Finding 1st order neighbors using shapefile polygons

Question

I am looking a efficient way to find the 1st order neighbors of a given polygon. My data are in shapefile format.

My first idea was to calculate the x and y coordinates of the polygons' centroids in order to find the neighbor's centroids.

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)

This approach could be valid for a regular grid but in my case, I need to find a "more general" solution. The figure shows the problem. Consider the yellow polygon has the referee. The neighbor's polygons are the gray polygons. Using the centroids-neighbors approach, the clear blue polygon is considered a neighbor but it doesn't have a side in common with the yellow polygon.

A recent solution modified from Efficiently finding the 1st order neighbors of 200k polygons can be the following:

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]

With this solution, I have a dictionary with vertex coordinates as the keys and a list of block group IDs that have a vertex at that coordinate as the value.

>>> 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']}

Answer 1

Just incase this is still an open question for the OP or someone else stumbles here.

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