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update notebook
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@gboeing
gboeing committed Feb 21, 2024
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Showing 1 changed file with 41 additions and 34 deletions.
75 changes: 41 additions & 34 deletions modules/07-urban-networks-i/lecture.ipynb
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"source": [
"# download/model a street network for some city then visualize it\n",
"place = \"Piedmont, California, USA\"\n",
"G = ox.graph_from_place(place, network_type=\"drive\")\n",
"fig, ax = ox.plot_graph(G)"
"G = ox.graph.graph_from_place(place, network_type=\"drive\")\n",
"fig, ax = ox.plot.plot_graph(G)"
]
},
{
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"outputs": [],
"source": [
"# convert your graph to node and edge GeoPandas GeoDataFrames\n",
"gdf_nodes, gdf_edges = ox.graph_to_gdfs(G)\n",
"gdf_nodes, gdf_edges = ox.utils_graph.graph_to_gdfs(G)\n",
"gdf_nodes.head()"
]
},
Expand All @@ -314,7 +314,7 @@
"outputs": [],
"source": [
"# convert node/edge GeoPandas GeoDataFrames to a NetworkX MultiDiGraph\n",
"G2 = ox.graph_from_gdfs(gdf_nodes, gdf_edges, graph_attrs=G.graph)\n",
"G2 = ox.utils_graph.graph_from_gdfs(gdf_nodes, gdf_edges, graph_attrs=G.graph)\n",
"print(len(G2.nodes))\n",
"print(len(G2.edges))"
]
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"outputs": [],
"source": [
"# get our study site's geometry\n",
"gdf = ox.geocode_to_gdf(place)\n",
"gdf_proj = ox.project_gdf(gdf)\n",
"gdf = ox.geocoder.geocode_to_gdf(place)\n",
"gdf_proj = ox.projection.project_gdf(gdf)\n",
"geom_proj = gdf_proj[\"geometry\"].iloc[0]\n",
"geom_proj"
]
Expand All @@ -367,7 +367,7 @@
"outputs": [],
"source": [
"# project the graph (automatically) then check its new CRS\n",
"G_proj = ox.project_graph(G)\n",
"G_proj = ox.projection.project_graph(G)\n",
"G_proj.graph[\"crs\"]"
]
},
Expand All @@ -378,7 +378,7 @@
"outputs": [],
"source": [
"# show some basic stats about the (projected) network\n",
"ox.basic_stats(G_proj, area=area_m, clean_int_tol=10)"
"ox.stats.basic_stats(G_proj, area=area_m, clean_int_tol=10)"
]
},
{
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"outputs": [],
"source": [
"# save graph to disk as geopackage (for GIS) or GraphML file (for Gephi etc)\n",
"ox.save_graph_geopackage(G, filepath=\"./data/mynetwork.gpkg\")\n",
"ox.save_graphml(G, filepath=\"./data/mynetwork.graphml\")"
"ox.io.save_graph_geopackage(G, filepath=\"./data/mynetwork.gpkg\")\n",
"ox.io.save_graphml(G, filepath=\"./data/mynetwork.graphml\")"
]
},
{
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"source": [
"# color edges in original graph with centralities from line graph\n",
"ec = ox.plot.get_edge_colors_by_attr(G, \"edge_centrality\", cmap=\"inferno\")\n",
"fig, ax = ox.plot_graph(G, edge_color=ec, edge_linewidth=2, node_size=0)"
"fig, ax = ox.plot.plot_graph(G, edge_color=ec, edge_linewidth=2, node_size=0)"
]
},
{
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"outputs": [],
"source": [
"# impute missing edge speeds then calculate edge (free-flow) travel times\n",
"G = ox.add_edge_speeds(G)\n",
"G = ox.add_edge_travel_times(G)"
"G = ox.speed.add_edge_speeds(G)\n",
"G = ox.speed.add_edge_travel_times(G)"
]
},
{
Expand All @@ -455,8 +455,8 @@
"outputs": [],
"source": [
"# get the nearest network nodes to two lat/lng points\n",
"orig = ox.nearest_nodes(G, -122.245846, 37.828903)\n",
"dest = ox.nearest_nodes(G, -122.215006, 37.812303)"
"orig = ox.distance.nearest_nodes(G, -122.245846, 37.828903)\n",
"dest = ox.distance.nearest_nodes(G, -122.215006, 37.812303)"
]
},
{
Expand All @@ -466,8 +466,8 @@
"outputs": [],
"source": [
"# find the shortest path between these nodes, minimizing travel time, then plot it\n",
"route = ox.shortest_path(G, orig, dest, weight=\"travel_time\")\n",
"fig, ax = ox.plot_graph_route(G, route, node_size=0)"
"route = ox.routing.shortest_path(G, orig, dest, weight=\"travel_time\")\n",
"fig, ax = ox.plot.plot_graph_route(G, route, node_size=0)"
]
},
{
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"outputs": [],
"source": [
"# how long is our route in meters?\n",
"edge_lengths = ox.utils_graph.get_route_edge_attributes(G, route, \"length\")\n",
"edge_lengths = ox.utils_graph.route_to_gdf(G, route)[\"length\"]\n",
"sum(edge_lengths)"
]
},
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"outputs": [],
"source": [
"# how far is it between these two nodes as the crow flies (haversine)?\n",
"ox.distance.great_circle_vec(\n",
"ox.distance.great_circle(\n",
" G.nodes[orig][\"y\"], G.nodes[orig][\"x\"], G.nodes[dest][\"y\"], G.nodes[dest][\"x\"]\n",
")"
]
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"source": [
"# you can make query an unambiguous dict to help the geocoder find it\n",
"place = {\"city\": \"San Francisco\", \"state\": \"California\", \"country\": \"USA\"}\n",
"G = ox.graph_from_place(place, network_type=\"drive\", truncate_by_edge=True)\n",
"fig, ax = ox.plot_graph(G, figsize=(10, 10), node_size=0, edge_color=\"y\", edge_linewidth=0.2)"
"G = ox.graph.graph_from_place(place, network_type=\"drive\", truncate_by_edge=True)\n",
"fig, ax = ox.plot.plot_graph(G, figsize=(10, 10), node_size=0, edge_color=\"y\", edge_linewidth=0.2)"
]
},
{
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"outputs": [],
"source": [
"# you can get networks anywhere in the world\n",
"G = ox.graph_from_place(\"Sinalunga, Italy\", network_type=\"all\")\n",
"fig, ax = ox.plot_graph(G, node_size=0, edge_linewidth=0.5)"
"G = ox.graph.graph_from_place(\"Sinalunga, Italy\", network_type=\"all\")\n",
"fig, ax = ox.plot.plot_graph(G, node_size=0, edge_linewidth=0.5)"
]
},
{
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"# ...useful when OSM just doesn't already have a polygon for the place you want\n",
"lewis_hall = (34.019267, -118.283566)\n",
"one_mile = 1609 # meters\n",
"G = ox.graph_from_point(lewis_hall, dist=one_mile, network_type=\"drive\")\n",
"fig, ax = ox.plot_graph(G, node_size=0)"
"G = ox.graph.graph_from_point(lewis_hall, dist=one_mile, network_type=\"drive\")\n",
"fig, ax = ox.plot.plot_graph(G, node_size=0)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Consider study area boundaries and the artificial perimeter problem."
]
},
{
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"outputs": [],
"source": [
"# get NY subway rail network\n",
"G = ox.graph_from_place(\n",
"G = ox.graph.graph_from_place(\n",
" \"New York City, New York, USA\",\n",
" retain_all=False,\n",
" truncate_by_edge=True,\n",
" simplify=True,\n",
" custom_filter='[\"railway\"~\"subway\"]',\n",
")\n",
"\n",
"fig, ax = ox.plot_graph(G, node_size=0, edge_color=\"c\", edge_linewidth=0.2)"
"fig, ax = ox.plot.plot_graph(G, node_size=0, edge_color=\"c\", edge_linewidth=0.2)"
]
},
{
Expand All @@ -594,7 +601,7 @@
"source": [
"## Get any geospatial entities' geometries and attributes\n",
"\n",
"Use the `geometries` module to download entities, such as local amenities, points of interest, or building footprints, and turn them into a GeoDataFrame."
"Use the `features` module to download entities, such as local amenities, points of interest, or building footprints, and turn them into a GeoDataFrame."
]
},
{
Expand All @@ -604,9 +611,9 @@
"outputs": [],
"source": [
"# get all building footprints in some neighborhood\n",
"place = \"Civic Center, Los Angeles, California\"\n",
"place = \"Soho, New York, NY, USA\"\n",
"tags = {\"building\": True}\n",
"gdf = ox.geometries_from_place(place, tags)\n",
"gdf = ox.features.features_from_place(place, tags)\n",
"gdf.shape"
]
},
Expand All @@ -616,7 +623,7 @@
"metadata": {},
"outputs": [],
"source": [
"fig, ax = ox.plot_footprints(gdf, figsize=(3, 3))"
"fig, ax = ox.plot.plot_footprints(gdf, figsize=(3, 3))"
]
},
{
Expand All @@ -627,7 +634,7 @@
"source": [
"# get all parks and bus stops in some neighborhood\n",
"tags = {\"leisure\": \"park\", \"highway\": \"bus_stop\"}\n",
"gdf = ox.geometries_from_place(place, tags)\n",
"gdf = ox.features.features_from_place(place, tags)\n",
"gdf.shape"
]
},
Expand All @@ -640,7 +647,7 @@
"# restaurants near the empire state buildings\n",
"address = \"350 5th Ave, New York, NY 10001\"\n",
"tags = {\"amenity\": \"restaurant\"}\n",
"gdf = ox.geometries_from_address(address, tags=tags, dist=500)\n",
"gdf = ox.features.features_from_address(address, tags=tags, dist=500)\n",
"gdf[[\"name\", \"cuisine\", \"geometry\"]].dropna().head()"
]
},
Expand All @@ -662,7 +669,7 @@
"source": [
"## In-class workshop\n",
"\n",
"Git clone the [OSMnx examples](https://github.com/gboeing/osmnx-examples) repo to your desktop, then work through the notebooks (skip notebook 00, which covers a similar overview as this lecture)."
"Git clone the OSMnx [Examples Gallery](https://github.com/gboeing/osmnx-examples) repository to your desktop, then work through the notebooks (skip notebook 00, which covers a similar overview as this lecture)."
]
},
{
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