Transportation Emissions

Sankey Chart and Road Map for Santa Monica data.

Data Source: Google Environmental Insights Explorer.

Link to Project Code

Modes in Santa Monica Road Network

Import Libraries

import pandas as pd
import geopandas as gpd
import networkx as nx
import osmnx as ox
import matplotlib.pyplot as plt
import matplotlib.lines as mlines

Download OpenStreetMap Data

# Define the place name or address
place = "Santa Monica, California, USA"

# Retrieve the road network for the place
G = ox.graph_from_place(place, network_type='all', buffer_dist=1000)

# Retrieve the boundary polygon for the place
boundary = ox.geocode_to_gdf(place)

# Plot the road network within the boundary
fig, ax = plt.subplots()
ox.plot_graph(G, bgcolor='w', edge_color='gray', node_size=0, ax=ax)
boundary.plot(ax=ax, edgecolor='black', facecolor='none', linewidth=2)
plt.show();

<Figure size 672x480 with 0 Axes>

Acquire data as GeoDataFrames

# Convert the NetworkX graph to GeoDataFrames
nodes, edges = ox.graph_to_gdfs(G)

# Look at the data columns
edges.columns

# Look at counts per edge type
edge_types = edges['highway'].value_counts()
edge_types

footway                          10472
service                           9159
residential                       7450
tertiary                          2536
secondary                         1346
primary                            993
cycleway                           336
unclassified                       232
[steps, footway]                   148
pedestrian                          84
path                                76
trunk                               50
steps                               42
motorway_link                       38
secondary_link                      31
[residential, service]              27
[residential, footway]              26
[path, footway]                     16
motorway                            15
trunk_link                          13
[footway, service]                  12
[service, footway]                  12
tertiary_link                       10
[residential, path]                 10
[unclassified, service]              8
primary_link                         6
[footway, pedestrian]                6
[steps, path]                        6
[footway, cycleway]                  4
[steps, residential, footway]        4
[steps, pedestrian]                  4
[service, cycleway]                  2
[steps, residential]                 2
[steps, footway, corridor]           2
[residential, tertiary]              2
track                                2
[track, footway]                     2
[steps, path, footway]               2
Name: highway, dtype: int64

# Generalize highway types
# https://wiki.openstreetmap.org/wiki/Tag:highway%3Dpedestrian
edges['highway'] = edges['highway'].apply(lambda x: ', '.join(x) if isinstance(x, list) else x)
edges['highway'] = edges['highway'].str.replace('footway|steps|pedestrian|corridor|track', 'path')
edges['highway'] = edges['highway'].str.replace('roads_link|motorway_link|trunk_link|primary_link|secondary_link|motorway_junction', 'roads')
edges['highway'] = edges['highway'].str.replace('motorway|trunk|primary|secondary|tertiary|unclassified|residential|service', 'roads')
edges.loc[edges['highway'].str.contains('roads'), 'highway'] = 'roads'
edges.loc[edges['highway'].str.contains('cycleway'), 'highway'] = 'cycleway'
edges.loc[edges['highway'].str.contains('path'), 'highway'] = 'path'
edge_types = edges['highway'].value_counts()
edge_types

/var/folders/13/hf_sfnls1lq6kn8vsc92q40h0000gn/T/ipykernel_8329/3233845876.py:4: FutureWarning:

The default value of regex will change from True to False in a future version.

/var/folders/13/hf_sfnls1lq6kn8vsc92q40h0000gn/T/ipykernel_8329/3233845876.py:5: FutureWarning:

The default value of regex will change from True to False in a future version.

/var/folders/13/hf_sfnls1lq6kn8vsc92q40h0000gn/T/ipykernel_8329/3233845876.py:6: FutureWarning:

The default value of regex will change from True to False in a future version.

roads       21984
path        10862
cycleway      340
Name: highway, dtype: int64

Color-code roads by type

#https://stackoverflow.com/questions/63102436/coloring-edges-in-osmnx-graph-based-on-edge-attribute

# get one color for each highway type in the graph
color_list = ['#B0CFDE','green','red'] #
#color_list = ox.plot.get_colors(n=len(edge_types), cmap='hsv', return_hex=True)
#color_list = ['#00e600','#ffcc99','fuchsia']
#color_list = ['#ccccff','green','magenta']
#color_list = ['white','lime','magenta']
color_mapper = pd.Series(color_list, index=edge_types.index).to_dict()
edges['color'] = edges['highway'].map(color_mapper)

Plot details

# check the bounds for xlim and ylim
minx , miny , maxx , maxy = edges.total_bounds # = [-118.5250193,   33.9888725, -118.4328995,   34.0586015]

# custom legend
black_line = mlines.Line2D([], [], color='black', label='City Boundary')
blue_line = mlines.Line2D([], [], color='#B0CFDE', label='Road')
green_line = mlines.Line2D([], [], color='green', label='Path')
red_line = mlines.Line2D([], [], color='red', label='Cycleway')

fig, ax = plt.subplots(figsize=(10, 8))
edges.plot(ax=ax, color = edges['color'], linewidth=0.5, zorder=1);
boundary.plot(ax=ax, edgecolor='black', color='none', zorder=10);
ax.legend(handles=[black_line,blue_line,green_line,red_line], bbox_to_anchor=(1.2, 1.1)) #loc='lower left',
ax.set_ylim(miny, maxy)
ax.set_xlim(minx, maxx)
#ax.set_xticklabels([])
#ax.set_yticklabels([])
#ax.spines['top'].set_visible(False)
#ax.spines['right'].set_visible(False)
#ax.spines['bottom'].set_visible(False)
#ax.spines['left'].set_visible(False)
ax.axis('off')
plt.show();