Street Suffix Analysis & Colouring with Python

Posted on Fri 24 July 2020 in Data Science • 5 min read

Street Suffix Visualisation with Python

Ever thought about how roads and streets are named where you live? How many are roads versus how many are streets? Is there a specific pattern to it where you live or is it just random? This post is going to go into how to visualise this using Python, and in particular `osmnx`.

To understand what the terms are in the naming convention, here's an excerpt from a blog post over at: https://360.here.com/2016/12/30/whats-the-difference-between-a-road-a-street-and-an-avenue/

So a 'road' is anything that connects two points, while 'streets' are public ways which have buildings on either side. Avenues, meanwhile, have the same attributes as streets but run perpendicular to them, while a boulevard is essentially a wide street (or avenue), with a median through the middle. A lane is, predictably, smaller.

Some of the fantastic resources used in creating this post are:

Let's get started, always beginning by importing the necessary packages. This environment was created using Anaconda, as setting up `GDAL` for Windows can be painful at the best of time, and Anaconda automates this process for us. This stackoverflow question also helped with setting up https://stackoverflow.com/questions/59802791/installing-osmnx-with-anaconda.

Note that we set up the format for matplotlib as svg, as this will help out for our plots in this case. Svg is a file format which is a vector graphic. This means that everything in the 'image' is drawn mathematically allowing for 'infinite' resolution.

In [1]:
```import osmnx as ox
ox.config(log_console=True, use_cache=True)
import matplotlib.pyplot as plt
from IPython.display import set_matplotlib_formats
import pandas as pd
import seaborn as sns
%matplotlib inline
set_matplotlib_formats('svg')
ox.__version__
```
Out[1]:
`'0.14.0'`

`osmnx` allows us to access the plethora of data that is OpenStreetMap. OpenStreetMap is a map of the world, created by people like you and free to use under an open license. OpenStreetMap contains data on streets, buildings, elevations, terrain and more. `osmnx` can extract this data in a variety of methods, the main one we are interested in is the graph representation of the data (which is used for the streets). We extract the data for our area by providing an address, filter by the type of network ('drive' in this case) and providing a specific distance away from this address. We plot this data to see the extent of the extracted data.

In [2]:
```address = "Beach Rd, Redhead NSW 2290, Australia"

fig, ax = ox.plot_graph(graph)

plt.tight_layout()
```
`<Figure size 432x288 with 0 Axes>`

Next we need to query & analyse this data, which `osmnx` conveniently provides us with a function to convert the extracted graph into a `GeoDataFrame`.

In [3]:
```nodes, streets = ox.graph_to_gdfs(graph)

display(streets)
```
u v key osmid highway name maxspeed oneway length geometry junction lanes ref width bridge access
0 4812701696 4812701699 0 488979392 residential Callemonda Close 50 False 89.588 LINESTRING (151.68153 -33.01304, 151.68144 -33... NaN NaN NaN NaN NaN NaN
1 4812701699 1844333226 0 172707768 tertiary Dalrymple Street 50 False 91.022 LINESTRING (151.68130 -33.01380, 151.68228 -33... NaN NaN NaN NaN NaN NaN
2 4812701699 1844333215 0 172707768 tertiary Dalrymple Street 50 False 162.463 LINESTRING (151.68130 -33.01380, 151.68105 -33... NaN NaN NaN NaN NaN NaN
3 4812701699 4812701696 0 488979392 residential Callemonda Close 50 False 89.588 LINESTRING (151.68130 -33.01380, 151.68138 -33... NaN NaN NaN NaN NaN NaN
4 4812701700 4812701707 0 488979393 residential Twin View Court 50 False 78.934 LINESTRING (151.67519 -33.01230, 151.67523 -33... NaN NaN NaN NaN NaN NaN
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
2733 1847021562 1847021587 0 173954199 residential Boldon Close 50 False 112.986 LINESTRING (151.68051 -32.97493, 151.68038 -32... NaN NaN NaN NaN NaN NaN
2734 1847021565 1847021545 0 418259583 tertiary_link Algona Road 60 False 131.581 LINESTRING (151.69368 -32.97523, 151.69442 -32... NaN NaN NaN NaN NaN NaN
2735 1847021565 2157256210 0 254493907 residential Lees Street 50 False 342.531 LINESTRING (151.69368 -32.97523, 151.69353 -32... NaN NaN NaN NaN NaN NaN
2736 1847021565 1847021576 0 418259583 tertiary_link Algona Road 60 False 37.266 LINESTRING (151.69368 -32.97523, 151.69365 -32... NaN NaN NaN NaN NaN NaN
2737 1847021566 1847021583 0 173954217 residential Church Street 50 False 52.110 LINESTRING (151.68817 -32.97526, 151.68808 -32... NaN NaN NaN NaN NaN NaN

2738 rows × 16 columns

To query the dataset for the road type, we need to extract this from the roads name. For some roads in the dataset, multiple road names are provided so we just take the first in this case and drop all roads without a name. To get the last word in the road name, we split the string by spaces into a list and then take the last element in the list.

In [4]:
```def get_road_type(street_name):
if isinstance(street_name,list):
street_name = street_name[0]
return street_name.split()[-1]

streets = streets[streets['name'].notna()]

display(streets)
```
u v key osmid highway name maxspeed oneway length geometry junction lanes ref width bridge access street_type
0 4812701696 4812701699 0 488979392 residential Callemonda Close 50 False 89.588 LINESTRING (151.68153 -33.01304, 151.68144 -33... NaN NaN NaN NaN NaN NaN Close
1 4812701699 1844333226 0 172707768 tertiary Dalrymple Street 50 False 91.022 LINESTRING (151.68130 -33.01380, 151.68228 -33... NaN NaN NaN NaN NaN NaN Street
2 4812701699 1844333215 0 172707768 tertiary Dalrymple Street 50 False 162.463 LINESTRING (151.68130 -33.01380, 151.68105 -33... NaN NaN NaN NaN NaN NaN Street
3 4812701699 4812701696 0 488979392 residential Callemonda Close 50 False 89.588 LINESTRING (151.68130 -33.01380, 151.68138 -33... NaN NaN NaN NaN NaN NaN Close
4 4812701700 4812701707 0 488979393 residential Twin View Court 50 False 78.934 LINESTRING (151.67519 -33.01230, 151.67523 -33... NaN NaN NaN NaN NaN NaN Court
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
2733 1847021562 1847021587 0 173954199 residential Boldon Close 50 False 112.986 LINESTRING (151.68051 -32.97493, 151.68038 -32... NaN NaN NaN NaN NaN NaN Close
2734 1847021565 1847021545 0 418259583 tertiary_link Algona Road 60 False 131.581 LINESTRING (151.69368 -32.97523, 151.69442 -32... NaN NaN NaN NaN NaN NaN Road
2735 1847021565 2157256210 0 254493907 residential Lees Street 50 False 342.531 LINESTRING (151.69368 -32.97523, 151.69353 -32... NaN NaN NaN NaN NaN NaN Street
2736 1847021565 1847021576 0 418259583 tertiary_link Algona Road 60 False 37.266 LINESTRING (151.69368 -32.97523, 151.69365 -32... NaN NaN NaN NaN NaN NaN Road
2737 1847021566 1847021583 0 173954217 residential Church Street 50 False 52.110 LINESTRING (151.68817 -32.97526, 151.68808 -32... NaN NaN NaN NaN NaN NaN Street

2662 rows × 17 columns

Finally time to plot! Since the legend in this case can potentially be very large, we provide some specifics on locating the legend.

In [5]:
```ax = streets.plot(column='street_type',legend=True,legend_kwds={'bbox_to_anchor':(0.5,-0.05),'shadow':True,'ncol':3,'loc':'upper center'})
ax.axis('off')
fig = ax.get_figure()
fig.savefig('street_type_map.svg',bbox_inches='tight')
```