In [1]:

#CrossCompute

# Prompts users to enter a zipcode in the tool
# The default zipcode is 11419
target_folder = '/tmp'

ZipcodeInput = 11419

In [2]:

import subprocess
import sys

# This function is used to install packages using pip
# It's equivalent to doing 'pip install ______'
def install(package):
    subprocess.call([sys.executable, "-m", "pip", "install", package])

install('sodapy') # Package for NYC OpenData API
install('folium') # Package to generate map
install('Fiona')

In [3]:

import pandas as pd
from sodapy import Socrata # Used to access/ work with NYCOpenData API
import folium

In [4]:

#################################
# WORKING WITH CATCH BASIN DATA #
#################################


# Grabbing data from API
client = Socrata("data.cityofnewyork.us",
                'YFHnlAd1f74IprxACGOlr46td',
                username="nycopendataninjas@gmail.com",
                password="DataNinjas4TheWin!")

# Limits the data to only clogged catch basin complaints in a specified zipcode^
results = client.get("fhrw-4uyv", 
                     incident_zip = ZipcodeInput,
                     complaint_type="Sewer",
                     descriptor = "Catch Basin Clogged/Flooding (Use Comments) (SC)",
                     limit=10000)

# Convert to pandas DataFrame
df_threeOneOneReq = pd.DataFrame.from_records(results)

# Only gets the location of these complaints
complaintLoc = df_threeOneOneReq[['latitude','longitude']]

In [5]:

#################################
# WORKING WITH TREE CENSUS DATA #
#################################


# Limits the data to only trees that are ALIVE in that specified zipcode that was entered above^
results = client.get("5rq2-4hqu",
                     zipcode = ZipcodeInput,
                     status = 'Alive',
                     limit=10000)

# Convert to pandas DataFrame
results_df = pd.DataFrame.from_records(results)

# Only get the columns that are useful
results_df = results_df[['tree_dbh', 'health','status','latitude','longitude','spc_latin']]            
            
# Replaces words with numbers so that it is easier to create a 'grade' for each tree
results_df = results_df.replace(['Poor','Fair','Good'],[0,50,100])

# 'tree_dbh' was an object, this converts it to an int so that it can be added to 'health' and 'status'
results_df['tree_dbh'] = pd.to_numeric(results_df['tree_dbh'])

# Anywhere there is an 'NaN', make it a zero
results_df = results_df.fillna(0)

# Looks through list of each species and it's type
df = pd.read_csv('Species_Types.csv')
df = df.set_index('Species')

# Decides whether each tree is deciduous, conferous, etc.
results_df['Type'] = df.loc[results_df.spc_latin,'Type'].values

# Replaces words with numbers so that it is easier to create a 'grade' for each tree
results_df = results_df.replace(['deciduous','coniferous','evergreen','both'],[1,0,0,0])

# Generates a final grade that will be the value of the weight on the heat map for each tree
results_df['Final Grade'] = ((results_df.tree_dbh + results_df.health)/100)*results_df.Type

# Removes all the trees that dont lose leaves
results_df = results_df[results_df.Type != 0]
results_df = results_df.fillna(0)

In [6]:

# Only gets the location of these trees

treesLoc = results_df[['latitude', 'longitude']].copy()
treesLoc.dropna(subset=['latitude','longitude'], inplace=True)
treesList = [treesLoc]
treesList

Out[6]:

[         latitude     longitude
 0     40.69199129  -73.82114902
 1     40.69161541  -73.82096547
 2     40.69153616  -73.82092678
 3     40.69210017  -73.82832551
 4     40.69166655    -73.829862
 5      40.6921299  -73.82821961
 6     40.69382431  -73.82753579
 7     40.69385554  -73.82742485
 8     40.69394083   -73.8271218
 9     40.69379328  -73.82764605
 10    40.68967489  -73.82221726
 11    40.69407543  -73.82728544
 12    40.69412078  -73.82707632
 13    40.69322963  -73.82436567
 14    40.69321395  -73.82441918
 15    40.69190202  -73.82110543
 16    40.69409821  -73.82718036
 17     40.6932463   -73.8243088
 18    40.69318198  -73.82452821
 19    40.69319875    -73.824471
 20    40.69316679  -73.82458003
 21     40.6935223  -73.82331912
 22    40.69313473   -73.8246894
 23    40.69388705   -73.8273129
 24    40.69404925  -73.82740613
 25    40.69427445  -73.82240225
 26    40.69031163  -73.83471829
 27    40.69315239  -73.83071821
 28    40.69282391  -73.83188216
 29    40.69289219  -73.83164428
 ...           ...           ...
 2595  40.68934814  -73.83143042
 2596  40.69026481   -73.8318954
 2597  40.68919706  -73.83135379
 2598  40.68756899  -73.83061058
 2599   40.6891561  -73.83133301
 2600  40.68718106  -73.83041891
 2601  40.68810577  -73.83087582
 2602  40.68737631  -73.83051538
 2603  40.68723758  -73.83044683
 2604  40.68676744  -73.83021454
 2605  40.69054903  -73.83203958
 2606  40.69017519  -73.83184994
 2607  40.68877714  -73.83114079
 2608  40.69083581  -73.83218505
 2609  40.68661329  -73.83013838
 2610  40.69067705  -73.83210452
 2611  40.69091518  -73.83222531
 2612  40.68958883  -73.83155251
 2613   40.6890716  -73.83129015
 2614  40.69073338  -73.83213309
 2615  40.69092456  -73.83208086
 2616  40.68829705  -73.83155471
 2617  40.68838596  -73.83124422
 2618  40.68834731  -73.83137922
 2619  40.68933935   -73.8312878
 2620   40.6906412  -73.83208633
 2621   40.6895702   -73.8314033
 2622  40.68843287  -73.83158551
 2623  40.68854021  -73.83120708
 2624  40.69067062  -73.83195382
 
 [2456 rows x 2 columns]]

In [7]:

df_threeOneOneReq_LOC = df_threeOneOneReq[['latitude', 'longitude']].copy()
df_threeOneOneReq_LOC.dropna(subset=['latitude','longitude'], inplace=True)

In [9]:

####################################
#   GETTING COMPLAINT COUNTS       #                            
#   WITHIN A 100 METER RADIUS      #
#         OF EACH TREE             #
####################################

import numpy as np
from pysal.cg.kdtree import KDTree
from pysal.cg import RADIUS_EARTH_MILES

complaints_xys = df_threeOneOneReq_LOC[['latitude', 'longitude']].astype(np.float).values 
complaints_tree = KDTree(complaints_xys, distance_metric='Arc', radius=RADIUS_EARTH_MILES)

---------------------------------------------------------------------------
ModuleNotFoundError                       Traceback (most recent call last)
<ipython-input-9-f947cb74f055> in <module>
      6 
      7 import numpy as np
----> 8 from pysal.cg.kdtree import KDTree
      9 from pysal.cg import RADIUS_EARTH_MILES
     10 

ModuleNotFoundError: No module named 'pysal.cg'

In [10]:

complaints_count = len(complaints_xys)
complaints_count

Out[10]:

In [11]:

xy = 40.682460735128025,-73.8300148272251
distances, indices = complaints_tree.query(xy, k=complaints_count, distance_upper_bound=0.5)

In [12]:

indices
indices[~np.isnan(indices)]
len(indices[~np.isnan(indices)])

Out[12]:

In [13]:

# Setting radius equal to ~ 100 meters
radius_in_miles = 0.0497097

# Function that can find the number of complaints within 100 meters from each tree
def get_complaint_count(r):
    xy = r['latitude'], r['longitude']
    distances, indices = complaints_tree.query(xy, k=complaints_count, distance_upper_bound=radius_in_miles)
    indices = indices[~np.isnan(indices)]
    return len(indices)

# Applying functtion to each tree
treesLoc = treesLoc.apply(pd.to_numeric)
treesLoc['# of Complaints within 0.5 miles'] = treesLoc.apply(get_complaint_count,axis=1)

In [14]:

# Adding that column to the results_df
results_df['complaints'] = treesLoc['# of Complaints within 0.5 miles']

In [15]:

# This is what the final dataframe will look like
#results_df

In [16]:

# Used to print table in final tool result
# We most likely will not need it
# because we are using a map

from os.path import join
target_path = join(target_folder, 'results.csv')
results_df.to_csv(target_path, index=False)
print('result_table_path = %s' % target_path)

result_table_path = /tmp/results.csv

In [17]:

#################################
#     Generating a Heatmap      #
#################################


from folium import plugins
from folium.plugins import HeatMap

# Centers the map at the first coordinate in that zipcode
starting_Lat = results_df.iloc[0]['latitude']
starting_Long = results_df.iloc[0]['longitude']

# Coverts the starting points from string to float
starting_Lat = pd.to_numeric(starting_Lat, downcast='float')
starting_Long = pd.to_numeric(starting_Long, downcast='float')

# Creates the map centered at that point^, b/w, zoomed in
map_hooray = folium.Map(location=[starting_Lat, starting_Long],
                    tiles = "Stamen Toner",
                    zoom_start = 14.5)

# Ensure you're handing it floats
results_df['Latitude'] = results_df['latitude'].astype(float)
results_df['Longitude'] = results_df['longitude'].astype(float)
results_df['Final_Grade'] = results_df['Final Grade'].astype(float)

results_df = results_df.fillna(0)

# This is what we will be putting onto the map: Latitude, longitude, and a "weight"
heat_data = [[row['Latitude'],row['Longitude'],row['Final Grade']] for index, row in results_df.iterrows()]

# Plot it on the map
HeatMap(heat_data, 
        min_opacity = 0.01, 
        max_val = 1.5, 
        blur = 20, 
       ).add_to(map_hooray)

# Allows the map to go fullscreen
folium.plugins.Fullscreen(position='topright',
                          title='Full Screen',
                          title_cancel='Exit Full Screen',
                          force_separate_button=True
                         ).add_to(map_hooray)

# Display the map
map_hooray

Out[17]:

In [18]:

#################################
#       Training a Model        #
#################################

In [19]:

x = results_df[[
    'tree_dbh',
    'health',
    'Type'
]]
y = results_df['complaints']

In [20]:

from sklearn.model_selection import cross_val_score
from sklearn.linear_model import LinearRegression
model1 = LinearRegression()
model1.fit(x,y)

Out[20]:

LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,
         normalize=False)

In [21]:

cross_val_score(model1, x,y,cv=3,scoring = 'neg_mean_absolute_error')

Out[21]:

array([-6.66012407, -6.79546739, -5.40887688])

In [22]:

q = [19,50,1]
model1.predict([q])

Out[22]:

array([11.25671526])

In [23]:

from sklearn.linear_model import BayesianRidge
model2 = BayesianRidge()
model2.fit(x,y)

Out[23]:

BayesianRidge(alpha_1=1e-06, alpha_2=1e-06, compute_score=False, copy_X=True,
       fit_intercept=True, lambda_1=1e-06, lambda_2=1e-06, n_iter=300,
       normalize=False, tol=0.001, verbose=False)

In [24]:

cross_val_score(model2, x,y,cv=3,scoring = 'neg_mean_absolute_error').mean()

Out[24]:

-6.285259013190195

In [25]:

model2.predict([q])

Out[25]:

array([11.17912212])

In [26]:

import os
import webbrowser
map_hooray.save('map.html')

In [27]:

'''
import os
import subprocess
outdir = "Screenshots"
map_hooray.save("map.html")
url = "file://map.html".format(os.getcwd())
outfn = os.path.join(outdir,"outfig.png")
subprocess.check_call(["cutycapt","--url={}".format(url), "--out={}".format(outfn)])
'''
# WIP
# Code to save map as a pgn to be accessible on CrossCompute

Out[27]:

'\nimport os\nimport subprocess\noutdir = "Screenshots"\nmap_hooray.save("map.html")\nurl = "file://map.html".format(os.getcwd())\noutfn = os.path.join(outdir,"outfig.png")\nsubprocess.check_call(["cutycapt","--url={}".format(url), "--out={}".format(outfn)])\n'

In [28]:

%matplotlib inline
import numpy as np
import numpy.random
import matplotlib.pyplot as plt

In [29]:

treesLoc

Out[29]:

	latitude	longitude	# of Complaints within 0.5 miles
0	40.691991	-73.821149	10
1	40.691615	-73.820965	7
2	40.691536	-73.820927	7
3	40.692100	-73.828326	8
4	40.691667	-73.829862	11
5	40.692130	-73.828220	8
6	40.693824	-73.827536	4
7	40.693856	-73.827425	2
8	40.693941	-73.827122	2
9	40.693793	-73.827646	4
10	40.689675	-73.822217	13
11	40.694075	-73.827285	2
12	40.694121	-73.827076	1
13	40.693230	-73.824366	4
14	40.693214	-73.824419	3
15	40.691902	-73.821105	10
16	40.694098	-73.827180	2
17	40.693246	-73.824309	4
18	40.693182	-73.824528	3
19	40.693199	-73.824471	3
20	40.693167	-73.824580	3
21	40.693522	-73.823319	5
22	40.693135	-73.824689	2
23	40.693887	-73.827313	2
24	40.694049	-73.827406	2
25	40.694274	-73.822402	4
26	40.690312	-73.834718	7
27	40.693152	-73.830718	9
28	40.692824	-73.831882	19
29	40.692892	-73.831644	6
...	...	...	...
2595	40.689348	-73.831430	25
2596	40.690265	-73.831895	17
2597	40.689197	-73.831354	11
2598	40.687569	-73.830611	14
2599	40.689156	-73.831333	11
2600	40.687181	-73.830419	15
2601	40.688106	-73.830876	10
2602	40.687376	-73.830515	14
2603	40.687238	-73.830447	15
2604	40.686767	-73.830215	24
2605	40.690549	-73.832040	19
2606	40.690175	-73.831850	19
2607	40.688777	-73.831141	9
2608	40.690836	-73.832185	18
2609	40.686613	-73.830138	24
2610	40.690677	-73.832105	19
2611	40.690915	-73.832225	18
2612	40.689589	-73.831553	25
2613	40.689072	-73.831290	11
2614	40.690733	-73.832133	17
2615	40.690925	-73.832081	18
2616	40.688297	-73.831555	9
2617	40.688386	-73.831244	9
2618	40.688347	-73.831379	9
2619	40.689339	-73.831288	11
2620	40.690641	-73.832086	19
2621	40.689570	-73.831403	25
2622	40.688433	-73.831586	9
2623	40.688540	-73.831207	8
2624	40.690671	-73.831954	19

<p>2456 rows × 3 columns</p>

In [30]:

from shapely.geometry import Point
lls = treesLoc[['longitude', 'latitude']].values
ll_points = [Point(_) for _ in lls]

In [31]:

target_proj4 = (
    '+proj=lcc +lat_1=41.03333333333333 +lat_2=40.66666666666666 '
    '+lat_0=40.16666666666666 +lon_0=-74 +x_0=300000.0000000001 +y_0=0 +ellps=GRS80 ' 
    '+datum=NAD83 +to_meter=0.3048006096012192 +no_defs ')

In [32]:

from geotable.projections import get_transform_shapely_geometry, LONGITUDE_LATITUDE_PROJ4
transform_shapely_geometry = get_transform_shapely_geometry(LONGITUDE_LATITUDE_PROJ4, target_proj4)

In [33]:

xy_points = [transform_shapely_geometry(g) for g in ll_points]

In [34]:

xys = [[_.x, _.y] for _ in xy_points]
xys

Out[34]:

[[1033847.8707557486, 191438.16226065008],
 [1033899.0515508064, 191301.32240691432],
 [1033909.8398974979, 191272.4712977005],
 [1031857.6548645254, 191473.84853362272],
 [1031431.8728140886, 191315.03701283535],
 [1031887.001086411, 191484.73759774523],
 [1032075.417874307, 192102.43241733414],
 [1032106.1597834653, 192113.87099342345],
 [1032190.1359940091, 192145.1103000095],
 [1032044.8643610342, 192091.06711697386],
 [1033553.3461153246, 190593.62853743866],
 [1032144.6610346106, 192194.0594357817],
 [1032202.6184250781, 192210.69615250683],
 [1032954.9454699566, 191887.52011803782],
 [1032940.1181464486, 191881.777696558],
 [1033860.0252749054, 191405.66332976424],
 [1032173.783862781, 192202.41631585],
 [1032970.7038191559, 191893.62510275698],
 [1032909.9065332928, 191870.06952374193],
 [1032925.7591169882, 191876.21110100677],
 [1032895.5474827107, 191864.50658935402],
 [1033244.9475374835, 191994.73182205242],
 [1032865.2416072892, 191852.7654937561],
 [1032137.1815413903, 192125.41217265363],
 [1032111.2118956717, 192184.45534352827],
 [1033498.6466136669, 192269.2758142716],
 [1030086.0822640383, 190818.82242729404],
 [1031193.3868353553, 191855.91116543187],
 [1030870.8429464652, 191735.61484307164],
 [1030936.7616773117, 191760.61790275908],
 [1033387.8555658556, 192434.79983943404],
 [1031165.0799509926, 191845.3929182683],
 [1033369.8309241201, 192427.90668913658],
 [1032367.626486273, 192256.89162216499],
 [1030066.9638009464, 190811.4232532727],
 [1033489.4709674016, 192293.71105754637],
 [1033870.9557261895, 191376.43723261394],
 [1033778.577535409, 191623.4284872874],
 [1033814.5889148266, 191527.14390136945],
 [1030148.3098543205, 191464.9479854939],
 [1030120.6454988508, 191389.77816777883],
 [1032930.0067801966, 192438.3738110241],
 [1033794.9729222504, 191579.5929285359],
 [1030138.5650344017, 191343.10126133822],
 [1031557.6323014728, 191993.121368697],
 [1031591.5942303599, 192005.93544493252],
 [1031788.3385993503, 192079.72491574887],
 [1031854.4160144486, 192104.87287153376],
 [1030489.0052128154, 191592.66149022375],
 [1030745.1466000318, 191688.99471173246],
 [1032441.7831986122, 192277.99490318767],
 [1030184.4733779969, 191223.5092653456],
 [1032656.9921161905, 191773.85832554562],
 [1031628.831489578, 192019.98739277144],
 [1033787.0568330595, 191600.76258227226],
 [1030915.1147369054, 191168.77680027118],
 [1033607.9552557922, 190448.35354214616],
 [1033653.5558591317, 190327.04055301854],
 [1032115.0826359701, 191571.08404749585],
 [1030969.7653264615, 191773.1378015433],
 [1033198.0625509603, 191977.0729437715],
 [1032819.2715048206, 191834.95251757518],
 [1033830.705586093, 191484.05831931805],
 [1031506.8871314911, 191343.37468621126],
 [1033807.9331333507, 191544.93877913593],
 [1030211.0298038158, 191154.3295112585],
 [1030262.9016745123, 191507.39062178964],
 [1032050.3525286487, 192055.70510127733],
 [1033824.5377552444, 191500.5425997625],
 [1032848.832032214, 191846.40788695132],
 [1030194.0408165172, 191198.58168392387],
 [1032399.5297589609, 191678.0524457012],
 [1033464.5844366333, 192359.9942284334],
 [1031405.7687619538, 191305.18949076443],
 [1033121.5140729863, 191948.2423882925],
 [1030291.2232627907, 191517.87869640914],
 [1032367.8786848598, 191666.1927203252],
 [1032327.2382419138, 191650.96340618876],
 [1030656.6369591039, 191359.5111778766],
 [1030419.7003198379, 190439.36336348858],
 [1030840.7477818041, 191564.2636787716],
 [1030387.0454121003, 190522.47784097336],
 [1031331.4801752643, 191277.16321755323],
 [1033222.0204513586, 191986.09832728503],
 [1030848.8727345204, 191542.74735729725],
 [1030822.0283654884, 191613.8495366787],
 [1030703.9511327232, 191238.4254475278],
 [1030675.5620175786, 191311.07687301518],
 [1030684.6608957002, 191287.79171607003],
 [1030267.9054775143, 191006.17310256662],
 [1030203.9343201573, 191172.81676430014],
 [1032703.1408756562, 191791.1977209629],
 [1030735.2526403002, 191158.32543784726],
 [1030710.8652107008, 191220.72858860253],
 [1030639.8940303833, 191402.3535559775],
 [1030854.1686147422, 191528.7125934485],
 [1030640.6125280821, 191502.41817230577],
 [1030660.3693718904, 191451.08901818978],
 [1030631.1602924807, 191424.71038603003],
 [1030666.8282212887, 191333.43004625343],
 [1030510.4680549054, 190208.35779588684],
 [1030255.7230321659, 190856.70552206502],
 [1030745.8063678655, 191131.3123496616],
 [1030692.6673717964, 191267.29888537078],
 [1030615.8721421221, 191463.8284629088],
 [1034221.8882163037, 191919.25439734999],
 [1030529.8792194608, 190154.00396086197],
 [1030293.2787820562, 190761.1182048619],
 [1030672.2251791635, 191420.29299436545],
 [1030737.6054996187, 191250.4398527165],
 [1030766.7048846273, 191174.84971772085],
 [1034185.0362490427, 192017.89752641792],
 [1029924.2326370938, 191079.45798034684],
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 [1030835.5695393711, 189197.29706994662],
 [1032881.0022465406, 189620.47784228163],
 [1032896.0551753987, 189579.34599119044],
 [1030819.5033495177, 189191.44427777498],
 [1030879.4473484096, 189213.28424216213],
 [1034554.3472983199, 190651.7547808301],
 [1031180.3377878038, 190032.2504181761],
 [1030805.378956276, 187243.00052384645],
 [1031706.6510542344, 187956.15968327248],
 [1031822.8642057986, 188092.7008984571],
 [1031716.1526675652, 187930.88648194037],
 [1031725.5148903388, 187905.98827163462],
 [1031854.7087138551, 188104.6076315143],
 [1030447.8933638581, 188193.32397308535],
 [1030801.0509444497, 190024.16484454897],
 [1031259.83939168, 189111.09053137508],
 [1030106.6133195341, 190602.36540218236],
 [1030725.9117552112, 187453.28511671597],
 [1030836.0956444556, 188820.24594872503],
 [1032126.0063890314, 189693.72778446582],
 [1030839.9138695628, 187151.60908547023],
 [1030733.866096394, 189219.0567711501],
 [1030128.1161565451, 189281.15293744038],
 [1029597.6801539024, 190462.23966168353],
 [1031196.6261660741, 189987.37459004534],
 [1032257.5101477972, 189789.14297256025],
 [1030835.3677886355, 189257.67341136924],
 [1030789.385139862, 189240.17760424287],
 [1031697.3871454509, 188098.3175880956],
 [1030553.6284242106, 188858.19009104345],
 [1031716.0809419808, 188052.77646465256],
 [1030558.6311304442, 187899.60824106066],
 [1030803.6906879209, 188907.1019725551],
 [1029888.7551121964, 190912.73949267215],
 [1029973.378424949, 190854.3617316207],
 [1031085.2144418645, 189351.26555393697],
 [1030814.6185143285, 189249.77868249995],
 [1030141.3368430509, 189245.95450765177],
 [1032492.3887233323, 189829.99058577017],
 [1032048.1446522299, 189664.1584730367],
 [1032740.2597862143, 190002.71375163124],
 [1030574.7360084376, 188802.02518607563],
 [1029685.9431160727, 190227.26972858646],
 [1029676.8740822953, 190251.21109960193],
 [1030582.9004262756, 187835.23011100345],
 [1029915.2259519409, 190922.75001925405],
 [1031509.4291816246, 190579.79496072038],
 [1032534.6441447071, 189939.73783199085],
 [1030917.752185554, 186945.6244854812],
 [1031449.9049539007, 190266.85462720602],
 [1029783.0865142529, 189970.85410483126],
 [1030068.3141316874, 190607.16422716383],
 [1031820.0811337152, 190346.7856456756],
 [1030820.9246105778, 188860.90884795782],
 [1032628.4384189276, 190300.0864266685],
 [1030756.5599656876, 187372.18207249473],
 [1031781.6545190387, 191245.46878499823],
 [1030821.7096920465, 187199.78226500913],
 [1031925.6214306323, 189617.23977603938],
 [1031420.9317169753, 190255.8320210696],
 [1030955.793654108, 189918.60945949546],
 [1031448.8992967976, 189945.14938065773],
 [1032199.6869998296, 189767.62075140406],
 [1031618.6376168738, 190188.2327586483],
 [1029708.5460850135, 190167.60928112257],
 [1030593.52439145, 188752.03855113222],
 [1031711.3703606857, 189941.48552528126],
 [1029629.9363783384, 191116.66922866338],
 [1032685.0360190325, 190149.57427663967],
 [1030037.98409777, 190686.14112981295],
 [1029582.668197441, 190502.53567494274],
 [1031223.9534801553, 189206.1403330297],
 [1030532.6240951706, 188914.0710347777],
 [1031674.5277486275, 188089.78407482087],
 [1032200.7516926295, 189721.38939280258],
 [1031232.6426743543, 189183.1278508167],
 [1032711.678962556, 190078.71787732668],
 [1031750.487793732, 187230.7813454036],
 [1032389.8299348246, 187557.44067944397],
 [1032462.8863021977, 189818.67786724024],
 [1030191.6889686753, 189111.8998215471],
 [1030572.8098589606, 189519.82762438303],
 [1032235.2067515053, 189780.83954990256],
 [1029617.408599012, 191050.5527308524],
 [1030880.8832953249, 187043.190356624],
 [1031975.7302169515, 188150.47006888586],
 [1030204.770458431, 189077.07642434243],
 [1032048.4322994681, 191007.79326958788],
 [1030242.406634876, 189814.91179301418],
 [1032490.7469581674, 189877.4522234871],
 [1030634.4616822508, 189355.72725220403],
 [1030169.5371978225, 189170.8757149437],
 [1031175.0481635764, 190164.8198437961],
 [1029573.0172341248, 190756.739786349],
 [1031811.4194985967, 190369.8125543554],
 [1030705.798452701, 187506.51162931212],
 [1030482.8956921428, 189957.89775099157],
 [1031738.2333213806, 190566.81924101184],
 [1031698.8097421682, 190671.4360642392],
 [1031712.915433642, 190634.00685601],
 [1029793.2900994143, 189943.92375446483],
 [1029720.0259773665, 190137.31123573612],
 [1032095.1949167358, 187651.49328738358],
 [1030459.8854101509, 189949.19749380613],
 [1032061.9719119802, 191012.98611406502],
 [1031632.0926129845, 190848.48315396422],
 [1029905.0866176722, 191033.68400581751],
 [1031675.6051364766, 190733.0098797031],
 ...]

In [35]:

import numpy as np
xy_array = np.array(xys)

In [36]:

xs = xy_array[:, 0] 
ys = xy_array[:, 1]

xs

Out[36]:

array([1033847.87075575, 1033899.05155081, 1033909.8398975 , ...,
       1030956.18275112, 1031061.0567902 , 1030852.47589388])

In [37]:

ys

Out[37]:

array([191438.16226065, 191301.32240691, 191272.4712977 , ...,
       190135.99126526, 190175.30029261, 190951.07139433])

In [38]:

#Create heatmap
heatmap, xedges, yedges = np.histogram2d(xs, ys, bins=(64,64))
extent = [xedges[1], xedges[-1], yedges[1], yedges[-1]]
 
# Plot heatmap
plt.clf()
plt.title('Pythonspot.com heatmap example')
plt.ylabel('y')
plt.xlabel('x')

Out[38]:

<matplotlib.text.Text at 0x7f99b67817b8>

In [39]:

plt.imshow(heatmap, extent=extent)
plt.imshow(heatmap)

plt.show()

In [40]:

xys = np.array(xys)

In [41]:

filtered_xys = xys[(xys[:,0] > 1) & (xys[:,1] > 1)]
xs = filtered_xys[:, 0] 
ys = filtered_xys[:, 1]

xs

Out[41]:

array([1033847.87075575, 1033899.05155081, 1033909.8398975 , ...,
       1030956.18275112, 1031061.0567902 , 1030852.47589388])

In [42]:

# Libraries
import numpy as np
import matplotlib.pyplot as plt
from scipy.stats import kde
 
# Create data: 200 points
#data = np.random.multivariate_normal([0, 0], [[1, 0.5], [0.5, 3]], 200)
#x, y = data.T
x = xs
y = ys

# Create a figure with 6 plot areas
fig, axes = plt.subplots(ncols=3, nrows=1, figsize=(20, 5)) #og 21,5

# Everything sarts with a Scatterplot
axes[0].set_title('Scatterplot')
axes[0].plot(x, y, 'ko', markersize=3)
# As you can see there is a lot of overplottin here!
 
# Thus we can cut the plotting window in several hexbins
nbins = 70
axes[1].set_title('Hexbin')
axes[1].hexbin(x, y, gridsize=60,bins='log', cmap='inferno')
 
# 2D Histogram

axes[2].set_title('2D Histogram')
axes[2].hist2d(x, y, bins=nbins, cmap='jet')
plt.show()

In [43]:

#save by doing targetpath
# Save file to target folder to include it in the result download
target_path = target_folder + '/Businesses.png'
#figure = axes.get_figure()

fig.savefig(target_path)
print(f'Businesses_image_path = {target_path}')
#print('a_image_path = %s' % target_path)

Businesses_image_path = /tmp/Businesses.png

In [44]:

blocks = client.get("twhy-dzjp",
                    boro_code = 2,
                     limit=10000)

# Convert to pandas DataFrame
blocksDF = pd.DataFrame.from_records(blocks)

from geotable import GeoTable

# Link for the 2010 Census BLOCKS shapefile data
t = GeoTable.load("2010 Census Blocks.zip")

t

Out[44]:

	bctcb2010	boro_code	boro_name	cb2010	ct2010	shape_area	shape_leng	geometry_object	geometry_layer	geometry_proj4
0	50009001000	5	Staten Island	1000	000900	2.445896e+05	2508.948115	POLYGON ((-74.07920577013245 40.64343078374567...	geo_export_7753596b-be5c-4ff9-a64c-ddfbaf0c5d08	+proj=longlat +ellps=WGS84 +no_defs
1	50020011000	5	Staten Island	1000	002001	1.110063e+05	1345.886422	POLYGON ((-74.07061992438017 40.61085506705416...	geo_export_7753596b-be5c-4ff9-a64c-ddfbaf0c5d08	+proj=longlat +ellps=WGS84 +no_defs
2	50027001000	5	Staten Island	1000	002700	1.504068e+05	1703.381172	POLYGON ((-74.07524403910642 40.62600632452712...	geo_export_7753596b-be5c-4ff9-a64c-ddfbaf0c5d08	+proj=longlat +ellps=WGS84 +no_defs
3	50040001000	5	Staten Island	1000	004000	1.412966e+05	1511.173743	POLYGON ((-74.08708799689826 40.61116883123925...	geo_export_7753596b-be5c-4ff9-a64c-ddfbaf0c5d08	+proj=longlat +ellps=WGS84 +no_defs
4	50064001000	5	Staten Island	1000	006400	2.007850e+05	1978.243852	POLYGON ((-74.07643744511992 40.60100160655585...	geo_export_7753596b-be5c-4ff9-a64c-ddfbaf0c5d08	+proj=longlat +ellps=WGS84 +no_defs
5	50074001000	5	Staten Island	1000	007400	1.390840e+05	1540.875503	POLYGON ((-74.06589403843647 40.59682603642131...	geo_export_7753596b-be5c-4ff9-a64c-ddfbaf0c5d08	+proj=longlat +ellps=WGS84 +no_defs
6	50075001000	5	Staten Island	1000	007500	1.235605e+05	1412.642887	POLYGON ((-74.08568983334587 40.63660727984283...	geo_export_7753596b-be5c-4ff9-a64c-ddfbaf0c5d08	+proj=longlat +ellps=WGS84 +no_defs
7	50077001000	5	Staten Island	1000	007700	2.057710e+05	2421.037307	POLYGON ((-74.08709163679771 40.64033437636213...	geo_export_7753596b-be5c-4ff9-a64c-ddfbaf0c5d08	+proj=longlat +ellps=WGS84 +no_defs
8	50112011000	5	Staten Island	1000	011201	1.961123e+05	1943.029020	POLYGON ((-74.0865414171051 40.58373352623683,...	geo_export_7753596b-be5c-4ff9-a64c-ddfbaf0c5d08	+proj=longlat +ellps=WGS84 +no_defs
9	50112021000	5	Staten Island	1000	011202	9.273776e+04	1233.682178	POLYGON ((-74.09940090442285 40.57918675553432...	geo_export_7753596b-be5c-4ff9-a64c-ddfbaf0c5d08	+proj=longlat +ellps=WGS84 +no_defs
10	50114011000	5	Staten Island	1000	011401	6.668409e+04	1094.120092	POLYGON ((-74.09719237050396 40.58828333684241...	geo_export_7753596b-be5c-4ff9-a64c-ddfbaf0c5d08	+proj=longlat +ellps=WGS84 +no_defs
11	50114021000	5	Staten Island	1000	011402	1.830613e+05	1942.556759	POLYGON ((-74.10061310938735 40.58271119102469...	geo_export_7753596b-be5c-4ff9-a64c-ddfbaf0c5d08	+proj=longlat +ellps=WGS84 +no_defs
12	50121001000	5	Staten Island	1000	012100	1.702508e+05	1793.986645	POLYGON ((-74.10474881613307 40.62580546479904...	geo_export_7753596b-be5c-4ff9-a64c-ddfbaf0c5d08	+proj=longlat +ellps=WGS84 +no_defs
13	50122001000	5	Staten Island	1000	012200	1.398193e+05	1587.811375	POLYGON ((-74.10559988905946 40.5775774710855,...	geo_export_7753596b-be5c-4ff9-a64c-ddfbaf0c5d08	+proj=longlat +ellps=WGS84 +no_defs
14	50125001000	5	Staten Island	1000	012500	2.151780e+05	1934.120821	POLYGON ((-74.11666642606224 40.63171010196231...	geo_export_7753596b-be5c-4ff9-a64c-ddfbaf0c5d08	+proj=longlat +ellps=WGS84 +no_defs
15	50132011000	5	Staten Island	1000	013201	8.806900e+05	4338.685959	POLYGON ((-74.12395063694647 40.56843071865556...	geo_export_7753596b-be5c-4ff9-a64c-ddfbaf0c5d08	+proj=longlat +ellps=WGS84 +no_defs
16	50134001000	5	Staten Island	1000	013400	4.323219e+04	1076.670462	POLYGON ((-74.1206434158707 40.57490815363254,...	geo_export_7753596b-be5c-4ff9-a64c-ddfbaf0c5d08	+proj=longlat +ellps=WGS84 +no_defs
17	50138001000	5	Staten Island	1000	013800	1.105674e+05	1351.102708	POLYGON ((-74.13690607650334 40.57215488411653...	geo_export_7753596b-be5c-4ff9-a64c-ddfbaf0c5d08	+proj=longlat +ellps=WGS84 +no_defs
18	50146051000	5	Staten Island	1000	014605	1.598831e+05	1766.454298	POLYGON ((-74.15150507695977 40.56538269160452...	geo_export_7753596b-be5c-4ff9-a64c-ddfbaf0c5d08	+proj=longlat +ellps=WGS84 +no_defs
19	50146071000	5	Staten Island	1000	014607	3.052821e+05	2316.414101	POLYGON ((-74.16246054798417 40.56003557382821...	geo_export_7753596b-be5c-4ff9-a64c-ddfbaf0c5d08	+proj=longlat +ellps=WGS84 +no_defs
20	50146081000	5	Staten Island	1000	014608	4.003076e+05	2688.759398	POLYGON ((-74.15573077130404 40.56348664559846...	geo_export_7753596b-be5c-4ff9-a64c-ddfbaf0c5d08	+proj=longlat +ellps=WGS84 +no_defs
21	50156031000	5	Staten Island	1000	015603	1.642635e+05	1941.242465	POLYGON ((-74.15046462220288 40.53419468029215...	geo_export_7753596b-be5c-4ff9-a64c-ddfbaf0c5d08	+proj=longlat +ellps=WGS84 +no_defs
22	50169011000	5	Staten Island	1000	016901	2.033579e+05	2111.303257	POLYGON ((-74.10686367243076 40.61309934521687...	geo_export_7753596b-be5c-4ff9-a64c-ddfbaf0c5d08	+proj=longlat +ellps=WGS84 +no_defs
23	50170051000	5	Staten Island	1000	017005	1.214575e+05	2457.600150	POLYGON ((-74.18083755625177 40.54673239030795...	geo_export_7753596b-be5c-4ff9-a64c-ddfbaf0c5d08	+proj=longlat +ellps=WGS84 +no_defs
24	50170081000	5	Staten Island	1000	017008	1.690098e+05	1956.655428	POLYGON ((-74.1722550766378 40.56170483715575,...	geo_export_7753596b-be5c-4ff9-a64c-ddfbaf0c5d08	+proj=longlat +ellps=WGS84 +no_defs
25	50170111000	5	Staten Island	1000	017011	1.533049e+05	1705.532918	POLYGON ((-74.16741722331244 40.54988134637112...	geo_export_7753596b-be5c-4ff9-a64c-ddfbaf0c5d08	+proj=longlat +ellps=WGS84 +no_defs
26	50173001000	5	Staten Island	1000	017300	1.180859e+05	1392.506406	POLYGON ((-74.11706965339282 40.60820624975425...	geo_export_7753596b-be5c-4ff9-a64c-ddfbaf0c5d08	+proj=longlat +ellps=WGS84 +no_defs
27	50187011000	5	Staten Island	1000	018701	1.696581e+05	1955.146098	POLYGON ((-74.12811936127744 40.61059539235342...	geo_export_7753596b-be5c-4ff9-a64c-ddfbaf0c5d08	+proj=longlat +ellps=WGS84 +no_defs
28	50189011000	5	Staten Island	1000	018901	1.095069e+05	1436.410144	POLYGON ((-74.13418513192617 40.6153616160391,...	geo_export_7753596b-be5c-4ff9-a64c-ddfbaf0c5d08	+proj=longlat +ellps=WGS84 +no_defs
29	50197001000	5	Staten Island	1000	019700	6.439962e+04	1099.138528	POLYGON ((-74.13513182128254 40.61980539254179...	geo_export_7753596b-be5c-4ff9-a64c-ddfbaf0c5d08	+proj=longlat +ellps=WGS84 +no_defs
...	...	...	...	...	...	...	...	...	...	...
38766	10006005001	1	Manhattan	5001	000600	2.584757e+05	2110.261252	POLYGON ((-73.98741944956207 40.71147054341841...	geo_export_7753596b-be5c-4ff9-a64c-ddfbaf0c5d08	+proj=longlat +ellps=WGS84 +no_defs
38767	10006006001	1	Manhattan	6001	000600	7.571292e+03	509.101831	POLYGON ((-73.98821773094718 40.71076478101609...	geo_export_7753596b-be5c-4ff9-a64c-ddfbaf0c5d08	+proj=longlat +ellps=WGS84 +no_defs
38768	10243021005	1	Manhattan	1005	024302	3.470288e+05	2930.378834	POLYGON ((-73.93674454853043 40.82989431214514...	geo_export_7753596b-be5c-4ff9-a64c-ddfbaf0c5d08	+proj=longlat +ellps=WGS84 +no_defs
38769	10243023000	1	Manhattan	3000	024302	6.472890e+05	3870.362883	POLYGON ((-73.93549629092554 40.83174234901332...	geo_export_7753596b-be5c-4ff9-a64c-ddfbaf0c5d08	+proj=longlat +ellps=WGS84 +no_defs
38770	10158021000	1	Manhattan	1000	015802	1.217140e+05	1815.345960	POLYGON ((-73.94933170627112 40.78519312605533...	geo_export_7753596b-be5c-4ff9-a64c-ddfbaf0c5d08	+proj=longlat +ellps=WGS84 +no_defs
38771	30352001020	3	Brooklyn	1020	035200	4.715105e+05	2828.880224	POLYGON ((-73.98528886657739 40.57253160673621...	geo_export_7753596b-be5c-4ff9-a64c-ddfbaf0c5d08	+proj=longlat +ellps=WGS84 +no_defs
38772	30326002001	3	Brooklyn	2001	032600	1.743703e+05	1773.948939	POLYGON ((-73.98660774857026 40.57489309626504...	geo_export_7753596b-be5c-4ff9-a64c-ddfbaf0c5d08	+proj=longlat +ellps=WGS84 +no_defs
38773	10230001000	1	Manhattan	1000	023000	3.091705e+04	752.264999	POLYGON ((-73.93764373525447 40.81851059183536...	geo_export_7753596b-be5c-4ff9-a64c-ddfbaf0c5d08	+proj=longlat +ellps=WGS84 +no_defs
38774	10230001001	1	Manhattan	1001	023000	2.010833e+05	2073.907682	POLYGON ((-73.9380268984241 40.81868092712311,...	geo_export_7753596b-be5c-4ff9-a64c-ddfbaf0c5d08	+proj=longlat +ellps=WGS84 +no_defs
38775	10219004000	1	Manhattan	4000	021900	2.197990e+05	2325.840957	POLYGON ((-73.95614878227988 40.81635610248966...	geo_export_7753596b-be5c-4ff9-a64c-ddfbaf0c5d08	+proj=longlat +ellps=WGS84 +no_defs
38776	10219004001	1	Manhattan	4001	021900	1.658021e+04	685.702971	POLYGON ((-73.95659705290377 40.8157674621146,...	geo_export_7753596b-be5c-4ff9-a64c-ddfbaf0c5d08	+proj=longlat +ellps=WGS84 +no_defs
38777	30391002005	3	Brooklyn	2005	039100	1.676642e+05	2236.207276	POLYGON ((-73.92996413032211 40.69824992819299...	geo_export_7753596b-be5c-4ff9-a64c-ddfbaf0c5d08	+proj=longlat +ellps=WGS84 +no_defs
38778	30391002003	3	Brooklyn	2003	039100	1.933658e+05	2039.950761	POLYGON ((-73.93128036584677 40.69920248184159...	geo_export_7753596b-be5c-4ff9-a64c-ddfbaf0c5d08	+proj=longlat +ellps=WGS84 +no_defs
38779	30391002004	3	Brooklyn	2004	039100	1.015944e+05	1770.208727	POLYGON ((-73.93052028738461 40.69877228327239...	geo_export_7753596b-be5c-4ff9-a64c-ddfbaf0c5d08	+proj=longlat +ellps=WGS84 +no_defs
38780	30702014007	3	Brooklyn	4007	070201	2.448044e+05	2348.763119	POLYGON ((-73.90845283308809 40.60789747045573...	geo_export_7753596b-be5c-4ff9-a64c-ddfbaf0c5d08	+proj=longlat +ellps=WGS84 +no_defs
38781	30702014008	3	Brooklyn	4008	070201	2.161195e+05	2217.346253	POLYGON ((-73.9092253116946 40.60834532801371,...	geo_export_7753596b-be5c-4ff9-a64c-ddfbaf0c5d08	+proj=longlat +ellps=WGS84 +no_defs
38782	30702014009	3	Brooklyn	4009	070201	2.077416e+05	2110.428787	POLYGON ((-73.90996420854586 40.60878350822895...	geo_export_7753596b-be5c-4ff9-a64c-ddfbaf0c5d08	+proj=longlat +ellps=WGS84 +no_defs
38783	30702014010	3	Brooklyn	4010	070201	1.935942e+05	2001.583778	POLYGON ((-73.91070445132921 40.60922123573495...	geo_export_7753596b-be5c-4ff9-a64c-ddfbaf0c5d08	+proj=longlat +ellps=WGS84 +no_defs
38784	30375001000	3	Brooklyn	1000	037500	1.132881e+05	2086.002825	POLYGON ((-73.91789200384292 40.68644915917891...	geo_export_7753596b-be5c-4ff9-a64c-ddfbaf0c5d08	+proj=longlat +ellps=WGS84 +no_defs
38785	30375001001	3	Brooklyn	1001	037500	9.493392e+04	1683.746644	POLYGON ((-73.92066796895084 40.68628662815645...	geo_export_7753596b-be5c-4ff9-a64c-ddfbaf0c5d08	+proj=longlat +ellps=WGS84 +no_defs
38786	40281003001	4	Queens	3001	028100	2.024700e+05	2074.453654	POLYGON ((-73.88113747601183 40.74792729299265...	geo_export_7753596b-be5c-4ff9-a64c-ddfbaf0c5d08	+proj=longlat +ellps=WGS84 +no_defs
38787	40281004001	4	Queens	4001	028100	2.015936e+05	2071.624978	POLYGON ((-73.88207112127155 40.74782992681795...	geo_export_7753596b-be5c-4ff9-a64c-ddfbaf0c5d08	+proj=longlat +ellps=WGS84 +no_defs
38788	50007001002	5	Staten Island	1002	000700	7.937377e+05	4630.322131	POLYGON ((-74.08697080193778 40.64702859356238...	geo_export_7753596b-be5c-4ff9-a64c-ddfbaf0c5d08	+proj=longlat +ellps=WGS84 +no_defs
38789	50081003005	5	Staten Island	3005	008100	3.639689e+05	3399.852412	POLYGON ((-74.08773666349921 40.64389446122451...	geo_export_7753596b-be5c-4ff9-a64c-ddfbaf0c5d08	+proj=longlat +ellps=WGS84 +no_defs
38790	50081003000	5	Staten Island	3000	008100	4.497355e+05	4065.970844	POLYGON ((-74.08652380457303 40.64256408357222...	geo_export_7753596b-be5c-4ff9-a64c-ddfbaf0c5d08	+proj=longlat +ellps=WGS84 +no_defs
38791	50128051000	5	Staten Island	1000	012805	6.382500e+05	4633.747592	POLYGON ((-74.11937726656176 40.55998256805508...	geo_export_7753596b-be5c-4ff9-a64c-ddfbaf0c5d08	+proj=longlat +ellps=WGS84 +no_defs
38792	50128051002	5	Staten Island	1002	012805	4.637241e+04	1454.837984	POLYGON ((-74.12051090293794 40.5578116241892,...	geo_export_7753596b-be5c-4ff9-a64c-ddfbaf0c5d08	+proj=longlat +ellps=WGS84 +no_defs
38793	50128051004	5	Staten Island	1004	012805	2.925027e+05	2678.393485	POLYGON ((-74.12368247752183 40.55963388471181...	geo_export_7753596b-be5c-4ff9-a64c-ddfbaf0c5d08	+proj=longlat +ellps=WGS84 +no_defs
38794	50128051011	5	Staten Island	1011	012805	2.315392e+06	9899.744624	POLYGON ((-74.12125019302078 40.55791315343746...	geo_export_7753596b-be5c-4ff9-a64c-ddfbaf0c5d08	+proj=longlat +ellps=WGS84 +no_defs
38795	10158021002	1	Manhattan	1002	015802	2.194009e+04	609.293625	POLYGON ((-73.95033804130387 40.78561715001314...	geo_export_7753596b-be5c-4ff9-a64c-ddfbaf0c5d08	+proj=longlat +ellps=WGS84 +no_defs

<p>38796 rows × 10 columns</p>

In [45]:

target_path = t.save_csv(target_folder + '/choropleth.csv')
print('borough_choropleth_geotable_path = %s' % target_path)

borough_choropleth_geotable_path = /tmp/choropleth.csv

Pay Notebook Creator: Naiem Gafar	0
Set Container: Numerical CPU with TINY Memory for 10 Minutes	0
Total	0

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