【python010】获取任意多边形区域内的经纬度点并可视化

1.熟悉、梳理、总结项目研发实战中的Python开发日常使用中的问题、知识点等，如获取任意多边形区域内的经纬度点并可视化，找了N篇文章没发现有效的解决方案。
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文章目录

- 1.获取任意多边形边界经纬度`json`数据
- 2.根据多边形区域生成区域内的坐标点数据`Python`代码
- 3.经纬度点集压缩`Python`代码
- 4.生成点位可视化`html`代码
- 5.多边形及区域内经纬度点集可视化效果

1.获取任意多边形边界经纬度`json`数据

方法一：http://www.guihuayun.com/maps/region_datav.php
方法二（高德平台）：https://lbs.amap.com/demo/javascript-api-v2/example/district-search/draw-district-boundaries

json数据坐标点位解析python代码示例

jsonp_480370_1717574386373_ = {"status": "1","info": "OK","infocode": "10000","count": "1","suggestion": {"keywords": [],"cities": []},"districts": [{"citycode": "0571","adcode": "330112","name": "临安区","polyline": "119.826803,30.134153;119.826136,30.133364;119.824951,30.133047;119.821774,30.132589;119.817282,30.131128;119.814503,30.130486;119.813499,30.129761;119.811872,30.126984;119.81059,30.125801;119.807812,30.124766;119.804677,30.122295;119.80324,30.12009;119.802538,30.117812;119.802746,30.114885;119.802495,30.112591;119.802087,30.111689;119.800962,30.111131;119.797126,30.11034;119.791012,30.107358;119.790137,30.106339;119.789781,30.105194;119.789997,30.099349;119.789391,30.098497","center": "119.724457,30.234375","level": "district","districts": []}]
}import geohash
import pandas as pdll_result = []
lnglats = []
for ii in jsonp_480370_1717574386373_['districts'][0]['polyline'].split(';'):lng, lat = ii.split(',')lnglats.append( [float(lng), float(lat)] )ll_result.append( [lng, lat, geohash.encode( float(lat), float(lng), 6 )] )ll_result_df = pd.DataFrame( ll_result )
ll_result_df.columns = ['lng', 'lat', 'geohash6']
ll_result_df.head(3)

边界数据可视化效果

2.根据多边形区域生成区域内的坐标点数据`Python`代码

polygon_coords = lnglats

# 导入必要的库
# pip install shapely
import numpy as np
from shapely.geometry import Point, Polygon# ================================================
# 经纬度坐标点 lnglats（list） 数组赋值
polygon_coords = lnglats # 定义多边形区域的顶点坐标
grid_interval = 0.005 # 定义格网的经纬度间隔# 定义格网范围
min_lon = min(polygon_coords, key=lambda x: x[0])[0]
max_lon = max(polygon_coords, key=lambda x: x[0])[0]
min_lat = min(polygon_coords, key=lambda x: x[1])[1]
max_lat = max(polygon_coords, key=lambda x: x[1])[1]# 划分格网
lon_range = np.arange(min_lon, max_lon, grid_interval)
lat_range = np.arange(min_lat, max_lat, grid_interval)polygon = Polygon(polygon_coords)
grid_points = []
for lon in lon_range:for lat in lat_range:
#         print( lon, lat )point = Point(lon, lat)
#         print( polygon.contains(point) )if polygon.contains(point): # 判断格网是否在多边形内grid_points.append((lon, lat))# 打印落入多边形内的经纬度坐标格网
for point in grid_points:print(point)

3.经纬度点集压缩`Python`代码

提示：如果经纬度点太多，可视化过程比较慢，可能导致浏览器崩溃。

import math
EARTH_RADIUS = 6371000 # 地球平均半径，单位：米
def haversine(lat1, lon1, lat2, lon2):"""计算两个经纬度点之间的距离（单位：米）"""lat1, lon1, lat2, lon2 = map(math.radians, [lat1, lon1, lat2, lon2]) # 将经纬度转换为弧度dlon = lon2 - lon1 # Haversine公式dlat = lat2 - lat1a = math.sin(dlat/2)**2 + math.cos(lat1) * math.cos(lat2) * math.sin(dlon/2)**2c = 2 * math.asin(math.sqrt(a))r = EARTH_RADIUSreturn c * rdef merge_points(points, threshold=10):"""合并距离在threshold（单位：米）以内的经纬度点"""merged_points = []for point in points:merged = Falsefor merged_point in merged_points:distance = haversine(point[0], point[1], merged_point[0], merged_point[1])if distance <= threshold:merged = True # 更新已合并的点（这里简单地取第一个点的经纬度）breakif not merged:merged_points.append(point)return merged_pointspoints = lnglats
merged_points = merge_points(points, threshold=500)
print(len(merged_points), len(lnglats))

4.生成点位可视化`html`代码

ak=tUUW8mROQP659ly3fqG7eOjRz2kku5xt 替换为自己的百度ak

<!DOCTYPE html>
<html>
<head><meta http-equiv="Content-Type" content="text/html; charset=utf-8" /><meta name="viewport" content="initial-scale=1.0, user-scalable=no" /><style type="text/css">body, html,#allmap {width: 100%;height: 100%;overflow: hidden;margin:0;font-family:"微软雅黑";}#l-map{height:100%;width:78%;float:left;border-right:2px solid #bcbcbc;}#r-result{height:100%;width:20%;float:left;}</style><script type="text/javascript" src="http://api.map.baidu.com/api?v=2.0&ak=tUUW8mROQP659ly3fqG7eOjRz2kku5xt"></script><title>任意多边形区域及内部经纬度点位可视化</title>
</head>
<body><div id="allmap"></div>
</body>
</html>
<script type="text/javascript">
// 经纬度数据对象
var pointDataBJ = [ ]; // 多边形区域边界经纬度点位集合
var pointData = [ ]; // 多边形内部经纬度点位集合
var pointD = new BMap.Point(119.79569599999995, 30.133075); // 经纬度对象
var map = new BMap.Map("allmap");  // 创建百度地图实例
map.enableScrollWheelZoom(); // 允许百度地图缩放
map.centerAndZoom(pointD, 1);  // 初始化地图级别和中心店
function showPointBJ (pointArr) {var myIconBJ = new BMap.Icon("./map_normal_bj.svg", new BMap.Size(50,50));pointArr.forEach(function(item, index){var point = new BMap.Point(item.longitude, item.latitude); // 经纬度对象var marker = new BMap.Marker(point, {icon: myIconBJ});  // 创建标注marker.mapDataIndex = index; // 将数据对象的索引存到对象中map.addOverlay(marker);  // 将标注添加到地图中marker.addEventListener("click",getAttr);});
}
function showPoint (pointArr) {var myIcon = new BMap.Icon("./map_normal.svg", new BMap.Size(50,50));pointArr.forEach(function(item, index){var point = new BMap.Point(item.longitude, item.latitude); // 经纬度对象var marker = new BMap.Marker(point, {icon: myIcon});  // 创建标注marker.mapDataIndex = index; // 将数据对象的索引存到对象中map.addOverlay(marker);  // 将标注添加到地图中marker.addEventListener("click",getAttr);});
}
// 调用的点击事件
function getAttr(){// maker对象调用this就会指向他var obj = this.getPosition();       //获取marker的位置alert("marker的位置是" + obj.lng + "," + obj.lat);alert("marker的索引是" + this.mapDataIndex);// window.location.href="http://www.baidu.com";   
}
showPointBJ(pointDataBJ);
showPoint(pointData);
</script>