Question

我正在寻找一种有效的方法来查找给定多边形的第一阶邻域。我的数据采用shapefile格式。

我的第一个想法是计算多边形质心的x和y坐标，以便找到邻居的质心。

import pysal
from pysal.common import *
import pysal.weights
import numpy as np
from scipy import sparse,float32
import scipy.spatial
import os, gc, operator


def get_points_array_from_shapefile(inFile):
    """
    Gets a data array of x and y coordinates from a given shape file

    Parameters
    ----------
    shapefile: string name of a shape file including suffix

    Returns
    -------
    points: array (n,2) a data array of x and y coordinates

    Notes
    -----
    If the given shape file includes polygons,
    this function returns x and y coordinates of the polygons' centroids

    Examples
    --------
    Point shapefile
    >>> from pysal.weights.util import get_points_array_from_shapefile
    >>> xy = get_points_array_from_shapefile('../examples/juvenile.shp')
    >>> xy[:3]
    array([[ 94.,  93.],
           [ 80.,  95.],
           [ 79.,  90.]])

    Polygon shapefile
    >>> xy = get_points_array_from_shapefile('../examples/columbus.shp')
    >>> xy[:3]
    array([[  8.82721847,  14.36907602],
           [  8.33265837,  14.03162401],
           [  9.01226541,  13.81971908]])

    (source: https://code.google.com/p/pysal/source/browse/trunk/pysal/weights/util.py?r=1013)

    """
    f = pysal.open(inFile)
    shapes = f.read()
    if f.type.__name__ == 'Polygon':
        data = np.array([shape.centroid for shape in shapes])
    elif f.type.__name__ == 'Point':
        data = np.array([shape for shape in shapes])
    f.close()
    return data


inFile = "../examples/myshapefile.shp"
my_centr = get_points_array_from_shapefile(inFile)

这种方法对于常规网格可能有效但在我的情况下，我需要找到“更通用”的解决方案。该图显示了问题。考虑黄色多边形有裁判。邻居的多边形是灰色多边形。使用质心 - 邻域方法，清晰的蓝色多边形被认为是邻居，但它与黄色多边形没有共同点。

从Efficiently finding the 1st order neighbors of 200k polygons修改的最新解决方案如下：

from collections import defaultdict
inFile = 'C:\\MultiShapefile.shp'

shp = osgeo.ogr.Open(inFile)
layer = shp.GetLayer()
BlockGroupVertexDictionary = dict()
for index in xrange(layer.GetFeatureCount()):
    feature = layer.GetFeature(index)
    FID = str(feature.GetFID())
    geometry = feature.GetGeometryRef()
    pts = geometry.GetGeometryRef(0)
    # delete last points because is the first (see shapefile polygon topology)
    for p in xrange(pts.GetPointCount()-1):
        PointText = str(pts.GetX(p))+str(pts.GetY(p))
        # If coordinate is already in dictionary, append this BG's ID
        if PointText in BlockGroupVertexDictionary:
            BlockGroupVertexDictionary[PointText].append(FID)
        # If coordinate is not already in dictionary, create new list with this BG's ID
        else:
            BlockGroupVertexDictionary[PointText] = [FID]

通过这个解决方案，我有一个以顶点坐标为键的字典和一个以该坐标为顶点的块组ID列表作为值。

>>> BlockGroupVertexDictionary
{'558324.3057036361423.57178': ['18'],
 '558327.4401686361422.40755': ['18', '19'],
 '558347.5890836361887.12271': ['1'],
 '558362.8645026361662.38757': ['17', '18'],
 '558378.7836876361760.98381': ['14', '17'],
 '558389.9225016361829.97259': ['14'],
 '558390.1235856361830.41498': ['1', '14'],
 '558390.1870856361652.96599': ['17', '18', '19'],
 '558391.32786361398.67786': ['19', '20'],
 '558400.5058556361853.25597': ['1'],
 '558417.6037156361748.57558': ['14', '15', '17', '19'],
 '558425.0594576362017.45522': ['1', '3'],
 '558438.2518686361813.61726': ['14', '15'],
 '558453.8892486362065.9571': ['3', '5'],
 '558453.9626046361375.4135': ['20', '21'],
 '558464.7845966361733.49493': ['15', '16'],
 '558474.6171066362100.82867': ['4', '5'],
 '558476.3606496361467.63697': ['21'],
 '558476.3607186361467.63708': ['26'],
 '558483.1668826361727.61931': ['19', '20'],
 '558485.4911846361797.12981': ['15', '16'],
 '558520.6376956361649.94611': ['25', '26'],
 '558525.9186066361981.57914': ['1', '3'],
 '558527.5061096362189.80664': ['4'],
 '558529.0036896361347.5411': ['21'],
 '558529.0037236361347.54108': ['26'],
 '558529.8873646362083.17935': ['4', '5'],
 '558533.062376362006.9792': ['1', '3'],
 '558535.4436256361710.90985': ['9', '16', '20'],
 '558535.4437266361710.90991': ['25'],
 '558548.7071816361705.956': ['9', '10'],
 '558550.2603156361432.56769': ['26'],
 '558550.2603226361432.56763': ['21'],
 '558559.5872216361771.26884': ['9', '16'],
 '558560.3288756362178.39003': ['4', '5'],
 '558568.7811926361768.05997': ['1', '9', '10'],
 '558572.749956362041.11051': ['3', '5'],
 '558573.5437016362012.53546': ['1', '3'],
 '558575.3048386362048.77518': ['2', '3'],
 '558576.189546362172.87328': ['5'],
 '558577.1149386361695.34587': ['7', '10'],
 '558579.0999636362020.47297': ['1', '3'],
 '558581.6312396362025.36096': ['0', '1'],
 '558586.7728956362035.28967': ['0', '3'],
 '558589.8015336362043.7987': ['2', '3'],
 '558601.3250076361686.30355': ['7'],
 '558601.3250736361686.30353': ['25'],
 '558613.7793476362164.19871': ['2', '5'],
 '558616.4062876361634.7097': ['7'],
 '558616.4063116361634.70972': ['25'],
 '558618.129066361634.29952': ['7', '11', '22'],
 '558618.1290896361634.2995': ['25'],
 '558626.9644156361875.47515': ['10', '11'],
 '558631.2229836362160.17325': ['2'],
 '558632.0261236361600.77448': ['25', '26'],
 '558639.495586361898.60961': ['11', '13'],
 '558650.4935686361918.91358': ['12', '13'],
 '558659.2473416361624.50945': ['8', '11', '22', '24'],
 '558664.5218136361857.94836': ['7', '10'],
 '558666.4126376361622.80343': ['8', '24'],
 '558675.1439056361912.52276': ['12', '13'],
 '558686.3385396361985.08892': ['0', '1'],
..................
.................
 '558739.4377836361931.57279': ['11', '13'],
 '558746.8758486361973.84475': ['11', '13'],
 '558751.3440576361902.20399': ['6', '11'],
 '558768.8067026361258.4715': ['26'],
 '558779.9170276361961.16408': ['6', '11'],
 '558785.7399596361571.47416': ['22', '24'],
 '558791.5596546361882.09619': ['8', '11'],
 '558800.2351726361877.75843': ['6', '8'],
 '558802.7700816361332.39227': ['26'],
 '558802.770176361332.39218': ['22'],
 '558804.7899976361336.78827': ['22'],
 '558812.9707376361565.14513': ['23', '24'],
 '558833.2667696361940.68932': ['6', '24'],
 '558921.2068976361539.98868': ['22', '23'],
 '558978.3570116361885.00604': ['23', '24'],
 '559022.80716361982.3729': ['23'],
 '559096.8905816361239.42141': ['22'],
 '559130.7573166361935.80614': ['23'],
 '559160.3907086361434.15513': ['22']}

enter image description here

Answer 1

对于OP或其他人偶然发现这仍然是一个悬而未决的问题。

import pysal as ps 
w = ps.queen_from_shapefile('shapefile.shp')

http://pysal.readthedocs.io/en/latest/users/tutorials/weights.html#pysal-spatial-weight-types

Answer 2

我不熟悉所使用的特定数据格式，但无论如何，请认为以下想法可行。

在Python中，您可以使用数字元组创建集合，即(x,y)和(x1,y1,x2,y2)，因此应该可以创建一个表示给定多边形中所有点或边的集合。之后，您将能够使用非常快速的设置交叉点操作来查找所有第一个订单邻居。

您可以使用某种微不足道的拒绝测试来加快这一过程，以避免进一步处理不可能是邻居的多边形 - 也许使用您的多边形的质心概念。

这种解释有意义吗？

使用shapefile多边形查找一阶邻居

2 个答案: