Distance based calculation of connection costs

Distance_based_calculation_of_connection_costs.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import shapely.geometry as spg"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We specify location of a cleint and location of an access hub as points. The two locations are different, so obviously they do not intersect."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False\n"
     ]
    }
   ],
   "source": [
    "client_location = spg.point.Point(0, 1.6) # this will come from the geocoder lib \n",
    "access_hub1 = spg.point.Point(0, 0) # this will come from the KML files\n",
    "print(client_location.intersects(access_hub1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we use `buffer()` to define three circles around the access hub point. Each circle denotes costs for connecting to the access hub for the points inside the circle. The lcient is located inside the medium cost circle, but not in the low cost circle, so the connection costs will be medium."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False\n",
      "True\n",
      "True\n"
     ]
    }
   ],
   "source": [
    "access_hub1_low_costs = access_hub1.buffer(1)\n",
    "access_hub1_medium_costs = access_hub1.buffer(2)\n",
    "access_hub1_high_costs = access_hub1.buffer(3)\n",
    "\n",
    "print(client_location.intersects(access_hub1_low_costs))\n",
    "print(client_location.intersects(access_hub1_medium_costs))\n",
    "print(client_location.intersects(access_hub1_high_costs))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With many access hub points and cost levels iterating over all them and checking for intersection may become too slow, so we will use GIS index to speed up the lookup."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "access_hub2 = spg.point.Point(0, 4.5) # this will come from the KML files\n",
    "access_hub2_low_costs = access_hub2.buffer(1)\n",
    "access_hub2_medium_costs = access_hub2.buffer(2)\n",
    "access_hub2_high_costs = access_hub2.buffer(3)\n",
    "\n",
    "access_hub1_low_costs.tag = 'hub1_low'\n",
    "access_hub1_medium_costs.tag = 'hub1_medium'\n",
    "access_hub1_high_costs.tag = 'hub1_high'\n",
    "access_hub2_low_costs.tag = 'hub2_low'\n",
    "access_hub2_medium_costs.tag = 'hub2_medium'\n",
    "access_hub2_high_costs.tag = 'hub2_high'\n",
    "\n",
    "all_costs = [\n",
    "    access_hub1_low_costs,\n",
    "    access_hub1_medium_costs,\n",
    "    access_hub1_high_costs,\n",
    "    access_hub2_low_costs,\n",
    "    access_hub2_medium_costs,\n",
    "    access_hub2_high_costs\n",
    "    ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from shapely.strtree import STRtree\n",
    "\n",
    "gis_index = STRtree(all_costs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hub1_medium\n",
      "hub1_high\n",
      "hub2_high\n"
     ]
    }
   ],
   "source": [
    "possible_costs = gis_index.query(client_location)\n",
    "for pc in possible_costs:\n",
    "    if client_location.intersects(pc):\n",
    "        print(pc.tag)"
   ]
  }
 ],
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