notebooks/snkit-demo.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create a topological network from spatial data\n",
    "\n",
    "`snkit` helps with spatial networks problems, mainly the kind of thing that comes up in data cleaning, tidying and pre-processing.\n",
    "\n",
    "This notebook goes through a cleaning process:\n",
    "- read in a set of nearly-connected points and lines\n",
    "- add nodes at the line endpoints\n",
    "- links the remaining points to the existing lines (if they're within a specified distance)\n",
    "- give everything a unique ID\n",
    "- add from/to ids to the edges\n",
    "\n",
    "The data is output in a state ready to be used as a network graph for further analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import snkit\n",
    "import snkit.network\n",
    "import networkx\n",
    "import pandas\n",
    "import geopandas\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "                                             geometry\n",
       "0   LINESTRING (0.00412 -0.04086, 0.00034 -0.03347...\n",
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      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = geopandas.read_file(\"small-demo.geojson\")\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "edges = data[:3]\n",
    "nodes = data[3:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_network(n):\n",
    "    df = pandas.concat([n.nodes, n.edges], axis=0, sort=False)\n",
    "    df.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A network consists of nodes and edges - `snkit` stores these as two separate `GeoDataFrame`s."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "base_network = snkit.Network(nodes, edges)\n",
    "plot_network(base_network)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Add points at the end of each line. This uses the first and last coordinate pair from each `LineString` geometry and creates a node at each point."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "with_endpoints = snkit.network.add_endpoints(base_network)\n",
    "plot_network(with_endpoints)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Links each point to any (all) edges within a distance threshold.\n",
    "\n",
    "For a typical edge, this:\n",
    "- finds any nodes within the given distance\n",
    "- creates new straight-line edges from the edge directly to each node\n",
    "- splits the original edge at each point where the new edges intersect"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "linked = snkit.network.link_nodes_to_edges_within(with_endpoints, 0.01)\n",
    "plot_network(linked)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Linking nodes is sensitive to the distance parameter - depending on the dataset or problem, some nodes will not be connected if they are too far from the nearest line (see top right in the figure below)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "linked_n = snkit.network.link_nodes_to_edges_within(with_endpoints, 0.005)\n",
    "plot_network(linked_n)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Add IDs to each node and edge, then add topology, recording the ids of nodes at each end of each edge. \n",
    "\n",
    "This is saved as `from_id` and `to_id` but `snkit` currently has no opinion about direction or ordering - from/to could easily be reversed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "with_topology = snkit.network.add_topology(snkit.network.add_ids(linked))\n",
    "plot_network(with_topology)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Preview data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>geometry</th>\n",
       "      <th>id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>POINT (0.03090 0.04429)</td>\n",
       "      <td>node_0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>POINT (0.03090 0.03759)</td>\n",
       "      <td>node_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>POINT (0.02129 0.03948)</td>\n",
       "      <td>node_2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>POINT (0.02987 -0.02180)</td>\n",
       "      <td>node_3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>POINT (0.03759 -0.02798)</td>\n",
       "      <td>node_4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   geometry      id\n",
       "0   POINT (0.03090 0.04429)  node_0\n",
       "1   POINT (0.03090 0.03759)  node_1\n",
       "2   POINT (0.02129 0.03948)  node_2\n",
       "3  POINT (0.02987 -0.02180)  node_3\n",
       "4  POINT (0.03759 -0.02798)  node_4"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with_topology.nodes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>geometry</th>\n",
       "      <th>id</th>\n",
       "      <th>from_id</th>\n",
       "      <th>to_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LINESTRING (0.00412 -0.04086, 0.00190 -0.03652)</td>\n",
       "      <td>edge_0</td>\n",
       "      <td>node_10</td>\n",
       "      <td>node_22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LINESTRING (0.00190 -0.03652, 0.00034 -0.03347...</td>\n",
       "      <td>edge_1</td>\n",
       "      <td>node_22</td>\n",
       "      <td>node_21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LINESTRING (-0.00010 -0.02843, -0.00034 -0.025...</td>\n",
       "      <td>edge_2</td>\n",
       "      <td>node_21</td>\n",
       "      <td>node_25</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LINESTRING (0.01717 -0.01081, 0.02283 -0.00893...</td>\n",
       "      <td>edge_3</td>\n",
       "      <td>node_25</td>\n",
       "      <td>node_26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LINESTRING (0.03044 0.01900, 0.03021 0.02043, ...</td>\n",
       "      <td>edge_4</td>\n",
       "      <td>node_26</td>\n",
       "      <td>node_17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "                                            geometry      id  from_id    to_id\n",
       "0    LINESTRING (0.00412 -0.04086, 0.00190 -0.03652)  edge_0  node_10  node_22\n",
       "1  LINESTRING (0.00190 -0.03652, 0.00034 -0.03347...  edge_1  node_22  node_21\n",
       "2  LINESTRING (-0.00010 -0.02843, -0.00034 -0.025...  edge_2  node_21  node_25\n",
       "3  LINESTRING (0.01717 -0.01081, 0.02283 -0.00893...  edge_3  node_25  node_26\n",
       "4  LINESTRING (0.03044 0.01900, 0.03021 0.02043, ...  edge_4  node_26  node_17"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with_topology.edges.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Save the data to a [geopackage](https://www.geopackage.org/) for later visualisation or analysis:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "with_topology.nodes.to_file(\"network.gpkg\", layer=\"nodes\", driver=\"GPKG\", index=False)\n",
    "with_topology.edges.to_file(\"network.gpkg\", layer=\"edges\", driver=\"GPKG\", index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Convert to [`networkx.Graph`](https://networkx.org/documentation/stable/reference/classes/graph.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "G = snkit.network.to_networkx(with_topology)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "..which gives us access to a whole host of algorithms in the `networkx` library.\n",
    "\n",
    "For example, we can find the shortest path between two nodes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['node_0',\n",
       " 'node_16',\n",
       " 'node_18',\n",
       " 'node_17',\n",
       " 'node_26',\n",
       " 'node_25',\n",
       " 'node_21',\n",
       " 'node_22',\n",
       " 'node_10']"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "networkx.shortest_path(G, \"node_0\", \"node_10\")"
   ]
  }
 ],
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