In [35]:

    

%matplotlib inline  
from mpl_toolkits.basemap import Basemap
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
import fiona
from fiona.crs import to_string,from_epsg
from shapely.geometry import Point, Polygon, MultiPoint, MultiPolygon, shape
from shapely.prepared import prep
from itertools import chain
from mpl_toolkits.basemap.pyproj import Proj, transform
matplotlib.rcParams['figure.figsize'] = (20.0, 16.0)


    

In [29]:

    

shp = fiona.open('data/CITY_LIMITS.shp')
lat_0 = shp.crs['lat_0']
lon_0 = shp.crs['lon_0']
shp.close()
shp = fiona.open('data/SJ.shp')
extra = 0
bds = shp.bounds
ll = (bds[0], bds[1])
ur = (bds[2], bds[3])
coords = list(chain(ll, ur))
w, h = coords[2] - coords[0], coords[3] - coords[1]
m = Basemap(
    projection='tmerc',
    lon_0=-121.848729129722,
    lat_0=37.3020714417266,
    ellps = 'WGS84',
    llcrnrlon=coords[0],
    llcrnrlat=coords[1],
    urcrnrlon=coords[2],
    urcrnrlat=coords[3],
    lat_ts=0,
    resolution='i',
    suppress_ticks=True)
shp.close()


    

In [30]:

    

data = pd.read_excel('data/dumping_data.xlsx')


    

In [31]:

    

data.describe()


    

    
Out[31]:






  

    

      

      
id
      
BAS_INTERSECTION_ID
      
apn_lat
      
apn_lng
      
intersection_lat
      
intersection_lng
      
DISPATCHED
      
ENTRY_INCOMPLETE
      
DISP_PRIORITY
      
CubicYardsGC
    
  
  

    

      
count
      
20715.000000
      
15340.000000
      
8003.000000
      
8003.000000
      
15340.000000
      
15340.000000
      
20715
      
20715
      
20715
      
7.450000e+03
    
    

      
mean
      
532666.511127
      
12718.732464
      
36.996764
      
-120.939870
      
37.208712
      
-121.639322
      
1
      
0.000144823
      
0.0199372
      
1.631249e+09
    
    

      
std
      
19662.582716
      
8438.803313
      
4.396029
      
10.697223
      
3.423451
      
5.406255
      
0
      
0.0120336
      
0.139788
      
1.402063e+11
    
    

      
min
      
497463.000000
      
0.000000
      
-121.925560
      
-122.507423
      
-121.927660
      
-122.036665
      
True
      
False
      
False
      
0.000000e+00
    
    

      
25%
      
516521.500000
      
6544.000000
      
37.301988
      
-121.896127
      
37.295987
      
-121.889590
      
1
      
0
      
0
      
5.000000e-01
    
    

      
50%
      
533282.000000
      
11673.000000
      
37.324659
      
-121.863477
      
37.322706
      
-121.856706
      
1
      
0
      
0
      
1.750000e+00
    
    

      
75%
      
550288.500000
      
14921.000000
      
37.347360
      
-121.829147
      
37.347712
      
-121.824050
      
1
      
0
      
0
      
3.000000e+00
    
    

      
max
      
565290.000000
      
51895.000000
      
37.896717
      
37.303200
      
37.434373
      
37.357880
      
True
      
True
      
True
      
1.210158e+13
    
  

In [44]:

    

lat_loc = data['intersection_lat'].dropna()
lon_loc = data['intersection_lng'].dropna()
m.fillcontinents(color='#ddaa66',lake_color='aqua')
m.drawcoastlines()
m.readshapefile('data/SJ','San Jose')
m.scatter(lon_loc.tolist(),
    lat_loc.tolist(),
    10, marker='o', lw=.25,
    facecolor='#33ccff', edgecolor='w',
    alpha=0.9, antialiased=True,
    label='Dumping Locations', zorder=3, latlon=True)


    

    
Out[44]:





<matplotlib.collections.PathCollection at 0x67e5090>






    

In [ ]: