data_api.py

import json  
import pandas as pd  
from pandas.io.json import json_normalize  
import requests
from tabulate import tabulate
from sklearn.cluster import KMeans
import random
import numpy as np
import pandas as pd
import folium



#Fetching data form HERE API for IIT Bombay
url = 'https://discover.search.hereapi.com/v1/discover?in=circle:19.1334,72.9133;r=10000&q=apartment&apiKey=uJHMEjeagmFGldXp661-pDMf4R-PxvWIu7I68UjYC5Q'
data = requests.get(url).json()
d=json_normalize(data['items'])
d.to_csv('api-data/apartment.csv')



#Cleaning API data
d2=d[['title','address.label','distance','access','position.lat','position.lng','address.postalCode','contacts','id']]
d2.to_csv('api-data/cleaned_apartment.csv')



#Counting no. of cafes, department stores and gyms near apartments around IIT Bombay
df_final=d2[['position.lat','position.lng']]

CafeList=[]
DepList=[]
GymList=[]
latitudes = list(d2['position.lat'])
longitudes = list( d2['position.lng'])
for lat, lng in zip(latitudes, longitudes):    
    radius = '1000' #Set the radius to 1000 metres
    latitude=lat
    longitude=lng
    
    search_query = 'cafe' #Search for any cafes
    url = 'https://discover.search.hereapi.com/v1/discover?in=circle:{},{};r={}&q={}&apiKey=uJHMEjeagmFGldXp661-pDMf4R-PxvWIu7I68UjYC5Q'.format(latitude, longitude, radius, search_query)
    results = requests.get(url).json()
    venues=json_normalize(results['items'])
    CafeList.append(venues['title'].count())
	
    search_query = 'gym' #Search for any gyms
    url = 'https://discover.search.hereapi.com/v1/discover?in=circle:{},{};r={}&q={}&apiKey=uJHMEjeagmFGldXp661-pDMf4R-PxvWIu7I68UjYC5Q'.format(latitude, longitude, radius, search_query)
    results = requests.get(url).json()
    venues=json_normalize(results['items'])
    GymList.append(venues['title'].count())

    search_query = 'department-store' #search for supermarkets
    url = 'https://discover.search.hereapi.com/v1/discover?in=circle:{},{};r={}&q={}&apiKey=uJHMEjeagmFGldXp661-pDMf4R-PxvWIu7I68UjYC5Q'.format(latitude, longitude, radius, search_query)
    results = requests.get(url).json()
    venues=json_normalize(results['items'])
    DepList.append(venues['title'].count())

df_final['Cafes'] = CafeList
df_final['Department Stores'] = DepList
df_final['Gyms'] = GymList

print(tabulate(df_final,headers='keys',tablefmt='github'))



#Run K-means clustering on dataframe
kclusters = 3

kmeans = KMeans(n_clusters=kclusters, random_state=0).fit(df_final)
df_final['Cluster']=kmeans.labels_
df_final['Cluster']=df_final['Cluster'].apply(str)

print(tabulate(df_final,headers='keys',tablefmt='github'))



#Plotting clustered locations on map using Folium

#define coordinates of the college
map_bom=folium.Map(location=[19.1334,72.9133],zoom_start=12)

# instantiate a feature group for the incidents in the dataframe
locations = folium.map.FeatureGroup()

# set color scheme for the clusters
def color_producer(cluster):
    if cluster=='0':
        return 'green'
    elif cluster=='1':
        return 'orange'
    else:
        return 'red'

latitudes = list(df_final['position.lat'])
longitudes = list(df_final['position.lng'])
labels = list(df_final['Cluster'])
names=list(d2['title'])
for lat, lng, label,names in zip(latitudes, longitudes, labels,names):
    folium.CircleMarker(
            [lat,lng],
            fill=True,
            fill_opacity=1,
            popup=folium.Popup(names, max_width = 300),
            radius=5,
            color=color_producer(label)
        ).add_to(map_bom)

# add locations to map
map_bom.add_child(locations)
folium.Marker([19.1334,72.9133],popup='IIT Bombay').add_to(map_bom)

#saving the map 
map_bom.save("map-IITBombay.html")