In [21]:
# Make all plots inline.
%matplotlib inline
# Import dependencies
import folium
import numpy as np
import pandas as pd
import pickle
from matplotlib import pyplot as plt
from IPython.core.display import display
In [ ]:
# Load accident data.
accident_data = pd.read_csv('./data/NYPD_Motor_Vehicle_Collisions_sampled.csv')
In [2]:
accident_data.head()
Out[2]:
DATE
TIME
BOROUGH
ZIP CODE
LATITUDE
LONGITUDE
LOCATION
ON STREET NAME
CROSS STREET NAME
OFF STREET NAME
...
CONTRIBUTING FACTOR VEHICLE 2
CONTRIBUTING FACTOR VEHICLE 3
CONTRIBUTING FACTOR VEHICLE 4
CONTRIBUTING FACTOR VEHICLE 5
UNIQUE KEY
VEHICLE TYPE CODE 1
VEHICLE TYPE CODE 2
VEHICLE TYPE CODE 3
VEHICLE TYPE CODE 4
VEHICLE TYPE CODE 5
0
06/18/2016
5:20
BRONX
10456
40.824067
-73.908710
(40.8240665, -73.9087095)
EAST 163 STREET
3 AVENUE
NaN
...
Unspecified
NaN
NaN
NaN
3463614
PASSENGER VEHICLE
NaN
NaN
NaN
NaN
1
06/18/2016
7:10
BRONX
10472
40.826916
-73.872030
(40.8269163, -73.8720302)
METCALF AVENUE
WATSON AVENUE
NaN
...
Unspecified
NaN
NaN
NaN
3464214
PASSENGER VEHICLE
PASSENGER VEHICLE
NaN
NaN
NaN
2
06/18/2016
7:20
NaN
NaN
40.701455
-73.989620
(40.7014547, -73.9896203)
NaN
NaN
NaN
...
Unspecified
NaN
NaN
NaN
3463782
PASSENGER VEHICLE
PASSENGER VEHICLE
NaN
NaN
NaN
3
06/18/2016
7:30
NaN
NaN
NaN
NaN
NaN
47 STREET
NaN
NaN
...
Unspecified
NaN
NaN
NaN
3465413
PASSENGER VEHICLE
OTHER
NaN
NaN
NaN
4
06/18/2016
7:45
QUEENS
11422
40.665256
-73.735334
(40.665256, -73.7353338)
SOUTH CONDUIT AVENUE
FRANCIS LEWIS BOULEVARD
NaN
...
Unspecified
NaN
NaN
NaN
3463318
PASSENGER VEHICLE
PASSENGER VEHICLE
NaN
NaN
NaN
5 rows × 29 columns
In [3]:
accident_data.describe()
Out[3]:
ZIP CODE
LATITUDE
LONGITUDE
NUMBER OF PERSONS INJURED
NUMBER OF PERSONS KILLED
NUMBER OF PEDESTRIANS INJURED
NUMBER OF PEDESTRIANS KILLED
NUMBER OF CYCLIST INJURED
NUMBER OF CYCLIST KILLED
NUMBER OF MOTORIST INJURED
NUMBER OF MOTORIST KILLED
UNIQUE KEY
count
6395.000000
6687.000000
6687.000000
10000.000000
10000.000000
10000.000000
10000.000000
10000.000000
10000
10000.000000
10000.000000
10000.000000
mean
10846.141048
40.723501
-73.917075
0.253500
0.000900
0.057600
0.000600
0.013900
0
0.206300
0.000400
3554134.235000
std
554.570810
0.079065
0.086132
0.625361
0.029988
0.260939
0.024489
0.131562
0
0.702417
0.024493
127228.305605
min
10000.000000
40.504925
-74.247136
0.000000
0.000000
0.000000
0.000000
0.000000
0
0.000000
0.000000
125912.000000
25%
10309.000000
40.667784
-73.974463
0.000000
0.000000
0.000000
0.000000
0.000000
0
0.000000
0.000000
3525040.500000
50%
11207.000000
40.721536
-73.926085
0.000000
0.000000
0.000000
0.000000
0.000000
0
0.000000
0.000000
3586995.000000
75%
11238.000000
40.767516
-73.861872
0.000000
0.000000
0.000000
0.000000
0.000000
0
0.000000
0.000000
3605235.250000
max
11697.000000
40.912295
-73.700597
12.000000
1.000000
6.000000
1.000000
3.000000
0
14.000000
2.000000
3612908.000000
In [4]:
# Num rows in data.
print(accident_data.count())
DATE 10000
TIME 10000
BOROUGH 6396
ZIP CODE 6395
LATITUDE 6687
LONGITUDE 6687
LOCATION 6687
ON STREET NAME 6610
CROSS STREET NAME 5197
OFF STREET NAME 2174
NUMBER OF PERSONS INJURED 10000
NUMBER OF PERSONS KILLED 10000
NUMBER OF PEDESTRIANS INJURED 10000
NUMBER OF PEDESTRIANS KILLED 10000
NUMBER OF CYCLIST INJURED 10000
NUMBER OF CYCLIST KILLED 10000
NUMBER OF MOTORIST INJURED 10000
NUMBER OF MOTORIST KILLED 10000
CONTRIBUTING FACTOR VEHICLE 1 9950
CONTRIBUTING FACTOR VEHICLE 2 8320
CONTRIBUTING FACTOR VEHICLE 3 700
CONTRIBUTING FACTOR VEHICLE 4 196
CONTRIBUTING FACTOR VEHICLE 5 40
UNIQUE KEY 10000
VEHICLE TYPE CODE 1 9779
VEHICLE TYPE CODE 2 7002
VEHICLE TYPE CODE 3 623
VEHICLE TYPE CODE 4 173
VEHICLE TYPE CODE 5 34
dtype: int64
In [5]:
# Map data.
# Starting coordinates to load map view.
NYC_coordinates = (40.7142700, -74.0059700)
# Create Map object.
map = folium.Map(location=NYC_coordinates,
zoom_start=12)
# Creating different tile layers
cycle_tile = folium.TileLayer(tiles = 'http://b.tile.opencyclemap.org/cycle/{z}/{x}/{y}.png', attr='Attributed').add_to(map)
cycle_tile.layer_name = 'Cycling Route Map'
# Create a feature group for controlling multiple aspects
col_group = folium.FeatureGroup(name='Collisions').add_to(map)
injury_group = folium.FeatureGroup(name='Injuries').add_to(map)
# Plot accidents.
# Limit number of points to plot for testing.
MAX_RECORDS = 1000
collision_cluster = folium.MarkerCluster().add_to(col_group)
for row in accident_data[0:MAX_RECORDS].iterrows():
# Only plot point if lat/long is available.
if (not np.isnan(row[1]['LATITUDE']) and not np.isnan(row[1]['LONGITUDE'])):
accident_metadata = """
<ul>
<li><strong>On street</strong>: {0}</li>
<li><strong>Cross street</strong>: {1}</li>
<li><strong>Reason</strong>: {2}</li>
</ul>""".format(
str(row[1]['ON STREET NAME']), str(row[1]['CROSS STREET NAME']),
str(row[1]['CONTRIBUTING FACTOR VEHICLE 1']))
iframe = folium.element.IFrame(html=accident_metadata, width=250, height=100)
popup = folium.Popup(iframe, max_width=2650)
folium.Marker(
location = [row[1]['LATITUDE'], row[1]['LONGITUDE']],
icon = folium.Icon(color='red', icon='asterisk'),
popup=popup).add_to(collision_cluster)
# Create new cluster group for cyclist injuries
bike_cluster = folium.MarkerCluster().add_to(injury_group)
for row in accident_data[0:MAX_RECORDS].iterrows():
# Only plot point if lat/long is available.
if (not np.isnan(row[1]['LATITUDE']) and not np.isnan(row[1]['LONGITUDE']) and row[1]['NUMBER OF CYCLIST INJURED'] >= 1 or row[1]['NUMBER OF CYCLIST KILLED'] >= 1):
accident_metadata = """
<ul>
<li><strong>On street</strong>: {0}</li>
<li><strong>Cross street</strong>: {1}</li>
<li><strong>Cyclists Injured</strong>: {2}</li>
<li><strong>Cyclists Killed</strong>: {3}</li>
</ul>""".format(
str(row[1]['ON STREET NAME']), str(row[1]['CROSS STREET NAME']),
str(row[1]['NUMBER OF CYCLIST INJURED']), str(row[1]['NUMBER OF CYCLIST KILLED']))
iframe = folium.element.IFrame(html=accident_metadata, width=250, height=100)
popup = folium.Popup(iframe, max_width=2650)
folium.Marker(
location = [row[1]['LATITUDE'], row[1]['LONGITUDE']],
icon = folium.Icon(color='blue', icon='asterisk'),
popup=popup).add_to(bike_cluster)
# Create new cluster group for pedestrian injuries
ped_cluster = folium.MarkerCluster().add_to(injury_group)
for row in accident_data[0:MAX_RECORDS].iterrows():
# Only plot point if lat/long is available.
if (not np.isnan(row[1]['LATITUDE']) and not np.isnan(row[1]['LONGITUDE']) and row[1]['NUMBER OF PEDESTRIANS INJURED'] >= 1 or row[1]['NUMBER OF PEDESTRIANS KILLED'] >= 1):
accident_metadata = """
<ul>
<li><strong>On street</strong>: {0}</li>
<li><strong>Cross street</strong>: {1}</li>
<li><strong>Pedestrians Injured</strong>: {2}</li>
<li><strong>Pedestrians Killed</strong>: {3}</li>
</ul>""".format(
str(row[1]['ON STREET NAME']), str(row[1]['CROSS STREET NAME']),
str(row[1]['NUMBER OF PEDESTRIANS INJURED']), str(row[1]['NUMBER OF PEDESTRIANS KILLED']))
iframe = folium.element.IFrame(html=accident_metadata, width=250, height=100)
popup = folium.Popup(iframe, max_width=2650)
folium.Marker(
location = [row[1]['LATITUDE'], row[1]['LONGITUDE']],
icon = folium.Icon(color='green', icon='asterisk'),
popup=popup).add_to(ped_cluster)
# Create new cluster group for automobile injuries
auto_cluster = folium.MarkerCluster().add_to(injury_group)
for row in accident_data[0:MAX_RECORDS].iterrows():
# Only plot point if lat/long is available.
if (not np.isnan(row[1]['LATITUDE']) and not np.isnan(row[1]['LONGITUDE']) and row[1]['NUMBER OF MOTORIST INJURED'] >= 1 or row[1]['NUMBER OF MOTORIST KILLED'] >= 1):
accident_metadata = """
<ul>
<li><strong>On street</strong>: {0}</li>
<li><strong>Cross street</strong>: {1}</li>
<li><strong>Motorists Injured</strong>: {2}</li>
<li><strong>Motorists Killed</strong>: {3}</li>
</ul>""".format(
str(row[1]['ON STREET NAME']), str(row[1]['CROSS STREET NAME']),
str(row[1]['NUMBER OF MOTORIST INJURED']), str(row[1]['NUMBER OF MOTORIST KILLED']))
iframe = folium.element.IFrame(html=accident_metadata, width=250, height=100)
popup = folium.Popup(iframe, max_width=2650)
folium.Marker(
location = [row[1]['LATITUDE'], row[1]['LONGITUDE']],
icon = folium.Icon(color='red', icon='asterisk'),
popup=popup).add_to(auto_cluster)
# layer names for items separately
# collision_cluster.layer_name = 'Collisions'
# bike_cluster.layer_name = 'Cyclist Injuries'
# ped_cluster.layer_name = 'Pedestrian Injuries'
folium.LayerControl().add_to(map)
map
Out[5]:
In [ ]:
# Save html version of map.
map.save('accidents_map.html')
In [3]:
# Load pickle files containing aggregate data.
agg_accident_data = pickle.load(open("./data/accident_counts.pkl", "rb"))
agg_traffic_data = pickle.load(open("./data/intersection_counts_yellow.pkl", "rb"))
nodes_to_coord_data = pickle.load(open("./data/nodes_to_coordinates.pkl", "rb"))
In [16]:
# Get sizes of data sets.
print(len(agg_accident_data))
print(len(agg_traffic_data))
print(len(nodes_to_coord_data))
3894
4471
4483
In [32]:
# Print accident dataset.
print(agg_accident_data)
defaultdict(<class 'int'>, {0: 21, 1: 19, 2: 58, 3: 34, 5: 16, 7: 37, 8: 50, 10: 23, 11: 10, 12: 37, 13: 21, 14: 16, 15: 12, 16: 23, 17: 44, 18: 50, 19: 31, 21: 30, 22: 23, 23: 11, 24: 70, 26: 7, 27: 46, 28: 62, 30: 18, 31: 15, 32: 23, 33: 1, 34: 50, 35: 3, 36: 35, 37: 44, 38: 42, 39: 27, 40: 47, 41: 38, 43: 13, 44: 13, 45: 152, 46: 7, 47: 38, 48: 42, 49: 28, 50: 18, 51: 28, 53: 2, 54: 2, 55: 3, 56: 39, 57: 14, 58: 87, 59: 127, 60: 7, 61: 27, 62: 1, 63: 65, 64: 5, 66: 140, 67: 65, 69: 389, 70: 40, 71: 76, 72: 32, 73: 35, 74: 134, 75: 1, 77: 1, 78: 83, 79: 35, 80: 25, 81: 2, 82: 1, 84: 27, 85: 100, 86: 13, 88: 161, 90: 7, 91: 18, 93: 44, 95: 199, 96: 51, 97: 105, 98: 20, 99: 561, 100: 269, 101: 5, 102: 32, 103: 3, 104: 313, 106: 55, 107: 20, 111: 1, 112: 2, 113: 71, 114: 69, 115: 93, 117: 23, 119: 19, 120: 19, 121: 499, 122: 52, 125: 40, 126: 57, 127: 39, 128: 38, 129: 58, 130: 27, 131: 78, 133: 72, 134: 120, 135: 13, 136: 47, 137: 2, 138: 11, 140: 32, 141: 94, 142: 79, 143: 6, 144: 50, 145: 2, 146: 34, 148: 44, 149: 106, 151: 2, 152: 11, 154: 32, 156: 31, 157: 2, 161: 57, 164: 1, 165: 43, 166: 2, 168: 1, 169: 13, 171: 4, 172: 16, 173: 84, 174: 27, 176: 41, 177: 2, 179: 10, 180: 71, 182: 23, 183: 30, 184: 28, 185: 29, 186: 37, 187: 156, 188: 105, 189: 28, 190: 32, 191: 21, 192: 36, 193: 28, 194: 15, 195: 11, 196: 28, 197: 32, 198: 21, 199: 70, 200: 19, 201: 8, 202: 32, 203: 31, 204: 19, 205: 39, 206: 6, 207: 6, 208: 5, 209: 3, 210: 23, 212: 2, 213: 53, 214: 45, 215: 19, 216: 43, 217: 187, 218: 19, 219: 21, 220: 47, 221: 77, 222: 57, 223: 15, 224: 42, 226: 25, 227: 26, 228: 106, 229: 58, 230: 95, 231: 67, 232: 89, 233: 36, 234: 55, 235: 76, 236: 18, 237: 14, 238: 98, 239: 64, 240: 64, 241: 54, 242: 89, 243: 136, 244: 103, 245: 2, 246: 23, 247: 13, 248: 13, 249: 30, 250: 13, 251: 13, 252: 20, 253: 33, 254: 17, 255: 11, 256: 174, 257: 26, 258: 7, 259: 38, 260: 11, 261: 52, 262: 3, 263: 53, 264: 56, 265: 134, 266: 117, 267: 112, 268: 69, 269: 179, 270: 37, 271: 76, 272: 22, 273: 1, 274: 10, 275: 16, 276: 32, 277: 29, 278: 96, 279: 33, 280: 48, 281: 96, 282: 20, 283: 18, 284: 21, 285: 9, 286: 7, 287: 13, 288: 2, 289: 176, 290: 8, 291: 184, 292: 273, 293: 261, 294: 60, 295: 39, 296: 51, 297: 65, 298: 131, 299: 32, 300: 113, 301: 130, 302: 138, 303: 83, 304: 53, 305: 51, 306: 61, 307: 43, 308: 46, 309: 78, 310: 43, 311: 42, 312: 43, 313: 44, 314: 33, 315: 38, 316: 54, 317: 40, 318: 31, 319: 37, 320: 51, 321: 47, 322: 47, 323: 27, 324: 53, 326: 8, 327: 15, 328: 57, 329: 49, 330: 47, 331: 14, 332: 76, 333: 16, 334: 18, 335: 7, 336: 47, 337: 16, 338: 10, 339: 35, 340: 37, 341: 47, 342: 28, 343: 27, 344: 62, 345: 26, 346: 51, 347: 6, 348: 25, 349: 47, 350: 67, 351: 58, 352: 28, 353: 58, 354: 48, 355: 67, 356: 44, 357: 39, 358: 35, 359: 57, 360: 49, 361: 37, 362: 112, 363: 248, 364: 144, 365: 101, 366: 63, 367: 84, 368: 61, 369: 64, 370: 48, 371: 73, 372: 58, 373: 61, 374: 123, 375: 74, 376: 1, 377: 72, 378: 59, 379: 14, 380: 1, 381: 14, 382: 45, 383: 19, 384: 14, 385: 47, 386: 91, 387: 54, 388: 60, 389: 18, 390: 71, 391: 48, 392: 47, 393: 52, 394: 64, 395: 14, 396: 379, 397: 19, 398: 74, 399: 88, 400: 124, 401: 106, 402: 2, 403: 19, 404: 36, 405: 31, 406: 19, 407: 24, 408: 26, 409: 52, 410: 44, 411: 30, 412: 39, 413: 57, 414: 59, 415: 107, 416: 18, 417: 70, 419: 3, 420: 1, 421: 6, 422: 106, 423: 83, 424: 2, 425: 2, 426: 3, 428: 70, 429: 4, 431: 3, 432: 1, 434: 1, 438: 1, 439: 3, 443: 1, 450: 2, 451: 1, 452: 1, 454: 1, 455: 1, 456: 3, 461: 1, 462: 2, 463: 1, 465: 1, 467: 47, 468: 2, 469: 1, 470: 8, 475: 32, 476: 1, 477: 2, 479: 2, 480: 5, 481: 1, 482: 1, 483: 1, 485: 1, 486: 3, 487: 2, 490: 1, 491: 1, 495: 1, 497: 1, 498: 2, 501: 2, 503: 5, 504: 58, 505: 44, 506: 40, 507: 42, 508: 33, 509: 47, 510: 34, 511: 64, 512: 1, 513: 6, 514: 142, 515: 78, 516: 89, 517: 31, 518: 33, 519: 72, 520: 20, 521: 33, 522: 60, 523: 27, 524: 31, 525: 41, 526: 47, 527: 28, 528: 29, 529: 30, 530: 19, 531: 129, 532: 51, 533: 54, 534: 105, 535: 146, 536: 53, 537: 33, 538: 25, 539: 28, 541: 20, 542: 1, 543: 30, 544: 55, 545: 1, 546: 113, 547: 116, 548: 147, 549: 88, 550: 1, 551: 14, 552: 13, 553: 20, 554: 13, 555: 82, 556: 88, 557: 50, 558: 24, 559: 27, 560: 5, 561: 34, 562: 55, 563: 22, 564: 17, 565: 17, 566: 30, 567: 4, 568: 18, 569: 18, 570: 5, 571: 6, 572: 66, 573: 53, 574: 7, 575: 35, 576: 44, 577: 21, 579: 71, 580: 32, 581: 39, 582: 210, 585: 1, 586: 8, 587: 19, 588: 49, 589: 29, 590: 32, 591: 40, 593: 21, 594: 26, 595: 88, 596: 123, 597: 66, 598: 69, 599: 58, 600: 50, 601: 76, 602: 26, 603: 31, 605: 34, 606: 54, 607: 88, 608: 84, 609: 9, 610: 14, 611: 4, 612: 9, 613: 19, 614: 11, 615: 9, 616: 42, 617: 14, 618: 28, 619: 19, 620: 19, 621: 15, 622: 15, 624: 295, 625: 2, 627: 235, 628: 6, 629: 169, 630: 290, 631: 128, 632: 4, 633: 1, 635: 4, 636: 2, 637: 11, 638: 24, 639: 1, 640: 4, 641: 13, 642: 13, 643: 4, 644: 37, 645: 29, 646: 5, 647: 29, 648: 69, 649: 55, 650: 1, 651: 44, 652: 59, 653: 92, 654: 18, 655: 128, 656: 8, 657: 14, 658: 36, 659: 40, 660: 58, 661: 69, 662: 137, 663: 305, 664: 116, 665: 19, 666: 52, 667: 38, 668: 20, 670: 4, 671: 94, 672: 82, 673: 74, 674: 135, 675: 40, 676: 19, 677: 40, 679: 39, 680: 50, 681: 90, 682: 60, 683: 16, 684: 69, 685: 40, 686: 39, 687: 64, 688: 19, 689: 6, 690: 5, 691: 165, 692: 1, 693: 8, 694: 19, 695: 27, 696: 41, 697: 17, 698: 20, 699: 54, 700: 104, 701: 29, 702: 71, 703: 91, 704: 73, 705: 61, 706: 109, 707: 277, 708: 57, 709: 80, 710: 85, 711: 120, 712: 172, 713: 101, 714: 274, 715: 126, 716: 140, 717: 139, 718: 153, 719: 123, 720: 145, 721: 162, 722: 120, 723: 229, 724: 132, 725: 152, 726: 171, 727: 135, 728: 169, 729: 127, 730: 80, 731: 348, 732: 133, 733: 159, 734: 170, 735: 112, 736: 145, 737: 147, 738: 144, 739: 136, 740: 155, 741: 140, 742: 179, 743: 181, 744: 131, 745: 163, 746: 319, 747: 138, 748: 201, 749: 1, 750: 35, 752: 63, 753: 46, 754: 81, 755: 74, 756: 9, 759: 12, 760: 34, 761: 29, 764: 34, 765: 72, 766: 36, 767: 38, 768: 79, 769: 49, 770: 179, 771: 19, 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2, 4364: 27, 4365: 44, 4367: 58, 4368: 69, 4369: 5, 4370: 13, 4371: 39, 4372: 1, 4373: 1, 4374: 42, 4376: 27, 4377: 95, 4378: 5, 4379: 7, 4380: 3, 4381: 2, 4382: 3, 4384: 20, 4386: 6, 4387: 5, 4388: 1, 4393: 48, 4394: 22, 4395: 34, 4399: 4, 4401: 41, 4404: 8, 4406: 7, 4408: 32, 4411: 384, 4413: 2, 4414: 18, 4418: 64, 4419: 1, 4420: 127, 4422: 181, 4423: 4, 4424: 5, 4426: 160, 4427: 337, 4428: 212, 4430: 120, 4433: 6, 4434: 1, 4437: 2, 4439: 48, 4441: 71, 4442: 102, 4445: 1, 4446: 49, 4447: 3, 4448: 8, 4449: 61, 4450: 66, 4451: 83, 4452: 47, 4453: 91, 4454: 112, 4455: 63, 4456: 49, 4457: 63, 4458: 126, 4459: 68, 4460: 99, 4461: 74, 4462: 196, 4463: 74, 4464: 91, 4465: 59, 4466: 128, 4467: 1, 4468: 62, 4469: 1, 4470: 50, 4471: 39, 4472: 73, 4473: 10, 4478: 2, 4482: 43})
In [33]:
print(agg_traffic_data)
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1885, 663: 0, 4081416473: 82587, 3639382768: 68270, 1764400318: 5247, 42438295: 647, 589520065: 893, 86: 0, 1773055862: 2, 42441926: 8952, 42458313: 13132, 42433743: 52357, 791: 0, 60918583: 102, 4597668028: 206732, 828: 0, 42441942: 7869, 42430329: 294727, 278608420: 5658, 42441947: 2285, 42458333: 129829, 42441950: 1699, 42428965: 2591, 42441952: 1355, 42441953: 987, 42425570: 114472, 1241742563: 121990, 42441956: 4171, 42441254: 4664, 42458342: 24514, 922: 0, 519: 0, 1692450029: 199177, 792: 0, 42442621: 9626, 593984752: 739, 42452026: 74274, 61275380: 12408, 42433781: 992, 42457209: 837, 3786728660: 1015, 42433789: 2369, 765367023: 29, 42433797: 11858, 42438529: 141798, 42433800: 1015, 42453548: 15023, 358448394: 151811, 793: 0, 595295501: 6566, 4679523598: 26179, 42428973: 2752, 42438531: 166488, 42439897: 1163, 42442628: 7963, 42428975: 1999, 42433821: 5425, 42433822: 36161, 4629486879: 4388, 1241742627: 127180, 521: 0, 42433834: 10525, 595254571: 2131, 4597668031: 265875, 42446728: 2866, 836: 0, 4618691806: 34557, 595352889: 123, 249: 0, 595352897: 2179, 42458435: 17, 42450080: 7160, 595352904: 4819, 42442058: 3007, 595352907: 15, 42437177: 23701, 42433881: 3519, 523: 0, 42430352: 306918, 42450275: 388, 42435814: 11272, 42442750: 536, 595352935: 1517, 587934268: 754, 541: 0, 730: 0, 42444461: 180767, 42450290: 249, 42443574: 1052, 42425718: 2, 216: 0, 42442107: 19789, 246857084: 53220, 42442645: 6231, 432: 0, 797: 0, 42433927: 134497, 42445757: 45504, 42442123: 2597, 42428994: 51888, 60929261: 1986, 42442128: 2786, 42425747: 842, 42450325: 36061, 1241742743: 80617, 486864450: 3, 370924955: 6790, 42428640: 126909, 42442142: 3957, 42454214: 136, 42446849: 24982, 42450341: 127602, 374463910: 215, 593: 0, 42453575: 21613, 42442158: 80684, 4347534767: 303778, 4347534768: 116414, 595729841: 7850, 42443534: 154490, 42442164: 80679, 607911351: 235, 881: 0, 3099327930: 2514, 3099327931: 1557, 595729852: 208, 595729853: 1289, 4347534783: 202916, 3099327937: 2734, 3099327940: 1355, 42459041: 24086, 3099327944: 1791, 3099327945: 1556, 2504882466: 1384, 3099327948: 1449, 2821304137: 110809, 3099327951: 1296, 61275600: 7876, 61275601: 18540, 42430371: 275670, 42429006: 47263, 42453792: 67, 42439929: 254, 3099327961: 1269, 3099327962: 3838, 3099327964: 18364, 3099327965: 3274, 3099327966: 5100, 800: 0, 3099327968: 2637, 3099327970: 1730, 42433212: 1010, 3099327972: 2474, 3099327974: 41789, 248708583: 7953, 3099327976: 1115, 255: 0, 1251958257: 8725, 1692433907: 156323, 651: 0, 42442230: 16691, 1692433911: 81634, 42450426: 197215, 1692433916: 197372, 801: 0, 1692433918: 83626, 1692433919: 210513, 1692433920: 77915, 42450434: 120517, 1692433924: 60608, 42442247: 51517, 1692433928: 141843, 42424757: 966, 1692433932: 101476, 42442255: 34531, 1692433938: 148979, 1692433939: 68041, 1692433940: 129453, 1692433941: 101789, 1692433942: 80342, 4332563033: 28680, 42434072: 252411, 42434074: 310408, 42440916: 2272, 42450460: 27844, 42442269: 26620, 42427768: 109, 42442273: 77844, 42447126: 130300, 42442276: 83472, 42434085: 227624, 402480678: 42308, 42434087: 396337, 4158807592: 154237, 205019740: 11802, 42434090: 216966, 42434092: 24628, 42442286: 24437, 42442288: 12548, 42434097: 10112, 42434098: 20030, 42442291: 48672, 42434101: 774, 42442294: 70915, 910: 0, 42422258: 3043, 42434110: 5764, 42434111: 2674, 42434113: 10199, 42446036: 149084, 42434117: 2975, 42453601: 60892, 42434122: 8139, 42446775: 11204, 42448141: 1464, 42434129: 1454, 42434131: 1043, 3099327952: 8163, 42434134: 2472, 1669480023: 2826, 42425945: 121, 42434140: 395327, 42430394: 493595, 42434142: 205405, 42445413: 244239, 42434145: 84348, 42425955: 24150, 42434148: 46905, 42425959: 195, 42458728: 1454, 42453607: 29277, 42434156: 190655, 42434158: 57328, 42434160: 93215, 42434161: 7219, 246890098: 46485, 42439955: 419581, 42434165: 31602, 3831856758: 178424, 4597668042: 185725, 42434169: 49873, 4708064890: 24722, 270: 0, 42434172: 183852, 42453354: 820, 42434175: 44059, 370897467: 282, 2141002593: 262, 42434179: 127306, 42429242: 205, 42444055: 21664, 42458766: 89083, 42442384: 519, 1544809889: 5647, 806: 0, 42434196: 231677, 42434197: 23670, 42436716: 77040, 42434201: 207014, 4477147271: 36, 42434205: 82936, 1538237248: 5767, 42423579: 466, 2099150501: 9368, 42434215: 57655, 925: 0, 42442410: 86898, 3099327955: 2386, 42442415: 100068, 42442417: 78960, 42434226: 19329, 205020852: 76736, 42442422: 90324, 42442697: 2575, 42434233: 184105, 42424823: 175, 42434237: 3195, 42442432: 315501, 42439968: 342404, 785: 0, 4332563027: 62092, 272195270: 156908, 272195271: 128322, 272195272: 34393, 42434249: 6213, 42450634: 77218, 42434251: 5026, 272195276: 13056, 42442445: 127744, 42424218: 10758, 1061531597: 219647, 42450641: 77177, 42442451: 132801, 42434262: 9790, 42434264: 1160, 42442459: 134319, 42434268: 128329, 42434270: 205827, 42442463: 138607, 263: 0, 42442469: 124157, 42434279: 143412, 42426089: 18143, 3099327957: 3540, 42442475: 96683, 42434285: 82113, 42442480: 130752, 1773084402: 169081, 42450675: 60704, 1773084405: 188487, 1773084407: 146032, 1773084410: 120797, 42442492: 77494, 42450688: 31560, 42434306: 432, 42434308: 3534, 42442502: 81276, 335: 0, 42434314: 1041, 60928695: 2, 42441951: 1319, 42431789: 95543, 804: 0, 588455697: 26854, 42442514: 70441, 588455700: 1162, 588455701: 141665, 1544809872: 379, 42426135: 1009, 42426138: 185, 611: 0, 42442528: 101613, 658: 0, 42434338: 655, 42434340: 7290, 42442534: 109929, 42434343: 3830, 811: 0, 42457735: 33007, 42442542: 83138, 42434352: 123682, 593854257: 12912, 42434354: 19625, 42434355: 28788, 42442549: 67431, 42434358: 16412, 4616503095: 221933, 42434360: 30500, 42434361: 3120, 588455738: 1196, 371225085: 180349, 42426172: 3075, 42442557: 58395, 588455742: 73887, 588455743: 168502, 42442561: 59337, 42434370: 166, 42434372: 292, 42442565: 54908, 42442567: 37657, 42442569: 44199, 42442571: 37252, 287: 0, 42442722: 1582, 42434382: 22879, 42442575: 30644, 42458960: 68333, 42434385: 19326, 42434386: 14554, 990: 0, 42434389: 182828, 42444954: 157995, 42442584: 32207, 42442586: 17649, 42431802: 127929, 278609760: 25084, 42436390: 48557, 637: 0, 217: 0, 42442596: 16787, 813: 0, 42431804: 120635, 42434410: 3756, 42434412: 59518, 42434413: 23381, 661: 0, 42442609: 12121, 42434418: 21303, 42434420: 22390, 42442613: 11540, 2499802997: 37189, 42442618: 9881, 42434427: 20304, 42434428: 8313, 42434429: 7967, 42450814: 90765, 591454081: 37213, 42434434: 96776, 814: 0, 42450820: 112097, 589298326: 180, 3786728724: 162, 42434439: 82391, 173: 0, 42442635: 7017, 311: 0, 42442638: 6640, 42442733: 1021, 42431044: 73922, 42442642: 6443, 1533280147: 2832, 4478001045: 41237, 42431812: 107964, 42442651: 6368, 42442656: 6683, 42434465: 6282, 42431992: 20508, 42442659: 5560, 596776132: 88583, 272: 0, 42442664: 7405, 370914027: 1249, 62915514: 19361, 1061531634: 179561, 589928030: 7910, 42442671: 43875, 588455857: 27068, 588455858: 401, 1205714869: 254, 1205714871: 56, 565: 0, 42442684: 40272, 816: 0, 3884569931: 172896, 278609863: 24832, 54: 0, 42435916: 7823, 620: 0, 42442702: 2235, 42442703: 2210, 42442710: 2091, 544: 0, 42459098: 195329, 60921167: 9, 42442717: 1970, 1205714910: 171, 42453669: 1695, 42433229: 137215, 42442723: 1410, 42442728: 1187, 42424334: 69, 42459114: 292378, 42437287: 193937, 279151954: 11009, 42442734: 898, 42449576: 48712, 42442738: 833, 42435923: 2536, 956: 0, 42442741: 712, 42445481: 20002, 42442745: 606, 1506445821: 9927, 373268478: 3332, 42434559: 119})
In [34]:
print(nodes_to_coord_data)
[(42421728, {'lon': -73.96004, 'x': 587729.35665455484, 'lat': 40.7980472, 'y': 4516859.0270689027, 'ref': nan, 'osmid': '42421728', 'highway': 'traffic_signals'}), (42421731, {'lon': -73.9614743, 'x': 587607.57342485804, 'lat': 40.798645, 'y': 4516923.9536821311, 'ref': nan, 'osmid': '42421731', 'highway': 'traffic_signals'}), (42421737, {'lon': -73.962876, 'x': 587488.55019145994, 'lat': 40.7992369, 'y': 4516988.2598536853, 'ref': nan, 'osmid': '42421737', 'highway': 'traffic_signals'}), (42421741, {'lon': -73.9657046, 'x': 587248.37035306217, 'lat': 40.8004313, 'y': 4517118.0294734156, 'ref': nan, 'osmid': '42421741', 'highway': 'traffic_signals'}), (42421745, {'lon': -73.9679942, 'x': 587053.96052313899, 'lat': 40.8014007, 'y': 4517223.3649709234, 'ref': nan, 'osmid': '42421745', 'highway': 'traffic_signals'}), (42421749, {'lon': -73.9685615, 'x': 587005.78869773983, 'lat': 40.8016434, 'y': 4517249.7435469087, 'ref': nan, 'osmid': '42421749', 'highway': 'traffic_signals'}), (42421751, {'lon': -73.970054, 'x': 586879.07380520157, 'lat': 40.8022682, 'y': 4517317.6214588294, 'ref': nan, 'osmid': '42421751', 'highway': nan}), (42421769, {'lon': -73.9749879, 'x': 586488.35834397585, 'lat': 40.7826312, 'y': 4515132.8774666963, 'ref': nan, 'osmid': '42421769', 'highway': 'traffic_signals'}), (42421772, {'lon': -73.9778254, 'x': 586247.38394269417, 'lat': 40.783828, 'y': 4515262.9375207862, 'ref': nan, 'osmid': '42421772', 'highway': 'traffic_signals'}), (42421775, {'lon': -73.9795827, 'x': 586098.14984667348, 'lat': 40.7845691, 'y': 4515343.4788722955, 'ref': nan, 'osmid': '42421775', 'highway': 'traffic_signals'}), (42421776, {'lon': -73.9806779, 'x': 586005.14436290669, 'lat': 40.7850311, 'y': 4515393.6899548359, 'ref': nan, 'osmid': '42421776', 'highway': 'traffic_signals'}), (42421778, {'lon': -73.9820114, 'x': 585891.90409287298, 'lat': 40.7855935, 'y': 4515454.8139766185, 'ref': nan, 'osmid': '42421778', 'highway': nan}), (42421783, {'lon': -73.9313326, 'x': 590068.1766291135, 'lat': 40.8593027, 'y': 4523688.0608103117, 'ref': nan, 'osmid': '42421783', 'highway': 'traffic_signals'}), (42421785, {'lon': -73.930122, 'x': 590170.96646138036, 'lat': 40.858744, 'y': 4523627.2856032271, 'ref': nan, 'osmid': '42421785', 'highway': nan}), (42421786, {'lon': -73.927625, 'x': 590381.0567395722, 'lat': 40.859012, 'y': 4523659.6103372704, 'ref': nan, 'osmid': '42421786', 'highway': nan}), (42421791, {'lon': -73.926786, 'x': 590450.50191733357, 'lat': 40.859944, 'y': 4523763.9375762669, 'ref': nan, 'osmid': '42421791', 'highway': nan}), (42421794, {'lon': -73.9250224, 'x': 590597.09673579375, 'lat': 40.8614439, 'y': 4523932.2641010908, 'ref': nan, 'osmid': '42421794', 'highway': nan}), (42421800, {'lon': -73.9703286, 'x': 586879.75028916076, 'lat': 40.7839788, 'y': 4515287.0768701946, 'ref': nan, 'osmid': '42421800', 'highway': 'traffic_signals'}), (42421803, {'lon': -73.9731396, 'x': 586641.02266634163, 'lat': 40.7851655, 'y': 4515416.0292394999, 'ref': nan, 'osmid': '42421803', 'highway': 'traffic_signals'}), (42421806, {'lon': -73.9759753, 'x': 586400.2058235975, 'lat': 40.7863627, 'y': 4515546.1304542292, 'ref': nan, 'osmid': '42421806', 'highway': 'traffic_signals'}), (42421809, {'lon': -73.9776927, 'x': 586254.36290726159, 'lat': 40.7870877, 'y': 4515624.9207098922, 'ref': nan, 'osmid': '42421809', 'highway': 'traffic_signals'}), (42421810, {'lon': -73.9788289, 'x': 586157.87772952241, 'lat': 40.7875673, 'y': 4515677.0432723248, 'ref': nan, 'osmid': '42421810', 'highway': 'traffic_signals'}), (42421820, {'lon': -73.9810702, 'x': 585967.55223883223, 'lat': 40.7885135, 'y': 4515779.8795028413, 'ref': nan, 'osmid': '42421820', 'highway': 'traffic_signals'}), (42421828, {'lon': -73.976212, 'x': 586459.28071941552, 'lat': 40.725326, 'y': 4508770.3703466281, 'ref': nan, 'osmid': '42421828', 'highway': nan}), (42421852, {'lon': -73.9568144, 'x': 587982.23532793601, 'lat': 40.8126067, 'y': 4518478.4870921411, 'ref': nan, 'osmid': '42421852', 'highway': nan}), (42421857, {'lon': -73.9598159, 'x': 587727.46210072527, 'lat': 40.8138385, 'y': 4518612.2190041021, 'ref': nan, 'osmid': '42421857', 'highway': nan}), (42421865, {'lon': -73.960687, 'x': 587653.52842956164, 'lat': 40.814192, 'y': 4518650.589039552, 'ref': nan, 'osmid': '42421865', 'highway': nan}), (42421877, {'lon': -73.995914, 'x': 584785.86603430111, 'lat': 40.732847, 'y': 4509586.0399496052, 'ref': nan, 'osmid': '42421877', 'highway': 'traffic_signals'}), (42421889, {'lon': -73.999188, 'x': 584507.77389509883, 'lat': 40.734134, 'y': 4509725.7491801521, 'ref': nan, 'osmid': '42421889', 'highway': 'traffic_signals'}), (42421941, {'lon': -74.008789, 'x': 583734.55146516033, 'lat': 40.704211, 'y': 4506394.9020589059, 'ref': nan, 'osmid': '42421941', 'highway': 'traffic_signals'}), (42421946, {'lon': -74.008325, 'x': 583774.24813008297, 'lat': 40.703813, 'y': 4506351.1639415864, 'ref': nan, 'osmid': '42421946', 'highway': nan}), (42421951, {'lon': -74.0077787, 'x': 583821.01174843579, 'lat': 40.7033242, 'y': 4506297.4251864692, 'ref': nan, 'osmid': '42421951', 'highway': 'traffic_signals'}), (42421960, {'lon': -74.0089636, 'x': 583719.96084691433, 'lat': 40.7040838, 'y': 4506380.6156452233, 'ref': nan, 'osmid': '42421960', 'highway': 'traffic_signals'}), (42421965, {'lon': -74.0079887, 'x': 583803.42888658773, 'lat': 40.7031983, 'y': 4506283.2491426878, 'ref': nan, 'osmid': '42421965', 'highway': 'traffic_signals'}), (42421969, {'lon': -73.9810292, 'x': 585995.82146430807, 'lat': 40.7692746, 'y': 4513644.2511247089, 'ref': nan, 'osmid': '42421969', 'highway': 'traffic_signals'}), (42421972, {'lon': -73.9822548, 'x': 585891.71916674543, 'lat': 40.7697921, 'y': 4513700.4969295636, 'ref': nan, 'osmid': '42421972', 'highway': 'traffic_signals'}), (42421974, {'lon': -73.9838503, 'x': 585756.18431432371, 'lat': 40.770478, 'y': 4513775.0762150688, 'ref': nan, 'osmid': '42421974', 'highway': 'traffic_signals'}), (42421982, {'lon': 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584869.81076184125, 'lat': 40.755791, 'y': 4512134.2817672081, 'ref': nan, 'osmid': '4491359483', 'highway': nan}), (4506716195, {'lon': -73.9954452, 'x': 584846.84175643127, 'lat': 40.7160039, 'y': 4507716.7951776618, 'ref': nan, 'osmid': '4506716195', 'highway': 'traffic_signals'}), (4515465264, {'lon': -73.9401576, 'x': 589363.23122649244, 'lat': 40.8303809, 'y': 4520468.4334114892, 'ref': nan, 'osmid': '4515465264', 'highway': 'traffic_signals'}), (4557482266, {'lon': -73.9816187, 'x': 585948.04260449845, 'lat': 40.7677437, 'y': 4513473.731855371, 'ref': nan, 'osmid': '4557482266', 'highway': 'traffic_signals'}), (4557495130, {'lon': -73.9811168, 'x': 585982.04387467762, 'lat': 40.7742266, 'y': 4514193.8753783489, 'ref': nan, 'osmid': '4557495130', 'highway': 'traffic_signals'}), (4557517549, {'lon': -73.997716, 'x': 584610.05837474111, 'lat': 40.7515002, 'y': 4511654.9381558923, 'ref': nan, 'osmid': '4557517549', 'highway': 'traffic_signals'}), (4557517550, {'lon': -74.0049194, 'x': 584014.31556673639, 'lat': 40.7416809, 'y': 4510558.006324403, 'ref': nan, 'osmid': '4557517550', 'highway': 'traffic_signals'}), (4557517552, {'lon': -74.0045542, 'x': 584044.59044022532, 'lat': 40.7421252, 'y': 4510607.6763436897, 'ref': nan, 'osmid': '4557517552', 'highway': nan}), (4557517554, {'lon': -74.0054494, 'x': 583970.43334743578, 'lat': 40.7409922, 'y': 4510481.0486675939, 'ref': nan, 'osmid': '4557517554', 'highway': 'traffic_signals'}), (4557517555, {'lon': -74.005864, 'x': 583936.3471526854, 'lat': 40.740261, 'y': 4510399.483835103, 'ref': nan, 'osmid': '4557517555', 'highway': nan}), (4557539295, {'lon': -73.9887918, 'x': 585399.98637704796, 'lat': 40.7229078, 'y': 4508489.6232913993, 'ref': nan, 'osmid': '4557539295', 'highway': 'traffic_signals'}), (4562863353, {'lon': -73.9494518, 'x': 588659.75838434359, 'lat': 40.7700832, 'y': 4513765.4450881006, 'ref': nan, 'osmid': '4562863353', 'highway': nan}), (4597668020, {'lon': -73.9823187, 'x': 585917.1751895909, 'lat': 40.7458297, 'y': 4511040.4299074318, 'ref': nan, 'osmid': '4597668020', 'highway': 'traffic_signals'}), (4597668026, {'lon': -73.9886195, 'x': 585396.30625240423, 'lat': 40.7371659, 'y': 4510072.5380578926, 'ref': nan, 'osmid': '4597668026', 'highway': 'traffic_signals'}), (4597668028, {'lon': -73.9877674, 'x': 585466.75119394658, 'lat': 40.7383406, 'y': 4510203.7672581309, 'ref': nan, 'osmid': '4597668028', 'highway': 'traffic_signals'}), (4597668029, {'lon': -73.9859284, 'x': 585618.78543297434, 'lat': 40.7408689, 'y': 4510486.2185034063, 'ref': nan, 'osmid': '4597668029', 'highway': 'traffic_signals'}), (4597668030, {'lon': -73.9841259, 'x': 585767.79547746934, 'lat': 40.7433432, 'y': 4510762.6431179559, 'ref': nan, 'osmid': '4597668030', 'highway': 'traffic_signals'}), (4597668031, {'lon': -73.9845756, 'x': 585730.61934246682, 'lat': 40.7427268, 'y': 4510693.7790001901, 'ref': nan, 'osmid': '4597668031', 'highway': 'traffic_signals'}), (4597668032, {'lon': -73.9850243, 'x': 585693.53141991189, 'lat': 40.7421069, 'y': 4510624.5275382679, 'ref': nan, 'osmid': '4597668032', 'highway': 'traffic_signals'}), (4597668033, {'lon': -73.9832289, 'x': 585841.94080439454, 'lat': 40.7445777, 'y': 4510900.5583320912, 'ref': nan, 'osmid': '4597668033', 'highway': 'traffic_signals'}), (4597668035, {'lon': -73.9899797, 'x': 585283.74304361106, 'lat': 40.7353747, 'y': 4509872.380071003, 'ref': nan, 'osmid': '4597668035', 'highway': 'traffic_signals'}), (4597668036, {'lon': -73.9890626, 'x': 585359.64475901448, 'lat': 40.7365773, 'y': 4510006.7683960004, 'ref': nan, 'osmid': '4597668036', 'highway': 'traffic_signals'}), (4597668038, {'lon': -73.9882005, 'x': 585430.93652102584, 'lat': 40.7377511, 'y': 4510137.9070401797, 'ref': nan, 'osmid': '4597668038', 'highway': 'traffic_signals'}), (4597668039, {'lon': -73.9873494, 'x': 585501.30907573551, 'lat': 40.7389154, 'y': 4510267.9811545173, 'ref': nan, 'osmid': '4597668039', 'highway': 'traffic_signals'}), (4597668040, {'lon': -73.9868973, 'x': 585538.68250262574, 'lat': 40.7395394, 'y': 4510337.6900029853, 'ref': nan, 'osmid': '4597668040', 'highway': 'traffic_signals'}), (4597668041, {'lon': -73.9864091, 'x': 585579.04222375376, 'lat': 40.740211, 'y': 4510412.7181889191, 'ref': nan, 'osmid': '4597668041', 'highway': 'traffic_signals'}), (4597668042, {'lon': -73.9854809, 'x': 585655.7830903345, 'lat': 40.7414813, 'y': 4510554.6358306473, 'ref': nan, 'osmid': '4597668042', 'highway': 'traffic_signals'}), (4597668043, {'lon': -73.9836775, 'x': 585804.85728951613, 'lat': 40.7439626, 'y': 4510831.8391909199, 'ref': nan, 'osmid': '4597668043', 'highway': 'traffic_signals'}), (4597668044, {'lon': -73.9827781, 'x': 585879.20291810599, 'lat': 40.7451978, 'y': 4510969.8348653037, 'ref': nan, 'osmid': '4597668044', 'highway': 'traffic_signals'}), (4602414021, {'lon': -74.0061172, 'x': 583935.70313348423, 'lat': 40.7237608, 'y': 4508567.606912652, 'ref': nan, 'osmid': '4602414021', 'highway': nan}), (4616503095, {'lon': -73.9679563, 'x': 587111.81211547414, 'lat': 40.7595572, 'y': 4512578.4423404569, 'ref': nan, 'osmid': '4616503095', 'highway': nan}), (4616536348, {'lon': -74.006055, 'x': 583940.12397552992, 'lat': 40.7244231, 'y': 4508641.1860928796, 'ref': nan, 'osmid': '4616536348', 'highway': 'traffic_signals'}), (4616536351, {'lon': -74.0058541, 'x': 583955.49745766493, 'lat': 40.7256907, 'y': 4508782.0902489433, 'ref': nan, 'osmid': '4616536351', 'highway': 'traffic_signals'}), (4616536352, {'lon': -74.0059652, 'x': 583946.93601877918, 'lat': 40.7250373, 'y': 4508709.4522490548, 'ref': nan, 'osmid': '4616536352', 'highway': 'traffic_signals'}), (4618691806, {'lon': -74.003395, 'x': 584154.70462459628, 'lat': 40.7324104, 'y': 4509530.3766690372, 'ref': nan, 'osmid': '4618691806', 'highway': 'traffic_signals'}), (4618691808, {'lon': -74.0035391, 'x': 584142.82847665902, 'lat': 40.7321786, 'y': 4509504.5071683312, 'ref': nan, 'osmid': '4618691808', 'highway': nan}), (4625583181, {'lon': -74.0034172, 'x': 584175.87402900308, 'lat': 40.7141186, 'y': 4507499.8434571344, 'ref': nan, 'osmid': '4625583181', 'highway': 'traffic_signals'}), (4629486879, {'lon': -74.0077224, 'x': 583800.47101497988, 'lat': 40.7234955, 'y': 4508536.6237286804, 'ref': nan, 'osmid': '4629486879', 'highway': nan}), (4679523598, {'lon': -74.0026261, 'x': 584218.07947808551, 'lat': 40.733642, 'y': 4509667.8300192012, 'ref': nan, 'osmid': '4679523598', 'highway': 'traffic_signals'}), (4680481156, {'lon': -74.0051183, 'x': 584038.11078376882, 'lat': 40.7094091, 'y': 4506975.4295998225, 'ref': nan, 'osmid': '4680481156', 'highway': 'stop'}), (4680481158, {'lon': -74.005561, 'x': 584001.15990116878, 'lat': 40.7090552, 'y': 4506935.7209729571, 'ref': nan, 'osmid': '4680481158', 'highway': nan}), (4685453634, {'lon': -74.005416, 'x': 583976.0683553461, 'lat': 40.738754, 'y': 4510232.6248011757, 'ref': nan, 'osmid': '4685453634', 'highway': nan}), (4685453636, {'lon': -74.0056066, 'x': 583961.48703038297, 'lat': 40.7375516, 'y': 4510098.9677375928, 'ref': nan, 'osmid': '4685453636', 'highway': nan}), (4685462237, {'lon': -74.0054685, 'x': 583971.26781208918, 'lat': 40.7390464, 'y': 4510265.0330149913, 'ref': nan, 'osmid': '4685462237', 'highway': nan}), (4685462240, {'lon': -74.0056556, 'x': 583956.97585109877, 'lat': 40.7378489, 'y': 4510131.9232358141, 'ref': nan, 'osmid': '4685462240', 'highway': nan}), (4708064890, {'lon': -73.9544228, 'x': 588193.05160206161, 'lat': 40.8057298, 'y': 4517717.4958519423, 'ref': nan, 'osmid': '4708064890', 'highway': nan})]
In [36]:
nodes_to_coord_data[0]
Out[36]:
(42421728,
{'highway': 'traffic_signals',
'lat': 40.7980472,
'lon': -73.96004,
'osmid': '42421728',
'ref': nan,
'x': 587729.35665455484,
'y': 4516859.0270689027})
In [4]:
# Compute histogram of accident counts.
accident_numbers_list = list(agg_accident_data.values())
print(min(accident_numbers_list))
print(max(accident_numbers_list))
plt.xlim(min(accident_numbers_list), max(accident_numbers_list))
plt.hist(accident_numbers_list)
plt.show()
1
668
In [6]:
# Compute histogram of traffic counts.
traffic_numbers_list = list(agg_traffic_data.values())
print(min(traffic_numbers_list))
print(max(traffic_numbers_list))
plt.xlim(min(traffic_numbers_list), max(traffic_numbers_list))
plt.hist(traffic_numbers_list)
plt.show()
0
667966
In [11]:
# Compute scores.
scores = []
for k, num_accidents in agg_accident_data.items():
if (num_accidents > 0):
node = nodes_to_coord_data[k]
node_id = node[0]
# Get amount of traffic at that node.
traffic = agg_traffic_data[node_id]
# There are some weird cases where traffic is 0 but accidents is > 0.
# For now, we update traffic to equal the num_accidents.
# Maybe this assumption should be revisited?
if (traffic < num_accidents):
traffic = num_accidents
# Calculate intersection score.
score = num_accidents/traffic
scores.append(score)
# Compute histogram of scores.
print(min(scores))
print(max(scores))
plt.xlim(min(scores), max(scores))
plt.hist(scores)
plt.show()
# Compute histogram of scores after filtering out high scores.
filtered_scores = [x for x in scores if x < 0.01]
plt.xlim(min(filtered_scores), max(filtered_scores))
plt.hist(filtered_scores)
plt.show()
2.904325702701604e-06
1.0
In [22]:
# Map data.
# Change this to false if you want to generate the full dataset.
sampleData = True
sampleSize = 100
# Starting coordinates to load map view.
NYC_coordinates = (40.7142700, -74.0059700)
# Create Map object.
agg_map = folium.Map(location=NYC_coordinates,
zoom_start=12)
# Plot data.
marker_cluster = folium.MarkerCluster().add_to(agg_map)
for k, num_accidents in agg_accident_data.items():
if (sampleData and k > sampleSize):
break;
if (num_accidents > 0):
# Get lat/long of node.
node = nodes_to_coord_data[k]
node_id = node[0]
lat = node[1]['lat']
lon = node[1]['lon']
# Get amount of traffic at that node.
traffic = agg_traffic_data[node_id]
# There are some weird cases where traffic is less than the num accidents.
# For now, we update traffic to equal the num_accidents.
# Maybe this assumption should be revisited?
if (traffic < num_accidents):
traffic = num_accidents
# Calculate intersection score.
score = num_accidents/traffic
# Plot point.
node_metadata = """
<ul>
<li><strong>Num accidents</strong>: {0}</li>
<li><strong>Traffic</strong>: {1}</li>
<li><strong>Score</strong>: {2}</li>
</ul>""".format(
str(num_accidents), str(traffic), str(score))
iframe = folium.element.IFrame(html=node_metadata, width=250, height=100)
popup = folium.Popup(iframe, max_width=2650)
score_color = "red"
if (score > 0.001 and score < 0.01):
score_color = "blue"
if (score <= 0.001):
score_color = "green"
folium.Marker(
location = [lat, lon],
icon = folium.Icon(color=score_color, icon='asterisk'),
popup=popup).add_to(marker_cluster)
# Only print the map in the notebook if sampling is on.
# Otherwise, it will crash your notebook.
# If not sampling the data, run the method in the next section
# to write out the generated html file and open that to see the results.
if (sampleData):
display(agg_map)
In [15]:
# Save html version of map.
agg_map.save('agg_accidents_map.html')
Content source: JasonSanchez/dangerous-intersections
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