# Open Data: Germany Maps Viz

In this post I want to show how to use public available (open) data to create geo visualizations in python. Maps are a great way to communicate and compare information when working with geolocation data. There are many frameworks to plot maps, here I focus on matplotlib and geopandas (and give a glimpse of mplleaflet).

Reference: A very good introduction to matplotlib is the chapter on Visualization with Matplotlib from the Python Data Science Handbook by Jake VanderPlas.

Remark: When I finished writing this notebook, I discovered a similar analysis done in R here. Please check it out!

## Prepare Notebook

import geopandas as gpd
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
plt.style.use('seaborn')

%matplotlib inline

## Get Germany Data

• plz-gebiete.shp: shapefile with germany postal codes polygons.
• zuordnung_plz_ort.csv: postal code to city and bundesland mapping.
• plz_einwohner.csv: population is assigned to each postal code area.

## Germany Maps

We begin by generating a Germany map with the most important cities.

# Make sure you read postal codes as strings, otherwise
# the postal code 01110 will be parsed as the number 1110.

plz_shape_df.head()
plz note geometry
0 52538 52538 Gangelt, Selfkant POLYGON ((5.86632 51.05110, 5.86692 51.05124, …
1 47559 47559 Kranenburg POLYGON ((5.94504 51.82354, 5.94580 51.82409, …
2 52525 52525 Waldfeucht, Heinsberg POLYGON ((5.96811 51.05556, 5.96951 51.05660, …
3 52074 52074 Aachen POLYGON ((5.97486 50.79804, 5.97495 50.79809, …
4 52531 52531 Ãbach-Palenberg POLYGON ((6.01507 50.94788, 6.03854 50.93561, …

The geometry column contains the polygons which define the postal code’s shape.

We can use geopandas mapping tools to generate the map with the plot method.

plt.rcParams['figure.figsize'] = [16, 11]

# Get lat and lng of Germany's main cities.
top_cities = {
'Berlin': (13.404954, 52.520008),
'Cologne': (6.953101, 50.935173),
'Düsseldorf': (6.782048, 51.227144),
'Frankfurt am Main': (8.682127, 50.110924),
'Hamburg': (9.993682, 53.551086),
'Leipzig': (12.387772, 51.343479),
'Munich': (11.576124, 48.137154),
'Dortmund': (7.468554, 51.513400),
'Stuttgart': (9.181332, 48.777128),
'Nuremberg': (11.077438, 49.449820),
'Hannover': (9.73322, 52.37052)
}

fig, ax = plt.subplots()

plz_shape_df.plot(ax=ax, color='orange', alpha=0.8)

# Plot cities.
for c in top_cities.keys():
# Plot city name.
ax.text(
x=top_cities[c][0],
# Add small shift to avoid overlap with point.
y=top_cities[c][1] + 0.08,
s=c,
fontsize=12,
ha='center',
)
# Plot city location centroid.
ax.plot(
top_cities[c][0],
top_cities[c][1],
marker='o',
c='black',
alpha=0.5
)

ax.set(
title='Germany',
aspect=1.3,
facecolor='lightblue'
);

### First-Digit-Postalcodes Areas

Next, let us plot different regions corresponding to the first digit of each postal code.

# Create feature.
plz_shape_df = plz_shape_df \
.assign(first_dig_plz = lambda x: x['plz'].str.slice(start=0, stop=1))
fig, ax = plt.subplots()

plz_shape_df.plot(
ax=ax,
column='first_dig_plz',
categorical=True,
legend=True,
legend_kwds={'title':'First Digit', 'loc':'lower right'},
cmap='tab20',
alpha=0.9
)

for c in top_cities.keys():

ax.text(
x=top_cities[c][0],
y=top_cities[c][1] + 0.08,
s=c,
fontsize=12,
ha='center',
)

ax.plot(
top_cities[c][0],
top_cities[c][1],
marker='o',
c='black',
alpha=0.5
)

ax.set(
title='Germany First-Digit-Postal Codes Areas',
aspect=1.3,
facecolor='white'
);

### Bundesland Map

Let us now map each postal code to the corresponding region:

plz_region_df = pd.read_csv(
'../Data/zuordnung_plz_ort.csv',
sep=',',
dtype={'plz': str}
)

plz_region_df.drop('osm_id', axis=1, inplace=True)

plz_region_df.head()
ort plz bundesland
1 Aach 54298 Rheinland-Pfalz
2 Aachen 52062 Nordrhein-Westfalen
3 Aachen 52064 Nordrhein-Westfalen
4 Aachen 52066 Nordrhein-Westfalen
# Merge data.
germany_df = pd.merge(
left=plz_shape_df,
right=plz_region_df,
on='plz',
how='inner'
)

germany_df.drop(['note'], axis=1, inplace=True)

germany_df.head()
plz geometry first_dig_plz ort bundesland
0 52538 POLYGON ((5.86632 51.05110, 5.86692 51.05124, … 5 Gangelt Nordrhein-Westfalen
1 52538 POLYGON ((5.86632 51.05110, 5.86692 51.05124, … 5 Selfkant Nordrhein-Westfalen
2 47559 POLYGON ((5.94504 51.82354, 5.94580 51.82409, … 4 Kranenburg Nordrhein-Westfalen
3 52525 POLYGON ((5.96811 51.05556, 5.96951 51.05660, … 5 Heinsberg Nordrhein-Westfalen
4 52525 POLYGON ((5.96811 51.05556, 5.96951 51.05660, … 5 Waldfeucht Nordrhein-Westfalen

Generate Bundesland map:

fig, ax = plt.subplots()

germany_df.plot(
ax=ax,
column='bundesland',
categorical=True,
legend=True,
legend_kwds={'title':'Bundesland', 'bbox_to_anchor': (1.35, 0.8)},
cmap='tab20',
alpha=0.9
)

for c in top_cities.keys():

ax.text(
x=top_cities[c][0],
y=top_cities[c][1] + 0.08,
s=c,
fontsize=12,
ha='center',
)

ax.plot(
top_cities[c][0],
top_cities[c][1],
marker='o',
c='black',
alpha=0.5
)

ax.set(
title='Germany - Bundesländer',
aspect=1.3,
facecolor='white'
);

### Number of Inhabitants

Now we include the number of inhabitants per postal code:

plz_einwohner_df = pd.read_csv(
'../Data/plz_einwohner.csv',
sep=',',
dtype={'plz': str, 'einwohner': int}
)

plz_einwohner_df.head()
plz einwohner
0 01067 11957
1 01069 25491
2 01097 14811
3 01099 28021
4 01108 5876
# Merge data.
germany_df = pd.merge(
left=germany_df,
right=plz_einwohner_df,
on='plz',
how='left'
)

germany_df.head()
plz geometry first_dig_plz ort bundesland einwohner
0 52538 POLYGON ((5.86632 51.05110, 5.86692 51.05124, … 5 Gangelt Nordrhein-Westfalen 21390
1 52538 POLYGON ((5.86632 51.05110, 5.86692 51.05124, … 5 Selfkant Nordrhein-Westfalen 21390
2 47559 POLYGON ((5.94504 51.82354, 5.94580 51.82409, … 4 Kranenburg Nordrhein-Westfalen 10220
3 52525 POLYGON ((5.96811 51.05556, 5.96951 51.05660, … 5 Heinsberg Nordrhein-Westfalen 49737
4 52525 POLYGON ((5.96811 51.05556, 5.96951 51.05660, … 5 Waldfeucht Nordrhein-Westfalen 49737

Generate map:

fig, ax = plt.subplots()

germany_df.plot(
ax=ax,
column='einwohner',
categorical=False,
legend=True,
cmap='autumn_r',
alpha=0.8
)

for c in top_cities.keys():

ax.text(
x=top_cities[c][0],
y=top_cities[c][1] + 0.08,
s=c,
fontsize=12,
ha='center',
)

ax.plot(
top_cities[c][0],
top_cities[c][1],
marker='o',
c='black',
alpha=0.5
)

ax.set(
title='Germany: Number of Inhabitants per Postal Code',
aspect=1.3,
facecolor='lightblue'
);

## City Maps

We can now filter for cities using the ort feature.

• Munich
munich_df = germany_df.query('ort == "München"')

fig, ax = plt.subplots()

munich_df.plot(
ax=ax,
column='einwohner',
categorical=False,
legend=True,
cmap='autumn_r',
)

ax.set(
title='Munich: Number of Inhabitants per Postal Code',
aspect=1.3,
facecolor='lightblue'
);
• Berlin
berlin_df = germany_df.query('ort == "Berlin"')

fig, ax = plt.subplots()

berlin_df.plot(
ax=ax,
column='einwohner',
categorical=False,
legend=True,
cmap='autumn_r',
)

ax.set(
title='Berlin: Number of Inhabitants per Postal Code',
aspect=1.3,
facecolor='lightblue'
);

/

## Berlin

We can use the portal https://www.statistik-berlin-brandenburg.de to get the official postal code to area mapping in Berlin here. After some formating (not structured raw data):

berlin_plz_area_df = pd.read_excel(
'../Data/ZuordnungderBezirkezuPostleitzahlen.xls',
sheet_name='plz_bez_tidy',
dtype={'plz': str}
)

berlin_plz_area_df.head()
plz area
0 10115 Mitte
1 10117 Mitte
2 10119 Mitte
3 10178 Mitte
4 10179 Mitte

Note however that this map is not one-to-one, i.e. a postal code can correspond to many areas:

berlin_plz_area_df \
[berlin_plz_area_df['plz'].duplicated(keep=False)] \
.sort_values('plz')
plz area
2 10119 Mitte
41 10119 Pankow
4 10179 Mitte
26 10179 Friedrichshain-Kreuzberg
42 10247 Pankow
133 14197 Steglitz-Zehlendorf
95 14197 Charlottenburg-Wilmersdorf
165 14197 Tempelhof-Schöneberg
134 14199 Steglitz-Zehlendorf
96 14199 Charlottenburg-Wilmersdorf

99 rows × 2 columns

Hence, we need to change the postal code grouping variable.

### Berlin Neighbourhoods

Fortunately, the website http://insideairbnb.com/get-the-data.html, containing AirBnB data for many cities (which is definitely worth investigatinig!), has a convinient data set neighbourhoods.geojson which maps Berlin’s area to neighbourhoods:

berlin_neighbourhoods_df = gpd.read_file('../Data/neighbourhoods.geojson')

berlin_neighbourhoods_df = berlin_neighbourhoods_df \
[~ berlin_neighbourhoods_df['neighbourhood_group'].isnull()]

berlin_neighbourhoods_df.head()
neighbourhood neighbourhood_group geometry
0 Blankenfelde/Niederschönhausen Pankow MULTIPOLYGON (((13.41191 52.61487, 13.41183 52…
1 Helmholtzplatz Pankow MULTIPOLYGON (((13.41405 52.54929, 13.41422 52…
2 Wiesbadener Straße Charlottenburg-Wilm. MULTIPOLYGON (((13.30748 52.46788, 13.30743 52…
3 Schmöckwitz/Karolinenhof/Rauchfangswerder Treptow - Köpenick MULTIPOLYGON (((13.70973 52.39630, 13.70926 52…
4 Müggelheim Treptow - Köpenick MULTIPOLYGON (((13.73762 52.40850, 13.73773 52…
fig, ax = plt.subplots()

berlin_df.plot(
ax=ax,
alpha=0.2
)

berlin_neighbourhoods_df.plot(
ax=ax,
column='neighbourhood_group',
categorical=True,
legend=True,
legend_kwds={'title': 'Neighbourhood', 'loc': 'upper right'},
cmap='tab20',
edgecolor='black'
)

ax.set(
title='Berlin Neighbourhoods',
aspect=1.3
);

Here the divisions correspond to Neighbourhood $$\subset$$ Neighbourhood Group.

### Selected Locations in Berlin

Sometimes it is useful to include well-known locations on the maps so that the user can identify them and understand the distances and scales. One way to do it is to manualy input the latitude and longitude of each point (as above). This of course can be time consuming and prone to errors. As expected, there is a library which can fetch this type of data automatically, namely geopy.

Here is a simple example:

from geopy import Nominatim

locator = Nominatim(user_agent='myGeocoder')

location = locator.geocode('Humboldt Universität zu Berlin')

print(location)

Humboldt-Universität zu Berlin, Dorotheenstraße, Spandauer Vorstadt, Mitte, Berlin, 10117, Deutschland

Let us write a function to get the latitude and longitude coordinates:

def lat_lng_from_string_loc(x):

locator = Nominatim(user_agent='myGeocoder')

location = locator.geocode(x)

if location is None:
None
else:
return location.longitude, location.latitude
# Define some well-known Berlin locations.
berlin_locations = [
'Alexander Platz',
'Zoo Berlin',
'Berlin Tegel',
'Berlin Schönefeld',
'Berlin Südkreuz',
'Frei Universität Berlin',
'Mauerpark',
'Treptower Park',
]

# Get geodata.
berlin_locations_geo = {
x: lat_lng_from_string_loc(x)
for x in berlin_locations
}

# Remove None.
berlin_locations_geo = {
k: v
for k, v in berlin_locations_geo.items()
if v is not None
}

Let us see the resulting map:

berlin_df = germany_df.query('ort == "Berlin"')

fig, ax = plt.subplots()

berlin_df.plot(
ax=ax,
color='orange',
alpha=0.8
)

for c in berlin_locations_geo.keys():

ax.text(
x=berlin_locations_geo[c][0],
y=berlin_locations_geo[c][1] + 0.005,
s=c,
fontsize=12,
ha='center',
)

ax.plot(
berlin_locations_geo[c][0],
berlin_locations_geo[c][1],
marker='o',
c='black',
alpha=0.5
)

ax.set(
title='Berlin - Some Relevant Locations',
aspect=1.3,
facecolor='lightblue'
);

### Christmas Markets

Let us enrich the maps by including other type of information. There is a great resource for publicly available data for Berlin: Berlin Open Data. Among many interesting datasets, I found one on the Christmas markets around the city (which are really fun to visit!) here. You can acces the data via a public API. Let us use the requests module to do this:

import requests

# GET request.
response = requests.get(
'https://www.berlin.de/sen/web/service/maerkte-feste/weihnachtsmaerkte/index.php/index/all.json?q='
)

response_json = response.json()

Convert to pandas dataframe.

berlin_maerkte_raw_df = pd.DataFrame(response_json['index'])

We do not have a postal code feature, but we can create one by extracting it from the plz_ort column.

berlin_maerkte_df = berlin_maerkte_raw_df[['name', 'bezirk', 'plz_ort', 'lat', 'lng']]

berlin_maerkte_df = berlin_maerkte_df \
.query('lat != ""') \
.assign(plz = lambda x: x['plz_ort'].str.split(' ').apply(lambda x: x[0]).astype(str)) \
.drop('plz_ort', axis=1)

# Convert to float.
berlin_maerkte_df['lat'] = berlin_maerkte_df['lat'].str.replace(',', '.').astype(float)
berlin_maerkte_df['lng'] = berlin_maerkte_df['lng'].str.replace(',', '.').astype(float)

berlin_maerkte_df.head()
name bezirk lat lng plz
0 Weihnachtsmarkt vor dem Schloss Charlottenburg Charlottenburg-Wilmersdorf 52.519951 13.295946 14059
1
1. Weihnachtsmarkt an der Gedächtniskirche
Charlottenburg-Wilmersdorf 52.504886 13.335511 10789
2 Weihnachtsmarkt in der Fußgängerzone Wilmersdo… Charlottenburg-Wilmersdorf 52.509313 13.305994 10627
3 Weihnachten in Westend Charlottenburg-Wilmersdorf 52.512538 13.259213 14052
4 Weihnachtsmarkt Berlin-Grunewald des Johannisc… Charlottenburg-Wilmersdorf 52.488350 13.277250 14193

Let us plot the Christmas markets locations:

fig, ax = plt.subplots()

berlin_df.plot(ax=ax, color= 'green', alpha=0.4)

for c in berlin_locations_geo.keys():

ax.text(
x=berlin_locations_geo[c][0],
y=berlin_locations_geo[c][1] + 0.005,
s=c,
fontsize=12,
ha='center',
)

ax.plot(
berlin_locations_geo[c][0],
berlin_locations_geo[c][1],
marker='o',
c='black',
alpha=0.5
)

berlin_maerkte_df.plot(
kind='scatter',
x='lng',
y='lat',
c='r',
marker='*',
s=50,
label='Christmas Market',
ax=ax
)

ax.set(
title='Berlin Christmas Markets (2019)',
aspect=1.3,
facecolor='white'
);

## Interactive Maps

We can use the mplleaflet library which converts a matplotlib plot into a webpage containing a pannable, zoomable Leaflet map.

• Berlin Neighbourhoods
import mplleaflet

fig, ax = plt.subplots()

berlin_df.plot(
ax=ax,
alpha=0.2
)

berlin_neighbourhoods_df.plot(
ax=ax,
column='neighbourhood_group',
categorical=True,
cmap='tab20',
)

mplleaflet.display(fig=fig)
• Christmas Markets
fig, ax = plt.subplots()

berlin_df.plot(ax=ax, color= 'green', alpha=0.4)

berlin_maerkte_df.plot(
kind='scatter',
x='lng',
y='lat',
c='r',
marker='*',
s=30,
ax=ax
)

mplleaflet.display(fig=fig)

I hope these data sources and code snippets serve as a starting point to explore geodata analysis and visualization with python.