04-simplify-graph-consolidate-nodes.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Simplify network topology and consolidate intersections\n",
    "\n",
    "Author: [Geoff Boeing](https://geoffboeing.com/)\n",
    "\n",
    "  - [Overview of OSMnx](http://geoffboeing.com/2016/11/osmnx-python-street-networks/)\n",
    "  - [GitHub repo](https://github.com/gboeing/osmnx)\n",
    "  - [Examples, demos, tutorials](https://github.com/gboeing/osmnx-examples)\n",
    "  - [Documentation](https://osmnx.readthedocs.io/en/stable/)\n",
    "  - [Journal article/citation](http://geoffboeing.com/publications/osmnx-complex-street-networks/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1.2.2'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import networkx as nx\n",
    "import osmnx as ox\n",
    "\n",
    "%matplotlib inline\n",
    "ox.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Complex intersection consolidation\n",
    "\n",
    "Many real-world street networks feature complex intersections and traffic circles, resulting in a cluster of graph nodes where there is really just one true intersection, as we would think of it in transportation or urban design. Similarly, divided roads are often represented by separate centerline edges: the intersection of two divided roads thus creates 4 nodes, representing where each edge intersects a perpendicular edge, but these 4 nodes represent a single intersection in the real world. Traffic circles similarly create a cluster of nodes where each street's edge intersects the roundabout.\n",
    "\n",
    "OSMnx can consolidate nearby intersections and optionally rebuild the graph's topology."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# get a street network and plot it with all edge intersections\n",
    "point = 37.858495, -122.267468\n",
    "G = ox.graph_from_point(point, network_type=\"drive\", dist=500)\n",
    "fig, ax = ox.plot_graph(G, node_color=\"r\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice the complex intersections and traffic circles creating clusters of nodes.\n",
    "\n",
    "We'll specify that any nodes with 15 meter buffers of each other in this network are part of the same intersection. Adjust this tolerance based on the street design standards in the community you are examining, and use a projected graph to work in meaningful units like meters. We'll also specify that we do not want dead-ends returned in our list of consolidated intersections."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get a GeoSeries of consolidated intersections\n",
    "G_proj = ox.project_graph(G)\n",
    "ints = ox.consolidate_intersections(G_proj, rebuild_graph=False, tolerance=15, dead_ends=False)\n",
    "len(ints)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# compare to number of nodes in original graph\n",
    "len(G)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that these cleaned up intersections give us more accurate intersection counts and densities, but do not alter or integrate with the network's topology.\n",
    "\n",
    "To do that, we need to **rebuild the graph**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# consolidate intersections and rebuild graph topology\n",
    "# this reconnects edge geometries to the new consolidated nodes\n",
    "G2 = ox.consolidate_intersections(G_proj, rebuild_graph=True, tolerance=15, dead_ends=False)\n",
    "len(G2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, ax = ox.plot_graph(G2, node_color=\"r\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice how the traffic circles' many nodes are merged into a new single centroid node, with edge geometries extended to connect to it. Similar consolidation occurs at the intersection of the divided roads.\n",
    "\n",
    "Running `consolidate_intersections` with `rebuild_graph=True` may yield somewhat (but not very) different intersection counts/densities compared to `rebuild_graph=False`. The difference lies in that the latter just merges buffered node points that overlap, whereas the former checks the topology of the overlapping node buffers before merging them.\n",
    "\n",
    "This prevents topologically remote but spatially proximate nodes from being merged. For example:\n",
    "\n",
    "  - A street intersection may lie directly below a freeway overpass's intersection with an on-ramp. We would not want to merge these together and connnect their edges: they are distinct junctions in the system of roads.\n",
    "  - In a residential neighborhood, a bollarded street may create a dead-end immediately next to an intersection or traffic circle. We would not want to merge this dead-end with the intersection and connect their edges.\n",
    "\n",
    "These examples illustrate (two-dimensional) geometric proximity, but topological remoteness. Accordingly, in some situations we may expect higher intersection counts when using `rebuild_graph=True` because it is more cautious with merging in these cases. The trade-off is that it has higher time complexity than `rebuild_graph=False`.\n",
    "\n",
    "## 2. Graph simplification\n",
    "\n",
    "Use simplification to clean-up nodes that are not intersections or dead-ends while retaining the complete edge geometry. OSMnx does this automatically by default when constructing a graph."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a network around some (lat, lng) point and plot it\n",
    "location_point = (33.299896, -111.831638)\n",
    "G = ox.graph_from_point(location_point, dist=500, simplify=False)\n",
    "fig, ax = ox.plot_graph(G, node_color=\"r\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# show which nodes we'd remove if we simplify it (yellow)\n",
    "nc = [\"r\" if ox.simplification._is_endpoint(G, node) else \"y\" for node in G.nodes()]\n",
    "fig, ax = ox.plot_graph(G, node_color=nc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# simplify the network\n",
    "G2 = ox.simplify_graph(G)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plot the simplified network and highlight any self-loop edges\n",
    "loops = [edge[0] for edge in nx.selfloop_edges(G2)]\n",
    "nc = [\"r\" if node in loops else \"y\" for node in G2.nodes()]\n",
    "fig, ax = ox.plot_graph(G2, node_color=nc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# turn off strict mode and see what nodes we'd remove\n",
    "nc = [\"r\" if ox.simplification._is_endpoint(G, node, strict=False) else \"y\" for node in G.nodes()]\n",
    "fig, ax = ox.plot_graph(G, node_color=nc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# simplify network with strict mode turned off\n",
    "G3 = ox.simplify_graph(G.copy(), strict=False)\n",
    "fig, ax = ox.plot_graph(G3, node_color=\"r\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Cleaning up the periphery of the network\n",
    "\n",
    "This is related to simplification. OSMnx by default (with clean_periphery parameter equal to True) buffers the area you request by 0.5km, and then retrieves the street network within this larger, buffered area. Then it simplifies the topology so that nodes represent intersections of streets (rather than including all the interstitial OSM nodes). Then it calculates the (undirected) degree of each node in this larger network. Next it truncates this network by the actual area you requested (either by bounding box, or by polygon). Finally it saves a dictionary of node degree values as a graph attribute.\n",
    "\n",
    "This has two primary benefits. First, it cleans up stray false edges around the periphery. If clean_periphery=False, peripheral non-intersection nodes within the requested area appear to be cul-de-sacs, as the rest of the edge leading to an intersection outside the area is ignored. If clean_periphery=True, the larger graph is first created, allowing simplification of such edges to their true intersections, allowing their entirety to be pruned after truncating down to the actual requested area. Second, it gives accurate node degrees by both a) counting node neighbors even if they fall outside the retained network (so you don't claim a degree-4 node is degree-2 because only 2 of its neighbors lie within the area), and b) not counting all those stray false edges' terminus nodes as cul-de-sacs that otherwise grossly inflate the count of nodes with degree=1, even though these nodes are really just interstitial nodes in the middle of a chopped-off street segment between intersections.\n",
    "\n",
    "See two examples below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get some bbox\n",
    "bbox = ox.utils_geo.bbox_from_point((45.518698, -122.679964), dist=300)\n",
    "north, south, east, west = bbox"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "G = ox.graph_from_bbox(north, south, east, west, network_type=\"drive\", clean_periphery=False)\n",
    "fig, ax = ox.plot_graph(G, node_color=\"r\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# the node degree distribution for this graph has many false cul-de-sacs\n",
    "k = dict(G.degree())\n",
    "{n: list(k.values()).count(n) for n in range(max(k.values()) + 1)}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Above, notice all the peripheral stray edge stubs. Below, notice these are cleaned up and that the node degrees are accurate with regards to the wider street network that may extend beyond the limits of the requested area."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "G = ox.graph_from_bbox(north, south, east, west, network_type=\"drive\")\n",
    "fig, ax = ox.plot_graph(G, node_color=\"r\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# the streets per node distribution for this cleaned up graph is more accurate\n",
    "# dict keys = count of streets emanating from the node (ie, intersections and dead-ends)\n",
    "# dict vals = number of nodes with that count\n",
    "k = nx.get_node_attributes(G, \"street_count\")\n",
    "{n: list(k.values()).count(n) for n in range(max(k.values()) + 1)}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A final example. Compare the network below to the ones in the section above. It has the stray peripheral edges cleaned up. Also notice toward the bottom left, two interstitial nodes remain in that east-west street. Why? These are actually intersections, but their (southbound) edges were removed because these edges' next intersections were south of the requested area's boundaries. However, OSMnx correctly kept these nodes in the graph because they are in fact intersections and should be counted in measures of intersection density, etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "location_point = (33.299896, -111.831638)\n",
    "G = ox.graph_from_point(location_point, dist=500, simplify=True)\n",
    "fig, ax = ox.plot_graph(G, node_color=\"r\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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