将多边形与最近的邻居线连接起来，创建可切割的模板

Question

我的python脚本连接单独的多边形，带有基于最近的邻居算法的“连接器线”，以便我可以将它们切成 像这样的形状连接的多边形。

这可以正常工作，但我有三个问题：

• 这些线实际上是多边形之间最接近的连接不是。为什么该算法找不到红线（手动插入），而是错误黑线？请参阅此处：错误最近的邻居行 - 最近的点功能只能连接到节点但是不是要在多边形线上指向吗？那可以解释偏移吗？
• 要创建不太“摇摆”的稳定模板，我想添加其他连接线。显然需要一种非常不同的算法。我如何定义“颤抖”，找到这些连接的标准是什么，这些连接会使模板更稳定？我相信答案在于一种方法，可以找到多边形点之间的最短路径，然后将最短路径长度与直线连接的长度进行比较。然后，我会添加连接a）a）通过直接行驶和b）高于Treshold Inn总体“保存”而具有最高百分比。这将必须在迭代上进行，因为新连接将改变所有其他路径的计算。 I have found a number of interesting but complex algorithms that address the first part of the problem: finding a shortest path within one polygon:
• https://pdfs.semanticscholar.org/d59f/b891cac975a3b1d627e096916e352235235ff2.pdff.2.pdf2.pdf2.pdf2.pdf2.pdf
• 100 .1319＆amp = rep1＆amp = pdf“ rel =“ nofollow noreferrer”> https：//citeseerx.ist.ist.ist.psu.edu.edu.edu/viewdoc/viewdoc/download?doi=10.1.1.1.1.1.1.1.1.1.100.100.100.100.1319＆amp.empt = rep=rep=rep = rep1&.amp.tepepepepepepectepectectectectectectecte=pdf < /a>

3. 一个简单得多的问题：用三个多边形创建GeodataFrame的脚本的一部分似乎非常复杂。我相信有很多更简单的方法可以做到吗？

import osmnx as ox
import matplotlib.pyplot as plt
import geopandas as gpd
from shapely.geometry import Point, Polygon, LineString
from shapely.ops import nearest_points

box=(51.4616,51.4522, -0.1354, -0.1628)

p1lat=[51.4596626, 51.4580315, 51.4579446, 51.4589807, 51.4599099, 51.4595690, 51.4587935, 51.4578643, 51.4577373, 51.4590141, 51.4603845, 51.4603578, 51.4596626]
p1long=[-0.1544094,-0.1543021,-0.1536155,-0.1533473,-0.1532292,-0.1521242,-0.1519632,-0.1526713,-0.1520705,-0.1510084,-0.1519740,-0.1538301,-0.1544094]
p1=Polygon(zip(p1long,p1lat))
p2lat=[51.4582654, 51.4572092, 51.4566744, 51.4562599, 51.4561930, 51.4569953, 51.4585930, 51.4594553, 51.4594553, 51.4580983, 51.4569084, 51.4567813, 51.4569552, 51.4571758, 51.4579379, 51.4581652, 51.4582989, 51.4585796, 51.4587601, 51.4582654]
p2long=[-0.1493561,-0.1497531,-0.1492059,-0.1475108,-0.1460624,-0.1456332,-0.1454616,-0.1456439,-0.1460516,-0.1460302,-0.1463413,-0.1471460,-0.1477575,-0.1486588,-0.1486266,-0.1473176,-0.1467168,-0.1466846,-0.1468456,-0.1493561,]
p2=Polygon(zip(p2long,p2lat))
p3lat=[51.4608859, 51.4608424, 51.4604213, 51.4600904, 51.4600235, 51.4602241, 51.4603110, 51.4606385, 51.4606185, 51.4608859]
p3long=[-0.1481116,-0.1507562,-0.1511103,-0.1510674,-0.1480097,-0.1480311,-0.1504290,-0.1504987, -0.1480740,-0.1481116]
p3=Polygon(zip(p3long,p3lat))

polygons = gpd.GeoDataFrame(index=[0], geometry=[p1])
polygons=polygons.append(gpd.GeoDataFrame(index=[0], geometry=[p2]))
polygons=polygons.append(gpd.GeoDataFrame(index=[0], geometry=[p3]))
polygons.set_crs(epsg=4326, inplace=True)

def createcard(layer,lcolor, framewidth, connectorwidth):
  dflines = gpd.GeoDataFrame(columns=['ID','Location','geometry']) #connector lines
  dfframes =gpd.GeoDataFrame(columns=['ID','Location','geometry']) #frames
  df=layer
  df=df.dissolve().explode()
  df.insert(1,'nearest_geometry', None)
  df.insert(2,'queried_geometry', None)
  while df.shape[0]>1: #add connector lines to the dataframe, explode and dissolve until therev is only 1 polygon, i.e. everything is connected
    for index, row in df.iterrows():
        point = row.geometry
        multipoint = df.drop(index, axis=0).geometry.unary_union
        queried_geom, nearest_geom = nearest_points(point, multipoint)
        df.loc[index, 'nearest_geometry'] = nearest_geom
        df.loc[index, 'queried_geometry'] = queried_geom
        x=LineString([nearest_geom,queried_geom]).buffer(connectorwidth)
        dflines=dflines.append({'geometry' : x, 'ID':'2','Location':'test'},ignore_index=True)
    df=df.append(dflines).dissolve().explode() # add the connector lines to the df and make it one polygon 
  fig, ax = plt.subplots(figsize=(15,15))
  ox.plot.plot_footprints(df, ax=ax, bbox=box ,  color=lcolor, alpha=1, bgcolor='#FFFF', save=True, show=False, close=False)

Google COLAB笔记本带有代码：

Answer 1

连接线无关，但是我忽略了一些更基本的东西：预测。在上面的示例中，整个地图被扭曲了，但是我只通过查看添加的内容来注意到地图，连接器线。所有预测都扭曲了，您需要为您的目的选择合适的预测。我仍然需要了解有关预测的更多信息，但是FWIW最终以UTM CRS的0,0坐标为中心创建了一个自定义UTM CRS。