In [1]:

    

import os
# import folium
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline
import plotly.offline as py
py.init_notebook_mode(connected=True)
import plotly.graph_objs as go
import plotly.tools as tls
import warnings
warnings.filterwarnings('ignore')

# print(folium.__version__)


    

    












    




0.2.1

In [2]:

    

dfloc = pd.read_csv('kycd_loc.csv', header=9)
dfloc.head()


    

    
Out[2]:






  

    

      

      
(
      
)
      
578
      
8595
    
  
  

    

      
0
      
(40.498060701
      
-74.248893743)
      
351
      
1
    
    

      
1
      
(40.49876753
      
-74.240511403)
      
107
      
1
    
    

      
2
      
(40.49876753
      
-74.240511403)
      
341
      
6
    
    

      
3
      
(40.49876753
      
-74.240511403)
      
344
      
3
    
    

      
4
      
(40.49876753
      
-74.240511403)
      
351
      
2
    
  

In [3]:

    

dfloc.columns = ['lat', 'lon', 'kycd', 'cnt']
dfloc.head()


    

    
Out[3]:






  

    

      

      
lat
      
lon
      
kycd
      
cnt
    
  
  

    

      
0
      
(40.498060701
      
-74.248893743)
      
351
      
1
    
    

      
1
      
(40.49876753
      
-74.240511403)
      
107
      
1
    
    

      
2
      
(40.49876753
      
-74.240511403)
      
341
      
6
    
    

      
3
      
(40.49876753
      
-74.240511403)
      
344
      
3
    
    

      
4
      
(40.49876753
      
-74.240511403)
      
351
      
2
    
  

In [4]:

    

dfloc['lat'] = np.array([float(str(i)[1:]) for i in dfloc.lat.values])
dfloc['lon'] = np.array([float(str(i)[:-1]) for i in dfloc.lon.values])
dfloc.head()


    

    
Out[4]:






  

    

      

      
lat
      
lon
      
kycd
      
cnt
    
  
  

    

      
0
      
40.498061
      
-74.248894
      
351
      
1
    
    

      
1
      
40.498768
      
-74.240511
      
107
      
1
    
    

      
2
      
40.498768
      
-74.240511
      
341
      
6
    
    

      
3
      
40.498768
      
-74.240511
      
344
      
3
    
    

      
4
      
40.498768
      
-74.240511
      
351
      
2
    
  

In [5]:

    

print( sum(dfloc.isnull().values) )
print( dfloc.shape )
dfloc.describe()


    

    




[0 0 0 0]
(557461, 4)






    
Out[5]:






  

    

      

      
lat
      
lon
      
kycd
      
cnt
    
  
  

    

      
count
      
557461.000000
      
557461.000000
      
557461.000000
      
557461.000000
    
    

      
mean
      
40.713864
      
-73.918996
      
297.803785
      
6.621538
    
    

      
std
      
0.086010
      
0.099872
      
150.649372
      
29.262535
    
    

      
min
      
40.498061
      
-74.255076
      
105.000000
      
1.000000
    
    

      
25%
      
40.650653
      
-73.972612
      
109.000000
      
1.000000
    
    

      
50%
      
40.706045
      
-73.916128
      
341.000000
      
3.000000
    
    

      
75%
      
40.768576
      
-73.854069
      
351.000000
      
6.000000
    
    

      
max
      
40.912723
      
-73.700316
      
578.000000
      
13566.000000
    
  

In [6]:

    

# dfloc = dfloc[(dfloc.year <= 2015) & (dfloc.year >= 2006)]


    

In [7]:

    

dfloc[dfloc.kycd == 351].lat.values


    

    
Out[7]:





array([ 40.4980607 ,  40.49876753,  40.49890536, ...,  40.91235528,
        40.91247643,  40.9127234 ])

In [8]:

    

f, axes = plt.subplots(3, 3, figsize=(10, 10), sharex=True, sharey=True)

s = np.linspace(0, 3, 10)
cmap = sns.cubehelix_palette(start=0.0, light=1, as_cmap=True)


# 341: Petit Larceny
x = dfloc[dfloc.kycd == 341].lon.values
y = dfloc[dfloc.kycd == 341].lat.values
sns.kdeplot(x, y, cmap=cmap, shade=True, cut=5, ax=axes[0,0])
axes[0,0].set(ylim=(40.498061, 40.912723), xlim=(-74.255076, -73.700316), title = 'Petit Larceny')

cmap = sns.cubehelix_palette(start=0.333333333333, light=1, as_cmap=True)

# 578: Harrassment 2
x = dfloc[dfloc.kycd == 578].lon.values
y = dfloc[dfloc.kycd == 578].lat.values
sns.kdeplot(x, y, cmap=cmap, shade=True, cut=5, ax=axes[0,1])
axes[0,1].set(ylim=(40.498061, 40.912723), xlim=(-74.255076, -73.700316), title = 'Harrassment 2')

cmap = sns.cubehelix_palette(start=0.666666666667, light=1, as_cmap=True)

# 344: Assault 3 & Related Offenses
x = dfloc[dfloc.kycd == 344].lon.values
y = dfloc[dfloc.kycd == 344].lat.values
sns.kdeplot(x, y, cmap=cmap, shade=True, cut=5, ax=axes[0,2])
axes[0,2].set(ylim=(40.498061, 40.912723), xlim=(-74.255076, -73.700316), title = 'Assault 3 & Related Offenses')

cmap = sns.cubehelix_palette(start=1.0, light=1, as_cmap=True)

# 351: Criminal Mischief
x = dfloc[dfloc.kycd == 351].lon.values
y = dfloc[dfloc.kycd == 351].lat.values
sns.kdeplot(x, y, cmap=cmap, shade=True, cut=5, ax=axes[1,0])
axes[1,0].set(ylim=(40.498061, 40.912723), xlim=(-74.255076, -73.700316), title = 'Criminal Mischief')

cmap = sns.cubehelix_palette(start=1.333333333333, light=1, as_cmap=True)

# 109: Grand Larceny
x = dfloc[dfloc.kycd == 109].lon.values
y = dfloc[dfloc.kycd == 109].lat.values
sns.kdeplot(x, y, cmap=cmap, shade=True, cut=5, ax=axes[1,1])
axes[1,1].set(ylim=(40.498061, 40.912723), xlim=(-74.255076, -73.700316), title = 'Grand Larceny')

cmap = sns.cubehelix_palette(start=1.666666666667, light=1, as_cmap=True)

# 235: Dangerous Drugs
x = dfloc[dfloc.kycd == 235].lon.values
y = dfloc[dfloc.kycd == 235].lat.values
sns.kdeplot(x, y, cmap=cmap, shade=True, cut=5, ax=axes[1,2])
axes[1,2].set(ylim=(40.498061, 40.912723), xlim=(-74.255076, -73.700316), title = 'Dangerous Drugs')

cmap = sns.cubehelix_palette(start=2.0, light=1, as_cmap=True)

# 361: Offense Against Public Order Sensibility
x = dfloc[dfloc.kycd == 361].lon.values
y = dfloc[dfloc.kycd == 361].lat.values
sns.kdeplot(x, y, cmap=cmap, shade=True, cut=5, ax=axes[2,0])
axes[2,0].set(ylim=(40.498061, 40.912723), xlim=(-74.255076, -73.700316), title = 'Offense Against Public Order Sensibility')

cmap = sns.cubehelix_palette(start=2.333333333333, light=1, as_cmap=True)

# 105: Robbery
x = dfloc[dfloc.kycd == 105].lon.values
y = dfloc[dfloc.kycd == 105].lat.values
sns.kdeplot(x, y, cmap=cmap, shade=True, cut=5, ax=axes[2,1])
axes[2,1].set(ylim=(40.498061, 40.912723), xlim=(-74.255076, -73.700316), title = 'Robbery')

cmap = sns.cubehelix_palette(start=2.666666666667, light=1, as_cmap=True)

# 107: Burglary
x = dfloc[dfloc.kycd == 107].lon.values
y = dfloc[dfloc.kycd == 107].lat.values
sns.kdeplot(x, y, cmap=cmap, shade=True, cut=5, ax=axes[2,2])
axes[2,2].set(ylim=(40.498061, 40.912723), xlim=(-74.255076, -73.700316), title = 'Burglary')


    

    
Out[8]:





[(40.498061, 40.912723),
 (-74.255076, -73.700316),
 <matplotlib.text.Text at 0x112f256a0>]






    

In [9]:

    

dfloc


    

    
Out[9]:






  

    

      

      
lat
      
lon
      
kycd
      
cnt
    
  
  

    

      
0
      
40.498061
      
-74.248894
      
351
      
1
    
    

      
1
      
40.498768
      
-74.240511
      
107
      
1
    
    

      
2
      
40.498768
      
-74.240511
      
341
      
6
    
    

      
3
      
40.498768
      
-74.240511
      
344
      
3
    
    

      
4
      
40.498768
      
-74.240511
      
351
      
2
    
    

      
5
      
40.498768
      
-74.240511
      
361
      
1
    
    

      
6
      
40.498768
      
-74.240511
      
578
      
5
    
    

      
7
      
40.498905
      
-74.241537
      
105
      
1
    
    

      
8
      
40.498905
      
-74.241537
      
107
      
3
    
    

      
9
      
40.498905
      
-74.241537
      
341
      
3
    
    

      
10
      
40.498905
      
-74.241537
      
344
      
2
    
    

      
11
      
40.498905
      
-74.241537
      
351
      
1
    
    

      
12
      
40.498905
      
-74.241537
      
361
      
3
    
    

      
13
      
40.498905
      
-74.241537
      
578
      
7
    
    

      
14
      
40.498957
      
-74.244367
      
109
      
1
    
    

      
15
      
40.498957
      
-74.244367
      
344
      
1
    
    

      
16
      
40.498957
      
-74.244367
      
351
      
1
    
    

      
17
      
40.499143
      
-74.243486
      
235
      
1
    
    

      
18
      
40.499143
      
-74.243486
      
344
      
1
    
    

      
19
      
40.499229
      
-74.243055
      
105
      
1
    
    

      
20
      
40.499229
      
-74.243055
      
107
      
1
    
    

      
21
      
40.499229
      
-74.243055
      
235
      
1
    
    

      
22
      
40.499229
      
-74.243055
      
341
      
2
    
    

      
23
      
40.499229
      
-74.243055
      
361
      
1
    
    

      
24
      
40.499324
      
-74.239740
      
107
      
2
    
    

      
25
      
40.499324
      
-74.239740
      
341
      
5
    
    

      
26
      
40.499324
      
-74.239740
      
578
      
3
    
    

      
27
      
40.499401
      
-74.242175
      
109
      
1
    
    

      
28
      
40.499401
      
-74.242175
      
351
      
1
    
    

      
29
      
40.499401
      
-74.242175
      
361
      
2
    
    

      
...
      
...
      
...
      
...
      
...
    
    

      
557431
      
40.912088
      
-73.900786
      
341
      
1
    
    

      
557432
      
40.912088
      
-73.900786
      
351
      
2
    
    

      
557433
      
40.912113
      
-73.901043
      
109
      
1
    
    

      
557434
      
40.912113
      
-73.901043
      
341
      
1
    
    

      
557435
      
40.912113
      
-73.901043
      
351
      
1
    
    

      
557436
      
40.912231
      
-73.901676
      
109
      
1
    
    

      
557437
      
40.912231
      
-73.901676
      
341
      
1
    
    

      
557438
      
40.912231
      
-73.901676
      
351
      
1
    
    

      
557439
      
40.912231
      
-73.901676
      
578
      
1
    
    

      
557440
      
40.912351
      
-73.900695
      
107
      
1
    
    

      
557441
      
40.912351
      
-73.900695
      
109
      
3
    
    

      
557442
      
40.912351
      
-73.900695
      
351
      
1
    
    

      
557443
      
40.912351
      
-73.900695
      
578
      
1
    
    

      
557444
      
40.912355
      
-73.902132
      
109
      
1
    
    

      
557445
      
40.912355
      
-73.902132
      
341
      
1
    
    

      
557446
      
40.912355
      
-73.902132
      
351
      
1
    
    

      
557447
      
40.912355
      
-73.902132
      
578
      
2
    
    

      
557448
      
40.912476
      
-73.902591
      
235
      
1
    
    

      
557449
      
40.912476
      
-73.902591
      
341
      
2
    
    

      
557450
      
40.912476
      
-73.902591
      
344
      
2
    
    

      
557451
      
40.912476
      
-73.902591
      
351
      
2
    
    

      
557452
      
40.912476
      
-73.902591
      
578
      
3
    
    

      
557453
      
40.912723
      
-73.902525
      
107
      
12
    
    

      
557454
      
40.912723
      
-73.902525
      
109
      
25
    
    

      
557455
      
40.912723
      
-73.902525
      
235
      
5
    
    

      
557456
      
40.912723
      
-73.902525
      
341
      
32
    
    

      
557457
      
40.912723
      
-73.902525
      
344
      
13
    
    

      
557458
      
40.912723
      
-73.902525
      
351
      
17
    
    

      
557459
      
40.912723
      
-73.902525
      
361
      
20
    
    

      
557460
      
40.912723
      
-73.902525
      
578
      
26
    
  

557461 rows × 4 columns

In [89]:

    

f, axes = plt.subplots(3, 3, figsize=(10, 10), sharex=True, sharey=True)

s = np.linspace(0, 3, 10)
cmap = sns.cubehelix_palette(start=0.0, light=1, as_cmap=True)


# 341: Petit Larceny
x = dfloc[dfloc.kycd == 341].lon.values
y = dfloc[dfloc.kycd == 341].lat.values
sns.kdeplot(x, y, cmap=cmap, shade=True, cut=5, ax=axes[0,0])
axes[0,0].set(ylim=(40.498061, 40.912723), xlim=(-74.255076, -73.700316), title = 'Petit Larceny')

cmap = sns.cubehelix_palette(start=0.333333333333, light=1, as_cmap=True)

# 578: Harrassment 2
x = dfloc[dfloc.kycd == 578].lon.values
y = dfloc[dfloc.kycd == 578].lat.values
sns.kdeplot(x, y, cmap=cmap, shade=True, cut=5, ax=axes[0,1])
axes[0,1].set(ylim=(40.498061, 40.912723), xlim=(-74.255076, -73.700316), title = 'Harrassment 2')

cmap = sns.cubehelix_palette(start=0.666666666667, light=1, as_cmap=True)

# 344: Assault 3 & Related Offenses
x = dfloc[dfloc.kycd == 344].lon.values
y = dfloc[dfloc.kycd == 344].lat.values
sns.kdeplot(x, y, cmap=cmap, shade=True, cut=5, ax=axes[0,2])
axes[0,2].set(ylim=(40.498061, 40.912723), xlim=(-74.255076, -73.700316), title = 'Assault 3 & Related Offenses')

cmap = sns.cubehelix_palette(start=1.0, light=1, as_cmap=True)

# 351: Criminal Mischief
x = dfloc[dfloc.kycd == 351].lon.values
y = dfloc[dfloc.kycd == 351].lat.values
sns.kdeplot(x, y, cmap=cmap, shade=True, cut=5, ax=axes[1,0])
axes[1,0].set(ylim=(40.498061, 40.912723), xlim=(-74.255076, -73.700316), title = 'Criminal Mischief')

cmap = sns.cubehelix_palette(start=1.333333333333, light=1, as_cmap=True)

# 109: Grand Larceny
x = dfloc[dfloc.kycd == 109].lon.values
y = dfloc[dfloc.kycd == 109].lat.values
sns.kdeplot(x, y, cmap=cmap, shade=True, cut=5, ax=axes[1,1])
axes[1,1].set(ylim=(40.498061, 40.912723), xlim=(-74.255076, -73.700316), title = 'Grand Larceny')

cmap = sns.cubehelix_palette(start=1.666666666667, light=1, as_cmap=True)

# 235: Dangerous Drugs
x = dfloc[dfloc.kycd == 235].lon.values
y = dfloc[dfloc.kycd == 235].lat.values
sns.kdeplot(x, y, cmap=cmap, shade=True, cut=5, ax=axes[1,2])
axes[1,2].set(ylim=(40.498061, 40.912723), xlim=(-74.255076, -73.700316), title = 'Dangerous Drugs')

cmap = sns.cubehelix_palette(start=2.0, light=1, as_cmap=True)

# 361: Offense Against Public Order Sensibility
x = dfloc[dfloc.kycd == 361].lon.values
y = dfloc[dfloc.kycd == 361].lat.values
sns.kdeplot(x, y, cmap=cmap, shade=True, cut=5, ax=axes[2,0])
axes[2,0].set(ylim=(40.498061, 40.912723), xlim=(-74.255076, -73.700316), title = 'Offense Against Public Order Sensibility')

cmap = sns.cubehelix_palette(start=2.333333333333, light=1, as_cmap=True)

# 105: Robbery
x = dfloc[dfloc.kycd == 105].lon.values
y = dfloc[dfloc.kycd == 105].lat.values
sns.kdeplot(x, y, cmap=cmap, shade=True, cut=5, ax=axes[2,1])
axes[2,1].set(ylim=(40.498061, 40.912723), xlim=(-74.255076, -73.700316), title = 'Robbery')

cmap = sns.cubehelix_palette(start=2.666666666667, light=1, as_cmap=True)

# 107: Burglary
x = dfloc[dfloc.kycd == 107].lon.values
y = dfloc[dfloc.kycd == 107].lat.values
sns.kdeplot(x, y, cmap=cmap, shade=True, cut=5, ax=axes[2,2])
axes[2,2].set(ylim=(40.498061, 40.912723), xlim=(-74.255076, -73.700316), title = 'Burglary')


    

    
Out[89]:





[(40.498061, 40.912723),
 (-74.255076, -73.700316),
 <matplotlib.text.Text at 0x1137fd080>]






    

In [ ]: