#Read zip file containing csv from url.
from io import BytesIO as bIO
import requests
from zipfile import ZipFile as zf
#General Packages.
from datetime import date
from datetime import timedelta
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import re
import warnings
#Specific Packages for visualization.
from wordcloud import WordCloud as WC
import bar_chart_race as bcr
from IPython.display import Video
import plotly
import plotly.graph_objects as go
from pywaffle import Waffle
#Max pd df display.
pd.set_option("display.max_rows", 999,
"display.max_columns", 999)
#No warnings.
warnings.filterwarnings('ignore')
#My jobs spreadsheet.
df = pd.read_excel(r'C:\Users\Rohan\Documents\Jobs\Jobs.xlsx')
#Convert datetime columns to date.
df['Date_Applied'] = df['Date_Applied'].apply(lambda x: x.date())
df['Rejection_Email'] = df['Rejection_Email'].apply(lambda x: x.date())
df['Viewed_Email'] = df['Viewed_Email'].apply(lambda x: x.date())
#Latitudes and Longitudes for missing cities. Select only city, state, and coordinates columns.
url = "https://simplemaps.com/static/data/us-cities/1.73/basic/simplemaps_uscities_basicv1.73.zip"
lat_lon = pd.read_csv(zf(bIO(requests.get(url).content)).open("uscities.csv"))[['city', 'state_id', 'lat', 'lng']]
df
#Current Numbers
print("As of " + date.today().strftime("%A, %B %d, %Y") + ", I have applied to " + str(df.shape[0]) + " jobs.")
As of Saturday, July 03, 2021, I have applied to 1162 jobs.
#Combine all job titles into one string.
jobs_string = ' '.join(df['Title'])
#Only letters are useful.
regex = re.compile('[^a-zA-Z]')
#Remove all non letters, and remove ' I ', ' II ', ' III '.
jobs_string = regex.sub(' ', jobs_string).replace(' I ',' ').replace(' II ',' ').replace(' III ',' ')
#Specific Replacement.
jobs_string = jobs_string.replace('AR VR','AR/VR').replace('C C', 'C2C')
jobs_string = jobs_string.replace('Microsoft', 'Microsoft-365').replace('Non IT', 'Non-IT')
jobs_string = jobs_string.replace('E Commerce','E-Commerce')
#Create a frequency distribution of all words in the job titles.
jobs_dict = {}
jobs_words = jobs_string.split()
for word in jobs_words :
if word not in jobs_dict.keys() :
jobs_dict[word] = 1
else :
jobs_dict[word] += 1
#Convert frequency distribution to dataframe, sort by frequency.
jobs_df = pd.DataFrame({'Word' : list(jobs_dict.keys()),
'Count' : list(jobs_dict.values())}).sort_values(by = 'Count', ascending = False, axis = 0).reset_index(drop = True)
jobs_df.head(10).transpose()
| 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | |
|---|---|---|---|---|---|---|---|---|---|---|
| Word | Data | Scientist | Analyst | Engineer | Python | Developer | Machine | Learning | Analytics | Tableau |
| Count | 1025 | 547 | 375 | 152 | 88 | 77 | 68 | 67 | 45 | 41 |
jobs_df.tail(10).transpose()
| 328 | 329 | 330 | 331 | 332 | 333 | 334 | 335 | 336 | 337 | |
|---|---|---|---|---|---|---|---|---|---|---|
| Word | Coder | GAN | Al | TS | SCI | Clearance | Required | Discovery | E-Commerce | Dashboard |
| Count | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
jobs_wc = WC(background_color = 'black',
max_words = 400,
collocations = False,
relative_scaling = 0,
width = 900,
height = 400)
jobs_wc.generate(jobs_string)
#plt.figure(figsize = (15, 11))
#plt.imshow(jobs_wc, interpolation = 'bilinear')
#plt.axis('off');
WC.to_file(jobs_wc, filename = "wordcloud.png");
#List of all companies.
companies = df['Company'].unique()
#Dates from first application to today.
date_index = pd.date_range(start = min(df['Date_Applied']), end = date.today())
#Create new data frame of 0s.
application_df = pd.DataFrame(index = date_index, columns = companies).fillna(0)
#Create cumulative count of job applications by company and date.
for i in range(len(df)) :
company = df.iloc[i, 1]
date_app = df.iloc[i, 7]
application_df.loc[date_app:, company] += 1
#Alphabetical
application_df = application_df.reindex(sorted(application_df.columns), axis=1)
#Total number of applications to each company.
cumulative_app_count = application_df.iloc[[-1]]
major_companies = cumulative_app_count.columns[(cumulative_app_count >= 6).iloc[0]]
minor_companies = cumulative_app_count.columns[(cumulative_app_count < 6).iloc[0]].tolist()
#Create a dummy company called 'Other', containing all companies with less than 6 applications.
major_df = pd.DataFrame.copy(application_df)
major_df['Other'] = major_df[minor_companies].sum(axis = 1)
major_df.drop(minor_companies, axis = 1, inplace = True)
#Shape check.
major_df.shape
#pd.set_option("display.max_rows", None, "display.max_columns", None)
#major_df.sum(axis=1)
(327, 36)
bold_colors = ['#f0f0f0', '#3cb44b', '#e6194b', '#fffac8', '#9a6324', '#e6beff', '#fabebe', '#000075',
'#ffe119', '#008080', '#4363d8', '#ffffff', '#bcf60c', '#46f0f0', '#911eb4', '#800000',
'#f032e6', '#808000', '#ffd8b1', '#f58231', '#aaffc3', '#ca1699', '#800080', '#000000']
apps = bcr.bar_chart_race(df = major_df,
filename = 'applications.mp4',
orientation = 'h',
sort = 'desc',
cmap = bold_colors,
filter_column_colors = True,
period_fmt = '%B %d, %Y',
period_label = {'x': 0.99,
'y': 0.26,
'ha': 'right',
'size': 14},
period_summary_func = lambda v, r: {'x': 0.99,
'y': 0.05,
's': f"{v.sum():,.0f} applications completed.\n\nCompanies with less than 6 applications are grouped in 'Other'.",
'ha': 'right',
'size': 9,
'weight': 'normal'},
steps_per_period = 20,
period_length = 250,
title = 'Total Number of Jobs Applied to by Company and Date')
#Video("applications.mp4")
df[df['City'] == "Remote"]
| Title | Company | Size | City | State_abbv | State | Date_Posted | Date_Applied | Rejection_Email | Viewed_Email | CoID | JobID | URL | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 195 | Data Scientist | Pilot Flying J | 10001+ | Remote | NaN | NaN | 2020-09-04 00:00:00 | 2020-09-06 | 2020-09-17 | NaT | NaN | 2.019452e+09 | https://www.linkedin.com/jobs/search/?currentJ... |
| 419 | Forward Deployed Data Scientist | Cresta | 11-50 | Remote | NaN | NaN | 2020-09-29 00:00:00 | 2020-09-30 | NaT | NaT | NaN | 2.155368e+09 | https://www.linkedin.com/jobs/search/?currentJ... |
| 420 | Data Scientist | White Ops | 51-200 | Remote | NaN | NaN | > 1 week | 2020-09-30 | 2020-10-15 | NaT | NaN | 2.023661e+09 | https://www.linkedin.com/jobs/search/?currentJ... |
| 433 | Intern - AI/Machine Learning | Seagate Technology | 10001+ | Remote | OR | Oregon | 2020-09-30 00:00:00 | 2020-10-01 | NaT | NaT | 201783 | 2.183372e+09 | https://www.linkedin.com/jobs/search/?currentJ... |
| 437 | Platform Data Engineer | Demyst | 51-200 | Remote | NaN | NaN | 2020-10-01 00:00:00 | 2020-10-01 | NaT | NaT | NaN | 2.152177e+09 | https://www.linkedin.com/jobs/search/?currentJ... |
| 522 | Data Scientist | Possible | 11-50 | Remote | NaN | NaN | 2020-10-14 00:00:00 | 2020-10-15 | NaT | 2020-10-16 | NaN | 2.183079e+09 | https://www.linkedin.com/jobs/search/?currentJ... |
| 588 | Data Scientist | Imperfect Foods | 1001-5000 | Remote | NaN | NaN | 2020-12-16 00:00:00 | 2020-12-23 | NaT | NaT | NaN | 2.306004e+09 | https://www.linkedin.com/jobs/search/?currentJ... |
| 639 | Data Analyst | Concentric | 11-50 | Remote | NaN | NaN | 2020-12-31 00:00:00 | 2020-12-31 | NaT | NaT | NaN | 2.363471e+09 | https://www.linkedin.com/jobs/search/?currentJ... |
| 662 | Data Engineer | Peterson Technology Partners | 201-500 | Remote | NaN | NaN | 2020-12-31 00:00:00 | 2021-01-02 | NaT | NaT | NaN | 2.332354e+09 | https://www.linkedin.com/jobs/search/?currentJ... |
| 711 | Quantitative Analyst | Silverpips | 2-10 | Remote | NaN | NaN | 2021-01-09 00:00:00 | 2021-01-09 | NaT | NaT | NaN | 2.374559e+09 | https://www.linkedin.com/jobs/search/?currentJ... |
| 744 | Python Developer/ML/Data Engineer | X-Team | NaN | Remote | NaN | NaN | >1 week | 2021-01-11 | 2021-01-14 | 2021-01-12 | NaN | NaN | https://www.linkedin.com/jobs/search/?currentJ... |
| 824 | Data Analyst / SQL Developer | Modis | 10001 | Remote | MI | Michigan | 2021-01-20 00:00:00 | 2021-01-21 | NaT | NaT | NaN | 2.367098e+09 | https://www.linkedin.com/jobs/search/?currentJ... |
| 838 | Data Scientist | Yelp | 1001-5000 | Remote | OR | Oregon | 2021-01-22 00:00:00 | 2021-01-23 | NaT | NaT | NaN | 2.377888e+09 | https://www.linkedin.com/jobs/search/?currentJ... |
| 938 | Junior Data Analyst | Abzooba | 51-200 | Remote | NaN | NaN | 2021-02-12 00:00:00 | 2021-02-14 | NaT | NaT | NaN | 2.420192e+09 | https://www.linkedin.com/jobs/search/?currentJ... |
| 941 | Data Scientist | Pixalate | 51-200 | Remote | NaN | NaN | 2021-02-12 00:00:00 | 2021-02-14 | NaT | NaT | NaN | 2.420264e+09 | https://www.linkedin.com/jobs/search/?currentJ... |
| 945 | Python Developer | Kavaliro | 501-1000 | Remote | NaN | NaN | 2021-02-12 00:00:00 | 2021-02-14 | NaT | 2021-02-16 | NaN | 2.419931e+09 | https://www.linkedin.com/jobs/search/?currentJ... |
| 956 | Data Engineer / Data Scientist | Advantage Tech | 51-200 | Remote | NaN | NaN | 2021-02-10 00:00:00 | 2021-02-15 | NaT | NaT | NaN | 2.416364e+09 | https://www.linkedin.com/jobs/search/?currentJ... |
| 1002 | Data Scientist - Job ID 12846 | infor | 10001+ | Remote | NaN | NaN | 2021-02-19 00:00:00 | 2021-02-22 | NaT | NaT | NaN | 2.416721e+09 | https://www.linkedin.com/jobs/search/?currentJ... |
| 1020 | Data Engineer - Remote | Diversant, LLC | 1001-5000 | Remote | NaN | NaN | 2021-02-24 00:00:00 | 2021-02-25 | NaT | NaT | NaN | NaN | https://www.linkedin.com/jobs/search/?currentJ... |
| 1030 | Data Scientist | Bernard Nickels & Associates | 51-200 | Remote | NaN | NaN | 2021-02-25 00:00:00 | 2021-02-26 | NaT | NaT | NaN | 2.438540e+09 | https://www.linkedin.com/jobs/search/?currentJ... |
| 1031 | Data Scientist | o9 Solutions, Inc. | 501-1000 | Remote | NaN | NaN | 2021-02-25 00:00:00 | 2021-02-26 | NaT | NaT | NaN | 2.438657e+09 | https://www.linkedin.com/jobs/search/?currentJ... |
| 1070 | Data Scientist | Curate Partners | 11-50 | Remote | NaN | NaN | 2021-03-05 00:00:00 | 2021-03-08 | NaT | NaT | NaN | 2.448626e+09 | https://www.linkedin.com/jobs/search/?currentJ... |
| 1114 | Tableau Developer | Wipro | 10001+ | Remote | NaN | NaN | >1 week | 2021-03-14 | 2021-03-17 | NaT | NaN | 2.430087e+09 | https://www.linkedin.com/jobs/search/?currentJ... |
| 1140 | Data Analyst | Insight Global | 1001-5000 | Remote | NaN | NaN | 2021-03-23 00:00:00 | 2021-03-24 | NaT | NaT | NaN | 2.473891e+09 | https://www.linkedin.com/jobs/search/?currentJ... |
| 1153 | Data Scientist | PolyScale | 2-10 | Remote | NaN | NaN | 2021-04-07 00:00:00 | 2021-04-07 | NaT | 2021-04-07 | NaN | 2.475137e+09 | https://www.linkedin.com/jobs/search/?currentJ... |
I retrieved their office locations and manually entered them.
#Manual City, State_abbv, and State entry.
dict_cs = {195: ("Knoxville", "TN", "Tennessee"),
419: ("San Francisco", "CA", "California"),
420: ("New York", "NY", "New York"),
433: ("New York", "NY", "New York"),
437: ("Portland", "OR", "Oregon"),
522: ("Seattle", "WA", "Washington"),
588: ("San Francisco", "CA", "California"),
639: ("Cambridge", "MA", "Massachusetts"),
662: ("Park Ridge", "IL", "Illinois"),
711: ("Wilmington", "DE", "Delaware"),
744: ("Melbourne", "AU", "Australia"),
824: ("Southfield", "MI", "Michigan"),
838: ("Portland", "OR", "Oregon"),
938: ("Milpitas", "CA", "California"),
941: ("Palo Alto", "CA", "California"),
945: ("Orlando", "FL", "Florida"),
956: ("Overland Park", "KS", "Kansas"),
1002: ("New York", "NY", "New York"),
1020: ("Richmond", "VA", "Virginia"),
1030: ("New York", "NY", "New York"),
1031: ("Dallas - Ft. Worth", "TX", "Texas"),
1070: ("Stoneham", "MA", "Massachusetts"),
1114: ("Washington", "DC", "District Of Columbia"),
1140: ("Raleigh", "NC", "North Carolina"),
1153: ("San Francisco", "CA", "California")}
#Loop to manually enter missing City, State_abbv, and State entry for Remote locations.
for idx in dict_cs :
temp_city = dict_cs[idx][0]
temp_state_abbv = dict_cs[idx][1]
temp_state = dict_cs[idx][2]
df.at[idx, 'City'] = temp_city
df.at[idx, 'State_abbv'] = temp_state_abbv
df.at[idx, 'State'] = temp_state
Several jobs I applied to were in cities not in the spreadsheet I downloaded.
#Select relevant columns.
df_loc = df[['City', 'State_abbv', 'State', 'Date_Applied']]
#Count the number of applications.
city_tally = df_loc.groupby(['City', 'State_abbv', 'State']).count().reset_index()
#Merge to get latitude, longitude for each city.
merged = pd.merge(city_tally,
lat_lon,
how = 'left',
left_on = ['City', 'State_abbv'],
right_on = ['city', 'state_id'])
#Several cities are not in the list.
merged[merged['city'].isna()]
| City | State_abbv | State | Date_Applied | city | state_id | lat | lng | |
|---|---|---|---|---|---|---|---|---|
| 3 | Andover | MA | Massachusetts | 1 | NaN | NaN | NaN | NaN |
| 11 | Battle Creek | MI | Michigan | 3 | NaN | NaN | NaN | NaN |
| 13 | Bedford | MA | Massachusetts | 4 | NaN | NaN | NaN | NaN |
| 30 | Bridgewater | NJ | New Jersey | 2 | NaN | NaN | NaN | NaN |
| 38 | Center Valley | PA | Pennsylvania | 1 | NaN | NaN | NaN | NaN |
| 55 | Concord | MA | Massachusetts | 3 | NaN | NaN | NaN | NaN |
| 58 | Dallas - Ft. Worth | TX | Texas | 24 | NaN | NaN | NaN | NaN |
| 69 | East Hanover | NJ | New Jersey | 2 | NaN | NaN | NaN | NaN |
| 100 | Hunt Valley | MD | Maryland | 1 | NaN | NaN | NaN | NaN |
| 104 | Irving | TX | Texas | 1 | NaN | NaN | NaN | NaN |
| 126 | Melbourne | AU | Australia | 1 | NaN | NaN | NaN | NaN |
| 153 | Parsippany | NJ | New Jersey | 1 | NaN | NaN | NaN | NaN |
| 154 | Patuxent River | MD | Maryland | 1 | NaN | NaN | NaN | NaN |
| 174 | Ridgefield | CT | Connecticut | 2 | NaN | NaN | NaN | NaN |
| 184 | Sandy Hook | CT | Connecticut | 1 | NaN | NaN | NaN | NaN |
| 202 | Stoneham | MA | Massachusetts | 1 | NaN | NaN | NaN | NaN |
| 204 | Sydney | AU | Australia | 1 | NaN | NaN | NaN | NaN |
| 208 | Toronto | CN | Canada | 1 | NaN | NaN | NaN | NaN |
| 211 | Utica Rome | NY | New York | 1 | NaN | NaN | NaN | NaN |
| 219 | Wellesley | MA | Massachusetts | 6 | NaN | NaN | NaN | NaN |
| 221 | West Shokan | NY | New York | 2 | NaN | NaN | NaN | NaN |
| 222 | Westchester | CA | California | 1 | NaN | NaN | NaN | NaN |
| 223 | Weston | MA | Massachusetts | 1 | NaN | NaN | NaN | NaN |
| 227 | Woodbridge | NJ | New Jersey | 1 | NaN | NaN | NaN | NaN |
Two were from Australia and one from Canada. For the others, I retrieved values from Google. An approximate average value was chosen for Dallas-Ft. Worth.
#Huxley job from Sydney, Australia was removed.
#Remote X-Team job from Melbourne, Australia was removed.
merged = merged[merged.State_abbv != 'AU']
#Prodigy Academy job from Toronto, Canada was removed.
merged = merged[merged.State_abbv != 'CN']
#Manual latitude and longitude entry.
dict_loc = {3: (42.3212, -85.1797), #Andover
11: (42.3212, -85.1797), #Battle Creek
13: (42.4906, -71.2760), #Bedford
30: (40.5940, -74.6049), #Bridgewater
37: (40.5294, -75.3937), #Center Valley
54: (42.4604, -71.3489), #Concord
58: (32.7598, -97.0646), #Dallas - Ft Worth
69: (40.8201, -74.3647), #East Hanover
100: (39.4900, -76.6585), #Hunt Valley
104: (32.8140, -96.9489), #Irving
153: (40.8653, -74.4174), #Parsippany
154: (38.2773, -76.4229), #Patuxent River
174: (41.2841, -73.4975), #Ridgefield
184: (41.4218, -73.2833), #Sandy Hook
202: (42.4802, -71.1000), #Stoneham
211: (43.1615, -75.3405), #Utica Rome
219: (42.2968, -71.2924), #Wellesley
221: (41.9671, -74.2871), #West Shokan
222: (33.9626, 118.3988), #Westchester
223: (42.3668, -71.3031), #Weston
227: (40.5576, -74.2846)} #Woodbridge
#Loop to manually enter missing latitude and longitudes.
for idx in dict_loc :
lat = dict_loc[idx][0]
lon = dict_loc[idx][1]
merged.at[idx, 'lat'] = lat
merged.at[idx, 'lng'] = lon
#Rename columns and drop redundant columns
merged = merged.rename(columns = {'Date_Applied' : 'Count',
'lat': 'Latitude',
'lng': 'Longitude'}).drop(['city', 'state_id'], axis = 1)
#Group job applications by State, count them, reset the index, drop the date, and rename City to Count.
state_tally = merged[['Count', 'State_abbv', 'State']].groupby(['State_abbv', 'State']).sum().sort_values(by = 'Count', ascending = False, axis = 0).reset_index()
#Singular or Plural.
def f(row) :
if row['Count'] == 1 :
string_val = ' application in '
else :
string_val = ' applications in '
return string_val
#Number of applications per city as text.
merged['Text'] = merged['Count'].astype(str) + merged.apply(f, axis = 1) + merged['City'] + ', ' + merged['State_abbv'] + '.'
#Number of applications per state as text.
state_tally['Text'] = state_tally['Count'].astype(str) + state_tally.apply(f, axis = 1) + state_tally['State'] + '.'
#Color in states by number of applications.
state_map_data = go.Choropleth(locations = state_tally['State_abbv'],
z = state_tally['Count'],
text = state_tally['Text'],
hoverinfo = 'text',
locationmode = 'USA-states',
colorbar = {'title': "<b>Applications</b>",
'thicknessmode': "pixels",
'thickness': 70,
'lenmode': "pixels",
'len': 400,
'titlefont': {'size': 16},
'tickfont': {'size': 12},
'tickvals': [0, 50, 100, 150, 200, 250]},
colorscale = 'Blues')
#Plot cities, size corresponds to number of applications.
city_map_data = go.Scattergeo(lon = merged['Longitude'],
lat = merged['Latitude'],
text = merged['Text'],
hoverinfo = 'text',
locationmode = 'USA-states',
marker = {'size': 10*np.sqrt(merged['Count']),
'color': 'Darkgreen'})
layout = go.Layout(
margin = go.layout.Margin(l = 0,
r = 0,
b = 0))
fig = go.Figure(data = [state_map_data,
city_map_data],
layout = layout)
fig.update_layout(title = {'text': 'Where I Have Applied (Hover and Zoom)',
'font': {'size': 30}},
geo_scope = 'usa',
width = 900,
height = 500)
plotly.offline.plot(fig, filename = "usa_jobs.html");
#Sort by number of applications for each state.
waffle_data = state_tally[['State', 'Count']]
#Add a row for states with less than 3 applications. 'Other' also includes Australia and Canada.
waffle_data = waffle_data.append(pd.DataFrame({'State': ['Other'], 'Count': [3]})).reset_index(drop = True)
to_drop = []
#Add applications from states with less than 8 to 'Other'.
for i in waffle_data.index :
if waffle_data.iloc[i]['Count'] < 8:
temp = waffle_data.iloc[i]['Count']
waffle_data.at[36, 'Count'] += temp
to_drop.append(i)
#Remove states with less than 3. Change orientation of data.
waffle_data = waffle_data.drop(labels = to_drop, axis = 0).reset_index(drop = True).set_index('State').transpose()
print(waffle_data.sum(axis = 1))
waffle_data
Count 1162 dtype: int64
| State | California | New York | Texas | Illinois | North Carolina | Washington | Massachusetts | Georgia | Virginia | Colorado | Pennsylvania | Maryland | District Of Columbia | New Jersey | Missouri | Florida | Oregon | Ohio | Minnesota | Michigan | Tennessee | Connecticut | Arizona | Other |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Count | 263 | 122 | 92 | 74 | 69 | 66 | 58 | 55 | 44 | 37 | 34 | 32 | 31 | 24 | 22 | 17 | 16 | 15 | 15 | 14 | 10 | 8 | 8 | 36 |
fig = plt.figure(FigureClass = Waffle,
rows = 14,
values = waffle_data.values.tolist()[0],
labels = waffle_data.columns.tolist(),
figsize = (17, 7),
colors = bold_colors[0:24],
legend = {'loc': 'upper left',
'ncol': 2,
'fontsize': 12})
fig.savefig('waffle.png');
merged
| City | State_abbv | State | Count | Latitude | Longitude | Text | |
|---|---|---|---|---|---|---|---|
| 0 | Allentown | PA | Pennsylvania | 1 | 40.5961 | -75.4756 | 1 application in Allentown, PA. |
| 1 | Alpharetta | GA | Georgia | 4 | 34.0704 | -84.2739 | 4 applications in Alpharetta, GA. |
| 2 | Anaheim | CA | California | 1 | 33.8390 | -117.8572 | 1 application in Anaheim, CA. |
| 3 | Andover | MA | Massachusetts | 1 | 42.3212 | -85.1797 | 1 application in Andover, MA. |
| 4 | Arlington | VA | Virginia | 8 | 38.8786 | -77.1011 | 8 applications in Arlington, VA. |
| 5 | Ashburn | VA | Virginia | 2 | 39.0300 | -77.4711 | 2 applications in Ashburn, VA. |
| 6 | Asheville | NC | North Carolina | 1 | 35.5704 | -82.5536 | 1 application in Asheville, NC. |
| 7 | Atlanta | GA | Georgia | 46 | 33.7627 | -84.4224 | 46 applications in Atlanta, GA. |
| 8 | Auburn Hills | MI | Michigan | 1 | 42.6735 | -83.2447 | 1 application in Auburn Hills, MI. |
| 9 | Austin | TX | Texas | 32 | 30.3004 | -97.7522 | 32 applications in Austin, TX. |
| 10 | Baltimore | MD | Maryland | 12 | 39.3051 | -76.6144 | 12 applications in Baltimore, MD. |
| 11 | Battle Creek | MI | Michigan | 3 | 42.3212 | -85.1797 | 3 applications in Battle Creek , MI. |
| 12 | Beaverton | OR | Oregon | 3 | 45.4779 | -122.8168 | 3 applications in Beaverton, OR. |
| 13 | Bedford | MA | Massachusetts | 4 | 42.4906 | -71.2760 | 4 applications in Bedford, MA. |
| 14 | Bellevue | WA | Washington | 9 | 47.5951 | -122.1535 | 9 applications in Bellevue, WA. |
| 15 | Bend | OR | Oregon | 1 | 44.0562 | -121.3087 | 1 application in Bend, OR. |
| 16 | Bentonville | AR | Arkansas | 1 | 36.3546 | -94.2306 | 1 application in Bentonville, AR. |
| 17 | Berkeley | CA | California | 1 | 37.8723 | -122.2760 | 1 application in Berkeley, CA. |
| 18 | Berkeley | IL | Illinois | 1 | 41.8891 | -87.9114 | 1 application in Berkeley, IL. |
| 19 | Bethesda | MD | Maryland | 1 | 38.9866 | -77.1188 | 1 application in Bethesda, MD. |
| 20 | Beverly Hills | CA | California | 1 | 34.0786 | -118.4021 | 1 application in Beverly Hills, CA. |
| 21 | Birmingham | AL | Alabama | 1 | 33.5277 | -86.7987 | 1 application in Birmingham, AL. |
| 22 | Bloomington | MN | Minnesota | 2 | 44.8306 | -93.3151 | 2 applications in Bloomington, MN. |
| 23 | Boca Raton | FL | Florida | 1 | 26.3752 | -80.1080 | 1 application in Boca Raton, FL. |
| 24 | Boise | ID | Idaho | 1 | 43.6007 | -116.2312 | 1 application in Boise, ID. |
| 25 | Boston | MA | Massachusetts | 33 | 42.3188 | -71.0846 | 33 applications in Boston, MA. |
| 26 | Bothell | WA | Washington | 5 | 47.7735 | -122.2044 | 5 applications in Bothell, WA. |
| 27 | Boulder | CO | Colorado | 5 | 40.0249 | -105.2523 | 5 applications in Boulder, CO. |
| 28 | Bound Brook | NJ | New Jersey | 1 | 40.5676 | -74.5383 | 1 application in Bound Brook, NJ. |
| 29 | Brentwood | CA | California | 1 | 37.9355 | -121.7191 | 1 application in Brentwood, CA. |
| 30 | Bridgewater | NJ | New Jersey | 2 | 40.5940 | -74.6049 | 2 applications in Bridgewater, NJ. |
| 31 | Buckhead | GA | Georgia | 1 | 33.5685 | -83.3623 | 1 application in Buckhead, GA. |
| 32 | Calabasas | CA | California | 1 | 34.1375 | -118.6689 | 1 application in Calabasas, CA. |
| 33 | Cambridge | MA | Massachusetts | 4 | 42.3759 | -71.1185 | 4 applications in Cambridge, MA. |
| 34 | Campbell | CA | California | 1 | 37.2802 | -121.9538 | 1 application in Campbell, CA. |
| 35 | Carmel | IN | Indiana | 2 | 39.9650 | -86.1460 | 2 applications in Carmel, IN. |
| 36 | Carrollton | TX | Texas | 1 | 32.9890 | -96.8999 | 1 application in Carrollton, TX. |
| 37 | Cary | NC | North Carolina | 2 | 40.5294 | -75.3937 | 2 applications in Cary, NC. |
| 38 | Center Valley | PA | Pennsylvania | 1 | NaN | NaN | 1 application in Center Valley, PA. |
| 39 | Champaign | IL | Illinois | 1 | 40.1144 | -88.2735 | 1 application in Champaign, IL. |
| 40 | Chandler | AZ | Arizona | 1 | 33.2826 | -111.8516 | 1 application in Chandler, AZ. |
| 41 | Charleston | SC | South Carolina | 1 | 32.8153 | -79.9628 | 1 application in Charleston, SC. |
| 42 | Charlotte | NC | North Carolina | 23 | 35.2080 | -80.8304 | 23 applications in Charlotte, NC. |
| 43 | Charlottesville | VA | Virginia | 1 | 38.0375 | -78.4855 | 1 application in Charlottesville, VA. |
| 44 | Chesterfield | MO | Missouri | 1 | 38.6588 | -90.5804 | 1 application in Chesterfield, MO. |
| 45 | Chevy Chase | MD | Maryland | 1 | 38.9943 | -77.0737 | 1 application in Chevy Chase, MD. |
| 46 | Chicago | IL | Illinois | 49 | 41.8373 | -87.6862 | 49 applications in Chicago, IL. |
| 47 | Cincinnati | OH | Ohio | 7 | 39.1413 | -84.5061 | 7 applications in Cincinnati, OH. |
| 48 | Clearwater | FL | Florida | 1 | 27.9789 | -82.7622 | 1 application in Clearwater, FL. |
| 49 | Cleveland | OH | Ohio | 1 | 41.4767 | -81.6804 | 1 application in Cleveland, OH. |
| 50 | Colorado Springs | CO | Colorado | 1 | 38.8674 | -104.7606 | 1 application in Colorado Springs, CO. |
| 51 | Columbia | SC | South Carolina | 1 | 34.0376 | -80.9037 | 1 application in Columbia, SC. |
| 52 | Columbus | IN | Indiana | 1 | 39.2093 | -85.9183 | 1 application in Columbus, IN. |
| 53 | Columbus | OH | Ohio | 4 | 39.9862 | -82.9850 | 4 applications in Columbus, OH. |
| 54 | Concord | CA | California | 1 | 42.4604 | -71.3489 | 1 application in Concord, CA. |
| 55 | Concord | MA | Massachusetts | 3 | NaN | NaN | 3 applications in Concord, MA. |
| 56 | Coraopolis | PA | Pennsylvania | 5 | 40.5148 | -80.1627 | 5 applications in Coraopolis, PA. |
| 57 | Cupertino | CA | California | 2 | 37.3168 | -122.0465 | 2 applications in Cupertino, CA. |
| 58 | Dallas - Ft. Worth | TX | Texas | 24 | 32.7598 | -97.0646 | 24 applications in Dallas - Ft. Worth, TX. |
| 59 | Danbury | CT | Connecticut | 1 | 41.4016 | -73.4710 | 1 application in Danbury, CT. |
| 60 | Dearborn | MI | Michigan | 1 | 42.3127 | -83.2129 | 1 application in Dearborn, MI. |
| 61 | Deerfield | IL | Illinois | 1 | 42.1654 | -87.8516 | 1 application in Deerfield, IL. |
| 62 | Deerfield Beach | FL | Florida | 1 | 26.3050 | -80.1277 | 1 application in Deerfield Beach, FL. |
| 63 | Denver | CO | Colorado | 22 | 39.7621 | -104.8759 | 22 applications in Denver, CO. |
| 64 | Des Plaines | IL | Illinois | 2 | 42.0345 | -87.9009 | 2 applications in Des Plaines, IL. |
| 65 | Detroit | MI | Michigan | 2 | 42.3834 | -83.1024 | 2 applications in Detroit, MI. |
| 66 | Devens | MA | Massachusetts | 1 | 42.5438 | -71.6147 | 1 application in Devens, MA. |
| 67 | Dublin | OH | Ohio | 2 | 40.1112 | -83.1454 | 2 applications in Dublin, OH. |
| 68 | Durham | NC | North Carolina | 7 | 35.9794 | -78.9031 | 7 applications in Durham, NC. |
| 69 | East Hanover | NJ | New Jersey | 2 | 40.8201 | -74.3647 | 2 applications in East Hanover, NJ. |
| 70 | El Segundo | CA | California | 2 | 33.9169 | -118.4021 | 2 applications in El Segundo, CA. |
| 71 | Elk Grove Village | IL | Illinois | 3 | 42.0063 | -87.9921 | 3 applications in Elk Grove Village, IL. |
| 72 | Englewood | CO | Colorado | 2 | 39.6468 | -104.9942 | 2 applications in Englewood, CO. |
| 73 | Erlanger | KY | Kentucky | 2 | 39.0109 | -84.5864 | 2 applications in Erlanger, KY. |
| 74 | Evanston | IL | Illinois | 2 | 42.0463 | -87.6942 | 2 applications in Evanston, IL. |
| 75 | Everett | WA | Washington | 1 | 47.9525 | -122.1669 | 1 application in Everett, WA. |
| 76 | Fairview Heights | IL | Illinois | 1 | 38.5974 | -90.0053 | 1 application in Fairview Heights, IL. |
| 77 | Fort Lauderdale | FL | Florida | 2 | 26.1412 | -80.1464 | 2 applications in Fort Lauderdale, FL. |
| 78 | Fort Lee | NJ | New Jersey | 1 | 40.8509 | -73.9712 | 1 application in Fort Lee, NJ. |
| 79 | Fort Walton Beach | FL | Florida | 1 | 30.4249 | -86.6194 | 1 application in Fort Walton Beach, FL. |
| 80 | Framingham | MA | Massachusetts | 1 | 42.3085 | -71.4368 | 1 application in Framingham, MA. |
| 81 | Franklin Lakes | NJ | New Jersey | 3 | 41.0086 | -74.2083 | 3 applications in Franklin Lakes, NJ. |
| 82 | Frederick | MD | Maryland | 1 | 39.4336 | -77.4141 | 1 application in Frederick, MD. |
| 83 | Fremont | CA | California | 2 | 37.5265 | -121.9852 | 2 applications in Fremont, CA. |
| 84 | Frisco | TX | Texas | 1 | 33.1555 | -96.8215 | 1 application in Frisco, TX. |
| 85 | Gaithersburg | MD | Maryland | 1 | 39.1346 | -77.2132 | 1 application in Gaithersburg, MD. |
| 86 | Gallatin | TN | Tennessee | 2 | 36.3782 | -86.4696 | 2 applications in Gallatin, TN. |
| 87 | Germantown | MD | Maryland | 6 | 39.1755 | -77.2643 | 6 applications in Germantown, MD. |
| 88 | Glendale | CA | California | 1 | 34.1818 | -118.2468 | 1 application in Glendale, CA. |
| 89 | Golden | CO | Colorado | 1 | 39.7406 | -105.2118 | 1 application in Golden, CO. |
| 90 | Grand Blanc | MI | Michigan | 2 | 42.9258 | -83.6181 | 2 applications in Grand Blanc, MI. |
| 91 | Green Bay | WI | Wisconsin | 1 | 44.5150 | -87.9896 | 1 application in Green Bay, WI. |
| 92 | Greensboro | NC | North Carolina | 1 | 36.0956 | -79.8269 | 1 application in Greensboro, NC. |
| 93 | Greenville | SC | South Carolina | 1 | 34.8353 | -82.3647 | 1 application in Greenville, SC. |
| 94 | Greenwood Village | CO | Colorado | 3 | 39.6152 | -104.9130 | 3 applications in Greenwood Village, CO. |
| 95 | Groveport | OH | Ohio | 1 | 39.8585 | -82.8978 | 1 application in Groveport, OH. |
| 96 | Hartford | CT | Connecticut | 3 | 41.7661 | -72.6834 | 3 applications in Hartford, CT. |
| 97 | Herndon | VA | Virginia | 2 | 38.9699 | -77.3867 | 2 applications in Herndon, VA. |
| 98 | Hillsboro | OR | Oregon | 3 | 45.5272 | -122.9361 | 3 applications in Hillsboro, OR. |
| 99 | Houston | TX | Texas | 16 | 29.7863 | -95.3889 | 16 applications in Houston, TX. |
| 100 | Hunt Valley | MD | Maryland | 1 | 39.4900 | -76.6585 | 1 application in Hunt Valley, MD. |
| 101 | Indianapolis | IN | Indiana | 4 | 39.7771 | -86.1458 | 4 applications in Indianapolis, IN. |
| 102 | Irvine | CA | California | 3 | 33.6772 | -117.7738 | 3 applications in Irvine, CA. |
| 103 | Irving | TX | Texas | 1 | 32.8584 | -96.9702 | 1 application in Irving, TX. |
| 104 | Irving | TX | Texas | 1 | 32.8140 | -96.9489 | 1 application in Irving , TX. |
| 105 | Jacksonville | FL | Florida | 1 | 30.3322 | -81.6749 | 1 application in Jacksonville, FL. |
| 106 | Jersey City | NJ | New Jersey | 3 | 40.7161 | -74.0682 | 3 applications in Jersey City, NJ. |
| 107 | Kendall Park | NJ | New Jersey | 1 | 40.4138 | -74.5626 | 1 application in Kendall Park, NJ. |
| 108 | King of Prussia | PA | Pennsylvania | 4 | 40.0963 | -75.3820 | 4 applications in King of Prussia, PA. |
| 109 | Knoxville | TN | Tennessee | 1 | 35.9692 | -83.9496 | 1 application in Knoxville, TN. |
| 110 | Lake Forest | IL | Illinois | 1 | 42.2380 | -87.8596 | 1 application in Lake Forest, IL. |
| 111 | Lavallette | NJ | New Jersey | 2 | 39.9699 | -74.0722 | 2 applications in Lavallette, NJ. |
| 112 | Lewisville | TX | Texas | 1 | 33.0452 | -96.9823 | 1 application in Lewisville, TX. |
| 113 | Lexington | KY | Kentucky | 1 | 38.0423 | -84.4587 | 1 application in Lexington, KY. |
| 114 | Livermore | CA | California | 1 | 37.6862 | -121.7608 | 1 application in Livermore, CA. |
| 115 | Livonia | MI | Michigan | 1 | 42.3972 | -83.3733 | 1 application in Livonia, MI. |
| 116 | Longmont | CO | Colorado | 2 | 40.1686 | -105.1005 | 2 applications in Longmont, CO. |
| 117 | Los Angeles | CA | California | 21 | 34.1139 | -118.4068 | 21 applications in Los Angeles, CA. |
| 118 | Louisville | KY | Kentucky | 1 | 38.1663 | -85.6485 | 1 application in Louisville, KY. |
| 119 | Loveland | CO | Colorado | 1 | 40.4166 | -105.0623 | 1 application in Loveland, CO. |
| 120 | Maplewood | MN | Minnesota | 1 | 44.9842 | -93.0247 | 1 application in Maplewood, MN. |
| 121 | Marietta | GA | Georgia | 1 | 33.9533 | -84.5422 | 1 application in Marietta, GA. |
| 122 | Marina del Rey | CA | California | 1 | 33.9765 | -118.4486 | 1 application in Marina del Rey, CA. |
| 123 | Marlborough | MA | Massachusetts | 1 | 42.3494 | -71.5468 | 1 application in Marlborough, MA. |
| 124 | Maryland Heights | MO | Missouri | 1 | 38.7189 | -90.4749 | 1 application in Maryland Heights, MO. |
| 125 | McLean | VA | Virginia | 16 | 38.9436 | -77.1943 | 16 applications in McLean, VA. |
| 127 | Menlo Park | CA | California | 13 | 37.4686 | -122.1672 | 13 applications in Menlo Park, CA. |
| 128 | Merrifield | VA | Virginia | 1 | 38.8731 | -77.2426 | 1 application in Merrifield, VA. |
| 129 | Miami | FL | Florida | 2 | 25.7839 | -80.2102 | 2 applications in Miami, FL. |
| 130 | Milpitas | CA | California | 1 | 37.4339 | -121.8921 | 1 application in Milpitas, CA. |
| 131 | Minneapolis | MN | Minnesota | 8 | 44.9635 | -93.2678 | 8 applications in Minneapolis, MN. |
| 132 | Mobile | AL | Alabama | 1 | 30.6783 | -88.1162 | 1 application in Mobile, AL. |
| 133 | Mooresville | NC | North Carolina | 1 | 35.5849 | -80.8265 | 1 application in Mooresville, NC. |
| 134 | Morgantown | WV | West Virginia | 1 | 39.6380 | -79.9468 | 1 application in Morgantown, WV. |
| 135 | Morristown | NJ | New Jersey | 1 | 40.7966 | -74.4772 | 1 application in Morristown, NJ. |
| 136 | Morrisville | NC | North Carolina | 4 | 35.8367 | -78.8348 | 4 applications in Morrisville, NC. |
| 137 | Mount Pleasant | SC | South Carolina | 1 | 32.8538 | -79.8204 | 1 application in Mount Pleasant, SC. |
| 138 | Mountain View | CA | California | 18 | 37.4000 | -122.0796 | 18 applications in Mountain View, CA. |
| 139 | Nashville | TN | Tennessee | 7 | 36.1715 | -86.7843 | 7 applications in Nashville, TN. |
| 140 | New Brunswick | NJ | New Jersey | 2 | 40.4870 | -74.4450 | 2 applications in New Brunswick, NJ. |
| 141 | New York | NY | New York | 118 | 40.6943 | -73.9249 | 118 applications in New York, NY. |
| 142 | Newark | CA | California | 1 | 37.5204 | -122.0312 | 1 application in Newark, CA. |
| 143 | Norcross | GA | Georgia | 1 | 33.9379 | -84.2065 | 1 application in Norcross, GA. |
| 144 | Normal | IL | Illinois | 1 | 40.5218 | -88.9881 | 1 application in Normal, IL. |
| 145 | Northbrook | IL | Illinois | 1 | 42.1292 | -87.8353 | 1 application in Northbrook, IL. |
| 146 | Oak Brook | IL | Illinois | 4 | 41.8372 | -87.9513 | 4 applications in Oak Brook, IL. |
| 147 | Oakland | CA | California | 9 | 37.7903 | -122.2165 | 9 applications in Oakland, CA. |
| 148 | Orange | CA | California | 1 | 33.8038 | -117.8218 | 1 application in Orange, CA. |
| 149 | Orlando | FL | Florida | 1 | 28.4772 | -81.3369 | 1 application in Orlando, FL. |
| 150 | Overland Park | KS | Kansas | 2 | 38.8870 | -94.6870 | 2 applications in Overland Park, KS. |
| 151 | Palo Alto | CA | California | 12 | 37.3913 | -122.1468 | 12 applications in Palo Alto, CA. |
| 152 | Park Ridge | IL | Illinois | 1 | 42.0125 | -87.8436 | 1 application in Park Ridge, IL. |
| 153 | Parsippany | NJ | New Jersey | 1 | 40.8653 | -74.4174 | 1 application in Parsippany, NJ. |
| 154 | Patuxent River | MD | Maryland | 1 | 38.2773 | -76.4229 | 1 application in Patuxent River, MD. |
| 155 | Peoria | IL | Illinois | 3 | 40.7520 | -89.6153 | 3 applications in Peoria, IL. |
| 156 | Philadelphia | PA | Pennsylvania | 8 | 40.0077 | -75.1339 | 8 applications in Philadelphia, PA. |
| 157 | Phoenix | AZ | Arizona | 4 | 33.5722 | -112.0891 | 4 applications in Phoenix, AZ. |
| 158 | Pittsburgh | PA | Pennsylvania | 15 | 40.4396 | -79.9762 | 15 applications in Pittsburgh, PA. |
| 159 | Plano | TX | Texas | 12 | 33.0502 | -96.7487 | 12 applications in Plano, TX. |
| 160 | Pleasanton | CA | California | 2 | 37.6663 | -121.8805 | 2 applications in Pleasanton, CA. |
| 161 | Plymouth | MN | Minnesota | 2 | 45.0225 | -93.4618 | 2 applications in Plymouth, MN. |
| 162 | Portland | OR | Oregon | 9 | 45.5372 | -122.6500 | 9 applications in Portland, OR. |
| 163 | Portsmouth | NH | New Hampshire | 1 | 43.0580 | -70.7826 | 1 application in Portsmouth, NH. |
| 164 | Princeton | NJ | New Jersey | 2 | 40.3563 | -74.6693 | 2 applications in Princeton, NJ. |
| 165 | Providence | RI | Rhode Island | 1 | 41.8230 | -71.4187 | 1 application in Providence, RI. |
| 166 | Quincy | MA | Massachusetts | 1 | 42.2516 | -71.0183 | 1 application in Quincy, MA. |
| 167 | Raleigh | NC | North Carolina | 29 | 35.8325 | -78.6435 | 29 applications in Raleigh, NC. |
| 168 | Redmond | WA | Washington | 4 | 47.6762 | -122.1169 | 4 applications in Redmond, WA. |
| 169 | Redwood City | CA | California | 6 | 37.5026 | -122.2252 | 6 applications in Redwood City, CA. |
| 170 | Reston | VA | Virginia | 1 | 38.9497 | -77.3461 | 1 application in Reston, VA. |
| 171 | Richardson | TX | Texas | 2 | 32.9717 | -96.7093 | 2 applications in Richardson, TX. |
| 172 | Richfield | MN | Minnesota | 2 | 44.8762 | -93.2833 | 2 applications in Richfield, MN. |
| 173 | Richmond | VA | Virginia | 9 | 37.5295 | -77.4756 | 9 applications in Richmond, VA. |
| 174 | Ridgefield | CT | Connecticut | 2 | 41.2841 | -73.4975 | 2 applications in Ridgefield, CT. |
| 175 | Rockville | MD | Maryland | 7 | 39.0834 | -77.1552 | 7 applications in Rockville, MD. |
| 176 | Roseland | NJ | New Jersey | 1 | 40.8208 | -74.3085 | 1 application in Roseland, NJ. |
| 177 | Sacramento | CA | California | 1 | 38.5667 | -121.4683 | 1 application in Sacramento, CA. |
| 178 | Salt Lake City | UT | Utah | 2 | 40.7777 | -111.9306 | 2 applications in Salt Lake City, UT. |
| 179 | San Bruno | CA | California | 2 | 37.6254 | -122.4313 | 2 applications in San Bruno, CA. |
| 180 | San Diego | CA | California | 8 | 32.8312 | -117.1225 | 8 applications in San Diego, CA. |
| 181 | San Francisco | CA | California | 82 | 37.7562 | -122.4430 | 82 applications in San Francisco, CA. |
| 182 | San Jose | CA | California | 26 | 37.3019 | -121.8486 | 26 applications in San Jose, CA. |
| 183 | San Mateo | CA | California | 6 | 37.5521 | -122.3122 | 6 applications in San Mateo, CA. |
| 184 | Sandy Hook | CT | Connecticut | 1 | 41.4218 | -73.2833 | 1 application in Sandy Hook, CT. |
| 185 | Santa Clara | CA | California | 5 | 37.3645 | -121.9680 | 5 applications in Santa Clara, CA. |
| 186 | Santa Cruz | CA | California | 1 | 36.9789 | -122.0346 | 1 application in Santa Cruz, CA. |
| 187 | Santa Fe Springs | CA | California | 1 | 33.9330 | -118.0625 | 1 application in Santa Fe Springs, CA. |
| 188 | Santa Maria | CA | California | 1 | 34.9334 | -120.4432 | 1 application in Santa Maria, CA. |
| 189 | Santa Monica | CA | California | 7 | 34.0232 | -118.4813 | 7 applications in Santa Monica, CA. |
| 190 | Schaumburg | IL | Illinois | 1 | 42.0307 | -88.0838 | 1 application in Schaumburg, IL. |
| 191 | Scottsdale | AZ | Arizona | 2 | 33.6872 | -111.8651 | 2 applications in Scottsdale, AZ. |
| 192 | Seattle | WA | Washington | 47 | 47.6211 | -122.3244 | 47 applications in Seattle, WA. |
| 193 | Smyrna | GA | Georgia | 2 | 33.8633 | -84.5168 | 2 applications in Smyrna, GA. |
| 194 | Somerset | NJ | New Jersey | 1 | 40.5083 | -74.5010 | 1 application in Somerset, NJ. |
| 195 | Southfield | MI | Michigan | 3 | 42.4765 | -83.2605 | 3 applications in Southfield, MI. |
| 196 | Spring | TX | Texas | 1 | 30.0613 | -95.3830 | 1 application in Spring, TX. |
| 197 | Springfield | MO | Missouri | 1 | 37.1943 | -93.2916 | 1 application in Springfield, MO. |
| 198 | Springfield | VA | Virginia | 3 | 38.7809 | -77.1839 | 3 applications in Springfield, VA. |
| 199 | St. Louis | MO | Missouri | 19 | 38.6358 | -90.2451 | 19 applications in St. Louis, MO. |
| 200 | St. Petersburg | FL | Florida | 2 | 27.7931 | -82.6652 | 2 applications in St. Petersburg, FL. |
| 201 | Stamford | CT | Connecticut | 1 | 41.1035 | -73.5583 | 1 application in Stamford, CT. |
| 202 | Stoneham | MA | Massachusetts | 1 | 42.4802 | -71.1000 | 1 application in Stoneham, MA. |
| 203 | Sunnyvale | CA | California | 15 | 37.3836 | -122.0256 | 15 applications in Sunnyvale, CA. |
| 205 | Tampa | FL | Florida | 4 | 27.9942 | -82.4451 | 4 applications in Tampa, FL. |
| 206 | Tempe | AZ | Arizona | 1 | 33.3881 | -111.9318 | 1 application in Tempe, AZ. |
| 207 | Timonium | MD | Maryland | 1 | 39.4463 | -76.6083 | 1 application in Timonium, MD. |
| 209 | Troy | MI | Michigan | 1 | 42.5818 | -83.1457 | 1 application in Troy, MI. |
| 210 | Tustin | CA | California | 1 | 33.7309 | -117.8106 | 1 application in Tustin, CA. |
| 211 | Utica Rome | NY | New York | 1 | 43.1615 | -75.3405 | 1 application in Utica Rome, NY. |
| 212 | Vernon Hills | IL | Illinois | 1 | 42.2340 | -87.9608 | 1 application in Vernon Hills, IL. |
| 213 | Vero Beach | FL | Florida | 1 | 27.6463 | -80.3930 | 1 application in Vero Beach, FL. |
| 214 | Virginia Beach | VA | Virginia | 1 | 36.7335 | -76.0435 | 1 application in Virginia Beach, VA. |
| 215 | Waltham | MA | Massachusetts | 1 | 42.3889 | -71.2423 | 1 application in Waltham, MA. |
| 216 | Washington | DC | District Of Columbia | 31 | 38.9047 | -77.0163 | 31 applications in Washington, DC. |
| 217 | Waukegan | IL | Illinois | 1 | 42.3697 | -87.8716 | 1 application in Waukegan, IL. |
| 218 | Wauwatosa | WI | Wisconsin | 1 | 43.0615 | -88.0347 | 1 application in Wauwatosa, WI. |
| 219 | Wellesley | MA | Massachusetts | 6 | 42.2968 | -71.2924 | 6 applications in Wellesley, MA. |
| 220 | West Menlo Park | CA | California | 2 | 37.4338 | -122.2034 | 2 applications in West Menlo Park, CA. |
| 221 | West Shokan | NY | New York | 2 | 41.9671 | -74.2871 | 2 applications in West Shokan, NY. |
| 222 | Westchester | CA | California | 1 | 33.9626 | 118.3988 | 1 application in Westchester, CA. |
| 223 | Weston | MA | Massachusetts | 1 | 42.3668 | -71.3031 | 1 application in Weston, MA. |
| 224 | White Plains | NY | New York | 1 | 41.0220 | -73.7549 | 1 application in White Plains, NY. |
| 225 | Wilmington | DE | Delaware | 3 | 39.7415 | -75.5413 | 3 applications in Wilmington, DE. |
| 226 | Winston-Salem | NC | North Carolina | 1 | 36.1029 | -80.2611 | 1 application in Winston-Salem, NC. |
| 227 | Woodbridge | NJ | New Jersey | 1 | 40.5576 | -74.2846 | 1 application in Woodbridge, NJ. |
| 228 | Woonsocket | RI | Rhode Island | 2 | 42.0010 | -71.4993 | 2 applications in Woonsocket, RI. |
| 229 | Yuba City | CA | California | 1 | 39.1357 | -121.6381 | 1 application in Yuba City, CA. |
state_tally
| State_abbv | State | Count | Text | |
|---|---|---|---|---|
| 0 | CA | California | 263 | 263 applications in California. |
| 1 | NY | New York | 122 | 122 applications in New York. |
| 2 | TX | Texas | 92 | 92 applications in Texas. |
| 3 | IL | Illinois | 74 | 74 applications in Illinois. |
| 4 | NC | North Carolina | 69 | 69 applications in North Carolina. |
| 5 | WA | Washington | 66 | 66 applications in Washington. |
| 6 | MA | Massachusetts | 58 | 58 applications in Massachusetts. |
| 7 | GA | Georgia | 55 | 55 applications in Georgia. |
| 8 | VA | Virginia | 44 | 44 applications in Virginia. |
| 9 | CO | Colorado | 37 | 37 applications in Colorado. |
| 10 | PA | Pennsylvania | 34 | 34 applications in Pennsylvania. |
| 11 | MD | Maryland | 32 | 32 applications in Maryland. |
| 12 | DC | District Of Columbia | 31 | 31 applications in District Of Columbia. |
| 13 | NJ | New Jersey | 24 | 24 applications in New Jersey. |
| 14 | MO | Missouri | 22 | 22 applications in Missouri. |
| 15 | FL | Florida | 17 | 17 applications in Florida. |
| 16 | OR | Oregon | 16 | 16 applications in Oregon. |
| 17 | OH | Ohio | 15 | 15 applications in Ohio. |
| 18 | MN | Minnesota | 15 | 15 applications in Minnesota. |
| 19 | MI | Michigan | 14 | 14 applications in Michigan. |
| 20 | TN | Tennessee | 10 | 10 applications in Tennessee. |
| 21 | CT | Connecticut | 8 | 8 applications in Connecticut. |
| 22 | AZ | Arizona | 8 | 8 applications in Arizona. |
| 23 | IN | Indiana | 7 | 7 applications in Indiana. |
| 24 | KY | Kentucky | 4 | 4 applications in Kentucky. |
| 25 | SC | South Carolina | 4 | 4 applications in South Carolina. |
| 26 | DE | Delaware | 3 | 3 applications in Delaware. |
| 27 | RI | Rhode Island | 3 | 3 applications in Rhode Island. |
| 28 | UT | Utah | 2 | 2 applications in Utah. |
| 29 | WI | Wisconsin | 2 | 2 applications in Wisconsin. |
| 30 | AL | Alabama | 2 | 2 applications in Alabama. |
| 31 | KS | Kansas | 2 | 2 applications in Kansas. |
| 32 | NH | New Hampshire | 1 | 1 application in New Hampshire. |
| 33 | AR | Arkansas | 1 | 1 application in Arkansas. |
| 34 | ID | Idaho | 1 | 1 application in Idaho. |
| 35 | WV | West Virginia | 1 | 1 application in West Virginia. |