Overview

Dataset statistics

Number of variables7
Number of observations47
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.7 KiB
Average record size in memory59.8 B

Variable types

Numeric1
Categorical3
Text3

Dataset

Description부산광역시수영구_그늘막현황_20221221
Author부산광역시 수영구
URLhttp://data.busan.go.kr/dataSet/detail.nm?contentId=10&publicdatapk=15088918

Alerts

연번 is highly overall correlated with 행정동 구분High correlation
행정동 구분 is highly overall correlated with 연번High correlation
설치일시 is highly overall correlated with 그늘막 유형High correlation
그늘막 유형 is highly overall correlated with 설치일시High correlation
그늘막 유형 is highly imbalanced (51.1%)Imbalance
연번 has unique valuesUnique
관리번호 has unique valuesUnique
설치장소 has unique valuesUnique

Reproduction

Analysis started2023-12-10 17:28:23.405565
Analysis finished2023-12-10 17:28:25.850449
Duration2.44 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct47
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24
Minimum1
Maximum47
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size555.0 B
2023-12-11T02:28:26.062200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.3
Q112.5
median24
Q335.5
95-th percentile44.7
Maximum47
Range46
Interquartile range (IQR)23

Descriptive statistics

Standard deviation13.711309
Coefficient of variation (CV)0.57130455
Kurtosis-1.2
Mean24
Median Absolute Deviation (MAD)12
Skewness0
Sum1128
Variance188
MonotonicityStrictly increasing
2023-12-11T02:28:26.452332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
1 1
 
2.1%
2 1
 
2.1%
27 1
 
2.1%
28 1
 
2.1%
29 1
 
2.1%
30 1
 
2.1%
31 1
 
2.1%
32 1
 
2.1%
33 1
 
2.1%
34 1
 
2.1%
Other values (37) 37
78.7%
ValueCountFrequency (%)
1 1
2.1%
2 1
2.1%
3 1
2.1%
4 1
2.1%
5 1
2.1%
6 1
2.1%
7 1
2.1%
8 1
2.1%
9 1
2.1%
10 1
2.1%
ValueCountFrequency (%)
47 1
2.1%
46 1
2.1%
45 1
2.1%
44 1
2.1%
43 1
2.1%
42 1
2.1%
41 1
2.1%
40 1
2.1%
39 1
2.1%
38 1
2.1%

행정동 구분
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)21.3%
Missing0
Missing (%)0.0%
Memory size508.0 B
광안제2동
10 
민락동
8 
남천제1동
7 
수영동
7 
망미제1동
4 
Other values (5)
11 

Length

Max length5
Median length5
Mean length4.3617021
Min length3

Unique

Unique2 ?
Unique (%)4.3%

Sample

1st row남천제1동
2nd row남천제1동
3rd row남천제1동
4th row남천제1동
5th row남천제1동

Common Values

ValueCountFrequency (%)
광안제2동 10
21.3%
민락동 8
17.0%
남천제1동 7
14.9%
수영동 7
14.9%
망미제1동 4
 
8.5%
망미제2동 4
 
8.5%
광안제1동 3
 
6.4%
남천제2동 2
 
4.3%
광안제3동 1
 
2.1%
광안제4동 1
 
2.1%

Length

2023-12-11T02:28:26.853138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T02:28:27.240177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
광안제2동 10
21.3%
민락동 8
17.0%
남천제1동 7
14.9%
수영동 7
14.9%
망미제1동 4
 
8.5%
망미제2동 4
 
8.5%
광안제1동 3
 
6.4%
남천제2동 2
 
4.3%
광안제3동 1
 
2.1%
광안제4동 1
 
2.1%

관리번호
Text

UNIQUE 

Distinct47
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size508.0 B
2023-12-11T02:28:27.870995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length6
Mean length6.0212766
Min length5

Characters and Unicode

Total characters283
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique47 ?
Unique (%)100.0%

Sample

1st row수영구-1
2nd row수영구-2
3rd row수영구-3
4th row수영구-4
5th row수영구-32
ValueCountFrequency (%)
수영구-1 1
 
2.1%
수영구-15 1
 
2.1%
수영구-스마트3 1
 
2.1%
수영구-17 1
 
2.1%
수영구-18 1
 
2.1%
수영구-19 1
 
2.1%
수영구-20 1
 
2.1%
수영구-21 1
 
2.1%
수영구-22 1
 
2.1%
수영구-23 1
 
2.1%
Other values (37) 37
78.7%
2023-12-11T02:28:28.741197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
수 47
16.6%
영 47
16.6%
구 47
16.6%
- 47
16.6%
2 16
 
5.7%
1 16
 
5.7%
3 15
 
5.3%
4 8
 
2.8%
스 5
 
1.8%
마 5
 
1.8%
Other values (7) 30
10.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 156
55.1%
Decimal Number 80
28.3%
Dash Punctuation 47
 
16.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 16
20.0%
1 16
20.0%
3 15
18.8%
4 8
10.0%
5 5
 
6.2%
0 4
 
5.0%
8 4
 
5.0%
6 4
 
5.0%
7 4
 
5.0%
9 4
 
5.0%
Other Letter
ValueCountFrequency (%)
수 47
30.1%
영 47
30.1%
구 47
30.1%
스 5
 
3.2%
마 5
 
3.2%
트 5
 
3.2%
Dash Punctuation
ValueCountFrequency (%)
- 47
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 156
55.1%
Common 127
44.9%

Most frequent character per script

Common
ValueCountFrequency (%)
- 47
37.0%
2 16
 
12.6%
1 16
 
12.6%
3 15
 
11.8%
4 8
 
6.3%
5 5
 
3.9%
0 4
 
3.1%
8 4
 
3.1%
6 4
 
3.1%
7 4
 
3.1%
Hangul
ValueCountFrequency (%)
수 47
30.1%
영 47
30.1%
구 47
30.1%
스 5
 
3.2%
마 5
 
3.2%
트 5
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 156
55.1%
ASCII 127
44.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
수 47
30.1%
영 47
30.1%
구 47
30.1%
스 5
 
3.2%
마 5
 
3.2%
트 5
 
3.2%
ASCII
ValueCountFrequency (%)
- 47
37.0%
2 16
 
12.6%
1 16
 
12.6%
3 15
 
11.8%
4 8
 
6.3%
5 5
 
3.9%
0 4
 
3.1%
8 4
 
3.1%
6 4
 
3.1%
7 4
 
3.1%

설치장소
Text

UNIQUE 

Distinct47
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size508.0 B
2023-12-11T02:28:29.254043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length15
Mean length10.212766
Min length5

Characters and Unicode

Total characters480
Distinct characters158
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique47 ?
Unique (%)100.0%

Sample

1st rowKBS 삼거리 앞
2nd row사단법인 인구보건복지협회 앞
3rd row대남교차로
4th row남천마리나 앞
5th row남천역 4번출구 앞
ValueCountFrequency (%)
앞 37
31.4%
횡단보도 5
 
4.2%
103동 3
 
2.5%
맞은편 2
 
1.7%
코스트코 2
 
1.7%
센텀비스타동원2차 1
 
0.8%
출구 1
 
0.8%
미니스톱 1
 
0.8%
대우아이빌 1
 
0.8%
비치비키니 1
 
0.8%
Other values (64) 64
54.2%
2023-12-11T02:28:30.170386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
72
 
15.0%
앞 37
 
7.7%
스 13
 
2.7%
1 10
 
2.1%
동 9
 
1.9%
영 8
 
1.7%
수 8
 
1.7%
트 8
 
1.7%
구 8
 
1.7%
파 7
 
1.5%
Other values (148) 300
62.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 373
77.7%
Space Separator 72
 
15.0%
Decimal Number 24
 
5.0%
Uppercase Letter 9
 
1.9%
Dash Punctuation 1
 
0.2%
Lowercase Letter 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
앞 37
 
9.9%
스 13
 
3.5%
동 9
 
2.4%
영 8
 
2.1%
수 8
 
2.1%
트 8
 
2.1%
구 8
 
2.1%
파 7
 
1.9%
단 6
 
1.6%
보 6
 
1.6%
Other values (133) 263
70.5%
Uppercase Letter
ValueCountFrequency (%)
K 2
22.2%
S 2
22.2%
T 1
11.1%
I 1
11.1%
B 1
11.1%
L 1
11.1%
G 1
11.1%
Decimal Number
ValueCountFrequency (%)
1 10
41.7%
0 6
25.0%
2 3
 
12.5%
3 3
 
12.5%
4 2
 
8.3%
Space Separator
ValueCountFrequency (%)
72
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 373
77.7%
Common 97
 
20.2%
Latin 10
 
2.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
앞 37
 
9.9%
스 13
 
3.5%
동 9
 
2.4%
영 8
 
2.1%
수 8
 
2.1%
트 8
 
2.1%
구 8
 
2.1%
파 7
 
1.9%
단 6
 
1.6%
보 6
 
1.6%
Other values (133) 263
70.5%
Latin
ValueCountFrequency (%)
K 2
20.0%
S 2
20.0%
T 1
10.0%
I 1
10.0%
B 1
10.0%
e 1
10.0%
L 1
10.0%
G 1
10.0%
Common
ValueCountFrequency (%)
72
74.2%
1 10
 
10.3%
0 6
 
6.2%
2 3
 
3.1%
3 3
 
3.1%
4 2
 
2.1%
- 1
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 373
77.7%
ASCII 107
 
22.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
72
67.3%
1 10
 
9.3%
0 6
 
5.6%
2 3
 
2.8%
3 3
 
2.8%
K 2
 
1.9%
4 2
 
1.9%
S 2
 
1.9%
T 1
 
0.9%
I 1
 
0.9%
Other values (5) 5
 
4.7%
Hangul
ValueCountFrequency (%)
앞 37
 
9.9%
스 13
 
3.5%
동 9
 
2.4%
영 8
 
2.1%
수 8
 
2.1%
트 8
 
2.1%
구 8
 
2.1%
파 7
 
1.9%
단 6
 
1.6%
보 6
 
1.6%
Other values (133) 263
70.5%

설치위치
Text

Distinct43
Distinct (%)91.5%
Missing0
Missing (%)0.0%
Memory size508.0 B
2023-12-11T02:28:30.815646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length41
Median length40
Mean length26.595745
Min length11

Characters and Unicode

Total characters1250
Distinct characters77
Distinct categories8 ?
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 (%)83.0%

Sample

1st row부산광역시 수영구 광남로10번길 2 (남천동)
2nd row부산광역시 수영구 수영로 425 (남천동)
3rd row부산광역시 수영구 수영로 371 (남천동)
4th row부산광역시 수영구 광안해변로 44 (남천동)
5th row부산광역시 수영구 수영로 381 (남천동)
ValueCountFrequency (%)
수영구 45
18.5%
부산광역시 44
18.1%
수영로 15
 
6.2%
광안동 15
 
6.2%
광안해변로 14
 
5.8%
수영동 8
 
3.3%
남천동 8
 
3.3%
망미동 6
 
2.5%
민락동 6
 
2.5%
연수로 5
 
2.1%
Other values (65) 77
31.7%
2023-12-11T02:28:31.855117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
240
19.2%
수 80
 
6.4%
광 78
 
6.2%
영 75
 
6.0%
부 47
 
3.8%
구 47
 
3.8%
산 47
 
3.8%
로 47
 
3.8%
역 45
 
3.6%
동 44
 
3.5%
Other values (67) 500
40.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 753
60.2%
Space Separator 240
 
19.2%
Decimal Number 158
 
12.6%
Open Punctuation 43
 
3.4%
Close Punctuation 43
 
3.4%
Other Punctuation 9
 
0.7%
Dash Punctuation 3
 
0.2%
Lowercase Letter 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
수 80
10.6%
광 78
 
10.4%
영 75
 
10.0%
부 47
 
6.2%
구 47
 
6.2%
산 47
 
6.2%
로 47
 
6.2%
역 45
 
6.0%
동 44
 
5.8%
시 44
 
5.8%
Other values (51) 199
26.4%
Decimal Number
ValueCountFrequency (%)
1 30
19.0%
2 24
15.2%
7 21
13.3%
4 17
10.8%
3 16
10.1%
5 15
9.5%
0 11
 
7.0%
6 11
 
7.0%
9 8
 
5.1%
8 5
 
3.2%
Space Separator
ValueCountFrequency (%)
240
100.0%
Open Punctuation
ValueCountFrequency (%)
( 43
100.0%
Close Punctuation
ValueCountFrequency (%)
) 43
100.0%
Other Punctuation
ValueCountFrequency (%)
, 9
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 753
60.2%
Common 496
39.7%
Latin 1
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
수 80
10.6%
광 78
 
10.4%
영 75
 
10.0%
부 47
 
6.2%
구 47
 
6.2%
산 47
 
6.2%
로 47
 
6.2%
역 45
 
6.0%
동 44
 
5.8%
시 44
 
5.8%
Other values (51) 199
26.4%
Common
ValueCountFrequency (%)
240
48.4%
( 43
 
8.7%
) 43
 
8.7%
1 30
 
6.0%
2 24
 
4.8%
7 21
 
4.2%
4 17
 
3.4%
3 16
 
3.2%
5 15
 
3.0%
0 11
 
2.2%
Other values (5) 36
 
7.3%
Latin
ValueCountFrequency (%)
e 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 753
60.2%
ASCII 497
39.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
240
48.3%
( 43
 
8.7%
) 43
 
8.7%
1 30
 
6.0%
2 24
 
4.8%
7 21
 
4.2%
4 17
 
3.4%
3 16
 
3.2%
5 15
 
3.0%
0 11
 
2.2%
Other values (6) 37
 
7.4%
Hangul
ValueCountFrequency (%)
수 80
10.6%
광 78
 
10.4%
영 75
 
10.0%
부 47
 
6.2%
구 47
 
6.2%
산 47
 
6.2%
로 47
 
6.2%
역 45
 
6.0%
동 44
 
5.8%
시 44
 
5.8%
Other values (51) 199
26.4%

설치일시
Categorical

HIGH CORRELATION 

Distinct9
Distinct (%)19.1%
Missing0
Missing (%)0.0%
Memory size508.0 B
2018-07-17
25 
2019-05-07
6 
2020-08-27
4 
2022-10-12
4 
2018-07-24
3 
Other values (4)
5 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique3 ?
Unique (%)6.4%

Sample

1st row2018-07-17
2nd row2018-07-17
3rd row2018-07-17
4th row2018-07-17
5th row2020-08-27

Common Values

ValueCountFrequency (%)
2018-07-17 25
53.2%
2019-05-07 6
 
12.8%
2020-08-27 4
 
8.5%
2022-10-12 4
 
8.5%
2018-07-24 3
 
6.4%
2022-08-26 2
 
4.3%
2022-05-31 1
 
2.1%
2019-06-24 1
 
2.1%
2019-07-24 1
 
2.1%

Length

2023-12-11T02:28:32.278383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T02:28:32.580742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2018-07-17 25
53.2%
2019-05-07 6
 
12.8%
2020-08-27 4
 
8.5%
2022-10-12 4
 
8.5%
2018-07-24 3
 
6.4%
2022-08-26 2
 
4.3%
2022-05-31 1
 
2.1%
2019-06-24 1
 
2.1%
2019-07-24 1
 
2.1%

그늘막 유형
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Memory size508.0 B
고정형
42 
스마트형
5 

Length

Max length4
Median length3
Mean length3.106383
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
고정형 42
89.4%
스마트형 5
 
10.6%

Length

2023-12-11T02:28:32.902855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T02:28:33.155298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
고정형 42
89.4%
스마트형 5
 
10.6%

Interactions

2023-12-11T02:28:24.295378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T02:28:33.314951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번행정동 구분관리번호설치장소설치위치설치일시그늘막 유형
연번1.0000.9591.0001.0000.8780.5460.351
행정동 구분0.9591.0001.0001.0000.9870.0000.000
관리번호1.0001.0001.0001.0001.0001.0001.000
설치장소1.0001.0001.0001.0001.0001.0001.000
설치위치0.8780.9871.0001.0001.0000.9421.000
설치일시0.5460.0001.0001.0000.9421.0000.780
그늘막 유형0.3510.0001.0001.0001.0000.7801.000
2023-12-11T02:28:33.568182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
설치일시행정동 구분그늘막 유형
설치일시1.0000.0000.734
행정동 구분0.0001.0000.000
그늘막 유형0.7340.0001.000
2023-12-11T02:28:33.818096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번행정동 구분설치일시그늘막 유형
연번1.0000.6860.2600.236
행정동 구분0.6861.0000.0000.000
설치일시0.2600.0001.0000.734
그늘막 유형0.2360.0000.7341.000

Missing values

2023-12-11T02:28:25.343908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T02:28:25.707637image/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

연번행정동 구분관리번호설치장소설치위치설치일시그늘막 유형
01남천제1동수영구-1KBS 삼거리 앞부산광역시 수영구 광남로10번길 2 (남천동)2018-07-17고정형
12남천제1동수영구-2사단법인 인구보건복지협회 앞부산광역시 수영구 수영로 425 (남천동)2018-07-17고정형
23남천제1동수영구-3대남교차로부산광역시 수영구 수영로 371 (남천동)2018-07-17고정형
34남천제1동수영구-4남천마리나 앞부산광역시 수영구 광안해변로 44 (남천동)2018-07-17고정형
45남천제1동수영구-32남천역 4번출구 앞부산광역시 수영구 수영로 381 (남천동)2020-08-27고정형
56남천제1동수영구-38LG베트스샵 맞은편부산광역시 수영구 황령대로 473번길 15, 건너편 횡단보도2022-08-26고정형
67남천제1동수영구-스마트1농협 남천동지점 앞부산광역시 수영구 광남로 35 (남천동)2019-05-07스마트형
78남천제2동수영구-5협진태양아파트 앞부산광역시 수영구 광안해변로 141 (남천동, 협진태양아파트)2018-07-17고정형
89남천제2동수영구-6협진태양 맞은편부산광역시 수영구 광안해변로 141 (남천동, 협진태양아파트)2018-07-17고정형
910수영동수영구-7더샵센텀포레 104동 앞부산광역시 수영구 수영로 775 (수영동)2018-07-17고정형
연번행정동 구분관리번호설치장소설치위치설치일시그늘막 유형
3738광안제3동수영구-25타이어뱅크 앞부산광역시 수영구 망미번영로38번길 3 (광안동)2018-07-17고정형
3839광안제4동수영구-26부산IT전문학교 앞부산광역시 수영구 수영로 525 (광안동)2018-07-17고정형
3940민락동수영구-27부산광역시 수영구민체육센터부산광역시 수영구 광남로257번길 12 (민락동)2019-07-24고정형
4041민락동수영구-28광안참사랑요양병원 앞부산광역시 수영구 광안해변로 233 (민락동)2018-07-17고정형
4142민락동수영구-29현가네 콩나물해장국 앞부산광역시 수영구 광안해변로 243 (민락동)2018-07-17고정형
4243민락동수영구-30커피스미스 앞부산광역시 수영구 광안해변로255번길 5 (민락동)2018-07-17고정형
4344민락동수영구-31새벽집 앞부산광역시 수영구 광안해변로 267 (민락동)2018-07-17고정형
4445민락동수영구-35민락역 1번출구 앞부산광역시 수영구 수영로 776 (민락동, 부산 더샵 센텀포레)2020-08-27고정형
4546민락동수영구-40센텀비스타동원2차 앞 교차로무학로63번길 1422022-10-12고정형
4647민락동수영구-41수영교 사거리수영로 776, 부산더샵센텀포레 104동 앞2022-10-12고정형