Backend/locationAnalize.py at master · Location-Intelligence/Backend · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
from shapely.geometry import Point
from shapely.geometry.polygon import Polygon
import pandas as pd
import googlemaps
import numpy as np
import json
import os
import time
from const import API_KEY
gmaps = googlemaps.Client(API_KEY)

# Cleans the data extracted from google maps by removing unnecessary features and splitting coordinates
def cleanData(option, data_list):
    # Inner Functions for dataframe cleaning
    def lat(row):
        if 'location' in row:
            return row['location']['lat']
        return None
    def lng(row):
        if 'location' in row:
            return row['location']['lng']
        return None

    df = pd.DataFrame(data_list)
    if len(data_list)>0:
        cleaned_df = df[ df.columns & ['name', 'place_id', 'rating', 'geometry']]
        # print('0: ',cleaned_df.columns)
        cleaned_df['latitude'] = cleaned_df['geometry'].apply(lat)
        cleaned_df['longitude'] = cleaned_df['geometry'].apply(lng)
        cleaned_df = cleaned_df.drop(['geometry'],axis=1, errors='ignore')
        # print('Before dropping duplicates, the size was,',len(cleaned_df))
        cleaned_df.sort_values("place_id", inplace=True)
        # print('1: ',cleaned_df.columns)
        cleaned_df.drop_duplicates(subset=['place_id'], ignore_index=True, inplace=True)
        # print('After dropping duplicates, the size was,',len(cleaned_df))
        return cleaned_df
    else:
        return df

#Extract nearby places from google maps based on a 1km radius and list of categories
def extractFeatures(options, latitude, longitude):
    dataframes = []
    for opt in options:
        data_list = []
        params = {
            'location':[latitude, longitude],
            'radius':1000,
            'type': opt
        }

        first_page = gmaps.places_nearby(**params)

        second_page = {'results':[]}
        third_page = {'results':[]}

        #Fetching the second page if there is any
        if 'next_page_token' in first_page:
            params['page_token'] = first_page['next_page_token']
            time.sleep(2)
            second_page = gmaps.places_nearby(**params)

        #Fetching the third or last page if there is any
        if 'next_page_token' in second_page:
            params['page_token'] = second_page['next_page_token']
            time.sleep(2)
            third_page = gmaps.places_nearby(**params)

        data_list.extend(first_page['results'])
        data_list.extend(second_page['results'])
        data_list.extend(third_page['results'])
        cleaned_data = cleanData(opt, data_list)
        if cleaned_data.shape[0]>0:
            dataframes.append((opt, cleaned_data))
    return dataframes

#Calculate Distance between two coordinates
def calculateDistance(lat1, lon1, lat2, lon2):
    from math import sin, cos, sqrt, atan2, radians
    # approximate radius of earth in km
    R = 6373.0
    lat1, lon1 = radians(lat1), radians(lon1)
    lat2, lon2 = radians(lat2), radians(lon2)

    dlon = lon2 - lon1
    dlat = lat2 - lat1
    a = sin(dlat / 2)**2 + cos(lat1) * cos(lat2) * sin(dlon / 2)**2
    c = 2 * atan2(sqrt(a), sqrt(1 - a))
    distance = R * c
    return distance

#Collect Nearest Supermarkets to the coordinate given
def nearestSupermarkets(lat, lon, dataframe, count):
    import heapq
    nearest = []
    if len(dataframe)==0: return nearest
    for i in dataframe.itertuples(index=True, name='Pandas'):
        heapq.heappush(nearest, (calculateDistance(lat, lon, i.latitude, i.longitude), {
            'name': i.name.replace("'", "\'").split('|')[0],
            'latitude': i.latitude,
            'longitude': i.longitude,
            # 'google_rating': i.rating if i.rating else 'NaN'
        }))
    return [i[1] for i in heapq.nsmallest(count, nearest)]

#Preprocess each extracted dataframe and return their counts alongside nearest supermarkets
def collectNearbyPlaces(current_features, latitude, longitude):
    dataframes = extractFeatures(current_features, latitude, longitude)
    nearby_places = {}
    supermarkets = []
    for key in current_features:
        nearby_places[key] = 0
    for key, df in dataframes:
        if key=='shopping_mall' or key=='supermarket':
            if len(supermarkets)==0:
                supermarkets = df
            else:
                supermarkets = supermarkets.append(df)
        if 'permanently_closed' in df.columns:
            df = df[df['permanently_closed']!=True]
        if key=='shopping_mall' or key=='supermarket':
            nearby_places['competitors'] += df.shape[0]
        else:
            nearby_places[key] = df.shape[0]
    if len(supermarkets)!=0:
        supermarkets.drop_duplicates(inplace = True, subset=['name', 'latitude', 'longitude'])
    nearest_supermarkets = nearestSupermarkets(latitude, longitude, supermarkets, 5)
    return nearby_places, nearest_supermarkets

# Import population File
def importPopulationFile(location = 'Population_per_subcity.json'):
    script_dir = os.path.dirname("__file__")
    subcity_population_json = os.path.join(script_dir, location )
    subcity_population = open(subcity_population_json,)
    subcity_population_data = json.load(subcity_population)
    return subcity_population_data

# Based on coordinate determine the poplation per gender
# def determineSubcityAndAddPopulation(latitude, longitude):
#     subcity_population_data = importPopulationFile()

#     for subcity in subcity_population_data:
#         point = Point(latitude,longitude)
#         polygon = Polygon([(i,j) for i, j in subcity_population_data[subcity]['coordinates']])
#         if polygon.contains(point):
#             total_males = total_females = 0
#             for age in subcity_population_data[subcity]['population']:
#               total_males += subcity_population_data[subcity]['population'][age]['Males']
#               total_females += subcity_population_data[subcity]['population'][age]['Females']
#             return [total_males, total_females]
#     return [0,0]
def determineSubcityAndAddPopulation(latitude, longitude):
    subcity_population_data = importPopulationFile()
    for subcity in subcity_population_data:
        point = Point(latitude, longitude)
        polygon = Polygon([(i,j) for i, j in subcity_population_data[subcity]['coordinates']])
        if polygon.contains(point):
            total_children = total_working = total_elderly = 0
            total_males = total_females = 0
            for age in subcity_population_data[subcity]['population']:
                if age=="0 - 4" or age=="5 - 9" or age=="10-14":
                    total_children += subcity_population_data[subcity]['population'][age]['Total']
                elif age=="65-69" or age=="70-74" or age=="75-79" or age=="80-84" or age=='85-89' or age=="90-94" or age=="95+":
                    total_elderly += subcity_population_data[subcity]['population'][age]['Total']
                else:
                    total_working += subcity_population_data[subcity]['population'][age]['Total']
                total_males += subcity_population_data[subcity]['population'][age]['Males']
                total_females += subcity_population_data[subcity]['population'][age]['Females']
            return [total_males, total_females, total_children, total_working, total_elderly]
    return [0,0,0,0,0]
# Main function to extract places and population count
def extractData(current_features, latitude, longitude):
    nearby_places, supermarkets = collectNearbyPlaces(current_features, latitude, longitude)
    total_males, total_females, total_children, total_working, total_elderly = determineSubcityAndAddPopulation(latitude, longitude)
    nearby_places['Males'] = total_males
    nearby_places['Females'] = total_females
    nearby_places['Children'] = total_children
    nearby_places['Working'] = total_working
    nearby_places['Elderly'] = total_elderly

    return nearby_places, supermarkets