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jessekafor/Kafor-Final-Project.ipynb

Created September 1, 2020 18:11
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Kafor-Final-Project.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Introduction"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The objective is to look at the different venues between University of Chicago and Georgia Institute of Technology (Georgia Tech) since these are universities people as myself would want to attend for a degree. Both locations have good reputations as public institutions so the problem to solve was to find out what venues are available in the areas that does not require long travel. Many people who decide to stay near a campus ride a bike or use there four legs as a mode of transportation and need close by locations for daily activities and community.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To get started, before even running any code first library modules had to be imported."
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Collecting package metadata (current_repodata.json): done\n",
"Solving environment: done\n",
"\n",
"\n",
"==> WARNING: A newer version of conda exists. <==\n",
" current version: 4.8.3\n",
" latest version: 4.8.4\n",
"\n",
"Please update conda by running\n",
"\n",
" $ conda update -n base -c defaults conda\n",
"\n",
"\n",
"\n",
"# All requested packages already installed.\n",
"\n",
"Collecting package metadata (current_repodata.json): done\n",
"Solving environment: done\n",
"\n",
"\n",
"==> WARNING: A newer version of conda exists. <==\n",
" current version: 4.8.3\n",
" latest version: 4.8.4\n",
"\n",
"Please update conda by running\n",
"\n",
" $ conda update -n base -c defaults conda\n",
"\n",
"\n",
"\n",
"# All requested packages already installed.\n",
"\n",
"Libraries Imported\n"
]
}
],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import requests\n",
"\n",
"!conda install -c conda-forge geopy --yes\n",
"import geopy as geopy\n",
"from geopy.geocoders import Nominatim \n",
"\n",
"# Matplotlib and associated plotting modules\n",
"import matplotlib.cm as cm\n",
"import matplotlib.colors as colors\n",
"\n",
"!conda install -c conda-forge folium=0.5.0 --yes\n",
"import folium # map rendering library\n",
"\n",
"print(\"Libraries Imported\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Data Collection"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The data for the venues was retrieved using the Foursquare Places API. The Places API offers real-time access to Foursquare's global database of rich venue data and user content to power your location-based experiences."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Foursquare Credentials"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Credentials:\n",
"CLIENT_ID: MWBMLLHDCHCNOFVSO405PNGLHJP0EW0MAD1MEEO0SKMOFSJX\n",
"CLIENT_SECRET:3J3OZ5F4I5WW5OTRZOOWNMREHZEOANOANIOUC3VG2AN5DIM3\n"
]
}
],
"source": [
"CLIENT_ID = 'MWBMLLHDCHCNOFVSO405PNGLHJP0EW0MAD1MEEO0SKMOFSJX'\n",
"CLIENT_SECRET = '3J3OZ5F4I5WW5OTRZOOWNMREHZEOANOANIOUC3VG2AN5DIM3'\n",
"VERSION = '20200825'\n",
"\n",
"print('Credentials:')\n",
"print('CLIENT_ID: ' + CLIENT_ID)\n",
"print('CLIENT_SECRET:' + CLIENT_SECRET)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Coordinates"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The addresses from both institutions were retrieved from Google Knowledge Graph through search. The Geocoder module was used to get the coordinates for both institutions."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Get nearby venues by Chicago - University of Chicago\n",
"Address: 5801 S Ellis Ave, Chicago, IL 60637"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The geographical coordinate of University of Chicago are 41.784977, -87.5905237123017.\n"
]
}
],
"source": [
"address = \"5801 S Ellis Ave, Chicago, IL 60637\"\n",
"\n",
"geolocator = Nominatim(user_agent=\"ca_explorer\")\n",
"location = geolocator.geocode(address)\n",
"chi_latitude = location.latitude\n",
"chi_longitude = location.longitude\n",
"print('The geographical coordinate of University of Chicago are {}, {}.'.format(chi_latitude, chi_longitude))"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"width:100%;\"><div style=\"position:relative;width:100%;height:0;padding-bottom:60%;\"><span style=\"color:#565656\">Make this Notebook Trusted to load map: File -> Trust Notebook</span><iframe src=\"about:blank\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" data-html=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 onload=\"this.contentDocument.open();this.contentDocument.write(atob(this.getAttribute('data-html')));this.contentDocument.close();\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
],
"text/plain": [
"<folium.folium.Map at 0x7fdc7830ccf8>"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Map of Chicago using latitude and longitude values\n",
"map_chicago = folium.Map(location=[chi_latitude, chi_longitude], zoom_start=10)\n",
"map_chicago"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Get nearby venues by Atlanta - Georgia Institute of Technology\n",
"Address: North Ave NW, Atlanta, GA 30332"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The geographical coordinates of Georgia Tech university are 33.7713526, -84.396345.\n"
]
}
],
"source": [
"address = \"North Ave NW, Atlanta, GA 30332\"\n",
"\n",
"geolocator = Nominatim(user_agent=\"ca_explorer\")\n",
"location = geolocator.geocode(address)\n",
"atl_latitude = location.latitude\n",
"atl_longitude = location.longitude\n",
"print('The geographical coordinates of Georgia Tech are {}, {}.'.format(atl_latitude, atl_longitude))"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"width:100%;\"><div style=\"position:relative;width:100%;height:0;padding-bottom:60%;\"><span style=\"color:#565656\">Make this Notebook Trusted to load map: File -> Trust Notebook</span><iframe src=\"about:blank\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" data-html=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 onload=\"this.contentDocument.open();this.contentDocument.write(atob(this.getAttribute('data-html')));this.contentDocument.close();\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
],
"text/plain": [
"<folium.folium.Map at 0x7f232e05e198>"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Map of Atlanta using latitude and longitude values\n",
"map_atlanta = folium.Map(location=[atl_latitude, atl_longitude], zoom_start=10)\n",
"map_atlanta"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Explore Venues "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" At first I reduced the radius and then increased it to 500 in order to generate enough venues nearby the universities. The campus size of University of Chicago is 217 acres and as for Georgia Institute of Technology (Georgia Tech), its almost twice as large with over 400 acres according to Google's Knowledge Graph. I completely forgot how large universities can be so this made sense to extend the radius to an appropriate size (500). Even within a 500 radius of the area there was only 13 venue listings nearby the University of Chicago and 17 nearby Georgia Institute of Technology (Georgia Tech). I did set the limit of venues to 100 although the cap was not necessary after seeing that not many locations were not available from either institution."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'meta': {'code': 200, 'requestId': '5f4e60c482a8164a55496de5'},\n",
" 'response': {'headerLocation': 'Woodlawn',\n",
" 'headerFullLocation': 'Woodlawn, Chicago',\n",
" 'headerLocationGranularity': 'neighborhood',\n",
" 'totalResults': 16,\n",
" 'suggestedBounds': {'ne': {'lat': 41.7894770045, 'lng': -87.58449997727499},\n",
" 'sw': {'lat': 41.780476995499995, 'lng': -87.59654744732842}},\n",
" 'groups': [{'type': 'Recommended Places',\n",
" 'name': 'recommended',\n",
" 'items': [{'reasons': {'count': 0,\n",
" 'items': [{'summary': 'This spot is popular',\n",
" 'type': 'general',\n",
" 'reasonName': 'globalInteractionReason'}]},\n",
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},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Limit 100\n",
"#Radius 500 miles\n",
"\n",
"LIMIT = 100 \n",
"radius = 500\n",
"url = 'https://api.foursquare.com/v2/venues/explore?&client_id={}&client_secret={}&v={}&ll={},{}&radius={}&limit={}'.format(\n",
" CLIENT_ID, \n",
" CLIENT_SECRET, \n",
" VERSION, \n",
" chi_latitude, \n",
" chi_longitude, \n",
" radius, \n",
" LIMIT)\n",
"\n",
"# get the result to a json file\n",
"chi_results = requests.get(url).json()\n",
"chi_results"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Explore Venues nearby Georgia Institute of Technology"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
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" 'response': {'suggestedFilters': {'header': 'Tap to show:',\n",
" 'filters': [{'name': 'Open now', 'key': 'openNow'}]},\n",
" 'headerLocation': 'Marietta Street Artery',\n",
" 'headerFullLocation': 'Marietta Street Artery, Atlanta',\n",
" 'headerLocationGranularity': 'neighborhood',\n",
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" 'groups': [{'type': 'Recommended Places',\n",
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" 'venue': {'id': '555639d2498ea5420402bccd',\n",
" 'name': 'Coca-Cola Mainstreet @ AOC',\n",
" 'location': {'address': '1 Coca Cola Plz NW',\n",
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" 'city': 'Atlanta',\n",
" 'state': 'GA',\n",
" 'country': 'United States',\n",
" 'formattedAddress': ['1 Coca Cola Plz NW',\n",
" 'Atlanta, GA 30313',\n",
" 'United States']},\n",
" 'categories': [{'id': '4bf58dd8d48988d14e941735',\n",
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" 'venue': {'id': '512e4185e0e26a825ba3c825',\n",
" 'name': 'Highland Bakery',\n",
" 'location': {'address': '225 North Ave NW',\n",
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" 'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/food/bakery_',\n",
" 'suffix': '.png'},\n",
" 'primary': True}],\n",
" 'delivery': {'id': '1490525',\n",
" 'url': 'https://www.grubhub.com/restaurant/highland-bakery-224-uncle-heinie-way-northwest-atlanta/1490525?affiliate=1131&utm_source=foursquare-affiliate-network&utm_medium=affiliate&utm_campaign=1131&utm_content=1490525',\n",
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" 'venue': {'id': '4b6a8415f964a5206ad72be3',\n",
" 'name': 'Ferst Center For The Arts',\n",
" 'location': {'address': '349 North Ave NW',\n",
" 'crossStreet': 'Georgia Tech',\n",
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" 'lng': -84.39919356481722,\n",
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" 'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/arts_entertainment/performingarts_theater_',\n",
" 'suffix': '.png'},\n",
" 'primary': True}],\n",
" 'photos': {'count': 0, 'groups': []}},\n",
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" 'name': 'Hampton Inn by Hilton',\n",
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" 'suffix': '.png'},\n",
" 'primary': True}],\n",
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" 'name': 'Wells Fargo',\n",
" 'location': {'address': '645 State St NW',\n",
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" 'lng': -84.39843042541548,\n",
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" 'suffix': '.png'},\n",
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" 'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/food/asian_',\n",
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" 'Atlanta, GA 30332',\n",
" 'United States']},\n",
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" 'lat': 33.771245913278335,\n",
" 'lng': -84.3920373916626}],\n",
" 'distance': 398,\n",
" 'postalCode': '30332',\n",
" 'cc': 'US',\n",
" 'city': 'Atlanta',\n",
" 'state': 'GA',\n",
" 'country': 'United States',\n",
" 'formattedAddress': ['Techwood Dr NW (at North Ave NW)',\n",
" 'Atlanta, GA 30332',\n",
" 'United States']},\n",
" 'categories': [{'id': '52f2ab2ebcbc57f1066b8b4f',\n",
" 'name': 'Bus Stop',\n",
" 'pluralName': 'Bus Stops',\n",
" 'shortName': 'Bus Stop',\n",
" 'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/travel/busstation_',\n",
" 'suffix': '.png'},\n",
" 'primary': True}],\n",
" 'photos': {'count': 0, 'groups': []}},\n",
" 'referralId': 'e-0-506b929eb0edcdd34cd227c8-14'},\n",
" {'reasons': {'count': 0,\n",
" 'items': [{'summary': 'This spot is popular',\n",
" 'type': 'general',\n",
" 'reasonName': 'globalInteractionReason'}]},\n",
" 'venue': {'id': '4dadda617abacefe5c8b201e',\n",
" 'name': 'Student Center Food Court',\n",
" 'location': {'address': '350 Ferst Dr NW',\n",
" 'crossStreet': 'at Georgia Tech',\n",
" 'lat': 33.77445091089932,\n",
" 'lng': -84.39890435841951,\n",
" 'labeledLatLngs': [{'label': 'display',\n",
" 'lat': 33.77445091089932,\n",
" 'lng': -84.39890435841951}],\n",
" 'distance': 418,\n",
" 'postalCode': '30332',\n",
" 'cc': 'US',\n",
" 'neighborhood': 'Georgia Tech',\n",
" 'city': 'Atlanta',\n",
" 'state': 'GA',\n",
" 'country': 'United States',\n",
" 'formattedAddress': ['350 Ferst Dr NW (at Georgia Tech)',\n",
" 'Atlanta, GA 30332',\n",
" 'United States']},\n",
" 'categories': [{'id': '4bf58dd8d48988d120951735',\n",
" 'name': 'Food Court',\n",
" 'pluralName': 'Food Courts',\n",
" 'shortName': 'Food Court',\n",
" 'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/shops/food_foodcourt_',\n",
" 'suffix': '.png'},\n",
" 'primary': True}],\n",
" 'photos': {'count': 0, 'groups': []}},\n",
" 'referralId': 'e-0-4dadda617abacefe5c8b201e-15'},\n",
" {'reasons': {'count': 0,\n",
" 'items': [{'summary': 'This spot is popular',\n",
" 'type': 'general',\n",
" 'reasonName': 'globalInteractionReason'}]},\n",
" 'venue': {'id': '4d5bd186fb186dcb8b03f89a',\n",
" 'name': \"Dunkin'\",\n",
" 'location': {'address': 'North Ave NW',\n",
" 'crossStreet': 'at Georgia Tech',\n",
" 'lat': 33.77442791392891,\n",
" 'lng': -84.39880812280745,\n",
" 'labeledLatLngs': [{'label': 'display',\n",
" 'lat': 33.77442791392891,\n",
" 'lng': -84.39880812280745}],\n",
" 'distance': 411,\n",
" 'postalCode': '30332',\n",
" 'cc': 'US',\n",
" 'city': 'Atlanta',\n",
" 'state': 'GA',\n",
" 'country': 'United States',\n",
" 'formattedAddress': ['North Ave NW (at Georgia Tech)',\n",
" 'Atlanta, GA 30332',\n",
" 'United States']},\n",
" 'categories': [{'id': '4bf58dd8d48988d148941735',\n",
" 'name': 'Donut Shop',\n",
" 'pluralName': 'Donut Shops',\n",
" 'shortName': 'Donuts',\n",
" 'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/food/donuts_',\n",
" 'suffix': '.png'},\n",
" 'primary': True}],\n",
" 'photos': {'count': 0, 'groups': []}},\n",
" 'referralId': 'e-0-4d5bd186fb186dcb8b03f89a-16'},\n",
" {'reasons': {'count': 0,\n",
" 'items': [{'summary': 'This spot is popular',\n",
" 'type': 'general',\n",
" 'reasonName': 'globalInteractionReason'}]},\n",
" 'venue': {'id': '5701c802498ef642af8ca74c',\n",
" 'name': 'Einstein Statue',\n",
" 'location': {'lat': 33.77522394551474,\n",
" 'lng': -84.39718993342497,\n",
" 'labeledLatLngs': [{'label': 'display',\n",
" 'lat': 33.77522394551474,\n",
" 'lng': -84.39718993342497}],\n",
" 'distance': 437,\n",
" 'cc': 'US',\n",
" 'city': 'Atlanta',\n",
" 'state': 'GA',\n",
" 'country': 'United States',\n",
" 'formattedAddress': ['Atlanta, GA', 'United States']},\n",
" 'categories': [{'id': '52e81612bcbc57f1066b79ed',\n",
" 'name': 'Outdoor Sculpture',\n",
" 'pluralName': 'Outdoor Sculptures',\n",
" 'shortName': 'Outdoor Sculpture',\n",
" 'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/parks_outdoors/sculpture_',\n",
" 'suffix': '.png'},\n",
" 'primary': True}],\n",
" 'photos': {'count': 0, 'groups': []}},\n",
" 'referralId': 'e-0-5701c802498ef642af8ca74c-17'}]}]}}"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Limit 100\n",
"#Radius 500 miles\n",
"\n",
"LIMIT = 100 \n",
"radius = 500\n",
"url = 'https://api.foursquare.com/v2/venues/explore?&client_id={}&client_secret={}&v={}&ll={},{}&radius={}&limit={}'.format(\n",
" CLIENT_ID, \n",
" CLIENT_SECRET, \n",
" VERSION, \n",
" atl_latitude, \n",
" atl_longitude, \n",
" radius, \n",
" LIMIT)\n",
"\n",
"# get the result to a json file\n",
"atl_results = requests.get(url).json()\n",
"atl_results"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Data Preparation"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Venues Nearby "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The venues were grouped by categories in Foursquare to know what types are listed and available around each university. Then the venues were order by which are closer to the universities. After previewing the columns available in the JSON response I seen that we did not need all the columns. The name, categories, and coordinates should be efficient data to continue on for further data analysis."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We stored the venue results for University of Chicago and Georgia Institute of Technology (Georgia Tech) in a pandas dataframe."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Since the venues were not many I did not have to run any code to see unique or common categories and venues. There were not many similar category types. "
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Name</th>\n",
" <th>Category Type</th>\n",
" <th>Latitude</th>\n",
" <th>Longitude</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Build Coffee</td>\n",
" <td>Coffee Shop</td>\n",
" <td>41.784288</td>\n",
" <td>-87.590419</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>61st Street Farmers' Market</td>\n",
" <td>Market</td>\n",
" <td>41.784296</td>\n",
" <td>-87.590790</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Midway Plaisance Park</td>\n",
" <td>Park</td>\n",
" <td>41.786526</td>\n",
" <td>-87.594458</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>University of Chicago @ Midway Plaisance</td>\n",
" <td>Park</td>\n",
" <td>41.787751</td>\n",
" <td>-87.588782</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>B'Gabs Goodies</td>\n",
" <td>Food &amp; Drink Shop</td>\n",
" <td>41.784245</td>\n",
" <td>-87.590372</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>Plum Cafe at Chicago Press Building</td>\n",
" <td>Café</td>\n",
" <td>41.786028</td>\n",
" <td>-87.590035</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>Jackson Towers Fitness</td>\n",
" <td>Gym / Fitness Center</td>\n",
" <td>41.784879</td>\n",
" <td>-87.588799</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>Metra - 59th St (University of Chicago)</td>\n",
" <td>Train Station</td>\n",
" <td>41.787729</td>\n",
" <td>-87.588789</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>Jackson Park Track &amp; Soccer Field</td>\n",
" <td>Soccer Field</td>\n",
" <td>41.783368</td>\n",
" <td>-87.586806</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>Tiffin Cafe</td>\n",
" <td>Coffee Shop</td>\n",
" <td>41.788219</td>\n",
" <td>-87.590965</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>International House at the University of Chicago</td>\n",
" <td>Concert Hall</td>\n",
" <td>41.788240</td>\n",
" <td>-87.590804</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>Divvy Station</td>\n",
" <td>Bike Rental / Bike Share</td>\n",
" <td>41.787943</td>\n",
" <td>-87.588315</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>Huckleberry Park</td>\n",
" <td>Playground</td>\n",
" <td>41.782321</td>\n",
" <td>-87.594810</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>CTA Bus Stop 1513</td>\n",
" <td>Bus Station</td>\n",
" <td>41.787987</td>\n",
" <td>-87.586560</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>Leon's Barbecue</td>\n",
" <td>BBQ Joint</td>\n",
" <td>41.780854</td>\n",
" <td>-87.588451</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>Tres Original Pancakes</td>\n",
" <td>Food</td>\n",
" <td>41.780869</td>\n",
" <td>-87.588188</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Name \\\n",
"0 Build Coffee \n",
"1 61st Street Farmers' Market \n",
"2 Midway Plaisance Park \n",
"3 University of Chicago @ Midway Plaisance \n",
"4 B'Gabs Goodies \n",
"5 Plum Cafe at Chicago Press Building \n",
"6 Jackson Towers Fitness \n",
"7 Metra - 59th St (University of Chicago) \n",
"8 Jackson Park Track & Soccer Field \n",
"9 Tiffin Cafe \n",
"10 International House at the University of Chicago \n",
"11 Divvy Station \n",
"12 Huckleberry Park \n",
"13 CTA Bus Stop 1513 \n",
"14 Leon's Barbecue \n",
"15 Tres Original Pancakes \n",
"\n",
" Category Type Latitude Longitude \n",
"0 Coffee Shop 41.784288 -87.590419 \n",
"1 Market 41.784296 -87.590790 \n",
"2 Park 41.786526 -87.594458 \n",
"3 Park 41.787751 -87.588782 \n",
"4 Food & Drink Shop 41.784245 -87.590372 \n",
"5 Café 41.786028 -87.590035 \n",
"6 Gym / Fitness Center 41.784879 -87.588799 \n",
"7 Train Station 41.787729 -87.588789 \n",
"8 Soccer Field 41.783368 -87.586806 \n",
"9 Coffee Shop 41.788219 -87.590965 \n",
"10 Concert Hall 41.788240 -87.590804 \n",
"11 Bike Rental / Bike Share 41.787943 -87.588315 \n",
"12 Playground 41.782321 -87.594810 \n",
"13 Bus Station 41.787987 -87.586560 \n",
"14 BBQ Joint 41.780854 -87.588451 \n",
"15 Food 41.780869 -87.588188 "
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"chi_venues = chi_results['response']['groups'][0]['items']\n",
"#chi_nearby_venues = pd.json_normalize(chi_venues) #flatten JSON\n",
"\n",
"chi_venues_list = []\n",
"chi_venues_list.append([\n",
" (\n",
" v['venue']['name'], \n",
" v['venue']['categories'][0]['name'],\n",
" v['venue']['location']['lat'], \n",
" v['venue']['location']['lng'] \n",
" ) for v in chi_venues])\n",
"\n",
"chi_df = pd.DataFrame([item for chi_venue_list in chi_venues_list for item in chi_venue_list])\n",
"chi_df.columns = ['Name', 'Category Type', 'Latitude', 'Longitude']\n",
"chi_df\n"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(16, 4)\n"
]
}
],
"source": [
"print(chi_df.shape)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Name</th>\n",
" <th>Latitude</th>\n",
" <th>Longitude</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Category Type</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>BBQ Joint</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Bike Rental / Bike Share</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Bus Station</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Café</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Coffee Shop</th>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Concert Hall</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Food</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Food &amp; Drink Shop</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Gym / Fitness Center</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Market</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Park</th>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Playground</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Soccer Field</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Train Station</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Name Latitude Longitude\n",
"Category Type \n",
"BBQ Joint 1 1 1\n",
"Bike Rental / Bike Share 1 1 1\n",
"Bus Station 1 1 1\n",
"Café 1 1 1\n",
"Coffee Shop 2 2 2\n",
"Concert Hall 1 1 1\n",
"Food 1 1 1\n",
"Food & Drink Shop 1 1 1\n",
"Gym / Fitness Center 1 1 1\n",
"Market 1 1 1\n",
"Park 2 2 2\n",
"Playground 1 1 1\n",
"Soccer Field 1 1 1\n",
"Train Station 1 1 1"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"chi_df.groupby('Category Type').count()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Categories for Venues nearby Georgia Tech"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Name</th>\n",
" <th>Category Type</th>\n",
" <th>Latitude</th>\n",
" <th>Longitude</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Coca-Cola Mainstreet @ AOC</td>\n",
" <td>American Restaurant</td>\n",
" <td>33.770980</td>\n",
" <td>-84.398037</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Highland Bakery</td>\n",
" <td>Bakery</td>\n",
" <td>33.772646</td>\n",
" <td>-84.394585</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Ferst Center For The Arts</td>\n",
" <td>College Theater</td>\n",
" <td>33.774820</td>\n",
" <td>-84.399194</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Hampton Inn by Hilton</td>\n",
" <td>Hotel</td>\n",
" <td>33.771146</td>\n",
" <td>-84.395785</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>T-Mobile</td>\n",
" <td>Mobile Phone Shop</td>\n",
" <td>33.771179</td>\n",
" <td>-84.396202</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>Wells Fargo</td>\n",
" <td>Bank</td>\n",
" <td>33.772548</td>\n",
" <td>-84.398430</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>Chick-fil-A</td>\n",
" <td>Fast Food Restaurant</td>\n",
" <td>33.773673</td>\n",
" <td>-84.398203</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>Starbucks</td>\n",
" <td>Coffee Shop</td>\n",
" <td>33.774261</td>\n",
" <td>-84.396411</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>Panda Express</td>\n",
" <td>Chinese Restaurant</td>\n",
" <td>33.773779</td>\n",
" <td>-84.398137</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>Under The Couch</td>\n",
" <td>Music Venue</td>\n",
" <td>33.773898</td>\n",
" <td>-84.398869</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>Harrison Square</td>\n",
" <td>Plaza</td>\n",
" <td>33.773019</td>\n",
" <td>-84.395299</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>Taco Bell</td>\n",
" <td>Fast Food Restaurant</td>\n",
" <td>33.773780</td>\n",
" <td>-84.398206</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>Burdell's</td>\n",
" <td>College Bookstore</td>\n",
" <td>33.773770</td>\n",
" <td>-84.398083</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>Rooftop Garden</td>\n",
" <td>Roof Deck</td>\n",
" <td>33.774428</td>\n",
" <td>-84.396552</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>GT Stinger Stop - Techwood and North Ave</td>\n",
" <td>Bus Stop</td>\n",
" <td>33.771246</td>\n",
" <td>-84.392037</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>Student Center Food Court</td>\n",
" <td>Food Court</td>\n",
" <td>33.774451</td>\n",
" <td>-84.398904</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>Dunkin'</td>\n",
" <td>Donut Shop</td>\n",
" <td>33.774428</td>\n",
" <td>-84.398808</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>Einstein Statue</td>\n",
" <td>Outdoor Sculpture</td>\n",
" <td>33.775224</td>\n",
" <td>-84.397190</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Name Category Type Latitude \\\n",
"0 Coca-Cola Mainstreet @ AOC American Restaurant 33.770980 \n",
"1 Highland Bakery Bakery 33.772646 \n",
"2 Ferst Center For The Arts College Theater 33.774820 \n",
"3 Hampton Inn by Hilton Hotel 33.771146 \n",
"4 T-Mobile Mobile Phone Shop 33.771179 \n",
"5 Wells Fargo Bank 33.772548 \n",
"6 Chick-fil-A Fast Food Restaurant 33.773673 \n",
"7 Starbucks Coffee Shop 33.774261 \n",
"8 Panda Express Chinese Restaurant 33.773779 \n",
"9 Under The Couch Music Venue 33.773898 \n",
"10 Harrison Square Plaza 33.773019 \n",
"11 Taco Bell Fast Food Restaurant 33.773780 \n",
"12 Burdell's College Bookstore 33.773770 \n",
"13 Rooftop Garden Roof Deck 33.774428 \n",
"14 GT Stinger Stop - Techwood and North Ave Bus Stop 33.771246 \n",
"15 Student Center Food Court Food Court 33.774451 \n",
"16 Dunkin' Donut Shop 33.774428 \n",
"17 Einstein Statue Outdoor Sculpture 33.775224 \n",
"\n",
" Longitude \n",
"0 -84.398037 \n",
"1 -84.394585 \n",
"2 -84.399194 \n",
"3 -84.395785 \n",
"4 -84.396202 \n",
"5 -84.398430 \n",
"6 -84.398203 \n",
"7 -84.396411 \n",
"8 -84.398137 \n",
"9 -84.398869 \n",
"10 -84.395299 \n",
"11 -84.398206 \n",
"12 -84.398083 \n",
"13 -84.396552 \n",
"14 -84.392037 \n",
"15 -84.398904 \n",
"16 -84.398808 \n",
"17 -84.397190 "
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"atl_venues = atl_results['response']['groups'][0]['items']\n",
"\n",
"atl_venues_list = []\n",
"atl_venues_list.append([\n",
" (\n",
" v['venue']['name'], \n",
" v['venue']['categories'][0]['name'],\n",
" v['venue']['location']['lat'], \n",
" v['venue']['location']['lng'] \n",
" ) for v in atl_venues])\n",
"\n",
"atl_df = pd.DataFrame([item for atl_venue_list in atl_venues_list for item in atl_venue_list])\n",
"atl_df.columns = ['Name', 'Category Type', 'Latitude', 'Longitude']\n",
"atl_df"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(18, 4)\n"
]
}
],
"source": [
"print(atl_df.shape)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Name</th>\n",
" <th>Latitude</th>\n",
" <th>Longitude</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Category Type</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>American Restaurant</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Bakery</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Bank</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Bus Stop</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Chinese Restaurant</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Coffee Shop</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>College Bookstore</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>College Theater</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Donut Shop</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Fast Food Restaurant</th>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Food Court</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Hotel</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Mobile Phone Shop</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Music Venue</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Outdoor Sculpture</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Plaza</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Roof Deck</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Name Latitude Longitude\n",
"Category Type \n",
"American Restaurant 1 1 1\n",
"Bakery 1 1 1\n",
"Bank 1 1 1\n",
"Bus Stop 1 1 1\n",
"Chinese Restaurant 1 1 1\n",
"Coffee Shop 1 1 1\n",
"College Bookstore 1 1 1\n",
"College Theater 1 1 1\n",
"Donut Shop 1 1 1\n",
"Fast Food Restaurant 2 2 2\n",
"Food Court 1 1 1\n",
"Hotel 1 1 1\n",
"Mobile Phone Shop 1 1 1\n",
"Music Venue 1 1 1\n",
"Outdoor Sculpture 1 1 1\n",
"Plaza 1 1 1\n",
"Roof Deck 1 1 1"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"atl_df.groupby('Category Type').count()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Data Exploration (Results)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The University of Chicago had only two repeated category types which were parks and coffee shops. \n",
"\n",
"There is a nearby bus station at the University of Chicago, so a user does not need a car or uber as much if there is a bus route and a gym, but most universities already have a recreation center that someone could use. If a break is needed a person has the choice of more than one park to walk or go running at. If they do not want to eat coffee shop food and want something more hearty there is a BBQ joint nearby.\n",
"\n",
"At Georgia Institute of Technology (Georgia Tech), they have many different kind of restuarants nearby that are just not inside the college. One of the greatest perks is a music venue so people can probably get to hear local bands and if they do not want to stay at a dorm can have a night at the nearby hotel at the Hampton Inn and not too far from the hotel is a bank. \n",
"\n",
"There could be shopping center in the area."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Data Analysis"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Maps with Markers"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"After looking at the venues for each university, maps were created with markers for each of the venues around each of the universities. We remove the 'Name' and 'Category Type' columns since we only need the coordinates for k-Means cluster labels. The maps will not be ordinary maps with markers, but will have clusters using k-Means with a initial k/clusters of 3. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"After creating the dataframes with the clusters we can see the clusters were color-coded and of course grouped by distances. The elbow method was not neccessary since the data points are very few. The perfect zoom size at 16 let us see some data points that were like outliers, but as you know they don't exist in k-Means. For instance, based on Google maps Leon's Barbecue takes 19 minutes by car and up to two hours walking distance. This venue is about 6.6 miles away from the University of Chicago. The venue with the farthest distance from Georgia Institute of Technology (Georgia Tech) would be Cocoa Cola Mainstreet at AOC although GT Stinger Shop was close and has it's own cluster group. The location for Coca Cola @ AOC was not easy to locate by name in Google Maps and the address was retrieved for the restaurant through Foursquare. Again, Georgia Tech wins with the farthest distance being less than 1.7 miles with only a 35 minute walk."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.cluster import KMeans\n",
"\n",
"# set number of clusters\n",
"kclusters = 3\n",
"\n",
"chi_df_clustering = chi_df.drop(['Name','Category Type'], 1)\n",
"\n",
"# run k-means clustering\n",
"kmeans = KMeans(n_clusters=kclusters, random_state=0).fit(chi_df_clustering)\n",
"\n",
"# check cluster labels generated for each row in the dataframe\n",
"kmeans.labels_[0:13]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We then need to create a new dataframe with the cluster."
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Cluster Labels</th>\n",
" <th>Latitude</th>\n",
" <th>Longitude</th>\n",
" <th>Name</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2</td>\n",
" <td>41.784288</td>\n",
" <td>-87.590419</td>\n",
" <td>Build Coffee</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>41.784296</td>\n",
" <td>-87.590790</td>\n",
" <td>61st Street Farmers' Market</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0</td>\n",
" <td>41.786526</td>\n",
" <td>-87.594458</td>\n",
" <td>Midway Plaisance Park</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1</td>\n",
" <td>41.787751</td>\n",
" <td>-87.588782</td>\n",
" <td>University of Chicago @ Midway Plaisance</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2</td>\n",
" <td>41.784245</td>\n",
" <td>-87.590372</td>\n",
" <td>B'Gabs Goodies</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>1</td>\n",
" <td>41.786028</td>\n",
" <td>-87.590035</td>\n",
" <td>Plum Cafe at Chicago Press Building</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>2</td>\n",
" <td>41.784879</td>\n",
" <td>-87.588799</td>\n",
" <td>Jackson Towers Fitness</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>1</td>\n",
" <td>41.787729</td>\n",
" <td>-87.588789</td>\n",
" <td>Metra - 59th St (University of Chicago)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>2</td>\n",
" <td>41.783368</td>\n",
" <td>-87.586806</td>\n",
" <td>Jackson Park Track &amp; Soccer Field</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>1</td>\n",
" <td>41.788219</td>\n",
" <td>-87.590965</td>\n",
" <td>Tiffin Cafe</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>1</td>\n",
" <td>41.788240</td>\n",
" <td>-87.590804</td>\n",
" <td>International House at the University of Chicago</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>1</td>\n",
" <td>41.787943</td>\n",
" <td>-87.588315</td>\n",
" <td>Divvy Station</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>0</td>\n",
" <td>41.782321</td>\n",
" <td>-87.594810</td>\n",
" <td>Huckleberry Park</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>1</td>\n",
" <td>41.787987</td>\n",
" <td>-87.586560</td>\n",
" <td>CTA Bus Stop 1513</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>2</td>\n",
" <td>41.780854</td>\n",
" <td>-87.588451</td>\n",
" <td>Leon's Barbecue</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>2</td>\n",
" <td>41.780869</td>\n",
" <td>-87.588188</td>\n",
" <td>Tres Original Pancakes</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Cluster Labels Latitude Longitude \\\n",
"0 2 41.784288 -87.590419 \n",
"1 2 41.784296 -87.590790 \n",
"2 0 41.786526 -87.594458 \n",
"3 1 41.787751 -87.588782 \n",
"4 2 41.784245 -87.590372 \n",
"5 1 41.786028 -87.590035 \n",
"6 2 41.784879 -87.588799 \n",
"7 1 41.787729 -87.588789 \n",
"8 2 41.783368 -87.586806 \n",
"9 1 41.788219 -87.590965 \n",
"10 1 41.788240 -87.590804 \n",
"11 1 41.787943 -87.588315 \n",
"12 0 41.782321 -87.594810 \n",
"13 1 41.787987 -87.586560 \n",
"14 2 41.780854 -87.588451 \n",
"15 2 41.780869 -87.588188 \n",
"\n",
" Name \n",
"0 Build Coffee \n",
"1 61st Street Farmers' Market \n",
"2 Midway Plaisance Park \n",
"3 University of Chicago @ Midway Plaisance \n",
"4 B'Gabs Goodies \n",
"5 Plum Cafe at Chicago Press Building \n",
"6 Jackson Towers Fitness \n",
"7 Metra - 59th St (University of Chicago) \n",
"8 Jackson Park Track & Soccer Field \n",
"9 Tiffin Cafe \n",
"10 International House at the University of Chicago \n",
"11 Divvy Station \n",
"12 Huckleberry Park \n",
"13 CTA Bus Stop 1513 \n",
"14 Leon's Barbecue \n",
"15 Tres Original Pancakes "
]
},
"execution_count": 66,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"chi_df_merged = chi_df_clustering\n",
"chi_df_merged.insert(0, 'Cluster Labels', kmeans.labels_)\n",
"name_column = chi_df['Name']\n",
"chi_df_with_clusters = pd.concat([chi_df_merged,name_column], axis = 1)\n",
"chi_df_with_clusters"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"One of the most distance locations are two restuarants in one cluster group (green). Many woud have though the bus station may have been closer to the University of Chicago. Midway Plaisance Park is the closest data point to the college while the Huckleberry Park is one of the farthest."
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {},
"outputs": [
{
"data": {
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</script> onload=\"this.contentDocument.open();this.contentDocument.write(atob(this.getAttribute('data-html')));this.contentDocument.close();\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
],
"text/plain": [
"<folium.folium.Map at 0x7f22f6b92518>"
]
},
"execution_count": 67,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# create map\n",
"map_clusters = folium.Map(location=[chi_latitude, chi_longitude], zoom_start=16)\n",
"\n",
"# set color scheme for the clusters\n",
"x = np.arange(kclusters)\n",
"ys = [i + x + (i*x)**2 for i in range(kclusters)]\n",
"colors_array = cm.rainbow(np.linspace(0, 1, len(ys)))\n",
"rainbow = [colors.rgb2hex(i) for i in colors_array]\n",
"\n",
"# add markers to the map\n",
"markers_colors = []\n",
"for lat, lon, poi, cluster in zip(chi_df_with_clusters['Latitude'], chi_df_with_clusters['Longitude'], chi_df_with_clusters['Name'], chi_df_with_clusters['Cluster Labels']):\n",
" label = folium.Popup(str(poi) + ' Cluster ' + str(cluster), parse_html=True)\n",
" folium.CircleMarker(\n",
" [lat, lon],\n",
" radius=5,\n",
" popup=label,\n",
" color=rainbow[cluster-1],\n",
" fill=True,\n",
" fill_color=rainbow[cluster-1],\n",
" fill_opacity=0.7).add_to(map_clusters)\n",
" \n",
"map_clusters"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([2, 2, 0, 2, 2, 0, 0, 0, 0, 0, 2, 0, 0, 0, 1, 0, 0], dtype=int32)"
]
},
"execution_count": 50,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# set number of clusters\n",
"kclusters = 3\n",
"\n",
"atl_df_clustering = atl_df.drop(['Name','Category Type'], 1)\n",
"\n",
"# run k-means clustering\n",
"kmeans = KMeans(n_clusters=kclusters, random_state=0).fit(atl_df_clustering)\n",
"\n",
"# check cluster labels generated for each row in the dataframe\n",
"kmeans.labels_[0:17]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We then need to create a new dataframe with the clusters."
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
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" Cluster Labels Latitude Longitude \\\n",
"0 2 33.770980 -84.398037 \n",
"1 2 33.772646 -84.394585 \n",
"2 0 33.774820 -84.399194 \n",
"3 2 33.771146 -84.395785 \n",
"4 2 33.771179 -84.396202 \n",
"5 0 33.772548 -84.398430 \n",
"6 0 33.773673 -84.398203 \n",
"7 0 33.774261 -84.396411 \n",
"8 0 33.773779 -84.398137 \n",
"9 0 33.773898 -84.398869 \n",
"10 2 33.773019 -84.395299 \n",
"11 0 33.773780 -84.398206 \n",
"12 0 33.773770 -84.398083 \n",
"13 0 33.774428 -84.396552 \n",
"14 1 33.771246 -84.392037 \n",
"15 0 33.774451 -84.398904 \n",
"16 0 33.774428 -84.398808 \n",
"17 0 33.775224 -84.397190 \n",
"\n",
" Name \n",
"0 Coca-Cola Mainstreet @ AOC \n",
"1 Highland Bakery \n",
"2 Ferst Center For The Arts \n",
"3 Hampton Inn by Hilton \n",
"4 T-Mobile \n",
"5 Wells Fargo \n",
"6 Chick-fil-A \n",
"7 Starbucks \n",
"8 Panda Express \n",
"9 Under The Couch \n",
"10 Harrison Square \n",
"11 Taco Bell \n",
"12 Burdell's \n",
"13 Rooftop Garden \n",
"14 GT Stinger Stop - Techwood and North Ave \n",
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"source": [
"atl_df_merged = atl_df_clustering\n",
"#atl_df_merged.insert(0, 'Cluster Labels', kmeans.labels_)\n",
"name_column = atl_df['Name']\n",
"atl_df_with_clusters = pd.concat([atl_df_merged,name_column], axis = 1)\n",
"atl_df_with_clusters"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As you can see with the clusters, the prediction was correct that near Georgia Institute of Technology there is shopping enter based on the venues. There was a color coordinated data points and one outlier was present based on the map which is location GT Stringer Shop in its own cluster group as one data point."
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {},
"outputs": [
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  "radius": 5,
  "stroke": true,
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}
                ).addTo(map_bd9074bd7af14b34b6a11982ac03cc89);
            
    
            var popup_117e2fdf697d420591bbee1b960cf9bb = L.popup({maxWidth: '300'});

            
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            circle_marker_9f3b89503b524333b58781acf4d17f13.bindPopup(popup_117e2fdf697d420591bbee1b960cf9bb);

            
        
    
            var circle_marker_8af0c4c583d44b19adedd90c9ea27c9a = L.circleMarker(
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                {
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  "color": "#80ffb4",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "#80ffb4",
  "fillOpacity": 0.7,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_bd9074bd7af14b34b6a11982ac03cc89);
            
    
            var popup_31eb758d5637486b850ca3dff19af054 = L.popup({maxWidth: '300'});

            
                var html_8b4874e7630644d5b059cf3d2930890d = $('<div id="html_8b4874e7630644d5b059cf3d2930890d" style="width: 100.0%; height: 100.0%;">Harrison Square Cluster 2</div>')[0];
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            circle_marker_8af0c4c583d44b19adedd90c9ea27c9a.bindPopup(popup_31eb758d5637486b850ca3dff19af054);

            
        
    
            var circle_marker_059412bc19284c4ba4f089309f831f06 = L.circleMarker(
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                {
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  "dashArray": null,
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  "fill": true,
  "fillColor": "#ff0000",
  "fillOpacity": 0.7,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
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  "stroke": true,
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}
                ).addTo(map_bd9074bd7af14b34b6a11982ac03cc89);
            
    
            var popup_4260a1e761a84af5b9e9dc9f59cdb229 = L.popup({maxWidth: '300'});

            
                var html_45185bea0d7b4b56b25715356e9d1880 = $('<div id="html_45185bea0d7b4b56b25715356e9d1880" style="width: 100.0%; height: 100.0%;">Taco Bell Cluster 0</div>')[0];
                popup_4260a1e761a84af5b9e9dc9f59cdb229.setContent(html_45185bea0d7b4b56b25715356e9d1880);
            

            circle_marker_059412bc19284c4ba4f089309f831f06.bindPopup(popup_4260a1e761a84af5b9e9dc9f59cdb229);

            
        
    
            var circle_marker_89f6152b4fad4a81a9765298d2f6b57e = L.circleMarker(
                [33.77376958442543,-84.3980832286946],
                {
  "bubblingMouseEvents": true,
  "color": "#ff0000",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "#ff0000",
  "fillOpacity": 0.7,
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  "lineJoin": "round",
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}
                ).addTo(map_bd9074bd7af14b34b6a11982ac03cc89);
            
    
            var popup_0c97a8a105d5427aaf47330917def693 = L.popup({maxWidth: '300'});

            
                var html_d674b48cf95347c493210d69529e0c4a = $('<div id="html_d674b48cf95347c493210d69529e0c4a" style="width: 100.0%; height: 100.0%;">Burdell&#39;s Cluster 0</div>')[0];
                popup_0c97a8a105d5427aaf47330917def693.setContent(html_d674b48cf95347c493210d69529e0c4a);
            

            circle_marker_89f6152b4fad4a81a9765298d2f6b57e.bindPopup(popup_0c97a8a105d5427aaf47330917def693);

            
        
    
            var circle_marker_cd4b64ac3f41403a96abf6f7327fd328 = L.circleMarker(
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                {
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}
                ).addTo(map_bd9074bd7af14b34b6a11982ac03cc89);
            
    
            var popup_4208d1a01ac04f28b7356fd05f2f078c = L.popup({maxWidth: '300'});

            
                var html_9175bbb7f630457fac194625521a36e3 = $('<div id="html_9175bbb7f630457fac194625521a36e3" style="width: 100.0%; height: 100.0%;">Rooftop Garden Cluster 0</div>')[0];
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            circle_marker_cd4b64ac3f41403a96abf6f7327fd328.bindPopup(popup_4208d1a01ac04f28b7356fd05f2f078c);

            
        
    
            var circle_marker_fe0e9982421a4415b6c51cadcae2ea2b = L.circleMarker(
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                {
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  "stroke": true,
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}
                ).addTo(map_bd9074bd7af14b34b6a11982ac03cc89);
            
    
            var popup_b7ba2cef6c3d4b41a7a3f7827214ec0b = L.popup({maxWidth: '300'});

            
                var html_e5235812502e46cba9fa03f0754ed7b0 = $('<div id="html_e5235812502e46cba9fa03f0754ed7b0" style="width: 100.0%; height: 100.0%;">GT Stinger Stop - Techwood and North Ave Cluster 1</div>')[0];
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            circle_marker_fe0e9982421a4415b6c51cadcae2ea2b.bindPopup(popup_b7ba2cef6c3d4b41a7a3f7827214ec0b);

            
        
    
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  "fill": true,
  "fillColor": "#ff0000",
  "fillOpacity": 0.7,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
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}
                ).addTo(map_bd9074bd7af14b34b6a11982ac03cc89);
            
    
            var popup_916ddc802f714db080ca796fb2e65815 = L.popup({maxWidth: '300'});

            
                var html_afde8b918ec3459f9437c2a3450c7d13 = $('<div id="html_afde8b918ec3459f9437c2a3450c7d13" style="width: 100.0%; height: 100.0%;">Student Center Food Court Cluster 0</div>')[0];
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            circle_marker_9623fbd712a9455f8a98d597ee89bde4.bindPopup(popup_916ddc802f714db080ca796fb2e65815);

            
        
    
            var circle_marker_a1da0bd77861424fa5acea2392749b03 = L.circleMarker(
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                {
  "bubblingMouseEvents": true,
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  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "#ff0000",
  "fillOpacity": 0.7,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
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                ).addTo(map_bd9074bd7af14b34b6a11982ac03cc89);
            
    
            var popup_34c312143ec34e37b8ef43e8eaeabd28 = L.popup({maxWidth: '300'});

            
                var html_483e594b566346c189538423e3ef071b = $('<div id="html_483e594b566346c189538423e3ef071b" style="width: 100.0%; height: 100.0%;">Dunkin&#39; Cluster 0</div>')[0];
                popup_34c312143ec34e37b8ef43e8eaeabd28.setContent(html_483e594b566346c189538423e3ef071b);
            

            circle_marker_a1da0bd77861424fa5acea2392749b03.bindPopup(popup_34c312143ec34e37b8ef43e8eaeabd28);

            
        
    
            var circle_marker_e075716b8fc94805915a90bf9f4a13fb = L.circleMarker(
                [33.77522394551474,-84.39718993342497],
                {
  "bubblingMouseEvents": true,
  "color": "#ff0000",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "#ff0000",
  "fillOpacity": 0.7,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_bd9074bd7af14b34b6a11982ac03cc89);
            
    
            var popup_289eb80b676646e7ba9b9e79de7d5890 = L.popup({maxWidth: '300'});

            
                var html_8226ab187da4444d8ab00817889cf3ab = $('<div id="html_8226ab187da4444d8ab00817889cf3ab" style="width: 100.0%; height: 100.0%;">Einstein Statue Cluster 0</div>')[0];
                popup_289eb80b676646e7ba9b9e79de7d5890.setContent(html_8226ab187da4444d8ab00817889cf3ab);
            

            circle_marker_e075716b8fc94805915a90bf9f4a13fb.bindPopup(popup_289eb80b676646e7ba9b9e79de7d5890);

            
        
</script> onload=\"this.contentDocument.open();this.contentDocument.write(atob(this.getAttribute('data-html')));this.contentDocument.close();\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
],
"text/plain": [
"<folium.folium.Map at 0x7f22f6b4eb00>"
]
},
"execution_count": 69,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# create map\n",
"map_clusters = folium.Map(location=[atl_latitude, atl_longitude], zoom_start=16)\n",
"\n",
"# set color scheme for the clusters\n",
"x = np.arange(kclusters)\n",
"ys = [i + x + (i*x)**2 for i in range(kclusters)]\n",
"colors_array = cm.rainbow(np.linspace(0, 1, len(ys)))\n",
"rainbow = [colors.rgb2hex(i) for i in colors_array]\n",
"\n",
"# add markers to the map\n",
"markers_colors = []\n",
"for lat, lon, poi, cluster in zip(atl_df_with_clusters['Latitude'], atl_df_with_clusters['Longitude'], atl_df_with_clusters['Name'], atl_df_with_clusters['Cluster Labels']):\n",
" label = folium.Popup(str(poi) + ' Cluster ' + str(cluster), parse_html=True)\n",
" folium.CircleMarker(\n",
" [lat, lon],\n",
" radius=5,\n",
" popup=label,\n",
" color=rainbow[cluster-1],\n",
" fill=True,\n",
" fill_color=rainbow[cluster-1],\n",
" fill_opacity=0.7).add_to(map_clusters)\n",
" \n",
"map_clusters"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Conclusion"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"After viewing both universities and the only option was to choose one it would due to mobility it would definitely be Georgia Institute of Technology (Georgia Tech) since there is a shopping center that is walking distance from the institution. As a foodie, I would have preferred Chicago, but as you can see two of the nice restuarants are quite far. They do have the Divvy Station and bus route nearby for easy transportation if you do not want to commute by foot.\n",
"\n",
"Many people have different threshold of what is too far to travel without having a bike, vehicle or public transportation. Overall the venues surrounding both institutions (University of Chicago and Georgia Institute of Technology (Georgia Tech)) are good for pedestrians who need commutable distances to get around. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python",
"language": "python",
"name": "conda-env-python-py"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.11"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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