [117.5757, 41.4075],
‘拉萨‘: [91.1865, 30.1465],
‘无锡‘: [120.3442, 31.5527],
‘日照‘: [119.2786, 35.5023],
‘昆明‘: [102.9199, 25.4663],
‘杭州‘: [119.5313, 29.8773],
‘枣庄‘: [117.323, 34.8926],
‘柳州‘: [109.3799, 24.9774],
‘株洲‘: [113.5327, 27.0319],
‘武汉‘: [114.3896, 30.6628],
‘汕头‘: [117.1692, 23.3405],
‘江门‘: [112.6318, 22.1484],
‘沈阳‘: [123.1238, 42.1216],
‘沧州‘: [116.8286, 38.2104],
‘河源‘: [114.917, 23.9722],
‘泉州‘: [118.3228, 25.1147],
‘泰安‘: [117.0264, 36.0516],
‘泰州‘: [120.0586, 32.5525],
‘济南‘: [117.1582, 36.8701],
‘济宁‘: [116.8286, 35.3375],
‘海口‘: [110.3893, 19.8516],
‘淄博‘: [118.0371, 36.6064],
‘淮安‘: [118.927, 33.4039],
‘深圳‘: [114.5435, 22.5439],
‘清远‘: [112.9175, 24.3292],
‘温州‘: [120.498, 27.8119],
‘渭南‘: [109.7864, 35.0299],
‘湖州‘: [119.8608, 30.7782],
‘湘潭‘: [112.5439, 27.7075],
‘滨州‘: [117.8174, 37.4963],
‘潍坊‘: [119.0918, 36.524],
‘烟台‘: [120.7397, 37.5128],
‘玉溪‘: [101.9312, 23.8898],
‘珠海‘: [113.7305, 22.1155],
‘盐城‘: [120.2234, 33.5577],
‘盘锦‘: [121.9482, 41.0449],
‘石家庄‘: [114.4995, 38.1006],
‘福州‘: [119.4543, 25.9222],
‘秦皇岛‘: [119.2126, 40.0232],
‘绍兴‘: [120.564, 29.7565],
‘聊城‘: [115.9167, 36.4032],
‘肇庆‘: [112.1265, 23.5822],
‘舟山‘: [122.2559, 30.2234],
‘苏州‘: [120.6519, 31.3989],
‘莱芜‘: [117.6526, 36.2714],
‘菏泽‘: [115.6201, 35.2057],
‘营口‘: [122.4316, 40.4297],
‘葫芦岛‘: [120.1575, 40.578],
‘衡水‘: [115.8838, 37.7161],
‘衢州‘: [118.6853, 28.8666],
‘西宁‘: [101.4038, 36.8207],
‘西安‘: [109.1162, 34.2004],
‘贵阳‘: [106.6992, 26.7682],
‘连云港‘: [119.1248, 34.552],
‘邢台‘: [114.8071, 37.2821],
‘邯郸‘: [114.4775, 36.535],
‘郑州‘: [113.4668, 34.6234],
‘鄂尔多斯‘: [108.9734, 39.2487],
‘重庆‘: [107.7539, 30.1904],
‘金华‘: [120.0037, 29.1028],
‘铜川‘: [109.0393, 35.1947],
‘银川‘: [106.3586, 38.1775],
‘镇江‘: [119.4763, 31.9702],
‘长春‘: [125.8154, 44.2584],
‘长沙‘: [113.0823, 28.2568],
‘长治‘: [112.8625, 36.4746],
‘阳泉‘: [113.4778, 38.0951],
‘青岛‘: [120.4651, 36.3373],
‘韶关‘: [113.7964, 24.7028]
};
var BJData = [
[{name: ‘北京‘}, {name: ‘上海‘, value: 95}],
[{name: ‘北京‘}, {name: ‘广州‘, value: 90}],
[{name: ‘北京‘}, {name: ‘大连‘, value: 80}],
[{name: ‘北京‘}, {name: ‘南宁‘, value: 70}],
[{name: ‘北京‘}, {name: ‘南昌‘, value: 60}],
[{name: ‘北京‘}, {name: ‘拉萨‘, value: 50}],
[{name: ‘北京‘}, {name: ‘长春‘, value: 40}],
[{name: ‘北京‘}, {name: ‘包头‘, value: 30}],
[{name: ‘北京‘}, {name: ‘重庆‘, value: 20}],
[{name: ‘北京‘}, {name: ‘常州‘, value: 10}]
];
var SHData = [
[{name: ‘上海‘}, {name: ‘包头‘, value: 95}],
[{name: ‘上海‘}, {name: ‘昆明‘, value: 90}],
[{name: ‘上海‘}, {name: ‘广州‘, value: 80}],
[{name: ‘上海‘}, {name: ‘郑州‘, value: 70}],
[{name: ‘上海‘}, {name: ‘长春‘, value: 60}],
[{name: ‘上海‘}, {name: ‘重庆‘, value: 50}],
[{name: ‘上海‘}, {name: ‘长沙‘, value: 40}],
[{name: ‘上海‘}, {name: ‘北京‘, value: 30}],
[{name: ‘上海‘}, {name: ‘丹东‘, value: 20}],
[{name: ‘上海‘}, {name: ‘大连‘, value: 10}]
];
var GZData = [
[{name: ‘广州‘}, {name: ‘福州‘, value: 95}],
[{name: ‘广州‘}, {name: ‘太原‘, value: 90}],
[{name: ‘广州‘}, {name: ‘长春‘, value: 80}],
[{name: ‘广州‘}, {name: ‘重庆‘, value: 70}],
[{name: ‘广州‘}, {name: ‘西安‘, value: 60}],
[{name: ‘广州‘}, {name: ‘成都‘, value: 50}],
[{name: ‘广州‘}, {name: ‘常州‘, value: 40}],
[{name: ‘广州‘}, {name: ‘北京‘, value: 30}],
[{name: ‘广州‘}, {name: ‘北海‘, value: 20}],
[{name: ‘广州‘}, {name: ‘海口‘, value: 10}]
];
var planePath = ‘path://M1705.06,1318.313v-89.254l-319.9-221.799l0.073-208.063c0.521-84.662-26.629-121.796-63.961-121.491c-37.332-0.305-64.482,36.829-63.961,121.491l0.073,208.063l-319.9,221.799v89.254l330.343-157.288l12.238,241.308l-134.449,92.931l0.531,42.034l175.125-42.917l175.125,42.917l0.531-42.034l-134.449-92.931l12.238-241.308L1705.06,1318.313z‘;
var convertData = function (data) {
var res = [];
for (var i = 0; i < data.length; i++) {
var dataItem = data[i];
var fromCoord = geoCoordMap[dataItem[0].name];
var toCoord = geoCoordMap[dataItem[1].name];
if (fromCoord && toCoord) {
res.push({
fromName: dataItem[0].name,
toName: dataItem[1].name,
coords: [fromCoord, toCoord]
});
}
}
return res;
};
var color = [‘#a6c84c‘, ‘#ffa022‘, ‘#46bee9‘];
var series = [];
[
[‘北京‘, BJData], [‘上海‘, SHData], [‘广州‘, GZData]].forEach(
function (item, i) {
series.push({
name: item[0] + ‘ Top10‘,
type: ‘lines‘,
zlevel: 1,
effect: {
show: true,
period: 6,
trailLength: 0.7,
color: ‘#fff‘,
symbolSize: 3
},
lineStyle: {
normal: {
color: color[i],
width: 0,
curveness: 0.2
}
},
data: convertData(item[1])
},
{
name: item[0] + ‘ Top10‘,
type: ‘lines‘,
zlevel: 2,
effect: {
show: true,
period: 6,
trailLength: 0,
symbol: planePath,
symbolSize: 15
},
lineStyle: {
normal: {
color: color[i],
width: 1,
opacity: 0.4,
curveness: 0.2
}
},
data: convertData(item[1])
},
{
name: item[0] + ‘ Top10‘,
type: ‘effectScatter‘,
coordinateSystem: ‘geo‘,
zlevel: 2,
rippleEffect: {
brushType: ‘stroke‘
},
label: {
normal: {
show: true,
position: ‘right‘,
formatter: ‘{b}‘
}
},
symbolSize: function (val) {
return val[2] / 8;
},
itemStyle: {
normal: {
color: color[i]
}
},
data: item[1].map(function (dataItem) {
return {
name: dataItem[1].name,
value: geoCoordMap[dataItem[1].name].concat([dataItem[1].value])
};
})
});
});
return {
tooltip: {
trigger: ‘item‘
},
/*title: {
text: ‘模拟迁徙图‘,
subtext: ‘‘,
left: ‘center‘,
textStyle: {
color: ‘#fff‘
}
},*/
series: series
};
}
 
</script>
</body>
</html>

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