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yeesian/washingtonDC_osm.ipynb

Created October 16, 2014 03:11
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A demo of OpenStreetMap.jl on Washington DC
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washingtonDC_osm.ipynb
{
"metadata": {
"language": "Julia",
"name": "",
"signature": "sha256:a48e3d9e197e67b0afb43ea3d6214442015448dcd6256633ec5c679ab3b5f4b2"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"using OpenStreetMap"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"MAP_FILENAME = \"./data/OpenStreetMap/WashingtonDC.osm\""
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 2,
"text": [
"\"./data/OpenStreetMap/WashingtonDC.osm\""
]
}
],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"nodes, hwys, builds, feats = getOSMData(MAP_FILENAME, nodes=true, highways=true, buildings=true, features=true)\n",
"\n",
"println(\"Number of nodes: $(length(nodes))\")\n",
"println(\"Number of highways: $(length(hwys))\")\n",
"println(\"Number of buildings: $(length(builds))\")\n",
"println(\"Number of features: $(length(feats))\")"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Number of nodes: 2447660\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Number of highways: 65276\n",
"Number of buildings: 185828\n",
"Number of features: 12077\n"
]
}
],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# coordinates taken from dc-baltimore-mayland in \n",
"# https://github.com/mapzen/metroextractor-cities/blob/master/cities.json\n",
"bounds = OpenStreetMap.Bounds(38.539, 39.631, -77.599, -76.058)\n",
"cropMap!(nodes, bounds, highways=hwys, buildings=builds, features=feats, delete_nodes=false)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"roads = roadways(hwys)\n",
"peds = walkways(hwys)\n",
"cycles = cycleways(hwys)\n",
"bldg_classes = classify(builds)\n",
"feat_classes = classify(feats);"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"reference = OpenStreetMap.centerBounds(bounds)\n",
"nodesENU = lla2enu(nodes, reference)\n",
"boundsENU = lla2enu(bounds, reference)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 6,
"text": [
"Bounds{ENU}(-60326.806830156005,60897.68615543068,-66146.76253846746,67174.56415206788)"
]
}
],
"prompt_number": 6
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"network = createGraph(nodesENU, hwys, roads, Set(1:8))\n",
"println(\"Graph formed with $(Graphs.num_vertices(network.g)) vertices and $(Graphs.num_edges(network.g)) edges.\")"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Graph formed with 415727 vertices and 813049 edges."
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n"
]
}
],
"prompt_number": 7
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"length(network.v), length(nodesENU)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 8,
"text": [
"(415727,2447660)"
]
}
],
"prompt_number": 8
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There are alot more nodes in the original OSM data, than there are in the graph we've built, so we filter out the ones we don't need:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"filter!((k,v)->haskey(network.v,k), nodesENU)\n",
"length(nodesENU)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 9,
"text": [
"415727"
]
}
],
"prompt_number": 9
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now let's read in some firehouse data (taken from https://github.com/codefordc/ERDA)."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"using DataFrames\n",
"df = readtable(\"./data/DC_Emergency_Response_Data/FirehouseData.csv\")"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<table class=\"data-frame\"><tr><th></th><th>X</th><th>Y</th><th>OBJECTID_1</th><th>OBJECTID</th><th>NAME</th><th>GIS_ID</th><th>SSL</th><th>ADDRESS</th><th>PHONE</th><th>BFC</th><th>AMBULANCE</th><th>RESCSQUAD</th><th>MEDICUNIT</th><th>TRUCK</th><th>SPECIALTY</th><th>DETAIL</th><th>TYPE</th><th>ENGINE</th><th>BATTALION</th><th>RENSTATUS</th><th>AID</th><th>REL_UNITS</th><th>WEB_URL</th></tr><tr><th>1</th><td>-77.00514742400337</td><td>38.83089499410553</td><td>1</td><td>33.0</td><td>Engine 33 Station</td><td>Fire_032</td><td>NA</td><td>101 ATLANTIC STREET SE</td><td>(202) 673-3233</td><td>0</td><td>33.0</td><td>0.0</td><td>25.0</td><td>8.0</td><td>NA</td><td>NA</td><td>Other Station</td><td>33.0</td><td>3.0</td><td>OPEN</td><td>294471.0</td><td>NA</td><td>http://app.fems.dc.gov/details.asp?cid=77</td></tr><tr><th>2</th><td>-77.04979813195938</td><td>38.9054942897667</td><td>2</td><td>1.0</td><td>Engine 1 Station</td><td>Fire_026</td><td>0050 0822</td><td>2225 M STREET NW</td><td>(202) 673-3201</td><td>0</td><td>0.0</td><td>0.0</td><td>1.0</td><td>2.0</td><td>Twinned Agent Unit 2</td><td>NA</td><td>Other Station</td><td>1.0</td><td>6.0</td><td>OPEN</td><td>294540.0</td><td>NA</td><td>http://app.fems.dc.gov/details.asp?cid=45</td></tr><tr><th>3</th><td>-77.01948669436348</td><td>38.897074068143674</td><td>3</td><td>2.0</td><td>Engine 2 Station</td><td>Fire_022</td><td>0488 0833</td><td>500 F STREET NW</td><td>(202) 673-3202</td><td>0</td><td>0.0</td><td>1.0</td><td>0.0</td><td>0.0</td><td>EMS 1-6, EMS 5, RS-1 Support Unit w/small boat, FCU ,FFD</td><td>EMS Supervisor 1-6, EMS Supervisor 5, Office of the Firefighting DFC, Mobile Command Unit, Rescue Squad 1, RS-1 High-angle Rescue, Swift Water Rescu</td><td>Other Station</td><td>2.0</td><td>6.0</td><td>OPEN</td><td>299998.0</td><td>NA</td><td>http://app.fems.dc.gov/details.asp?cid=46</td></tr><tr><th>4</th><td>-77.0109004284146</td><td>38.895834462873715</td><td>4</td><td>3.0</td><td>Engine 3 Station</td><td>Fire_035</td><td>0630 0833</td><td>439 NEW JERSEY AVENUE NW</td><td>(202) 673-3203</td><td>0</td><td>0.0</td><td>0.0</td><td>3.0</td><td>0.0</td><td>NA</td><td>NA</td><td>Other Station</td><td>3.0</td><td>6.0</td><td>OPEN</td><td>237163.0</td><td>NA</td><td>http://app.fems.dc.gov/details.asp?cid=47</td></tr><tr><th>5</th><td>-77.02502518783706</td><td>38.923396559571565</td><td>5</td><td>4.0</td><td>Engine 4 Station</td><td>Fire_011</td><td>2882 1036</td><td>2531 SHERMAN AVENUE NW</td><td>(202) 673-3204</td><td>0</td><td>0.0</td><td>0.0</td><td>4.0</td><td>0.0</td><td>AIR 1</td><td>Safety Officer, Special Operations Office, Special Operations BC, Special Operations DC, Mask Room</td><td>Other Station</td><td>4.0</td><td>4.0</td><td>OPEN</td><td>232303.0</td><td>NA</td><td>http://app.fems.dc.gov/details.asp?cid=48</td></tr><tr><th>6</th><td>-77.06843588222283</td><td>38.91143793533217</td><td>6</td><td>5.0</td><td>Engine 5 Station</td><td>Fire_018</td><td>1277 0805</td><td>3412 DENT PLACE NW</td><td>(202) 673-3205</td><td>0</td><td>0.0</td><td>0.0</td><td>5.0</td><td>0.0</td><td>Rehab Unit, Rehab Support Unit, CU</td><td>NA</td><td>Other Station</td><td>5.0</td><td>5.0</td><td>OPEN</td><td>294568.0</td><td>NA</td><td>http://app.fems.dc.gov/details.asp?cid=49</td></tr><tr><th>7</th><td>-77.01607757351637</td><td>38.9076905728035</td><td>7</td><td>6.0</td><td>Engine 6 Station</td><td>Fire_028</td><td>NA</td><td>1300 NEW JERSEY AVENUE NW</td><td>(202) 673-3206</td><td>0</td><td>6.0</td><td>0.0</td><td>0.0</td><td>4.0</td><td>NA</td><td>EMS Equipment Office</td><td>Other Station</td><td>6.0</td><td>1.0</td><td>OPEN</td><td>294514.0</td><td>NA</td><td>http://app.fems.dc.gov/details.asp?cid=50</td></tr><tr><th>8</th><td>-77.01122977923366</td><td>38.87692863421768</td><td>8</td><td>7.0</td><td>Engine 7 Station</td><td>Fire_021</td><td>NA</td><td>1101 HALF STREET SW</td><td>(202) 673-3207</td><td>0</td><td>7.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>NA</td><td>NA</td><td>Other Station</td><td>7.0</td><td>2.0</td><td>OPEN</td><td>277735.0</td><td>NA</td><td>http://app.fems.dc.gov/details.asp?cid=51</td></tr><tr><th>9</th><td>-76.98294358225317</td><td>38.885539857826345</td><td>9</td><td>8.0</td><td>Engine 8 Station</td><td>Fire_019</td><td>1073 0806</td><td>1520 C STREET SE</td><td>(202) 673-3208</td><td>2</td><td>8.0</td><td>0.0</td><td>8.0</td><td>0.0</td><td>EMS 2, AIR 2,MEDIC UNIT 35,</td><td>Emergency Medical Supervisor 2</td><td>Other Station</td><td>8.0</td><td>2.0</td><td>OPEN</td><td>289723.0</td><td>NA</td><td>http://app.fems.dc.gov/details.asp?cid=52</td></tr><tr><th>10</th><td>-77.03752553414442</td><td>38.91729646933168</td><td>10</td><td>9.0</td><td>Engine 9 Station</td><td>Fire_025</td><td>0175 0826</td><td>1617 U STREET NW</td><td>(202) 673-3209</td><td>0</td><td>28.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>NA</td><td>NA</td><td>Other Station</td><td>9.0</td><td>4.0</td><td>OPEN</td><td>241846.0</td><td>NA</td><td>http://app.fems.dc.gov/details.asp?cid=53</td></tr><tr><th>11</th><td>-76.98669420018305</td><td>38.90158731196803</td><td>11</td><td>10.0</td><td>Engine 10 Station</td><td>Fire_029</td><td>NA</td><td>1342 FLORIDA AVENUE NE</td><td>(202) 673-3210</td><td>0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>13.0</td><td>NA</td><td>NA</td><td>Other Station</td><td>10.0</td><td>1.0</td><td>OPEN</td><td>294519.0</td><td>NA</td><td>http://app.fems.dc.gov/details.asp?cid=54</td></tr><tr><th>12</th><td>-77.03306259216362</td><td>38.93224649081721</td><td>12</td><td>11.0</td><td>Engine 11 Station</td><td>Fire_012</td><td>2678 0844</td><td>3420 14TH STREET NW</td><td>(202) 673-3211</td><td>4</td><td>11.0</td><td>0.0</td><td>0.0</td><td>6.0</td><td>EMS 4-5</td><td>EMS Supervisor 4-5</td><td>Other Station</td><td>11.0</td><td>4.0</td><td>OPEN</td><td>234580.0</td><td>NA</td><td>http://app.fems.dc.gov/details.asp?cid=55</td></tr><tr><th>13</th><td>-76.99918450431502</td><td>38.91991355574062</td><td>13</td><td>12.0</td><td>Engine 12 Station</td><td>Fire_030</td><td>NA</td><td>2225 5TH STREET NE</td><td>(202) 673-3212</td><td>1</td><td>12.0</td><td>0.0</td><td>12.0</td><td>0.0</td><td>Rapid Response Non-Transport Medic 12, HazMat Unit, HazMat Support</td><td>NA</td><td>Other Station</td><td>12.0</td><td>1.0</td><td>OPEN</td><td>294541.0</td><td>NA</td><td>http://app.fems.dc.gov/details.asp?cid=56</td></tr><tr><th>14</th><td>-77.00829363933359</td><td>38.94886630497476</td><td>14</td><td>14.0</td><td>Engine 14 Station</td><td>Fire_002</td><td>PAR 01240155</td><td>4801 NORTH CAPITOL STREET NE</td><td>(202) 673-3214</td><td>0</td><td>0.0</td><td>0.0</td><td>14.0</td><td>0.0</td><td>NA</td><td>NA</td><td>Other Station</td><td>14.0</td><td>1.0</td><td>OPEN</td><td>288276.0</td><td>NA</td><td>http://app.fems.dc.gov/details.asp?cid=58</td></tr><tr><th>15</th><td>-76.98607284808112</td><td>38.8648654596904</td><td>15</td><td>15.0</td><td>Engine 15 Station</td><td>Fire_031</td><td>NA</td><td>2101 14TH STREET SE</td><td>(202) 673-3215</td><td>3</td><td>15.0</td><td>3.0</td><td>15.0</td><td>0.0</td><td>Cave-in Unit, RS-3 Support Unit w/small boat</td><td>Rescue Squad 3, RS-3 Trench/Collapse Rescue, Swift Water Rescue, HazMat Ops, Cave-in Unit Support Unit,</td><td>Other Station</td><td>15.0</td><td>3.0</td><td>OPEN</td><td>156246.0</td><td>NA</td><td>http://app.fems.dc.gov/details.asp?cid=59</td></tr><tr><th>16</th><td>-77.03001561427483</td><td>38.90331066617566</td><td>16</td><td>16.0</td><td>Engine 16 Station</td><td>Fire_024</td><td>NA</td><td>1018 13TH STREET NW</td><td>(202) 673-3216</td><td>6</td><td>16.0</td><td>0.0</td><td>0.0</td><td>3.0</td><td>NA</td><td>EMS DC's and BC's Office, EMS Quality Assurance Office</td><td>Other Station</td><td>16.0</td><td>6.0</td><td>OPEN</td><td>240645.0</td><td>NA</td><td>http://app.fems.dc.gov/details.asp?cid=60</td></tr><tr><th>17</th><td>-76.98992820651962</td><td>38.932326221740425</td><td>17</td><td>17.0</td><td>Engine 17 Station</td><td>Fire_008</td><td>3929 0018</td><td>1227 MONROE STREET NE</td><td>(202) 673-3217</td><td>0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>NA</td><td>NA</td><td>Other Station</td><td>17.0</td><td>1.0</td><td>OPEN</td><td>294510.0</td><td>NA</td><td>http://app.fems.dc.gov/details.asp?cid=61</td></tr><tr><th>18</th><td>-76.99463683887988</td><td>38.883259246669</td><td>18</td><td>18.0</td><td>Engine 18 Station</td><td>Fire_020</td><td>0925 0826</td><td>414 8TH STREET SE</td><td>(202) 673-3218</td><td>0</td><td>18.0</td><td>0.0</td><td>0.0</td><td>7.0</td><td>NA</td><td>NA</td><td>Other Station</td><td>18.0</td><td>2.0</td><td>OPEN</td><td>280007.0</td><td>NA</td><td>http://app.fems.dc.gov/details.asp?cid=62</td></tr><tr><th>19</th><td>-76.96698728689411</td><td>38.87159603974435</td><td>19</td><td>19.0</td><td>Engine 19 Station</td><td>Fire_005</td><td>5582 0800</td><td>2813 PENNSYLVANIA AVENUE SE</td><td>(202) 673-3219</td><td>0</td><td>19.0</td><td>0.0</td><td>19.0</td><td>0.0</td><td>NA</td><td>NA</td><td>Other Station</td><td>19.0</td><td>3.0</td><td>OPEN</td><td>44167.0</td><td>NA</td><td>http://app.fems.dc.gov/details.asp?cid=63</td></tr><tr><th>20</th><td>-77.07842282793864</td><td>38.94497357972541</td><td>20</td><td>20.0</td><td>Engine 20 Station</td><td>Fire_016</td><td>1783 0804</td><td>4300 WISCONSIN AVENUE NW</td><td>(202) 673-3220</td><td>5</td><td>20.0</td><td>5.0</td><td>0.0</td><td>12.0</td><td>NA</td><td>NA</td><td>Other Station</td><td>20.0</td><td>5.0</td><td>OPEN</td><td>222965.0</td><td>NA</td><td>http://app.fems.dc.gov/details.asp?cid=64</td></tr><tr><th>21</th><td>-77.04221546112709</td><td>38.92477114817893</td><td>21</td><td>21.0</td><td>Engine 21 Station</td><td>Fire_013</td><td>2583 0063</td><td>1763 LANIER PLACE NW</td><td>(202) 673-3221</td><td>0</td><td>0.0</td><td>0.0</td><td>21.0</td><td>0.0</td><td>NA</td><td>NA</td><td>Other Station</td><td>21.0</td><td>5.0</td><td>OPEN</td><td>236004.0</td><td>Engine 28</td><td>http://app.fems.dc.gov/details.asp?cid=65</td></tr><tr><th>22</th><td>-77.02833879407437</td><td>38.95998107744306</td><td>22</td><td>22.0</td><td>Engine 22 Station</td><td>Fire_001</td><td>PAR 00870005</td><td>5760 GEORGIA AVENUE NW</td><td>(202) 673-3222</td><td>0</td><td>22.0</td><td>0.0</td><td>0.0</td><td>11.0</td><td>NA</td><td>NA</td><td>Other Station</td><td>22.0</td><td>4.0</td><td>OPEN</td><td>294612.0</td><td>NA</td><td>http://app.fems.dc.gov/details.asp?cid=66</td></tr><tr><th>23</th><td>-77.04762479297374</td><td>38.89858254354372</td><td>23</td><td>23.0</td><td>Engine 23 Station</td><td>Fire_027</td><td>0079 0005</td><td>2119 G STREET NW</td><td>(202) 673-3223</td><td>0</td><td>23.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>NA</td><td>NA</td><td>Other Station</td><td>23.0</td><td>6.0</td><td>OPEN</td><td>242505.0</td><td>NA</td><td>http://app.fems.dc.gov/details.asp?cid=67</td></tr><tr><th>24</th><td>-77.02703227038535</td><td>38.95214419705281</td><td>24</td><td>24.0</td><td>Engine 24 Station</td><td>Fire_010</td><td>3002 0064</td><td>5101 GEORGIA AVENUE NW</td><td>(202) 673-3224</td><td>0</td><td>0.0</td><td>2.0</td><td>24.0</td><td>0.0</td><td>RS-2 Support Unit w/small boat, CAR43, CAR44</td><td>RS-2 Confined Space Rescue, Swift Water Rescue, HazMat Ops, Office of the Fire Investigators</td><td>Other Station</td><td>24.0</td><td>4.0</td><td>OPEN</td><td>251752.0</td><td>NA</td><td>http://app.fems.dc.gov/details.asp?cid=68</td></tr><tr><th>25</th><td>-77.00078007337751</td><td>38.843156332407744</td><td>25</td><td>25.0</td><td>Engine 25 Station</td><td>Fire_003</td><td>NA</td><td>3203 MARTIN LUTHER KING JR AVENUE SE</td><td>(202) 673-3225</td><td>0</td><td>25.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>NA</td><td>NA</td><td>Other Station</td><td>25.0</td><td>3.0</td><td>OPEN</td><td>278344.0</td><td>NA</td><td>http://app.fems.dc.gov/details.asp?cid=69</td></tr><tr><th>26</th><td>-76.9867681807952</td><td>38.9247865806837</td><td>26</td><td>26.0</td><td>Engine 26 Station</td><td>Fire_009</td><td>3956 0802</td><td>1340 RHODE ISLAND AVENUE NE</td><td>(202) 673-3226</td><td>0</td><td>0.0</td><td>0.0</td><td>26.0</td><td>15.0</td><td>NA</td><td>NA</td><td>Other Station</td><td>26.0</td><td>1.0</td><td>OPEN</td><td>294518.0</td><td>NA</td><td>http://app.fems.dc.gov/details.asp?cid=70</td></tr><tr><th>27</th><td>-76.94324769061662</td><td>38.90108432157354</td><td>27</td><td>27.0</td><td>Engine 27 Station</td><td>Fire_007</td><td>5076 0800</td><td>4201 MINNESOTA AVENUE NE</td><td>(202) 673-3227</td><td>0</td><td>27.0</td><td>0.0</td><td>27.0</td><td>0.0</td><td>NA</td><td>NA</td><td>Other Station</td><td>27.0</td><td>2.0</td><td>OPEN</td><td>294591.0</td><td>NA</td><td>http://app.fems.dc.gov/details.asp?cid=71</td></tr><tr><th>28</th><td>-77.05940357016888</td><td>38.93634475833589</td><td>28</td><td>28.0</td><td>Engine 28 Station</td><td>Fire_014</td><td>2068 0809</td><td>3522 CONNECTICUT AVENUE NW</td><td>(202) 673-3228</td><td>0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>NA</td><td>NA</td><td>Other Station</td><td>28.0</td><td>5.0</td><td>CLOSED</td><td>220960.0</td><td>NA</td><td>http://app.fems.dc.gov/details.asp?cid=72</td></tr><tr><th>29</th><td>-77.0935236295225</td><td>38.915565015395615</td><td>29</td><td>29.0</td><td>Engine 29 Station</td><td>Fire_017</td><td>1372 0808</td><td>4811 MACARTHUR BOULEVARD NW</td><td>(202) 673-3229</td><td>0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>5.0</td><td>NA</td><td>NA</td><td>Other Station</td><td>29.0</td><td>5.0</td><td>OPEN</td><td>284959.0</td><td>NA</td><td>http://app.fems.dc.gov/details.asp?cid=73</td></tr><tr><th>30</th><td>-76.93339636130632</td><td>38.89069508531919</td><td>30</td><td>30.0</td><td>Engine 30 Station</td><td>Fire_006</td><td>5140 0810</td><td>50 49TH STREET NE</td><td>(202) 673-3230</td><td>0</td><td>30.0</td><td>0.0</td><td>30.0</td><td>17.0</td><td>NA</td><td>NA</td><td>Other Station</td><td>30.0</td><td>2.0</td><td>OPEN</td><td>156317.0</td><td>NA</td><td>http://app.fems.dc.gov/details.asp?cid=74</td></tr><tr><th>&vellip;</th><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td></tr></table>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 10,
"text": [
"35x23 DataFrame\n",
"| Row | X | Y | OBJECTID_1 | OBJECTID |\n",
"|-----|----------|---------|------------|----------|\n",
"| 1 | -77.0051 | 38.8309 | 1 | 33.0 |\n",
"| 2 | -77.0498 | 38.9055 | 2 | 1.0 |\n",
"| 3 | -77.0195 | 38.8971 | 3 | 2.0 |\n",
"| 4 | -77.0109 | 38.8958 | 4 | 3.0 |\n",
"| 5 | -77.025 | 38.9234 | 5 | 4.0 |\n",
"| 6 | -77.0684 | 38.9114 | 6 | 5.0 |\n",
"| 7 | -77.0161 | 38.9077 | 7 | 6.0 |\n",
"| 8 | -77.0112 | 38.8769 | 8 | 7.0 |\n",
"| 9 | -76.9829 | 38.8855 | 9 | 8.0 |\n",
"| 10 | -77.0375 | 38.9173 | 10 | 9.0 |\n",
"| 11 | -76.9867 | 38.9016 | 11 | 10.0 |\n",
"\u22ee\n",
"| 24 | -77.027 | 38.9521 | 24 | 24.0 |\n",
"| 25 | -77.0008 | 38.8432 | 25 | 25.0 |\n",
"| 26 | -76.9868 | 38.9248 | 26 | 26.0 |\n",
"| 27 | -76.9432 | 38.9011 | 27 | 27.0 |\n",
"| 28 | -77.0594 | 38.9363 | 28 | 28.0 |\n",
"| 29 | -77.0935 | 38.9156 | 29 | 29.0 |\n",
"| 30 | -76.9334 | 38.8907 | 30 | 30.0 |\n",
"| 31 | -77.0699 | 38.9542 | 31 | 31.0 |\n",
"| 32 | -76.9714 | 38.8526 | 32 | 32.0 |\n",
"| 33 | -77.0208 | 38.8739 | 33 | 34.0 |\n",
"| 34 | -77.0254 | 38.9163 | 34 | 35.0 |\n",
"| 35 | -77.0194 | 38.8836 | 35 | 13.0 |\n",
"\n",
"| Row | NAME | GIS_ID | SSL |\n",
"|-----|----------------------------------------|------------|----------------|\n",
"| 1 | \"Engine 33 Station\" | \"Fire_032\" | NA |\n",
"| 2 | \"Engine 1 Station\" | \"Fire_026\" | \"0050 0822\" |\n",
"| 3 | \"Engine 2 Station\" | \"Fire_022\" | \"0488 0833\" |\n",
"| 4 | \"Engine 3 Station\" | \"Fire_035\" | \"0630 0833\" |\n",
"| 5 | \"Engine 4 Station\" | \"Fire_011\" | \"2882 1036\" |\n",
"| 6 | \"Engine 5 Station\" | \"Fire_018\" | \"1277 0805\" |\n",
"| 7 | \"Engine 6 Station\" | \"Fire_028\" | NA |\n",
"| 8 | \"Engine 7 Station\" | \"Fire_021\" | NA |\n",
"| 9 | \"Engine 8 Station\" | \"Fire_019\" | \"1073 0806\" |\n",
"| 10 | \"Engine 9 Station\" | \"Fire_025\" | \"0175 0826\" |\n",
"| 11 | \"Engine 10 Station\" | \"Fire_029\" | NA |\n",
"\u22ee\n",
"| 24 | \"Engine 24 Station\" | \"Fire_010\" | \"3002 0064\" |\n",
"| 25 | \"Engine 25 Station\" | \"Fire_003\" | NA |\n",
"| 26 | \"Engine 26 Station\" | \"Fire_009\" | \"3956 0802\" |\n",
"| 27 | \"Engine 27 Station\" | \"Fire_007\" | \"5076 0800\" |\n",
"| 28 | \"Engine 28 Station\" | \"Fire_014\" | \"2068 0809\" |\n",
"| 29 | \"Engine 29 Station\" | \"Fire_017\" | \"1372 0808\" |\n",
"| 30 | \"Engine 30 Station\" | \"Fire_006\" | \"5140 0810\" |\n",
"| 31 | \"Engine 31 Station\" | \"Fire_015\" | \"1983 0807\" |\n",
"| 32 | \"Engine 32 Station\" | \"Fire_004\" | \"5845 0833\" |\n",
"| 33 | \"Fire Boats 1, 2, and 3\" | \"Fire_033\" | NA |\n",
"| 34 | \"HQ Fire and Emergency Medical Servic\" | \"Fire_034\" | NA |\n",
"| 35 | \"Engine 13 Station\" | \"Fire_023\" | \"0494 0028\" |\n",
"\n",
"| Row | ADDRESS | PHONE | BFC |\n",
"|-----|----------------------------------------|------------------|-----|\n",
"| 1 | \"101 ATLANTIC STREET SE\" | \"(202) 673-3233\" | 0 |\n",
"| 2 | \"2225 M STREET NW\" | \"(202) 673-3201\" | 0 |\n",
"| 3 | \"500 F STREET NW\" | \"(202) 673-3202\" | 0 |\n",
"| 4 | \"439 NEW JERSEY AVENUE NW\" | \"(202) 673-3203\" | 0 |\n",
"| 5 | \"2531 SHERMAN AVENUE NW\" | \"(202) 673-3204\" | 0 |\n",
"| 6 | \"3412 DENT PLACE NW\" | \"(202) 673-3205\" | 0 |\n",
"| 7 | \"1300 NEW JERSEY AVENUE NW\" | \"(202) 673-3206\" | 0 |\n",
"| 8 | \"1101 HALF STREET SW\" | \"(202) 673-3207\" | 0 |\n",
"| 9 | \"1520 C STREET SE\" | \"(202) 673-3208\" | 2 |\n",
"| 10 | \"1617 U STREET NW\" | \"(202) 673-3209\" | 0 |\n",
"| 11 | \"1342 FLORIDA AVENUE NE\" | \"(202) 673-3210\" | 0 |\n",
"\u22ee\n",
"| 24 | \"5101 GEORGIA AVENUE NW\" | \"(202) 673-3224\" | 0 |\n",
"| 25 | \"3203 MARTIN LUTHER KING JR AVENUE SE\" | \"(202) 673-3225\" | 0 |\n",
"| 26 | \"1340 RHODE ISLAND AVENUE NE\" | \"(202) 673-3226\" | 0 |\n",
"| 27 | \"4201 MINNESOTA AVENUE NE\" | \"(202) 673-3227\" | 0 |\n",
"| 28 | \"3522 CONNECTICUT AVENUE NW\" | \"(202) 673-3228\" | 0 |\n",
"| 29 | \"4811 MACARTHUR BOULEVARD NW\" | \"(202) 673-3229\" | 0 |\n",
"| 30 | \"50 49TH STREET NE\" | \"(202) 673-3230\" | 0 |\n",
"| 31 | \"4930 CONNECTICUT AVENUE NW\" | \"(202) 673-3231\" | 0 |\n",
"| 32 | \"2425 IRVING STREET SE\" | \"(202) 673-3232\" | 0 |\n",
"| 33 | \"550 WATER STREET SW\" | \"(202) 673-3200\" | 0 |\n",
"| 34 | \"1923 VERMONT AVENUE NW\" | \"(202) 673-3320\" | 0 |\n",
"| 35 | \"450 6TH STREET SW\" | \"(202) 673-3213\" | 0 |\n",
"\n",
"| Row | AMBULANCE | RESCSQUAD | MEDICUNIT | TRUCK |\n",
"|-----|-----------|-----------|-----------|-------|\n",
"| 1 | 33.0 | 0.0 | 25.0 | 8.0 |\n",
"| 2 | 0.0 | 0.0 | 1.0 | 2.0 |\n",
"| 3 | 0.0 | 1.0 | 0.0 | 0.0 |\n",
"| 4 | 0.0 | 0.0 | 3.0 | 0.0 |\n",
"| 5 | 0.0 | 0.0 | 4.0 | 0.0 |\n",
"| 6 | 0.0 | 0.0 | 5.0 | 0.0 |\n",
"| 7 | 6.0 | 0.0 | 0.0 | 4.0 |\n",
"| 8 | 7.0 | 0.0 | 0.0 | 0.0 |\n",
"| 9 | 8.0 | 0.0 | 8.0 | 0.0 |\n",
"| 10 | 28.0 | 0.0 | 0.0 | 0.0 |\n",
"| 11 | 0.0 | 0.0 | 0.0 | 13.0 |\n",
"\u22ee\n",
"| 24 | 0.0 | 2.0 | 24.0 | 0.0 |\n",
"| 25 | 25.0 | 0.0 | 0.0 | 0.0 |\n",
"| 26 | 0.0 | 0.0 | 26.0 | 15.0 |\n",
"| 27 | 27.0 | 0.0 | 27.0 | 0.0 |\n",
"| 28 | 0.0 | 0.0 | 0.0 | 0.0 |\n",
"| 29 | 0.0 | 0.0 | 0.0 | 5.0 |\n",
"| 30 | 30.0 | 0.0 | 30.0 | 17.0 |\n",
"| 31 | 0.0 | 0.0 | 31.0 | 14.0 |\n",
"| 32 | 32.0 | 0.0 | 0.0 | 16.0 |\n",
"| 33 | 0.0 | 0.0 | 0.0 | 0.0 |\n",
"| 34 | 0.0 | 0.0 | 0.0 | 0.0 |\n",
"| 35 | 13.0 | 0.0 | 0.0 | 10.0 |\n",
"\n",
"| Row | SPECIALTY |\n",
"|-----|---------------------------------------------------------------------|\n",
"| 1 | NA |\n",
"| 2 | \"Twinned Agent Unit 2\" |\n",
"| 3 | \"EMS 1-6, EMS 5, RS-1 Support Unit w/small boat, FCU ,FFD\" |\n",
"| 4 | NA |\n",
"| 5 | \"AIR 1\" |\n",
"| 6 | \"Rehab Unit, Rehab Support Unit, CU\" |\n",
"| 7 | NA |\n",
"| 8 | NA |\n",
"| 9 | \"EMS 2, AIR 2,MEDIC UNIT 35,\" |\n",
"| 10 | NA |\n",
"| 11 | NA |\n",
"\u22ee\n",
"| 24 | \"RS-2 Support Unit w/small boat, CAR43, CAR44\" |\n",
"| 25 | NA |\n",
"| 26 | NA |\n",
"| 27 | NA |\n",
"| 28 | NA |\n",
"| 29 | NA |\n",
"| 30 | NA |\n",
"| 31 | NA |\n",
"| 32 | \"EMS 3\" |\n",
"| 33 | \"Fire Boat Support Unit\" |\n",
"| 34 | NA |\n",
"| 35 | \"Foam Unit 1 & 2, Twinned Agent Unit 1, Truck 10 w/135 foot ladder\" |\n",
"\n",
"| Row | DETAIL |\n",
"|-----|--------------------------------------------------------------------------------------------------------------------------------------------------------|\n",
"| 1 | NA |\n",
"| 2 | NA |\n",
"| 3 | \"EMS Supervisor 1-6, EMS Supervisor 5, Office of the Firefighting DFC, Mobile Command Unit, Rescue Squad 1, RS-1 High-angle Rescue, Swift Water Rescu\" |\n",
"| 4 | NA |\n",
"| 5 | \"Safety Officer, Special Operations Office, Special Operations BC, Special Operations DC, Mask Room\" |\n",
"| 6 | NA |\n",
"| 7 | \"EMS Equipment Office\" |\n",
"| 8 | NA |\n",
"| 9 | \"Emergency Medical Supervisor 2\" |\n",
"| 10 | NA |\n",
"| 11 | NA |\n",
"\u22ee\n",
"| 24 | \"RS-2 Confined Space Rescue, Swift Water Rescue, HazMat Ops, Office of the Fire Investigators\" |\n",
"| 25 | NA |\n",
"| 26 | NA |\n",
"| 27 | NA |\n",
"| 28 | NA |\n",
"| 29 | NA |\n",
"| 30 | NA |\n",
"| 31 | NA |\n",
"| 32 | \"Emergency Medical Supervisor 3\" |\n",
"| 33 | NA |\n",
"| 34 | \"Old Grimke School\" |\n",
"| 35 | NA |\n",
"\n",
"| Row | TYPE | ENGINE | BATTALION | RENSTATUS | AID |\n",
"|-----|-----------------|--------|-----------|-----------|----------|\n",
"| 1 | \"Other Station\" | 33.0 | 3.0 | \"OPEN\" | 294471.0 |\n",
"| 2 | \"Other Station\" | 1.0 | 6.0 | \"OPEN\" | 294540.0 |\n",
"| 3 | \"Other Station\" | 2.0 | 6.0 | \"OPEN\" | 299998.0 |\n",
"| 4 | \"Other Station\" | 3.0 | 6.0 | \"OPEN\" | 237163.0 |\n",
"| 5 | \"Other Station\" | 4.0 | 4.0 | \"OPEN\" | 232303.0 |\n",
"| 6 | \"Other Station\" | 5.0 | 5.0 | \"OPEN\" | 294568.0 |\n",
"| 7 | \"Other Station\" | 6.0 | 1.0 | \"OPEN\" | 294514.0 |\n",
"| 8 | \"Other Station\" | 7.0 | 2.0 | \"OPEN\" | 277735.0 |\n",
"| 9 | \"Other Station\" | 8.0 | 2.0 | \"OPEN\" | 289723.0 |\n",
"| 10 | \"Other Station\" | 9.0 | 4.0 | \"OPEN\" | 241846.0 |\n",
"| 11 | \"Other Station\" | 10.0 | 1.0 | \"OPEN\" | 294519.0 |\n",
"\u22ee\n",
"| 24 | \"Other Station\" | 24.0 | 4.0 | \"OPEN\" | 251752.0 |\n",
"| 25 | \"Other Station\" | 25.0 | 3.0 | \"OPEN\" | 278344.0 |\n",
"| 26 | \"Other Station\" | 26.0 | 1.0 | \"OPEN\" | 294518.0 |\n",
"| 27 | \"Other Station\" | 27.0 | 2.0 | \"OPEN\" | 294591.0 |\n",
"| 28 | \"Other Station\" | 28.0 | 5.0 | \"CLOSED\" | 220960.0 |\n",
"| 29 | \"Other Station\" | 29.0 | 5.0 | \"OPEN\" | 284959.0 |\n",
"| 30 | \"Other Station\" | 30.0 | 2.0 | \"OPEN\" | 156317.0 |\n",
"| 31 | \"Other Station\" | 31.0 | 5.0 | \"OPEN\" | 294604.0 |\n",
"| 32 | \"Other Station\" | 32.0 | 3.0 | \"OPEN\" | 47304.0 |\n",
"| 33 | \"Other Station\" | 0.0 | 6.0 | \"OPEN\" | 294491.0 |\n",
"| 34 | \"HeadQuarters\" | 0.0 | 4.0 | \"OPEN\" | 239514.0 |\n",
"| 35 | \"Other Station\" | 13.0 | 6.0 | \"OPEN\" | 294488.0 |\n",
"\n",
"| Row | REL_UNITS | WEB_URL |\n",
"|-----|-----------|---------------------------------------------|\n",
"| 1 | NA | \"http://app.fems.dc.gov/details.asp?cid=77\" |\n",
"| 2 | NA | \"http://app.fems.dc.gov/details.asp?cid=45\" |\n",
"| 3 | NA | \"http://app.fems.dc.gov/details.asp?cid=46\" |\n",
"| 4 | NA | \"http://app.fems.dc.gov/details.asp?cid=47\" |\n",
"| 5 | NA | \"http://app.fems.dc.gov/details.asp?cid=48\" |\n",
"| 6 | NA | \"http://app.fems.dc.gov/details.asp?cid=49\" |\n",
"| 7 | NA | \"http://app.fems.dc.gov/details.asp?cid=50\" |\n",
"| 8 | NA | \"http://app.fems.dc.gov/details.asp?cid=51\" |\n",
"| 9 | NA | \"http://app.fems.dc.gov/details.asp?cid=52\" |\n",
"| 10 | NA | \"http://app.fems.dc.gov/details.asp?cid=53\" |\n",
"| 11 | NA | \"http://app.fems.dc.gov/details.asp?cid=54\" |\n",
"\u22ee\n",
"| 24 | NA | \"http://app.fems.dc.gov/details.asp?cid=68\" |\n",
"| 25 | NA | \"http://app.fems.dc.gov/details.asp?cid=69\" |\n",
"| 26 | NA | \"http://app.fems.dc.gov/details.asp?cid=70\" |\n",
"| 27 | NA | \"http://app.fems.dc.gov/details.asp?cid=71\" |\n",
"| 28 | NA | \"http://app.fems.dc.gov/details.asp?cid=72\" |\n",
"| 29 | NA | \"http://app.fems.dc.gov/details.asp?cid=73\" |\n",
"| 30 | NA | \"http://app.fems.dc.gov/details.asp?cid=74\" |\n",
"| 31 | NA | \"http://app.fems.dc.gov/details.asp?cid=75\" |\n",
"| 32 | NA | \"http://app.fems.dc.gov/details.asp?cid=76\" |\n",
"| 33 | NA | \"http://app.fems.dc.gov/details.asp?cid=78\" |\n",
"| 34 | NA | \"http://app.fems.dc.gov/details.asp?cid=83\" |\n",
"| 35 | NA | \"http://app.fems.dc.gov/details.asp?cid=57\" |"
]
}
],
"prompt_number": 10
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df[1,[:X,:Y]]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<table class=\"data-frame\"><tr><th></th><th>X</th><th>Y</th></tr><tr><th>1</th><td>-77.00514742400337</td><td>38.83089499410553</td></tr></table>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 11,
"text": [
"1x2 DataFrame\n",
"| Row | X | Y |\n",
"|-----|----------|---------|\n",
"| 1 | -77.0051 | 38.8309 |"
]
}
],
"prompt_number": 11
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We convert the coordinates of the stations (using the first one as an example) from lat/lon to ENU (east-north-up):"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"stn = lla2enu(OpenStreetMap.LLA(38.83089499410553,-77.00514742400337,0.0), reference)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 12,
"text": [
"ENU(-15338.664584558734,-28194.385597358065,-80.90697614598685)"
]
}
],
"prompt_number": 12
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can try requesting for nodes to use as starting-points from the station:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"local_nodes = nodesWithinRange(nodesENU, stn, 50.0)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 13,
"text": [
"0-element Array{Int64,1}"
]
}
],
"prompt_number": 13
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Seems like there are no nodes within 50metres from the station. We extend the search to 100metres:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"local_nodes = nodesWithinRange(nodesENU, stn, 100.0)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 14,
"text": [
"3-element Array{Int64,1}:\n",
" 640614126\n",
" 1443727014\n",
" 2000698376"
]
}
],
"prompt_number": 14
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Just for convenience, we use one of the nodes as a starting point, and compare it with routes that considers mutliple starting points later"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"start_index = nearestNode(nodesENU, stn)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 18,
"text": [
"1443727014"
]
}
],
"prompt_number": 18
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can request for the nodes within an (euclidean) distance from the station"
]
},
{
"cell_type": "code",
"collapsed": true,
"input": [
"radial_indices = nodesWithinRange(nodesENU, stn, 300.0) # 300metres\n",
"radial_indices[1:5]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 17,
"text": [
"5-element Array{Int64,1}:\n",
" 49756803\n",
" 1468892571\n",
" 1468892573\n",
" 1468892572\n",
" 49871146"
]
}
],
"prompt_number": 17
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Alternatively, we can use the road network as the topology for calculating (driving) distance. In this case, we're interested in the set of nodes that are within 500metres driving distance from the starting location."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"node_indices, distances = nodesWithinDrivingDistance(network, start_index, 500.0)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 19,
"text": [
"([49871144,49760993,1443727013,1489216543,49798593,1489216551,640614126,49830828,49798559,49830847 \u2026 49716511,49766733,49741480,49716543,1468886429,1489216554,1468886424,1506916978,49750298,49830844],[199.338,336.517,462.011,483.864,237.91,427.567,158.575,446.682,492.781,461.905 \u2026 302.525,463.333,228.065,328.925,301.846,451.355,486.483,263.018,447.192,368.382])"
]
}
],
"prompt_number": 19
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can also request for the set of nodes that are within 500metres driving distance from any of the locations in a given set of indices."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"ext_node_indices, ext_distances = nodesWithinDrivingDistance(network, local_nodes, 500.0)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 20,
"text": [
"([49871144,49760993,1443727013,1489216543,49798593,49741494,1489216551,640614126,49830828,49798559 \u2026 49716543,1468886429,49759975,1489216554,1468886424,1462089218,49864896,1506916978,49750298,49830844],[198.47,177.942,303.436,482.996,237.042,472.895,426.699,0.0,445.813,491.913 \u2026 328.056,300.978,488.24,450.487,485.615,352.291,342.83,104.443,446.323,367.513])"
]
}
],
"prompt_number": 20
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"println(\"number of nodes: $(length(node_indices))\")\n",
"println(\"number of (extended) nodes: $(length(ext_node_indices))\")\n",
"println(\"number of nodes (in 300 radius): $(length(radial_indices))\")"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"number of nodes: 207\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"number of (extended) nodes: 258\n",
"number of nodes (in 300 radius): 96\n"
]
}
],
"prompt_number": 21
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We extract the respective lat/lon coordinates from the nodes here, for plotting later."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"lat = Float64[]\n",
"lon = Float64[]\n",
"for i in node_indices\n",
" node = nodes[i]\n",
" push!(lat, node.lat)\n",
" push!(lon, node.lon)\n",
"end\n",
"lat[1:3], lon[1:3]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 22,
"text": [
"([38.8319,38.8317,38.831],[-77.0073,-77.0024,-77.0006])"
]
}
],
"prompt_number": 22
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"ext_lat = Float64[]\n",
"ext_lon = Float64[]\n",
"for i in ext_node_indices\n",
" node = nodes[i]\n",
" push!(ext_lat, node.lat)\n",
" push!(ext_lon, node.lon)\n",
"end\n",
"ext_lat[1:3], ext_lon[1:3]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 23,
"text": [
"([38.8319,38.8317,38.831],[-77.0073,-77.0024,-77.0006])"
]
}
],
"prompt_number": 23
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"radial_lat = Float64[]\n",
"radial_lon = Float64[]\n",
"for i in radial_indices\n",
" node = nodes[i]\n",
" push!(radial_lat, node.lat)\n",
" push!(radial_lon, node.lon)\n",
"end\n",
"radial_lat[1:3], radial_lon[1:3]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 24,
"text": [
"([38.833,38.833,38.8332],[-77.0057,-77.0048,-77.0047])"
]
}
],
"prompt_number": 24
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"using Winston"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 26
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"length(network.v), length(nodes)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 32,
"text": [
"(415727,2447660)"
]
}
],
"prompt_number": 32
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"filter!((k,v)->haskey(network.v,k), nodes)\n",
"length(nodes)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 33,
"text": [
"415727"
]
}
],
"prompt_number": 33
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"all_nodes = [n for n in values(nodes)]\n",
"all_lat = Float64[n.lat for n in all_nodes]\n",
"all_lon = Float64[n.lon for n in all_nodes];\n",
"length(all_nodes), length(all_lat), length(all_lon)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 34,
"text": [
"(415727,415727,415727)"
]
}
],
"prompt_number": 34
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here we plot the set of nodes (in green) that were within 300 metres from the station."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"p = FramedPlot(yrange=(38.825,38.838),xrange=(-77.015,-76.995))\n",
"plot(p, all_lon, all_lat, \".\")\n",
"plot(p, radial_lon, radial_lat, \"^\", color=\"green\")\n",
"plot(p, [-77.00514742400337], [38.83089499410553], \"s\", color=\"red\")"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"png": 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",
"prompt_number": 37,
"text": [
"FramedPlot(...)"
]
}
],
"prompt_number": 37
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We might want to compare how the set of nodes from \n",
"\n",
"1. a single location (yellow)\n",
"2. multiple starting locations (blue)\n",
"\n",
"compares with each other."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"p = FramedPlot(yrange=(38.825,38.838),xrange=(-77.015,-76.995))\n",
"plot(p, all_lon, all_lat, \".\")\n",
"plot(p, ext_lon, ext_lat, \"^\", color=\"blue\")\n",
"plot(p, lon, lat, \"^\", color=\"orange\")\n",
"plot(p, [-77.00514742400337], [38.83089499410553], \"s\", color=\"red\")"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"png": 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",
"prompt_number": 40,
"text": [
"FramedPlot(...)"
]
}
],
"prompt_number": 40
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"p = FramedPlot(yrange=(38.825,38.838),xrange=(-77.015,-76.995))\n",
"plot(p, all_lon, all_lat, \".\")\n",
"plot(p, ext_lon, ext_lat, \"^\", color=\"blue\")\n",
"plot(p, lon, lat, \"^\", color=\"orange\")\n",
"plot(p, [-77.00514742400337], [38.83089499410553], \"s\", color=\"red\")\n",
"savefig(\"./fig/demo.png\")"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 39
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Suppose we wanted to extract the drivetime isochrone of an ambulance from the station.\n",
"\n",
"A typical timing might be $11*60$ seconds. We multiply it by $0.5$ (to account road traffic/hindrance)."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"fast_indices, timings = nodesWithinDrivingTime(network, local_nodes, 0.5*11*60.0)\n",
"println(\"number of nodes: $(length(fast_indices))\")\n",
"fast_lat = Float64[]\n",
"fast_lon = Float64[]\n",
"for i in fast_indices\n",
" node = nodes[i]\n",
" push!(fast_lat, node.lat)\n",
" push!(fast_lon, node.lon)\n",
"end\n",
"fast_lat[1:3], fast_lon[1:3]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"number of nodes: 7283\n"
]
},
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 52,
"text": [
"([38.8361,38.837,38.853],[-76.9906,-77.0067,-76.9963])"
]
}
],
"prompt_number": 52
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"timings[1:5] # sample"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 53,
"text": [
"5-element Array{Float64,1}:\n",
" 262.964\n",
" 81.895\n",
" 312.24 \n",
" 216.781\n",
" 226.413"
]
}
],
"prompt_number": 53
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"p = FramedPlot()#(yrange=(38.825,38.838),xrange=(-77.015,-76.995))\n",
"plot(p, all_lon, all_lat, \".\")\n",
"#plot(p, ext_lon, ext_lat, \"^\", color=\"blue\")\n",
"plot(p, fast_lon, fast_lat, \"^\", color=\"orange\")\n",
"plot(p, [-77.00514742400337], [38.83089499410553], \"s\", color=\"red\")"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"png": 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",
"prompt_number": 54,
"text": [
"FramedPlot(...)"
]
}
],
"prompt_number": 54
}
],
"metadata": {}
}
]
}
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