The goal of this post is to visualize flights taken from Google location data using Python
Google Takeout is a Google service that allows users to export any personal Google data. We'll use Takeout to download our raw location history as a one-time snapshot. Since Latitude was retired, no API exists to access location history in real-time.
Download location data:
LocationHistory.json file. Working with location data in Pandas. Pandas is an incredibly powerful tool that simplifies working with complex datatypes and performing statistical analysis in the style of R. Chris Albon has great primers on using Pandas here under the "Data Wrangling" section.After completing the setup, we'll read in the LocationHistory.json file from Google Takeout and create a DataFrame.
In [1]:
import json
import time
import datetime
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.collections import PatchCollection
from IPython.display import Image
import fiona
from shapely.prepared import prep
from descartes import PolygonPatch
from mpl_toolkits.basemap import Basemap
from shapely.geometry import Point, Polygon, MultiPoint, MultiPolygon
import warnings
warnings.filterwarnings('ignore')
In [2]:
with open('LocationHistory.json', 'r') as fh:
raw = json.loads(fh.read())
# use location_data as an abbreviation for location data
location_data = pd.DataFrame(raw['locations'])
del raw #free up some memory
# convert to typical units
location_data['latitudeE7'] = location_data['latitudeE7']/float(1e7)
location_data['longitudeE7'] = location_data['longitudeE7']/float(1e7)
location_data['timestampMs'] = location_data['timestampMs'].map(lambda x: float(x)/1000) #to seconds
location_data['datetime'] = location_data.timestampMs.map(datetime.datetime.fromtimestamp)
# Rename fields based on the conversions we just did
location_data.rename(columns={'latitudeE7':'latitude', 'longitudeE7':'longitude', 'timestampMs':'timestamp'}, inplace=True)
location_data = location_data[location_data.accuracy < 1000] #Ignore locations with accuracy estimates over 1000m
location_data.reset_index(drop=True, inplace=True)
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location_data.head()
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location_data.dtypes
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location_data.describe()
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print("earliest observed date: {}".format(min(location_data["datetime"]).strftime('%m-%d-%Y')))
print("latest observed date: {}".format(max(location_data["datetime"]).strftime('%m-%d-%Y')))
earliest_obs = min(location_data["datetime"]).strftime('%m-%d-%Y')
latest_obs = max(location_data["datetime"]).strftime('%m-%d-%Y')
Consult this post for more info about degrees and radians in distance calculation.
In [7]:
degrees_to_radians = np.pi/180.0
location_data['phi'] = (90.0 - location_data.latitude) * degrees_to_radians
location_data['theta'] = location_data.longitude * degrees_to_radians
# Compute distance between two GPS points on a unit sphere
location_data['distance'] = np.arccos(
np.sin(location_data.phi)*np.sin(location_data.phi.shift(-1)) * np.cos(location_data.theta - location_data.theta.shift(-1)) +
np.cos(location_data.phi)*np.cos(location_data.phi.shift(-1))) * 6378.100 # radius of earth in km
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location_data['speed'] = location_data.distance/(location_data.timestamp - location_data.timestamp.shift(-1))*3600 #km/hr
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flight_data = pd.DataFrame(data={'end_lat':location_data.latitude,
'end_lon':location_data.longitude,
'end_datetime':location_data.datetime,
'distance':location_data.distance,
'speed':location_data.speed,
'start_lat':location_data.shift(-1).latitude,
'start_lon':location_data.shift(-1).longitude,
'start_datetime':location_data.shift(-1).datetime,
}).reset_index(drop=True)
In [10]:
def distance_on_unit_sphere(lat1, long1, lat2, long2):
# http://www.johndcook.com/python_longitude_latitude.html
# Convert latitude and longitude to spherical coordinates in radians.
degrees_to_radians = np.pi/180.0
# phi = 90 - latitude
phi1 = (90.0 - lat1)*degrees_to_radians
phi2 = (90.0 - lat2)*degrees_to_radians
# theta = longitude
theta1 = long1*degrees_to_radians
theta2 = long2*degrees_to_radians
cos = (np.sin(phi1)*np.sin(phi2)*np.cos(theta1 - theta2) +
np.cos(phi1)*np.cos(phi2))
arc = np.arccos( cos )
# Remember to multiply arc by the radius of the earth
# in your favorite set of units to get length.
return arc
In [11]:
flights = flight_data[(flight_data.speed > 40) & (flight_data.distance > 80)].reset_index()
# Combine instances of flight that are directly adjacent
# Find the indices of flights that are directly adjacent
_f = flights[flights['index'].diff() == 1]
adjacent_flight_groups = np.split(_f, (_f['index'].diff() > 1).nonzero()[0])
# Now iterate through the groups of adjacent flights and merge their data into
# one flight entry
for flight_group in adjacent_flight_groups:
idx = flight_group.index[0] - 1 #the index of flight termination
flights.loc[idx, ['start_lat', 'start_lon', 'start_datetime']] = [flight_group.iloc[-1].start_lat,
flight_group.iloc[-1].start_lon,
flight_group.iloc[-1].start_datetime]
# Recompute total distance of flight
flights.loc[idx, 'distance'] = distance_on_unit_sphere(flights.loc[idx].start_lat,
flights.loc[idx].start_lon,
flights.loc[idx].end_lat,
flights.loc[idx].end_lon)*6378.1
# Now remove the "flight" entries we don't need anymore.
flights = flights.drop(_f.index).reset_index(drop=True)
# Finally, we can be confident that we've removed instances of flights broken up by
# GPS data points during flight. We can now be more liberal in our constraints for what
# constitutes flight. Let's remove any instances below 200km as a final measure.
flights = flights[flights.distance > 200].reset_index(drop=True)
This algorithm worked 100% of the time for me - no false positives or negatives. But the adjacency-criteria of the algorithm is fairly brittle. The core of it centers around the assumption that inter-flight GPS data will be directly adjacent to one another. That's why the initial screening on line 1 of the previous cell had to be so liberal.
Now, the flights DataFrame contains only instances of true flights which facilitates plotting with Matplotlib's Basemap. If we plot on a flat projection like tmerc, the drawgreatcircle function will produce a true path arc just like we see in the in-flight magazines.
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fig = plt.figure(figsize=(18,12))
# Plotting across the international dateline is tough. One option is to break up flights
# by hemisphere. Otherwise, you'd need to plot using a different projection like 'robin'
# and potentially center on the Int'l Dateline (lon_0=-180)
# flights = flights[(flights.start_lon < 0) & (flights.end_lon < 0)]# Western Hemisphere Flights
# flights = flights[(flights.start_lon > 0) & (flights.end_lon > 0)] # Eastern Hemisphere Flights
xbuf = 0.2
ybuf = 0.35
min_lat = np.min([flights.end_lat.min(), flights.start_lat.min()])
min_lon = np.min([flights.end_lon.min(), flights.start_lon.min()])
max_lat = np.max([flights.end_lat.max(), flights.start_lat.max()])
max_lon = np.max([flights.end_lon.max(), flights.start_lon.max()])
width = max_lon - min_lon
height = max_lat - min_lat
m = Basemap(llcrnrlon=min_lon - width* xbuf,
llcrnrlat=min_lat - height*ybuf,
urcrnrlon=max_lon + width* xbuf,
urcrnrlat=max_lat + height*ybuf,
projection='merc',
resolution='l',
lat_0=min_lat + height/2,
lon_0=min_lon + width/2,)
m.drawmapboundary(fill_color='#EBF4FA')
m.drawcoastlines()
m.drawstates()
m.drawcountries()
m.fillcontinents()
current_date = time.strftime("printed: %a, %d %b %Y", time.localtime())
for idx, f in flights.iterrows():
m.drawgreatcircle(f.start_lon, f.start_lat, f.end_lon, f.end_lat, linewidth=3, alpha=0.4, color='b' )
m.plot(*m(f.start_lon, f.start_lat), color='g', alpha=0.8, marker='o')
m.plot(*m(f.end_lon, f.end_lat), color='r', alpha=0.5, marker='o' )
fig.text(0.125, 0.18, "Data collected from 2013-2017 on Android \nPlotted using Python, Basemap \n%s" % (current_date),
ha='left', color='#555555', style='italic')
fig.text(0.125, 0.15, "kivanpolimis.com", color='#555555', fontsize=16, ha='left')
plt.savefig('flights.png', dpi=150, frameon=False, transparent=False, bbox_inches='tight', pad_inches=0.2)
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Image(filename='flights.png')
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You can draw entertaining conclusions from the flight visualization. For instance, you can see some popular layover locations, all those lines in/out of Seattle, plus a recent trip to Germany. And Basemap has made it so simple for us - no Shapefiles to import because all map information is included in the Basemap module.
Calculate all the miles you have traveled in the years observed with a single line of code:
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flights_in_miles = round(flights.distance.sum()*.621371) # distance column is in km, convert to miles
flights_in_miles
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print("{0} miles traveled from {1} to {2}".format(flights_in_miles, earliest_obs, latest_obs))
You've now got the code to go ahead and reproduce these maps.
I'm working on creating functions to automate these visualizations
In [16]:
import time
print("last updated: {}".format(time.strftime("%a, %d %b %Y %H:%M", time.localtime())))