Python location, show distance from closest other location

Question

I am a location in a dataframe, underneath lat lon column names. I want to show how far that is from the lat lon of the nearest train station in a separate dataframe.

So for example, I have a lat lon of (37.814563 144.970267), and i have a list as below of other geospatial points. I want to find the point that is closest and then find the distance between those points, as an extra column in the dataframe in suburbs.

This is the example of the train dataset

<bound method NDFrame.to_clipboard of   STOP_ID                                          STOP_NAME   LATITUDE  \
0   19970             Royal Park Railway Station (Parkville) -37.781193   
1   19971  Flemington Bridge Railway Station (North Melbo... -37.788140   
2   19972         Macaulay Railway Station (North Melbourne) -37.794267   
3   19973   North Melbourne Railway Station (West Melbourne) -37.807419   
4   19974        Clifton Hill Railway Station (Clifton Hill) -37.788657   

    LONGITUDE TICKETZONE                                          ROUTEUSSP  \
0  144.952301          1                                            Upfield   
1  144.939323          1                                            Upfield   
2  144.936166          1                                            Upfield   
3  144.942570          1  Flemington,Sunbury,Upfield,Werribee,Williamsto...   
4  144.995417          1                                 Mernda,Hurstbridge   

                      geometry  
0  POINT (144.95230 -37.78119)  
1  POINT (144.93932 -37.78814)  
2  POINT (144.93617 -37.79427)  
3  POINT (144.94257 -37.80742)  
4  POINT (144.99542 -37.78866)  >

and this is an example of the suburbs

<bound method NDFrame.to_clipboard of       postcode              suburb state        lat         lon
4901      3000           MELBOURNE   VIC -37.814563  144.970267
4902      3002      EAST MELBOURNE   VIC -37.816640  144.987811
4903      3003      WEST MELBOURNE   VIC -37.806255  144.941123
4904      3005  WORLD TRADE CENTRE   VIC -37.822262  144.954856
4905      3006           SOUTHBANK   VIC -37.823258  144.965926>

Which I am trying to show, is the distance from the lat lon to the closet train station in a new column for the suburb list.

Using a solution get a weird output, wondering if it's correct?

With both solutions shown,

from sklearn.neighbors import NearestNeighbors
from haversine import haversine

NN = NearestNeighbors(n_neighbors=1, metric='haversine')
NN.fit(trains_shape[['LATITUDE', 'LONGITUDE']])

indices = NN.kneighbors(df_complete[['lat', 'lon']])[1]
indices = [index[0] for index in indices]
distances = NN.kneighbors(df_complete[['lat', 'lon']])[0]
df_complete['closest_station'] = trains_shape.iloc[indices]['STOP_NAME'].reset_index(drop=True)
df_complete['closest_station_distances'] = distances
print(df_complete)

The output here,

<bound method NDFrame.to_clipboard of    postcode        suburb state        lat         lon  Venues Cluster  \
1      3040    aberfeldie   VIC -37.756690  144.896259             4.0   
2      3042  airport west   VIC -37.711698  144.887037             1.0   
4      3206   albert park   VIC -37.840705  144.955710             0.0   
5      3020        albion   VIC -37.775954  144.819395             2.0   
6      3078    alphington   VIC -37.780767  145.031160             4.0   

                     #1                    #2             #3  \
1                  Café     Electronics Store  Grocery Store   
2  Fast Food Restaurant                  Café    Supermarket   
4                  Café                   Pub    Coffee Shop   
5                  Café  Fast Food Restaurant  Grocery Store   
6                  Café                  Park            Bar   

                      #4  ...                             #6  \
1            Coffee Shop  ...                         Bakery   
2          Grocery Store  ...             Italian Restaurant   
4         Breakfast Spot  ...                   Burger Joint   
5  Vietnamese Restaurant  ...                            Pub   
6            Pizza Place  ...  Vegetarian / Vegan Restaurant   

                      #7                   #8                         #9  \
1          Shopping Mall  Japanese Restaurant          Indian Restaurant   
2  Portuguese Restaurant    Electronics Store  Middle Eastern Restaurant   
4                    Bar               Bakery                  Gastropub   
5     Chinese Restaurant                  Gym                     Bakery   
6     Italian Restaurant            Gastropub                     Bakery   

                 #10 Ancestry Cluster  ClosestStopId  \
1   Greek Restaurant              8.0          20037   
2  Convenience Store              5.0          20032   
4              Beach              6.0          22180   
5  Convenience Store              5.0          20004   
6        Coffee Shop              5.0          19931   

                                   ClosestStopName  \
1              Essendon Railway Station (Essendon)   
2                Glenroy Railway Station (Glenroy)   
4  Southern Cross Railway Station (Melbourne City)   
5          Albion Railway Station (Sunshine North)   
6          Alphington Railway Station (Alphington)   

                                   closest_station closest_station_distances  
1                Glenroy Railway Station (Glenroy)                  0.019918  
2  Southern Cross Railway Station (Melbourne City)                  0.031020  
4          Alphington Railway Station (Alphington)                  0.023165  
5                  Altona Railway Station (Altona)                  0.005559  
6                Newport Railway Station (Newport)                  0.002375  

And the second function.

def ClosestStop(r):
    # Cartesin Distance: square root of (x2-x2)^2 + (y2-y1)^2
    distances = ((r['lat']-StationDf['LATITUDE'])**2 + (r['lon']-StationDf['LONGITUDE'])**2)**0.5
    
    # Stop with minimum Distance from the Suburb
    closestStationId = distances[distances == distances.min()].index.to_list()[0]
    return StationDf.loc[closestStationId, ['STOP_ID', 'STOP_NAME']]

df_complete[['ClosestStopId', 'ClosestStopName']] = df_complete.apply(ClosestStop, axis=1)

This is giving different answers oddly enough, and leads me to think that there is an issue with this code. the KM's seem wrong as well.

Completely unsure how to approach this problem - would love some guidance here, thanks!

Answer 1

A few key concepts

1. do a Cartesian product between two data frames to get all combinations (joining on identical value between two data frames is approach to this foo=1)
2. once both sets of data is together, have both sets of lat/lon to calculate distance) geopy has been used for this
3. cleanup the columns, use sort_values() to find smallest distance
4. finally a groupby() and agg() to get first values for shortest distance

There are two data frames for use

1. dfdist contains all the combinations and distances
2. dfnearest which contains result

dfstat = pd.DataFrame({'STOP_ID': ['19970', '19971', '19972', '19973', '19974'],
 'STOP_NAME': ['Royal Park Railway Station (Parkville)',
  'Flemington Bridge Railway Station (North Melbo...',
  'Macaulay Railway Station (North Melbourne)',
  'North Melbourne Railway Station (West Melbourne)',
  'Clifton Hill Railway Station (Clifton Hill)'],
 'LATITUDE': ['-37.781193',
  '-37.788140',
  '-37.794267',
  '-37.807419',
  '-37.788657'],
 'LONGITUDE': ['144.952301',
  '144.939323',
  '144.936166',
  '144.942570',
  '144.995417'],
 'TICKETZONE': ['1', '1', '1', '1', '1'],
 'ROUTEUSSP': ['Upfield',
  'Upfield',
  'Upfield',
  'Flemington,Sunbury,Upfield,Werribee,Williamsto...',
  'Mernda,Hurstbridge'],
 'geometry': ['POINT (144.95230 -37.78119)',
  'POINT (144.93932 -37.78814)',
  'POINT (144.93617 -37.79427)',
  'POINT (144.94257 -37.80742)',
  'POINT (144.99542 -37.78866)']})
dfsub = pd.DataFrame({'id': ['4901', '4902', '4903', '4904', '4905'],
 'postcode': ['3000', '3002', '3003', '3005', '3006'],
 'suburb': ['MELBOURNE',
  'EAST MELBOURNE',
  'WEST MELBOURNE',
  'WORLD TRADE CENTRE',
  'SOUTHBANK'],
 'state': ['VIC', 'VIC', 'VIC', 'VIC', 'VIC'],
 'lat': ['-37.814563', '-37.816640', '-37.806255', '-37.822262', '-37.823258'],
 'lon': ['144.970267', '144.987811', '144.941123', '144.954856', '144.965926']})

import geopy.distance
# cartesian product so we get all combinations
dfdist = (dfsub.assign(foo=1).merge(dfstat.assign(foo=1), on="foo")
    # calc distance in km between each suburb and each train station
     .assign(km=lambda dfa: dfa.apply(lambda r: 
                                      geopy.distance.geodesic(
                                          (r["LATITUDE"],r["LONGITUDE"]), 
                                          (r["lat"],r["lon"])).km, axis=1))
    # reduce number of columns to make it more digestable
     .loc[:,["postcode","suburb","STOP_ID","STOP_NAME","km"]]
    # sort so shortest distance station from a suburb is first
     .sort_values(["postcode","suburb","km"])
    # good practice
     .reset_index(drop=True)
)
# finally pick out stations nearest to suburb
# this can easily be joined back to source data frames as postcode and STOP_ID have been maintained
dfnearest = dfdist.groupby(["postcode","suburb"])\
    .agg({"STOP_ID":"first","STOP_NAME":"first","km":"first"}).reset_index()

print(dfnearest.to_string(index=False))
dfnearest

output

postcode              suburb STOP_ID                                         STOP_NAME        km
    3000           MELBOURNE   19973  North Melbourne Railway Station (West Melbourne)  2.564586
    3002      EAST MELBOURNE   19974       Clifton Hill Railway Station (Clifton Hill)  3.177320
    3003      WEST MELBOURNE   19973  North Melbourne Railway Station (West Melbourne)  0.181463
    3005  WORLD TRADE CENTRE   19973  North Melbourne Railway Station (West Melbourne)  1.970909
    3006           SOUTHBANK   19973  North Melbourne Railway Station (West Melbourne)  2.705553

an approach to reducing size of tested combinations

# pick nearer places,  based on lon/lat then all combinations
dfdist = (dfsub.assign(foo=1, latr=dfsub["lat"].round(1), lonr=dfsub["lon"].round(1))
          .merge(dfstat.assign(foo=1, latr=dfstat["LATITUDE"].round(1), lonr=dfstat["LONGITUDE"].round(1)), 
                 on=["foo","latr","lonr"])
    # calc distance in km between each suburb and each train station
     .assign(km=lambda dfa: dfa.apply(lambda r: 
                                      geopy.distance.geodesic(
                                          (r["LATITUDE"],r["LONGITUDE"]), 
                                          (r["lat"],r["lon"])).km, axis=1))
    # reduce number of columns to make it more digestable
     .loc[:,["postcode","suburb","STOP_ID","STOP_NAME","km"]]
    # sort so shortest distance station from a suburb is first
     .sort_values(["postcode","suburb","km"])
    # good practice
     .reset_index(drop=True)
)