{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Openpaths.cc visualization\n",
"For the last two years, I've used a [R Markdown](http://rmarkdown.rstudio.com/) [file](https://github.com/habi/jahresrueckblick/blob/master/OpenPaths.Rmd) to visualize my [OpenPaths.cc](http://openpaths.cc/)-data and give some insight on my wherebaouts in the passed year.\n",
"I've been using Python a *ton* more this year, and got to love both [Jupyter](http://jupyter.org) and [mplleaflet](https://github.com/jwass/mplleaflet) (for my [happy new year card](http://nbviewer.jupyter.org/github/habi/2017/blob/master/2017.ipynb)).\n",
"And also because I can more easily massage the data in Python, I've switched the old \"analysis\" to this Jupyter notebook."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To visualize the OpenPaths data, we first have to import some Python packages "
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Imports\n",
"import numpy # Numerical calculations\n",
"numpy.seterr(divide='ignore', invalid='ignore') # We use 'log' below for (sub)zero values and don't want to be warned\n",
"import matplotlib.pyplot as plt # The de-facto standard for Python plotting\n",
"%matplotlib inline\n",
"import mplleaflet # Easy matplotlib plots to interactive Leaflet web maps\n",
"import pandas # Data analysis and statistics\n",
"import geopy # Geodata handling\n",
"from geopy.geocoders import Nominatim # Address search from OpenStreetMap\n",
"import geopandas as gpd # Geodata handling, again\n",
"\n",
"# Setup some default values\n",
"plt.rcParams['image.cmap'] = 'viridis' # Change default colormap to a nice one (https://bids.github.io/colormap/)\n",
"plt.rcParams['figure.figsize'] = (16, 9) # We live in a widescreen world"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"At first we use the `pandas` CSV reader to import the data.\n",
"The fourth column is the date colums, so we immediately parse this as a date."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"
\n",
"
\n",
" \n",
" \n",
" | \n",
" lat | \n",
" lon | \n",
" alt | \n",
" version | \n",
"
\n",
" \n",
" \n",
" \n",
" count | \n",
" 28275.000000 | \n",
" 28275.000000 | \n",
" 28275.000000 | \n",
" 2.827500e+04 | \n",
"
\n",
" \n",
" mean | \n",
" 46.336489 | \n",
" 14.821240 | \n",
" 514.422474 | \n",
" 1.100000e+00 | \n",
"
\n",
" \n",
" std | \n",
" 3.143443 | \n",
" 29.446446 | \n",
" 362.733171 | \n",
" 2.220485e-16 | \n",
"
\n",
" \n",
" min | \n",
" 30.024679 | \n",
" -9.880663 | \n",
" -48.000000 | \n",
" 1.100000e+00 | \n",
"
\n",
" \n",
" 25% | \n",
" 46.931829 | \n",
" 7.437073 | \n",
" 356.000000 | \n",
" 1.100000e+00 | \n",
"
\n",
" \n",
" 50% | \n",
" 46.968079 | \n",
" 7.900918 | \n",
" 489.330231 | \n",
" 1.100000e+00 | \n",
"
\n",
" \n",
" 75% | \n",
" 47.481705 | \n",
" 8.220958 | \n",
" 553.736145 | \n",
" 1.100000e+00 | \n",
"
\n",
" \n",
" max | \n",
" 53.586563 | \n",
" 141.174377 | \n",
" 3893.417969 | \n",
" 1.100000e+00 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" lat lon alt version\n",
"count 28275.000000 28275.000000 28275.000000 2.827500e+04\n",
"mean 46.336489 14.821240 514.422474 1.100000e+00\n",
"std 3.143443 29.446446 362.733171 2.220485e-16\n",
"min 30.024679 -9.880663 -48.000000 1.100000e+00\n",
"25% 46.931829 7.437073 356.000000 1.100000e+00\n",
"50% 46.968079 7.900918 489.330231 1.100000e+00\n",
"75% 47.481705 8.220958 553.736145 1.100000e+00\n",
"max 53.586563 141.174377 3893.417969 1.100000e+00"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Load data from CSV\n",
"openpaths = pandas.read_csv('openpaths_habi.csv', parse_dates=[3])\n",
"openpaths.describe()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Since we're only interested in this years data, we subset the so called dataframe to 2016.\n",
"If we want to show *all* the data in the file, we could set `plot_current_year' to `False`, then no subsetting happens."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"
\n",
" \n",
" \n",
" | \n",
" lat | \n",
" lon | \n",
" alt | \n",
" date | \n",
" device | \n",
" os | \n",
" version | \n",
"
\n",
" \n",
" \n",
" \n",
" 22765 | \n",
" 46.286259 | \n",
" 7.798826 | \n",
" 1208.067993 | \n",
" 2016-01-01 07:57:52 | \n",
" iPhone6,2 | \n",
" 9.2.1 | \n",
" 1.1 | \n",
"
\n",
" \n",
" 22766 | \n",
" 46.294952 | \n",
" 7.800951 | \n",
" 1017.783081 | \n",
" 2016-01-01 10:57:04 | \n",
" iPhone6,2 | \n",
" 9.2.1 | \n",
" 1.1 | \n",
"
\n",
" \n",
" 22767 | \n",
" 46.304230 | \n",
" 7.801154 | \n",
" 640.996643 | \n",
" 2016-01-01 11:03:28 | \n",
" iPhone6,2 | \n",
" 9.2.1 | \n",
" 1.1 | \n",
"
\n",
" \n",
" 22768 | \n",
" 46.306042 | \n",
" 7.809315 | \n",
" 640.624329 | \n",
" 2016-01-01 11:14:08 | \n",
" iPhone6,2 | \n",
" 9.2.1 | \n",
" 1.1 | \n",
"
\n",
" \n",
" 22769 | \n",
" 46.313583 | \n",
" 7.823861 | \n",
" 635.348633 | \n",
" 2016-01-01 11:22:40 | \n",
" iPhone6,2 | \n",
" 9.2.1 | \n",
" 1.1 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" lat lon alt date device os \\\n",
"22765 46.286259 7.798826 1208.067993 2016-01-01 07:57:52 iPhone6,2 9.2.1 \n",
"22766 46.294952 7.800951 1017.783081 2016-01-01 10:57:04 iPhone6,2 9.2.1 \n",
"22767 46.304230 7.801154 640.996643 2016-01-01 11:03:28 iPhone6,2 9.2.1 \n",
"22768 46.306042 7.809315 640.624329 2016-01-01 11:14:08 iPhone6,2 9.2.1 \n",
"22769 46.313583 7.823861 635.348633 2016-01-01 11:22:40 iPhone6,2 9.2.1 \n",
"\n",
" version \n",
"22765 1.1 \n",
"22766 1.1 \n",
"22767 1.1 \n",
"22768 1.1 \n",
"22769 1.1 "
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Use only this years data\n",
"plot_current_year = True\n",
"if plot_current_year:\n",
" whichyear = 2016\n",
" thisyear = openpaths[pandas.Timestamp(str(whichyear)) < openpaths['date']]\n",
" thisyear = thisyear[thisyear['date'] < pandas.Timestamp(str(whichyear + 1))]\n",
" thisyear.describe()\n",
"else:\n",
" thisyear = openpaths\n",
"# Show the beginning of the dataframe\n",
"thisyear.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"I'm still using an iPhone 5S ([`iPhone6,2`](http://www.everymac.com/ultimate-mac-lookup/?search_keywords=iPhone6%2C2)) the whole year (when the new Salt-store opened in town one could get repairs for half the price. I wanted to fix my broken display and get a new battery on my nearly three year old phone. Insted of repairing the phone, I got a new one for a *very* good price, so it's actually two different phones, but the same model).\n",
"I went through 9 different versions of iOS.\n",
"If we assume that the app tracked the positions equally for each version, then I've used iOS 9.3 the longest, with nearly 2000 data points."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Iphone models: iPhone6,2\n",
"iOS versions: 9\n",
"Version 10.0:\t1149 data points\n",
"Version 10.0.2:\t 706 data points\n",
"Version 10.1.1:\t 378 data points\n",
"Version 10.2:\t 33 data points\n",
"Version 9.2.1:\t 256 data points\n",
"Version 9.3:\t1979 data points\n",
"Version 9.3.1:\t 214 data points\n",
"Version 9.3.2:\t 535 data points\n",
"Version 9.3.5:\t 256 data points\n"
]
}
],
"source": [
"print('Iphone models: %s' % thisyear['device'].unique()[0])\n",
"print('iOS versions: %s' % len(thisyear['os'].unique()))\n",
"for version in sorted(thisyear['os'].unique()):\n",
" print('Version %s:\\t%4s data points' % (version, len(thisyear[thisyear['os'] == version])))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's see where we've been all this year.\n",
"For the overview we use the nice ['Toner' maps from Stamen](http://maps.stamen.com/toner/#5/49.810/7.822).\n",
"\n",
"It seems that I've been only in Switzerland and Morocco.\n",
"Unfortunately, most of the data from May is missing, so there's no location points from our vacation in [Sardinia](https://flic.kr/s/aHskBCjBe5)..."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
""
],
"text/plain": [
""
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Plot a subset of the data points\n",
"# We use a subset of the data to not overwhelm the map...\n",
"subset=10\n",
"plt.scatter(thisyear['lon'][::subset],\n",
" thisyear['lat'][::subset], edgecolor='none', alpha=0.618, s=200)\n",
"mplleaflet.display(tiles='stamen_toner')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"By using [the geocoding library for Python](https://github.com/geopy/geopy) and the [OpenStreetMap reverse geocoding tool](https://wiki.openstreetmap.org/wiki/Nominatim) we can assign addresses to data points.\n",
"And display these on top of the standard 'OSM' map.\n",
"\n",
"Let's look at one particular location, namely the average location we had this year.\n",
"By using different keys of the `location` dictionary (`print(location.raw)`) we can get a nice text output."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The average location in 2016 was in Chardonnière in France\n"
]
},
{
"data": {
"text/html": [
""
],
"text/plain": [
""
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Address search with `geopy` and https://wiki.openstreetmap.org/wiki/Nominatim\n",
"geolocator = Nominatim()\n",
"location = geolocator.reverse('%s, %s' % (thisyear['lat'].mean(), thisyear['lon'].mean()))\n",
"#print(location.raw)\n",
"print('The average location in %s was in %s in %s' % (whichyear,\n",
" location.raw.get('address').get('suburb'),\n",
" location.raw.get('address').get('country')))\n",
"plt.scatter(thisyear['lon'].mean(), thisyear['lat'].mean(), s=500, alpha=0.618)\n",
"mplleaflet.display()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's look at the extremes of the four cardinal directions."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The most northern location in 2016 was in Döttingen in Schweiz, Suisse, Svizzera, Svizra\n"
]
},
{
"data": {
"text/html": [
""
],
"text/plain": [
""
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Cardinal directions extremes: north\n",
"point = (thisyear[thisyear['lat'] == thisyear['lat'].max()])\n",
"location = geolocator.reverse('%s, %s' % (point.iloc[0]['lat'], point.iloc[0]['lon']))\n",
"#print(location.raw)\n",
"print('The most northern location in %s was in %s in %s' % (whichyear,\n",
" location.raw.get('address').get('village'),\n",
" location.raw.get('address').get('country')))\n",
"plt.scatter(point.iloc[0]['lon'], point.iloc[0]['lat'], s=500, alpha=0.618)\n",
"mplleaflet.display()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The most eastern location in 2016 was in Landquart in Schweiz, Suisse, Svizzera, Svizra\n"
]
},
{
"data": {
"text/html": [
""
],
"text/plain": [
""
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Cardinal directions extremes: east\n",
"point = (thisyear[thisyear['lon'] == thisyear['lon'].max()])\n",
"location = geolocator.reverse('%s, %s' % (point.iloc[0]['lat'], point.iloc[0]['lon']))\n",
"#print(location.raw)\n",
"print('The most eastern location in %s was in %s in %s' % (whichyear,\n",
" location.raw.get('address').get('village'),\n",
" location.raw.get('address').get('country')))\n",
"plt.scatter(point.iloc[0]['lon'], point.iloc[0]['lat'], s=500, alpha=0.618)\n",
"mplleaflet.display()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The most southern location in 2016 was in Sidi Ouassay ⵙⵉⴷⵉ ⵡⴰⵙⴰⵢ سيدي وساي in Maroc ⵍⵎⵖⵔⵉⴱ المغرب\n"
]
},
{
"data": {
"text/html": [
""
],
"text/plain": [
""
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Cardinal directions extremes: south\n",
"point = (thisyear[thisyear['lat'] == thisyear['lat'].min()])\n",
"location = geolocator.reverse('%s, %s' % (point.iloc[0]['lat'], point.iloc[0]['lon']))\n",
"#print(location.raw)\n",
"print('The most southern location in %s was in %s in %s' % (whichyear,\n",
" location.raw.get('address').get('village'),\n",
" location.raw.get('address').get('country')))\n",
"plt.scatter(point.iloc[0]['lon'], point.iloc[0]['lat'], s=500, alpha=0.618)\n",
"mplleaflet.display(tiles='osm')"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The most western location in 2016 was in Tamri ⵜⴰⵎⵔⵉ تامري in Maroc ⵍⵎⵖⵔⵉⴱ المغرب\n"
]
},
{
"data": {
"text/html": [
""
],
"text/plain": [
""
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Cardinal directions extremes: west\n",
"point = (thisyear[thisyear['lon'] == thisyear['lon'].min()])\n",
"location = geolocator.reverse('%s, %s' % (point.iloc[0]['lat'], point.iloc[0]['lon']))\n",
"#print(location.raw)\n",
"print('The most western location in %s was in %s in %s' % (whichyear,\n",
" location.raw.get('address').get('town'),\n",
" location.raw.get('address').get('country')))\n",
"plt.scatter(point.iloc[0]['lon'], point.iloc[0]['lat'], s=500, alpha=0.618)\n",
"mplleaflet.display(tiles='osm')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"It's equally easy to grab the highest and lowest point in the year."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The highest point in 2016 was on the Klein Matterhorn in Zermatt at 3893.4 MASL\n"
]
},
{
"data": {
"text/html": [
""
],
"text/plain": [
""
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Highest point\n",
"point = thisyear.loc[thisyear['alt'] == thisyear['alt'].max()]\n",
"location = geolocator.reverse('%s, %s' % (point.iloc[0]['lat'], point.iloc[0]['lon']))\n",
"# print(location.raw)\n",
"print('The highest point in %s was on the %s in %s at %0.1f MASL' % (whichyear,\n",
" location.raw.get('address').get('footway'),\n",
" location.raw.get('address').get('village'),\n",
" point['alt']))\n",
"plt.scatter(point['lon'], point['lat'], s=500, alpha=0.618)\n",
"mplleaflet.display(tiles='osm')"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The lowest point in 2016 was in Sidi Ouassay ⵙⵉⴷⵉ ⵡⴰⵙⴰⵢ سيدي وساي in Maroc ⵍⵎⵖⵔⵉⴱ المغرب at 1.6 MASL\n"
]
},
{
"data": {
"text/html": [
""
],
"text/plain": [
""
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Lowest point\n",
"above_sealevel = thisyear.loc[thisyear['alt'] > 0] # there's some wrong GPS data, we didn't dive with the phone :)\n",
"point = above_sealevel.loc[above_sealevel['alt'] == above_sealevel['alt'].min()]\n",
"location = geolocator.reverse('%s, %s' % (point.iloc[0]['lat'], point.iloc[0]['lon']))\n",
"location = geolocator.reverse('%s, %s' % (point.iloc[0]['lat'], point.iloc[0]['lon']))\n",
"#print(location.raw)\n",
"print('The lowest point in %s was in %s in %s at %0.1f MASL' % (whichyear,\n",
" location.raw.get('address').get('village'),\n",
" location.raw.get('address').get('country'),\n",
" point.iloc[0]['alt']))\n",
"plt.scatter(point['lon'], point['lat'], s=500, alpha=0.618)\n",
"mplleaflet.display(tiles='osm')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's \"zoom\" in on the data just for Switzerland.\n",
"For this we subset the [Longitude](https://en.wikipedia.org/wiki/Longitude) and [Latitude](https://en.wikipedia.org/wiki/Latitude) data on Switzerland, which is around [47°N, 8°E](http://www.wolframalpha.com/input/?i=lat+lon+switzerland).\n",
"For kicks we color the data points according to their altitude."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Get the subset of swiss locations\n",
"location_in_switzerland = thisyear.loc[thisyear['lon'] > 8 - 3]\n",
"location_in_switzerland = location_in_switzerland.loc[location_in_switzerland['lon'] < 8 + 3]\n",
"location_in_switzerland = location_in_switzerland.loc[location_in_switzerland['lat'] > 47 - 1.5]\n",
"location_in_switzerland = location_in_switzerland.loc[location_in_switzerland['lat'] < 47 + 1.5]"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
""
],
"text/plain": [
""
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Plot all CH data points, colored by their altitude\n",
"plt.scatter(location_in_switzerland['lon'],\n",
" location_in_switzerland['lat'],\n",
" c=numpy.log(location_in_switzerland['alt']), edgecolor='none', alpha=0.618, s=200)\n",
"mplleaflet.display(tiles='stamen_toner')"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"# Bonus content!\n",
"Based on the idea found in [this blog post](http://lenguyenthedat.com/drawing-maps-python/) we get a shape file of Switzerland from [Mapzen](https://mapzen.com/data/borders/) (direct link [here](https://s3.amazonaws.com/osm-polygons.mapzen.com/switzerland_geojson.tgz)).\n",
"On top of this we do a [hexbin plot](http://matplotlib.org/examples/pylab_examples/hexbin_demo.html) of the location in Switzerland, especially for [Yves](http://habi.gna.ch/2016/01/15/where-have-i-been-in-2015/#comment-22434).\n",
"The color in this plot corresponds to the amount of data points in each bin, i.e. amount of time spent there.\n",
"It seems that I was quite a bit in Bern, Villigen and along the axis inbetween."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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wXZNblUrFk08+SVJSEmfOnGHdunWUlZURHR0NXPv9FuLPwsHBgV27dvHss8/S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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Load the shape file of Switzerland and display it\n",
"switzerland = gpd.GeoDataFrame.from_file('admin_level_2.geojson')\n",
"switzerland.geometry.plot(facecolor='none')\n",
"# Overplot Switzerland with a hexagon plot of the location data.\n",
"plt.hexbin(location_in_switzerland['lon'], location_in_switzerland['lat'],\n",
" cmap='viridis',\n",
" gridsize=20,\n",
" bins='log',\n",
" mincnt=1,\n",
" edgecolor='k')\n",
"plt.show()"
]
}
],
"metadata": {
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