Overview

Dataset statistics

Number of variables8
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.7 KiB
Average record size in memory68.3 B

Variable types

Categorical3
Text2
Numeric3

Alerts

ldgs_nm is highly overall correlated with fclty_la and 2 other fieldsHigh correlation
ldgs_addr is highly overall correlated with fclty_la and 2 other fieldsHigh correlation
fclty_la is highly overall correlated with ldgs_nm and 1 other fieldsHigh correlation
fclty_lo is highly overall correlated with ldgs_nm and 1 other fieldsHigh correlation
sccnt_sm_value is highly overall correlated with cl_cnHigh correlation
cl_cn is highly overall correlated with sccnt_sm_valueHigh correlation

Reproduction

Analysis started2023-12-10 10:15:44.720222
Analysis finished2023-12-10 10:15:47.528820
Duration2.81 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

ldgs_nm
Categorical

HIGH CORRELATION 

Distinct11
Distinct (%)11.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
라마다 앙코르 바이 윈덤 부산역
18 
시애틀비호텔
12 
아르반시티 호텔
12 
더킹호텔
12 
마리안느호텔
9 
Other values (6)
37 

Length

Max length17
Median length13
Mean length9.6
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row라마다 앙코르 바이 윈덤 부산역
2nd row씨엘오션호텔
3rd row라마다 앙코르 바이 윈덤 부산역
4th row라마다 앙코르 바이 윈덤 부산역
5th row라마다 앙코르 바이 윈덤 부산역

Common Values

ValueCountFrequency (%)
라마다 앙코르 바이 윈덤 부산역 18
18.0%
시애틀비호텔 12
12.0%
아르반시티 호텔 12
12.0%
더킹호텔 12
12.0%
마리안느호텔 9
9.0%
골든튤립 해운대 호텔 스위트 8
8.0%
아바니 센트럴 부산 8
8.0%
다이아몬드호텔 8
8.0%
호텔아쿠아펠리스 주식회사 6
 
6.0%
유토피아관광호텔 4
 
4.0%

Length

2023-12-10T19:15:47.666837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
호텔 20
 
8.7%
라마다 18
 
7.8%
바이 18
 
7.8%
윈덤 18
 
7.8%
부산역 18
 
7.8%
앙코르 18
 
7.8%
시애틀비호텔 12
 
5.2%
아르반시티 12
 
5.2%
더킹호텔 12
 
5.2%
마리안느호텔 9
 
3.9%
Other values (11) 75
32.6%

ldgs_addr
Categorical

HIGH CORRELATION 

Distinct11
Distinct (%)11.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
부산 동구 초량동 1204-1
18 
부산 연제구 연산동 1255-4
12 
부산 연제구 연산동 603-10
12 
부산 연제구 연산동 417-3
12 
부산 해운대구 중동 1400-24
9 
Other values (6)
37 

Length

Max length18
Median length17
Mean length16.65
Min length15

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row부산 동구 초량동 1204-1
2nd row부산 강서구 명지동 3592-9
3rd row부산 동구 초량동 1204-1
4th row부산 동구 초량동 1204-1
5th row부산 동구 초량동 1204-1

Common Values

ValueCountFrequency (%)
부산 동구 초량동 1204-1 18
18.0%
부산 연제구 연산동 1255-4 12
12.0%
부산 연제구 연산동 603-10 12
12.0%
부산 연제구 연산동 417-3 12
12.0%
부산 해운대구 중동 1400-24 9
9.0%
부산 해운대구 중동 1153-8 8
8.0%
부산 남구 문현동 1227-2 8
8.0%
부산 연제구 연산5동 1127-1 8
8.0%
부산 수영구 광안동 192-5 6
 
6.0%
부산 수영구 광안동 50-3 4
 
4.0%

Length

2023-12-10T19:15:47.891635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
부산 100
25.0%
연제구 44
11.0%
연산동 36
 
9.0%
초량동 18
 
4.5%
1204-1 18
 
4.5%
동구 18
 
4.5%
해운대구 17
 
4.2%
중동 17
 
4.2%
1255-4 12
 
3.0%
603-10 12
 
3.0%
Other values (15) 108
27.0%

fclty_nm
Text

Distinct63
Distinct (%)63.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:15:48.284814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length12
Mean length6.34
Min length3

Characters and Unicode

Total characters634
Distinct characters154
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique39 ?
Unique (%)39.0%

Sample

1st row부산항국제여객터미널
2nd row명지동근린공원축구장
3rd row부산과학체험관
4th row부산역
5th row부산항제4부두
ValueCountFrequency (%)
연산시장 4
 
4.0%
꼬마대통령연산토곡점 3
 
3.0%
황정사 3
 
3.0%
연봉공원 3
 
3.0%
연동시장 3
 
3.0%
연일시장 3
 
3.0%
샤르망라이프 3
 
3.0%
진여원부산정사 3
 
3.0%
혜원정사 3
 
3.0%
생활선원 3
 
3.0%
Other values (53) 69
69.0%
2023-12-10T19:15:48.895307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
산 30
 
4.7%
장 27
 
4.3%
시 23
 
3.6%
연 21
 
3.3%
부 21
 
3.3%
대 18
 
2.8%
사 17
 
2.7%
해 17
 
2.7%
점 17
 
2.7%
정 16
 
2.5%
Other values (144) 427
67.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 621
97.9%
Uppercase Letter 10
 
1.6%
Open Punctuation 1
 
0.2%
Decimal Number 1
 
0.2%
Close Punctuation 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 30
 
4.8%
장 27
 
4.3%
시 23
 
3.7%
연 21
 
3.4%
부 21
 
3.4%
대 18
 
2.9%
사 17
 
2.7%
해 17
 
2.7%
점 17
 
2.7%
정 16
 
2.6%
Other values (139) 414
66.7%
Uppercase Letter
ValueCountFrequency (%)
C 8
80.0%
N 2
 
20.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Decimal Number
ValueCountFrequency (%)
4 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 621
97.9%
Latin 10
 
1.6%
Common 3
 
0.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 30
 
4.8%
장 27
 
4.3%
시 23
 
3.7%
연 21
 
3.4%
부 21
 
3.4%
대 18
 
2.9%
사 17
 
2.7%
해 17
 
2.7%
점 17
 
2.7%
정 16
 
2.6%
Other values (139) 414
66.7%
Common
ValueCountFrequency (%)
( 1
33.3%
4 1
33.3%
) 1
33.3%
Latin
ValueCountFrequency (%)
C 8
80.0%
N 2
 
20.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 621
97.9%
ASCII 13
 
2.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
산 30
 
4.8%
장 27
 
4.3%
시 23
 
3.7%
연 21
 
3.4%
부 21
 
3.4%
대 18
 
2.9%
사 17
 
2.7%
해 17
 
2.7%
점 17
 
2.7%
정 16
 
2.6%
Other values (139) 414
66.7%
ASCII
ValueCountFrequency (%)
C 8
61.5%
N 2
 
15.4%
( 1
 
7.7%
4 1
 
7.7%
) 1
 
7.7%

fclty_addr
Text

Distinct60
Distinct (%)60.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:15:49.295085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length20
Mean length17.79
Min length14

Characters and Unicode

Total characters1779
Distinct characters84
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique35 ?
Unique (%)35.0%

Sample

1st row부산 동구 충장대로 206-0
2nd row부산 강서구 명지오션시티10로 80-0
3rd row부산 동구 중앙대로260번길 11-0
4th row부산 동구 중앙대로 206-0
5th row부산 동구 충장대로 206-0
ValueCountFrequency (%)
부산 98
24.5%
연제구 42
 
10.5%
동구 22
 
5.5%
해운대구 17
 
4.2%
수영구 10
 
2.5%
반송로 9
 
2.2%
24-0 7
 
1.8%
47-0 7
 
1.8%
8-0 5
 
1.2%
16-0 5
 
1.2%
Other values (98) 178
44.5%
2023-12-10T19:15:49.901806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
300
16.9%
0 119
 
6.7%
산 105
 
5.9%
부 102
 
5.7%
구 101
 
5.7%
- 98
 
5.5%
로 93
 
5.2%
1 62
 
3.5%
길 51
 
2.9%
2 48
 
2.7%
Other values (74) 700
39.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 958
53.9%
Decimal Number 423
23.8%
Space Separator 300
 
16.9%
Dash Punctuation 98
 
5.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 105
 
11.0%
부 102
 
10.6%
구 101
 
10.5%
로 93
 
9.7%
길 51
 
5.3%
연 46
 
4.8%
제 45
 
4.7%
번 44
 
4.6%
대 38
 
4.0%
동 33
 
3.4%
Other values (62) 300
31.3%
Decimal Number
ValueCountFrequency (%)
0 119
28.1%
1 62
14.7%
2 48
11.3%
3 43
 
10.2%
4 38
 
9.0%
6 33
 
7.8%
8 32
 
7.6%
7 19
 
4.5%
5 17
 
4.0%
9 12
 
2.8%
Space Separator
ValueCountFrequency (%)
300
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 98
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 958
53.9%
Common 821
46.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 105
 
11.0%
부 102
 
10.6%
구 101
 
10.5%
로 93
 
9.7%
길 51
 
5.3%
연 46
 
4.8%
제 45
 
4.7%
번 44
 
4.6%
대 38
 
4.0%
동 33
 
3.4%
Other values (62) 300
31.3%
Common
ValueCountFrequency (%)
300
36.5%
0 119
 
14.5%
- 98
 
11.9%
1 62
 
7.6%
2 48
 
5.8%
3 43
 
5.2%
4 38
 
4.6%
6 33
 
4.0%
8 32
 
3.9%
7 19
 
2.3%
Other values (2) 29
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 958
53.9%
ASCII 821
46.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
300
36.5%
0 119
 
14.5%
- 98
 
11.9%
1 62
 
7.6%
2 48
 
5.8%
3 43
 
5.2%
4 38
 
4.6%
6 33
 
4.0%
8 32
 
3.9%
7 19
 
2.3%
Other values (2) 29
 
3.5%
Hangul
ValueCountFrequency (%)
산 105
 
11.0%
부 102
 
10.6%
구 101
 
10.5%
로 93
 
9.7%
길 51
 
5.3%
연 46
 
4.8%
제 45
 
4.7%
번 44
 
4.6%
대 38
 
4.0%
동 33
 
3.4%
Other values (62) 300
31.3%

fclty_la
Real number (ℝ)

HIGH CORRELATION 

Distinct60
Distinct (%)60.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.160922
Minimum35.086179
Maximum35.190996
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:15:50.099144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.086179
5-th percentile35.117604
Q135.138959
median35.165125
Q335.185574
95-th percentile35.189619
Maximum35.190996
Range0.1048174
Interquartile range (IQR)0.046615375

Descriptive statistics

Standard deviation0.027481526
Coefficient of variation (CV)0.00078159289
Kurtosis-0.3724649
Mean35.160922
Median Absolute Deviation (MAD)0.0209524
Skewness-0.79934064
Sum3516.0922
Variance0.00075523428
MonotonicityNot monotonic
2023-12-10T19:15:50.286226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.1757979 4
 
4.0%
35.117747 3
 
3.0%
35.1894142 3
 
3.0%
35.1888389 3
 
3.0%
35.1854066 3
 
3.0%
35.1841224 3
 
3.0%
35.1883317 3
 
3.0%
35.1896192 3
 
3.0%
35.1881534 3
 
3.0%
35.1909965 3
 
3.0%
Other values (50) 69
69.0%
ValueCountFrequency (%)
35.0861791 1
 
1.0%
35.0897282 1
 
1.0%
35.0936865 1
 
1.0%
35.1151467 1
 
1.0%
35.116532 1
 
1.0%
35.11766 1
 
1.0%
35.1177214 1
 
1.0%
35.117747 3
3.0%
35.1180615 1
 
1.0%
35.1186379 1
 
1.0%
ValueCountFrequency (%)
35.1909965 3
3.0%
35.1896192 3
3.0%
35.1894142 3
3.0%
35.1888389 3
3.0%
35.1883317 3
3.0%
35.1881534 3
3.0%
35.1877482 1
 
1.0%
35.1868935 3
3.0%
35.1860776 3
3.0%
35.1854066 3
3.0%

fclty_lo
Real number (ℝ)

HIGH CORRELATION 

Distinct60
Distinct (%)60.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean129.08921
Minimum128.90016
Maximum129.18251
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:15:50.455032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum128.90016
5-th percentile129.03694
Q1129.06543
median129.0856
Q3129.10638
95-th percentile129.17301
Maximum129.18251
Range0.2823496
Interquartile range (IQR)0.0409494

Descriptive statistics

Standard deviation0.052330816
Coefficient of variation (CV)0.0004053849
Kurtosis3.3082331
Mean129.08921
Median Absolute Deviation (MAD)0.02059965
Skewness-0.82476273
Sum12908.921
Variance0.0027385143
MonotonicityNot monotonic
2023-12-10T19:15:50.652360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
129.0855995 4
 
4.0%
129.0491458 3
 
3.0%
129.0852796 3
 
3.0%
129.0925222 3
 
3.0%
129.0847479 3
 
3.0%
129.0905991 3
 
3.0%
129.0840961 3
 
3.0%
129.0933227 3
 
3.0%
129.099064 3
 
3.0%
129.0893346 3
 
3.0%
Other values (50) 69
69.0%
ValueCountFrequency (%)
128.9001625 1
1.0%
128.9047415 1
1.0%
128.9053736 1
1.0%
129.0338325 1
1.0%
129.036238 1
1.0%
129.0369721 1
1.0%
129.0378543 1
1.0%
129.0394779 1
1.0%
129.0400003 1
1.0%
129.0400111 1
1.0%
ValueCountFrequency (%)
129.1825121 1
 
1.0%
129.1770418 2
2.0%
129.1766569 2
2.0%
129.1728231 2
2.0%
129.1706545 2
2.0%
129.1696297 2
2.0%
129.1683765 2
2.0%
129.1673813 2
2.0%
129.164556 2
2.0%
129.1185235 3
3.0%

cl_cn
Categorical

HIGH CORRELATION 

Distinct19
Distinct (%)19.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
종교성지
21 
시장
20 
대형마트
10 
테마공원
6 
교통시설
5 
Other values (14)
38 

Length

Max length11
Median length4
Mean length4.08
Min length2

Unique

Unique3 ?
Unique (%)3.0%

Sample

1st row교통시설
2nd row레저스포츠시설
3rd row전시시설
4th row교통시설
5th row자연경관(하천/해양)

Common Values

ValueCountFrequency (%)
종교성지 21
21.0%
시장 20
20.0%
대형마트 10
10.0%
테마공원 6
 
6.0%
교통시설 5
 
5.0%
기타관광 5
 
5.0%
전시시설 4
 
4.0%
육상레저스포츠 4
 
4.0%
공연시설 4
 
4.0%
자연경관(하천/해양) 3
 
3.0%
Other values (9) 18
18.0%

Length

2023-12-10T19:15:50.866518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
종교성지 21
21.0%
시장 20
20.0%
대형마트 10
10.0%
테마공원 6
 
6.0%
교통시설 5
 
5.0%
기타관광 5
 
5.0%
전시시설 4
 
4.0%
육상레저스포츠 4
 
4.0%
공연시설 4
 
4.0%
쇼핑몰 3
 
3.0%
Other values (9) 18
18.0%

sccnt_sm_value
Real number (ℝ)

HIGH CORRELATION 

Distinct62
Distinct (%)62.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4688.42
Minimum8
Maximum80601
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:15:51.021128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile11
Q1118.25
median538
Q32743
95-th percentile22984.65
Maximum80601
Range80593
Interquartile range (IQR)2624.75

Descriptive statistics

Standard deviation12483.428
Coefficient of variation (CV)2.6626086
Kurtosis26.665486
Mean4688.42
Median Absolute Deviation (MAD)517
Skewness4.8521167
Sum468842
Variance1.5583596 × 108
MonotonicityNot monotonic
2023-12-10T19:15:51.174630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
300 4
 
4.0%
1938 3
 
3.0%
11 3
 
3.0%
10532 3
 
3.0%
2878 3
 
3.0%
1239 3
 
3.0%
538 3
 
3.0%
68 3
 
3.0%
25 3
 
3.0%
21 3
 
3.0%
Other values (52) 69
69.0%
ValueCountFrequency (%)
8 1
 
1.0%
10 3
3.0%
11 3
3.0%
16 1
 
1.0%
21 3
3.0%
25 3
3.0%
28 1
 
1.0%
31 1
 
1.0%
37 1
 
1.0%
42 1
 
1.0%
ValueCountFrequency (%)
80601 2
2.0%
26086 2
2.0%
24840 1
 
1.0%
22887 2
2.0%
21361 1
 
1.0%
12684 1
 
1.0%
11814 2
2.0%
10894 1
 
1.0%
10532 3
3.0%
6674 2
2.0%

Interactions

2023-12-10T19:15:46.716513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:15:45.464072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:15:45.888041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:15:46.870541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:15:45.599205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:15:46.363494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:15:47.021924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:15:45.729365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:15:46.542141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:15:51.289453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ldgs_nmldgs_addrfclty_nmfclty_addrfclty_lafclty_locl_cnsccnt_sm_value
ldgs_nm1.0001.0000.0000.4990.8740.9430.4720.197
ldgs_addr1.0001.0000.0000.4990.8740.9430.4720.197
fclty_nm0.0000.0001.0001.0001.0001.0001.0001.000
fclty_addr0.4990.4991.0001.0001.0001.0000.9960.983
fclty_la0.8740.8741.0001.0001.0000.9200.7640.428
fclty_lo0.9430.9431.0001.0000.9201.0000.7870.258
cl_cn0.4720.4721.0000.9960.7640.7871.0000.813
sccnt_sm_value0.1970.1971.0000.9830.4280.2580.8131.000
2023-12-10T19:15:51.405318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ldgs_nmldgs_addrcl_cn
ldgs_nm1.0001.0000.179
ldgs_addr1.0001.0000.179
cl_cn0.1790.1791.000
2023-12-10T19:15:51.498750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
fclty_lafclty_losccnt_sm_valueldgs_nmldgs_addrcl_cn
fclty_la1.0000.414-0.0720.6450.6450.402
fclty_lo0.4141.0000.3630.8210.8210.438
sccnt_sm_value-0.0720.3631.0000.1010.1010.528
ldgs_nm0.6450.8210.1011.0001.0000.179
ldgs_addr0.6450.8210.1011.0001.0000.179
cl_cn0.4020.4380.5280.1790.1791.000

Missing values

2023-12-10T19:15:47.225840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:15:47.444629image/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_nmldgs_addrfclty_nmfclty_addrfclty_lafclty_locl_cnsccnt_sm_value
0라마다 앙코르 바이 윈덤 부산역부산 동구 초량동 1204-1부산항국제여객터미널부산 동구 충장대로 206-035.117747129.049146교통시설10894
1씨엘오션호텔부산 강서구 명지동 3592-9명지동근린공원축구장부산 강서구 명지오션시티10로 80-035.086179128.904742레저스포츠시설854
2라마다 앙코르 바이 윈덤 부산역부산 동구 초량동 1204-1부산과학체험관부산 동구 중앙대로260번길 11-035.120272129.044133전시시설1955
3라마다 앙코르 바이 윈덤 부산역부산 동구 초량동 1204-1부산역부산 동구 중앙대로 206-035.117721129.044988교통시설1759
4라마다 앙코르 바이 윈덤 부산역부산 동구 초량동 1204-1부산항제4부두부산 동구 충장대로 206-035.117747129.049146자연경관(하천/해양)1451
5라마다 앙코르 바이 윈덤 부산역부산 동구 초량동 1204-1초량시장부산 동구 초량로13번길 8-035.118638129.040011시장596
6라마다 앙코르 바이 윈덤 부산역부산 동구 초량동 1204-1정란각부산 동구 홍곡로 75-035.125549129.042638역사유적지485
7씨엘오션호텔부산 강서구 명지동 3592-9몽키몽키롤러장부산 강서구 명지국제1로 56-135.093686128.905374육상레저스포츠573
8라마다 앙코르 바이 윈덤 부산역부산 동구 초량동 1204-1부산유라시아플랫폼부산 동구 중앙대로 210-035.115147129.040835기타관광295
9라마다 앙코르 바이 윈덤 부산역부산 동구 초량동 1204-1소림사부산 동구 초량상로65번길 7-035.11766129.036972종교성지217
ldgs_nmldgs_addrfclty_nmfclty_addrfclty_lafclty_locl_cnsccnt_sm_value
90호텔아쿠아펠리스 주식회사부산 수영구 광안동 192-5광안리해수욕장부산 수영구 광안해변로 219-035.153787129.118524자연경관(하천/해양)80601
91호텔아쿠아펠리스 주식회사부산 수영구 광안동 192-5광안종합시장부산 수영구 무학로49번길 71-035.164238129.118171시장562
92호텔아쿠아펠리스 주식회사부산 수영구 광안동 192-5금련사부산 수영구 광일로 71-035.162649129.106384종교성지330
93호텔아쿠아펠리스 주식회사부산 수영구 광안동 192-5광안시장부산 수영구 수영로603번길 18-035.160136129.1125시장297
94호텔아쿠아펠리스 주식회사부산 수영구 광안동 192-5수영구생활문화센터부산 수영구 광안해변로 219-035.153787129.118524기타문화관광지163
95호텔아쿠아펠리스 주식회사부산 수영구 광안동 192-5부산전통예술관부산 수영구 수영로521번길 63-035.152632129.105767전시시설113
96유토피아관광호텔부산 수영구 광안동 50-3광안리해수욕장부산 수영구 광안해변로 219-035.153787129.118524자연경관(하천/해양)80601
97유토피아관광호텔부산 수영구 광안동 50-3광안종합시장부산 수영구 무학로49번길 71-035.164238129.118171시장562
98유토피아관광호텔부산 수영구 광안동 50-3금련사부산 수영구 광일로 71-035.162649129.106384종교성지330
99유토피아관광호텔부산 수영구 광안동 50-3광안시장부산 수영구 수영로603번길 18-035.160136129.1125시장297