Calculate the cumulative distance of gps trackpoints

Prep:

import pandas as pd
import math

Function to calculate the distance:

#  function to approximately calculate the distance between 2 points
#  from: http://www.movable-type.co.uk/scripts/latlong.html
def rough_distance(lat1, lon1, lat2, lon2):
    lat1 = lat1 * math.pi / 180.0
    lon1 = lon1 * math.pi / 180.0
    lat2 = lat2 * math.pi / 180.0
    lon2 = lon2 * math.pi / 180.0
    r = 6371.0 #// km
    x = (lon2 - lon1) * math.cos((lat1+lat2)/2)
    y = (lat2 - lat1)
    d = math.sqrt(x*x+y*y) * r
    return d

Read data:

df=pd.io.parsers.read_table("trk.tsv",sep="\t")

# drop some columns (for clarity) 
df=df.drop(['track','ele','tm_str'],axis=1) 

Sample:

df.head()

         lat       lon
0  50.848408  4.787456
1  50.848476  4.787367
2  50.848572  4.787275
3  50.848675  4.787207
4  50.848728  4.787189

The prior-latitude column is the latitude column shifted by 1 unit:

df['prior_lat']= df['lat'].shift(1)
prior_lat_ix=df.columns.get_loc('prior_lat')
df.iloc[0,prior_lat_ix]= df.lat.iloc[0]

The prior-longitude column is the longitude column shifted by 1 unit:

df['prior_lon']= df['lon'].shift(1)
prior_lon_ix=df.columns.get_loc('prior_lon')
df.iloc[0,prior_lon_ix]= df.lon.iloc[0]

Calculate the distance:

df['dist']= df[ ['lat','lon','prior_lat','prior_lon'] ].apply(
                        lambda r : rough_distance ( r[0], r[1], r[2], r[3]) , axis=1)

Calculate the cumulative distance

cum=0
cum_dist=[]
for d in df['dist']:
    cum=cum+d
    cum_dist.append(cum)

df['cum_dist']=cum_dist

Sample:

df.head()

         lat       lon  prior_lat  prior_lon      dist  cum_dist
0  50.848408  4.787456  50.848408   4.787456  0.000000  0.000000
1  50.848476  4.787367  50.848408   4.787456  0.009831  0.009831
2  50.848572  4.787275  50.848476   4.787367  0.012435  0.022266
3  50.848675  4.787207  50.848572   4.787275  0.012399  0.034665
4  50.848728  4.787189  50.848675   4.787207  0.006067  0.040732



df.tail()

            lat       lon  prior_lat  prior_lon      dist   cum_dist
1012  50.847164  4.788163  50.846962   4.788238  0.023086  14.937470
1013  50.847267  4.788134  50.847164   4.788163  0.011634  14.949104
1014  50.847446  4.788057  50.847267   4.788134  0.020652  14.969756
1015  50.847630  4.787978  50.847446   4.788057  0.021097  14.990853
1016  50.847729  4.787932  50.847630   4.787978  0.011496  15.002349