Coloring Street Networks using OSMnx

OSMnx is a Python package for downloading boundary and street networks from OpenStreetMaps.
This notebook is a guide for using OSMnx to download OSM data for a specific place and color the network of roads within that area.
Inspiring Sources

Import needed libraries

import osmnx as ox
import pandas as pd
import geopandas as gpd
import networkx as nx
import numpy as np
from matplotlib.pyplot import figure

%matplotlib inline
ox.config(log_console=True, use_cache=True)

Instructions for setting up a GEO/GIS Python environment

Function to color roads by the highway type

# This function takes the road name and it returns the color
def colorcode(highway_type):
    if ('primary' in highway_type): 
        return '#f6cf71'  #yellow
    elif ('trunk' in highway_type):
        return '#f6cf71'  #yellow
    elif ('secondary' in highway_type):
        return '#15964d'  #green
    elif ('tertiary' in highway_type):
        return '#15964d'  #green
    elif ('motorway' in highway_type):  # interstates and motorways
        return '#1C94F0'  #blue
    else:
        return '#313131'  #dark gray - all residential, living-streets, & unclassified

Function to set the line width based on the highway type

# This function takes the highway type and a 3-letter code for the language and it returns the width of the line
def linewidth(highway_type):
    if ('primary' in highway_type): 
        return 0.5
    elif ('trunk' in highway_type):
        return 0.6
    elif ('secondary' in highway_type):
        return 0.35
    elif ('tertiary' in highway_type):
        return 0.35
    elif ('motorway' in highway_type):  # interstates and motorways
        return 0.7
    else:
        return 0.2  #dark gray - all residential, living-streets, & unclassified
    

Create a networkx graph from OSM data

parameter definitions
graph_from_place create a networkx graph from OSM data within the spatial boundaries of some geocodable place(s).
place an OSM location containing polygon boundaries
truncate_by_edge=True reveals the complete road where 1 node is out of bounding box
which_result can be a 1 or 2 whichever will work to get the polygon/polyline results and not a point
network_type='drive' select all driveable roads
more parameter info

place='Fayette County, Kentucky, United States'

graph = ox.graph_from_place(place, network_type='drive', truncate_by_edge=True, which_result=1) 

Convert the graph into GeoDataFrame

geo_df = ox.graph_to_gdfs(graph, nodes=False)

Create coloring and line width list for each polyline

# iterate over each row of the dataframe, get the line color for each polyline
edgeColorList = [colorcode(str(row['highway']).lower()) for index, row in geo_df.iterrows()]

# iterate over each row of the dataframe, get the line width for each polyline
lineWidthList = [linewidth(str(row['highway']).lower()) for index, row in geo_df.iterrows()]

Plot a networkx spatial graph

explanation of parameters

fig, ax = ox.plot_graph(graph,bgcolor='k', node_size=0, node_color='w', node_edgecolor='gray', node_zorder=2,
                        edge_color=edgeColorList, edge_linewidth=lineWidthList, edge_alpha=1, dpi=300)

Output spatial graph to PNG file

fig.patch.set_facecolor('k')

fig.set_size_inches(10, 8.5)
fig.savefig('Fayette_County_KY_highways.png', facecolor = fig.get_facecolor(), dpi=300)

---

Instructions for setting-up Python Conda environment

Setting up a GEO/GIS environment
conda create -n geo_env
conda activate geo_env
conda config --env --add channels conda-forge
conda config --env --set channel_priority strict

Install packages
conda install python=3 geopandas pandas numpy shapely fiona pyproj six matplotlib mapclassify descartes osmnx networkx jupyterlab rtree geojson contextily folium ipyleaflet pysal rasterio rasterstats pycrs

Sources

OSMnx: Python for Street Networks
gboeing/osmnx-examples Github
Retrieving OpenStreetMap data in Python
Creating beautiful maps with Python Python + OSMnx