data/ipynb/concave_hull_geographic_coordinates.ipynb

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   "source": [
    "import json\n",
    "import time\n",
    "\n",
    "import folium\n",
    "import numpy as np\n",
    "\n",
    "from uhull.alpha_shape import get_alpha_shape_polygons"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4ce4904e-2827-4bd6-b194-f949cbe6b7f2",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# loggibud dataset download: https://loggibud.s3.amazonaws.com/dataset.zip\n",
    "file_name_path_list = [\n",
    "    (\n",
    "        \"Brasília\",\n",
    "        \"../maps/points_brasilia_brazil.html\",\n",
    "        \"../dataset/data/delivery-instances-1.0/train/df/df-0.json\",\n",
    "    ),\n",
    "    (\n",
    "        \"Pará\",\n",
    "        \"../maps/points_para_brazil.html\",\n",
    "        \"../dataset/data/delivery-instances-1.0/train/pa/pa-0.json\",\n",
    "    ),\n",
    "    (\n",
    "        \"Rio de Janeiro\",\n",
    "        \"../maps/points_rio_de_janeiro_brazil.html\",\n",
    "        \"../dataset/data/delivery-instances-1.0/train/rj/rj-0.json\",\n",
    "    ),\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f4fc4cfa-65de-4eb4-84a2-e98ebdcb3d8b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "*** Location = Brasília ****\n",
      "Step 1: Loading json file for Brasília took 0.025 seconds\n",
      "Step 2: Create map and draw the points on the map for Brasília took 0.622 seconds.\n",
      "Step 3: Calculating the concave hull for the 10436 points of Brasília took 0.805 seconds.\n",
      "Step 4: Drawing the 22 calculated polygons on the map took 0.001 seconds\n",
      "Step 5: Saving map as html file took 7.125  seconds\n",
      "\n",
      "*** Location = Pará ****\n",
      "Step 1: Loading json file for Pará took 0.033 seconds\n",
      "Step 2: Create map and draw the points on the map for Pará took 0.415 seconds.\n",
      "Step 3: Calculating the concave hull for the 4238 points of Pará took 0.319 seconds.\n",
      "Step 4: Drawing the 11 calculated polygons on the map took 0.000 seconds\n",
      "Step 5: Saving map as html file took 2.915  seconds\n",
      "\n",
      "*** Location = Rio de Janeiro ****\n",
      "Step 1: Loading json file for Rio de Janeiro took 0.041 seconds\n",
      "Step 2: Create map and draw the points on the map for Rio de Janeiro took 1.735 seconds.\n",
      "Step 3: Calculating the concave hull for the 26833 points of Rio de Janeiro took 2.103 seconds.\n",
      "Step 4: Drawing the 28 calculated polygons on the map took 0.001 seconds\n",
      "Step 5: Saving map as html file took 19.439  seconds\n"
     ]
    }
   ],
   "source": [
    "MAP_COLORS = (\n",
    "    \"red\",\n",
    "    \"orange\",\n",
    "    \"green\",\n",
    "    \"purple\",\n",
    "    \"pink\",\n",
    "    \"darkblue\",\n",
    "    \"beige\",\n",
    "    \"gray\",\n",
    "    \"lightgreen\",\n",
    "    \"lightblue\",\n",
    "    \"lightgray\",\n",
    "    \"cadetblue\",\n",
    "    \"black\",\n",
    ")\n",
    "\n",
    "for location, file_name, file_path in file_name_path_list:\n",
    "    # Print location\n",
    "    print(f\"\\n*** Location = {location} ****\")\n",
    "\n",
    "    # Step 1: load json file\n",
    "    start_time = time.time()\n",
    "    with open(file_path) as file:\n",
    "        data = json.load(file)\n",
    "    print(\n",
    "        f\"Step 1: Loading json file for {location} took \"\n",
    "        f\"{time.time() - start_time:.3f} seconds\"\n",
    "    )\n",
    "\n",
    "    # Step 2: Create map and draw the points on the map\n",
    "    start_time = time.time()\n",
    "    locations = np.array(\n",
    "        [\n",
    "            (delivery[\"point\"][\"lat\"], delivery[\"point\"][\"lng\"])\n",
    "            for delivery in data[\"deliveries\"]\n",
    "        ]\n",
    "    )\n",
    "    region_map = folium.Map(\n",
    "        location=locations.mean(axis=0), zoom_start=11, tiles=\"cartodbpositron\"\n",
    "    )\n",
    "    for lat, lng in locations:\n",
    "        folium.Circle(\n",
    "            location=(lat, lng),\n",
    "            radius=20,\n",
    "            fill=True,\n",
    "            color=\"blue\",\n",
    "            popup=f\"location = ({lat}, {lng})\",\n",
    "        ).add_to(region_map)\n",
    "    print(\n",
    "        f\"Step 2: Create map and draw the points on the map for {location} \"\n",
    "        f\"took {time.time() - start_time:.3f} seconds.\"\n",
    "    )\n",
    "\n",
    "    # Step 3: Calculate the concave hull for the N location points.\n",
    "    start_time = time.time()\n",
    "    polygons = get_alpha_shape_polygons(\n",
    "        coordinates_points=[(lng, lat) for lat, lng in locations],\n",
    "        alpha=6 * 1.5,\n",
    "    )\n",
    "    print(\n",
    "        f\"Step 3: Calculating the concave hull for the {locations.shape[0]} points \"\n",
    "        f\"of {location} took {time.time() - start_time:.3f} seconds.\"\n",
    "    )\n",
    "\n",
    "    # Step 4: Draw the calculated polygons on the map.\n",
    "    start_time = time.time()\n",
    "    for i, polygon in enumerate(polygons):\n",
    "        folium.Polygon(\n",
    "            locations=[(lat, lng) for lng, lat in polygon],\n",
    "            color=MAP_COLORS[i % len(MAP_COLORS)],\n",
    "        ).add_to(region_map)\n",
    "    print(\n",
    "        f\"Step 4: Drawing the {len(polygons)} calculated polygons on the map took {time.time() - start_time:.3f} seconds\"\n",
    "    )\n",
    "\n",
    "    # Step 5: Save map as html file\n",
    "    start_time = time.time()\n",
    "    region_map.save(file_name)\n",
    "    print(\n",
    "        f\"Step 5: Saving map as html file took {time.time() - start_time:.3f}  seconds\"\n",
    "    )"
   ]
  }
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