Mapping 📍

Another Example of GeoPandas and Python Spatial Joins 🗺

Yesterday, I posted a much more complicated piece of code that pulled addresses from the SAM (State Address Management) database and did a spatial join to add a column to the file with Assembly District and Municipality. This was a bit too complex, so I made a simpler one for other purposes that doesn’t require the coordinates to be obtained from SAM.

This python script takes two parameters:

• Path to a CSV file that contains an X and Y coordinate
• Path to a Shapefile or Geopackage to Join Against

Then the code will create a new CSV file with the spatially joined attributes pulled from the Shapefile. I have only run it on a few large data sets, but I found it took roughly 1 second to join 1,000 records from call to end of end of script.

#!/usr/bin/python

import requests,sys,json,os,csv

import pandas as pd
import geopandas as gpd

lines=[]

# read list of addresses from parameter 1
with open(sys.argv[-2], newline='') as csvfile:	
	for line in csv.DictReader(csvfile):
		lines.append(line)

# convert to pandas
locPd = pd.DataFrame(lines,columns=lines[0].keys())
locPd.convert_dtypes()

locPd = gpd.GeoDataFrame(locPd,  geometry=gpd.points_from_xy(locPd.x.astype('float32'), locPd.y.astype('float32')))

# run spatial joins against parameter 2
ad = gpd.read_file(sys.argv[-1])
locPd = gpd.sjoin(locPd, ad, op="within")

# remove added geometery and index columns
del locPd['geometry']
del locPd['index_right']

# write pandas back to out csv
locPd.to_csv (os.path.splitext(sys.argv[-2])[0]+'-output.csv', index = False, header=True)