Which city would you like to live in?

In [1]:

    

import pandas as pd
import numpy as np

import cityfynders.data_processing as dp


    

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(natural, human, economy, tertiary) = dp.read_data()


    

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#natural

natural['Air'] = natural['Air'].rank(ascending=0)
natural['Water_quality'] = natural['Water_quality'].rank(ascending=0)
natural['Toxics'] = natural['Toxics'].rank(ascending=0)
natural['Hazardous'] = natural['Hazardous'].rank(ascending=0)
natural['Green_score_rank'] = natural['Green_score'].rank(ascending=1)
natural['Green_score_rank'].fillna(natural['Green_score_rank'].max()+1, inplace=True)
natural['Sanitation'].fillna(natural['Sanitation'].max()+1, inplace=True)

natural['Natural_total_score'] = (natural['Air'] + natural['Water_quality'] + natural['Toxics'] 
                                  + natural['Hazardous'] + natural['Green_score_rank'])
natural['Natural_total_rank'] = natural['Natural_total_score'].rank(ascending=1)


    

In [5]:

    

(natural, human, economy, tertiary) = dp.data_rank(natural, human, economy, tertiary)


    

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import geopy as gy
from geopy.geocoders import Nominatim

def find_loc(dataframe):
    geolocator = Nominatim()
    lat = []
    lon = []
    for index, row in dataframe.iterrows():
        loc = geolocator.geocode(row['City'] + ' ' + row['State'] + ' United States')
        lat.append(loc.latitude)
        lon.append(loc.longitude)
    return lat, lon

(Lat, Lon) = find_loc(human)


    

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rank = dp.create_rank(natural, human, economy, tertiary, Lat, Lon)


    

In [10]:

    

from cityfynders.plotly_usmap import usmap

usmap(rank)


    

    
Out[10]:





'file:///Users/han/Google Drive/CSE583/Project/uwseds-group-city-fynders/examples/general-ranking-map.html'

In [9]:

    

usmap(rank, 'natural')


    

    
Out[9]:





'file:///Users/han/Google Drive/CSE583/Project/uwseds-group-city-fynders/Docs/natural-ranking-map.html'

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import plotly
import plotly.plotly as py


# human related rank
df = rank
df = df.sort_values('Human_related_rank', ascending=1)
df['reverse_rank'] = df['Human_related_rank'].rank(ascending=0)

df['text'] = df['City'] + '<br># Final Rank ' + (df['Human_related_rank']).astype(str) +\
        '<br># Crime rank ' + (df['Crime_rank']).astype(str)+ '<br># Hospital rank ' +\
    (df['Hospital_rank']).astype(str)+'<br># Early education rank ' + (df['Early_education_rank']).astype(str)+\
    '<br># University education rank ' + (df['University_education_rank']).astype(str)



limits = [(0,10),(10,20),(20,30),(30,40),(40,50)]
colors = ["rgb(0,116,217)","rgb(255,65,54)","rgb(133,20,75)","rgb(255,133,27)","lightgrey"]
cities = []


for i in range(len(limits)):
    lim = limits[i]
    df_sub = df[lim[0]:lim[1]]
    city = dict(
        type = 'scattergeo',
        locationmode = 'USA-states',
        lon = df_sub['Longitude'],
        lat = df_sub['Latitude'],
        text = df_sub['text'],
        marker = dict(
            size = df_sub['reverse_rank']*15,
            color = colors[i],
            line = dict(width=0.5, color='rgb(40,40,40)'),
            sizemode = 'area'
        ),
        name = '{0} - {1}'.format(lim[0],lim[1]) )
    cities.append(city)

    layout = dict(
        title = 'The human related ranking of US big cities',
        showlegend = True,
        geo = dict(
            scope='usa',
            projection=dict( type='albers usa' ),
            showland = True,
            landcolor = 'rgb(217, 217, 217)',
            subunitwidth=1,
            countrywidth=1,
            subunitcolor="rgb(255, 255, 255)",
            countrycolor="rgb(255, 255, 255)"
        ),
    )

fig = dict( data=cities, layout=layout )
plotly.offline.plot( fig, validate=False, filename='human-related-ranking-map.html' )


    

    
Out[11]:





'file:///Users/han/Google Drive/CSE583/Project/uwseds-group-city-fynders/examples/human-related-ranking-map.html'

In [3]:

    

# Correction Matrix Plot
import matplotlib.pyplot as plt
import cityfynders.data_processing as dp

(natural, human, economy, tertiary) = dp.read_data()
alldata = human
for i in[natural, economy, tertiary]:
    factors = list(i.columns.values)
    for j in factors:
        alldata[j] = i[j]

df = alldata[['Population', 'Violent', 'Rape', 'Robbery', 'Colleges',
              'Percent_graduate_degree', 'AvgSATScore', 'NumTop200UnivInState',
              'NumHospital', 'Jan_T', 'April_T', 'july_T', 'Oct_T', 'Prep_inch',
              'Prep_days', 'Snowfall_inch', 'Green_score', 'Air', 'Water_quality',
              'Toxics', 'Hazardous', 'Sanitation', 'Percent unemployment',
              'State sale tax rate', 'Local tax rate', 'Total rate', 'Median Income',
              'AvgTuition', 'Bars', 'Restaurant', 'Museums', 'Libraries',
              'Pro_sports_team', 'Park_acres_per_1000_residents', 'NumTop200Restau']]             


names = ['Pop', 'Violent', 'Colleges', 'Rape', 'Robbery', 'Colleges',
         'Percent_graduate_degree', 'AvgSATScore', 'NumTop200UnivInState',
         'NumHospital', 'Jan_T', 'April_T', 'july_T', 'Oct_T', 'Prep_inch',
         'Prep_days', 'Snowfall_inch', 'Green_score', 'Air', 'Water_quality',
         'Toxics', 'Hazardous', 'Sanitation', 'Percent unemployment',
         'State sale tax rate', 'Local tax rate', 'Total rate', 'Median Income',
         'AvgTuition', 'Bars', 'Restaurant', 'Museums', 'Libraries',
         'Pro_sports_team', 'Park_acres_per_1000_residents', 'NumTop200Restau']
data = df
correlations = data.corr()
# plot correlation matrix
fig = plt.figure(figsize=(12,8))
ax = fig.add_subplot(111)
cax = ax.matshow(correlations, vmin=-1, vmax=1)
fig.colorbar(cax)
ticks = numpy.arange(0,34,1)
ax.set_xticks(ticks)
ax.set_yticks(ticks)
ax.set_xticklabels(names,rotation = 'vertical')
ax.set_yticklabels(names)
plt.show()


    

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