In [131]:

    

# Warning, Google geocode api has a daily limit of 2500 requests. You should only run this once tand then use the pickled data
# Currently limited to Arvada


import csv
from pygeocoder import Geocoder
import time
import random

SAMPLESIZE = 2000

def ramdom_rows(fname, rowcount):
    '''
    Randomly read rows
    This is not perfect some of the rows selected are though out later
    '''
    rows = []
    for i, row in enumerate(csv.DictReader(open(fname))):
        if (int(row["TOTACTVAL"]) > 50000 and row["STTSTRC"].strip(' ') == 'RESID' and row["PRPCTYNAM"].strip(' ') == 'ARVADA' and 
        int(row["TOTACTIMPV"]) > 50000):
            #print('V', row["VALACT"])
            rows.append(i)
    return random.sample(rows, rowcount)

readrows = ramdom_rows("Data2009.csv", SAMPLESIZE)

print('Len readrows', readrows)


# open samle data with per row dictionary
data = csv.DictReader(open("Data2009.csv"))


# GeoCode from address
latlon = []
propvalue = []
rowcount = 0
for row in data:
    if rowcount in readrows:
        addr = [row["PRPSTRNUM"], row["PRPSTRDIR"], row["PRPSTRNAM"], row["PRPSTRTYP"], row["PRPSTRSFX"], 
                row["PRPSTRUNT"], row["PRPCTYNAM"], row["PRPSTENAM"], row["PRPZIP5"]]
        addr = ' '.join(addr) 
        results = Geocoder.geocode(addr)[0].coordinates
        # this will result in lost rows
        if results not in latlon:
            print('RowCount', rowcount, 'Val', row["TOTACTVAL"])
            propvalue.append(int(row["TOTACTVAL"]))
            latlon.append(results)
            
        time.sleep(.25)
    rowcount += 1

# for coor in latlon:
#     print(coor)
# for val in propvalue:
#     print(val)


    

    




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In [132]:

    

# Run this after you get the lat lon so that it is saved
import cPickle as pickle
pickle.dump( latlon, open( "latlon3.p", "wb" ) )
pickle.dump( propvalue, open( "propvalue3.p", "wb" ) )


    

In [2]:

    

import cPickle as pickle
latlon = pickle.load( open( "latlon3.p", "rb" ) )
propvalue = pickle.load( open( "propvalue3.p", "rb" ) )

print(len(latlon), len(propvalue))
print(latlon[20], propvalue[20])
print(latlon[21], propvalue[21])
print(latlon[22], propvalue[22])
def removebad(ltln, propval):
    for i, lt in enumerate(ltln):
        if lt[0]>40 or lt[0]<39.7:
            #print(i)
            latlon.pop(i)
            propvalue.pop(i)
            
removebad(latlon, propvalue)


    

    




(1999, 1999)
((39.7918506, -105.064121), 120660)
((39.7869399, -105.0960013), 153400)
((39.7901733, -105.0879591), 305000)

In [5]:

    

import scipy.sparse.sparsetools
import matplotlib.pyplot as plt
from scipy.spatial import Delaunay

points = numpy.asarray(latlon)
points *= 100

tri = Delaunay(points)

pylab.rcParams['figure.figsize'] = (10.0, 10.0)
plt.triplot(points[:,1], points[:,0], tri.simplices.copy())
plt.plot(points[:,1], points[:,0], 'o')
plt.show()


    

In [24]:

    

#Example from: 
# http://matplotlib.org/examples/pylab_examples/trigradient_demo.html

from matplotlib.tri import Triangulation, CubicTriInterpolator, UniformTriRefiner
import numpy

propvalue = numpy.asarray(propvalue)
y = points[:,0]
x = points[:,1]
triang = Triangulation(x, y)

refiner = UniformTriRefiner(triang)
tri_refi, z_test_refi = refiner.refine_field(propvalue, subdiv=3)

tci = CubicTriInterpolator(triang, propvalue)
(Ex, Ey) = tci.gradient(triang.x, triang.y)
E_norm = np.sqrt(Ex**2 + Ey**2)

pylab.rcParams['figure.figsize'] = (15.0, 15.0)
plt.figure()
plt.gca().set_aspect('equal')
plt.triplot(triang, color='0.8')

# levels = np.arange(propvalue.min(), propvalue.max(), 100000)
levels = np.arange(z_test_refi.min(), z_test_refi.max(), 50000)
cmap = cm.get_cmap(name='seismic', lut=None)
# plt.tricontour(tri_refi, z_test_refi, levels=levels, cmap=cmap,
#                linewidths=[10.0, 5.0, 5.0, 5.0])
plt.tricontour(tri_refi, z_test_refi, levels=levels, linewidths=0.5, colors='k')  # This draws the contour lines

# plt.tricontourf(x, y, propvalue, 50, cmap=plt.cm.seismic,
#                 norm=plt.Normalize(vmax=abs(propvalue).max(), vmin=-abs(propvalue).max()))  # This colors the plot

plt.title('Property price gradient')
plt.show()


    

In [139]:

    

propvalue.max()
print(int(x.min()) - int(x.max()))
print(int(y.min()) - int(y.max()))
print(int(propvalue.min()), int(propvalue.max()))


    

    




-15
-9
(107066, 1170300)

In [14]:

    

# GridData example
# Based on http://matplotlib.org/examples/pylab_examples/tricontour_vs_griddata.html
import time

pylab.rcParams['figure.figsize'] = (15.0, 15.0)
npts = 10000
ngridx = 100 * int(x.max() - x.min())
ngridy = 100 * int(y.max() - y.min())
z = propvalue.copy()
start = time.clock()
plt.subplot(211)
xi = np.linspace(int(x.min()),int(x.max()),ngridx)
yi = np.linspace(int(y.min()),int(y.max()),ngridy)
# zi = griddata(x,y,z,xi,yi,interp='linear')
zi = griddata(x,y,z,xi,yi,interp='nn')
plt.contour(xi,yi,zi,50,linewidths=0.5,colors='k') # This draws the contour lines
# plt.contourf(xi,yi,zi,30,cmap=plt.cm.seismic,
#              norm=plt.Normalize(vmax=abs(zi).max(), vmin=-abs(zi).max()))  # This colors the plot
show()