[英]Filter and merge points from two dataframe within a specific distance in Geopandas
對於兩個 GeoPandas 數據框如下:
df1:
id sMiddleLng sMiddleLat p1_sum p2_sum \
0 325782 109.255034 34.691754 0.0 0.0
1 84867 107.957177 33.958289 0.0 0.0
2 13101 107.835338 33.739493 0.0 0.0
3 92771 109.464280 33.980666 0.0 0.0
4 86609 108.253830 33.963262 0.0 0.0
geometry
0 POINT (109.255033915 34.69175367)
1 POINT (107.957177305 33.95828929)
2 POINT (107.8353377 33.73949313)
3 POINT (109.46428019 33.98066616)
4 POINT (108.253830245 33.96326193)
df2:
fnid sMiddleLng sMiddleLat p1_sum p2_sum \
0 361104 102.677887 36.686408 0.0 0.0
1 276307 103.268356 36.425372 0.0 0.0
2 334778 103.242125 36.605224 0.0 0.0
3 205223 104.186869 36.206637 0.0 0.0
4 167892 104.387566 36.091905 0.0 0.0
geometry
0 POINT (102.67788654685 36.68640780045)
1 POINT (103.26835590025 36.42537187675)
2 POINT (103.2421246007 36.60522388845)
3 POINT (104.1868687253 36.2066370049)
4 POINT (104.38756565315 36.0919047206)
如何根據id
和geometry
從另一個類似的 Geodataframe df2
中找到所有點並將其合並到df1
,這些點與df1
和df2
中的點之間的距離小於10 km
? 謝謝。
Function 計算距離:
from math import radians, cos, sin, asin, sqrt
def haversine(lon1, lat1, lon2, lat2):
"""
Calculate the great circle distance between two points
on the earth (specified in decimal degrees)
"""
# convert decimal degrees to radians
lon1, lat1, lon2, lat2 = map(radians, [lon1, lat1, lon2, lat2])
# haversine formula
dlon = lon2 - lon1
dlat = lat2 - lat1
a = sin(dlat/2)**2 + cos(lat1) * cos(lat2) * sin(dlon/2)**2
c = 2 * asin(sqrt(a))
r = 6371 # Radius of earth in kilometers. Use 3956 for miles
return c * r
我的建議遵循以下邏輯:
GeoDataFrame
重新投影到使用米作為單位的投影,例如 WebMercator這可以按如下方式實現:
# Assuming your data uses WGS84 projection. only use the following line if crs has not been initialised
df1.crs = df2.crs = {'init': 'epsg:4326'}
# Now convert the Dataframes to WebMercator
df2 = df2.to_crs({'init': 'epsg:3857'})
df1 = df1.to_crs({'init': 'epsg:3857'})
# Create a buffer with a radius of 10000 meters around each point in df2
df2.geometry = df2.geometry.buffer(10000)
# Join the two Dataframes and convert back to original projection
df3 = gpd.sjoin(df1, df2, how='left', op='intersects', lsuffix='_df1', rsuffix='_df2')
df3.to_crs({'init': 'epsg:4326'}) # or whatever was used originally
現在,您可以在一個方便的GeoDataFrame
中獲得有關連接點的信息。 在給定數據的情況下, df2
中的一個點在df1
的 10 公里范圍內沒有任何點。
另外,我不完全確定您希望以何種形式合並數據,因此只需相應地適應您的需求即可。
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