notebook.community

Edit and run 





In [1]:



    


import numpy as np
import pandas as pd
import itertools
import geoplotlib as glp
from collections import Counter
from geoplotlib.utils import BoundingBox, DataAccessObject
from __future__ import division

%matplotlib inline



    











In [2]:



    


dataset = 'datasets/NYPD_Motor_Vehicle_Collisions.csv'
collisions = pd.read_csv(dataset)



    











In [3]:



    


collisions = collisions[pd.notnull(collisions.BOROUGH)]
collisions.head()



    


















    
Out[3]:










  
    

      
      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
      03/14/2016
      3:27
      QUEENS
      11372
      40.747734
      -73.882999
      (40.7477341, -73.8829986)
      ROOSEVELT AVENUE
      83 STREET
      NaN
      ...
      NaN
      NaN
      NaN
      NaN
      3405169
      OTHER
      NaN
      NaN
      NaN
      NaN
    

    

      3
      03/14/2016
      0:45
      MANHATTAN
      10035
      40.808279
      -73.938793
      (40.8082795, -73.9387929)
      EAST 129 STREET
      MADISON AVENUE
      NaN
      ...
      Unspecified
      NaN
      NaN
      NaN
      3405059
      PASSENGER VEHICLE
      PASSENGER VEHICLE
      NaN
      NaN
      NaN
    

    

      4
      03/13/2016
      23:00
      BROOKLYN
      11206
      40.706653
      -73.950406
      (40.7066527, -73.9504063)
      UNION AVENUE
      MONTROSE AVENUE
      NaN
      ...
      Unspecified
      NaN
      NaN
      NaN
      3405121
      PASSENGER VEHICLE
      PASSENGER VEHICLE
      NaN
      NaN
      NaN
    

    

      6
      03/13/2016
      9:48
      BROOKLYN
      11212
      40.661997
      -73.919593
      (40.661997, -73.9195931)
      KINGS HIGHWAY
      EAST 98 STREET
      NaN
      ...
      Unspecified
      NaN
      NaN
      NaN
      3404744
      PASSENGER VEHICLE
      UNKNOWN
      NaN
      NaN
      NaN
    

    

      9
      03/13/2016
      9:46
      QUEENS
      11106
      40.756580
      -73.929752
      (40.75658, -73.9297516)
      36 AVENUE
      31 STREET
      NaN
      ...
      Unspecified
      NaN
      NaN
      NaN
      3404995
      LIVERY VEHICLE
      PASSENGER VEHICLE
      NaN
      NaN
      NaN
    

  



5 rows × 29 columns



















In [4]:



    


print "Number of registered collisions since 2012: " + str(len(collisions))



    


















    






Number of registered collisions since 2012: 584753

















In [5]:



    


# Types of contributing factors
collisions['NUMBER OF PERSONS KILLED'].unique()



    


















    
Out[5]:








array([0, 1, 2, 3, 4, 5])

















In [3]:



    


# KDE MAP OF ALL COLLISION INCIDENTS
bbox = BoundingBox(north=collisions.LATITUDE.max()-0.055,\
                   west=collisions.LONGITUDE.min()+0.055,\
                   south=collisions.LATITUDE.min()-0.055,\
                   east=collisions.LONGITUDE.max()+0.055)

#bbox = BoundingBox(north=40.915256, west=-74.255735, south=40.496044, east=-73.700272)

coords = {'lat': collisions.LATITUDE.values.tolist(), 'lon': collisions.LONGITUDE.values.tolist()}

glp.kde(coords, bw=2, cut_below=1e-4)
glp.set_bbox(bbox)
glp.inline()
#glp.show()



    


















    






('smallest non-zero count', 8.955579620337621e-09)
('max count:', 54.536583126377039)










    























In [6]:



    


def filter_cause(cause):
    return collisions[(collisions['CONTRIBUTING FACTOR VEHICLE 1'] == cause) | \
                (collisions['CONTRIBUTING FACTOR VEHICLE 2'] == cause) | \
                (collisions['CONTRIBUTING FACTOR VEHICLE 3'] == cause) | \
                (collisions['CONTRIBUTING FACTOR VEHICLE 4'] == cause) | \
                (collisions['CONTRIBUTING FACTOR VEHICLE 5'] == cause)]

def draw_kde(data):
    bbox = BoundingBox(north=data.LATITUDE.max()-0.055,\
                       west=data.LONGITUDE.min()+0.055,\
                       south=data.LATITUDE.min()-0.055,\
                       east=data.LONGITUDE.max()+0.055)

    #bbox = BoundingBox(north=40.915256, west=-74.255735, south=40.496044, east=-73.700272)

    coords = {'lat': data.LATITUDE.values.tolist(), 'lon': data.LONGITUDE.values.tolist()}
    
    glp.kde(coords, bw=2, cut_below=1e-4)
    glp.set_bbox(bbox)
    glp.inline()

for c in collisions['CONTRIBUTING FACTOR VEHICLE 1'].unique().tolist():
    filtered = filter_cause(c)
    print "CAUSE: " + c
    print "COUNT: " + str(len(filtered)) + " of " + str(len(collisions))
    draw_kde(filtered)



    


















    






CAUSE: Pavement Slippery
COUNT: 9151 of 769054
('smallest non-zero count', 7.1647865443840454e-10)
('max count:', 0.23171796205253486)










    























In [8]:



    


def draw_dot(data, type_color):
    gridDots = {'lat': data.LATITUDE.values.tolist(), 'lon': data.LONGITUDE.values.tolist()}
    
    bbox = BoundingBox(north=collisions.LATITUDE.max()-0.055,\
                   west=collisions.LONGITUDE.min()+0.055,\
                   south=collisions.LATITUDE.min()-0.055,\
                   east=collisions.LONGITUDE.max()+0.055)
    glp.set_bbox(bbox)
    glp.dot(gridDots, color=type_color)

draw_dot(collisions, 'r')
glp.inline()
#collisions.head()



    


















    























In [9]:



    


inj_cyclists = collisions[collisions['NUMBER OF CYCLIST INJURED'] > 0]
inj_pedestrians = collisions[collisions['NUMBER OF PEDESTRIANS INJURED'] > 0]
inj_motorists = collisions[collisions['NUMBER OF MOTORIST INJURED'] > 0]

print "CYCLIST INJURIES: " + str(len(inj_cyclists))
print "PEDESTRIANS INJURIES: " + str(len(inj_pedestrians))
print "MOTORISTS INJURIES: " + str(len(inj_motorists))



    


















    






CYCLIST INJURIES: 13612
PEDESTRIANS INJURIES: 35368
MOTORISTS INJURIES: 61214

















In [10]:



    


draw_dot(inj_motorists, 'b')
draw_dot(inj_pedestrians, 'r')
draw_dot(inj_cyclists, 'g')
glp.inline()



    


















    























In [11]:



    


kill_cyclists = collisions[collisions['NUMBER OF CYCLIST KILLED'] > 0]
kill_pedestrians = collisions[collisions['NUMBER OF PEDESTRIANS KILLED'] > 0]
kill_motorists = collisions[collisions['NUMBER OF MOTORIST KILLED'] > 0]

print "CYCLIST KILLED: " + str(len(kill_cyclists))
print "PEDESTRIANS KILLED: " + str(len(kill_pedestrians))
print "MOTORISTS KILLED: " + str(len(kill_motorists))



    


















    






CYCLIST KILLED: 49
PEDESTRIANS KILLED: 423
MOTORISTS KILLED: 218

















In [12]:



    


draw_dot(kill_motorists, 'b')
draw_dot(kill_pedestrians, 'r')
draw_dot(kill_cyclists, 'g')
glp.inline()



    


















    























In [13]:



    


# Collisions on the hour for each borough
series = {}

for b in collisions.BOROUGH.unique():
    filtered = collisions[collisions.BOROUGH == b]
    collisions_on_hour = Counter([int(c.split(":")[0]) for c in filtered.TIME.values])
    series[b] = pd.Series([collisions_on_hour[h] for h in range(0,24)], index=range(0,24))

d = pd.DataFrame(series)
d.plot(kind='bar', figsize=(16,6), subplots=True, layout=(3,2), legend=False)
d.plot(kind='bar', figsize=(16,6), subplots=False, legend=True)



    


















    
Out[13]:








<matplotlib.axes._subplots.AxesSubplot at 0x111f98310>










    

















    
























In [14]:



    


# GET ALL ENTRIES FROM THE DATASET WHERE CAUSATION AND KILLED ARE FILLED OUT
killed = collisions[((collisions['NUMBER OF PERSONS KILLED'] > 0))]

killed2 = killed[(killed['CONTRIBUTING FACTOR VEHICLE 1'] != "Unspecified") & (pd.notnull(killed['CONTRIBUTING FACTOR VEHICLE 1'])) | \
                (killed['CONTRIBUTING FACTOR VEHICLE 2'] != "Unspecified") & (pd.notnull(killed['CONTRIBUTING FACTOR VEHICLE 2'])) | \
                (killed['CONTRIBUTING FACTOR VEHICLE 3'] != "Unspecified") & (pd.notnull(killed['CONTRIBUTING FACTOR VEHICLE 3'])) | \
                (killed['CONTRIBUTING FACTOR VEHICLE 4'] != "Unspecified") & (pd.notnull(killed['CONTRIBUTING FACTOR VEHICLE 4'])) | \
                (killed['CONTRIBUTING FACTOR VEHICLE 5'] != "Unspecified") & (pd.notnull(killed['CONTRIBUTING FACTOR VEHICLE 5']))]

print str(len(killed2)) + " of " + str(len(killed)) +  " KILLS"
killed2.head()[['CONTRIBUTING FACTOR VEHICLE 1', 'CONTRIBUTING FACTOR VEHICLE 2', 'CONTRIBUTING FACTOR VEHICLE 3', 'CONTRIBUTING FACTOR VEHICLE 4', 'CONTRIBUTING FACTOR VEHICLE 5']]



    


















    






372 of 688 KILLS










    
Out[14]:










  
    

      
      CONTRIBUTING FACTOR VEHICLE 1
      CONTRIBUTING FACTOR VEHICLE 2
      CONTRIBUTING FACTOR VEHICLE 3
      CONTRIBUTING FACTOR VEHICLE 4
      CONTRIBUTING FACTOR VEHICLE 5
    

  
  
    

      5718
      Following Too Closely
      NaN
      NaN
      NaN
      NaN
    

    

      7660
      Failure to Yield Right-of-Way
      NaN
      NaN
      NaN
      NaN
    

    

      8341
      Driver Inattention/Distraction
      Unspecified
      NaN
      NaN
      NaN
    

    

      8342
      Driver Inattention/Distraction
      Unspecified
      NaN
      NaN
      NaN
    

    

      8506
      Physical Disability
      NaN
      NaN
      NaN
      NaN
    

  




















In [15]:



    


# GET ALL ENTRIES FROM THE DATASET WHERE CAUSATION AND INJURED ARE FILLED OUT
injured = collisions[((collisions['NUMBER OF PERSONS INJURED'] > 0))]

injured2 = injured[(injured['CONTRIBUTING FACTOR VEHICLE 1'] != "Unspecified") & (pd.notnull(injured['CONTRIBUTING FACTOR VEHICLE 1'])) | \
                (injured['CONTRIBUTING FACTOR VEHICLE 2'] != "Unspecified") & (pd.notnull(injured['CONTRIBUTING FACTOR VEHICLE 2'])) | \
                (injured['CONTRIBUTING FACTOR VEHICLE 3'] != "Unspecified") & (pd.notnull(injured['CONTRIBUTING FACTOR VEHICLE 3'])) | \
                (injured['CONTRIBUTING FACTOR VEHICLE 4'] != "Unspecified") & (pd.notnull(injured['CONTRIBUTING FACTOR VEHICLE 4'])) | \
                (killed['CONTRIBUTING FACTOR VEHICLE 5'] != "Unspecified") & (pd.notnull(injured['CONTRIBUTING FACTOR VEHICLE 5']))]
injured
print str(len(injured2)) + " of " + str(len(injured)) +  " INJURED"
injured2.head()[['CONTRIBUTING FACTOR VEHICLE 1', 'CONTRIBUTING FACTOR VEHICLE 2', 'CONTRIBUTING FACTOR VEHICLE 3', 'CONTRIBUTING FACTOR VEHICLE 4', 'CONTRIBUTING FACTOR VEHICLE 5']]



    


















    






51364 of 109552 INJURED










    
Out[15]:










  
    

      
      CONTRIBUTING FACTOR VEHICLE 1
      CONTRIBUTING FACTOR VEHICLE 2
      CONTRIBUTING FACTOR VEHICLE 3
      CONTRIBUTING FACTOR VEHICLE 4
      CONTRIBUTING FACTOR VEHICLE 5
    

  
  
    

      36
      Other Vehicular
      Physical Disability
      NaN
      NaN
      NaN
    

    

      45
      Following Too Closely
      Unspecified
      NaN
      NaN
      NaN
    

    

      50
      Traffic Control Disregarded
      NaN
      NaN
      NaN
      NaN
    

    

      78
      Driver Inattention/Distraction
      Fatigued/Drowsy
      NaN
      NaN
      NaN
    

    

      83
      Physical Disability
      Physical Disability
      NaN
      NaN
      NaN
    

  




















In [16]:



    


collisions.columns



    


















    
Out[16]:








Index([u'DATE', u'TIME', u'BOROUGH', u'ZIP CODE', u'LATITUDE', u'LONGITUDE',
       u'LOCATION', u'ON STREET NAME', u'CROSS STREET NAME',
       u'OFF STREET NAME', u'NUMBER OF PERSONS INJURED',
       u'NUMBER OF PERSONS KILLED', u'NUMBER OF PEDESTRIANS INJURED',
       u'NUMBER OF PEDESTRIANS KILLED', u'NUMBER OF CYCLIST INJURED',
       u'NUMBER OF CYCLIST KILLED', u'NUMBER OF MOTORIST INJURED',
       u'NUMBER OF MOTORIST KILLED', u'CONTRIBUTING FACTOR VEHICLE 1',
       u'CONTRIBUTING FACTOR VEHICLE 2', u'CONTRIBUTING FACTOR VEHICLE 3',
       u'CONTRIBUTING FACTOR VEHICLE 4', u'CONTRIBUTING FACTOR VEHICLE 5',
       u'UNIQUE KEY', u'VEHICLE TYPE CODE 1', u'VEHICLE TYPE CODE 2',
       u'VEHICLE TYPE CODE 3', u'VEHICLE TYPE CODE 4', u'VEHICLE TYPE CODE 5'],
      dtype='object')

















In [17]:



    


print "TOP THREE MOST DANGEROUS STREET BY KILL COUNT"
collisions.groupby(by='ON STREET NAME').sum().sort_values(by='NUMBER OF PERSONS KILLED', ascending=False).head(3)



    


















    






TOP THREE MOST DANGEROUS STREET BY KILL COUNT










    
Out[17]:










  
    

      
      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
    

    

      ON STREET NAME
      
      
      
      
      
      
      
      
      
      
      
      
    

  
  
    

      ATLANTIC AVENUE
      77846497
      281639.996307
      -511764.262074
      2227
      13
      299
      9
      88
      0
      1840
      4
      9898840005
    

    

      HYLAN BOULEVARD
      27364377
      107699.757766
      -196792.205979
      890
      11
      132
      4
      13
      1
      745
      6
      3699354661
    

    

      LINDEN BOULEVARD
      43082235
      155350.329904
      -282160.813662
      1708
      11
      171
      8
      52
      0
      1485
      3
      5387103651
    

  




















In [18]:



    


print "TOP THREE MOST DANGEROUS STREET BY INJURY COUNT"
collisions.groupby(by='ON STREET NAME').sum().sort_values(by='NUMBER OF PERSONS INJURED', ascending=False).head(3)



    


















    






TOP THREE MOST DANGEROUS STREET BY INJURY COUNT










    
Out[18]:










  
    

      
      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
    

    

      ON STREET NAME
      
      
      
      
      
      
      
      
      
      
      
      
    

  
  
    

      BROADWAY
      82963096
      323402.733870
      -586578.169632
      2231
      11
      602
      8
      354
      0
      1275
      3
      11109402338
    

    

      ATLANTIC AVENUE
      77846497
      281639.996307
      -511764.262074
      2227
      13
      299
      9
      88
      0
      1840
      4
      9898840005
    

    

      LINDEN BOULEVARD
      43082235
      155350.329904
      -282160.813662
      1708
      11
      171
      8
      52
      0
      1485
      3
      5387103651
    

  




















In [19]:



    


collisions[pd.isnull(collisions['ON STREET NAME'])].fillna(0)['NUMBER OF PERSONS INJURED'].sum()



    


















    
Out[19]:








960

















In [21]:



    


collisions[collisions['ON STREET NAME']].fillna(0)['NUMBER OF PERSONS INJURED'].sum()



    


















    






---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-21-5a56a3110d95> in <module>()
----> 1 collisions[collisions['ON STREET NAME']].fillna(0)['NUMBER OF PERSONS INJURED'].sum()

/Users/masve/anaconda/lib/python2.7/site-packages/pandas/core/frame.pyc in __getitem__(self, key)
   1961         if isinstance(key, (Series, np.ndarray, Index, list)):
   1962             # either boolean or fancy integer index
-> 1963             return self._getitem_array(key)
   1964         elif isinstance(key, DataFrame):
   1965             return self._getitem_frame(key)

/Users/masve/anaconda/lib/python2.7/site-packages/pandas/core/frame.pyc in _getitem_array(self, key)
   1988     def _getitem_array(self, key):
   1989         # also raises Exception if object array with NA values
-> 1990         if com.is_bool_indexer(key):
   1991             # warning here just in case -- previously __setitem__ was
   1992             # reindexing but __getitem__ was not; it seems more reasonable to

/Users/masve/anaconda/lib/python2.7/site-packages/pandas/core/common.pyc in is_bool_indexer(key)
   1737             if not lib.is_bool_array(key):
   1738                 if isnull(key).any():
-> 1739                     raise ValueError('cannot index with vector containing '
   1740                                      'NA / NaN values')
   1741                 return False

ValueError: cannot index with vector containing NA / NaN values

Now lets focus on some contributing factors that happens to occur in specific areas...





In [ ]:

Content source: masve/saav-deliveries

Similar notebooks:

notebook.community | gallery | about