Question

我正在尝试在Python3中使用匀称的，fiona和bokeh来创建一个Choropleth。

我有一个大约有7000行的文件，其中有一个城镇和一个柜台的位置。

示例：

54.7604;9.55827;208
54.4004;9.95918;207
53.8434;9.95271;203
53.5979;10.0013;201
53.728;10.2526;197
53.646;10.0403;196
54.3977;10.1054;193
52.4385;9.39217;193
53.815;10.3476;192
...

我想在一个12.5公里的网格中显示这些图像，为此可以使用shapefile https://opendata-esri-de.opendata.arcgis.com/datasets/3c1f46241cbb4b669e18b002e4893711_0

我的代码有效。

这非常慢，因为它是一种蛮力算法，可以将7000个点中的所有7127个网格点进行检查。

import pandas as pd
import fiona
from shapely.geometry import Polygon, Point, MultiPoint, MultiPolygon
from shapely.prepared import prep
sf = r'c:\Temp\geo_de\Hexagone_125_km\Hexagone_125_km.shp'
shp = fiona.open(sf)

district_xy = [ [ xy for xy in feat["geometry"]["coordinates"][0]] for feat in shp] 
district_poly = [ Polygon(xy) for xy in district_xy] # coords to Polygon

df_p = pd.read_csv('points_file.csv', sep=';', header=None)
df_p.columns = ('lat', 'lon', 'count')

map_points = [Point(x,y) for x,y in zip(df_p.lon, df_p.lat)] # Convert Points to Shapely Points

all_points = MultiPoint(map_points) # all points

def calc_points_per_poly(poly, points, values): # Returns total for poly
    poly = prep(poly)
    return sum([v for p, v in zip(points, values) if poly.contains(p)])

# this is the slow part
# for each shape this sums um the points

sum_hex = [calc_points_per_poly(x, all_points, df_p['count']) for x in district_poly]

由于这太慢了，我想知道是否有一种更快的方法来获取 num_hex 值，特别是因为现实世界中的点列表可能要大得多且网格要小形状越多效果越好。

Answer 1

我建议使用“ geopandas”及其内置的rtree空间索引。仅当点可能位于多边形内时，它才允许您进行检查。

import pandas as pd
import geopandas as gpd
from shapely.geometry import Polygon, Point

sf = 'Hexagone_125_km.shp'
shp = gpd.read_file(sf)

df_p = pd.read_csv('points_file.csv', sep=';', header=None)
df_p.columns = ('lat', 'lon', 'count')

gdf_p = gpd.GeoDataFrame(df_p, geometry=[Point(x,y) for x,y in zip(df_p.lon, df_p.lat)])

sum_hex = []
spatial_index = gdf_p.sindex

for index, row in shp.iterrows():
    polygon = row.geometry
    possible_matches_index = list(spatial_index.intersection(polygon.bounds))
    possible_matches = gdf_p.iloc[possible_matches_index]
    precise_matches = possible_matches[possible_matches.within(polygon)]
    sum_hex.append(sum(precise_matches['count']))

shp['sum'] = sum_hex

此解决方案应该比您的解决方案快。然后，您可以通过散景绘制GeoDataFrame。如果您想了解有关空间索引的更多详细信息，我建议Geoff Boeing撰写这篇文章：https://geoffboeing.com/2016/10/r-tree-spatial-index-python/

整形的Python：聚集点以形成Choropleth映射的形状文件

1 个答案: