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bmount/application.py

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application.py
# This allows us to read the cities.json file. The format of the file
# is 'Javascript Object Notation' (JSON). It's a way of representing
# essential data structures, like lists and key-value pairs.
import json
# Our data will be in this format:
# {
# "city": "Beloit",
# "growth_from_2000_to_2013": "2.9%",
# "latitude": 42.5083482,
# "longitude": -89.03177649999999,
# "population": "36888",
# "rank": "999",
# "state": "Wisconsin"
# },
cities_dataset = json.load(open('cities.json'))
def manhattan_distance(starting_point, destination):
"""
This is a function that tells you basically, 'If my coordinates were city blocks, and
I couldn't walk through the middle of the block, how far would I have to walk to get from
a to b, using city streets?
"""
distance = 0
n_dimensions = len(starting_point)
for dimension in range(n_dimensions):
# Could be streets, could be avenues, could be anything!
distance_at_dimension = abs(starting_point[dimension] - destination[dimension])
distance += distance_at_dimension
return distance
def normalized_dot_product(a, b):
"""
This is a function that tells you basically, 'how much are two vectors pointing in the same direction?'
This is just the Pythagorean theorem!
"""
# normalize vectors.
# A ** 2 + B ** 2 = C ** 2
# Raise to power of 1/2 to get the square root
a_length = sum([x**2 for x in a]) ** 0.5
a_normalized = [x / a_length for x in a]
b_length = sum([x**2 for x in b]) ** 0.5
b_normalized = [x / b_length for x in b]
# calculate dot product
return sum([a_normalized[i] * b_normalized[i] for i in range(len(a))])
#if __name__ == '__main__':
# Run this code if we're running this file directly
# (as opposed to importing it from another file)
# Let's compare cities!
# First, we know that cities close together have some things in common.
# It's fair to say that Chicago and Minneapolis are 'similar' in some
# important ways. Let's see what computing the overlap of latitudes and
# longitudes looks like.
# We're going to store the cities in a dictionary
# The key will be the city name
# The value will be the coordinates of the city. Latitude and longitude
# are just a geographic, 2 dimensional vector:
lonlat = {}
for city in cities_dataset:
lonlat[city['city']] = [city['longitude'], city['latitude']]
# Woah! That's a lot of cities! Let's simplify with ones with a bigger population:
big_cities = {}
for city in cities_dataset:
if int(city['population']) > 100000:
big_cities[city['city']] = [city['longitude'], city['latitude']]
# Now, let's make a function that finds the nearest city to each other city,
# using the oversimplified-but-fine approximation of the manhattan distance:
def approximate_nearest_city(city_name, cities):
coordinates = cities[city_name]
# So far, we have nothing 'nearest', so start our search with a large value:
nearest = 1000
nearest_name = None
for other_city, other_coordinates in cities.items():
# calculate the distance between the two cities
distance = manhattan_distance(coordinates, other_coordinates)
print('distance from', city, 'to', other_city, 'is', distance)
# if the distance is less than the current nearest, update the nearest
# unless the distance is near 0, which means we're comparing the same city
# to itself
if distance < nearest and distance > 0.000001:
nearest = distance
nearest_name = other_city
# return the nearest city
return nearest_name, distance
# Now, let's test it out. Also, we are going to see how long it takes to find
# the nearest city to each other city:
import time
starting_time = time.time()
print('Now find the city nearest to each other city:')
for city in big_cities:
print(city, approximate_nearest_city(city, big_cities))
ending_time = time.time()
print('It took', ending_time - starting_time, 'seconds to find the nearest city to each other city.')
print('the nearest city to Chicago is', approximate_nearest_city('Chicago', big_cities))
# So this kind of works, but we want something a little deeper. More 'similarity' than just
# 'nearness'. Let's try to find the most similar city to each other city, using additional features.
# Intuitively, nearby cities do share a lot in common. But also, big cities have some of that bigness
# in common, apart from geography. Let's see what we can do with that.
# Now, let's start thinking of our cities list as a matrix. Each row is a city, and each column is a feature.
big_cities_list = [city for city in cities_dataset if int(city['population']) > 100000]
names = [city['city'] for city in big_cities_list]
longitudes = [city['longitude'] for city in big_cities_list]
latitudes = [city['latitude'] for city in big_cities_list]
populations = [float(city['population']) for city in big_cities_list]
growth_rates = [float(city['growth_from_2000_to_2013'].replace('%', '')) for city in big_cities_list]
# So far, so good, but how do we compare the number of people in one city to the degrees latitude of another?
# Answer: you don't! Intuitively, we know that longitude, the Eastness-Westness of a city, does mean something
# to us. Seattle has some things in common with San Francisco much further South. And likewise Phoenix to
# Jacksonville in terms of latitude -- there's a real "sunbelt" effect uniting these distant cities. So we want
# to keep all those features, but not over-emphasize any in particular. Again, we're designing our features here.
# And BTW, since we're designing features, let's throw in one more: the market share of pickup trucks in the state!
# Because why not?! Also lets us practice reading CSV files.
# our format will be:
# Connecticut,9.4%
# New Jersey,7.8%
# ...
import csv
share = csv.DictReader(open('pickup-trucks.csv'), delimiter = ',', fieldnames=['state', 'market_share'])
state_truck_share = {}
for state in share:
state_name = state['state']
market_share = float(state['market_share'].replace('%', ''))
state_truck_share[state_name] = market_share
pickup_market_share = [state_truck_share[city['state']] for city in big_cities_list]
# Back to our matrix! We definitely won't be comparing longitudes to pickups! So let's rescale all our features
# We'll use this common function:
def rescale(vector, range = (-0.5, 0.5)):
"""
This function takes a vector of numbers, and rescales it to be between
the range specified. The default range is -0.5 to 0.5, which is where
floating point numbers are most accurate.
"""
# find the minimum and maximum values in the vector
min_value = min(vector)
max_value = max(vector)
# find the range of the vector
vector_range = max_value - min_value
# find the range we want to rescale to
target_range = range[1] - range[0]
# rescale the vector
rescaled = [range[0] + (x - min_value) * target_range / vector_range for x in vector]
return rescaled
# Now, let's rescale all our features:
rescaled_longitudes = rescale(longitudes)
rescaled_latitudes = rescale(latitudes)
rescaled_populations = rescale(populations)
rescaled_growth_rates = rescale(growth_rates)
rescaled_pickup_market_share = rescale(pickup_market_share)
# Now, let's put all our features together into a matrix:
import numpy as np
matrix = np.array([rescaled_longitudes, rescaled_latitudes, rescaled_populations,
rescaled_growth_rates, rescaled_pickup_market_share])
# Now, let's transpose the matrix, so that each row is a city, and each column is a feature:
matrix = matrix.T
# Now, let's take a couple of approaches. Here we normalize so that each city's vector has length 1:
normalized_matrix = matrix / np.linalg.norm(matrix, axis = 1).reshape(-1, 1)
# Working with the normalized matrix is really like "how much are these two vectors pointing in the same direction?"
# Because the magnitude of each row is 1, we can just take the dot product of each row with every other row. This
# is the same as matrix multiplication, but we're multiplying a matrix by its transpose. So we can use the @ operator
similarities = normalized_matrix @ normalized_matrix.T
# Now, arrange the results such that the most similar city is at the top of the list (which will be itself), and
# the least similar city is at the bottom of the list (which will be the city furthest away in terms of our combined
# features):
# Axis = 1 means we're sorting each row, and -1 means we're sorting in descending order
# Argsort returns the indices of the sorted array -- where you would have to put stuff so that it appears in order
rankings = np.argsort(-similarities, axis = 1)
# Now, let's print out the most similar city to each other city:
print('Now find the most similar city to each other city:')
def show_rankings(rankings, names, n=5, preamble=''):
for index, name in enumerate(names):
top_n = rankings[index][1:1+n]
near = [names[i] for i in top_n]
far = [names[i] for i in rankings[index][-n:]]
print(preamble, name, 'is most similar to', ', '.join(near), 'and least similar to', ', '.join(far))
show_rankings(rankings, names)
# Now, just for fun, let's ignore geography and just look at the features.
no_geo = matrix[:, 2:]
# Re-normalize:
no_geo = no_geo / np.linalg.norm(no_geo, axis = 1).reshape(-1, 1)
# Let's talk about distances, without geography.
no_geo_distances = no_geo @ no_geo.T
no_geo_rankings = np.argsort(-no_geo_distances, axis = 1)
print('Now find the most similar city to each other city, ignoring geography:')
show_rankings(no_geo_rankings, names)
# Time for deep learning! We see above a couple of things:
# 1. Geography is either too dominant or not dominant enough.
# 2. We need more to capture the subtlety of underlying features of these cities.
# What if instead of making up a feature vector for each city, we let loose a neural network,
# with the broad instructions of: "find the features that best capture the semantic essence
# of every word and image on the internet"? Let's see what happens!
# First, we need to get our data into a format that a neural network can understand.
# How about English!
import sentence_transformers
model = sentence_transformers.SentenceTransformer('clip-ViT-B-32').to('cuda')
def embed_row(row):
# Little bit of practical engineering here: language models are not exceptionally
# good at dealing with numbers. And the really big ones don't entirely need to be!
# So let's just use the proper name for the city an state, and see what happens.
sentence = f"""{row.get('city')}, {row['state']}"""
print('getting embedding for', sentence)
return model.encode(sentence)
# Now, let's embed all our cities:
embeddings = np.array([embed_row(row) for row in big_cities_list])
# Now, let's see how similar these embeddings are to each other:
embeddings_distances = embeddings @ embeddings.T
embeddings_rankings = np.argsort(-embeddings_distances, axis = 1)
def top_n_ranking(rankings, index, n=5):
top_n = rankings[index][1:1+n]
near = [names[i] for i in top_n]
far = [names[i] for i in rankings[index][-n:]]
return near, far
def show_ranking(rankings, index, preamble=''):
near, far = top_n_ranking(rankings, index)
print(preamble, 'is most similar to:', ', '.join(near))
print(preamble, 'is least similar to:', ', '.join(far))
def compare_models(city_name, state_name):
# Almost 10 of America's top 300 cities share the same name. So we need to be more specific.
idx = 0
found = False
for row in big_cities_list:
if row['city'] == city_name and row['state'] == state_name:
found = True
break
idx += 1
if not found:
print("Couldn't find", city_name, 'in', state_name)
return
print('Now find the most similar city to', city_name, 'in each model:')
show_ranking(rankings, idx, preamble=f'With our manually created basic features, {city_name}, {state_name} ')
show_ranking(embeddings_rankings, idx, preamble=f'With our deep learning model, {city_name}, {state_name} ')
Raw
cities.json
[
{
"city": "New York",
"growth_from_2000_to_2013": "4.8%",
"latitude": 40.7127837,
"longitude": -74.0059413,
"population": "8405837",
"rank": "1",
"state": "New York"
},
{
"city": "Los Angeles",
"growth_from_2000_to_2013": "4.8%",
"latitude": 34.0522342,
"longitude": -118.2436849,
"population": "3884307",
"rank": "2",
"state": "California"
},
{
"city": "Chicago",
"growth_from_2000_to_2013": "-6.1%",
"latitude": 41.8781136,
"longitude": -87.6297982,
"population": "2718782",
"rank": "3",
"state": "Illinois"
},
{
"city": "Houston",
"growth_from_2000_to_2013": "11.0%",
"latitude": 29.7604267,
"longitude": -95.3698028,
"population": "2195914",
"rank": "4",
"state": "Texas"
},
{
"city": "Philadelphia",
"growth_from_2000_to_2013": "2.6%",
"latitude": 39.9525839,
"longitude": -75.1652215,
"population": "1553165",
"rank": "5",
"state": "Pennsylvania"
},
{
"city": "Phoenix",
"growth_from_2000_to_2013": "14.0%",
"latitude": 33.4483771,
"longitude": -112.0740373,
"population": "1513367",
"rank": "6",
"state": "Arizona"
},
{
"city": "San Antonio",
"growth_from_2000_to_2013": "21.0%",
"latitude": 29.4241219,
"longitude": -98.49362819999999,
"population": "1409019",
"rank": "7",
"state": "Texas"
},
{
"city": "San Diego",
"growth_from_2000_to_2013": "10.5%",
"latitude": 32.715738,
"longitude": -117.1610838,
"population": "1355896",
"rank": "8",
"state": "California"
},
{
"city": "Dallas",
"growth_from_2000_to_2013": "5.6%",
"latitude": 32.7766642,
"longitude": -96.79698789999999,
"population": "1257676",
"rank": "9",
"state": "Texas"
},
{
"city": "San Jose",
"growth_from_2000_to_2013": "10.5%",
"latitude": 37.3382082,
"longitude": -121.8863286,
"population": "998537",
"rank": "10",
"state": "California"
},
{
"city": "Austin",
"growth_from_2000_to_2013": "31.7%",
"latitude": 30.267153,
"longitude": -97.7430608,
"population": "885400",
"rank": "11",
"state": "Texas"
},
{
"city": "Indianapolis",
"growth_from_2000_to_2013": "7.8%",
"latitude": 39.768403,
"longitude": -86.158068,
"population": "843393",
"rank": "12",
"state": "Indiana"
},
{
"city": "Jacksonville",
"growth_from_2000_to_2013": "14.3%",
"latitude": 30.3321838,
"longitude": -81.65565099999999,
"population": "842583",
"rank": "13",
"state": "Florida"
},
{
"city": "San Francisco",
"growth_from_2000_to_2013": "7.7%",
"latitude": 37.7749295,
"longitude": -122.4194155,
"population": "837442",
"rank": "14",
"state": "California"
},
{
"city": "Columbus",
"growth_from_2000_to_2013": "14.8%",
"latitude": 39.9611755,
"longitude": -82.99879419999999,
"population": "822553",
"rank": "15",
"state": "Ohio"
},
{
"city": "Charlotte",
"growth_from_2000_to_2013": "39.1%",
"latitude": 35.2270869,
"longitude": -80.8431267,
"population": "792862",
"rank": "16",
"state": "North Carolina"
},
{
"city": "Fort Worth",
"growth_from_2000_to_2013": "45.1%",
"latitude": 32.7554883,
"longitude": -97.3307658,
"population": "792727",
"rank": "17",
"state": "Texas"
},
{
"city": "Detroit",
"growth_from_2000_to_2013": "-27.1%",
"latitude": 42.331427,
"longitude": -83.0457538,
"population": "688701",
"rank": "18",
"state": "Michigan"
},
{
"city": "El Paso",
"growth_from_2000_to_2013": "19.4%",
"latitude": 31.7775757,
"longitude": -106.4424559,
"population": "674433",
"rank": "19",
"state": "Texas"
},
{
"city": "Memphis",
"growth_from_2000_to_2013": "-5.3%",
"latitude": 35.1495343,
"longitude": -90.0489801,
"population": "653450",
"rank": "20",
"state": "Tennessee"
},
{
"city": "Seattle",
"growth_from_2000_to_2013": "15.6%",
"latitude": 47.6062095,
"longitude": -122.3320708,
"population": "652405",
"rank": "21",
"state": "Washington"
},
{
"city": "Denver",
"growth_from_2000_to_2013": "16.7%",
"latitude": 39.7392358,
"longitude": -104.990251,
"population": "649495",
"rank": "22",
"state": "Colorado"
},
{
"city": "Washington",
"growth_from_2000_to_2013": "13.0%",
"latitude": 38.9071923,
"longitude": -77.0368707,
"population": "646449",
"rank": "23",
"state": "District of Columbia"
},
{
"city": "Boston",
"growth_from_2000_to_2013": "9.4%",
"latitude": 42.3600825,
"longitude": -71.0588801,
"population": "645966",
"rank": "24",
"state": "Massachusetts"
},
{
"city": "Nashville-Davidson",
"growth_from_2000_to_2013": "16.2%",
"latitude": 36.1626638,
"longitude": -86.7816016,
"population": "634464",
"rank": "25",
"state": "Tennessee"
},
{
"city": "Baltimore",
"growth_from_2000_to_2013": "-4.0%",
"latitude": 39.2903848,
"longitude": -76.6121893,
"population": "622104",
"rank": "26",
"state": "Maryland"
},
{
"city": "Oklahoma City",
"growth_from_2000_to_2013": "20.2%",
"latitude": 35.4675602,
"longitude": -97.5164276,
"population": "610613",
"rank": "27",
"state": "Oklahoma"
},
{
"city": "Louisville/Jefferson County",
"growth_from_2000_to_2013": "10.0%",
"latitude": 38.2526647,
"longitude": -85.7584557,
"population": "609893",
"rank": "28",
"state": "Kentucky"
},
{
"city": "Portland",
"growth_from_2000_to_2013": "15.0%",
"latitude": 45.5230622,
"longitude": -122.6764816,
"population": "609456",
"rank": "29",
"state": "Oregon"
},
{
"city": "Las Vegas",
"growth_from_2000_to_2013": "24.5%",
"latitude": 36.1699412,
"longitude": -115.1398296,
"population": "603488",
"rank": "30",
"state": "Nevada"
},
{
"city": "Milwaukee",
"growth_from_2000_to_2013": "0.3%",
"latitude": 43.0389025,
"longitude": -87.9064736,
"population": "599164",
"rank": "31",
"state": "Wisconsin"
},
{
"city": "Albuquerque",
"growth_from_2000_to_2013": "23.5%",
"latitude": 35.0853336,
"longitude": -106.6055534,
"population": "556495",
"rank": "32",
"state": "New Mexico"
},
{
"city": "Tucson",
"growth_from_2000_to_2013": "7.5%",
"latitude": 32.2217429,
"longitude": -110.926479,
"population": "526116",
"rank": "33",
"state": "Arizona"
},
{
"city": "Fresno",
"growth_from_2000_to_2013": "18.3%",
"latitude": 36.7468422,
"longitude": -119.7725868,
"population": "509924",
"rank": "34",
"state": "California"
},
{
"city": "Sacramento",
"growth_from_2000_to_2013": "17.2%",
"latitude": 38.5815719,
"longitude": -121.4943996,
"population": "479686",
"rank": "35",
"state": "California"
},
{
"city": "Long Beach",
"growth_from_2000_to_2013": "1.5%",
"latitude": 33.7700504,
"longitude": -118.1937395,
"population": "469428",
"rank": "36",
"state": "California"
},
{
"city": "Kansas City",
"growth_from_2000_to_2013": "5.5%",
"latitude": 39.0997265,
"longitude": -94.5785667,
"population": "467007",
"rank": "37",
"state": "Missouri"
},
{
"city": "Mesa",
"growth_from_2000_to_2013": "13.5%",
"latitude": 33.4151843,
"longitude": -111.8314724,
"population": "457587",
"rank": "38",
"state": "Arizona"
},
{
"city": "Virginia Beach",
"growth_from_2000_to_2013": "5.1%",
"latitude": 36.8529263,
"longitude": -75.97798499999999,
"population": "448479",
"rank": "39",
"state": "Virginia"
},
{
"city": "Atlanta",
"growth_from_2000_to_2013": "6.2%",
"latitude": 33.7489954,
"longitude": -84.3879824,
"population": "447841",
"rank": "40",
"state": "Georgia"
},
{
"city": "Colorado Springs",
"growth_from_2000_to_2013": "21.4%",
"latitude": 38.8338816,
"longitude": -104.8213634,
"population": "439886",
"rank": "41",
"state": "Colorado"
},
{
"city": "Omaha",
"growth_from_2000_to_2013": "5.9%",
"latitude": 41.2523634,
"longitude": -95.99798829999999,
"population": "434353",
"rank": "42",
"state": "Nebraska"
},
{
"city": "Raleigh",
"growth_from_2000_to_2013": "48.7%",
"latitude": 35.7795897,
"longitude": -78.6381787,
"population": "431746",
"rank": "43",
"state": "North Carolina"
},
{
"city": "Miami",
"growth_from_2000_to_2013": "14.9%",
"latitude": 25.7616798,
"longitude": -80.1917902,
"population": "417650",
"rank": "44",
"state": "Florida"
},
{
"city": "Oakland",
"growth_from_2000_to_2013": "1.3%",
"latitude": 37.8043637,
"longitude": -122.2711137,
"population": "406253",
"rank": "45",
"state": "California"
},
{
"city": "Minneapolis",
"growth_from_2000_to_2013": "4.5%",
"latitude": 44.977753,
"longitude": -93.2650108,
"population": "400070",
"rank": "46",
"state": "Minnesota"
},
{
"city": "Tulsa",
"growth_from_2000_to_2013": "1.3%",
"latitude": 36.1539816,
"longitude": -95.99277500000001,
"population": "398121",
"rank": "47",
"state": "Oklahoma"
},
{
"city": "Cleveland",
"growth_from_2000_to_2013": "-18.1%",
"latitude": 41.49932,
"longitude": -81.6943605,
"population": "390113",
"rank": "48",
"state": "Ohio"
},
{
"city": "Wichita",
"growth_from_2000_to_2013": "9.7%",
"latitude": 37.688889,
"longitude": -97.336111,
"population": "386552",
"rank": "49",
"state": "Kansas"
},
{
"city": "Arlington",
"growth_from_2000_to_2013": "13.3%",
"latitude": 32.735687,
"longitude": -97.10806559999999,
"population": "379577",
"rank": "50",
"state": "Texas"
},
{
"city": "New Orleans",
"growth_from_2000_to_2013": "-21.6%",
"latitude": 29.95106579999999,
"longitude": -90.0715323,
"population": "378715",
"rank": "51",
"state": "Louisiana"
},
{
"city": "Bakersfield",
"growth_from_2000_to_2013": "48.4%",
"latitude": 35.3732921,
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]
Display the source blob
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city_features.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "ccd6c190",
"metadata": {},
"outputs": [],
"source": [
"\n",
"# This allows us to read the cities.json file. The format of the file\n",
"# is 'Javascript Object Notation' (JSON). It's a way of representing\n",
"# essential data structures, like lists and key-value pairs.\n",
"\n",
"import json\n",
"\n",
"# Our data will be in this format:\n",
"# {\n",
"# \"city\": \"Beloit\", \n",
"# \"growth_from_2000_to_2013\": \"2.9%\", \n",
"# \"latitude\": 42.5083482, \n",
"# \"longitude\": -89.03177649999999, \n",
"# \"population\": \"36888\", \n",
"# \"rank\": \"999\", \n",
"# \"state\": \"Wisconsin\"\n",
"# },\n",
"\n",
"cities_dataset = json.load(open('cities.json'))\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "e45a662c",
"metadata": {},
"outputs": [],
"source": [
"\n",
"def manhattan_distance(starting_point, destination):\n",
" \"\"\"\n",
" This is a function that tells you basically, 'If my coordinates were city blocks, and\n",
" I couldn't walk through the middle of the block, how far would I have to walk to get from\n",
" a to b, using city streets?\n",
" \"\"\"\n",
" distance = 0\n",
" n_dimensions = len(starting_point)\n",
" for dimension in range(n_dimensions):\n",
" # Could be streets, could be avenues, could be anything!\n",
" distance_at_dimension = abs(starting_point[dimension] - destination[dimension])\n",
" distance += distance_at_dimension\n",
" return distance\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "2add2f7d",
"metadata": {},
"outputs": [],
"source": [
"# Let's compare cities!\n",
"\n",
"# First, we know that cities close together have some things in common.\n",
"# It's fair to say that Chicago and Minneapolis are 'similar' in some\n",
"# important ways. Let's see what computing the overlap of latitudes and\n",
"# longitudes looks like.\n",
"\n",
"# We're going to store the cities in a dictionary\n",
"# The key will be the city name\n",
"# The value will be the coordinates of the city. Latitude and longitude\n",
"# are just a geographic, 2 dimensional vector:\n",
"\n",
"lonlat = {}\n",
"\n",
"for city in cities_dataset:\n",
" lonlat[city['city']] = [city['longitude'], city['latitude']]"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "2c621737",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'New York': [-74.0059413, 40.7127837],\n",
" 'Los Angeles': [-118.2436849, 34.0522342],\n",
" 'Chicago': [-87.6297982, 41.8781136],\n",
" 'Houston': [-95.3698028, 29.7604267],\n",
" 'Philadelphia': [-75.1652215, 39.9525839],\n",
" 'Phoenix': [-112.0740373, 33.4483771],\n",
" 'San Antonio': [-98.49362819999999, 29.4241219],\n",
" 'San Diego': [-117.1610838, 32.715738],\n",
" 'Dallas': [-96.79698789999999, 32.7766642],\n",
" 'San Jose': [-121.8863286, 37.3382082],\n",
" 'Austin': [-97.7430608, 30.267153],\n",
" 'Indianapolis': [-86.158068, 39.768403],\n",
" 'Jacksonville': [-77.4302414, 34.7540524],\n",
" 'San Francisco': [-122.4194155, 37.7749295],\n",
" 'Columbus': [-85.9213796, 39.2014404],\n",
" 'Charlotte': [-80.8431267, 35.2270869],\n",
" 'Fort Worth': [-97.3307658, 32.7554883],\n",
" 'Detroit': [-83.0457538, 42.331427],\n",
" 'El Paso': [-106.4424559, 31.7775757],\n",
" 'Memphis': [-90.0489801, 35.1495343],\n",
" 'Seattle': [-122.3320708, 47.6062095],\n",
" 'Denver': [-104.990251, 39.7392358],\n",
" 'Washington': [-77.0368707, 38.9071923],\n",
" 'Boston': [-71.0588801, 42.3600825],\n",
" 'Nashville-Davidson': [-86.7816016, 36.1626638],\n",
" 'Baltimore': [-76.6121893, 39.2903848],\n",
" 'Oklahoma City': [-97.5164276, 35.4675602],\n",
" 'Louisville/Jefferson County': [-85.7584557, 38.2526647],\n",
" 'Portland': [-70.2553259, 43.66147100000001],\n",
" 'Las Vegas': [-115.1398296, 36.1699412],\n",
" 'Milwaukee': [-87.9064736, 43.0389025],\n",
" 'Albuquerque': [-106.6055534, 35.0853336],\n",
" 'Tucson': [-110.926479, 32.2217429],\n",
" 'Fresno': [-119.7725868, 36.7468422],\n",
" 'Sacramento': [-121.4943996, 38.5815719],\n",
" 'Long Beach': [-118.1937395, 33.7700504],\n",
" 'Kansas City': [-94.6274636, 39.114053],\n",
" 'Mesa': [-111.8314724, 33.4151843],\n",
" 'Virginia Beach': [-75.97798499999999, 36.8529263],\n",
" 'Atlanta': [-84.3879824, 33.7489954],\n",
" 'Colorado Springs': [-104.8213634, 38.8338816],\n",
" 'Omaha': [-95.99798829999999, 41.2523634],\n",
" 'Raleigh': [-78.6381787, 35.7795897],\n",
" 'Miami': [-80.1917902, 25.7616798],\n",
" 'Oakland': [-122.2711137, 37.8043637],\n",
" 'Minneapolis': [-93.2650108, 44.977753],\n",
" 'Tulsa': [-95.99277500000001, 36.1539816],\n",
" 'Cleveland': [-84.8766115, 35.1595182],\n",
" 'Wichita': [-97.336111, 37.688889],\n",
" 'Arlington': [-97.10806559999999, 32.735687],\n",
" 'New Orleans': [-90.0715323, 29.95106579999999],\n",
" 'Bakersfield': [-119.0187125, 35.3732921],\n",
" 'Tampa': [-82.4571776, 27.950575],\n",
" 'Honolulu': [-157.8583333, 21.3069444],\n",
" 'Aurora': [-88.32007150000001, 41.7605849],\n",
" 'Anaheim': [-117.9145036, 33.8352932],\n",
" 'Santa Ana': [-117.8678338, 33.7455731],\n",
" 'St. Louis': [-90.19940419999999, 38.6270025],\n",
" 'Riverside': [-117.3961564, 33.9533487],\n",
" 'Corpus Christi': [-97.39638099999999, 27.8005828],\n",
" 'Lexington-Fayette': [-84.5037164, 38.0405837],\n",
" 'Pittsburgh': [-79.9958864, 40.44062479999999],\n",
" 'Anchorage': [-149.9002778, 61.2180556],\n",
" 'Stockton': [-121.2907796, 37.9577016],\n",
" 'Cincinnati': [-84.5120196, 39.1031182],\n",
" 'St. Paul': [-93.0899578, 44.9537029],\n",
" 'Toledo': [-83.55521200000001, 41.6639383],\n",
" 'Greensboro': [-79.7919754, 36.0726354],\n",
" 'Newark': [-122.0402399, 37.5296593],\n",
" 'Plano': [-96.6988856, 33.0198431],\n",
" 'Henderson': [-114.9817213, 36.0395247],\n",
" 'Lincoln': [-121.2930079, 38.891565],\n",
" 'Buffalo': [-78.8783689, 42.88644679999999],\n",
" 'Jersey City': [-74.0776417, 40.72815749999999],\n",
" 'Chula Vista': [-117.0841955, 32.6400541],\n",
" 'Fort Wayne': [-85.1393513, 41.079273],\n",
" 'Orlando': [-81.3792365, 28.5383355],\n",
" 'St. Petersburg': [-82.64, 27.773056],\n",
" 'Chandler': [-111.8412502, 33.3061605],\n",
" 'Laredo': [-99.48032409999999, 27.5305671],\n",
" 'Norfolk': [-76.28587259999999, 36.8507689],\n",
" 'Durham': [-78.898619, 35.9940329],\n",
" 'Madison': [-86.74833180000002, 34.6992579],\n",
" 'Lubbock': [-101.8551665, 33.5778631],\n",
" 'Irvine': [-117.7946942, 33.6839473],\n",
" 'Winston-Salem': [-80.244216, 36.09985959999999],\n",
" 'Glendale': [-118.255075, 34.1425078],\n",
" 'Garland': [-96.63888329999999, 32.912624],\n",
" 'Hialeah': [-80.2781057, 25.8575963],\n",
" 'Reno': [-119.8138027, 39.5296329],\n",
" 'Chesapeake': [-76.2874927, 36.7682088],\n",
" 'Gilbert': [-111.789027, 33.3528264],\n",
" 'Baton Rouge': [-91.1403196, 30.4582829],\n",
" 'Irving': [-96.9488945, 32.8140177],\n",
" 'Scottsdale': [-111.9260519, 33.4941704],\n",
" 'North Las Vegas': [-115.1175013, 36.1988592],\n",
" 'Fremont': [-121.9885719, 37.5482697],\n",
" 'Boise City': [-116.2146068, 43.6187102],\n",
" 'Richmond': [-122.3477486, 37.9357576],\n",
" 'San Bernardino': [-117.2897652, 34.1083449],\n",
" 'Birmingham': [-86.80248999999999, 33.5206608],\n",
" 'Spokane': [-117.4260466, 47.6587802],\n",
" 'Rochester': [-92.4801989, 44.0121221],\n",
" 'Des Moines': [-93.6091064, 41.6005448],\n",
" 'Modesto': [-120.9968782, 37.63909719999999],\n",
" 'Fayetteville': [-94.1574263, 36.0625795],\n",
" 'Tacoma': [-122.4442906, 47.2528768],\n",
" 'Oxnard': [-119.1770516, 34.1975048],\n",
" 'Fontana': [-117.435048, 34.0922335],\n",
" 'Montgomery': [-86.2999689, 32.3668052],\n",
" 'Moreno Valley': [-117.2296717, 33.9424658],\n",
" 'Shreveport': [-93.7501789, 32.5251516],\n",
" 'Yonkers': [-73.89874689999999, 40.9312099],\n",
" 'Akron': [-81.51900529999999, 41.0814447],\n",
" 'Huntington Beach': [-117.9992265, 33.660297],\n",
" 'Little Rock': [-92.28959479999999, 34.7464809],\n",
" 'Augusta-Richmond County': [-82.0105148, 33.4734978],\n",
" 'Amarillo': [-101.8312969, 35.2219971],\n",
" 'Mobile': [-88.0398912, 30.6953657],\n",
" 'Grand Rapids': [-85.6680863, 42.9633599],\n",
" 'Salt Lake City': [-111.8910474, 40.7607793],\n",
" 'Tallahassee': [-84.28073289999999, 30.4382559],\n",
" 'Huntsville': [-95.55077709999999, 30.7235263],\n",
" 'Grand Prairie': [-96.99778459999999, 32.7459645],\n",
" 'Knoxville': [-83.9207392, 35.9606384],\n",
" 'Worcester': [-71.8022934, 42.2625932],\n",
" 'Newport News': [-76.4730122, 37.0870821],\n",
" 'Brownsville': [-97.4974838, 25.9017472],\n",
" 'Overland Park': [-94.6707917, 38.9822282],\n",
" 'Santa Clarita': [-118.542586, 34.3916641],\n",
" 'Providence': [-71.4128343, 41.8239891],\n",
" 'Garden Grove': [-117.9414477, 33.7739053],\n",
" 'Chattanooga': [-85.3096801, 35.0456297],\n",
" 'Oceanside': [-117.3794834, 33.1958696],\n",
" 'Jackson': [-88.81394689999999, 35.6145169],\n",
" 'Fort Lauderdale': [-80.13731740000001, 26.1224386],\n",
" 'Santa Rosa': [-122.7140548, 38.440429],\n",
" 'Rancho Cucamonga': [-117.5931084, 34.10639889999999],\n",
" 'Port St. Lucie': [-80.3582261, 27.2730492],\n",
" 'Tempe': [-111.9400054, 33.4255104],\n",
" 'Ontario': [-117.6508876, 34.0633443],\n",
" 'Vancouver': [-122.6614861, 45.6387281],\n",
" 'Cape Coral': [-81.9495331, 26.5628537],\n",
" 'Sioux Falls': [-96.73110340000001, 43.5445959],\n",
" 'Springfield': [-83.8088171, 39.9242266],\n",
" 'Peoria': [-89.5889864, 40.6936488],\n",
" 'Pembroke Pines': [-80.2962555, 26.007765],\n",
" 'Elk Grove': [-121.3716178, 38.4087993],\n",
" 'Salem': [-70.8967155, 42.51954],\n",
" 'Lancaster': [-96.7561082, 32.5920798],\n",
" 'Corona': [-117.5664384, 33.8752935],\n",
" 'Eugene': [-123.0867536, 44.0520691],\n",
" 'Palmdale': [-118.1164613, 34.5794343],\n",
" 'Salinas': [-121.6555013, 36.6777372],\n",
" 'Pasadena': [-118.1445155, 34.1477849],\n",
" 'Fort Collins': [-105.084423, 40.5852602],\n",
" 'Hayward': [-122.0807964, 37.6688205],\n",
" 'Pomona': [-117.7499909, 34.055103],\n",
" 'Cary': [-78.7811169, 35.79154],\n",
" 'Rockford': [-89.0939952, 42.2711311],\n",
" 'Alexandria': [-92.4451371, 31.3112936],\n",
" 'Escondido': [-117.086421, 33.1192068],\n",
" 'McKinney': [-96.6397822, 33.1972465],\n",
" 'Joliet': [-88.0817251, 41.525031],\n",
" 'Sunnyvale': [-122.0363496, 37.36883],\n",
" 'Torrance': [-118.3406288, 33.8358492],\n",
" 'Bridgeport': [-73.19517669999999, 41.1865478],\n",
" 'Lakewood': [-81.7981908, 41.4819932],\n",
" 'Hollywood': [-80.1494901, 26.0112014],\n",
" 'Paterson': [-74.17181099999999, 40.9167654],\n",
" 'Naperville': [-88.1535352, 41.7508391],\n",
" 'Syracuse': [-76.14742439999999, 43.0481221],\n",
" 'Mesquite': [-96.5991593, 32.76679550000001],\n",
" 'Dayton': [-84.1916069, 39.7589478],\n",
" 'Savannah': [-81.09983419999999, 32.0835407],\n",
" 'Clarksville': [-87.3594528, 36.5297706],\n",
" 'Orange': [-117.8531119, 33.7877944],\n",
" 'Fullerton': [-117.9242966, 33.8703596],\n",
" 'Killeen': [-97.72779589999999, 31.1171194],\n",
" 'Frisco': [-96.82361159999999, 33.1506744],\n",
" 'Hampton': [-76.34522179999999, 37.0298687],\n",
" 'McAllen': [-98.23001239999999, 26.2034071],\n",
" 'Warren': [-80.81841659999999, 41.2375569],\n",
" 'Bellevue': [-95.9145568, 41.1543623],\n",
" 'West Valley City': [-112.0010501, 40.6916132],\n",
" 'Columbia': [-92.3340724, 38.9517053],\n",
" 'Olathe': [-94.81912849999999, 38.8813958],\n",
" 'Sterling Heights': [-83.0302033, 42.5803122],\n",
" 'New Haven': [-72.9278835, 41.308274],\n",
" 'Miramar': [-80.30356019999999, 25.9860762],\n",
" 'Waco': [-97.1466695, 31.549333],\n",
" 'Thousand Oaks': [-118.8375937, 34.1705609],\n",
" 'Cedar Rapids': [-91.6656232, 41.9778795],\n",
" 'Charleston': [-81.6326234, 38.3498195],\n",
" 'Visalia': [-119.2920585, 36.3302284],\n",
" 'Topeka': [-95.68901849999999, 39.0558235],\n",
" 'Elizabeth': [-74.2107006, 40.6639916],\n",
" 'Gainesville': [-82.32482619999999, 29.6516344],\n",
" 'Thornton': [-104.9719243, 39.8680412],\n",
" 'Roseville': [-82.9371409, 42.4972583],\n",
" 'Carrollton': [-96.8899636, 32.9756415],\n",
" 'Coral Springs': [-80.2706044, 26.271192],\n",
" 'Stamford': [-73.5387341, 41.0534302],\n",
" 'Simi Valley': [-118.781482, 34.2694474],\n",
" 'Concord': [-71.5375718, 43.2081366],\n",
" 'Hartford': [-72.6850932, 41.76371109999999],\n",
" 'Kent': [-122.2348431, 47.3809335],\n",
" 'Lafayette': [-86.87528689999999, 40.4167022],\n",
" 'Midland': [-84.2472116, 43.6155825],\n",
" 'Surprise': [-112.3679279, 33.6292337],\n",
" 'Denton': [-97.13306829999999, 33.2148412],\n",
" 'Victorville': [-117.2927641, 34.5362184],\n",
" 'Evansville': [-87.5710898, 37.9715592],\n",
" 'Santa Clara': [-121.9552356, 37.3541079],\n",
" 'Abilene': [-99.73314390000002, 32.4487364],\n",
" 'Athens-Clarke County': [-83.357567, 33.9519347],\n",
" 'Vallejo': [-122.2566367, 38.1040864],\n",
" 'Allentown': [-75.4901833, 40.6084305],\n",
" 'Norman': [-97.4394777, 35.2225668],\n",
" 'Beaumont': [-116.977248, 33.9294606],\n",
" 'Independence': [-94.41550679999999, 39.0911161],\n",
" 'Murfreesboro': [-86.39027, 35.8456213],\n",
" 'Ann Arbor': [-83.7430378, 42.2808256],\n",
" 'Berkeley': [-122.272747, 37.8715926],\n",
" 'Provo': [-111.6585337, 40.2338438],\n",
" 'El Monte': [-118.0275667, 34.0686206],\n",
" 'Lansing': [-84.5555347, 42.732535],\n",
" 'Fargo': [-96.7898034, 46.8771863],\n",
" 'Downey': [-118.1331593, 33.9401088],\n",
" 'Costa Mesa': [-117.9186689, 33.6411316],\n",
" 'Wilmington': [-75.5397878, 39.7390721],\n",
" 'Arvada': [-105.0874842, 39.8027644],\n",
" 'Inglewood': [-118.3531311, 33.9616801],\n",
" 'Miami Gardens': [-80.2456045, 25.9420377],\n",
" 'Carlsbad': [-117.3505939, 33.1580933],\n",
" 'Westminster': [-117.9939921, 33.7513419],\n",
" 'Odessa': [-102.3676431, 31.8456816],\n",
" 'Manchester': [-71.4547891, 42.9956397],\n",
" 'Elgin': [-88.2825668, 42.0354084],\n",
" 'West Jordan': [-111.9391031, 40.6096698],\n",
" 'Round Rock': [-97.678896, 30.5082551],\n",
" 'Clearwater': [-82.8001026, 27.9658533],\n",
" 'Waterbury': [-73.0514965, 41.5581525],\n",
" 'Gresham': [-122.4302013, 45.5001357],\n",
" 'Fairfield': [-84.5603187, 39.3454673],\n",
" 'Billings': [-108.5006904, 45.7832856],\n",
" 'Lowell': [-71.31617179999999, 42.6334247],\n",
" 'San Buenaventura (Ventura)': [-119.2290316, 34.274646],\n",
" 'Pueblo': [-104.6091409, 38.2544472],\n",
" 'High Point': [-80.0053176, 35.9556923],\n",
" 'West Covina': [-117.9389526, 34.0686208],\n",
" 'Murrieta': [-117.2139232, 33.5539143],\n",
" 'Cambridge': [-71.10973349999999, 42.3736158],\n",
" 'Antioch': [-121.805789, 38.0049214],\n",
" 'Temecula': [-117.1483648, 33.4936391],\n",
" 'Norwalk': [-73.4081575, 41.11774399999999],\n",
" 'Centennial': [-104.8771726, 39.5807452],\n",
" 'Everett': [-71.0536625, 42.40843],\n",
" 'Palm Bay': [-80.5886646, 28.0344621],\n",
" 'Wichita Falls': [-98.4933873, 33.9137085],\n",
" 'Green Bay': [-88.019826, 44.51915899999999],\n",
" 'Daly City': [-122.4702079, 37.6879241],\n",
" 'Burbank': [-118.3089661, 34.1808392],\n",
" 'Richardson': [-96.7298519, 32.9483335],\n",
" 'Pompano Beach': [-80.1247667, 26.2378597],\n",
" 'North Charleston': [-79.9748103, 32.8546197],\n",
" 'Broken Arrow': [-95.7974526, 36.060949],\n",
" 'Boulder': [-105.2705456, 40.0149856],\n",
" 'West Palm Beach': [-80.0533746, 26.7153424],\n",
" 'Santa Maria': [-120.4357191, 34.9530337],\n",
" 'El Cajon': [-116.9625269, 32.7947731],\n",
" 'Davenport': [-90.5776367, 41.5236437],\n",
" 'Rialto': [-117.3703235, 34.1064001],\n",
" 'Las Cruces': [-106.7636538, 32.3199396],\n",
" 'San Mateo': [-122.3255254, 37.5629917],\n",
" 'Lewisville': [-96.994174, 33.046233],\n",
" 'South Bend': [-86.25198979999999, 41.6763545],\n",
" 'Lakeland': [-81.9498042, 28.0394654],\n",
" 'Erie': [-80.085059, 42.12922409999999],\n",
" 'Tyler': [-95.30106239999999, 32.3512601],\n",
" 'Pearland': [-95.2860474, 29.5635666],\n",
" 'College Station': [-96.3344068, 30.627977],\n",
" 'Kenosha': [-87.82118539999999, 42.5847425],\n",
" 'Sandy Springs': [-84.3733147, 33.9304352],\n",
" 'Clovis': [-103.2052272, 34.4047987],\n",
" 'Flint': [-83.6874562, 43.0125274],\n",
" 'Roanoke': [-79.9414266, 37.2709704],\n",
" 'Albany': [-123.1059282, 44.6365107],\n",
" 'Jurupa Valley': [-117.4854802, 33.9971974],\n",
" 'Compton': [-118.2200712, 33.8958492],\n",
" 'San Angelo': [-100.4370375, 31.4637723],\n",
" 'Hillsboro': [-122.989827, 45.5228939],\n",
" 'Lawton': [-98.39592909999999, 34.6035669],\n",
" 'Renton': [-122.2170661, 47.48287759999999],\n",
" 'Vista': [-117.2425355, 33.2000368],\n",
" 'Davie': [-80.25211569999999, 26.0764783],\n",
" 'Greeley': [-104.7091322, 40.4233142],\n",
" 'Mission Viejo': [-117.6719953, 33.6000232],\n",
" 'Portsmouth': [-76.2982742, 36.8354258],\n",
" 'Dearborn': [-83.17631449999999, 42.3222599],\n",
" 'South Gate': [-118.2120161, 33.954737],\n",
" 'Tuscaloosa': [-87.56917349999999, 33.2098407],\n",
" 'Livonia': [-83.35270969999999, 42.36837],\n",
" 'New Bedford': [-70.93420499999999, 41.6362152],\n",
" 'Vacaville': [-121.9877444, 38.3565773],\n",
" 'Brockton': [-71.0183787, 42.0834335],\n",
" 'Roswell': [-104.5230242, 33.3942655],\n",
" 'Beaverton': [-122.8037102, 45.48706199999999],\n",
" 'Quincy': [-91.4098726, 39.9356016],\n",
" 'Sparks': [-119.7526886, 39.5349112],\n",
" 'Yakima': [-120.5058987, 46.6020711],\n",
" \"Lee's Summit\": [-94.3821724, 38.9108408],\n",
" 'Federal Way': [-122.3126222, 47.3223221],\n",
" 'Carson': [-118.281693, 33.8316745],\n",
" 'Santa Monica': [-118.4911912, 34.0194543],\n",
" 'Hesperia': [-117.3008784, 34.4263886],\n",
" 'Allen': [-96.67055030000002, 33.1031744],\n",
" 'Rio Rancho': [-106.6630437, 35.2327544],\n",
" 'Yuma': [-114.6276916, 32.6926512],\n",
" 'Orem': [-111.6946475, 40.2968979],\n",
" 'Lynn': [-70.9494938, 42.46676300000001],\n",
" 'Redding': [-122.3916754, 40.5865396],\n",
" 'Spokane Valley': [-117.2393748, 47.6732281],\n",
" 'Miami Beach': [-80.1300455, 25.790654],\n",
" 'League City': [-95.0949303, 29.5074538],\n",
" 'Lawrence': [-86.0252612, 39.8386516],\n",
" 'Santa Barbara': [-119.6981901, 34.4208305],\n",
" 'Plantation': [-80.23310359999999, 26.1275862],\n",
" 'Sandy': [-111.8389726, 40.5649781],\n",
" 'Sunrise': [-80.25659499999999, 26.1669711],\n",
" 'Macon': [-83.6324022, 32.8406946],\n",
" 'Longmont': [-105.1019275, 40.1672068],\n",
" 'Boca Raton': [-80.1289321, 26.3683064],\n",
" 'San Marcos': [-97.9413941, 29.8832749],\n",
" 'Greenville': [-82.3940104, 34.85261759999999],\n",
" 'Waukegan': [-87.84479379999999, 42.3636331],\n",
" 'Fall River': [-71.1550451, 41.7014912],\n",
" 'Chico': [-121.8374777, 39.7284944],\n",
" 'Newton': [-71.20922139999999, 42.3370413],\n",
" 'San Leandro': [-122.1560768, 37.7249296],\n",
" 'Reading': [-75.9268747, 40.3356483],\n",
" 'Fort Smith': [-94.39854749999999, 35.3859242],\n",
" 'Newport Beach': [-117.9289469, 33.6189101],\n",
" 'Asheville': [-82.5514869, 35.5950581],\n",
" 'Nashua': [-71.46756599999999, 42.7653662],\n",
" 'Edmond': [-97.47809540000002, 35.6528323],\n",
" 'Whittier': [-118.032844, 33.9791793],\n",
" 'Nampa': [-116.5634624, 43.5407172],\n",
" 'Bloomington': [-88.99368729999999, 40.4842027],\n",
" 'Deltona': [-81.26367379999999, 28.9005446],\n",
" 'Hawthorne': [-118.3525748, 33.9164032],\n",
" 'Duluth': [-92.1004852, 46.78667189999999],\n",
" 'Carmel': [-86.1180435, 39.978371],\n",
" 'Suffolk': [-76.5835621, 36.7282054],\n",
" 'Clifton': [-74.16375529999999, 40.8584328],\n",
" 'Citrus Heights': [-121.2810611, 38.7071247],\n",
" 'Livermore': [-121.7680088, 37.6818745],\n",
" 'Tracy': [-121.4252227, 37.7396513],\n",
" 'Alhambra': [-118.1270146, 34.095287],\n",
" 'Kirkland': [-122.2087353, 47.6814875],\n",
" 'Trenton': [-74.7429384, 40.2170534],\n",
" 'Ogden': [-111.9738304, 41.223],\n",
" 'Hoover': [-86.8113781, 33.4053867],\n",
" 'Cicero': [-87.7539448, 41.8455877],\n",
" 'Fishers': [-86.01335, 39.9567548],\n",
" 'Sugar Land': [-95.6349463, 29.6196787],\n",
" 'Danbury': [-73.4540111, 41.394817],\n",
" 'Meridian': [-88.703656, 32.3643098],\n",
" 'Indio': [-116.2155619, 33.7205771],\n",
" 'Menifee': [-117.185294, 33.6971468],\n",
" 'Champaign': [-88.2433829, 40.1164204],\n",
" 'Buena Park': [-117.9981181, 33.8675143],\n",
" 'Troy': [-73.69178509999999, 42.7284117],\n",
" \"O'Fallon\": [-90.69984769999999, 38.8106075],\n",
" 'Johns Creek': [-84.198579, 34.0289259],\n",
" 'Bellingham': [-122.4781473, 48.74908],\n",
" 'Westland': [-83.400211, 42.32420399999999],\n",
" 'Sioux City': [-96.40030689999999, 42.4999942],\n",
" 'Warwick': [-71.4161671, 41.7001009],\n",
" 'Hemet': [-116.9719684, 33.7475203],\n",
" 'Longview': [-94.74048909999999, 32.5007037],\n",
" 'Farmington Hills': [-83.3677168, 42.4989936],\n",
" 'Bend': [-121.3153096, 44.0581728],\n",
" 'Merced': [-120.4829677, 37.3021632],\n",
" 'Mission': [-98.32529319999999, 26.2159066],\n",
" 'Chino': [-117.688944, 34.0122346],\n",
" 'Redwood City': [-122.2363548, 37.48521520000001],\n",
" 'Edinburg': [-98.1633432, 26.3017374],\n",
" 'Cranston': [-71.4372796, 41.7798226],\n",
" 'Parma': [-81.7229086, 41.4047742],\n",
" 'New Rochelle': [-73.7823549, 40.9114882],\n",
" 'Lake Forest': [-117.689218, 33.6469661],\n",
" 'Napa': [-122.286865, 38.2975381],\n",
" 'Hammond': [-87.5000412, 41.5833688],\n",
" 'Avondale': [-112.3496021, 33.4355977],\n",
" 'Somerville': [-71.0994968, 42.3875968],\n",
" 'Palm Coast': [-81.20786989999999, 29.5844524],\n",
" 'Bryan': [-96.3699632, 30.6743643],\n",
" 'Gary': [-87.3464271, 41.5933696],\n",
" 'Largo': [-82.7873244, 27.9094665],\n",
" 'Brooklyn Park': [-93.3563405, 45.0941315],\n",
" 'Tustin': [-117.826166, 33.7458511],\n",
" 'Racine': [-87.78285230000002, 42.7261309],\n",
" 'Deerfield Beach': [-80.09976569999999, 26.3184123],\n",
" 'Lynchburg': [-79.14224639999999, 37.4137536],\n",
" 'Mountain View': [-122.0838511, 37.3860517],\n",
" 'Medford': [-71.1061639, 42.4184296],\n",
" 'Bellflower': [-118.1170117, 33.8816818],\n",
" 'Melbourne': [-80.60810889999999, 28.0836269],\n",
" 'St. Joseph': [-94.84668099999999, 39.7674578],\n",
" 'Camden': [-75.1196199, 39.9259463],\n",
" 'St. George': [-113.5684164, 37.0965278],\n",
" 'Kennewick': [-119.1372338, 46.2112458],\n",
" 'Baldwin Park': [-117.9608978, 34.0852868],\n",
" 'Chino Hills': [-117.7325848, 33.9898188],\n",
" 'Alameda': [-122.2416355, 37.7652065],\n",
" 'Arlington Heights': [-87.98062650000001, 42.0883603],\n",
" 'Scranton': [-75.66241219999999, 41.408969],\n",
" 'Evanston': [-87.68769689999999, 42.0450722],\n",
" 'Kalamazoo': [-85.5872286, 42.2917069],\n",
" 'Baytown': [-94.97742740000001, 29.7355047],\n",
" 'Upland': [-117.6483876, 34.09751],\n",
" 'Springdale': [-94.1288141, 36.18674420000001],\n",
" 'Bethlehem': [-75.37045789999999, 40.6259316],\n",
" 'Schaumburg': [-88.0834059, 42.0333607],\n",
" 'Mount Pleasant': [-79.82842579999999, 32.8323225],\n",
" 'Auburn': [-85.48078249999999, 32.6098566],\n",
" 'Decatur': [-86.9833417, 34.6059253],\n",
" 'San Ramon': [-121.9780153, 37.7799273],\n",
" 'Pleasanton': [-121.8746789, 37.6624312],\n",
" 'Wyoming': [-85.7053085, 42.9133602],\n",
" 'Lake Charles': [-93.2173758, 30.2265949],\n",
" 'Plymouth': [-93.4555093, 45.0105194],\n",
" 'Bolingbrook': [-88.0683955, 41.69864159999999],\n",
" 'Pharr': [-98.1836216, 26.1947962],\n",
" 'Appleton': [-88.41538469999999, 44.2619309],\n",
" 'Gastonia': [-81.18730049999999, 35.262082],\n",
" 'Folsom': [-121.1760583, 38.6779591],\n",
" 'Southfield': [-83.2218731, 42.4733688],\n",
" 'Rochester Hills': [-83.1499322, 42.65836609999999],\n",
" 'New Britain': [-72.7795419, 41.6612104],\n",
" 'Goodyear': [-112.3576567, 33.4353394],\n",
" 'Canton': [-81.378447, 40.79894729999999],\n",
" 'Warner Robins': [-83.624201, 32.6130007],\n",
" 'Union City': [-74.26316349999999, 40.6975898],\n",
" 'Perris': [-117.2286478, 33.7825194],\n",
" 'Manteca': [-121.2160526, 37.7974273],\n",
" 'Iowa City': [-91.5301683, 41.6611277],\n",
" 'Jonesboro': [-90.704279, 35.84229670000001],\n",
" 'Lynwood': [-118.2114603, 33.930293],\n",
" 'Loveland': [-105.0749801, 40.3977612],\n",
" 'Pawtucket': [-71.38255579999999, 41.878711],\n",
" 'Boynton Beach': [-80.0905465, 26.5317866],\n",
" 'Waukesha': [-88.2314813, 43.0116784],\n",
" 'Gulfport': [-89.0928155, 30.3674198],\n",
" 'Apple Valley': [-93.21772000000001, 44.7319094],\n",
" 'Passaic': [-74.1284764, 40.8567662],\n",
" 'Rapid City': [-103.2310149, 44.0805434],\n",
" 'Layton': [-111.9710529, 41.0602216],\n",
" 'Turlock': [-120.8465941, 37.4946568],\n",
" 'Muncie': [-85.3863599, 40.1933767],\n",
" 'Temple': [-97.342782, 31.0982344],\n",
" 'Missouri City': [-95.5377215, 29.6185669],\n",
" 'Redlands': [-117.1825381, 34.0555693],\n",
" 'Santa Fe': [-105.937799, 35.6869752],\n",
" 'Lauderhill': [-80.2133808, 26.1403635],\n",
" 'Milpitas': [-121.8995741, 37.4323341],\n",
" 'Palatine': [-88.03424000000001, 42.1103041],\n",
" 'Missoula': [-113.996586, 46.87871759999999],\n",
" 'Rock Hill': [-81.02507840000001, 34.9248667],\n",
" 'Franklin': [-86.8688899, 35.9250637],\n",
" 'Flagstaff': [-111.651302, 35.1982836],\n",
" 'Flower Mound': [-97.0969552, 33.0145673],\n",
" 'Weston': [-80.3997748, 26.1003654],\n",
" 'Waterloo': [-92.34257749999999, 42.492786],\n",
" 'Mount Vernon': [-73.8370786, 40.9125992],\n",
" 'Fort Myers': [-81.8723084, 26.640628],\n",
" 'Dothan': [-85.3904888, 31.2232313],\n",
" 'Rancho Cordova': [-121.302728, 38.5890723],\n",
" 'Redondo Beach': [-118.3884078, 33.8491816],\n",
" 'Pasco': [-119.1005657, 46.2395793],\n",
" 'St. Charles': [-90.4974359, 38.7881062],\n",
" 'Eau Claire': [-91.4984941, 44.811349],\n",
" 'North Richland Hills': [-97.2289029, 32.8342952],\n",
" 'Bismarck': [-100.7837392, 46.8083268],\n",
" 'Yorba Linda': [-117.8131125, 33.8886259],\n",
" 'Kenner': [-90.2417434, 29.9940924],\n",
" 'Walnut Creek': [-122.0651819, 37.9100783],\n",
" 'Frederick': [-77.4105409, 39.41426879999999],\n",
" 'Oshkosh': [-88.5426136, 44.0247062],\n",
" 'Pittsburg': [-121.8846806, 38.0279762],\n",
" 'Palo Alto': [-122.1430195, 37.4418834],\n",
" 'Bossier City': [-93.7321228, 32.5159852],\n",
" 'St. Cloud': [-81.2811801, 28.2489016],\n",
" 'Davis': [-121.7405167, 38.5449065],\n",
" 'South San Francisco': [-122.4077498, 37.654656],\n",
" 'Camarillo': [-119.0376023, 34.2163937],\n",
" 'North Little Rock': [-92.2670941, 34.769536],\n",
" 'Schenectady': [-73.9395687, 42.8142432],\n",
" 'Gaithersburg': [-77.2013705, 39.1434406],\n",
" 'Harlingen': [-97.69610259999999, 26.1906306],\n",
" 'Woodbury': [-92.9593797, 44.9238552],\n",
" 'Eagan': [-93.1668858, 44.8041322],\n",
" 'Yuba City': [-121.6169108, 39.1404477],\n",
" 'Maple Grove': [-93.4557877, 45.0724642],\n",
" 'Youngstown': [-80.6495194, 41.0997803],\n",
" 'Skokie': [-87.7416246, 42.0324025],\n",
" 'Kissimmee': [-81.40757099999999, 28.2919557],\n",
" 'Johnson City': [-82.3534727, 36.3134397],\n",
" 'Victoria': [-97.0035982, 28.8052674],\n",
" 'San Clemente': [-117.6119925, 33.4269728],\n",
" 'Bayonne': [-74.1143091, 40.6687141],\n",
" 'Laguna Niguel': [-117.7075526, 33.5225261],\n",
" 'East Orange': [-74.2048677, 40.767323],\n",
" 'Shawnee': [-94.7151865, 39.02284849999999],\n",
" 'Homestead': [-80.4775569, 25.4687224],\n",
" 'Rockville': [-77.1527578, 39.0839973],\n",
" 'Delray Beach': [-80.0728201, 26.4614625],\n",
" 'Janesville': [-89.0187222, 42.6827885],\n",
" 'Conway': [-92.4421011, 35.0886963],\n",
" 'Pico Rivera': [-118.096735, 33.9830688],\n",
" 'Lorain': [-82.1823746, 41.452819],\n",
" 'Montebello': [-118.1137535, 34.0165053],\n",
" 'Lodi': [-121.2722194, 38.1341477],\n",
" 'New Braunfels': [-98.1244531, 29.7030024],\n",
" 'Marysville': [-122.1770818, 48.0517637],\n",
" 'Tamarac': [-80.2497707, 26.2128609],\n",
" 'Madera': [-120.0607176, 36.9613356],\n",
" 'Conroe': [-95.45605119999999, 30.3118769],\n",
" 'Santa Cruz': [-122.0307963, 36.9741171],\n",
" 'Eden Prairie': [-93.47078599999999, 44.8546856],\n",
" 'Cheyenne': [-104.8202462, 41.1399814],\n",
" 'Daytona Beach': [-81.0228331, 29.2108147],\n",
" 'Alpharetta': [-84.2940899, 34.0753762],\n",
" 'Hamilton': [-84.5613355, 39.3995008],\n",
" 'Waltham': [-71.2356113, 42.3764852],\n",
" 'Coon Rapids': [-93.30300629999999, 45.1732394],\n",
" 'Haverhill': [-71.0772796, 42.7762015],\n",
" 'Council Bluffs': [-95.8608333, 41.2619444],\n",
" 'Taylor': [-83.2696509, 42.240872],\n",
" 'Utica': [-75.232664, 43.100903],\n",
" 'Ames': [-93.61999999999999, 42.034722],\n",
" 'La Habra': [-117.9461734, 33.9319578],\n",
" 'Encinitas': [-117.2919818, 33.0369867],\n",
" 'Bowling Green': [-86.4808043, 36.9685219],\n",
" 'Burnsville': [-93.27772259999999, 44.7677424],\n",
" 'West Des Moines': [-93.711332, 41.5772115],\n",
" 'Cedar Park': [-97.8202888, 30.505198],\n",
" 'Tulare': [-119.3473379, 36.2077288],\n",
" 'Monterey Park': [-118.1228476, 34.0625106],\n",
" 'Vineland': [-75.02596369999999, 39.4863773],\n",
" 'Terre Haute': [-87.41390919999999, 39.4667034],\n",
" 'North Miami': [-80.1867138, 25.8900949],\n",
" 'Mansfield': [-82.5154471, 40.75839],\n",
" 'West Allis': [-88.0070315, 43.0166806],\n",
" 'Bristol': [-72.9492703, 41.67176480000001],\n",
" 'Taylorsville': [-111.9388258, 40.66772479999999],\n",
" 'Malden': [-71.066163, 42.4250964],\n",
" 'Meriden': [-72.80704349999999, 41.5381535],\n",
" 'Blaine': [-93.23494889999999, 45.1607987],\n",
" 'Wellington': [-80.2683571, 26.6617635],\n",
" 'Cupertino': [-122.0321823, 37.3229978],\n",
" 'Rogers': [-94.1185366, 36.3320196],\n",
" 'St. Clair Shores': [-82.89636039999999, 42.4974085],\n",
" 'Gardena': [-118.3089624, 33.8883487],\n",
" 'Pontiac': [-83.29104679999999, 42.6389216],\n",
" 'National City': [-117.0991967, 32.6781085],\n",
" 'Grand Junction': [-108.5506486, 39.0638705],\n",
" 'Rocklin': [-121.2357828, 38.7907339],\n",
" 'Chapel Hill': [-79.0558445, 35.9131996],\n",
" 'Casper': [-106.313081, 42.866632],\n",
" 'Broomfield': [-105.0866504, 39.9205411],\n",
" 'Petaluma': [-122.6366524, 38.232417],\n",
" 'South Jordan': [-111.929658, 40.5621704],\n",
" 'Great Falls': [-111.2833449, 47.4941836],\n",
" 'North Port': [-82.2359254, 27.044224],\n",
" 'Marietta': [-84.5499327, 33.95260200000001],\n",
" 'San Rafael': [-122.5310874, 37.9735346],\n",
" 'Royal Oak': [-83.1446485, 42.4894801],\n",
" 'Des Plaines': [-87.88339909999999, 42.0333623],\n",
" 'Huntington Park': [-118.2250725, 33.9816812],\n",
" 'La Mesa': [-117.0230839, 32.7678287],\n",
" 'Orland Park': [-87.85394250000002, 41.6303103],\n",
" 'Lakeville': [-93.24271999999999, 44.6496868],\n",
" 'Owensboro': [-87.1111676, 37.7719074],\n",
" 'Moore': [-97.48670279999999, 35.3395079],\n",
" 'Jupiter': [-80.0942087, 26.9342246],\n",
" 'Idaho Falls': [-112.0339645, 43.49165139999999],\n",
" 'Dubuque': [-90.66457179999999, 42.5005583],\n",
" 'Bartlett': [-88.1856301, 41.9950276],\n",
" 'Rowlett': [-96.56388, 32.9029017],\n",
" 'Novi': [-83.4754913, 42.48059],\n",
" 'White Plains': [-73.7629097, 41.03398620000001],\n",
" 'Arcadia': [-118.0353449, 34.1397292],\n",
" 'Redmond': [-122.121512, 47.6739881],\n",
" 'Lake Elsinore': [-117.3272615, 33.6680772],\n",
" 'Ocala': [-82.14009229999999, 29.1871986],\n",
" 'Tinley Park': [-87.7932939, 41.5731442],\n",
" 'Port Orange': [-80.9956105, 29.1383165],\n",
" 'Oak Lawn': [-87.7479528, 41.719978],\n",
" 'Rocky Mount': [-77.7905339, 35.9382103],\n",
" 'Kokomo': [-86.13360329999999, 40.486427],\n",
" 'Coconut Creek': [-80.17893509999999, 26.2517482],\n",
" 'Bowie': [-76.77913649999999, 39.0067768],\n",
" 'Berwyn': [-87.7936685, 41.85058739999999],\n",
" 'Midwest City': [-97.3967019, 35.4495065],\n",
" 'Fountain Valley': [-117.9536697, 33.7091847],\n",
" 'Buckeye': [-112.5837766, 33.3703197],\n",
" 'Dearborn Heights': [-83.27326269999999, 42.3369816],\n",
" 'Woodland': [-121.7732971, 38.67851570000001],\n",
" 'Noblesville': [-86.0085955, 40.0455917],\n",
" 'Valdosta': [-83.2784851, 30.8327022],\n",
" 'Diamond Bar': [-117.8103367, 34.0286226],\n",
" 'Manhattan': [-96.57166939999999, 39.18360819999999],\n",
" 'Santee': [-116.9739167, 32.8383828],\n",
" 'Taunton': [-71.0897674, 41.900101],\n",
" 'Sanford': [-81.269453, 28.8028612],\n",
" 'Kettering': [-84.1688274, 39.68950359999999],\n",
" 'New Brunswick': [-74.4518188, 40.4862157],\n",
" 'Chicopee': [-72.6078672, 42.1487043],\n",
" 'Anderson': [-85.6802541, 40.1053196],\n",
" 'Margate': [-80.206436, 26.2445263],\n",
" 'Weymouth Town': [-70.94103559999999, 42.2180724],\n",
" 'Hempstead': [-73.6187397, 40.7062128],\n",
" 'Corvallis': [-123.2620435, 44.5645659],\n",
" 'Eastvale': [-117.5848025, 33.952463],\n",
" 'Porterville': [-119.0167679, 36.06523],\n",
" 'West Haven': [-72.9469711, 41.2705484],\n",
" 'Brentwood': [-86.78277720000001, 36.0331164],\n",
" 'Paramount': [-118.1597911, 33.8894598],\n",
" 'Grand Forks': [-97.0328547, 47.9252568],\n",
" 'Georgetown': [-97.6779842, 30.6332618],\n",
" 'St. Peters': [-90.6298922, 38.7874699],\n",
" 'Shoreline': [-122.3415178, 47.7556531],\n",
" 'Mount Prospect': [-87.9372908, 42.0664167],\n",
" 'Hanford': [-119.6456844, 36.3274502],\n",
" 'Normal': [-88.9906312, 40.5142026],\n",
" 'Rosemead': [-118.072846, 34.0805651],\n",
" 'Lehi': [-111.8507662, 40.3916172],\n",
" 'Pocatello': [-112.4455344, 42.8713032],\n",
" 'Highland': [-117.2086513, 34.1283442],\n",
" 'Novato': [-122.5697032, 38.1074198],\n",
" 'Port Arthur': [-93.93994699999999, 29.8849504],\n",
" 'Carson City': [-119.7674034, 39.1637984],\n",
" 'Hendersonville': [-86.6199957, 36.3047735],\n",
" 'Elyria': [-82.10764859999999, 41.3683798],\n",
" 'Revere': [-71.0119948, 42.4084302],\n",
" 'Pflugerville': [-97.62000429999999, 30.4393696],\n",
" 'Greenwood': [-86.10665259999999, 39.6136578],\n",
" 'Wheaton': [-88.1070127, 41.8661403],\n",
" 'Smyrna': [-86.5186045, 35.9828412],\n",
" 'Sarasota': [-82.53065269999999, 27.3364347],\n",
" 'Blue Springs': [-94.2816148, 39.0169509],\n",
" 'Colton': [-117.3136547, 34.0739016],\n",
" 'Euless': [-97.08195409999999, 32.8370727],\n",
" 'Castle Rock': [-104.8560902, 39.3722121],\n",
" 'Cathedral City': [-116.4668036, 33.7805388],\n",
" 'Kingsport': [-82.5618186, 36.548434],\n",
" 'Lake Havasu City': [-114.3224548, 34.483901],\n",
" 'Pensacola': [-87.2169149, 30.42130899999999],\n",
" 'Hoboken': [-74.0323626, 40.7439905],\n",
" 'Yucaipa': [-117.0430865, 34.033625],\n",
" 'Watsonville': [-121.7568946, 36.910231],\n",
" 'Richland': [-119.2844621, 46.2856907],\n",
" 'Delano': [-119.2470536, 35.7688425],\n",
" 'Hoffman Estates': [-88.12271989999999, 42.0629915],\n",
" 'Florissant': [-90.322614, 38.789217],\n",
" 'Placentia': [-117.8703363, 33.8722371],\n",
" 'West New York': [-74.0143064, 40.7878788],\n",
" 'Dublin': [-83.1140771, 40.0992294],\n",
" 'Oak Park': [-87.7845025, 41.8850317],\n",
" 'Peabody': [-70.9286609, 42.5278731],\n",
" 'Perth Amboy': [-74.2654234, 40.5067723],\n",
" 'Battle Creek': [-85.17971419999999, 42.3211522],\n",
" 'Bradenton': [-82.5748194, 27.4989278],\n",
" 'Gilroy': [-121.5682751, 37.0057816],\n",
" 'Milford': [-73.064036, 41.2306979],\n",
" 'Ankeny': [-93.6001278, 41.7317884],\n",
" 'La Crosse': [-91.23958069999999, 43.8013556],\n",
" 'Burlington': [-73.21207199999999, 44.4758825],\n",
" 'DeSoto': [-96.8570738, 32.5896998],\n",
" 'Harrisonburg': [-78.8689155, 38.4495688],\n",
" 'Minnetonka': [-93.4687489, 44.9211836],\n",
" 'Elkhart': [-85.9766671, 41.6819935],\n",
" 'Glendora': [-117.865339, 34.1361187],\n",
" 'Southaven': [-90.0125913, 34.9889818],\n",
" 'Joplin': [-94.51328099999999, 37.08422710000001],\n",
" 'Enid': [-97.8783911, 36.3955891],\n",
" 'Palm Beach Gardens': [-80.13865469999999, 26.8233946],\n",
" 'Brookhaven': [-84.3365917, 33.8651033],\n",
" 'Plainfield': [-88.2120315, 41.632223],\n",
" 'Grand Island': [-98.3420118, 40.9263957],\n",
" 'Palm Desert': [-116.3744556, 33.7222445],\n",
" 'Huntersville': [-80.84285040000002, 35.410694],\n",
" 'Tigard': [-122.7714861, 45.4312294],\n",
" 'Lenexa': [-94.73357089999999, 38.9536174],\n",
" 'Saginaw': [-83.9508068, 43.4194699],\n",
" 'Kentwood': [-85.64474919999999, 42.8694731],\n",
" 'Doral': [-80.3553302, 25.8195424],\n",
" 'Grapevine': [-97.0780654, 32.9342919],\n",
" 'Aliso Viejo': [-117.7256083, 33.5676842],\n",
" 'Sammamish': [-122.0355736, 47.61626829999999],\n",
" 'Casa Grande': [-111.7573521, 32.8795022],\n",
" 'Pinellas Park': [-82.6995443, 27.8428025],\n",
" 'West Sacramento': [-121.530234, 38.5804609],\n",
" 'Burien': [-122.3467918, 47.4703767],\n",
" 'Commerce City': [-104.9338675, 39.8083196],\n",
" 'Monroe': [-92.1193012, 32.5093109],\n",
" 'Cerritos': [-118.0647871, 33.8583483],\n",
" 'Downers Grove': [-88.01117459999999, 41.8089191],\n",
" 'Coral Gables': [-80.2683838, 25.72149],\n",
" 'Wilson': [-77.9155395, 35.7212689],\n",
" 'Niagara Falls': [-79.0377388, 43.0962143],\n",
" 'Poway': [-117.0358646, 32.9628232],\n",
" 'Edina': [-93.3499489, 44.8896866],\n",
" 'Cuyahoga Falls': [-81.48455849999999, 41.1339449],\n",
" 'Rancho Santa Margarita': [-117.603104, 33.640855],\n",
" 'Harrisburg': [-76.8867008, 40.2731911],\n",
" 'Huntington': [-82.44515400000002, 38.4192496],\n",
" 'La Mirada': [-118.0120086, 33.9172357],\n",
" 'Cypress': [-118.0372852, 33.8169599],\n",
" 'Caldwell': [-116.6873596, 43.66293839999999],\n",
" 'Logan': [-111.8338359, 41.7369803],\n",
" 'Galveston': [-94.7976958, 29.3013479],\n",
" 'Sheboygan': [-87.71453, 43.7508284],\n",
" 'Middletown': [-72.6506488, 41.5623209],\n",
" 'Murray': [-111.8879909, 40.6668916],\n",
" 'Parker': [-104.7613633, 39.5186002],\n",
" 'Bedford': [-97.1430671, 32.844017],\n",
" 'East Lansing': [-84.48386540000001, 42.7369792],\n",
" 'Methuen': [-71.1908924, 42.7262016],\n",
" 'Covina': [-117.8903397, 34.0900091],\n",
" 'Olympia': [-122.9006951, 47.0378741],\n",
" 'Euclid': [-81.5267873, 41.5931049],\n",
" 'Mishawaka': [-86.15861559999999, 41.6619927],\n",
" 'Salina': [-97.61142369999999, 38.8402805],\n",
" 'Azusa': [-117.9075627, 34.1336186],\n",
" 'Chesterfield': [-90.5770675, 38.6631083],\n",
" 'Leesburg': [-77.56360149999999, 39.1156615],\n",
" 'Dunwoody': [-84.3346473, 33.9462125],\n",
" 'Hattiesburg': [-89.29033919999999, 31.3271189],\n",
" 'Bonita Springs': [-81.7786972, 26.339806],\n",
" 'Portage': [-85.5800022, 42.2011538],\n",
" 'St. Louis Park': [-93.3702186, 44.9597376],\n",
" 'Collierville': [-89.6645266, 35.042036],\n",
" 'Stillwater': [-97.0583681, 36.1156071],\n",
" 'East Providence': [-71.3700545, 41.8137116],\n",
" 'Wauwatosa': [-88.0075875, 43.0494572],\n",
" 'Mentor': [-81.339552, 41.6661573],\n",
" 'Ceres': [-120.9577098, 37.5949316],\n",
" 'Cedar Hill': [-96.9561152, 32.5884689],\n",
" 'Binghamton': [-75.91797380000001, 42.09868669999999],\n",
" \"Coeur d'Alene\": [-116.7804664, 47.6776832],\n",
" 'San Luis Obispo': [-120.6596156, 35.2827524],\n",
" 'Minot': [-101.2922906, 48.2329668],\n",
" 'Palm Springs': [-116.5452921, 33.8302961],\n",
" 'Pine Bluff': [-92.00319549999999, 34.2284312],\n",
" 'Texas City': [-94.9027002, 29.383845],\n",
" 'Summerville': [-80.17564809999999, 33.0185039],\n",
" 'Twin Falls': [-114.4608711, 42.5629668],\n",
" 'Jeffersonville': [-85.7371847, 38.2775702],\n",
" 'San Jacinto': [-116.958635, 33.7839084],\n",
" 'Altoona': [-78.3947359, 40.5186809],\n",
" 'Beavercreek': [-84.06326849999999, 39.7092262],\n",
" 'Apopka': [-81.5322149, 28.6934076],\n",
" 'Elmhurst': [-87.9403418, 41.8994744],\n",
" 'Maricopa': [-112.0476423, 33.0581063],\n",
" 'Farmington': [-108.2186856, 36.72805830000001],\n",
" 'Glenview': [-87.7878408, 42.0697509],\n",
" 'Cleveland Heights': [-81.556235, 41.5200518],\n",
" 'Draper': [-111.8638226, 40.5246711],\n",
" 'Sierra Vista': [-110.2772856, 31.5455001],\n",
" 'Lacey': [-122.8231915, 47.03426289999999],\n",
" 'Biloxi': [-88.88530779999999, 30.3960318],\n",
" 'Strongsville': [-81.83569, 41.3144966],\n",
" 'Barnstable Town': [-70.3002024, 41.7003208],\n",
" 'Wylie': [-96.5388789, 33.0151201],\n",
" 'Sayreville': [-74.360846, 40.45940210000001],\n",
" 'Kannapolis': [-80.6217341, 35.4873613],\n",
" 'Charlottesville': [-78.47667810000002, 38.0293059],\n",
" 'Littleton': [-105.0166498, 39.613321],\n",
" 'Titusville': [-80.8075537, 28.6122187],\n",
" 'Hackensack': [-74.0434736, 40.8859325],\n",
" 'Pittsfield': [-73.2453824, 42.4500845],\n",
" 'York': [-76.727745, 39.9625984],\n",
" 'Lombard': [-88.00784349999999, 41.8800296],\n",
" 'Attleboro': [-71.2856082, 41.94454409999999],\n",
" 'DeKalb': [-88.75036469999999, 41.9294736],\n",
" 'Blacksburg': [-80.4139393, 37.2295733],\n",
" 'Haltom City': [-97.26918169999999, 32.7995738],\n",
" 'Lompoc': [-120.4579409, 34.6391501],\n",
" 'El Centro': [-115.5630514, 32.792],\n",
" 'Danville': [-79.39502279999999, 36.5859718],\n",
" 'Jefferson City': [-92.1735164, 38.57670170000001],\n",
" 'Cutler Bay': [-80.34685929999999, 25.5808323],\n",
" 'Oakland Park': [-80.1319893, 26.1723065],\n",
" 'North Miami Beach': [-80.1625463, 25.9331488],\n",
" 'Freeport': [-73.58318349999999, 40.6576022],\n",
" 'Moline': [-90.51513419999999, 41.5067003],\n",
" 'Coachella': [-116.173894, 33.6803003],\n",
" 'Fort Pierce': [-80.3256056, 27.4467056],\n",
" 'Bountiful': [-111.880771, 40.8893895],\n",
" 'Fond du Lac': [-88.4470508, 43.7730448],\n",
" 'Keller': [-97.229298, 32.9341893],\n",
" 'Belleville': [-89.9839935, 38.5200504],\n",
" 'Bell Gardens': [-118.1514588, 33.9652918],\n",
" 'North Lauderdale': [-80.2258811, 26.217305],\n",
" 'Rancho Palos Verdes': [-118.3870173, 33.7444613],\n",
" 'San Bruno': [-122.4110835, 37.6304904],\n",
" 'Apex': [-78.85028559999999, 35.732652],\n",
" 'Altamonte Springs': [-81.3656242, 28.6611089],\n",
" 'Hutchinson': [-97.92977429999999, 38.0608445],\n",
" 'Buffalo Grove': [-87.9631308, 42.1662831],\n",
" 'Urbandale': [-93.71216559999999, 41.6266555],\n",
" 'State College': [-77.8600012, 40.7933949],\n",
" 'Urbana': [-88.2072697, 40.1105875],\n",
" 'Manassas': [-77.47526669999999, 38.7509488],\n",
" 'Kearny': [-74.1454214, 40.7684342],\n",
" 'Oro Valley': [-110.966488, 32.3909071],\n",
" 'Findlay': [-83.6499321, 41.04422],\n",
" 'Rohnert Park': [-122.7010984, 38.3396367],\n",
" 'Westfield': [-72.749538, 42.1250929],\n",
" 'Linden': [-74.24459019999999, 40.6220478],\n",
" 'Sumter': [-80.3414693, 33.9204354],\n",
" 'Wilkes-Barre': [-75.88130749999999, 41.2459149],\n",
" 'Woonsocket': [-71.51478390000001, 42.00287609999999],\n",
" 'Leominster': [-71.759794, 42.5250906],\n",
" 'Shelton': [-73.0931641, 41.3164856],\n",
" 'Brea': [-117.9000604, 33.9166805],\n",
" 'Covington': [-84.5085536, 39.0836712],\n",
" 'Rockwall': [-96.4597089, 32.93123360000001],\n",
" 'Riverton': [-111.9391023, 40.521893],\n",
" 'Morgan Hill': [-121.6543901, 37.1305012],\n",
" 'Edmonds': [-122.3773552, 47.8106521],\n",
" 'Burleson': [-97.3208492, 32.5420821],\n",
" 'Beverly': [-70.880049, 42.5584283],\n",
" 'Mankato': [-93.99939959999999, 44.1635775],\n",
" 'Hagerstown': [-77.71999319999999, 39.6417629],\n",
" 'Prescott': [-112.4685025, 34.5400242],\n",
" 'Campbell': [-121.9499568, 37.2871651],\n",
" 'Cedar Falls': [-92.4453161, 42.5348993],\n",
" 'La Puente': [-117.9495083, 34.0200114],\n",
" 'Crystal Lake': [-88.31619649999999, 42.2411344],\n",
" 'Fitchburg': [-71.8022955, 42.5834228],\n",
" 'Carol Stream': [-88.13479269999999, 41.91252859999999],\n",
" 'Hickory': [-81.3444573, 35.7344538],\n",
" 'Streamwood': [-88.17840849999999, 42.0255827],\n",
" 'Norwich': [-72.07591049999999, 41.5242649],\n",
" 'Coppell': [-97.01500779999999, 32.9545687],\n",
" 'San Gabriel': [-118.1058333, 34.09611110000001],\n",
" 'Holyoke': [-72.6162009, 42.2042586],\n",
" 'Bentonville': [-94.2088172, 36.3728538],\n",
" 'Florence': [-79.7625625, 34.1954331],\n",
" 'Peachtree Corners': [-84.2214551, 33.9698929],\n",
" 'Bozeman': [-111.0429339, 45.6769979],\n",
" 'New Berlin': [-88.1084224, 42.9764027],\n",
" 'Goose Creek': [-80.03258670000001, 32.9810059],\n",
" 'Prescott Valley': [-112.315721, 34.6100243],\n",
" 'Maplewood': [-92.9952153, 44.9530215],\n",
" 'Romeoville': [-88.0895061, 41.6475306],\n",
" 'Duncanville': [-96.9083366, 32.6518004],\n",
" 'Atlantic City': [-74.4229266, 39.3642834],\n",
" 'The Colony': [-96.89283089999999, 33.0806083],\n",
" 'Culver City': [-118.3964665, 34.0211224],\n",
" 'Marlborough': [-71.5522874, 42.3459271],\n",
" 'Hilton Head Island': [-80.752608, 32.216316],\n",
" 'Moorhead': [-96.76780389999999, 46.8737648],\n",
" 'Calexico': [-115.4988834, 32.6789476],\n",
" 'Bullhead City': [-114.5285981, 35.1359386],\n",
" 'Germantown': [-89.8100858, 35.0867577],\n",
" 'La Quinta': [-116.3100095, 33.6633573],\n",
" 'Wausau': [-89.6301221, 44.9591352],\n",
" 'Sherman': [-96.6088805, 33.6356618],\n",
" 'Ocoee': [-81.5439619, 28.5691677],\n",
" 'Shakopee': [-93.5272861, 44.7973962],\n",
" 'Woburn': [-71.1522765, 42.4792618],\n",
" 'Bremerton': [-122.6329356, 47.5673202],\n",
" 'Rock Island': [-90.5787476, 41.5094771],\n",
" 'Muskogee': [-95.3696909, 35.7478769],\n",
" 'Cape Girardeau': [-89.51814759999999, 37.3058839],\n",
" 'Annapolis': [-76.4921829, 38.9784453],\n",
" 'Greenacres': [-80.1353896, 26.6276276],\n",
" 'Ormond Beach': [-81.0558894, 29.2858129],\n",
" 'Hallandale Beach': [-80.14837899999999, 25.9812024],\n",
" 'Stanton': [-117.9931165, 33.8025155],\n",
" 'Puyallup': [-122.2928974, 47.1853785],\n",
" 'Pacifica': [-122.4869194, 37.6138253],\n",
" 'Hanover Park': [-88.1450735, 41.9994722],\n",
" 'Hurst': [-97.1705678, 32.8234621],\n",
" 'Lima': [-84.1052256, 40.742551],\n",
" 'Marana': [-111.2224422, 32.436381],\n",
" 'Carpentersville': [-88.2578582, 42.1211364],\n",
" 'Oakley': [-121.7124536, 37.9974219],\n",
" 'Huber Heights': [-84.12466080000002, 39.843947],\n",
" 'Montclair': [-117.6897776, 34.0775104],\n",
" 'Wheeling': [-87.9289591, 42.1391927],\n",
" 'Brookfield': [-88.1064787, 43.0605671],\n",
" 'Park Ridge': [-87.84061919999999, 42.0111412],\n",
" 'Roy': [-112.0263313, 41.1616108],\n",
" 'Winter Garden': [-81.58618469999999, 28.5652787],\n",
" 'Chelsea': [-71.0328284, 42.3917638],\n",
" 'Valley Stream': [-73.70846449999999, 40.6642699],\n",
" 'Spartanburg': [-81.9320482, 34.9495672],\n",
" 'Lake Oswego': [-122.6706498, 45.42067489999999],\n",
" 'Friendswood': [-95.2010447, 29.5293998],\n",
" 'Westerville': [-82.92906959999999, 40.1261743],\n",
" 'Northglenn': [-104.9811468, 39.8961821],\n",
" 'Phenix City': [-85.0007653, 32.4709761],\n",
" 'Grove City': [-83.0929644, 39.88145189999999],\n",
" 'Texarkana': [-94.04768820000001, 33.425125],\n",
" 'Addison': [-87.9889556, 41.931696],\n",
" 'Dover': [-75.5243682, 39.158168],\n",
" 'Lincoln Park': [-83.1785361, 42.2505943],\n",
" 'Calumet City': [-87.5294871, 41.6155909],\n",
" 'Muskegon': [-86.24839209999999, 43.2341813],\n",
" 'Aventura': [-80.1392121, 25.9564812],\n",
" 'Martinez': [-122.1341321, 38.0193657],\n",
" 'Greenfield': [-88.0125865, 42.9614039],\n",
" 'Apache Junction': [-111.5495777, 33.4150485],\n",
" 'Monrovia': [-118.0019482, 34.1442616],\n",
" 'Weslaco': [-97.9908366, 26.1595194],\n",
" 'Keizer': [-123.0262077, 44.9901194],\n",
" 'Spanish Fork': [-111.654923, 40.114955],\n",
" 'Beloit': [-89.03177649999999, 42.5083482],\n",
" 'Panama City': [-85.6602058, 30.1588129]}"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lonlat"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "08f227e5",
"metadata": {},
"outputs": [],
"source": [
"# Woah! That's a lot of cities! Let's simplify with ones with a bigger population:\n",
"big_cities = {}\n",
"for city in cities_dataset:\n",
" if int(city['population']) > 100000:\n",
" big_cities[city['city']] = [city['longitude'], city['latitude']]"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "86447118",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'New York': [-74.0059413, 40.7127837],\n",
" 'Los Angeles': [-118.2436849, 34.0522342],\n",
" 'Chicago': [-87.6297982, 41.8781136],\n",
" 'Houston': [-95.3698028, 29.7604267],\n",
" 'Philadelphia': [-75.1652215, 39.9525839],\n",
" 'Phoenix': [-112.0740373, 33.4483771],\n",
" 'San Antonio': [-98.49362819999999, 29.4241219],\n",
" 'San Diego': [-117.1610838, 32.715738],\n",
" 'Dallas': [-96.79698789999999, 32.7766642],\n",
" 'San Jose': [-121.8863286, 37.3382082],\n",
" 'Austin': [-97.7430608, 30.267153],\n",
" 'Indianapolis': [-86.158068, 39.768403],\n",
" 'Jacksonville': [-81.65565099999999, 30.3321838],\n",
" 'San Francisco': [-122.4194155, 37.7749295],\n",
" 'Columbus': [-84.9877094, 32.4609764],\n",
" 'Charlotte': [-80.8431267, 35.2270869],\n",
" 'Fort Worth': [-97.3307658, 32.7554883],\n",
" 'Detroit': [-83.0457538, 42.331427],\n",
" 'El Paso': [-106.4424559, 31.7775757],\n",
" 'Memphis': [-90.0489801, 35.1495343],\n",
" 'Seattle': [-122.3320708, 47.6062095],\n",
" 'Denver': [-104.990251, 39.7392358],\n",
" 'Washington': [-77.0368707, 38.9071923],\n",
" 'Boston': [-71.0588801, 42.3600825],\n",
" 'Nashville-Davidson': [-86.7816016, 36.1626638],\n",
" 'Baltimore': [-76.6121893, 39.2903848],\n",
" 'Oklahoma City': [-97.5164276, 35.4675602],\n",
" 'Louisville/Jefferson County': [-85.7584557, 38.2526647],\n",
" 'Portland': [-122.6764816, 45.5230622],\n",
" 'Las Vegas': [-115.1398296, 36.1699412],\n",
" 'Milwaukee': [-87.9064736, 43.0389025],\n",
" 'Albuquerque': [-106.6055534, 35.0853336],\n",
" 'Tucson': [-110.926479, 32.2217429],\n",
" 'Fresno': [-119.7725868, 36.7468422],\n",
" 'Sacramento': [-121.4943996, 38.5815719],\n",
" 'Long Beach': [-118.1937395, 33.7700504],\n",
" 'Kansas City': [-94.6274636, 39.114053],\n",
" 'Mesa': [-111.8314724, 33.4151843],\n",
" 'Virginia Beach': [-75.97798499999999, 36.8529263],\n",
" 'Atlanta': [-84.3879824, 33.7489954],\n",
" 'Colorado Springs': [-104.8213634, 38.8338816],\n",
" 'Omaha': [-95.99798829999999, 41.2523634],\n",
" 'Raleigh': [-78.6381787, 35.7795897],\n",
" 'Miami': [-80.1917902, 25.7616798],\n",
" 'Oakland': [-122.2711137, 37.8043637],\n",
" 'Minneapolis': [-93.2650108, 44.977753],\n",
" 'Tulsa': [-95.99277500000001, 36.1539816],\n",
" 'Cleveland': [-81.6943605, 41.49932],\n",
" 'Wichita': [-97.336111, 37.688889],\n",
" 'Arlington': [-97.10806559999999, 32.735687],\n",
" 'New Orleans': [-90.0715323, 29.95106579999999],\n",
" 'Bakersfield': [-119.0187125, 35.3732921],\n",
" 'Tampa': [-82.4571776, 27.950575],\n",
" 'Honolulu': [-157.8583333, 21.3069444],\n",
" 'Aurora': [-88.32007150000001, 41.7605849],\n",
" 'Anaheim': [-117.9145036, 33.8352932],\n",
" 'Santa Ana': [-117.8678338, 33.7455731],\n",
" 'St. Louis': [-90.19940419999999, 38.6270025],\n",
" 'Riverside': [-117.3961564, 33.9533487],\n",
" 'Corpus Christi': [-97.39638099999999, 27.8005828],\n",
" 'Lexington-Fayette': [-84.5037164, 38.0405837],\n",
" 'Pittsburgh': [-79.9958864, 40.44062479999999],\n",
" 'Anchorage': [-149.9002778, 61.2180556],\n",
" 'Stockton': [-121.2907796, 37.9577016],\n",
" 'Cincinnati': [-84.5120196, 39.1031182],\n",
" 'St. Paul': [-93.0899578, 44.9537029],\n",
" 'Toledo': [-83.55521200000001, 41.6639383],\n",
" 'Greensboro': [-79.7919754, 36.0726354],\n",
" 'Newark': [-74.1723667, 40.735657],\n",
" 'Plano': [-96.6988856, 33.0198431],\n",
" 'Henderson': [-114.9817213, 36.0395247],\n",
" 'Lincoln': [-96.6851982, 40.8257625],\n",
" 'Buffalo': [-78.8783689, 42.88644679999999],\n",
" 'Jersey City': [-74.0776417, 40.72815749999999],\n",
" 'Chula Vista': [-117.0841955, 32.6400541],\n",
" 'Fort Wayne': [-85.1393513, 41.079273],\n",
" 'Orlando': [-81.3792365, 28.5383355],\n",
" 'St. Petersburg': [-82.64, 27.773056],\n",
" 'Chandler': [-111.8412502, 33.3061605],\n",
" 'Laredo': [-99.48032409999999, 27.5305671],\n",
" 'Norfolk': [-76.28587259999999, 36.8507689],\n",
" 'Durham': [-78.898619, 35.9940329],\n",
" 'Madison': [-89.4012302, 43.0730517],\n",
" 'Lubbock': [-101.8551665, 33.5778631],\n",
" 'Irvine': [-117.7946942, 33.6839473],\n",
" 'Winston-Salem': [-80.244216, 36.09985959999999],\n",
" 'Glendale': [-118.255075, 34.1425078],\n",
" 'Garland': [-96.63888329999999, 32.912624],\n",
" 'Hialeah': [-80.2781057, 25.8575963],\n",
" 'Reno': [-119.8138027, 39.5296329],\n",
" 'Chesapeake': [-76.2874927, 36.7682088],\n",
" 'Gilbert': [-111.789027, 33.3528264],\n",
" 'Baton Rouge': [-91.1403196, 30.4582829],\n",
" 'Irving': [-96.9488945, 32.8140177],\n",
" 'Scottsdale': [-111.9260519, 33.4941704],\n",
" 'North Las Vegas': [-115.1175013, 36.1988592],\n",
" 'Fremont': [-121.9885719, 37.5482697],\n",
" 'Boise City': [-116.2146068, 43.6187102],\n",
" 'Richmond': [-122.3477486, 37.9357576],\n",
" 'San Bernardino': [-117.2897652, 34.1083449],\n",
" 'Birmingham': [-86.80248999999999, 33.5206608],\n",
" 'Spokane': [-117.4260466, 47.6587802],\n",
" 'Rochester': [-92.4801989, 44.0121221],\n",
" 'Des Moines': [-93.6091064, 41.6005448],\n",
" 'Modesto': [-120.9968782, 37.63909719999999],\n",
" 'Fayetteville': [-78.87835849999999, 35.0526641],\n",
" 'Tacoma': [-122.4442906, 47.2528768],\n",
" 'Oxnard': [-119.1770516, 34.1975048],\n",
" 'Fontana': [-117.435048, 34.0922335],\n",
" 'Montgomery': [-86.2999689, 32.3668052],\n",
" 'Moreno Valley': [-117.2296717, 33.9424658],\n",
" 'Shreveport': [-93.7501789, 32.5251516],\n",
" 'Yonkers': [-73.89874689999999, 40.9312099],\n",
" 'Akron': [-81.51900529999999, 41.0814447],\n",
" 'Huntington Beach': [-117.9992265, 33.660297],\n",
" 'Little Rock': [-92.28959479999999, 34.7464809],\n",
" 'Augusta-Richmond County': [-82.0105148, 33.4734978],\n",
" 'Amarillo': [-101.8312969, 35.2219971],\n",
" 'Mobile': [-88.0398912, 30.6953657],\n",
" 'Grand Rapids': [-85.6680863, 42.9633599],\n",
" 'Salt Lake City': [-111.8910474, 40.7607793],\n",
" 'Tallahassee': [-84.28073289999999, 30.4382559],\n",
" 'Huntsville': [-86.5861037, 34.7303688],\n",
" 'Grand Prairie': [-96.99778459999999, 32.7459645],\n",
" 'Knoxville': [-83.9207392, 35.9606384],\n",
" 'Worcester': [-71.8022934, 42.2625932],\n",
" 'Newport News': [-76.4730122, 37.0870821],\n",
" 'Brownsville': [-97.4974838, 25.9017472],\n",
" 'Overland Park': [-94.6707917, 38.9822282],\n",
" 'Santa Clarita': [-118.542586, 34.3916641],\n",
" 'Providence': [-71.4128343, 41.8239891],\n",
" 'Garden Grove': [-117.9414477, 33.7739053],\n",
" 'Chattanooga': [-85.3096801, 35.0456297],\n",
" 'Oceanside': [-117.3794834, 33.1958696],\n",
" 'Jackson': [-90.1848103, 32.2987573],\n",
" 'Fort Lauderdale': [-80.13731740000001, 26.1224386],\n",
" 'Santa Rosa': [-122.7140548, 38.440429],\n",
" 'Rancho Cucamonga': [-117.5931084, 34.10639889999999],\n",
" 'Port St. Lucie': [-80.3582261, 27.2730492],\n",
" 'Tempe': [-111.9400054, 33.4255104],\n",
" 'Ontario': [-117.6508876, 34.0633443],\n",
" 'Vancouver': [-122.6614861, 45.6387281],\n",
" 'Cape Coral': [-81.9495331, 26.5628537],\n",
" 'Sioux Falls': [-96.73110340000001, 43.5445959],\n",
" 'Springfield': [-89.6501481, 39.78172130000001],\n",
" 'Peoria': [-89.5889864, 40.6936488],\n",
" 'Pembroke Pines': [-80.2962555, 26.007765],\n",
" 'Elk Grove': [-121.3716178, 38.4087993],\n",
" 'Salem': [-123.0350963, 44.9428975],\n",
" 'Lancaster': [-118.1541632, 34.6867846],\n",
" 'Corona': [-117.5664384, 33.8752935],\n",
" 'Eugene': [-123.0867536, 44.0520691],\n",
" 'Palmdale': [-118.1164613, 34.5794343],\n",
" 'Salinas': [-121.6555013, 36.6777372],\n",
" 'Pasadena': [-118.1445155, 34.1477849],\n",
" 'Fort Collins': [-105.084423, 40.5852602],\n",
" 'Hayward': [-122.0807964, 37.6688205],\n",
" 'Pomona': [-117.7499909, 34.055103],\n",
" 'Cary': [-78.7811169, 35.79154],\n",
" 'Rockford': [-89.0939952, 42.2711311],\n",
" 'Alexandria': [-77.0469214, 38.8048355],\n",
" 'Escondido': [-117.086421, 33.1192068],\n",
" 'McKinney': [-96.6397822, 33.1972465],\n",
" 'Joliet': [-88.0817251, 41.525031],\n",
" 'Sunnyvale': [-122.0363496, 37.36883],\n",
" 'Torrance': [-118.3406288, 33.8358492],\n",
" 'Bridgeport': [-73.19517669999999, 41.1865478],\n",
" 'Lakewood': [-105.0813734, 39.7047095],\n",
" 'Hollywood': [-80.1494901, 26.0112014],\n",
" 'Paterson': [-74.17181099999999, 40.9167654],\n",
" 'Naperville': [-88.1535352, 41.7508391],\n",
" 'Syracuse': [-76.14742439999999, 43.0481221],\n",
" 'Mesquite': [-96.5991593, 32.76679550000001],\n",
" 'Dayton': [-84.1916069, 39.7589478],\n",
" 'Savannah': [-81.09983419999999, 32.0835407],\n",
" 'Clarksville': [-87.3594528, 36.5297706],\n",
" 'Orange': [-117.8531119, 33.7877944],\n",
" 'Fullerton': [-117.9242966, 33.8703596],\n",
" 'Killeen': [-97.72779589999999, 31.1171194],\n",
" 'Frisco': [-96.82361159999999, 33.1506744],\n",
" 'Hampton': [-76.34522179999999, 37.0298687],\n",
" 'McAllen': [-98.23001239999999, 26.2034071],\n",
" 'Warren': [-83.01465259999999, 42.5144566],\n",
" 'Bellevue': [-122.2006786, 47.610377],\n",
" 'West Valley City': [-112.0010501, 40.6916132],\n",
" 'Columbia': [-92.3340724, 38.9517053],\n",
" 'Olathe': [-94.81912849999999, 38.8813958],\n",
" 'Sterling Heights': [-83.0302033, 42.5803122],\n",
" 'New Haven': [-72.9278835, 41.308274],\n",
" 'Miramar': [-80.30356019999999, 25.9860762],\n",
" 'Waco': [-97.1466695, 31.549333],\n",
" 'Thousand Oaks': [-118.8375937, 34.1705609],\n",
" 'Cedar Rapids': [-91.6656232, 41.9778795],\n",
" 'Charleston': [-79.93105120000001, 32.7764749],\n",
" 'Visalia': [-119.2920585, 36.3302284],\n",
" 'Topeka': [-95.68901849999999, 39.0558235],\n",
" 'Elizabeth': [-74.2107006, 40.6639916],\n",
" 'Gainesville': [-82.32482619999999, 29.6516344],\n",
" 'Thornton': [-104.9719243, 39.8680412],\n",
" 'Roseville': [-121.2880059, 38.7521235],\n",
" 'Carrollton': [-96.8899636, 32.9756415],\n",
" 'Coral Springs': [-80.2706044, 26.271192],\n",
" 'Stamford': [-73.5387341, 41.0534302],\n",
" 'Simi Valley': [-118.781482, 34.2694474],\n",
" 'Concord': [-122.0310733, 37.9779776],\n",
" 'Hartford': [-72.6850932, 41.76371109999999],\n",
" 'Kent': [-122.2348431, 47.3809335],\n",
" 'Lafayette': [-92.0198427, 30.2240897],\n",
" 'Midland': [-102.0779146, 31.9973456],\n",
" 'Surprise': [-112.3679279, 33.6292337],\n",
" 'Denton': [-97.13306829999999, 33.2148412],\n",
" 'Victorville': [-117.2927641, 34.5362184],\n",
" 'Evansville': [-87.5710898, 37.9715592],\n",
" 'Santa Clara': [-121.9552356, 37.3541079],\n",
" 'Abilene': [-99.73314390000002, 32.4487364],\n",
" 'Athens-Clarke County': [-83.357567, 33.9519347],\n",
" 'Vallejo': [-122.2566367, 38.1040864],\n",
" 'Allentown': [-75.4901833, 40.6084305],\n",
" 'Norman': [-97.4394777, 35.2225668],\n",
" 'Beaumont': [-94.1265562, 30.080174],\n",
" 'Independence': [-94.41550679999999, 39.0911161],\n",
" 'Murfreesboro': [-86.39027, 35.8456213],\n",
" 'Ann Arbor': [-83.7430378, 42.2808256],\n",
" 'Berkeley': [-122.272747, 37.8715926],\n",
" 'Provo': [-111.6585337, 40.2338438],\n",
" 'El Monte': [-118.0275667, 34.0686206],\n",
" 'Lansing': [-84.5555347, 42.732535],\n",
" 'Fargo': [-96.7898034, 46.8771863],\n",
" 'Downey': [-118.1331593, 33.9401088],\n",
" 'Costa Mesa': [-117.9186689, 33.6411316],\n",
" 'Wilmington': [-77.9447102, 34.2257255],\n",
" 'Arvada': [-105.0874842, 39.8027644],\n",
" 'Inglewood': [-118.3531311, 33.9616801],\n",
" 'Miami Gardens': [-80.2456045, 25.9420377],\n",
" 'Carlsbad': [-117.3505939, 33.1580933],\n",
" 'Westminster': [-105.0372046, 39.8366528],\n",
" 'Odessa': [-102.3676431, 31.8456816],\n",
" 'Manchester': [-71.4547891, 42.9956397],\n",
" 'Elgin': [-88.2825668, 42.0354084],\n",
" 'West Jordan': [-111.9391031, 40.6096698],\n",
" 'Round Rock': [-97.678896, 30.5082551],\n",
" 'Clearwater': [-82.8001026, 27.9658533],\n",
" 'Waterbury': [-73.0514965, 41.5581525],\n",
" 'Gresham': [-122.4302013, 45.5001357],\n",
" 'Fairfield': [-122.0399663, 38.24935809999999],\n",
" 'Billings': [-108.5006904, 45.7832856],\n",
" 'Lowell': [-71.31617179999999, 42.6334247],\n",
" 'San Buenaventura (Ventura)': [-119.2290316, 34.274646],\n",
" 'Pueblo': [-104.6091409, 38.2544472],\n",
" 'High Point': [-80.0053176, 35.9556923],\n",
" 'West Covina': [-117.9389526, 34.0686208],\n",
" 'Murrieta': [-117.2139232, 33.5539143],\n",
" 'Cambridge': [-71.10973349999999, 42.3736158],\n",
" 'Antioch': [-121.805789, 38.0049214],\n",
" 'Temecula': [-117.1483648, 33.4936391],\n",
" 'Norwalk': [-118.081733, 33.9022367],\n",
" 'Centennial': [-104.8771726, 39.5807452],\n",
" 'Everett': [-122.2020794, 47.9789848],\n",
" 'Palm Bay': [-80.5886646, 28.0344621],\n",
" 'Wichita Falls': [-98.4933873, 33.9137085],\n",
" 'Green Bay': [-88.019826, 44.51915899999999],\n",
" 'Daly City': [-122.4702079, 37.6879241],\n",
" 'Burbank': [-118.3089661, 34.1808392],\n",
" 'Richardson': [-96.7298519, 32.9483335],\n",
" 'Pompano Beach': [-80.1247667, 26.2378597],\n",
" 'North Charleston': [-79.9748103, 32.8546197],\n",
" 'Broken Arrow': [-95.7974526, 36.060949],\n",
" 'Boulder': [-105.2705456, 40.0149856],\n",
" 'West Palm Beach': [-80.0533746, 26.7153424],\n",
" 'Santa Maria': [-120.4357191, 34.9530337],\n",
" 'El Cajon': [-116.9625269, 32.7947731],\n",
" 'Davenport': [-90.5776367, 41.5236437],\n",
" 'Rialto': [-117.3703235, 34.1064001],\n",
" 'Las Cruces': [-106.7636538, 32.3199396],\n",
" 'San Mateo': [-122.3255254, 37.5629917],\n",
" 'Lewisville': [-96.994174, 33.046233],\n",
" 'South Bend': [-86.25198979999999, 41.6763545],\n",
" 'Lakeland': [-81.9498042, 28.0394654],\n",
" 'Erie': [-80.085059, 42.12922409999999],\n",
" 'Tyler': [-95.30106239999999, 32.3512601],\n",
" 'Pearland': [-95.2860474, 29.5635666],\n",
" 'College Station': [-96.3344068, 30.627977]}"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"big_cities"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "184d032a",
"metadata": {},
"outputs": [],
"source": [
"# Now, let's make a function that finds the nearest city to each other city,\n",
"# using the oversimplified-but-fine approximation of the Manhattan distance.\n",
"# We'll also take a little shortcut, in that extremely close cities are often\n",
"# really more of the same city (Houston-Pearland, San Francisco-Daly City). For\n",
"# cities to even be distinct at all geographically, let's say they have to be\n",
"# roughly 1,000 blocks apart (0.001 degree longitude is about a block in North\n",
"# America, appropriate for our Manhattan distance metric ;)\n",
"def approximate_nearest_city(city_name, cities, minimum_distance = 1.0):\n",
" coordinates = cities[city_name]\n",
" # So far, we have nothing 'nearest', so start our search with a large value:\n",
" nearest = 1000\n",
" nearest_name = None\n",
" for other_city, other_coordinates in cities.items():\n",
" # calculate the distance between the two cities\n",
" distance = manhattan_distance(coordinates, other_coordinates)\n",
" # if the distance is less than the current nearest, update the nearest\n",
" # unless the distance is so small we'd really be comparing the same city\n",
" # to itself\n",
" if distance < nearest and distance > minimum_distance:\n",
" nearest = distance\n",
" nearest_name = other_city\n",
"\n",
" # return the nearest city\n",
" return nearest_name, nearest\n"
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "2c16f8e8",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"the nearest city to New York is Bridgeport (1.2845287000000098)\n",
"the nearest city to Los Angeles is San Bernardino (1.010030399999998)\n",
"the nearest city to Chicago is Milwaukee (1.437464300000002)\n",
"the nearest city to Houston is Beaumont (1.5629939000000057)\n",
"the nearest city to Philadelphia is Elizabeth (1.665928600000008)\n",
"the nearest city to Phoenix is Tucson (2.3741924999999995)\n",
"the nearest city to San Antonio is Austin (1.5935984999999953)\n",
"the nearest city to San Diego is Moreno Valley (1.2953156999999962)\n",
"the nearest city to Dallas is Waco (1.5770128000000092)\n",
"the nearest city to San Jose is Richmond (1.0589694000000094)\n",
"the nearest city to Austin is San Antonio (1.5935984999999953)\n",
"the nearest city to Indianapolis is Louisville/Jefferson County (1.9153506000000036)\n",
"the nearest city to Jacksonville is Gainesville (1.3497245999999983)\n",
"the nearest city to San Francisco is Stockton (1.3114080000000072)\n",
"the nearest city to Columbus is Montgomery (1.4064306999999943)\n",
"the nearest city to Charlotte is Winston-Salem (1.4716833999999892)\n",
"the nearest city to Fort Worth is McKinney (1.132741799999991)\n",
"the nearest city to Detroit is Toledo (1.1769469000000115)\n",
"the nearest city to El Paso is Albuquerque (3.470855400000005)\n",
"the nearest city to Memphis is Little Rock (2.643668099999992)\n",
"the nearest city to Seattle is Gresham (2.2042042999999936)\n",
"the nearest city to Denver is Colorado Springs (1.07424180000001)\n",
"the nearest city to Washington is Newport News (2.38396869999999)\n",
"the nearest city to Boston is Manchester (1.031466200000004)\n",
"the nearest city to Nashville-Davidson is Huntsville (1.6277929000000029)\n",
"the nearest city to Baltimore is Philadelphia (2.109166899999998)\n",
"the nearest city to Oklahoma City is Tulsa (2.2100739999999917)\n",
"the nearest city to Louisville/Jefferson County is Lexington-Fayette (1.4668202999999949)\n",
"the nearest city to Portland is Eugene (1.8812650999999931)\n",
"the nearest city to Las Vegas is Victorville (3.7866572999999946)\n",
"the nearest city to Milwaukee is Elgin (1.3795872999999972)\n",
"the nearest city to Albuquerque is Las Cruces (2.9234943999999956)\n",
"the nearest city to Tucson is Gilbert (1.9936314999999993)\n",
"the nearest city to Fresno is Salinas (1.9520194999999987)\n",
"the nearest city to Sacramento is Concord (1.140268000000006)\n",
"the nearest city to Long Beach is Thousand Oaks (1.0443646999999885)\n",
"the nearest city to Kansas City is Topeka (1.1197843999999861)\n",
"the nearest city to Mesa is Tucson (2.0984347999999926)\n",
"the nearest city to Virginia Beach is Alexandria (3.0208456000000155)\n",
"the nearest city to Atlanta is Athens-Clarke County (1.2333546999999996)\n",
"the nearest city to Colorado Springs is Denver (1.07424180000001)\n",
"the nearest city to Omaha is Lincoln (1.1138108000000102)\n",
"the nearest city to Raleigh is Greensboro (1.4468424000000013)\n",
"the nearest city to Miami is West Palm Beach (1.092078200000003)\n",
"the nearest city to Oakland is Santa Rosa (1.0790063999999973)\n",
"the nearest city to Minneapolis is Rochester (1.7504427999999947)\n",
"the nearest city to Tulsa is Oklahoma City (2.2100739999999917)\n",
"the nearest city to Cleveland is Toledo (2.0254698000000104)\n",
"the nearest city to Wichita is Oklahoma City (2.4016453999999996)\n",
"the nearest city to Arlington is Waco (1.2249579000000104)\n",
"the nearest city to New Orleans is Baton Rouge (1.5760044000000057)\n",
"the nearest city to Bakersfield is Visalia (1.2302822999999918)\n",
"the nearest city to Tampa is Orlando (1.6657015999999878)\n",
"the nearest city to Honolulu is Anchorage (47.8691667)\n",
"the nearest city to Aurora is Rockford (1.2844698999999835)\n",
"the nearest city to Anaheim is Lancaster (1.0911509999999964)\n",
"the nearest city to Santa Ana is Oceanside (1.0380539000000013)\n",
"the nearest city to St. Louis is Springfield (1.7039748999999986)\n",
"the nearest city to Riverside is Glendale (1.0480777000000074)\n",
"the nearest city to Corpus Christi is Brownsville (1.9999384000000084)\n",
"the nearest city to Lexington-Fayette is Cincinnati (1.0708376999999984)\n",
"the nearest city to Pittsburgh is Erie (1.7777718999999976)\n",
"the nearest city to Anchorage is Everett (40.937269199999996)\n",
"the nearest city to Stockton is Fairfield (1.0408431999999976)\n",
"the nearest city to Cincinnati is Lexington-Fayette (1.0708376999999984)\n",
"the nearest city to St. Paul is Rochester (1.5513396999999927)\n",
"the nearest city to Toledo is Detroit (1.1769469000000115)\n",
"the nearest city to Greensboro is Cary (1.2919539000000029)\n",
"the nearest city to Newark is Bridgeport (1.4280808000000036)\n",
"the nearest city to Plano is Waco (1.9182940000000066)\n",
"the nearest city to Henderson is Victorville (3.814349099999994)\n",
"the nearest city to Lincoln is Omaha (1.1138108000000102)\n",
"the nearest city to Buffalo is Erie (1.9639128000000028)\n",
"the nearest city to Jersey City is Bridgeport (1.3408553000000225)\n",
"the nearest city to Chula Vista is Murrieta (1.0435878999999915)\n",
"the nearest city to Fort Wayne is South Bend (1.7097199999999901)\n",
"the nearest city to Orlando is Lakeland (1.0694377999999958)\n",
"the nearest city to St. Petersburg is Cape Coral (1.900669200000003)\n",
"the nearest city to Chandler is Tucson (1.9991887999999989)\n",
"the nearest city to Laredo is Corpus Christi (2.353958800000001)\n",
"the nearest city to Norfolk is Alexandria (2.715115400000016)\n",
"the nearest city to Durham is High Point (1.1450391999999994)\n",
"the nearest city to Madison is Rockford (1.1091556000000082)\n",
"the nearest city to Lubbock is Amarillo (1.6680035999999987)\n",
"the nearest city to Irvine is Burbank (1.0111638000000127)\n",
"the nearest city to Winston-Salem is Durham (1.4514236999999852)\n",
"the nearest city to Glendale is Riverside (1.0480777000000074)\n",
"the nearest city to Garland is Waco (1.8710772000000127)\n",
"the nearest city to Hialeah is West Palm Beach (1.0824771999999996)\n",
"the nearest city to Reno is Roseville (2.2517126000000047)\n",
"the nearest city to Chesapeake is Alexandria (2.7960554000000073)\n",
"the nearest city to Gilbert is Tucson (1.9936314999999993)\n",
"the nearest city to Baton Rouge is Lafayette (1.1137163)\n",
"the nearest city to Irving is Waco (1.4624597000000072)\n",
"the nearest city to Scottsdale is Tucson (2.272000400000003)\n",
"the nearest city to North Las Vegas is Victorville (3.837903599999997)\n",
"the nearest city to Fremont is Modesto (1.082521199999988)\n",
"the nearest city to Boise City is Spokane (5.251509800000008)\n",
"the nearest city to Richmond is San Jose (1.0589694000000094)\n",
"the nearest city to San Bernardino is Oceanside (1.00219349999999)\n",
"the nearest city to Birmingham is Huntsville (1.4260942999999955)\n",
"the nearest city to Spokane is Bellevue (4.8230352)\n",
"the nearest city to Rochester is St. Paul (1.5513396999999927)\n",
"the nearest city to Des Moines is Cedar Rapids (2.3208179000000015)\n",
"the nearest city to Modesto is Fremont (1.082521199999988)\n",
"the nearest city to Fayetteville is Wilmington (1.7605868999999856)\n",
"the nearest city to Tacoma is Gresham (1.7668304000000106)\n",
"the nearest city to Oxnard is Inglewood (1.0597451999999947)\n",
"the nearest city to Fontana is Carlsbad (1.0185942999999895)\n",
"the nearest city to Montgomery is Columbus (1.4064306999999943)\n",
"the nearest city to Moreno Valley is Huntington Beach (1.0517236000000096)\n",
"the nearest city to Shreveport is Tyler (1.724775000000001)\n",
"the nearest city to Yonkers is New Haven (1.3479274999999973)\n",
"the nearest city to Akron is Pittsburgh (2.163938799999997)\n",
"the nearest city to Huntington Beach is Temecula (1.017519600000007)\n",
"the nearest city to Little Rock is Memphis (2.643668099999992)\n",
"the nearest city to Augusta-Richmond County is Athens-Clarke County (1.8254891000000129)\n",
"the nearest city to Amarillo is Lubbock (1.6680035999999987)\n",
"the nearest city to Mobile is New Orleans (2.77594100000001)\n",
"the nearest city to Grand Rapids is Lansing (1.343376500000005)\n",
"the nearest city to Salt Lake City is Fort Collins (6.9821435000000065)\n",
"the nearest city to Tallahassee is Columbus (2.7296970000000087)\n",
"the nearest city to Huntsville is Murfreesboro (1.3110861999999912)\n",
"the nearest city to Grand Prairie is Waco (1.3455164000000117)\n",
"the nearest city to Knoxville is Chattanooga (2.3039495999999957)\n",
"the nearest city to Worcester is Manchester (1.0805507999999975)\n",
"the nearest city to Newport News is Alexandria (2.291662600000002)\n",
"the nearest city to Brownsville is McAllen (1.0341884999999955)\n",
"the nearest city to Overland Park is Topeka (1.0918220999999946)\n",
"the nearest city to Santa Clarita is Pomona (1.129156199999997)\n",
"the nearest city to Providence is Manchester (1.2136053999999987)\n",
"the nearest city to Garden Grove is Temecula (1.0733491000000015)\n",
"the nearest city to Chattanooga is Huntsville (1.5916844999999995)\n",
"the nearest city to Oceanside is San Bernardino (1.00219349999999)\n",
"the nearest city to Jackson is New Orleans (2.460969500000001)\n",
"the nearest city to Fort Lauderdale is Port St. Lucie (1.3715192999999815)\n",
"the nearest city to Santa Rosa is Berkeley (1.0101442000000063)\n",
"the nearest city to Rancho Cucamonga is Torrance (1.0180700999999885)\n",
"the nearest city to Port St. Lucie is Coral Springs (1.0894788999999996)\n",
"the nearest city to Tempe is Tucson (2.2172939000000014)\n",
"the nearest city to Ontario is Temecula (1.0722280000000026)\n",
"the nearest city to Vancouver is Salem (1.0694408000000024)\n",
"the nearest city to Cape Coral is Lakeland (1.4768828000000056)\n",
"the nearest city to Sioux Falls is Lincoln (2.764738600000001)\n",
"the nearest city to Springfield is St. Louis (1.7039748999999986)\n",
"the nearest city to Peoria is Davenport (1.818645200000006)\n",
"the nearest city to Pembroke Pines is Port St. Lucie (1.327254799999995)\n",
"the nearest city to Elk Grove is Concord (1.0902772000000027)\n",
"the nearest city to Salem is Vancouver (1.0694408000000024)\n",
"the nearest city to Lancaster is Victorville (1.0119653)\n",
"the nearest city to Corona is Burbank (1.048073400000014)\n",
"the nearest city to Eugene is Portland (1.8812650999999931)\n",
"the nearest city to Palmdale is Huntington Beach (1.0363720999999941)\n",
"the nearest city to Salinas is Sunnyvale (1.0719410999999965)\n",
"the nearest city to Pasadena is Oxnard (1.082256000000001)\n",
"the nearest city to Fort Collins is Centennial (1.2117654000000044)\n",
"the nearest city to Hayward is Stockton (1.0788979000000012)\n",
"the nearest city to Pomona is Lancaster (1.0358538999999993)\n",
"the nearest city to Cary is Greensboro (1.2919539000000029)\n",
"the nearest city to Rockford is Elgin (1.0471510999999936)\n",
"the nearest city to Alexandria is Newport News (2.291662600000002)\n",
"the nearest city to Escondido is Riverside (1.1438772999999927)\n",
"the nearest city to McKinney is Fort Worth (1.132741799999991)\n",
"the nearest city to Joliet is Milwaukee (1.6891230000000022)\n",
"the nearest city to Sunnyvale is Salinas (1.0719410999999965)\n",
"the nearest city to Torrance is Rancho Cucamonga (1.0180700999999885)\n",
"the nearest city to Bridgeport is Hartford (1.0872467999999813)\n",
"the nearest city to Lakewood is Colorado Springs (1.1308379000000102)\n",
"the nearest city to Hollywood is Port St. Lucie (1.4705838)\n",
"the nearest city to Paterson is Bridgeport (1.2464166999999975)\n",
"the nearest city to Naperville is Rockford (1.4607519999999994)\n",
"the nearest city to Syracuse is Buffalo (2.89261980000002)\n",
"the nearest city to Mesquite is Tyler (1.7136323000000147)\n",
"the nearest city to Dayton is Indianapolis (1.9759163000000015)\n",
"the nearest city to Savannah is Charleston (1.8617171999999726)\n",
"the nearest city to Clarksville is Murfreesboro (1.6533321)\n",
"the nearest city to Orange is Palmdale (1.0549892999999955)\n",
"the nearest city to Fullerton is Murrieta (1.0268187000000069)\n",
"the nearest city to Killeen is Waco (1.0133399999999888)\n",
"the nearest city to Frisco is Waco (1.9243993000000081)\n",
"the nearest city to Hampton is Alexandria (2.4766664000000134)\n",
"the nearest city to McAllen is Brownsville (1.0341884999999955)\n",
"the nearest city to Warren is Toledo (1.3910777000000252)\n",
"the nearest city to Bellevue is Gresham (2.339763999999988)\n",
"the nearest city to West Valley City is Fort Collins (7.022980099999998)\n",
"the nearest city to Columbia is Independence (2.2208451999999923)\n",
"the nearest city to Olathe is Topeka (1.0443177000000006)\n",
"the nearest city to Sterling Heights is Ann Arbor (1.0123211000000012)\n",
"the nearest city to New Haven is Yonkers (1.3479274999999973)\n",
"the nearest city to Miramar is Port St. Lucie (1.341638900000003)\n",
"the nearest city to Waco is Killeen (1.0133399999999888)\n",
"the nearest city to Thousand Oaks is West Covina (1.0005812000000063)\n",
"the nearest city to Cedar Rapids is Davenport (1.5422222999999988)\n",
"the nearest city to Charleston is Savannah (1.8617171999999726)\n",
"the nearest city to Visalia is Bakersfield (1.2302822999999918)\n",
"the nearest city to Topeka is Olathe (1.0443177000000006)\n",
"the nearest city to Elizabeth is Stamford (1.0614050999999947)\n",
"the nearest city to Gainesville is Jacksonville (1.3497245999999983)\n",
"the nearest city to Thornton is Colorado Springs (1.1847205000000045)\n",
"the nearest city to Roseville is Fairfield (1.254725800000017)\n",
"the nearest city to Carrollton is Waco (1.6830144000000011)\n",
"the nearest city to Coral Springs is Port St. Lucie (1.0894788999999996)\n",
"the nearest city to Stamford is Elizabeth (1.0614050999999947)\n",
"the nearest city to Simi Valley is West Covina (1.0433560000000028)\n",
"the nearest city to Concord is Elk Grove (1.0902772000000027)\n",
"the nearest city to Hartford is Bridgeport (1.0872467999999813)\n",
"the nearest city to Kent is Gresham (2.0761559999999832)\n",
"the nearest city to Lafayette is Baton Rouge (1.1137163)\n",
"the nearest city to Midland is Lubbock (1.8032656000000067)\n",
"the nearest city to Surprise is Tucson (2.8489396999999954)\n",
"the nearest city to Denton is Waco (1.6791094000000122)\n",
"the nearest city to Victorville is Lancaster (1.0119653)\n",
"the nearest city to Evansville is Clarksville (1.6534255999999985)\n",
"the nearest city to Santa Clara is Vallejo (1.0513796000000042)\n",
"the nearest city to Abilene is Wichita Falls (2.704728700000018)\n",
"the nearest city to Athens-Clarke County is Atlanta (1.2333546999999996)\n",
"the nearest city to Vallejo is Santa Clara (1.0513796000000042)\n",
"the nearest city to Allentown is Elizabeth (1.3350438000000082)\n",
"the nearest city to Norman is Denton (2.3141350000000074)\n",
"the nearest city to Beaumont is Houston (1.5629939000000057)\n",
"the nearest city to Independence is Topeka (1.3088042999999985)\n",
"the nearest city to Murfreesboro is Huntsville (1.3110861999999912)\n",
"the nearest city to Ann Arbor is Sterling Heights (1.0123211000000012)\n",
"the nearest city to Berkeley is Santa Rosa (1.0101442000000063)\n",
"the nearest city to Provo is Boulder (6.606846300000001)\n",
"the nearest city to El Monte is Victorville (1.2024003999999948)\n",
"the nearest city to Lansing is Ann Arbor (1.264206299999998)\n",
"the nearest city to Fargo is Sioux Falls (3.3912903999999884)\n",
"the nearest city to Downey is San Bernardino (1.011630199999999)\n",
"the nearest city to Costa Mesa is Rialto (1.0136139000000028)\n",
"the nearest city to Wilmington is Fayetteville (1.7605868999999856)\n",
"the nearest city to Arvada is Colorado Springs (1.235003600000013)\n",
"the nearest city to Inglewood is Fontana (1.0486365000000006)\n",
"the nearest city to Miami Gardens is Port St. Lucie (1.443633099999996)\n",
"the nearest city to Carlsbad is San Bernardino (1.0110803000000033)\n",
"the nearest city to Westminster is Colorado Springs (1.218612400000005)\n",
"the nearest city to Odessa is Lubbock (2.2446581000000023)\n",
"the nearest city to Manchester is Boston (1.031466200000004)\n",
"the nearest city to Elgin is Rockford (1.0471510999999936)\n",
"the nearest city to West Jordan is Fort Collins (6.879089699999994)\n",
"the nearest city to Round Rock is College Station (1.4642111)\n",
"the nearest city to Clearwater is Orlando (1.9933482999999974)\n",
"the nearest city to Waterbury is Yonkers (1.4741929999999925)\n",
"the nearest city to Gresham is Salem (1.1621332000000137)\n",
"the nearest city to Fairfield is Stockton (1.0408431999999976)\n",
"the nearest city to Billings is Salt Lake City (8.412863300000005)\n",
"the nearest city to Lowell is Hartford (2.2386350000000164)\n",
"the nearest city to San Buenaventura (Ventura) is Burbank (1.0138722999999885)\n",
"the nearest city to Pueblo is Centennial (1.5943296999999959)\n",
"the nearest city to High Point is Durham (1.1450391999999994)\n",
"the nearest city to West Covina is Thousand Oaks (1.0005812000000063)\n",
"the nearest city to Murrieta is El Cajon (1.010537499999998)\n",
"the nearest city to Cambridge is Hartford (2.1852644000000225)\n",
"the nearest city to Antioch is Modesto (1.1747350000000196)\n",
"the nearest city to Temecula is Huntington Beach (1.017519600000007)\n",
"the nearest city to Norwalk is Thousand Oaks (1.0241848999999945)\n",
"the nearest city to Centennial is Fort Collins (1.2117654000000044)\n",
"the nearest city to Everett is Gresham (2.7069709999999887)\n",
"the nearest city to Palm Bay is Orlando (1.2944453000000031)\n",
"the nearest city to Wichita Falls is Denton (2.0591863000000004)\n",
"the nearest city to Green Bay is Milwaukee (1.5936088999999853)\n",
"the nearest city to Daly City is Stockton (1.4492058000000156)\n",
"the nearest city to Burbank is Irvine (1.0111638000000127)\n",
"the nearest city to Richardson is Waco (1.8158180999999978)\n",
"the nearest city to Pompano Beach is Port St. Lucie (1.2686488999999987)\n",
"the nearest city to North Charleston is Savannah (1.8961028999999883)\n",
"the nearest city to Broken Arrow is Oklahoma City (2.3123638)\n",
"the nearest city to Boulder is Colorado Springs (1.6302862000000147)\n",
"the nearest city to West Palm Beach is Hialeah (1.0824771999999996)\n",
"the nearest city to Santa Maria is Bakersfield (1.837264999999995)\n",
"the nearest city to El Cajon is Murrieta (1.010537499999998)\n",
"the nearest city to Davenport is Cedar Rapids (1.5422222999999988)\n",
"the nearest city to Rialto is Burbank (1.0130817000000079)\n",
"the nearest city to Las Cruces is Albuquerque (2.9234943999999956)\n",
"the nearest city to San Mateo is Santa Rosa (1.2659667)\n",
"the nearest city to Lewisville is Waco (1.6493955000000007)\n",
"the nearest city to South Bend is Chicago (1.5795675000000031)\n",
"the nearest city to Lakeland is Orlando (1.0694377999999958)\n",
"the nearest city to Erie is Pittsburgh (1.7777718999999976)\n",
"the nearest city to Tyler is Mesquite (1.7136323000000147)\n",
"the nearest city to Pearland is Beaumont (1.676098600000003)\n",
"the nearest city to College Station is Round Rock (1.4642111)\n",
"It took 0.014703035354614258 seconds to find the nearest city to each other city.\n"
]
}
],
"source": [
"# Now, let's test it out. Also, we are going to see how long it takes to find\n",
"# the nearest city to each other city:\n",
"import time\n",
"starting_time = time.time()\n",
"for city in big_cities:\n",
" nearest, how_far = approximate_nearest_city(city, big_cities)\n",
" print(f'the nearest city to {city} is {nearest} ({how_far})')\n",
"\n",
"ending_time = time.time()\n",
"\n",
"print('It took', ending_time - starting_time, 'seconds to find the nearest city to each other city.')\n"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "62083e8a",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'New York': [-74.0059413, 40.7127837],\n",
" 'Los Angeles': [-118.2436849, 34.0522342],\n",
" 'Chicago': [-87.6297982, 41.8781136],\n",
" 'Houston': [-95.3698028, 29.7604267],\n",
" 'Philadelphia': [-75.1652215, 39.9525839],\n",
" 'Phoenix': [-112.0740373, 33.4483771],\n",
" 'San Antonio': [-98.49362819999999, 29.4241219],\n",
" 'San Diego': [-117.1610838, 32.715738],\n",
" 'Dallas': [-96.79698789999999, 32.7766642],\n",
" 'San Jose': [-121.8863286, 37.3382082],\n",
" 'Austin': [-97.7430608, 30.267153],\n",
" 'Indianapolis': [-86.158068, 39.768403],\n",
" 'Jacksonville': [-81.65565099999999, 30.3321838],\n",
" 'San Francisco': [-122.4194155, 37.7749295],\n",
" 'Columbus': [-84.9877094, 32.4609764],\n",
" 'Charlotte': [-80.8431267, 35.2270869],\n",
" 'Fort Worth': [-97.3307658, 32.7554883],\n",
" 'Detroit': [-83.0457538, 42.331427],\n",
" 'El Paso': [-106.4424559, 31.7775757],\n",
" 'Memphis': [-90.0489801, 35.1495343],\n",
" 'Seattle': [-122.3320708, 47.6062095],\n",
" 'Denver': [-104.990251, 39.7392358],\n",
" 'Washington': [-77.0368707, 38.9071923],\n",
" 'Boston': [-71.0588801, 42.3600825],\n",
" 'Nashville-Davidson': [-86.7816016, 36.1626638],\n",
" 'Baltimore': [-76.6121893, 39.2903848],\n",
" 'Oklahoma City': [-97.5164276, 35.4675602],\n",
" 'Louisville/Jefferson County': [-85.7584557, 38.2526647],\n",
" 'Portland': [-122.6764816, 45.5230622],\n",
" 'Las Vegas': [-115.1398296, 36.1699412],\n",
" 'Milwaukee': [-87.9064736, 43.0389025],\n",
" 'Albuquerque': [-106.6055534, 35.0853336],\n",
" 'Tucson': [-110.926479, 32.2217429],\n",
" 'Fresno': [-119.7725868, 36.7468422],\n",
" 'Sacramento': [-121.4943996, 38.5815719],\n",
" 'Long Beach': [-118.1937395, 33.7700504],\n",
" 'Kansas City': [-94.6274636, 39.114053],\n",
" 'Mesa': [-111.8314724, 33.4151843],\n",
" 'Virginia Beach': [-75.97798499999999, 36.8529263],\n",
" 'Atlanta': [-84.3879824, 33.7489954],\n",
" 'Colorado Springs': [-104.8213634, 38.8338816],\n",
" 'Omaha': [-95.99798829999999, 41.2523634],\n",
" 'Raleigh': [-78.6381787, 35.7795897],\n",
" 'Miami': [-80.1917902, 25.7616798],\n",
" 'Oakland': [-122.2711137, 37.8043637],\n",
" 'Minneapolis': [-93.2650108, 44.977753],\n",
" 'Tulsa': [-95.99277500000001, 36.1539816],\n",
" 'Cleveland': [-81.6943605, 41.49932],\n",
" 'Wichita': [-97.336111, 37.688889],\n",
" 'Arlington': [-97.10806559999999, 32.735687],\n",
" 'New Orleans': [-90.0715323, 29.95106579999999],\n",
" 'Bakersfield': [-119.0187125, 35.3732921],\n",
" 'Tampa': [-82.4571776, 27.950575],\n",
" 'Honolulu': [-157.8583333, 21.3069444],\n",
" 'Aurora': [-88.32007150000001, 41.7605849],\n",
" 'Anaheim': [-117.9145036, 33.8352932],\n",
" 'Santa Ana': [-117.8678338, 33.7455731],\n",
" 'St. Louis': [-90.19940419999999, 38.6270025],\n",
" 'Riverside': [-117.3961564, 33.9533487],\n",
" 'Corpus Christi': [-97.39638099999999, 27.8005828],\n",
" 'Lexington-Fayette': [-84.5037164, 38.0405837],\n",
" 'Pittsburgh': [-79.9958864, 40.44062479999999],\n",
" 'Anchorage': [-149.9002778, 61.2180556],\n",
" 'Stockton': [-121.2907796, 37.9577016],\n",
" 'Cincinnati': [-84.5120196, 39.1031182],\n",
" 'St. Paul': [-93.0899578, 44.9537029],\n",
" 'Toledo': [-83.55521200000001, 41.6639383],\n",
" 'Greensboro': [-79.7919754, 36.0726354],\n",
" 'Newark': [-74.1723667, 40.735657],\n",
" 'Plano': [-96.6988856, 33.0198431],\n",
" 'Henderson': [-114.9817213, 36.0395247],\n",
" 'Lincoln': [-96.6851982, 40.8257625],\n",
" 'Buffalo': [-78.8783689, 42.88644679999999],\n",
" 'Jersey City': [-74.0776417, 40.72815749999999],\n",
" 'Chula Vista': [-117.0841955, 32.6400541],\n",
" 'Fort Wayne': [-85.1393513, 41.079273],\n",
" 'Orlando': [-81.3792365, 28.5383355],\n",
" 'St. Petersburg': [-82.64, 27.773056],\n",
" 'Chandler': [-111.8412502, 33.3061605],\n",
" 'Laredo': [-99.48032409999999, 27.5305671],\n",
" 'Norfolk': [-76.28587259999999, 36.8507689],\n",
" 'Durham': [-78.898619, 35.9940329],\n",
" 'Madison': [-89.4012302, 43.0730517],\n",
" 'Lubbock': [-101.8551665, 33.5778631],\n",
" 'Irvine': [-117.7946942, 33.6839473],\n",
" 'Winston-Salem': [-80.244216, 36.09985959999999],\n",
" 'Glendale': [-118.255075, 34.1425078],\n",
" 'Garland': [-96.63888329999999, 32.912624],\n",
" 'Hialeah': [-80.2781057, 25.8575963],\n",
" 'Reno': [-119.8138027, 39.5296329],\n",
" 'Chesapeake': [-76.2874927, 36.7682088],\n",
" 'Gilbert': [-111.789027, 33.3528264],\n",
" 'Baton Rouge': [-91.1403196, 30.4582829],\n",
" 'Irving': [-96.9488945, 32.8140177],\n",
" 'Scottsdale': [-111.9260519, 33.4941704],\n",
" 'North Las Vegas': [-115.1175013, 36.1988592],\n",
" 'Fremont': [-121.9885719, 37.5482697],\n",
" 'Boise City': [-116.2146068, 43.6187102],\n",
" 'Richmond': [-122.3477486, 37.9357576],\n",
" 'San Bernardino': [-117.2897652, 34.1083449],\n",
" 'Birmingham': [-86.80248999999999, 33.5206608],\n",
" 'Spokane': [-117.4260466, 47.6587802],\n",
" 'Rochester': [-92.4801989, 44.0121221],\n",
" 'Des Moines': [-93.6091064, 41.6005448],\n",
" 'Modesto': [-120.9968782, 37.63909719999999],\n",
" 'Fayetteville': [-78.87835849999999, 35.0526641],\n",
" 'Tacoma': [-122.4442906, 47.2528768],\n",
" 'Oxnard': [-119.1770516, 34.1975048],\n",
" 'Fontana': [-117.435048, 34.0922335],\n",
" 'Montgomery': [-86.2999689, 32.3668052],\n",
" 'Moreno Valley': [-117.2296717, 33.9424658],\n",
" 'Shreveport': [-93.7501789, 32.5251516],\n",
" 'Yonkers': [-73.89874689999999, 40.9312099],\n",
" 'Akron': [-81.51900529999999, 41.0814447],\n",
" 'Huntington Beach': [-117.9992265, 33.660297],\n",
" 'Little Rock': [-92.28959479999999, 34.7464809],\n",
" 'Augusta-Richmond County': [-82.0105148, 33.4734978],\n",
" 'Amarillo': [-101.8312969, 35.2219971],\n",
" 'Mobile': [-88.0398912, 30.6953657],\n",
" 'Grand Rapids': [-85.6680863, 42.9633599],\n",
" 'Salt Lake City': [-111.8910474, 40.7607793],\n",
" 'Tallahassee': [-84.28073289999999, 30.4382559],\n",
" 'Huntsville': [-86.5861037, 34.7303688],\n",
" 'Grand Prairie': [-96.99778459999999, 32.7459645],\n",
" 'Knoxville': [-83.9207392, 35.9606384],\n",
" 'Worcester': [-71.8022934, 42.2625932],\n",
" 'Newport News': [-76.4730122, 37.0870821],\n",
" 'Brownsville': [-97.4974838, 25.9017472],\n",
" 'Overland Park': [-94.6707917, 38.9822282],\n",
" 'Santa Clarita': [-118.542586, 34.3916641],\n",
" 'Providence': [-71.4128343, 41.8239891],\n",
" 'Garden Grove': [-117.9414477, 33.7739053],\n",
" 'Chattanooga': [-85.3096801, 35.0456297],\n",
" 'Oceanside': [-117.3794834, 33.1958696],\n",
" 'Jackson': [-90.1848103, 32.2987573],\n",
" 'Fort Lauderdale': [-80.13731740000001, 26.1224386],\n",
" 'Santa Rosa': [-122.7140548, 38.440429],\n",
" 'Rancho Cucamonga': [-117.5931084, 34.10639889999999],\n",
" 'Port St. Lucie': [-80.3582261, 27.2730492],\n",
" 'Tempe': [-111.9400054, 33.4255104],\n",
" 'Ontario': [-117.6508876, 34.0633443],\n",
" 'Vancouver': [-122.6614861, 45.6387281],\n",
" 'Cape Coral': [-81.9495331, 26.5628537],\n",
" 'Sioux Falls': [-96.73110340000001, 43.5445959],\n",
" 'Springfield': [-89.6501481, 39.78172130000001],\n",
" 'Peoria': [-89.5889864, 40.6936488],\n",
" 'Pembroke Pines': [-80.2962555, 26.007765],\n",
" 'Elk Grove': [-121.3716178, 38.4087993],\n",
" 'Salem': [-123.0350963, 44.9428975],\n",
" 'Lancaster': [-118.1541632, 34.6867846],\n",
" 'Corona': [-117.5664384, 33.8752935],\n",
" 'Eugene': [-123.0867536, 44.0520691],\n",
" 'Palmdale': [-118.1164613, 34.5794343],\n",
" 'Salinas': [-121.6555013, 36.6777372],\n",
" 'Pasadena': [-118.1445155, 34.1477849],\n",
" 'Fort Collins': [-105.084423, 40.5852602],\n",
" 'Hayward': [-122.0807964, 37.6688205],\n",
" 'Pomona': [-117.7499909, 34.055103],\n",
" 'Cary': [-78.7811169, 35.79154],\n",
" 'Rockford': [-89.0939952, 42.2711311],\n",
" 'Alexandria': [-77.0469214, 38.8048355],\n",
" 'Escondido': [-117.086421, 33.1192068],\n",
" 'McKinney': [-96.6397822, 33.1972465],\n",
" 'Joliet': [-88.0817251, 41.525031],\n",
" 'Sunnyvale': [-122.0363496, 37.36883],\n",
" 'Torrance': [-118.3406288, 33.8358492],\n",
" 'Bridgeport': [-73.19517669999999, 41.1865478],\n",
" 'Lakewood': [-105.0813734, 39.7047095],\n",
" 'Hollywood': [-80.1494901, 26.0112014],\n",
" 'Paterson': [-74.17181099999999, 40.9167654],\n",
" 'Naperville': [-88.1535352, 41.7508391],\n",
" 'Syracuse': [-76.14742439999999, 43.0481221],\n",
" 'Mesquite': [-96.5991593, 32.76679550000001],\n",
" 'Dayton': [-84.1916069, 39.7589478],\n",
" 'Savannah': [-81.09983419999999, 32.0835407],\n",
" 'Clarksville': [-87.3594528, 36.5297706],\n",
" 'Orange': [-117.8531119, 33.7877944],\n",
" 'Fullerton': [-117.9242966, 33.8703596],\n",
" 'Killeen': [-97.72779589999999, 31.1171194],\n",
" 'Frisco': [-96.82361159999999, 33.1506744],\n",
" 'Hampton': [-76.34522179999999, 37.0298687],\n",
" 'McAllen': [-98.23001239999999, 26.2034071],\n",
" 'Warren': [-83.01465259999999, 42.5144566],\n",
" 'Bellevue': [-122.2006786, 47.610377],\n",
" 'West Valley City': [-112.0010501, 40.6916132],\n",
" 'Columbia': [-92.3340724, 38.9517053],\n",
" 'Olathe': [-94.81912849999999, 38.8813958],\n",
" 'Sterling Heights': [-83.0302033, 42.5803122],\n",
" 'New Haven': [-72.9278835, 41.308274],\n",
" 'Miramar': [-80.30356019999999, 25.9860762],\n",
" 'Waco': [-97.1466695, 31.549333],\n",
" 'Thousand Oaks': [-118.8375937, 34.1705609],\n",
" 'Cedar Rapids': [-91.6656232, 41.9778795],\n",
" 'Charleston': [-79.93105120000001, 32.7764749],\n",
" 'Visalia': [-119.2920585, 36.3302284],\n",
" 'Topeka': [-95.68901849999999, 39.0558235],\n",
" 'Elizabeth': [-74.2107006, 40.6639916],\n",
" 'Gainesville': [-82.32482619999999, 29.6516344],\n",
" 'Thornton': [-104.9719243, 39.8680412],\n",
" 'Roseville': [-121.2880059, 38.7521235],\n",
" 'Carrollton': [-96.8899636, 32.9756415],\n",
" 'Coral Springs': [-80.2706044, 26.271192],\n",
" 'Stamford': [-73.5387341, 41.0534302],\n",
" 'Simi Valley': [-118.781482, 34.2694474],\n",
" 'Concord': [-122.0310733, 37.9779776],\n",
" 'Hartford': [-72.6850932, 41.76371109999999],\n",
" 'Kent': [-122.2348431, 47.3809335],\n",
" 'Lafayette': [-92.0198427, 30.2240897],\n",
" 'Midland': [-102.0779146, 31.9973456],\n",
" 'Surprise': [-112.3679279, 33.6292337],\n",
" 'Denton': [-97.13306829999999, 33.2148412],\n",
" 'Victorville': [-117.2927641, 34.5362184],\n",
" 'Evansville': [-87.5710898, 37.9715592],\n",
" 'Santa Clara': [-121.9552356, 37.3541079],\n",
" 'Abilene': [-99.73314390000002, 32.4487364],\n",
" 'Athens-Clarke County': [-83.357567, 33.9519347],\n",
" 'Vallejo': [-122.2566367, 38.1040864],\n",
" 'Allentown': [-75.4901833, 40.6084305],\n",
" 'Norman': [-97.4394777, 35.2225668],\n",
" 'Beaumont': [-94.1265562, 30.080174],\n",
" 'Independence': [-94.41550679999999, 39.0911161],\n",
" 'Murfreesboro': [-86.39027, 35.8456213],\n",
" 'Ann Arbor': [-83.7430378, 42.2808256],\n",
" 'Berkeley': [-122.272747, 37.8715926],\n",
" 'Provo': [-111.6585337, 40.2338438],\n",
" 'El Monte': [-118.0275667, 34.0686206],\n",
" 'Lansing': [-84.5555347, 42.732535],\n",
" 'Fargo': [-96.7898034, 46.8771863],\n",
" 'Downey': [-118.1331593, 33.9401088],\n",
" 'Costa Mesa': [-117.9186689, 33.6411316],\n",
" 'Wilmington': [-77.9447102, 34.2257255],\n",
" 'Arvada': [-105.0874842, 39.8027644],\n",
" 'Inglewood': [-118.3531311, 33.9616801],\n",
" 'Miami Gardens': [-80.2456045, 25.9420377],\n",
" 'Carlsbad': [-117.3505939, 33.1580933],\n",
" 'Westminster': [-105.0372046, 39.8366528],\n",
" 'Odessa': [-102.3676431, 31.8456816],\n",
" 'Manchester': [-71.4547891, 42.9956397],\n",
" 'Elgin': [-88.2825668, 42.0354084],\n",
" 'West Jordan': [-111.9391031, 40.6096698],\n",
" 'Round Rock': [-97.678896, 30.5082551],\n",
" 'Clearwater': [-82.8001026, 27.9658533],\n",
" 'Waterbury': [-73.0514965, 41.5581525],\n",
" 'Gresham': [-122.4302013, 45.5001357],\n",
" 'Fairfield': [-122.0399663, 38.24935809999999],\n",
" 'Billings': [-108.5006904, 45.7832856],\n",
" 'Lowell': [-71.31617179999999, 42.6334247],\n",
" 'San Buenaventura (Ventura)': [-119.2290316, 34.274646],\n",
" 'Pueblo': [-104.6091409, 38.2544472],\n",
" 'High Point': [-80.0053176, 35.9556923],\n",
" 'West Covina': [-117.9389526, 34.0686208],\n",
" 'Murrieta': [-117.2139232, 33.5539143],\n",
" 'Cambridge': [-71.10973349999999, 42.3736158],\n",
" 'Antioch': [-121.805789, 38.0049214],\n",
" 'Temecula': [-117.1483648, 33.4936391],\n",
" 'Norwalk': [-118.081733, 33.9022367],\n",
" 'Centennial': [-104.8771726, 39.5807452],\n",
" 'Everett': [-122.2020794, 47.9789848],\n",
" 'Palm Bay': [-80.5886646, 28.0344621],\n",
" 'Wichita Falls': [-98.4933873, 33.9137085],\n",
" 'Green Bay': [-88.019826, 44.51915899999999],\n",
" 'Daly City': [-122.4702079, 37.6879241],\n",
" 'Burbank': [-118.3089661, 34.1808392],\n",
" 'Richardson': [-96.7298519, 32.9483335],\n",
" 'Pompano Beach': [-80.1247667, 26.2378597],\n",
" 'North Charleston': [-79.9748103, 32.8546197],\n",
" 'Broken Arrow': [-95.7974526, 36.060949],\n",
" 'Boulder': [-105.2705456, 40.0149856],\n",
" 'West Palm Beach': [-80.0533746, 26.7153424],\n",
" 'Santa Maria': [-120.4357191, 34.9530337],\n",
" 'El Cajon': [-116.9625269, 32.7947731],\n",
" 'Davenport': [-90.5776367, 41.5236437],\n",
" 'Rialto': [-117.3703235, 34.1064001],\n",
" 'Las Cruces': [-106.7636538, 32.3199396],\n",
" 'San Mateo': [-122.3255254, 37.5629917],\n",
" 'Lewisville': [-96.994174, 33.046233],\n",
" 'South Bend': [-86.25198979999999, 41.6763545],\n",
" 'Lakeland': [-81.9498042, 28.0394654],\n",
" 'Erie': [-80.085059, 42.12922409999999],\n",
" 'Tyler': [-95.30106239999999, 32.3512601],\n",
" 'Pearland': [-95.2860474, 29.5635666],\n",
" 'College Station': [-96.3344068, 30.627977]}"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": []
},
{
"cell_type": "code",
"execution_count": 25,
"id": "59552f6b",
"metadata": {},
"outputs": [],
"source": [
"# So this kind of works, but we want something a little deeper. More 'similarity' than just\n",
"# 'nearness'. Let's try to find the most similar city to each other city, using additional features.\n",
"# Intuitively, nearby cities do share a lot in common. But also, big cities have some of that bigness\n",
"# in common, apart from geography. Let's see what we can do with that.\n",
"\n",
"# Now, let's start thinking of our cities list as a matrix. Each row is a city, and each column is a feature.\n",
"big_cities_list = [city for city in cities_dataset if int(city['population']) > 100000]\n",
"names = [city['city'] for city in big_cities_list]\n",
"longitudes = [city['longitude'] for city in big_cities_list]\n",
"latitudes = [city['latitude'] for city in big_cities_list]\n",
"populations = [float(city['population']) for city in big_cities_list]\n",
"growth_rates = [float(city['growth_from_2000_to_2013'].replace('%', '')) for city in big_cities_list]\n"
]
},
{
"cell_type": "code",
"execution_count": 26,
"id": "7d222f86",
"metadata": {},
"outputs": [],
"source": [
"# So far, so good, but how do we compare the number of people in one city to the degrees latitude of another?\n",
"# Answer: you don't! Intuitively, we know that longitude, the Eastness-Westness of a city, does mean something\n",
"# to us. Seattle has some things in common with San Francisco much further South. And likewise Phoenix to\n",
"# Jacksonville in terms of latitude -- there's a real \"sunbelt\" effect uniting these distant cities. So we want\n",
"# to keep all those features, but not over-emphasize any in particular. Again, we're designing our features here.\n",
"# And BTW, since we're designing features, let's throw in one more: the market share of pickup trucks in the state!\n",
"# Because why not?! Also lets us practice reading CSV files.\n",
"\n",
"# our format will be:\n",
"# Connecticut,9.4%\n",
"# New Jersey,7.8%\n",
"# ...\n",
"\n",
"import csv\n",
"share = csv.DictReader(open('pickup-trucks.csv'), delimiter = ',', fieldnames=['state', 'market_share'])\n",
"state_truck_share = {}\n",
"for state in share:\n",
" state_name = state['state']\n",
" market_share = float(state['market_share'].replace('%', ''))\n",
" state_truck_share[state_name] = market_share\n",
"\n",
"pickup_market_share = [state_truck_share[city['state']] for city in big_cities_list]\n"
]
},
{
"cell_type": "code",
"execution_count": 27,
"id": "8f0adb4d",
"metadata": {},
"outputs": [
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],
"source": [
"pickup_market_share"
]
},
{
"cell_type": "code",
"execution_count": 28,
"id": "d47e0037",
"metadata": {},
"outputs": [],
"source": [
"# Back to our matrix! We definitely won't be comparing longitudes to pickups! So let's rescale all our features\n",
"\n",
"# We'll use this common function:\n",
"\n",
"def rescale(vector, range = (-0.5, 0.5)):\n",
" \"\"\"\n",
" This function takes a vector of numbers, and rescales it to be between\n",
" the range specified. The default range is -0.5 to 0.5, which is where\n",
" floating point numbers are most accurate.\n",
" \"\"\"\n",
" # find the minimum and maximum values in the vector\n",
" min_value = min(vector)\n",
" max_value = max(vector)\n",
" # find the range of the vector\n",
" vector_range = max_value - min_value\n",
" # find the range we want to rescale to\n",
" target_range = range[1] - range[0]\n",
" # rescale the vector\n",
" rescaled = [range[0] + (x - min_value) * target_range / vector_range for x in vector]\n",
" return rescaled\n",
"\n",
"# Now, let's rescale all our features:\n",
"rescaled_longitudes = rescale(longitudes)\n",
"rescaled_latitudes = rescale(latitudes)\n",
"rescaled_populations = rescale(populations)\n",
"rescaled_growth_rates = rescale(growth_rates)\n",
"rescaled_pickup_market_share = rescale(pickup_market_share)\n",
"\n",
"# Now, let's put all our features together into a matrix:\n",
"import numpy as np\n",
"\n",
"matrix = np.array([rescaled_longitudes, rescaled_latitudes, rescaled_populations,\n",
" rescaled_growth_rates, rescaled_pickup_market_share])"
]
},
{
"cell_type": "code",
"execution_count": 29,
"id": "ef29d497",
"metadata": {},
"outputs": [],
"source": [
"# Now, let's transpose the matrix, so that each row is a city, and each column is a feature:\n",
"\n",
"matrix = matrix.T\n",
"\n",
"# Now, let's take a couple of approaches. Here we normalize so that each city's vector has length 1:\n",
"normalized_matrix = matrix / np.linalg.norm(matrix, axis = 1).reshape(-1, 1)\n",
"\n",
"# Working with the normalized matrix is really like \"how much are these two vectors pointing in the same direction?\"\n",
"# Because the magnitude of each row is 1, we can just take the dot product of each row with every other row. This\n",
"# is the same as matrix multiplication, but we're multiplying a matrix by its transpose. So we can use the @ operator\n",
"\n",
"similarities = normalized_matrix @ normalized_matrix.T\n",
"\n",
"# Now, arrange the results such that the most similar city is at the top of the list (which will be itself), and\n",
"# the least similar city is at the bottom of the list (which will be the city furthest away in terms of our combined\n",
"# features):\n",
"\n",
"# Axis = 1 means we're sorting each row, and -1 means we're sorting in descending order\n",
"# Argsort returns the indices of the sorted array -- where you would have to put stuff so that it appears in order\n",
"rankings = np.argsort(-similarities, axis = 1)"
]
},
{
"cell_type": "code",
"execution_count": 32,
"id": "8ca377da",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" New York is most similar to Chicago, Los Angeles, Philadelphia, Boston, Baltimore and least similar to Billings, Boise City, Frisco, Surprise, Anchorage\n",
" Los Angeles is most similar to San Diego, San Jose, Phoenix, San Francisco, Long Beach and least similar to Sioux Falls, Anchorage, Surprise, Billings, Frisco\n",
" Chicago is most similar to Baltimore, Philadelphia, Newark, Paterson, Jersey City and least similar to Boise City, Billings, Anchorage, Surprise, Frisco\n",
" Houston is most similar to San Antonio, Dallas, Austin, Mobile, New Orleans and least similar to Billings, New York, Frisco, Surprise, Anchorage\n",
" Philadelphia is most similar to Boston, Baltimore, Yonkers, Washington, Virginia Beach and least similar to Billings, Honolulu, Frisco, Surprise, Anchorage\n",
" Phoenix is most similar to Tucson, Mesa, Glendale, Tempe, Santa Ana and least similar to Billings, Surprise, New York, Anchorage, Frisco\n",
" San Antonio is most similar to Corpus Christi, Austin, Pasadena, Beaumont, Dallas and least similar to Billings, Frisco, Surprise, New York, Anchorage\n",
" San Diego is most similar to Long Beach, Santa Ana, Anaheim, Huntington Beach, Oceanside and least similar to Surprise, New York, Billings, Anchorage, Frisco\n",
" Dallas is most similar to Mobile, Beaumont, Arlington, Garland, Birmingham and least similar to Billings, New York, Anchorage, Frisco, Surprise\n",
" San Jose is most similar to San Francisco, Oakland, Fresno, Sacramento, Fremont and least similar to Anchorage, Surprise, Billings, New York, Frisco\n",
" Austin is most similar to Corpus Christi, Pasadena, Beaumont, Brownsville, Fort Worth and least similar to Billings, Frisco, Surprise, Anchorage, New York\n",
" Indianapolis is most similar to Cincinnati, Dayton, Akron, Cleveland, Toledo and least similar to Honolulu, New York, Anchorage, Frisco, Surprise\n",
" Jacksonville is most similar to Tampa, Gainesville, St. Petersburg, Clearwater, Fort Lauderdale and least similar to Boise City, Frisco, Surprise, Billings, Anchorage\n",
" San Francisco is most similar to San Jose, Oakland, Sacramento, Daly City, Fremont and least similar to Anchorage, Surprise, Billings, New York, Frisco\n",
" Columbus is most similar to Pittsburgh, Allentown, Washington, Akron, Indianapolis and least similar to Honolulu, New York, Frisco, Surprise, Anchorage\n",
" Charlotte is most similar to Wilmington, Durham, Raleigh, Greensboro, High Point and least similar to Honolulu, Frisco, Surprise, New York, Anchorage\n",
" Fort Worth is most similar to Tyler, Grand Prairie, Lewisville, College Station, Plano and least similar to Billings, Frisco, Surprise, Anchorage, New York\n",
" Detroit is most similar to Milwaukee, Lansing, Grand Rapids, Warren, Ann Arbor and least similar to Honolulu, Anchorage, New York, Frisco, Surprise\n",
" El Paso is most similar to Odessa, Abilene, Lubbock, Midland, Waco and least similar to Billings, Anchorage, Surprise, Frisco, New York\n",
" Memphis is most similar to Birmingham, Nashville-Davidson, Knoxville, Chattanooga, St. Louis and least similar to Honolulu, New York, Anchorage, Frisco, Surprise\n",
" Seattle is most similar to Tacoma, Everett, Bellevue, Spokane, Vancouver and least similar to Port St. Lucie, McKinney, Surprise, Frisco, New York\n",
" Denver is most similar to Lakewood, Centennial, Arvada, Westminster, Boulder and least similar to Billings, Anchorage, Surprise, Frisco, New York\n",
" Washington is most similar to Columbus, Allentown, Providence, Hampton, Newport News and least similar to New York, Honolulu, Frisco, Surprise, Anchorage\n",
" Boston is most similar to Worcester, Cambridge, Lowell, Springfield, Hartford and least similar to Billings, Honolulu, Frisco, Surprise, Anchorage\n",
" Nashville-Davidson is most similar to Knoxville, Chattanooga, Columbia, Fayetteville, Augusta-Richmond County and least similar to Honolulu, Frisco, New York, Surprise, Anchorage\n",
" Baltimore is most similar to Paterson, Newark, Bridgeport, Waterbury, Hartford and least similar to Boise City, Billings, Surprise, Frisco, Anchorage\n",
" Oklahoma City is most similar to Tulsa, Norman, Little Rock, Broken Arrow, Jackson and least similar to Anchorage, Los Angeles, Frisco, Surprise, New York\n",
" Louisville/Jefferson County is most similar to Lexington-Fayette, Knoxville, Nashville-Davidson, St. Louis, Chattanooga and least similar to Honolulu, Anchorage, Frisco, New York, Surprise\n",
" Portland is most similar to Salem, Tacoma, Vancouver, Gresham, Eugene and least similar to Port St. Lucie, McKinney, Surprise, Frisco, New York\n",
" Las Vegas is most similar to West Covina, El Monte, Burbank, Norwalk, Rialto and least similar to Billings, Surprise, Anchorage, Frisco, New York\n",
" Milwaukee is most similar to Grand Rapids, Lansing, Warren, Green Bay, Ann Arbor and least similar to Anchorage, Honolulu, New York, Frisco, Surprise\n",
" Albuquerque is most similar to Las Cruces, Norman, Oklahoma City, Tulsa, Broken Arrow and least similar to Anchorage, Los Angeles, Frisco, Surprise, New York\n",
" Tucson is most similar to Mesa, Glendale, Tempe, Scottsdale, Phoenix and least similar to Billings, Surprise, Anchorage, Frisco, New York\n",
" Fresno is most similar to Salinas, Modesto, Fremont, Sunnyvale, Stockton and least similar to Surprise, Anchorage, Billings, Frisco, New York\n",
" Sacramento is most similar to Santa Rosa, Fairfield, Stockton, Fremont, Richmond and least similar to Anchorage, Surprise, Billings, Frisco, New York\n",
" Long Beach is most similar to Santa Ana, Anaheim, Glendale, Huntington Beach, Inglewood and least similar to Surprise, Billings, Anchorage, Frisco, New York\n",
" Kansas City is most similar to Independence, Kansas City, Topeka, St. Louis, Wichita and least similar to Honolulu, Anchorage, Frisco, Surprise, New York\n",
" Mesa is most similar to Scottsdale, Glendale, Tempe, Tucson, Chandler and least similar to Billings, Surprise, Anchorage, Frisco, New York\n",
" Virginia Beach is most similar to Norfolk, Newport News, Hampton, Richmond, Chesapeake and least similar to New York, Billings, Frisco, Surprise, Anchorage\n",
" Atlanta is most similar to Augusta-Richmond County, Columbus, Savannah, Athens-Clarke County, Fayetteville and least similar to Billings, Frisco, New York, Surprise, Anchorage\n",
" Colorado Springs is most similar to Pueblo, Aurora, Arvada, Westminster, Centennial and least similar to Billings, Anchorage, Surprise, Frisco, New York\n",
" Omaha is most similar to Lincoln, Des Moines, Cedar Rapids, Davenport, Madison and least similar to Honolulu, Los Angeles, Frisco, Surprise, New York\n",
" Raleigh is most similar to Cary, Durham, Charlotte, Wilmington, High Point and least similar to Billings, Surprise, Honolulu, New York, Anchorage\n",
" Miami is most similar to Pembroke Pines, Miami Gardens, Hialeah, Hollywood, Coral Springs and least similar to Surprise, Boise City, New York, Billings, Anchorage\n",
" Oakland is most similar to Daly City, Vallejo, Concord, Modesto, Hayward and least similar to Anchorage, Surprise, Billings, Frisco, New York\n",
" Minneapolis is most similar to St. Paul, Green Bay, Rochester, Grand Rapids, Madison and least similar to Anchorage, Honolulu, New York, Frisco, Surprise\n",
" Tulsa is most similar to Oklahoma City, Little Rock, Norman, Broken Arrow, Jackson and least similar to Anchorage, Los Angeles, Frisco, Surprise, New York\n",
" Cleveland is most similar to Toledo, Akron, Indianapolis, Dayton, Pittsburgh and least similar to Honolulu, New York, Anchorage, Frisco, Surprise\n",
" Wichita is most similar to Springfield, Topeka, Kansas City, Independence, Kansas City and least similar to Honolulu, Anchorage, Frisco, Surprise, New York\n",
" Arlington is most similar to Garland, Mesquite, Richardson, Carrollton, Irving and least similar to Los Angeles, Frisco, Surprise, Anchorage, New York\n",
" New Orleans is most similar to Baton Rouge, Jackson, Shreveport, Lafayette, Mobile and least similar to Los Angeles, Anchorage, Frisco, New York, Surprise\n",
" Bakersfield is most similar to Lancaster, Fontana, Palmdale, Moreno Valley, Santa Maria and least similar to Sioux Falls, Frisco, Anchorage, Billings, New York\n",
" Tampa is most similar to Lakeland, Coral Springs, Pompano Beach, Miami Gardens, Hollywood and least similar to Surprise, Frisco, New York, Billings, Anchorage\n",
" Honolulu is most similar to Long Beach, San Buenaventura (Ventura), San Diego, Oxnard, El Cajon and least similar to Sioux Falls, Billings, Fargo, Frisco, New York\n",
" Aurora is most similar to Westminster, Colorado Springs, Arvada, Boulder, Fort Collins and least similar to Billings, Anchorage, Surprise, Frisco, New York\n",
" Anaheim is most similar to Huntington Beach, Santa Ana, Garden Grove, Glendale, Long Beach and least similar to Surprise, Billings, Anchorage, Frisco, New York\n",
" Santa Ana is most similar to Long Beach, Anaheim, Glendale, Huntington Beach, Inglewood and least similar to Surprise, Billings, Anchorage, Frisco, New York\n",
" St. Louis is most similar to Kansas City, Kansas City, Independence, Springfield, Topeka and least similar to Honolulu, Anchorage, New York, Frisco, Surprise\n",
" Riverside is most similar to Corona, Oxnard, San Bernardino, Santa Clarita, Escondido and least similar to Sioux Falls, Frisco, Billings, Anchorage, New York\n",
" Corpus Christi is most similar to Austin, Pasadena, McAllen, Brownsville, Laredo and least similar to Billings, Frisco, Surprise, Anchorage, New York\n",
" Lexington-Fayette is most similar to Louisville/Jefferson County, Knoxville, Huntsville, Nashville-Davidson, Chattanooga and least similar to Honolulu, Frisco, Anchorage, Surprise, New York\n",
" Pittsburgh is most similar to Columbus, Akron, Erie, Indianapolis, Toledo and least similar to Honolulu, New York, Frisco, Surprise, Anchorage\n",
" Anchorage is most similar to Boise City, Billings, Portland, Everett, Gresham and least similar to Surprise, Miramar, Port St. Lucie, Frisco, New York\n",
" Stockton is most similar to Antioch, Santa Clara, Berkeley, Fairfield, Santa Rosa and least similar to Surprise, Anchorage, Billings, Frisco, New York\n",
" Cincinnati is most similar to Dayton, Indianapolis, Akron, Toledo, Evansville and least similar to Honolulu, New York, Anchorage, Frisco, Surprise\n",
" St. Paul is most similar to Minneapolis, Rochester, Green Bay, Grand Rapids, Madison and least similar to Anchorage, Honolulu, Frisco, New York, Surprise\n",
" Toledo is most similar to Akron, Cleveland, Fort Wayne, Dayton, South Bend and least similar to Honolulu, New York, Anchorage, Frisco, Surprise\n",
" Greensboro is most similar to Winston-Salem, High Point, Durham, Wilmington, Fayetteville and least similar to Honolulu, Frisco, Surprise, New York, Anchorage\n",
" Newark is most similar to Jersey City, Paterson, Elizabeth, Stamford, Bridgeport and least similar to Boise City, Surprise, Frisco, Billings, Anchorage\n",
" Plano is most similar to Irving, Tyler, Mesquite, Carrollton, Richardson and least similar to Los Angeles, Frisco, Surprise, Anchorage, New York\n",
" Henderson is most similar to Peoria, Chandler, Lancaster, Palmdale, Rancho Cucamonga and least similar to Surprise, Frisco, Billings, Anchorage, New York\n",
" Lincoln is most similar to Omaha, Des Moines, Cedar Rapids, Davenport, Madison and least similar to Honolulu, Los Angeles, Frisco, Surprise, New York\n",
" Buffalo is most similar to Rochester, Syracuse, Springfield, Erie, Yonkers and least similar to New York, Honolulu, Surprise, Frisco, Anchorage\n",
" Jersey City is most similar to Elizabeth, Newark, Paterson, Stamford, Bridgeport and least similar to Boise City, Surprise, Frisco, Billings, Anchorage\n",
" Chula Vista is most similar to Carlsbad, Moreno Valley, Temecula, Fontana, Irvine and least similar to Sioux Falls, Frisco, Billings, Anchorage, New York\n",
" Fort Wayne is most similar to South Bend, Akron, Toledo, Dayton, Indianapolis and least similar to Honolulu, Anchorage, New York, Frisco, Surprise\n",
" Orlando is most similar to Palm Bay, West Palm Beach, Lakeland, Tampa, Pembroke Pines and least similar to Surprise, Boise City, New York, Billings, Anchorage\n",
" St. Petersburg is most similar to Clearwater, Fort Lauderdale, Pompano Beach, Tampa, Coral Springs and least similar to Surprise, Frisco, New York, Billings, Anchorage\n",
" Chandler is most similar to Peoria, Scottsdale, Mesa, Tempe, Glendale and least similar to Surprise, Billings, Frisco, Anchorage, New York\n",
" Laredo is most similar to McAllen, Brownsville, Corpus Christi, Austin, College Station and least similar to Billings, Frisco, Surprise, Anchorage, New York\n",
" Norfolk is most similar to Newport News, Richmond, Virginia Beach, Chesapeake, Hampton and least similar to Billings, New York, Frisco, Surprise, Anchorage\n",
" Durham is most similar to High Point, Greensboro, Wilmington, Winston-Salem, Charlotte and least similar to Honolulu, Frisco, Surprise, New York, Anchorage\n",
" Madison is most similar to Green Bay, Grand Rapids, Lansing, Ann Arbor, Sterling Heights and least similar to Anchorage, Honolulu, Frisco, Surprise, New York\n",
" Lubbock is most similar to Abilene, Odessa, Amarillo, Carrollton, Richardson and least similar to Los Angeles, Anchorage, Frisco, Surprise, New York\n",
" Irvine is most similar to Temecula, Chula Vista, Carlsbad, Moreno Valley, Bakersfield and least similar to Boise City, Sioux Falls, Billings, Anchorage, New York\n",
" Winston-Salem is most similar to Greensboro, High Point, Fayetteville, Durham, Wilmington and least similar to Honolulu, Frisco, Surprise, New York, Anchorage\n",
" Glendale is most similar to Tempe, Scottsdale, Mesa, Tucson, Las Vegas and least similar to Billings, Surprise, Anchorage, Frisco, New York\n",
" Garland is most similar to Arlington, Richardson, Mesquite, Carrollton, Irving and least similar to Los Angeles, Frisco, Anchorage, Surprise, New York\n",
" Hialeah is most similar to Fort Lauderdale, Hollywood, Pompano Beach, Coral Springs, Miami Gardens and least similar to Frisco, Surprise, New York, Billings, Anchorage\n",
" Reno is most similar to Fairfield, Antioch, Richmond, Berkeley, Vallejo and least similar to Anchorage, Billings, Surprise, Frisco, New York\n",
" Chesapeake is most similar to Norfolk, Richmond, Newport News, Virginia Beach, Alexandria and least similar to Billings, New York, Frisco, Surprise, Anchorage\n",
" Gilbert is most similar to North Las Vegas, Victorville, Murrieta, Peoria, Temecula and least similar to Sioux Falls, Boise City, Billings, Anchorage, New York\n",
" Baton Rouge is most similar to Lafayette, Shreveport, New Orleans, Jackson, Mobile and least similar to Los Angeles, Frisco, Anchorage, Surprise, New York\n",
" Irving is most similar to Mesquite, Plano, Carrollton, Richardson, Tyler and least similar to Los Angeles, Frisco, Surprise, Anchorage, New York\n",
" Scottsdale is most similar to Tempe, Glendale, Mesa, Tucson, Chandler and least similar to Billings, Surprise, Anchorage, Frisco, New York\n",
" North Las Vegas is most similar to Victorville, Gilbert, Murrieta, Irvine, Temecula and least similar to Sioux Falls, Boise City, Billings, Anchorage, New York\n",
" Fremont is most similar to Hayward, San Mateo, Sunnyvale, Modesto, Richmond and least similar to Anchorage, Surprise, Billings, Frisco, New York\n",
" Boise City is most similar to Billings, Sioux Falls, Fargo, Albuquerque, Tulsa and least similar to Chicago, Los Angeles, Frisco, Surprise, New York\n",
" Richmond is most similar to Newport News, Norfolk, Chesapeake, Hampton, Virginia Beach and least similar to Billings, New York, Frisco, Surprise, Anchorage\n",
" San Bernardino is most similar to Rialto, Fullerton, Orange, Santa Clarita, Ontario and least similar to Surprise, Billings, Anchorage, Frisco, New York\n",
" Birmingham is most similar to Montgomery, Mobile, Memphis, Huntsville, Chattanooga and least similar to Honolulu, Frisco, Anchorage, New York, Surprise\n",
" Spokane is most similar to Everett, Tacoma, Bellevue, Seattle, Vancouver and least similar to Port St. Lucie, McKinney, Surprise, Frisco, New York\n",
" Rochester is most similar to Syracuse, Buffalo, Springfield, Lowell, Yonkers and least similar to New York, Honolulu, Surprise, Frisco, Anchorage\n",
" Des Moines is most similar to Cedar Rapids, Davenport, Omaha, Lincoln, Madison and least similar to Anchorage, Honolulu, Frisco, Surprise, New York\n",
" Modesto is most similar to Hayward, Fremont, Concord, San Mateo, Richmond and least similar to Anchorage, Surprise, Billings, Frisco, New York\n",
" Fayetteville is most similar to Winston-Salem, Greensboro, Augusta-Richmond County, Atlanta, High Point and least similar to Honolulu, Frisco, New York, Surprise, Anchorage\n",
" Tacoma is most similar to Everett, Seattle, Bellevue, Vancouver, Portland and least similar to Port St. Lucie, McKinney, Surprise, Frisco, New York\n",
" Oxnard is most similar to Santa Clarita, Simi Valley, Thousand Oaks, Riverside, Fullerton and least similar to Sioux Falls, Anchorage, Billings, Frisco, New York\n",
" Fontana is most similar to Moreno Valley, Rancho Cucamonga, Palmdale, Lancaster, Carlsbad and least similar to Sioux Falls, Frisco, Billings, Anchorage, New York\n",
" Columbus is most similar to Savannah, Atlanta, Augusta-Richmond County, Athens-Clarke County, Fayetteville and least similar to Billings, Frisco, Surprise, New York, Anchorage\n",
" Montgomery is most similar to Mobile, Birmingham, Huntsville, Chattanooga, Memphis and least similar to Honolulu, Frisco, Surprise, Anchorage, New York\n",
" Moreno Valley is most similar to Fontana, Carlsbad, Rancho Cucamonga, Palmdale, Lancaster and least similar to Sioux Falls, Frisco, Billings, Anchorage, New York\n",
" Shreveport is most similar to Jackson, Baton Rouge, Lafayette, New Orleans, Little Rock and least similar to Los Angeles, Anchorage, Frisco, Surprise, New York\n",
" Aurora is most similar to Joliet, Elgin, Naperville, Peoria, Springfield and least similar to Surprise, Frisco, Billings, New York, Anchorage\n",
" Yonkers is most similar to Springfield, Worcester, Cambridge, Lowell, Syracuse and least similar to Billings, Honolulu, Frisco, Surprise, Anchorage\n",
" Akron is most similar to Toledo, Cleveland, Fort Wayne, Indianapolis, Dayton and least similar to Honolulu, New York, Anchorage, Frisco, Surprise\n",
" Huntington Beach is most similar to Costa Mesa, Garden Grove, Norwalk, Torrance, Ontario and least similar to Surprise, Billings, Anchorage, Frisco, New York\n",
" Little Rock is most similar to Oklahoma City, Jackson, Tulsa, Norman, Shreveport and least similar to Los Angeles, Anchorage, Frisco, Surprise, New York\n",
" Augusta-Richmond County is most similar to Atlanta, Savannah, Columbus, Athens-Clarke County, Fayetteville and least similar to Billings, Frisco, New York, Surprise, Anchorage\n",
" Amarillo is most similar to Wichita Falls, Lubbock, Abilene, Richardson, Carrollton and least similar to Los Angeles, Anchorage, Frisco, Surprise, New York\n",
" Glendale is most similar to Inglewood, El Monte, Pomona, Norwalk, West Covina and least similar to Surprise, Billings, Anchorage, Frisco, New York\n",
" Mobile is most similar to Montgomery, Beaumont, Birmingham, Pasadena, Arlington and least similar to Honolulu, Frisco, Surprise, Anchorage, New York\n",
" Grand Rapids is most similar to Lansing, Warren, Ann Arbor, Sterling Heights, Milwaukee and least similar to Honolulu, Anchorage, New York, Frisco, Surprise\n",
" Salt Lake City is most similar to Provo, West Valley City, West Jordan, Lakewood, Boulder and least similar to Los Angeles, Anchorage, Surprise, Frisco, New York\n",
" Tallahassee is most similar to Gainesville, Lakeland, Orlando, Tampa, Clearwater and least similar to Surprise, Frisco, Billings, New York, Anchorage\n",
" Huntsville is most similar to Chattanooga, Montgomery, Knoxville, Birmingham, Columbia and least similar to Honolulu, Frisco, Surprise, Anchorage, New York\n",
" Grand Prairie is most similar to Denton, Lewisville, Fort Worth, College Station, Killeen and least similar to Los Angeles, Frisco, Surprise, Anchorage, New York\n",
" Knoxville is most similar to Nashville-Davidson, Chattanooga, Columbia, Louisville/Jefferson County, Lexington-Fayette and least similar to Honolulu, Frisco, Surprise, New York, Anchorage\n",
" Worcester is most similar to Cambridge, Lowell, Springfield, Yonkers, Hartford and least similar to Billings, Honolulu, Frisco, Surprise, Anchorage\n",
" Newport News is most similar to Norfolk, Hampton, Richmond, Virginia Beach, Chesapeake and least similar to Billings, New York, Frisco, Surprise, Anchorage\n",
" Brownsville is most similar to McAllen, Laredo, Corpus Christi, Austin, Pasadena and least similar to Billings, Frisco, Surprise, Anchorage, New York\n",
" Overland Park is most similar to Independence, Topeka, Kansas City, Springfield, Columbia and least similar to Honolulu, Anchorage, Frisco, Surprise, New York\n",
" Santa Clarita is most similar to Simi Valley, Oxnard, Thousand Oaks, San Bernardino, Fullerton and least similar to Surprise, Anchorage, Billings, Frisco, New York\n",
" Providence is most similar to Allentown, Washington, Columbus, Erie, Yonkers and least similar to New York, Honolulu, Frisco, Surprise, Anchorage\n",
" Garden Grove is most similar to Torrance, Huntington Beach, Orange, Downey, Costa Mesa and least similar to Surprise, Billings, Anchorage, Frisco, New York\n",
" Chattanooga is most similar to Nashville-Davidson, Knoxville, Columbia, Huntsville, Clarksville and least similar to Honolulu, Frisco, Surprise, Anchorage, New York\n",
" Oceanside is most similar to El Cajon, Escondido, Garden Grove, Huntington Beach, Orange and least similar to Surprise, Billings, Anchorage, Frisco, New York\n",
" Jackson is most similar to Shreveport, Baton Rouge, Little Rock, New Orleans, Lafayette and least similar to Los Angeles, Anchorage, Frisco, Surprise, New York\n",
" Fort Lauderdale is most similar to Hialeah, Hollywood, Pompano Beach, Coral Springs, Miami Gardens and least similar to Frisco, Surprise, New York, Billings, Anchorage\n",
" Santa Rosa is most similar to Fairfield, Berkeley, Antioch, Richmond, San Mateo and least similar to Anchorage, Surprise, Billings, Frisco, New York\n",
" Rancho Cucamonga is most similar to Palmdale, Fontana, Lancaster, Moreno Valley, Corona and least similar to Sioux Falls, Frisco, Billings, Anchorage, New York\n",
" Port St. Lucie is most similar to Miramar, Cape Coral, Palm Bay, Orlando, West Palm Beach and least similar to Fargo, Boise City, Billings, New York, Anchorage\n",
" Tempe is most similar to Glendale, Scottsdale, Mesa, Tucson, Chandler and least similar to Billings, Surprise, Anchorage, Frisco, New York\n",
" Ontario is most similar to Downey, Pasadena, Garden Grove, West Covina, Pomona and least similar to Surprise, Billings, Anchorage, Frisco, New York\n",
" Vancouver is most similar to Tacoma, Salem, Gresham, Portland, Bellevue and least similar to Port St. Lucie, McKinney, Surprise, Frisco, New York\n",
" Cape Coral is most similar to Miramar, Palm Bay, Port St. Lucie, West Palm Beach, Orlando and least similar to Fargo, Boise City, Billings, New York, Anchorage\n",
" Sioux Falls is most similar to Fargo, Billings, Boise City, Broken Arrow, Tulsa and least similar to Honolulu, Frisco, Surprise, Los Angeles, New York\n",
" Springfield is most similar to Wichita, Independence, Overland Park, Kansas City, Kansas City and least similar to Honolulu, Anchorage, Frisco, Surprise, New York\n",
" Peoria is most similar to Chandler, Henderson, Scottsdale, Mesa, Tempe and least similar to Surprise, Frisco, Billings, Anchorage, New York\n",
" Pembroke Pines is most similar to Miami Gardens, West Palm Beach, Miami, Coral Springs, Hollywood and least similar to Surprise, Boise City, New York, Billings, Anchorage\n",
" Elk Grove is most similar to Victorville, Roseville, Murrieta, North Las Vegas, Irvine and least similar to Boise City, Sioux Falls, Anchorage, Billings, New York\n",
" Salem is most similar to Gresham, Eugene, Portland, Vancouver, Tacoma and least similar to Port St. Lucie, McKinney, Surprise, Frisco, New York\n",
" Lancaster is most similar to Palmdale, Rancho Cucamonga, Fontana, Santa Maria, Moreno Valley and least similar to Sioux Falls, Frisco, Anchorage, Billings, New York\n",
" Corona is most similar to Riverside, Rancho Cucamonga, Oxnard, Santa Clarita, Palmdale and least similar to Sioux Falls, Frisco, Billings, Anchorage, New York\n",
" Eugene is most similar to Salem, Gresham, Portland, Vancouver, Tacoma and least similar to Port St. Lucie, McKinney, Surprise, Frisco, New York\n",
" Palmdale is most similar to Lancaster, Rancho Cucamonga, Fontana, Moreno Valley, Santa Maria and least similar to Sioux Falls, Frisco, Anchorage, Billings, New York\n",
" Salinas is most similar to Sunnyvale, Fremont, San Mateo, Modesto, Hayward and least similar to Surprise, Anchorage, Billings, Frisco, New York\n",
" Springfield is most similar to Worcester, Lowell, Cambridge, Yonkers, Hartford and least similar to Billings, Honolulu, Frisco, Surprise, Anchorage\n",
" Pasadena is most similar to Beaumont, Waco, Austin, Corpus Christi, Arlington and least similar to Billings, Frisco, Surprise, Anchorage, New York\n",
" Fort Collins is most similar to Aurora, Boulder, Westminster, Arvada, Colorado Springs and least similar to Los Angeles, Anchorage, Surprise, Frisco, New York\n",
" Hayward is most similar to Fremont, San Mateo, Richmond, Modesto, Concord and least similar to Anchorage, Surprise, Billings, Frisco, New York\n",
" Pomona is most similar to West Covina, Norwalk, El Monte, Pasadena, Ontario and least similar to Surprise, Billings, Anchorage, Frisco, New York\n",
" Cary is most similar to Raleigh, Durham, Wilmington, Charlotte, High Point and least similar to Billings, Surprise, Honolulu, New York, Anchorage\n",
" Rockford is most similar to Peoria, Naperville, Elgin, Springfield, Buffalo and least similar to Billings, New York, Surprise, Anchorage, Frisco\n",
" Alexandria is most similar to Richmond, Chesapeake, Newport News, Norfolk, Allentown and least similar to Billings, New York, Frisco, Surprise, Anchorage\n",
" Escondido is most similar to El Cajon, Oceanside, Orange, Fullerton, San Bernardino and least similar to Surprise, Billings, Anchorage, Frisco, New York\n",
" McKinney is most similar to Pearland, Round Rock, Frisco, Port St. Lucie, Murfreesboro and least similar to Chicago, Billings, Los Angeles, Anchorage, New York\n",
" Kansas City is most similar to Topeka, Independence, Kansas City, Wichita, Overland Park and least similar to Honolulu, Anchorage, Frisco, Surprise, New York\n",
" Joliet is most similar to Aurora, Elgin, Naperville, Peoria, Springfield and least similar to Surprise, Frisco, Billings, New York, Anchorage\n",
" Sunnyvale is most similar to San Mateo, Fremont, Berkeley, Hayward, Santa Clara and least similar to Anchorage, Surprise, Billings, Frisco, New York\n",
" Torrance is most similar to Downey, Garden Grove, Orange, Fullerton, Ontario and least similar to Surprise, Billings, Anchorage, Frisco, New York\n",
" Bridgeport is most similar to New Haven, Stamford, Waterbury, Hartford, Paterson and least similar to Honolulu, Billings, Surprise, Frisco, Anchorage\n",
" Lakewood is most similar to Centennial, Arvada, Westminster, Boulder, Denver and least similar to Billings, Anchorage, Surprise, Frisco, New York\n",
" Hollywood is most similar to Coral Springs, Pompano Beach, Hialeah, Fort Lauderdale, Miami Gardens and least similar to Frisco, Surprise, New York, Billings, Anchorage\n",
" Paterson is most similar to Newark, Elizabeth, Jersey City, Bridgeport, Stamford and least similar to Boise City, Surprise, Frisco, Billings, Anchorage\n",
" Naperville is most similar to Elgin, Peoria, Rockford, Springfield, Joliet and least similar to Billings, Surprise, New York, Frisco, Anchorage\n",
" Syracuse is most similar to Rochester, Springfield, Buffalo, Lowell, Worcester and least similar to New York, Honolulu, Frisco, Surprise, Anchorage\n",
" Mesquite is most similar to Carrollton, Richardson, Irving, Plano, Tyler and least similar to Los Angeles, Frisco, Surprise, Anchorage, New York\n",
" Dayton is most similar to Cincinnati, Indianapolis, Akron, Toledo, Fort Wayne and least similar to Honolulu, New York, Anchorage, Frisco, Surprise\n",
" Savannah is most similar to Columbus, Augusta-Richmond County, Atlanta, Athens-Clarke County, Fayetteville and least similar to Billings, Frisco, Surprise, New York, Anchorage\n",
" Clarksville is most similar to Chattanooga, Columbia, Columbia, Knoxville, High Point and least similar to Honolulu, Frisco, Surprise, Anchorage, New York\n",
" Orange is most similar to Fullerton, Torrance, Garden Grove, Downey, Ontario and least similar to Surprise, Billings, Anchorage, Frisco, New York\n",
" Pasadena is most similar to West Covina, Burbank, Norwalk, Pomona, Ontario and least similar to Surprise, Billings, Anchorage, Frisco, New York\n",
" Fullerton is most similar to Orange, Rialto, Torrance, San Bernardino, Downey and least similar to Surprise, Billings, Anchorage, Frisco, New York\n",
" Killeen is most similar to College Station, Grand Prairie, Denton, Fort Worth, Lewisville and least similar to Los Angeles, Frisco, Surprise, Anchorage, New York\n",
" Frisco is most similar to Surprise, McKinney, Pearland, Port St. Lucie, Round Rock and least similar to Seattle, Chicago, Anchorage, Los Angeles, New York\n",
" Hampton is most similar to Newport News, Norfolk, Richmond, Virginia Beach, Chesapeake and least similar to Billings, New York, Frisco, Surprise, Anchorage\n",
" McAllen is most similar to Brownsville, Laredo, Corpus Christi, Austin, College Station and least similar to Billings, Frisco, Surprise, Anchorage, New York\n",
" Warren is most similar to Ann Arbor, Lansing, Sterling Heights, Grand Rapids, Milwaukee and least similar to Honolulu, Anchorage, New York, Frisco, Surprise\n",
" Bellevue is most similar to Everett, Tacoma, Seattle, Vancouver, Spokane and least similar to Port St. Lucie, McKinney, Surprise, Frisco, New York\n",
" West Valley City is most similar to Provo, West Jordan, Salt Lake City, Boulder, Arvada and least similar to Anchorage, Los Angeles, Surprise, Frisco, New York\n",
" Columbia is most similar to Chattanooga, Nashville-Davidson, North Charleston, Knoxville, Charleston and least similar to Honolulu, Frisco, Surprise, New York, Anchorage\n",
" Olathe is most similar to Columbia, Overland Park, Clarksville, Springfield, Independence and least similar to Los Angeles, Frisco, Surprise, Anchorage, New York\n",
" Sterling Heights is most similar to Ann Arbor, Warren, Lansing, Grand Rapids, Madison and least similar to Honolulu, Anchorage, Frisco, New York, Surprise\n",
" New Haven is most similar to Bridgeport, Stamford, Waterbury, Hartford, Springfield and least similar to Honolulu, Billings, Surprise, Frisco, Anchorage\n",
" Miramar is most similar to Cape Coral, Port St. Lucie, Palm Bay, West Palm Beach, Orlando and least similar to Fargo, Boise City, Billings, New York, Anchorage\n",
" Waco is most similar to Irving, Arlington, Mesquite, Carrollton, Plano and least similar to Los Angeles, Frisco, Surprise, Anchorage, New York\n",
" Thousand Oaks is most similar to San Buenaventura (Ventura), Simi Valley, Torrance, Fullerton, Downey and least similar to Surprise, Billings, Anchorage, Frisco, New York\n",
" Cedar Rapids is most similar to Davenport, Des Moines, Madison, Green Bay, Omaha and least similar to Los Angeles, Honolulu, Frisco, Surprise, New York\n",
" Charleston is most similar to North Charleston, Columbia, Wilmington, Savannah, Athens-Clarke County and least similar to Honolulu, Frisco, Surprise, New York, Anchorage\n",
" Visalia is most similar to Santa Maria, Lancaster, Palmdale, Bakersfield, Rancho Cucamonga and least similar to Sioux Falls, Anchorage, Frisco, Billings, New York\n",
" Topeka is most similar to Kansas City, Independence, Kansas City, Wichita, Overland Park and least similar to Honolulu, Anchorage, Frisco, Surprise, New York\n",
" Elizabeth is most similar to Jersey City, Newark, Paterson, Stamford, Bridgeport and least similar to Boise City, Surprise, Frisco, Billings, Anchorage\n",
" Gainesville is most similar to Tallahassee, Lakeland, Clearwater, Tampa, St. Petersburg and least similar to Surprise, Frisco, Billings, New York, Anchorage\n",
" Thornton is most similar to Fort Collins, Aurora, Colorado Springs, Westminster, Arvada and least similar to Los Angeles, Surprise, Anchorage, Frisco, New York\n",
" Roseville is most similar to Visalia, Bakersfield, Stockton, Antioch, Santa Clara and least similar to Sioux Falls, Anchorage, Frisco, Billings, New York\n",
" Carrollton is most similar to Richardson, Mesquite, Irving, Plano, Garland and least similar to Los Angeles, Frisco, Surprise, Anchorage, New York\n",
" Coral Springs is most similar to Pompano Beach, Hollywood, Fort Lauderdale, Miami Gardens, Hialeah and least similar to Frisco, Surprise, New York, Billings, Anchorage\n",
" Stamford is most similar to Bridgeport, New Haven, Waterbury, Hartford, Elizabeth and least similar to Honolulu, Surprise, Billings, Frisco, Anchorage\n",
" Simi Valley is most similar to Santa Clarita, Thousand Oaks, San Buenaventura (Ventura), Oxnard, Fullerton and least similar to Surprise, Anchorage, Billings, Frisco, New York\n",
" Concord is most similar to Vallejo, Daly City, Hayward, Richmond, Modesto and least similar to Anchorage, Surprise, Billings, Frisco, New York\n",
" Hartford is most similar to Waterbury, New Haven, Bridgeport, Stamford, Springfield and least similar to Honolulu, Billings, Surprise, Frisco, Anchorage\n",
" Kent is most similar to Bellevue, Everett, Gresham, Vancouver, Tacoma and least similar to Houston, Los Angeles, Surprise, Frisco, New York\n",
" Lafayette is most similar to Baton Rouge, Shreveport, Jackson, New Orleans, Mobile and least similar to Los Angeles, Frisco, Anchorage, Surprise, New York\n",
" Midland is most similar to Odessa, Lubbock, Lewisville, Waco, Irving and least similar to Los Angeles, Frisco, Surprise, Anchorage, New York\n",
" Surprise is most similar to Frisco, McKinney, Murrieta, Gilbert, Pearland and least similar to Los Angeles, Boise City, Billings, Anchorage, New York\n",
" Denton is most similar to Grand Prairie, Lewisville, Killeen, College Station, Fort Worth and least similar to Los Angeles, Frisco, Surprise, Anchorage, New York\n",
" Victorville is most similar to Murrieta, Irvine, North Las Vegas, Temecula, Elk Grove and least similar to Sioux Falls, Boise City, Anchorage, Billings, New York\n",
" Evansville is most similar to Cincinnati, Dayton, Indianapolis, Fort Wayne, St. Louis and least similar to Honolulu, Frisco, Anchorage, Surprise, New York\n",
" Santa Clara is most similar to Sunnyvale, Antioch, Berkeley, Stockton, San Mateo and least similar to Surprise, Anchorage, Billings, Frisco, New York\n",
" Abilene is most similar to Lubbock, Wichita Falls, Garland, Arlington, Waco and least similar to Los Angeles, Anchorage, Frisco, Surprise, New York\n",
" Athens-Clarke County is most similar to Savannah, Augusta-Richmond County, Columbus, Atlanta, Wilmington and least similar to Billings, Frisco, Surprise, New York, Anchorage\n",
" Vallejo is most similar to Concord, Daly City, Richmond, Hayward, Modesto and least similar to Anchorage, Surprise, Billings, Frisco, New York\n",
" Allentown is most similar to Providence, Columbus, Alexandria, Washington, Yonkers and least similar to Honolulu, New York, Frisco, Surprise, Anchorage\n",
" Norman is most similar to Broken Arrow, Oklahoma City, Little Rock, Tulsa, Albuquerque and least similar to Anchorage, Los Angeles, Frisco, Surprise, New York\n",
" Beaumont is most similar to Pasadena, Waco, Arlington, Tyler, Austin and least similar to Billings, Frisco, Surprise, Anchorage, New York\n",
" Independence is most similar to Kansas City, Topeka, Kansas City, Overland Park, Wichita and least similar to Honolulu, Anchorage, Frisco, Surprise, New York\n",
" Murfreesboro is most similar to Clarksville, Cary, Charleston, North Charleston, Raleigh and least similar to Surprise, Los Angeles, Honolulu, Anchorage, New York\n",
" Ann Arbor is most similar to Warren, Sterling Heights, Lansing, Grand Rapids, Madison and least similar to Honolulu, Anchorage, Frisco, New York, Surprise\n",
" Springfield is most similar to Peoria, Naperville, Rockford, Elgin, Joliet and least similar to Billings, Surprise, New York, Frisco, Anchorage\n",
" Berkeley is most similar to Fairfield, Antioch, San Mateo, Sunnyvale, Richmond and least similar to Anchorage, Surprise, Billings, Frisco, New York\n",
" Peoria is most similar to Springfield, Rockford, Naperville, Elgin, Joliet and least similar to Billings, Surprise, New York, Frisco, Anchorage\n",
" Provo is most similar to Salt Lake City, West Valley City, West Jordan, Boulder, Lakewood and least similar to Los Angeles, Anchorage, Surprise, Frisco, New York\n",
" El Monte is most similar to Inglewood, Pomona, Glendale, West Covina, Norwalk and least similar to Surprise, Billings, Anchorage, Frisco, New York\n",
" Columbia is most similar to Olathe, Overland Park, Clarksville, Springfield, Independence and least similar to Honolulu, Frisco, Surprise, Anchorage, New York\n",
" Lansing is most similar to Grand Rapids, Warren, Ann Arbor, Sterling Heights, Milwaukee and least similar to Honolulu, Anchorage, New York, Frisco, Surprise\n",
" Fargo is most similar to Sioux Falls, Billings, Boise City, Lincoln, Omaha and least similar to Honolulu, Frisco, Los Angeles, Surprise, New York\n",
" Downey is most similar to Torrance, Burbank, Ontario, Garden Grove, Pasadena and least similar to Surprise, Billings, Anchorage, Frisco, New York\n",
" Costa Mesa is most similar to Huntington Beach, Norwalk, Garden Grove, Downey, Pomona and least similar to Surprise, Billings, Anchorage, Frisco, New York\n",
" Wilmington is most similar to Durham, Charlotte, High Point, Greensboro, Winston-Salem and least similar to Honolulu, Frisco, Surprise, New York, Anchorage\n",
" Arvada is most similar to Westminster, Boulder, Centennial, Lakewood, Aurora and least similar to Billings, Anchorage, Surprise, Frisco, New York\n",
" Inglewood is most similar to El Monte, Glendale, Norwalk, Pomona, West Covina and least similar to Surprise, Billings, Anchorage, Frisco, New York\n",
" Miami Gardens is most similar to Coral Springs, Hollywood, Pembroke Pines, Pompano Beach, Hialeah and least similar to Frisco, Surprise, New York, Billings, Anchorage\n",
" Carlsbad is most similar to Moreno Valley, Fontana, Chula Vista, Rancho Cucamonga, Palmdale and least similar to Sioux Falls, Frisco, Billings, Anchorage, New York\n",
" Westminster is most similar to Arvada, Boulder, Centennial, Lakewood, Aurora and least similar to Billings, Anchorage, Surprise, Frisco, New York\n",
" Rochester is most similar to St. Paul, Madison, Minneapolis, Green Bay, Grand Rapids and least similar to Anchorage, Honolulu, Frisco, Surprise, New York\n",
" Odessa is most similar to Midland, Lubbock, Abilene, Waco, El Paso and least similar to Los Angeles, Frisco, Surprise, Anchorage, New York\n",
" Manchester is most similar to Sterling Heights, Providence, Warren, Ann Arbor, Lansing and least similar to New York, Frisco, Honolulu, Anchorage, Surprise\n",
" Elgin is most similar to Naperville, Rockford, Peoria, Joliet, Aurora and least similar to Billings, Surprise, New York, Frisco, Anchorage\n",
" West Jordan is most similar to West Valley City, Provo, Salt Lake City, Fort Collins, Boulder and least similar to Anchorage, Los Angeles, Surprise, Frisco, New York\n",
" Round Rock is most similar to Killeen, College Station, Pearland, Denton, Grand Prairie and least similar to Surprise, Billings, Los Angeles, Anchorage, New York\n",
" Clearwater is most similar to St. Petersburg, Tampa, Pompano Beach, Fort Lauderdale, Gainesville and least similar to Surprise, Frisco, Billings, New York, Anchorage\n",
" Waterbury is most similar to Hartford, New Haven, Bridgeport, Stamford, Springfield and least similar to Honolulu, Billings, Surprise, Frisco, Anchorage\n",
" Gresham is most similar to Salem, Eugene, Vancouver, Portland, Bellevue and least similar to Port St. Lucie, McKinney, Surprise, Frisco, New York\n",
" Fairfield is most similar to Berkeley, Santa Rosa, Antioch, Richmond, San Mateo and least similar to Anchorage, Surprise, Billings, Frisco, New York\n",
" Billings is most similar to Boise City, Sioux Falls, Fargo, Broken Arrow, Tulsa and least similar to Murrieta, Frisco, Surprise, Los Angeles, New York\n",
" Lowell is most similar to Cambridge, Worcester, Springfield, Yonkers, Syracuse and least similar to Billings, Honolulu, Frisco, Surprise, Anchorage\n",
" San Buenaventura (Ventura) is most similar to Thousand Oaks, Burbank, Simi Valley, Torrance, Downey and least similar to Surprise, Anchorage, Billings, Frisco, New York\n",
" Pueblo is most similar to Colorado Springs, Centennial, Lakewood, Arvada, Westminster and least similar to Billings, Anchorage, Surprise, Frisco, New York\n",
" High Point is most similar to Greensboro, Durham, Winston-Salem, Wilmington, Fayetteville and least similar to Honolulu, Frisco, Surprise, New York, Anchorage\n",
" West Covina is most similar to Norwalk, Pasadena, Pomona, Burbank, El Monte and least similar to Surprise, Billings, Anchorage, Frisco, New York\n",
" Richmond is most similar to Hayward, San Mateo, Berkeley, Fremont, Concord and least similar to Anchorage, Surprise, Billings, Frisco, New York\n",
" Murrieta is most similar to Victorville, North Las Vegas, Elk Grove, Irvine, Gilbert and least similar to Sioux Falls, Boise City, Billings, Anchorage, New York\n",
" Cambridge is most similar to Worcester, Lowell, Springfield, Yonkers, Boston and least similar to Billings, Honolulu, Frisco, Surprise, Anchorage\n",
" Antioch is most similar to Berkeley, Fairfield, Santa Clara, Stockton, Santa Rosa and least similar to Surprise, Anchorage, Billings, Frisco, New York\n",
" Temecula is most similar to Irvine, Chula Vista, Carlsbad, Moreno Valley, Fontana and least similar to Boise City, Sioux Falls, Billings, Anchorage, New York\n",
" Norwalk is most similar to West Covina, Pomona, Pasadena, Costa Mesa, El Monte and least similar to Surprise, Billings, Anchorage, Frisco, New York\n",
" Centennial is most similar to Lakewood, Arvada, Westminster, Boulder, Denver and least similar to Billings, Anchorage, Surprise, Frisco, New York\n",
" Everett is most similar to Bellevue, Tacoma, Seattle, Vancouver, Spokane and least similar to Port St. Lucie, McKinney, Surprise, Frisco, New York\n",
" Palm Bay is most similar to Orlando, West Palm Beach, Lakeland, Pembroke Pines, Tampa and least similar to Surprise, Boise City, Billings, New York, Anchorage\n",
" Wichita Falls is most similar to Abilene, Garland, Amarillo, Richardson, Carrollton and least similar to Los Angeles, Anchorage, Frisco, Surprise, New York\n",
" Green Bay is most similar to Madison, Milwaukee, Grand Rapids, Lansing, St. Paul and least similar to Anchorage, Honolulu, Frisco, New York, Surprise\n",
" Daly City is most similar to Concord, Vallejo, Hayward, Richmond, Modesto and least similar to Anchorage, Surprise, Billings, Frisco, New York\n",
" Burbank is most similar to Pasadena, West Covina, Downey, Norwalk, Ontario and least similar to Surprise, Billings, Anchorage, Frisco, New York\n",
" Richardson is most similar to Carrollton, Mesquite, Irving, Plano, Garland and least similar to Los Angeles, Frisco, Surprise, Anchorage, New York\n",
" Pompano Beach is most similar to Coral Springs, Hollywood, Fort Lauderdale, Hialeah, Miami Gardens and least similar to Frisco, Surprise, Billings, New York, Anchorage\n",
" North Charleston is most similar to Charleston, Columbia, Wilmington, Savannah, Athens-Clarke County and least similar to Honolulu, Frisco, Surprise, New York, Anchorage\n",
" Broken Arrow is most similar to Norman, Oklahoma City, Little Rock, Tulsa, Jackson and least similar to Anchorage, Los Angeles, Frisco, Surprise, New York\n",
" Boulder is most similar to Westminster, Arvada, Centennial, Lakewood, Aurora and least similar to Billings, Anchorage, Surprise, Frisco, New York\n",
" West Palm Beach is most similar to Pembroke Pines, Palm Bay, Orlando, Miami Gardens, Lakeland and least similar to Surprise, Boise City, Billings, New York, Anchorage\n",
" Santa Maria is most similar to Lancaster, Palmdale, Visalia, Rancho Cucamonga, Oxnard and least similar to Sioux Falls, Anchorage, Frisco, Billings, New York\n",
" El Cajon is most similar to Escondido, Oceanside, Orange, Garden Grove, Fullerton and least similar to Surprise, Billings, Anchorage, Frisco, New York\n",
" Davenport is most similar to Cedar Rapids, Des Moines, Madison, Omaha, Green Bay and least similar to Anchorage, Honolulu, Frisco, Surprise, New York\n",
" Rialto is most similar to Fullerton, San Bernardino, Orange, Downey, Ontario and least similar to Surprise, Billings, Anchorage, Frisco, New York\n",
" Las Cruces is most similar to Albuquerque, Norman, Midland, Odessa, Broken Arrow and least similar to Los Angeles, Anchorage, Frisco, Surprise, New York\n",
" San Mateo is most similar to Hayward, Sunnyvale, Richmond, Fremont, Berkeley and least similar to Anchorage, Surprise, Billings, Frisco, New York\n",
" Lewisville is most similar to Plano, Tyler, Irving, Carrollton, Mesquite and least similar to Los Angeles, Frisco, Surprise, Anchorage, New York\n",
" South Bend is most similar to Fort Wayne, Toledo, Akron, Dayton, Cleveland and least similar to Honolulu, Anchorage, New York, Frisco, Surprise\n",
" Lakeland is most similar to Tampa, Orlando, West Palm Beach, Gainesville, Palm Bay and least similar to Surprise, Boise City, Billings, New York, Anchorage\n",
" Erie is most similar to Pittsburgh, Akron, Toledo, Columbus, Cleveland and least similar to Honolulu, New York, Frisco, Surprise, Anchorage\n",
" Tyler is most similar to Plano, Irving, Mesquite, Carrollton, Richardson and least similar to Los Angeles, Frisco, Surprise, Anchorage, New York\n",
" Pearland is most similar to Round Rock, McKinney, Killeen, College Station, Port St. Lucie and least similar to Boise City, Billings, Los Angeles, Anchorage, New York\n",
" College Station is most similar to Killeen, Grand Prairie, Fort Worth, Denton, Lewisville and least similar to Los Angeles, Frisco, Surprise, Anchorage, New York\n"
]
}
],
"source": [
"# Now, let's print out the most similar city to each other city:\n",
"\n",
"def show_rankings(rankings, names, n=5, preamble=''):\n",
" for index, name in enumerate(names):\n",
" top_n = rankings[index][1:1+n]\n",
" near = [names[i] for i in top_n]\n",
" far = [names[i] for i in rankings[index][-n:]]\n",
" print(preamble, name, 'is most similar to', ', '.join(near), 'and least similar to', ', '.join(far))\n",
"\n",
"show_rankings(rankings, names)"
]
},
{
"cell_type": "code",
"execution_count": 31,
"id": "6398f805",
"metadata": {},
"outputs": [],
"source": [
"# There are some interesting results here. For example, this record:\n",
"\n",
"\"\"\"{'city': 'Frisco',\n",
" 'growth_from_2000_to_2013': '287.7%',\n",
" 'latitude': 33.1506744,\n",
" 'longitude': -96.82361159999999,\n",
" 'population': '136791',\n",
" 'rank': '186',\n",
" 'state': 'Texas'}\"\"\"\n",
"\n",
"# Frisco, Texas has such an astounding growth rate that it really is very unlike anywhere on the list!"
]
},
{
"cell_type": "code",
"execution_count": 34,
"id": "a4cbab2b",
"metadata": {},
"outputs": [],
"source": [
"# So now we have some intuition about feature vectors. A \"feature\" is just a value like \"population growth rate\",\n",
"# or latitude, or market share of pickup trucks. We've got a few manually assembled features like that.\n",
"\n",
"# Deep learning is capable of finding those features for us. We tell the model, find us the latitude, and frombitude,\n",
"# and glouflitude... for hundreds of dimensions. Find whatever we need to do our task.\n",
"\n",
"# So let's try some deep learning. Instead of making up a feature vector for each city, we let loose\n",
"# a neural network, with the broad instructions of: \"find the 512 dimensions (*-tudes!) that best capture\n",
"# the semantic essence of every word and image on the internet\". The below model learns which images go\n",
"# with which captions -- we'll use it here to do the caption part, independent of any images. We need to\n",
"# get our data into a format that a neural network can understand -- for this model that just means English!\n",
"\n",
"import sentence_transformers\n",
"model = sentence_transformers.SentenceTransformer('clip-ViT-B-32').to('cuda')\n",
"\n",
"def embed_row(row):\n",
" # Little bit of practical engineering here: language models are not exceptionally\n",
" # good at dealing with numbers. And the really big ones don't entirely need to be!\n",
" # So let's just use the proper name for the city an state, and see what happens:\n",
" sentence = f\"\"\"{row.get('city')}, {row['state']}\"\"\"\n",
" print('getting embedding for', sentence)\n",
" return model.encode(sentence)\n"
]
},
{
"cell_type": "code",
"execution_count": 35,
"id": "2f1e42b1",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"getting embedding for New York, New York\n",
"getting embedding for Los Angeles, California\n",
"getting embedding for Chicago, Illinois\n",
"getting embedding for Houston, Texas\n",
"getting embedding for Philadelphia, Pennsylvania\n",
"getting embedding for Phoenix, Arizona\n",
"getting embedding for San Antonio, Texas\n",
"getting embedding for San Diego, California\n",
"getting embedding for Dallas, Texas\n",
"getting embedding for San Jose, California\n",
"getting embedding for Austin, Texas\n",
"getting embedding for Indianapolis, Indiana\n",
"getting embedding for Jacksonville, Florida\n",
"getting embedding for San Francisco, California\n",
"getting embedding for Columbus, Ohio\n",
"getting embedding for Charlotte, North Carolina\n",
"getting embedding for Fort Worth, Texas\n",
"getting embedding for Detroit, Michigan\n",
"getting embedding for El Paso, Texas\n",
"getting embedding for Memphis, Tennessee\n",
"getting embedding for Seattle, Washington\n",
"getting embedding for Denver, Colorado\n",
"getting embedding for Washington, District of Columbia\n",
"getting embedding for Boston, Massachusetts\n",
"getting embedding for Nashville-Davidson, Tennessee\n",
"getting embedding for Baltimore, Maryland\n",
"getting embedding for Oklahoma City, Oklahoma\n",
"getting embedding for Louisville/Jefferson County, Kentucky\n",
"getting embedding for Portland, Oregon\n",
"getting embedding for Las Vegas, Nevada\n",
"getting embedding for Milwaukee, Wisconsin\n",
"getting embedding for Albuquerque, New Mexico\n",
"getting embedding for Tucson, Arizona\n",
"getting embedding for Fresno, California\n",
"getting embedding for Sacramento, California\n",
"getting embedding for Long Beach, California\n",
"getting embedding for Kansas City, Missouri\n",
"getting embedding for Mesa, Arizona\n",
"getting embedding for Virginia Beach, Virginia\n",
"getting embedding for Atlanta, Georgia\n",
"getting embedding for Colorado Springs, Colorado\n",
"getting embedding for Omaha, Nebraska\n",
"getting embedding for Raleigh, North Carolina\n",
"getting embedding for Miami, Florida\n",
"getting embedding for Oakland, California\n",
"getting embedding for Minneapolis, Minnesota\n",
"getting embedding for Tulsa, Oklahoma\n",
"getting embedding for Cleveland, Ohio\n",
"getting embedding for Wichita, Kansas\n",
"getting embedding for Arlington, Texas\n",
"getting embedding for New Orleans, Louisiana\n",
"getting embedding for Bakersfield, California\n",
"getting embedding for Tampa, Florida\n",
"getting embedding for Honolulu, Hawaii\n",
"getting embedding for Aurora, Colorado\n",
"getting embedding for Anaheim, California\n",
"getting embedding for Santa Ana, California\n",
"getting embedding for St. Louis, Missouri\n",
"getting embedding for Riverside, California\n",
"getting embedding for Corpus Christi, Texas\n",
"getting embedding for Lexington-Fayette, Kentucky\n",
"getting embedding for Pittsburgh, Pennsylvania\n",
"getting embedding for Anchorage, Alaska\n",
"getting embedding for Stockton, California\n",
"getting embedding for Cincinnati, Ohio\n",
"getting embedding for St. Paul, Minnesota\n",
"getting embedding for Toledo, Ohio\n",
"getting embedding for Greensboro, North Carolina\n",
"getting embedding for Newark, New Jersey\n",
"getting embedding for Plano, Texas\n",
"getting embedding for Henderson, Nevada\n",
"getting embedding for Lincoln, Nebraska\n",
"getting embedding for Buffalo, New York\n",
"getting embedding for Jersey City, New Jersey\n",
"getting embedding for Chula Vista, California\n",
"getting embedding for Fort Wayne, Indiana\n",
"getting embedding for Orlando, Florida\n",
"getting embedding for St. Petersburg, Florida\n",
"getting embedding for Chandler, Arizona\n",
"getting embedding for Laredo, Texas\n",
"getting embedding for Norfolk, Virginia\n",
"getting embedding for Durham, North Carolina\n",
"getting embedding for Madison, Wisconsin\n",
"getting embedding for Lubbock, Texas\n",
"getting embedding for Irvine, California\n",
"getting embedding for Winston-Salem, North Carolina\n",
"getting embedding for Glendale, Arizona\n",
"getting embedding for Garland, Texas\n",
"getting embedding for Hialeah, Florida\n",
"getting embedding for Reno, Nevada\n",
"getting embedding for Chesapeake, Virginia\n",
"getting embedding for Gilbert, Arizona\n",
"getting embedding for Baton Rouge, Louisiana\n",
"getting embedding for Irving, Texas\n",
"getting embedding for Scottsdale, Arizona\n",
"getting embedding for North Las Vegas, Nevada\n",
"getting embedding for Fremont, California\n",
"getting embedding for Boise City, Idaho\n",
"getting embedding for Richmond, Virginia\n",
"getting embedding for San Bernardino, California\n",
"getting embedding for Birmingham, Alabama\n",
"getting embedding for Spokane, Washington\n",
"getting embedding for Rochester, New York\n",
"getting embedding for Des Moines, Iowa\n",
"getting embedding for Modesto, California\n",
"getting embedding for Fayetteville, North Carolina\n",
"getting embedding for Tacoma, Washington\n",
"getting embedding for Oxnard, California\n",
"getting embedding for Fontana, California\n",
"getting embedding for Columbus, Georgia\n",
"getting embedding for Montgomery, Alabama\n",
"getting embedding for Moreno Valley, California\n",
"getting embedding for Shreveport, Louisiana\n",
"getting embedding for Aurora, Illinois\n",
"getting embedding for Yonkers, New York\n",
"getting embedding for Akron, Ohio\n",
"getting embedding for Huntington Beach, California\n",
"getting embedding for Little Rock, Arkansas\n",
"getting embedding for Augusta-Richmond County, Georgia\n",
"getting embedding for Amarillo, Texas\n",
"getting embedding for Glendale, California\n",
"getting embedding for Mobile, Alabama\n",
"getting embedding for Grand Rapids, Michigan\n",
"getting embedding for Salt Lake City, Utah\n",
"getting embedding for Tallahassee, Florida\n",
"getting embedding for Huntsville, Alabama\n",
"getting embedding for Grand Prairie, Texas\n",
"getting embedding for Knoxville, Tennessee\n",
"getting embedding for Worcester, Massachusetts\n",
"getting embedding for Newport News, Virginia\n",
"getting embedding for Brownsville, Texas\n",
"getting embedding for Overland Park, Kansas\n",
"getting embedding for Santa Clarita, California\n",
"getting embedding for Providence, Rhode Island\n",
"getting embedding for Garden Grove, California\n",
"getting embedding for Chattanooga, Tennessee\n",
"getting embedding for Oceanside, California\n",
"getting embedding for Jackson, Mississippi\n",
"getting embedding for Fort Lauderdale, Florida\n",
"getting embedding for Santa Rosa, California\n",
"getting embedding for Rancho Cucamonga, California\n",
"getting embedding for Port St. Lucie, Florida\n",
"getting embedding for Tempe, Arizona\n",
"getting embedding for Ontario, California\n",
"getting embedding for Vancouver, Washington\n",
"getting embedding for Cape Coral, Florida\n",
"getting embedding for Sioux Falls, South Dakota\n",
"getting embedding for Springfield, Missouri\n",
"getting embedding for Peoria, Arizona\n",
"getting embedding for Pembroke Pines, Florida\n",
"getting embedding for Elk Grove, California\n",
"getting embedding for Salem, Oregon\n",
"getting embedding for Lancaster, California\n",
"getting embedding for Corona, California\n",
"getting embedding for Eugene, Oregon\n",
"getting embedding for Palmdale, California\n",
"getting embedding for Salinas, California\n",
"getting embedding for Springfield, Massachusetts\n",
"getting embedding for Pasadena, Texas\n",
"getting embedding for Fort Collins, Colorado\n",
"getting embedding for Hayward, California\n",
"getting embedding for Pomona, California\n",
"getting embedding for Cary, North Carolina\n",
"getting embedding for Rockford, Illinois\n",
"getting embedding for Alexandria, Virginia\n",
"getting embedding for Escondido, California\n",
"getting embedding for McKinney, Texas\n",
"getting embedding for Kansas City, Kansas\n",
"getting embedding for Joliet, Illinois\n",
"getting embedding for Sunnyvale, California\n",
"getting embedding for Torrance, California\n",
"getting embedding for Bridgeport, Connecticut\n",
"getting embedding for Lakewood, Colorado\n",
"getting embedding for Hollywood, Florida\n",
"getting embedding for Paterson, New Jersey\n",
"getting embedding for Naperville, Illinois\n",
"getting embedding for Syracuse, New York\n",
"getting embedding for Mesquite, Texas\n",
"getting embedding for Dayton, Ohio\n",
"getting embedding for Savannah, Georgia\n",
"getting embedding for Clarksville, Tennessee\n",
"getting embedding for Orange, California\n",
"getting embedding for Pasadena, California\n",
"getting embedding for Fullerton, California\n",
"getting embedding for Killeen, Texas\n",
"getting embedding for Frisco, Texas\n",
"getting embedding for Hampton, Virginia\n",
"getting embedding for McAllen, Texas\n",
"getting embedding for Warren, Michigan\n",
"getting embedding for Bellevue, Washington\n",
"getting embedding for West Valley City, Utah\n",
"getting embedding for Columbia, South Carolina\n",
"getting embedding for Olathe, Kansas\n",
"getting embedding for Sterling Heights, Michigan\n",
"getting embedding for New Haven, Connecticut\n",
"getting embedding for Miramar, Florida\n",
"getting embedding for Waco, Texas\n",
"getting embedding for Thousand Oaks, California\n",
"getting embedding for Cedar Rapids, Iowa\n",
"getting embedding for Charleston, South Carolina\n",
"getting embedding for Visalia, California\n",
"getting embedding for Topeka, Kansas\n",
"getting embedding for Elizabeth, New Jersey\n",
"getting embedding for Gainesville, Florida\n",
"getting embedding for Thornton, Colorado\n",
"getting embedding for Roseville, California\n",
"getting embedding for Carrollton, Texas\n",
"getting embedding for Coral Springs, Florida\n",
"getting embedding for Stamford, Connecticut\n",
"getting embedding for Simi Valley, California\n",
"getting embedding for Concord, California\n",
"getting embedding for Hartford, Connecticut\n",
"getting embedding for Kent, Washington\n",
"getting embedding for Lafayette, Louisiana\n",
"getting embedding for Midland, Texas\n",
"getting embedding for Surprise, Arizona\n",
"getting embedding for Denton, Texas\n",
"getting embedding for Victorville, California\n",
"getting embedding for Evansville, Indiana\n",
"getting embedding for Santa Clara, California\n",
"getting embedding for Abilene, Texas\n",
"getting embedding for Athens-Clarke County, Georgia\n",
"getting embedding for Vallejo, California\n",
"getting embedding for Allentown, Pennsylvania\n",
"getting embedding for Norman, Oklahoma\n",
"getting embedding for Beaumont, Texas\n",
"getting embedding for Independence, Missouri\n",
"getting embedding for Murfreesboro, Tennessee\n",
"getting embedding for Ann Arbor, Michigan\n",
"getting embedding for Springfield, Illinois\n",
"getting embedding for Berkeley, California\n",
"getting embedding for Peoria, Illinois\n",
"getting embedding for Provo, Utah\n",
"getting embedding for El Monte, California\n",
"getting embedding for Columbia, Missouri\n",
"getting embedding for Lansing, Michigan\n",
"getting embedding for Fargo, North Dakota\n",
"getting embedding for Downey, California\n",
"getting embedding for Costa Mesa, California\n",
"getting embedding for Wilmington, North Carolina\n",
"getting embedding for Arvada, Colorado\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"getting embedding for Inglewood, California\n",
"getting embedding for Miami Gardens, Florida\n",
"getting embedding for Carlsbad, California\n",
"getting embedding for Westminster, Colorado\n",
"getting embedding for Rochester, Minnesota\n",
"getting embedding for Odessa, Texas\n",
"getting embedding for Manchester, New Hampshire\n",
"getting embedding for Elgin, Illinois\n",
"getting embedding for West Jordan, Utah\n",
"getting embedding for Round Rock, Texas\n",
"getting embedding for Clearwater, Florida\n",
"getting embedding for Waterbury, Connecticut\n",
"getting embedding for Gresham, Oregon\n",
"getting embedding for Fairfield, California\n",
"getting embedding for Billings, Montana\n",
"getting embedding for Lowell, Massachusetts\n",
"getting embedding for San Buenaventura (Ventura), California\n",
"getting embedding for Pueblo, Colorado\n",
"getting embedding for High Point, North Carolina\n",
"getting embedding for West Covina, California\n",
"getting embedding for Richmond, California\n",
"getting embedding for Murrieta, California\n",
"getting embedding for Cambridge, Massachusetts\n",
"getting embedding for Antioch, California\n",
"getting embedding for Temecula, California\n",
"getting embedding for Norwalk, California\n",
"getting embedding for Centennial, Colorado\n",
"getting embedding for Everett, Washington\n",
"getting embedding for Palm Bay, Florida\n",
"getting embedding for Wichita Falls, Texas\n",
"getting embedding for Green Bay, Wisconsin\n",
"getting embedding for Daly City, California\n",
"getting embedding for Burbank, California\n",
"getting embedding for Richardson, Texas\n",
"getting embedding for Pompano Beach, Florida\n",
"getting embedding for North Charleston, South Carolina\n",
"getting embedding for Broken Arrow, Oklahoma\n",
"getting embedding for Boulder, Colorado\n",
"getting embedding for West Palm Beach, Florida\n",
"getting embedding for Santa Maria, California\n",
"getting embedding for El Cajon, California\n",
"getting embedding for Davenport, Iowa\n",
"getting embedding for Rialto, California\n",
"getting embedding for Las Cruces, New Mexico\n",
"getting embedding for San Mateo, California\n",
"getting embedding for Lewisville, Texas\n",
"getting embedding for South Bend, Indiana\n",
"getting embedding for Lakeland, Florida\n",
"getting embedding for Erie, Pennsylvania\n",
"getting embedding for Tyler, Texas\n",
"getting embedding for Pearland, Texas\n",
"getting embedding for College Station, Texas\n"
]
}
],
"source": [
"# Now, let's embed all our cities:\n",
"embeddings = np.array([embed_row(row) for row in big_cities_list])"
]
},
{
"cell_type": "code",
"execution_count": 37,
"id": "29e043bb",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
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" -1.45773236e-02, -5.09472609e-01, 3.42719555e-02, 4.97731194e-02,\n",
" 1.33101434e-01, 1.76771834e-01, 7.86391050e-02, 1.23100551e-02,\n",
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" -1.09754831e-01, 2.90967189e-02, -3.56597424e-01, 2.09372997e-01,\n",
" -9.01909918e-02, 1.58672348e-01, 4.51118588e-01, 8.60635489e-02,\n",
" 2.34368265e-01, -1.20324977e-02, -2.12158769e-01, -2.56564319e-01,\n",
" 1.08701974e-01, -2.89285421e-01, -2.19394296e-01, -3.51048678e-01,\n",
" 6.20470457e-02, -2.45039359e-01, 1.53113604e-01, 1.29182488e-01,\n",
" 1.48216099e-01, 4.86298561e-01, 3.02182138e-03, 3.86681467e-01,\n",
" -4.19597805e-01, 8.33263546e-02, -5.97608350e-02, -1.00717902e-01,\n",
" 1.70298681e-01, -3.26323546e-02, 1.65129870e-01, 1.10249147e-01,\n",
" -1.12583205e-01, -2.55914927e-01, -4.99160811e-02, 5.90843499e-01,\n",
" 1.80812240e-01, 1.32644162e-01, -1.84739649e-01, 9.33255106e-02,\n",
" -1.13921076e-01, -8.65843147e-04, 1.17979810e-01, 1.12772420e-01,\n",
" -1.01463765e-01, -3.23023885e-01, -4.52458441e-01, -2.08374962e-01,\n",
" -1.71134800e-01, 4.69274044e-01, -4.77617979e-01, -6.52769446e-01,\n",
" -6.74332678e-02, 1.95676610e-02, -1.31601810e-01, -1.36253700e-01,\n",
" 2.18159854e-01, -2.19907969e-01, 1.73880666e-01, 6.58025891e-02,\n",
" 3.66426975e-01, -2.77260393e-01, 1.74390405e-01, 3.63743901e-01,\n",
" -4.85156737e-02, -8.91619995e-02, 2.82244772e-01, 2.10864469e-01,\n",
" -2.82512307e-01, -6.73852563e-02, -1.00473985e-01, -3.47391635e-01,\n",
" 9.23286676e-02, -3.41670305e-01, -3.66410613e-01, 1.16003722e-01,\n",
" 6.48514330e-02, 2.19546884e-01, -2.79180855e-01, -2.81040668e-01,\n",
" 1.95235312e-02, 1.57393843e-01, -5.01551807e-01, -4.60700512e-01,\n",
" 3.08292925e-01, -3.25243115e-01, 1.54953882e-01, -2.97841787e-01,\n",
" 2.26038411e-01, -2.59255111e-01, 5.11020005e-01, -1.24711096e-01,\n",
" -3.42533179e-02, -1.35495067e-02, -4.02491763e-02, 2.67736852e-01,\n",
" 1.04850903e-01, 9.72002000e-03, 6.40995950e-02, -4.78959233e-01,\n",
" 1.57557175e-01, -2.58380353e-01, -5.28326869e-01, -5.66866755e-01,\n",
" -5.49890280e-01, 1.22575313e-02, 1.07006446e-01, -7.44501948e-02,\n",
" 2.14609057e-01, -1.80439055e-02, -3.34003568e-03, -5.61832011e-01,\n",
" -3.88701558e-01, -2.50885397e-01, -8.59784633e-02, -3.17563772e-01,\n",
" -7.69940391e-02, 4.52337950e-01, 2.38023609e-01, -3.01256359e-01,\n",
" 5.87214530e-02, 1.54753044e-01, -4.79396254e-01, 1.97381571e-01,\n",
" 4.09321457e-01, 1.94423139e-01, -4.69209924e-02, 1.13154836e-01,\n",
" 2.74294704e-01, 2.60041922e-01, -5.27779758e-01, 6.79041296e-02,\n",
" -3.17419589e-01, -1.41729802e-01, 2.16767378e-02, -1.55712083e-01,\n",
" -1.57082826e-03, -6.75858930e-02, 3.26404572e-01, -3.02955627e-01,\n",
" -9.65613872e-02, 1.82333961e-01, -5.37505262e-02, -4.81158197e-01,\n",
" 6.01245388e-02, 2.03389272e-01, -9.98227745e-02, -1.15079790e-01,\n",
" -4.99836989e-02, -2.54199147e-01, 9.25733149e-03, -4.79371190e-01,\n",
" 5.17728508e-01, 3.39941859e-01, -2.30439603e-01, 7.00263977e-02,\n",
" -6.88191056e-02, -1.70307264e-01, -6.99588358e-02, -2.04142451e-01,\n",
" 1.78137988e-01, -7.10208237e-01, 3.91780913e-01, -2.78083205e-01,\n",
" 7.63812512e-02, 1.91101432e-03, 1.06407460e-02, 2.34999657e-01,\n",
" -2.27935076e-01, 4.89234179e-02, -1.12586498e-01, -4.11055274e-02,\n",
" 1.11136541e-01, -4.58816648e-01, -3.78083169e-01, -7.14376047e-02,\n",
" 1.62236720e-01, -3.93031836e-01, -5.17099142e-01, -2.79259622e-01],\n",
" dtype=float32)"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"embeddings[4]\n",
"# These are just normalized vectors, same as the ones we made up with latitude, trucks, etc:"
]
},
{
"cell_type": "code",
"execution_count": 38,
"id": "a781cd00",
"metadata": {},
"outputs": [],
"source": [
"# Getting pretty close now. "
]
},
{
"cell_type": "code",
"execution_count": 39,
"id": "9a0499c6",
"metadata": {},
"outputs": [],
"source": [
"# Now, let's see how similar these embeddings are to each other:\n",
"embeddings_distances = embeddings @ embeddings.T\n",
"embeddings_rankings = np.argsort(-embeddings_distances, axis = 1)\n",
"\n",
"\n",
"def top_n_ranking(rankings, index, n=5):\n",
" top_n = rankings[index][1:1+n]\n",
" near = [names[i] for i in top_n]\n",
" far = [names[i] for i in rankings[index][-n:]]\n",
" return near, far\n",
"\n",
"\n",
"def show_ranking(rankings, index, preamble=''):\n",
" near, far = top_n_ranking(rankings, index)\n",
" print(preamble, 'is most similar to:', ', '.join(near))\n",
" print(preamble, 'is least similar to:', ', '.join(far))\n",
"\n",
"\n",
"def compare_models(city_name, state_name):\n",
" # Almost 10 of America's top 300 cities share the same name. So we need to be more specific.\n",
" idx = 0\n",
" found = False\n",
" for row in big_cities_list:\n",
" if row['city'] == city_name and row['state'] == state_name:\n",
" found = True\n",
" break\n",
" idx += 1\n",
" if not found:\n",
" print(\"Couldn't find\", city_name, 'in', state_name)\n",
" return\n",
" print('Now find the most similar city to', city_name, 'in each model:')\n",
" show_ranking(rankings, idx, preamble=f'With our manually created basic features, {city_name}, {state_name} ')\n",
" show_ranking(embeddings_rankings, idx, preamble=f'With our deep learning model, {city_name}, {state_name} ')\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 40,
"id": "6227b0ce",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Now find the most similar city to San Diego in each model:\n",
"With our manually created basic features, San Diego, California is most similar to: Long Beach, Santa Ana, Anaheim, Huntington Beach, Oceanside\n",
"With our manually created basic features, San Diego, California is least similar to: Surprise, New York, Billings, Anchorage, Frisco\n",
"With our deep learning model, San Diego, California is most similar to: Norwalk, Riverside, New York, Fullerton, Hayward\n",
"With our deep learning model, San Diego, California is least similar to: Stamford, New Haven, Raleigh, Waterbury, Henderson\n"
]
}
],
"source": [
"compare_models('San Diego', 'California')\n"
]
},
{
"cell_type": "code",
"execution_count": 41,
"id": "79196fb7",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Now find the most similar city to Anchorage in each model:\n",
"With our manually created basic features, Anchorage, Alaska is most similar to: Boise City, Billings, Portland, Everett, Gresham\n",
"With our manually created basic features, Anchorage, Alaska is least similar to: Surprise, Miramar, Port St. Lucie, Frisco, New York\n",
"With our deep learning model, Anchorage, Alaska is most similar to: Everett, Anchorage, Thornton, Norwalk, Hayward\n",
"With our deep learning model, Anchorage, Alaska is least similar to: Stamford, New Haven, Raleigh, Waterbury, Henderson\n"
]
}
],
"source": [
"compare_models('Anchorage', 'Alaska')\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e1a6944a",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.6"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
Raw
pickup-trucks.csv
Wyoming 37.2%
Montana 34.6%
Idaho 31.1%
South Dakota 30.2%
Alaska 29.5%
North Dakota 28.5%
Vermont 24.2%
Oklahoma 24.2%
Arkansas 23.5%
West Virginia 23.5%
New Mexico 23.1%
Mississippi 23.0%
Maine 22.5%
Nebraska 22.2%
Louisiana 22.1%
Iowa 20.5%
Alabama 19.5%
Kentucky 19.4%
Wisconsin 19.3%
Texas 19.0%
Utah 18.8%
Kansas 18.3%
Oregon 18.3%
Michigan 18.1%
Missouri 18.0%
Minnesota 17.9%
Tennessee 17.9%
South Carolina 17.6%
New Hampshire 17.3%
Washington 17.1%
Colorado 17.1%
Indiana 15.9%
North Carolina 15.7%
Georgia 15.6%
Ohio 14.8%
Hawaii 14.2%
Rhode Island 14.2%
Arizona 14.0%
Pennsylvania 13.6%
Nevada 13.3%
Virginia 12.4%
Florida 12.1%
New York 11.4%
Delaware 11.3%
Massachusetts 10.8%
Illinois 10.8%
California 10.3%
Maryland 10.0%
Connecticut 9.4%
New Jersey 7.8%
District of Columbia 15.0%
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