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reverse_geocoder.py
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reverse_geocoder.py
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#Import libraries
from geopy.geocoders import Nominatim
from geopy.exc import GeocoderTimedOut
from geopy.extra.rate_limiter import RateLimiter
from geopy.point import Point
import pandas as pd
import geopandas as gpd
import numpy as np
#reads in National Address Database data for the Bronx (36005) - 'county_data'
file_path = '../dspg23_reverse_geocoder/Data/36005.csv'
county_data = pd.read_csv(file_path)
#Prints number of missing addresses in 'county_data'
print("No. of missing Addresses:", sum(county_data['address'].isna()))
#Creates Reverse geocoder tool
#Input: df w/ a 'latitude' and 'longitude' column, Output: same df with updated 'address' column
def reverse_geocode_tool(df):
#Creates Geo column: combines latitude and longitude
df["Geo"] = df["latitude"].astype(str)+ ',' + df["longitude"].astype(str)
geolocator = Nominatim(user_agent="reverse_geocoder", timeout=10)
rgeocode = RateLimiter(geolocator.reverse, min_delay_seconds=0.001)
#Reverse geocodes 'Geo' column and populates 'address' column with results
df['address'] = df['Geo'].apply(rgeocode)
return df
#'Missing_data': df containing all the rows of 'county_data' that have missing values in 'address' columns
missing_data = county_data[county_data['address'].isna()]
#Reads in shape file - 'county_shape'
shape_file_path = '../dspg23_reverse_geocoder/Data/tl_2020_36005_tabblock20/tl_2020_36005_tabblock20.shp'
county_shape = gpd.read_file(shape_file_path)
#Creates 'centroid' column based on 'geometry' - gives us latitude and longitude values
county_shape['centroid'] = county_shape['geometry'].centroid
#Converts 'GEOID20' column to int, so it can be merged with 'missing_data'
county_shape['GEOID20']= county_shape['GEOID20'].astype(int)
#Merges 'missing_data' with 'county_shape' on 'GEOID20' column (left join)
merged_df = missing_data.merge(county_shape, on='GEOID20', how="left")
merged_df = merged_df.drop(['STATEFP20', 'COUNTYFP20', 'TRACTCE20', 'BLOCKCE20', 'NAME20', 'MTFCC20', 'UR20', 'UACE20', 'UATYPE20', 'FUNCSTAT20', 'ALAND20', 'AWATER20', 'INTPTLAT20', 'INTPTLON20', 'geometry'], axis=1)
#Poulates 'latitude' and 'longitude' columns of 'merged_df' with latitude and longitude vals from 'centroid'
for i in merged_df.index:
point = merged_df['centroid'][i]
lat = point.y
long = point.x
merged_df['latitude'][i] = lat
merged_df['longitude'][i] = long
merged_df = merged_df.drop('centroid', axis=1)
#Reverse geocodes 'merged_df'
reverse_geocode_tool(merged_df)
merged_df = merged_df.drop('Geo', axis=1)
#Checks if 'address' column in 'bronx_data' is NaN
#if so, updates 'address', 'latitude', and 'longitude' columns of 'county_data'
#with 'address', 'latitude', and 'longitude' columns of 'merged_df', based on 'GEOID20'
for i in county_data.index:
if pd.isna(county_data['address'][i]):
county_data['address'][i] = merged_df['address'][merged_df.loc[merged_df['GEOID20']==county_data['GEOID20'][i]].index[0]]
county_data['latitude'][i] = merged_df['latitude'][merged_df.loc[merged_df['GEOID20']==county_data['GEOID20'][i]].index[0]]
county_data['longitude'][i] = merged_df['longitude'][merged_df.loc[merged_df['GEOID20']==county_data['GEOID20'][i]].index[0]]
#Saves output df as csv
county_data.to_csv('../dspg23_reverse_geocoder/36005_output.csv')
print("No. of missing Addresses:", sum(county_data['address'].isna()))
print("Reverse geocoding complete!")