
import pandas as pd
url = 'https://data.opendevelopmentmekong.net/dataset/ab20b509-2b7f-442e-8448-05d3a17651ac/resource/719e0ba4-637a-46c5-9ad7-9bc0e4c3752d/download/health_facilities_en.csv'
df_Origin = pd.read_csv(url)

df_Origin.head()


df_3_town =df_Origin[df_Origin['Address'].str.contains('Khon Kaen|Chiang Mai|Chonburi',case=False, regex=True)]
df_3_town.head()


location_list = df_3_town['Address'].tolist()


list_of_lat_hos = df_3_town['Lat']
list_of_long_hos = df_3_town['Long']

# Zip Lat & Long to List of Coordinates
list_of_coordinates_hos = []
for lat, long in zip(list_of_lat_hos, list_of_long_hos):
    list_of_coordinates_hos.append((lat,long))


url_1 = 'https://data.go.th/dataset/9bccd66e-8b14-414d-93d5-da044569350c/resource/a15653e5-05a2-48e7-aeaa-c721f2b088f9/download/data_station.csv'
df_sta= pd.read_csv(url_1)


print(df_sta.shape)
df_sta.head()

(691, 18)


df_sta["lat"] = df_sta["lat"].map(lambda x: x.rstrip(','))
df_sta["long"] = df_sta["long"].map(lambda x: x.rstrip(" "))
df_sta["long"] = df_sta["long"].str.strip()


df_sta["lat"] = pd.to_numeric(df_sta["lat"])
df_sta["long"] = pd.to_numeric(df_sta["long"])


df_sta.drop(df_sta.index[df_sta['long'] == 0], inplace = True)
df_sta.drop(df_sta.index[df_sta['lat'] > 90], inplace = True)


print(df_sta.shape)
df_sta.head()

(487, 18)


location_list_sta = df_sta['en_name'].tolist()


list_of_lat_sta= df_sta['lat']
list_of_long_sta = df_sta['long']

# Zip Lat & Long to List of Coordinates
list_of_coordinates_sta = []
for lat, long in zip(list_of_lat_sta, list_of_long_sta):
    list_of_coordinates_sta.append((lat,long))


location_list_sta = df_sta['en_name'].tolist()


from geopy.distance import geodesic

list_of_dist_sta = []

# list_of_coordinates -> Hospital Unit Coordinates
# Loop Theough the hospital Coordinates
for origin in list_of_coordinates_hos:
    # Loop Through All Train Station in the List in -> "list_of_sta_coordinates"
    for destination in range(0, len(list_of_coordinates_sta)):

        #geodesic is a  Function -> is use to Calculate the Distance between The Hospital
        list_of_dist_sta.append(geodesic(origin,list_of_coordinates_sta[destination]).meters)
        list_of_dist_sta.append(location_list_sta[destination])
        list_of_dist_sta.append(list_of_coordinates_sta[destination])


len(location_list_sta)

487


len(list_of_dist_sta)

1025622


chunked_list2 = list()
chunk_size2 = 1461

for i in range(0, len(list_of_dist_sta), chunk_size2):
    chunked_list2.append(list_of_dist_sta[i:i+chunk_size2])
list_of_dist_sta.clear()
#     for i in range(0, len(chunked_list2)): 
#         print(chunked_list2[i])

# 1 index equal 1 Hospital

xnew = []
dist_sta = []
for j in range(0,len(chunked_list2)):
    xnew = []
    x = chunked_list2[j]
    for i in range(0, len(x), 3):
        xnew.append(x[i:i+3])
    c = (min(xnew))
    dist_sta.append(c)
len(dist_sta)

702


print("sta",len(list_of_coordinates_sta))
print("hos",len(list_of_coordinates_hos))

sta 487
hos 702


import geocoder
from geopy.geocoders import Nominatim
address = "thailand, ON"

geolocator = Nominatim(user_agent="thailand_explorer")
location = geolocator.geocode(address)
latitude = location.latitude
longitude = location.longitude
print('The geograpical coordinate of Thailand are {}, {}.'.format(latitude, longitude))

The geograpical coordinate of Thailand are 12.9330537, 100.8888005.


import folium 
map_th = folium.Map(location=[latitude, longitude], zoom_start=5)


for lat, lng in zip(
        df_3_town['Lat'], 
        df_3_town['Long']): 
       
   
    folium.Marker(
        [lat, lng],
        radius=5,
       
        color='blue',
        fill=True,
        fill_color='#3186cc',
        fill_opacity=0.7,
        parse_html=False).add_to(map_th)  

map_th


df_dist_sta = pd.DataFrame(dist_sta, columns =['Dist_sta', 'Station', 'Lat_Long_Sta'])
df_dist_sta.head()


import requests
venues_list=[]

for i in range(len(df_3_town.index)):
    lat = df_3_town['Lat'].iloc[i]
    long = df_3_town['Long'].iloc[i]
    names = df_3_town['Agency'].iloc[i]
    location = df_3_town['Address'].iloc[i]
    query = 'cafe'
#     radius = 100000


    url = f"https://api.foursquare.com/v3/places/nearby?&ll={lat}%2C{long}&query={query}"
    headers = {
       "Accept": "application/json",
       "Authorization": "fsq3bdN5alD3QfFN/WdCXx8BPv7rqaMZGb1FvJf/NN1krOM="
        }

    response = requests.get(url, headers=headers)
    response = response.json()["results"][0]

    venues_list.append(response['name'])
    if len(response['categories']) > 0:
        venues_list.append(response['categories'][0]['name'])
    else:
        venues_list.append("no cat")
        pass
    venues_list.append(response['distance'])
    venues_list.append(response['geocodes']['main']['latitude'])
    venues_list.append(response['geocodes']['main']['longitude'])
    venues_list.append(names)
    venues_list.append(location)
    
chunked_list = list()
chunk_size = 7
for i in range(0, len(venues_list), chunk_size):
    chunked_list.append(venues_list[i:i+chunk_size])
    
nearby_venues = pd.DataFrame(chunked_list, columns =['Name', 'Category', 'Dist_Venue', 'Lat_Venue', 'Long_Venue','Hospital',"Location"])
nearby_venues.head()


df_3_town = df_3_town.reset_index().drop('index', axis=1)


df = nearby_venues
df['Dist_sta'] = df_dist_sta['Dist_sta']
df['Station'] = df_dist_sta['Station']
df['Lat_Long_Sta'] = df_dist_sta['Lat_Long_Sta']
df['H_lat'] = df_3_town['Lat']
df['H_long'] = df_3_town['Long']


cm =df[df['Location'].str.contains('Chiang Mai',case=False, regex=True)]
kk =df[df['Location'].str.contains('Khon Kaen',case=False, regex=True)]
cb =df[df['Location'].str.contains('Chonburi',case=False, regex=True)]
print("cm",cm.shape)
print("kk",kk.shape)
print("cb",cb.shape)

cm (296, 12)
kk (278, 12)
cb (128, 12)


cm2 = cm.sort_values(by=['Dist_sta'])
cm3 = cm.sort_values(by=['Dist_Venue'])


cm2.head(3)


cm3.head(10)


import folium 

index = 1

Hlat= cm['H_lat'][index]
Hlong = cm['H_long'][index]
Slat = cm['Lat_Long_Sta'][index][0]
Slong = cm['Lat_Long_Sta'][index][1]
Vlat = cm['Lat_Venue'][index]
Vlong = cm['Long_Venue'][index]

a = (Hlat+Slat+Vlat)/3
b = (Hlong+Slong+Vlong)/3
map_th2 = folium.Map(location=[a, b], zoom_start=14)

tooltip1 = cm['Hospital'][index]
tooltip2 = cm['Station'][index] + " Station"
tooltip3 = cm['Name'][index]
    
folium.Marker(
    location=[Hlat, Hlong],
    popup=tooltip1,
    icon=folium.Icon(color="red", icon="glyphicon glyphicon-plus"),tooltip=tooltip1
).add_to(map_th2)

folium.Marker(
    location=[Slat, Slong],
    popup=tooltip2,
    icon=folium.Icon(color="blue",icon="train", prefix='fa'),tooltip=tooltip2
).add_to(map_th2)

folium.Marker(
    location=[Vlat, Vlong],
    popup=tooltip3,
    icon=folium.Icon(color="green", icon="glyphicon glyphicon-cutlery"),tooltip=tooltip3
#     icon="coffee", prefix='fa'
).add_to(map_th2)

print(cm['Hospital'][index])
print(cm['Station'][index] + " Station" ,"| Distance: " , "{:.0f}".format(cm['Dist_sta'][index])+ " m.")
print(cm['Name'][index],"| Distance: " , cm['Dist_Venue'][index],"m.")
    
map_th2

Department of Mental Health, Suan Prung Hospital
Chiang Mai Station | Distance:  3942 m.
Cafe de Garden (กาแฟในสวน) | Distance:  131 m.


import folium

index = 616

Hlat= cm['H_lat'][index]
Hlong = cm['H_long'][index]
Slat = cm['Lat_Long_Sta'][index][0]
Slong = cm['Lat_Long_Sta'][index][1]
Vlat = cm['Lat_Venue'][index]
Vlong = cm['Long_Venue'][index]

a = (Hlat+Slat+Vlat)/3
b = (Hlong+Slong+Vlong)/3
map_th2 = folium.Map(location=[a, b], zoom_start=16)

tooltip1 = cm['Hospital'][index]
tooltip2 = cm['Station'][index] + " Station"
tooltip3 = cm['Name'][index]

folium.Marker(
    location=[Hlat, Hlong],
    popup=tooltip1,
    icon=folium.Icon(color="red", icon="glyphicon glyphicon-plus"),tooltip=tooltip1
).add_to(map_th2)

folium.Marker(
    location=[Slat, Slong],
    popup=tooltip2,
    icon=folium.Icon(color="blue",icon="train", prefix='fa'),tooltip=tooltip2
).add_to(map_th2)

folium.Marker(
    location=[Vlat, Vlong],
    popup=tooltip3,
    icon=folium.Icon(color="green", icon="glyphicon glyphicon-cutlery"),tooltip=tooltip3
#     icon="coffee", prefix='fa'
).add_to(map_th2)

print(cm['Hospital'][index])
print(cm['Station'][index] + " Station" ,"| Distance: " , "{:.0f}".format(cm['Dist_sta'][index])+ " m.")
print(cm['Name'][index],"| Distance: " , cm['Dist_Venue'][index],"m.")

map_th2

Ban Yang Nueng Subdistrict Health Promoting Hospital, Yang Nueng District, Chiang Mai Province
Saraphi Station | Distance:  1290 m.
PUMPNOM CAFÉ (กาแฟ ปั๊มนม) | Distance:  323 m.


kk2 = kk.sort_values(by=['Dist_sta'])
kk3 = kk.sort_values(by=['Dist_Venue'])


kk3.head(10)


import folium

index = 95

Hlat= kk['H_lat'][index]
Hlong = kk['H_long'][index]
Slat = kk['Lat_Long_Sta'][index][0]
Slong = kk['Lat_Long_Sta'][index][1]
Vlat = kk['Lat_Venue'][index]
Vlong = kk['Long_Venue'][index]

a = (Hlat+Slat+Vlat)/3
b = (Hlong+Slong+Vlong)/3
map_th2 = folium.Map(location=[a, b], zoom_start=14)

tooltip1 = kk['Hospital'][index]
tooltip2 = kk['Station'][index] + " Station"
tooltip3 = kk['Name'][index]

folium.Marker(
    location=[Hlat, Hlong],
    popup=tooltip1,
    icon=folium.Icon(color="red", icon="glyphicon glyphicon-plus"),tooltip=tooltip1
).add_to(map_th2)

folium.Marker(
    location=[Slat, Slong],
    popup=tooltip2,
    icon=folium.Icon(color="blue",icon="train", prefix='fa'),tooltip=tooltip2
).add_to(map_th2)

folium.Marker(
    location=[Vlat, Vlong],
    popup=tooltip3,
    icon=folium.Icon(color="green", icon="glyphicon glyphicon-cutlery"),tooltip=tooltip3
#     icon="coffee", prefix='fa'
).add_to(map_th2)

print(kk['Hospital'][index])
print(kk['Station'][index] + " Station" ,"| Distance: " , "{:.0f}".format(kk['Dist_sta'][index])+ " m.")
print(kk['Name'][index],"| Distance: " , kk['Dist_Venue'][index],"m.")

map_th2

Ban Non Muang Health Promoting Hospital Khon Kaen Province
Samran Station | Distance:  3951 m.
Warehouse Café 360 (แวร์เฮ้าส์คาเฟ่ 360) | Distance:  224 m.


import folium

index = 439

Hlat= kk['H_lat'][index]
Hlong = kk['H_long'][index]
Slat = kk['Lat_Long_Sta'][index][0]
Slong = kk['Lat_Long_Sta'][index][1]
Vlat = kk['Lat_Venue'][index]
Vlong = kk['Long_Venue'][index]

a = (Hlat+Slat+Vlat)/3
b = (Hlong+Slong+Vlong)/3
map_th2 = folium.Map(location=[a, b], zoom_start=15)

tooltip1 = kk['Hospital'][index]
tooltip2 = kk['Station'][index] + " Station"
tooltip3 = kk['Name'][index]

folium.Marker(
    location=[Hlat, Hlong],
    popup=tooltip1,
    icon=folium.Icon(color="red", icon="glyphicon glyphicon-plus"),tooltip=tooltip1
).add_to(map_th2)

folium.Marker(
    location=[Slat, Slong],
    popup=tooltip2,
    icon=folium.Icon(color="blue",icon="train", prefix='fa'),tooltip=tooltip2
).add_to(map_th2)

folium.Marker(
    location=[Vlat, Vlong],
    popup=tooltip3,
    icon=folium.Icon(color="green", icon="glyphicon glyphicon-cutlery"),tooltip=tooltip3
#     icon="coffee", prefix='fa'
).add_to(map_th2)

print(kk['Hospital'][index])
print(kk['Station'][index] + " Station" ,"| Distance: " , "{:.0f}".format(kk['Dist_sta'][index])+ " m.")
print(kk['Name'][index],"| Distance: " , kk['Dist_Venue'][index],"m.")
    
map_th2

Khon Kaen Hospital Khon Kaen Province
Khon Kaen Station | Distance:  2544 m.
YING cafe | Distance:  389 m.


cb2 = cb.sort_values(by=['Dist_sta'])
cb3 = cb.sort_values(by=['Dist_Venue'])
cb3.head(10)


import folium

index = 10

Hlat= cb['H_lat'][index]
Hlong = cb['H_long'][index]
Slat = cb['Lat_Long_Sta'][index][0]
Slong = cb['Lat_Long_Sta'][index][1]
Vlat = cb['Lat_Venue'][index]
Vlong = cb['Long_Venue'][index]

a = (Hlat+Slat+Vlat)/3
b = (Hlong+Slong+Vlong)/3
map_th2 = folium.Map(location=[a, b], zoom_start=15)

tooltip1 = cb['Hospital'][index]
tooltip2 = cb['Station'][index] + " Station"
tooltip3 = cb['Name'][index]

folium.Marker(
    location=[Hlat, Hlong],
    popup=tooltip1,
    icon=folium.Icon(color="red", icon="glyphicon glyphicon-plus"),tooltip=tooltip1
).add_to(map_th2)

folium.Marker(
    location=[Slat, Slong],
    popup=tooltip2,
    icon=folium.Icon(color="blue",icon="train", prefix='fa'),tooltip=tooltip2
).add_to(map_th2)

folium.Marker(
    location=[Vlat, Vlong],
    popup=tooltip3,
    icon=folium.Icon(color="green", icon="glyphicon glyphicon-cutlery"),tooltip=tooltip3
#     icon="coffee", prefix='fa'
).add_to(map_th2)

print(cb['Hospital'][index])
print(cb['Station'][index] + " Station" ,"| Distance: " , "{:.0f}".format(cb['Dist_sta'][index])+ " m.")
print(cb['Name'][index],"| Distance: " , cb['Dist_Venue'][index],"m.")
   
map_th2

Chonburi Hospital Chonburi Province
Chon Buri Station | Distance:  1981 m.
Café Amazon (คาเฟ่ อเมซอน) | Distance:  66 m.


import folium

index = 411

Hlat= cb['H_lat'][index]
Hlong = cb['H_long'][index]
Slat = cb['Lat_Long_Sta'][index][0]
Slong = cb['Lat_Long_Sta'][index][1]
Vlat = cb['Lat_Venue'][index]
Vlong = cb['Long_Venue'][index]

a = (Hlat+Slat+Vlat)/3
b = (Hlong+Slong+Vlong)/3
map_th2 = folium.Map(location=[a, b], zoom_start=15)

tooltip1 = cb['Hospital'][index]
tooltip2 = cb['Station'][index] + " Station"
tooltip3 = cb['Name'][index]

folium.Marker(
    location=[Hlat, Hlong],
    popup=tooltip1,
    icon=folium.Icon(color="red", icon="glyphicon glyphicon-plus"),tooltip=tooltip1
).add_to(map_th2)

folium.Marker(
    location=[Slat, Slong],
    popup=tooltip2,
    icon=folium.Icon(color="blue",icon="train", prefix='fa'),tooltip=tooltip2
).add_to(map_th2)

folium.Marker(
    location=[Vlat, Vlong],
    popup=tooltip3,
    icon=folium.Icon(color="green", icon="glyphicon glyphicon-cutlery"),tooltip=tooltip3
#     icon="coffee", prefix='fa'
).add_to(map_th2)

print(cb['Hospital'][index])
print(cb['Station'][index] + " Station" ,"| Distance: " , "{:.0f}".format(cb['Dist_sta'][index])+ " m.")
print(cb['Name'][index],"| Distance: " , cb['Dist_Venue'][index],"m.")

map_th2

Phanthong Hospital Chonburi Province
Phan Thong Station | Distance:  1979 m.
CHIC Café & Restaurant | Distance:  280 m.

	ID	Ministry	Department	Agency	Lat	Long	Address
0	1.110000e+11	Ministry of Defence	Naval Medical Department	Royal Thai Army Phramongkutklao Hospital	13.76733	100.5342	Thung Phaya Thai Subdistrict, Ratchathewi Dist...
1	1.110000e+11	Ministry of Justice	Department of Corrections	Department of Corrections Correctional Institu...	13.85018	100.5531	Ladyao Subdistrict, Chatuchak District, Bangko...
2	1.110000e+11	Royal Thai Police	Police Hospial	Police Hospital	13.74414	100.5391	Pathumwan, Pathumwan, Bangkok 10330
3	1.130000e+11	Ministry of Defence	Naval Medical Department	Royal Thai Army Ananda Mahidol Hospital	14.84966	100.6670	Khao Sam Yot Subdistrict, Mueang Lopburi Distr...
4	1.130000e+11	Ministry of Public Health	Department of Mental Health	Department of Mental Health, Srithanya Hospital	13.84513	100.5171	Talat Khwan Subdistrict, Mueang Nonthaburi Dis...

	ID	Ministry	Department	Agency	Lat	Long	Address
10	1.140000e+11	Ministry of Public Health	Department of Medical Services	Department of Medicine, Neurological Hospital,...	18.78978	98.96952	Number 2, Suthep Road, Suthep Subdistrict, Mue...
11	1.140000e+11	Ministry of Public Health	Department of Mental Health	Department of Mental Health, Suan Prung Hospital	18.78074	98.97915	Hai Ya Subdistrict, Mueang Chiang Mai District...
18	1.140000e+11	Ministry of Public Health	Department of Mental Health	Department of Mental Health, Khon Kaen Rajanag...	16.42467	102.84870	169 Soi Kalaya, Chataphadung Road, Nai Mueang ...
41	8.900000e+12	Ministry of Public Health	Office of the Permanent Secretary, Ministry of...	Mae Ai Hospital Chiang Mai Province	20.03843	99.30260	191 Moo 8, Fang-Thaton Road, Moo 8, Mae Ai Sub...
87	8.900000e+12	Ministry of Public Health	Office of the Permanent Secretary, Ministry of...	Banglamung Hospital Chonburi Province	12.96664	100.90620	No. 6 Moo 5, Naklua Subdistrict, Bang Lamung D...

	id	station_code	name	en_name	th_short	en_short	chname	controldivision	exact_km	exact_distance	km	class	lat	long	active	giveway	dual_track	comment
0	1	1001	กรุงเทพ	Bangkok	กท.	BKK	曼谷	14	0	0	0	11	13.739277	100.517391	1	1	1	NaN
1	2	1002	ยมราช	Yommaraj	ยช.	YMR	勇马拉	14	2	2	2	99	0.00000	0.00000	1	1	1	NaN
2	3	1004	สามเสน	Sam Sen	สส.	SAM	三森	14	5	4	4	1	13.779293	100.529235	1	1	1	NaN
3	4	1006	ประดิพัทธ์	Pra Dipat	ปอิ.	PDP	帕涤潘	14	6	6	6	99	0.00000	0.00000	1	1	1	ป.สระอิ
4	5	1007	ชุมทางบางซื่อ	Bang Sue Junction	บซ.	BSJ	邦苏总站	14	7	7	7	1	13.8043	100.54005	1	1	1	NaN

	id	station_code	name	en_name	th_short	en_short	chname	controldivision	exact_km	exact_distance	km	class	lat	long	active	giveway	dual_track	comment
0	1	1001	กรุงเทพ	Bangkok	กท.	BKK	曼谷	14	0	0	0	11	13.739277	100.517391	1	1	1	NaN
2	3	1004	สามเสน	Sam Sen	สส.	SAM	三森	14	5	4	4	1	13.779293	100.529235	1	1	1	NaN
4	5	1007	ชุมทางบางซื่อ	Bang Sue Junction	บซ.	BSJ	邦苏总站	14	7	7	7	1	13.804300	100.540050	1	1	1	NaN
6	7	1011	บางเขน	Bang Khen	บข.	BKH	邦肯	14	13	13	13	1	13.846929	100.560654	1	1	1	NaN
8	9	1015	หลักสี่	Lak Si	ลส.	LKS	拉四	14	18	17	17	1	13.883674	100.580690	1	1	1	NaN

	Dist_sta	Station	Lat_Long_Sta
0	5010.118103	Chiang Mai	(18.782835, 99.01648)
1	3942.471731	Chiang Mai	(18.782835, 99.01648)
2	2481.573228	Khon Kaen	(16.426943, 102.825585)
3	142202.140997	Chiang Mai	(18.782835, 99.01648)
4	2942.686004	Pattaya	(12.940214, 100.909291)

The workplace relocation of Medical-Staff.¶

Table of Contents

Hospital¶

Read Hospital location data¶

Filter the data according to the criteria of interest as below.¶

Add address to List¶

Add Latitude & Longtitude to List¶

Train Station¶

Read Train station data¶

Cleansing Data¶

Add Station name to List¶

Add Latitude & Longtitude to List¶

Find the distance between Hospital and Station coordinate¶

Filter the minimize of distance¶

Show point of Hospital coordinate on map¶

Get Latitude, Longitude of Thailand¶

Using Folium to show map¶

Find coordinate nearby places (Cafe)¶

Using Foursquare API find coordinate¶

Split the data into 3 cities of we interest¶

Visualization¶

Chiang Mai¶

Department of Mental Health, Suan Prung Hospital, Chiang Mai Province¶

Ban Yang Nueng Subdistrict Health Promoting Hospital, Chiang Mai Province¶

Khon Kaen¶

Ban Non Muang Health Promoting Hospital, Khon Kaen Province¶

Khon Kaen Hospital, Khon Kaen Province¶

Chonburi¶

Chonburi Hospital, Chonburi Province¶

Phanthong Hospital, Chonburi Province¶

	Name	Category	Dist_Venue	Lat_Venue	Long_Venue	Hospital	Location
0	Café Amazon (คาเฟ่ อเมซอน)	Coffee Shop	99	18.789836	98.970468	Department of Medicine, Neurological Hospital,...	Number 2, Suthep Road, Suthep Subdistrict, Mue...
1	Cafe de Garden (กาแฟในสวน)	Coffee Shop	131	18.781916	98.979057	Department of Mental Health, Suan Prung Hospital	Hai Ya Subdistrict, Mueang Chiang Mai District...
2	Changlong Cafe & Restaurant (ชางหลง Cafe & Res...	Chinese Restaurant	1560	16.431177	102.835731	Department of Mental Health, Khon Kaen Rajanag...	169 Soi Kalaya, Chataphadung Road, Nai Mueang ...
3	Café Amazon (คาเฟ่ อเมซอน)	Coffee Shop	2214	20.027149	99.285114	Mae Ai Hospital Chiang Mai Province	191 Moo 8, Fang-Thaton Road, Moo 8, Mae Ai Sub...
4	Café Amazon (คาเฟ่ อเมซอน)	Coffee Shop	421	12.965142	100.909780	Banglamung Hospital Chonburi Province	No. 6 Moo 5, Naklua Subdistrict, Bang Lamung D...

	Name	Category	Dist_Venue	Lat_Venue	Long_Venue	Hospital	Location	Dist_sta	Station	Lat_Long_Sta	H_lat	H_long
616	PUMPNOM CAFÉ (กาแฟ ปั๊มนม)	Café	323	18.717873	99.028921	Ban Yang Nueng Subdistrict Health Promoting Ho...	Moo 3, Yangneng Subdistrict, Saraphi District,...	1289.889726	Saraphi	(18.710173, 99.041886)	18.71609	99.03135
618	Nekoemon Café (เนโกะเอม่อน คาเฟ่)	Café	2229	18.714073	99.059645	Ban Si Song Muang Subdistrict Health Promoting...	Moo 2, Chaisathan Subdistrict, Saraphi Distric...	2015.961792	Saraphi	(18.710173, 99.041886)	18.72820	99.04461
105	Nekoemon Café (เนโกะเอม่อน คาเฟ่)	Café	719	18.714073	99.059645	Ban Phaya Chomphu Health Promoting Hospital, C...	Moo 2, Chompoo Subdistrict, Saraphi District, ...	2040.205163	Saraphi	(18.710173, 99.041886)	18.70774	99.06106

	Name	Category	Dist_Venue	Lat_Venue	Long_Venue	Hospital	Location	Dist_sta	Station	Lat_Long_Sta	H_lat	H_long
556	MOON CAFE' Sansai Nongjom Chiangmai	Coffee Shop	67	18.846888	99.019325	Ban Nong Krai Health Promoting Hospital, Nong ...	Moo 6, Nong Chom Subdistrict, San Sai District...	7104.198137	Chiang Mai	(18.782835, 99.01648)	18.84693	99.01997
398	Come Cafe	Café	71	17.949830	98.689887	Doi Tao Hospital Chiang Mai Province	105 Moo 3, Hot-Mae Taoy Road, Moo 3, Doi Tao S...	68689.666137	Nong Lom	(18.462368, 99.056907)	17.94997	98.69055
525	BKY steak cafe	Bistro	73	18.618248	98.947560	Ban Buak Khrok Subdistrict Health Promoting Ho...	Moo 2, Nong Tong Subdistrict, Hang Dong Distri...	8113.991706	Lamphun	(18.593556, 99.020686)	18.61799	98.94820
9	ฮ้านปันนา Punna Bookstore & Cafe	Café	82	19.912554	99.206125	Fang Hospital, Chiang Mai Province	30 Moo 4, Chotana Road, Moo 4, Wiang Subdistri...	126710.180102	Chiang Mai	(18.782835, 99.01648)	19.91320	99.20651
443	Café Amazon (คาเฟ่ อเมซอน)	Coffee Shop	91	18.851850	98.968399	Nakhon Phing Hospital Chiang Mai Province	159 Chotina Road, Moo 4, Mae Sa Subdistrict, M...	9249.987285	Chiang Mai	(18.782835, 99.01648)	18.85227	98.96765
0	Café Amazon (คาเฟ่ อเมซอน)	Coffee Shop	99	18.789836	98.970468	Department of Medicine, Neurological Hospital,...	Number 2, Suthep Road, Suthep Subdistrict, Mue...	5010.118103	Chiang Mai	(18.782835, 99.01648)	18.78978	98.96952
474	Cafe' De Atinge	no cat	115	18.407656	98.673765	Chomthong Hospital Chiang Mai Province	259 Moo 2, Chiang Mai-Hot Road, Moo 2, Doi Kae...	40873.020321	Nong Lom	(18.462368, 99.056907)	18.40682	98.67441
1	Cafe de Garden (กาแฟในสวน)	Coffee Shop	131	18.781916	98.979057	Department of Mental Health, Suan Prung Hospital	Hai Ya Subdistrict, Mueang Chiang Mai District...	3942.471731	Chiang Mai	(18.782835, 99.01648)	18.78074	98.97915
415	Café Amazon (คาเฟ่ อเมซอน)	Coffee Shop	179	19.369038	99.200552	Phrao Hospital Chiang Mai Province	181 Moo 4 Moo 4, Wiang Subdistrict, Phrao Dist...	67540.839962	Chiang Mai	(18.782835, 99.01648)	19.36745	99.20024
635	Kummoon Cafe	Café	182	19.306933	99.189920	Ban Pa Khaem Health Promoting Hospital, Mae Wa...	Moo 4, Mae Wan Subdistrict, Phrao District, Ch...	60924.838264	Chiang Mai	(18.782835, 99.01648)	19.30744	99.19158

	Name	Category	Dist_Venue	Lat_Venue	Long_Venue	Hospital	Location	Dist_sta	Station	Lat_Long_Sta	H_lat	H_long
407	Café Amazon (คาเฟ่ อเมซอน)	Coffee Shop	132	16.134293	102.532805	Petrola Hospital Khon Kaen Province	316 Moo 14, Kut Khao Subdistrict, Manchayri Di...	22126.502334	Ban Phai	(16.059662, 102.725377)	16.13501	102.5338
441	CHA-LEE Cafe	Café	152	16.708052	103.085952	Somdej Phra Yupparat Kranuan Hospital Khon Kae...	1 Moo 11, Nong Ko Subdistrict, Kranuan Distric...	22979.175977	Huai Sieo	(16.747454, 102.874905)	16.70670	103.0862
95	Warehouse Café 360 (แวร์เฮ้าส์คาเฟ่ 360)	Café	224	16.489716	102.819715	Ban Non Muang Health Promoting Hospital Khon K...	Moo 3, Sila Subdistrict, Mueang Khon Kaen Dist...	3951.350782	Samran	(16.51724, 102.842976)	16.49089	102.8180
137	Café Amazon (คาเฟ่ อเมซอน)	Coffee Shop	280	16.671336	102.805016	Ban Kham Kaen Koon Health Promoting Hospital K...	Moo 7, Muang Wan Subdistrict, Nam Phong Distri...	3152.542474	Non Phayom	(16.643394, 102.818675)	16.66948	102.8068
409	Café Amazon (คาเฟ่ อเมซอน)	Coffee Shop	378	15.733382	102.790890	Nong Song Hong Hospital Khon Kaen Province	803 Moo 16 Nong Song Hong Subdistrict Nong Son...	22885.752489	Muang Phon	(15.819684, 102.600494)	15.73298	102.7944
439	YING cafe	Café	389	16.432556	102.849203	Khon Kaen Hospital Khon Kaen Province	Nai Mueang Subdistrict, Mueang Khon Kaen Distr...	2543.556385	Khon Kaen	(16.426943, 102.825585)	16.42905	102.8493
308	Café Amazon	Coffee Shop	403	16.679657	102.709937	Khok Sung Health Promoting Hospital Khon Kaen ...	Moo 1, Khok Sung Subdistrict, Ubolrat District...	12672.711110	Non Phayom	(16.643394, 102.818675)	16.68137	102.7066
376	Cafe' Amazon	Coffee Shop	458	16.341665	102.800517	Tha Phra Health Promoting Hospital Khon Kaen P...	Moo 10, Tha Phra Subdistrict, Mueang Khon Kaen...	595.330858	Tha Phra	(16.339858, 102.804867)	16.34496	102.8031
457	นมนัว	Coffee Shop	526	16.092231	102.623590	Rural hospital Khon Kaen Province	231 Moo 11, Rural Subdistrict, Rural District,...	11922.315010	Ban Phai	(16.059662, 102.725377)	16.09111	102.6188
469	Café Amazon (คาเฟ่ อเมซอน)	Coffee Shop	779	16.529749	102.829149	Samran Health Promoting Hospital Khon Kaen Pro...	Moo 1, Samran Subdistrict, Mueang Khon Kaen Di...	2569.122374	Samran	(16.51724, 102.842976)	16.53673	102.8299

	Name	Category	Dist_Venue	Lat_Venue	Long_Venue	Hospital	Location	Dist_sta	Station	Lat_Long_Sta	H_lat	H_long
192	เครื่องหวาน Thai Dessert Cafe in Sattahip	Pastry Shop	64	12.708670	100.888562	Tambon Tao Than Health Promoting Hospital Chon...	Moo 8, Sattahip Subdistrict, Sattahip District...	7626.636310	Kao Chi Chan Junction	(12.733525, 100.954502)	12.70842	100.8891
10	Café Amazon (คาเฟ่ อเมซอน)	Coffee Shop	66	13.351722	100.982093	Chonburi Hospital Chonburi Province	No. 69 Moo 2, Ban Suan Subdistrict, Mueang Cho...	1980.750677	Chon Buri	(13.3423, 100.998185)	13.35182	100.9827
280	บ้านช่อง Café	Café	131	12.600367	100.960151	Ban Chong Samae San Health Promoting Hospital ...	Moo 2, Samaesarn Subdistrict, Sattahip Distric...	11553.534829	Ban Plu Ta Luang	(12.704562, 100.972436)	12.60095	100.9591
220	Cafe Amazon อีเทค	Coffee Shop	219	13.415422	101.062670	Nong Tamlueng Health Promoting Hospital Chonbu...	Moo 2, Nong Tamlueng Subdistrict, Phanthong Di...	5846.253049	Phan Thong	(13.46293, 101.081229)	13.41359	101.0619
35	Eye's Cafe	Café	226	13.050408	101.065693	Ban Bo Win Health Promoting Hospital Chonburi ...	Moo 5, Bowin Subdistrict, Sriracha District, C...	13528.108673	Bang Lamung	(13.034486, 100.939951)	13.05049	101.0636
424	Liep-Ing Cafe	Café	235	13.162184	100.805138	Koh Sichang Hospital Chonburi Province	Moo 1, Tha Thewa Wong Subdistrict, Koh Si Chan...	12126.796441	Laem Chabang	(13.098904, 100.898474)	13.16239	100.8073
411	CHIC Café & Restaurant	Café	280	13.471496	101.097737	Phanthong Hospital Chonburi Province	No. 1/10 Moo 8, Tambol Phanthong, Amphur Phant...	1979.083185	Phan Thong	(13.46293, 101.081229)	13.46906	101.0984
379	BKNT Cafe	Coffee Shop	283	13.400895	101.189228	Thung Khwang Health Promoting Hospital Chonbur...	Moo 8, Thung Khwang Subdistrict, Phanat Nikhom...	13835.664313	Phan Thong	(13.46293, 101.081229)	13.39911	101.1911
405	Café Amazon (คาเฟ่อเมซอน)	Coffee Shop	287	13.117604	100.920637	Laem Chabang Hospital Chonburi Province	Thungsukla Subdistrict, Sriracha District, Cho...	2884.318049	Laem Chabang	(13.098904, 100.898474)	13.11595	100.9186
276	Café Amazon (คาเฟ่ อเมซอน)	Coffee Shop	329	12.759357	100.903125	Bang Saray Health Promoting Hospital Chonburi ...	Moo 8, Bang Saray Subdistrict, Sattahip Distri...	2999.212754	Suan Nong Nuch	(12.7747625, 100.923996)	12.76067	100.9004