Overview

Dataset statistics

Number of variables7
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.8 KiB
Average record size in memory59.3 B

Variable types

Text3
Categorical1
Numeric2
Boolean1

Alerts

ldgs_la is highly overall correlated with ldgs_lo and 1 other fieldsHigh correlation
ldgs_lo is highly overall correlated with ldgs_la and 1 other fieldsHigh correlation
ctprvn_eng_nm is highly overall correlated with ldgs_la and 1 other fieldsHigh correlation
ldgs_nm has unique valuesUnique
ldgs_la has unique valuesUnique
ldgs_lo has unique valuesUnique

Reproduction

Analysis started2023-12-10 09:54:40.360951
Analysis finished2023-12-10 09:54:42.342015
Duration1.98 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

ldgs_nm
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T18:54:42.900359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length41
Median length32
Mean length26.17
Min length5

Characters and Unicode

Total characters2617
Distinct characters56
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique100 ?
Unique (%)100.0%

Sample

1st rowThe Millennials Shibuya
2nd rowhostel den
3rd rowNOHGA HOTEL UENO TOKYO
4th rowCourtyard by Marriott Tokyo Ginza Hotel
5th rowComfort Inn Tokyo Roppongi
ValueCountFrequency (%)
hotel 57
 
14.9%
tokyo 22
 
5.8%
roynet 14
 
3.7%
daiwa 14
 
3.7%
comfort 13
 
3.4%
kyoto 9
 
2.4%
osaka 8
 
2.1%
naha 7
 
1.8%
sapporo 6
 
1.6%
by 6
 
1.6%
Other values (159) 226
59.2%
2023-12-10T18:54:43.889413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
282
 
10.8%
o 227
 
8.7%
a 200
 
7.6%
e 147
 
5.6%
t 140
 
5.3%
i 111
 
4.2%
n 97
 
3.7%
H 92
 
3.5%
O 90
 
3.4%
y 73
 
2.8%
Other values (46) 1158
44.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1553
59.3%
Uppercase Letter 762
29.1%
Space Separator 282
 
10.8%
Dash Punctuation 9
 
0.3%
Other Punctuation 6
 
0.2%
Decimal Number 5
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 227
14.6%
a 200
12.9%
e 147
9.5%
t 140
 
9.0%
i 111
 
7.1%
n 97
 
6.2%
y 73
 
4.7%
k 71
 
4.6%
s 70
 
4.5%
l 65
 
4.2%
Other values (15) 352
22.7%
Uppercase Letter
ValueCountFrequency (%)
H 92
12.1%
O 90
11.8%
A 69
 
9.1%
T 69
 
9.1%
S 55
 
7.2%
I 52
 
6.8%
N 43
 
5.6%
R 42
 
5.5%
K 32
 
4.2%
E 30
 
3.9%
Other values (14) 188
24.7%
Other Punctuation
ValueCountFrequency (%)
' 3
50.0%
, 2
33.3%
& 1
 
16.7%
Decimal Number
ValueCountFrequency (%)
5 4
80.0%
3 1
 
20.0%
Space Separator
ValueCountFrequency (%)
282
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2315
88.5%
Common 302
 
11.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 227
 
9.8%
a 200
 
8.6%
e 147
 
6.3%
t 140
 
6.0%
i 111
 
4.8%
n 97
 
4.2%
H 92
 
4.0%
O 90
 
3.9%
y 73
 
3.2%
k 71
 
3.1%
Other values (39) 1067
46.1%
Common
ValueCountFrequency (%)
282
93.4%
- 9
 
3.0%
5 4
 
1.3%
' 3
 
1.0%
, 2
 
0.7%
3 1
 
0.3%
& 1
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2617
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
282
 
10.8%
o 227
 
8.7%
a 200
 
7.6%
e 147
 
5.6%
t 140
 
5.3%
i 111
 
4.2%
n 97
 
3.7%
H 92
 
3.5%
O 90
 
3.4%
y 73
 
2.8%
Other values (46) 1158
44.2%

ctprvn_eng_nm
Categorical

HIGH CORRELATION 

Distinct18
Distinct (%)18.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
Tokyo
30 
Okinawa
12 
Osaka
12 
Kyoto
10 
Hokkaido
7 
Other values (13)
29 

Length

Max length9
Median length5
Mean length5.87
Min length4

Unique

Unique6 ?
Unique (%)6.0%

Sample

1st rowTokyo
2nd rowTokyo
3rd rowTokyo
4th rowTokyo
5th rowTokyo

Common Values

ValueCountFrequency (%)
Tokyo 30
30.0%
Okinawa 12
 
12.0%
Osaka 12
 
12.0%
Kyoto 10
 
10.0%
Hokkaido 7
 
7.0%
Aichi 5
 
5.0%
Fukuoka 4
 
4.0%
Kanagawa 4
 
4.0%
Ishikawa 3
 
3.0%
Hyogo 3
 
3.0%
Other values (8) 10
 
10.0%

Length

2023-12-10T18:54:44.280324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
tokyo 30
30.0%
okinawa 12
 
12.0%
osaka 12
 
12.0%
kyoto 10
 
10.0%
hokkaido 7
 
7.0%
aichi 5
 
5.0%
fukuoka 4
 
4.0%
kanagawa 4
 
4.0%
hyogo 3
 
3.0%
ishikawa 3
 
3.0%
Other values (8) 10
 
10.0%

ldgs_addr
Text

Distinct99
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T18:54:44.780862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length105
Median length72
Mean length62.01
Min length39

Characters and Unicode

Total characters6201
Distinct characters61
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique98 ?
Unique (%)98.0%

Sample

1st row〒150-0041 Jinnan 1-20-13 Shibuya Ward, Tokyo
2nd row4-13-8 Nihonbashi Honcho, Chuo Ward, Tokyo, 103-0023
3rd row2-11-10 Higashi Ueno, Taito Ward, Tokyo, 110-0015
4th row〒104-0061 Ginza 6-14-10 Chuo Ward, Tokyo
5th rowRoppongi 3-9-8 Minato Ward, Tokyo, 106-0032
ValueCountFrequency (%)
ward 72
 
8.9%
prefecture 62
 
7.6%
city 52
 
6.4%
tokyo 30
 
3.7%
osaka 23
 
2.8%
chuo 22
 
2.7%
kyoto 16
 
2.0%
okinawa 12
 
1.5%
minato 10
 
1.2%
naha 8
 
1.0%
Other values (378) 506
62.2%
2023-12-10T18:54:45.730596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
713
 
11.5%
a 488
 
7.9%
o 339
 
5.5%
i 315
 
5.1%
- 303
 
4.9%
0 300
 
4.8%
, 289
 
4.7%
r 255
 
4.1%
e 251
 
4.0%
1 216
 
3.5%
Other values (51) 2732
44.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3210
51.8%
Decimal Number 1064
 
17.2%
Space Separator 713
 
11.5%
Uppercase Letter 596
 
9.6%
Dash Punctuation 303
 
4.9%
Other Punctuation 292
 
4.7%
Other Symbol 23
 
0.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 488
15.2%
o 339
10.6%
i 315
9.8%
r 255
 
7.9%
e 251
 
7.8%
t 196
 
6.1%
k 178
 
5.5%
u 162
 
5.0%
h 158
 
4.9%
y 144
 
4.5%
Other values (14) 724
22.6%
Uppercase Letter
ValueCountFrequency (%)
C 87
14.6%
W 72
12.1%
P 65
10.9%
S 55
9.2%
K 48
8.1%
N 43
7.2%
T 42
7.0%
O 39
6.5%
M 35
5.9%
H 34
 
5.7%
Other values (11) 76
12.8%
Decimal Number
ValueCountFrequency (%)
0 300
28.2%
1 216
20.3%
2 122
11.5%
3 89
 
8.4%
4 73
 
6.9%
5 69
 
6.5%
6 67
 
6.3%
8 49
 
4.6%
9 43
 
4.0%
7 36
 
3.4%
Other Punctuation
ValueCountFrequency (%)
, 289
99.0%
. 2
 
0.7%
& 1
 
0.3%
Space Separator
ValueCountFrequency (%)
713
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 303
100.0%
Other Symbol
ValueCountFrequency (%)
〒 23
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3806
61.4%
Common 2395
38.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 488
 
12.8%
o 339
 
8.9%
i 315
 
8.3%
r 255
 
6.7%
e 251
 
6.6%
t 196
 
5.1%
k 178
 
4.7%
u 162
 
4.3%
h 158
 
4.2%
y 144
 
3.8%
Other values (35) 1320
34.7%
Common
ValueCountFrequency (%)
713
29.8%
- 303
12.7%
0 300
12.5%
, 289
12.1%
1 216
 
9.0%
2 122
 
5.1%
3 89
 
3.7%
4 73
 
3.0%
5 69
 
2.9%
6 67
 
2.8%
Other values (6) 154
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6178
99.6%
None 23
 
0.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
713
 
11.5%
a 488
 
7.9%
o 339
 
5.5%
i 315
 
5.1%
- 303
 
4.9%
0 300
 
4.9%
, 289
 
4.7%
r 255
 
4.1%
e 251
 
4.1%
1 216
 
3.5%
Other values (50) 2709
43.8%
None
ValueCountFrequency (%)
〒 23
100.0%

ldgs_la
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.690385
Minimum24.335732
Maximum43.062144
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:54:46.065536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum24.335732
5-th percentile26.213537
Q134.692641
median35.334924
Q335.687451
95-th percentile43.048183
Maximum43.062144
Range18.726411
Interquartile range (IQR)0.9948095

Descriptive statistics

Standard deviation3.8884531
Coefficient of variation (CV)0.11209023
Kurtosis1.8576382
Mean34.690385
Median Absolute Deviation (MAD)0.38436275
Skewness-0.68693136
Sum3469.0385
Variance15.120068
MonotonicityNot monotonic
2023-12-10T18:54:46.405166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.6622153 1
 
1.0%
36.5800473 1
 
1.0%
34.6841637 1
 
1.0%
43.0621438 1
 
1.0%
43.0577983 1
 
1.0%
35.7008451 1
 
1.0%
43.0558524 1
 
1.0%
35.6707458 1
 
1.0%
35.3888304 1
 
1.0%
34.6955586 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
24.3357324 1
1.0%
24.7905733 1
1.0%
26.2056526 1
1.0%
26.2117672 1
1.0%
26.2132974 1
1.0%
26.2135499 1
1.0%
26.2162192 1
1.0%
26.2189068 1
1.0%
26.2214184 1
1.0%
26.2256728 1
1.0%
ValueCountFrequency (%)
43.0621438 1
1.0%
43.0577983 1
1.0%
43.0558524 1
1.0%
43.0555461 1
1.0%
43.0529872 1
1.0%
43.0479306 1
1.0%
42.735919 1
1.0%
38.2629612 1
1.0%
38.2594593 1
1.0%
36.5800473 1
1.0%

ldgs_lo
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean136.48878
Minimum124.18581
Maximum141.35701
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:54:46.729472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum124.18581
5-th percentile127.67763
Q1135.49487
median136.90169
Q3139.75532
95-th percentile141.34045
Maximum141.35701
Range17.171203
Interquartile range (IQR)4.2604519

Descriptive statistics

Standard deviation4.3596288
Coefficient of variation (CV)0.031941298
Kurtosis0.29231191
Mean136.48878
Median Absolute Deviation (MAD)2.8366118
Skewness-1.122353
Sum13648.878
Variance19.006363
MonotonicityNot monotonic
2023-12-10T18:54:47.033435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
139.6996339 1
 
1.0%
136.6478362 1
 
1.0%
135.5029142 1
 
1.0%
141.3456279 1
 
1.0%
141.3507923 1
 
1.0%
139.7491321 1
 
1.0%
141.3570094 1
 
1.0%
139.762253 1
 
1.0%
136.9414315 1
 
1.0%
135.198133 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
124.1858066 1
1.0%
125.3054672 1
1.0%
127.6639388 1
1.0%
127.6697577 1
1.0%
127.6766983 1
1.0%
127.6776746 1
1.0%
127.6791045 1
1.0%
127.6792537 1
1.0%
127.6827032 1
1.0%
127.6844589 1
1.0%
ValueCountFrequency (%)
141.3570094 1
1.0%
141.3558441 1
1.0%
141.3507923 1
1.0%
141.3456279 1
1.0%
141.3427965 1
1.0%
141.3403276 1
1.0%
140.8781637 1
1.0%
140.8722154 1
1.0%
140.8470174 1
1.0%
139.9298329 1
1.0%

trnsport_info_cn
Text

Distinct85
Distinct (%)85.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T18:54:47.917912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length42
Median length30
Mean length19.79
Min length12

Characters and Unicode

Total characters1979
Distinct characters45
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique75 ?
Unique (%)75.0%

Sample

1st rowShibuya Station
2nd rowKodenmacho Station Hibiya Line
3rd rowJR Ueno Station
4th rowHigashi Ginza Station
5th rowRoppongi Station
ValueCountFrequency (%)
station 91
31.4%
jr 30
 
10.3%
line 13
 
4.5%
airport 7
 
2.4%
5
 
1.7%
naha 4
 
1.4%
ginza 4
 
1.4%
osaka 4
 
1.4%
kyoto 4
 
1.4%
shin 4
 
1.4%
Other values (95) 124
42.8%
2023-12-10T18:54:48.771191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 250
12.6%
i 223
11.3%
t 211
10.7%
190
 
9.6%
o 179
 
9.0%
n 156
 
7.9%
S 119
 
6.0%
h 59
 
3.0%
s 55
 
2.8%
u 49
 
2.5%
Other values (35) 488
24.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1469
74.2%
Uppercase Letter 315
 
15.9%
Space Separator 190
 
9.6%
Dash Punctuation 5
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 250
17.0%
i 223
15.2%
t 211
14.4%
o 179
12.2%
n 156
10.6%
h 59
 
4.0%
s 55
 
3.7%
u 49
 
3.3%
e 45
 
3.1%
k 42
 
2.9%
Other values (13) 200
13.6%
Uppercase Letter
ValueCountFrequency (%)
S 119
37.8%
R 32
 
10.2%
J 30
 
9.5%
K 18
 
5.7%
N 18
 
5.7%
A 15
 
4.8%
T 14
 
4.4%
L 13
 
4.1%
H 12
 
3.8%
M 11
 
3.5%
Other values (10) 33
 
10.5%
Space Separator
ValueCountFrequency (%)
190
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1784
90.1%
Common 195
 
9.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 250
14.0%
i 223
12.5%
t 211
11.8%
o 179
10.0%
n 156
 
8.7%
S 119
 
6.7%
h 59
 
3.3%
s 55
 
3.1%
u 49
 
2.7%
e 45
 
2.5%
Other values (33) 438
24.6%
Common
ValueCountFrequency (%)
190
97.4%
- 5
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1979
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 250
12.6%
i 223
11.3%
t 211
10.7%
190
 
9.6%
o 179
 
9.0%
n 156
 
7.9%
S 119
 
6.0%
h 59
 
3.0%
s 55
 
2.8%
u 49
 
2.5%
Other values (35) 488
24.7%

prkplce_exst_at
Boolean

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size232.0 B
True
71 
False
29 
ValueCountFrequency (%)
True 71
71.0%
False 29
29.0%
2023-12-10T18:54:49.079536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Interactions

2023-12-10T18:54:41.488469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:54:41.050375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:54:41.689885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:54:41.280632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T18:54:49.278562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ldgs_nmctprvn_eng_nmldgs_addrldgs_laldgs_lotrnsport_info_cnprkplce_exst_at
ldgs_nm1.0001.0001.0001.0001.0001.0001.000
ctprvn_eng_nm1.0001.0001.0000.9790.9761.0000.347
ldgs_addr1.0001.0001.0001.0001.0001.0001.000
ldgs_la1.0000.9791.0001.0000.9580.9750.256
ldgs_lo1.0000.9761.0000.9581.0000.9990.302
trnsport_info_cn1.0001.0001.0000.9750.9991.0000.908
prkplce_exst_at1.0000.3471.0000.2560.3020.9081.000
2023-12-10T18:54:49.698997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
prkplce_exst_atctprvn_eng_nm
prkplce_exst_at1.0000.247
ctprvn_eng_nm0.2471.000
2023-12-10T18:54:50.415694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ldgs_laldgs_loctprvn_eng_nmprkplce_exst_at
ldgs_la1.0000.8990.8590.185
ldgs_lo0.8991.0000.8440.219
ctprvn_eng_nm0.8590.8441.0000.247
prkplce_exst_at0.1850.2190.2471.000

Missing values

2023-12-10T18:54:41.940250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T18:54:42.228793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

ldgs_nmctprvn_eng_nmldgs_addrldgs_laldgs_lotrnsport_info_cnprkplce_exst_at
0The Millennials ShibuyaTokyo〒150-0041 Jinnan 1-20-13 Shibuya Ward, Tokyo35.662215139.699634Shibuya StationN
1hostel denTokyo4-13-8 Nihonbashi Honcho, Chuo Ward, Tokyo, 103-002335.690993139.77668Kodenmacho Station Hibiya LineN
2NOHGA HOTEL UENO TOKYOTokyo2-11-10 Higashi Ueno, Taito Ward, Tokyo, 110-001535.710167139.778188JR Ueno StationY
3Courtyard by Marriott Tokyo Ginza HotelTokyo〒104-0061 Ginza 6-14-10 Chuo Ward, Tokyo35.66839139.764968Higashi Ginza StationY
4Comfort Inn Tokyo RoppongiTokyoRoppongi 3-9-8 Minato Ward, Tokyo, 106-003235.663573139.733781Roppongi StationY
5HOTEL KAZUSAYATokyo4-7-15 Nihonbashi Honcho, Chuo Ward, Tokyo, 103-002335.6897139.774357Nihonbashi StationY
6OMO5 Kyoto Gion by Hoshino ResortsKyoto〒605-0073 288 north side of Gion-cho, Higashiyama Ward, Kyoto Prefecture35.004175135.776871Gion Shijo StationN
7Rusutsu Holiday ChaletHokkaido327-11 Toyooka, Suzutomura, Shibata-Gun, Hokkaido, 048-171242.735919140.847017Niseko StationY
8OMO3 Tokyo Akasaka by Hoshino ResortsTokyoAkasaka 4-3-2 Minato Ward, Tokyo, 107-005235.675031139.735953Akasaka Mitsuke StationN
9OMO5 Tokyo Otsuka by Hoshino ResortsTokyo2-26-1 Kitaotsuka, Toshima Ward, Tokyo, 170-000435.732965139.72922Otsuka StationN
ldgs_nmctprvn_eng_nmldgs_addrldgs_laldgs_lotrnsport_info_cnprkplce_exst_at
90The Tokyo Station HotelTokyo1-9-1 Marunouchi, Chiyoda Ward, Tokyo, 100-000535.67987139.767515JR Tokyo StationY
91Daiwa Roynet Hotel NISHI-SHINJUKUTokyo6-12-39 Nishi-Shinjuku, Shinjuku Ward, Tokyo, 160-002335.69434139.689529Nishi - Shinjuku StationY
92La'gent Hotel Tokyo BayChibaSunrise 5-7-1 Urayasu City, Chiba Prefecture, 279-001335.640813139.929833Shin - Urayasu StationY
93Comfort Hotel Sendai WestMiyagi3-5-11 Chuo Aoba Ward, Sendai City, Miyagi Prefecture, 980-002138.259459140.878164JR Sendai StationY
94Comfort Suites Tokyo BayChiba5-8-15 Myohai, Urayasu City, Chiba Prefecture, 279-001435.638518139.927359JR Shin - Urayasu StationY
95GRIDS PREMIUM HOTEL OSAKA NAMBAOsaka1-7-7 Namba, Naniwa Ward, Osaka City, Osaka Prefecture, 556-001134.664647135.498692Namba StationY
96Daiwa Roynet Hotel Okinawa KenchomaeOkinawa1-11-2 Izumizaki, Naha City, Okinawa Prefecture, 900-002126.213297127.676698Asahibashi StationY
97Richmond Hotel Tokyo SuidobashiTokyo1-33-9, Hongo, Bunkyo Ward, Tokyo, 113-003335.706152139.754942Kasuga StationY
98Comfort Hotel Ishigaki IslandOkinawa340 Shinae-ri, Ishigaki City, Okinawa Prefecture, 907-000224.335732124.185807Ishigaki AirportY
99ASTIL HOTEL SHINOSAKAOsaka3-3-7 Nishi-Sankoku, Yodogawa Ward, Osaka City, Osaka Prefecture, 532-000634.7385135.484515Hankyu Sankuni StationY