Overview

Dataset statistics

Number of variables8
Number of observations27
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.9 KiB
Average record size in memory70.9 B

Variable types

Numeric1
Text4
Categorical3

Dataset

Description인천광역시 부평구 비상 급수 생활용수 시설 현황 데이터입니다.(연번,시도,시군구,읍면동,시설종류,개방유무,민방위 비상급수시설 명,소재지 도로명 주소,소재지 지번주소)ex) 1,인천,부평구,부평1동,정부지원,개방,부평서초,부평문화로53번길 19,부평동 542-18
Author인천광역시 부평구
URLhttps://www.data.go.kr/data/15089254/fileData.do

Alerts

개방유무 has constant value ""Constant
연번 is highly overall correlated with 시설종류High correlation
시설종류 is highly overall correlated with 연번High correlation
Unnamed: 7 is highly imbalanced (77.1%)Imbalance
연번 has unique valuesUnique
민방위 비상급수시설 명 has unique valuesUnique
소재지 도로명 주소 has unique valuesUnique
소재지 지번주소 has unique valuesUnique

Reproduction

Analysis started2023-12-12 17:38:14.760699
Analysis finished2023-12-12 17:38:15.410646
Duration0.65 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14
Minimum1
Maximum27
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-13T02:38:15.480406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.3
Q17.5
median14
Q320.5
95-th percentile25.7
Maximum27
Range26
Interquartile range (IQR)13

Descriptive statistics

Standard deviation7.9372539
Coefficient of variation (CV)0.56694671
Kurtosis-1.2
Mean14
Median Absolute Deviation (MAD)7
Skewness0
Sum378
Variance63
MonotonicityStrictly increasing
2023-12-13T02:38:15.610804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
1 1
 
3.7%
2 1
 
3.7%
27 1
 
3.7%
26 1
 
3.7%
25 1
 
3.7%
24 1
 
3.7%
23 1
 
3.7%
22 1
 
3.7%
21 1
 
3.7%
20 1
 
3.7%
Other values (17) 17
63.0%
ValueCountFrequency (%)
1 1
3.7%
2 1
3.7%
3 1
3.7%
4 1
3.7%
5 1
3.7%
6 1
3.7%
7 1
3.7%
8 1
3.7%
9 1
3.7%
10 1
3.7%
ValueCountFrequency (%)
27 1
3.7%
26 1
3.7%
25 1
3.7%
24 1
3.7%
23 1
3.7%
22 1
3.7%
21 1
3.7%
20 1
3.7%
19 1
3.7%
18 1
3.7%

읍면동
Text

Distinct16
Distinct (%)59.3%
Missing0
Missing (%)0.0%
Memory size348.0 B
2023-12-13T02:38:15.789984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.962963
Min length3

Characters and Unicode

Total characters107
Distinct characters19
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)22.2%

Sample

1st row부평1동
2nd row부평4동
3rd row부평4동
4th row부평5동
5th row부개1동
ValueCountFrequency (%)
갈산1동 3
11.1%
부평1동 2
 
7.4%
부평4동 2
 
7.4%
부평5동 2
 
7.4%
부개1동 2
 
7.4%
부개2동 2
 
7.4%
부개3동 2
 
7.4%
산곡2동 2
 
7.4%
산곡3동 2
 
7.4%
청천2동 2
 
7.4%
Other values (6) 6
22.2%
2023-12-13T02:38:16.061627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 27
25.2%
부 13
12.1%
산 10
 
9.3%
1 10
 
9.3%
평 7
 
6.5%
2 6
 
5.6%
곡 6
 
5.6%
개 6
 
5.6%
3 5
 
4.7%
갈 3
 
2.8%
Other values (9) 14
13.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 81
75.7%
Decimal Number 26
 
24.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 27
33.3%
부 13
16.0%
산 10
 
12.3%
평 7
 
8.6%
곡 6
 
7.4%
개 6
 
7.4%
갈 3
 
3.7%
청 2
 
2.5%
천 2
 
2.5%
삼 1
 
1.2%
Other values (4) 4
 
4.9%
Decimal Number
ValueCountFrequency (%)
1 10
38.5%
2 6
23.1%
3 5
19.2%
4 3
 
11.5%
5 2
 
7.7%

Most occurring scripts

ValueCountFrequency (%)
Hangul 81
75.7%
Common 26
 
24.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 27
33.3%
부 13
16.0%
산 10
 
12.3%
평 7
 
8.6%
곡 6
 
7.4%
개 6
 
7.4%
갈 3
 
3.7%
청 2
 
2.5%
천 2
 
2.5%
삼 1
 
1.2%
Other values (4) 4
 
4.9%
Common
ValueCountFrequency (%)
1 10
38.5%
2 6
23.1%
3 5
19.2%
4 3
 
11.5%
5 2
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 81
75.7%
ASCII 26
 
24.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 27
33.3%
부 13
16.0%
산 10
 
12.3%
평 7
 
8.6%
곡 6
 
7.4%
개 6
 
7.4%
갈 3
 
3.7%
청 2
 
2.5%
천 2
 
2.5%
삼 1
 
1.2%
Other values (4) 4
 
4.9%
ASCII
ValueCountFrequency (%)
1 10
38.5%
2 6
23.1%
3 5
19.2%
4 3
 
11.5%
5 2
 
7.7%

시설종류
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)11.1%
Missing0
Missing (%)0.0%
Memory size348.0 B
공공용
16 
정부지원
10 
지자체
 
1

Length

Max length4
Median length3
Mean length3.3703704
Min length3

Unique

Unique1 ?
Unique (%)3.7%

Sample

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

Common Values

ValueCountFrequency (%)
공공용 16
59.3%
정부지원 10
37.0%
지자체 1
 
3.7%

Length

2023-12-13T02:38:16.247661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:38:16.361501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
공공용 16
59.3%
정부지원 10
37.0%
지자체 1
 
3.7%

개방유무
Categorical

CONSTANT 

Distinct1
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Memory size348.0 B
개방
27 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
개방 27
100.0%

Length

2023-12-13T02:38:16.451764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:38:16.530809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
개방 27
100.0%

민방위 비상급수시설 명
Text

UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size348.0 B
2023-12-13T02:38:16.684042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length7
Mean length5
Min length3

Characters and Unicode

Total characters135
Distinct characters80
Distinct categories4 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique27 ?
Unique (%)100.0%

Sample

1st row부평서초
2nd row부평동초
3rd row부평중
4th row부흥초
5th row부개초
ValueCountFrequency (%)
부평서초 1
 
3.6%
부평동초 1
 
3.6%
보브프라자주유소 1
 
3.6%
㈜bl 1
 
3.6%
천제연불가마 1
 
3.6%
에이짐피트니스 1
 
3.6%
명품카센터 1
 
3.6%
지에스칼텍스 1
 
3.6%
㈜동서식품 1
 
3.6%
수랜드사우나 1
 
3.6%
Other values (18) 18
64.3%
2023-12-13T02:38:16.989369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
부 10
 
7.4%
평 6
 
4.4%
초 5
 
3.7%
사 4
 
3.0%
동 4
 
3.0%
산 4
 
3.0%
차 4
 
3.0%
나 4
 
3.0%
세 3
 
2.2%
장 3
 
2.2%
Other values (70) 88
65.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 130
96.3%
Other Symbol 2
 
1.5%
Uppercase Letter 2
 
1.5%
Space Separator 1
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
부 10
 
7.7%
평 6
 
4.6%
초 5
 
3.8%
사 4
 
3.1%
동 4
 
3.1%
산 4
 
3.1%
차 4
 
3.1%
나 4
 
3.1%
세 3
 
2.3%
장 3
 
2.3%
Other values (66) 83
63.8%
Uppercase Letter
ValueCountFrequency (%)
B 1
50.0%
L 1
50.0%
Other Symbol
ValueCountFrequency (%)
㈜ 2
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 132
97.8%
Latin 2
 
1.5%
Common 1
 
0.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
부 10
 
7.6%
평 6
 
4.5%
초 5
 
3.8%
사 4
 
3.0%
동 4
 
3.0%
산 4
 
3.0%
차 4
 
3.0%
나 4
 
3.0%
세 3
 
2.3%
장 3
 
2.3%
Other values (67) 85
64.4%
Latin
ValueCountFrequency (%)
B 1
50.0%
L 1
50.0%
Common
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 130
96.3%
ASCII 3
 
2.2%
None 2
 
1.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
부 10
 
7.7%
평 6
 
4.6%
초 5
 
3.8%
사 4
 
3.1%
동 4
 
3.1%
산 4
 
3.1%
차 4
 
3.1%
나 4
 
3.1%
세 3
 
2.3%
장 3
 
2.3%
Other values (66) 83
63.8%
None
ValueCountFrequency (%)
㈜ 2
100.0%
ASCII
ValueCountFrequency (%)
B 1
33.3%
L 1
33.3%
1
33.3%

소재지 도로명 주소
Text

UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size348.0 B
2023-12-13T02:38:17.194012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length11
Mean length8.5555556
Min length5

Characters and Unicode

Total characters231
Distinct characters48
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

Unique27 ?
Unique (%)100.0%

Sample

1st row부평문화로53번길 19
2nd row부평대로88번길 19
3rd row부흥북로57번길 10
4th row부흥로366번길 34
5th row마분로 9
ValueCountFrequency (%)
19 2
 
3.8%
안남로 2
 
3.8%
10 2
 
3.8%
평천로 2
 
3.8%
새벌로 1
 
1.9%
261 1
 
1.9%
마장로 1
 
1.9%
316 1
 
1.9%
마장로144번길 1
 
1.9%
2 1
 
1.9%
Other values (38) 38
73.1%
2023-12-13T02:38:17.511412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
로 26
 
11.3%
25
 
10.8%
1 16
 
6.9%
4 14
 
6.1%
3 13
 
5.6%
9 12
 
5.2%
번 11
 
4.8%
길 11
 
4.8%
6 8
 
3.5%
부 8
 
3.5%
Other values (38) 87
37.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 112
48.5%
Decimal Number 91
39.4%
Space Separator 25
 
10.8%
Dash Punctuation 3
 
1.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 26
23.2%
번 11
 
9.8%
길 11
 
9.8%
부 8
 
7.1%
평 7
 
6.2%
장 5
 
4.5%
마 4
 
3.6%
흥 3
 
2.7%
북 2
 
1.8%
화 2
 
1.8%
Other values (26) 33
29.5%
Decimal Number
ValueCountFrequency (%)
1 16
17.6%
4 14
15.4%
3 13
14.3%
9 12
13.2%
6 8
8.8%
5 7
7.7%
8 6
 
6.6%
0 6
 
6.6%
7 5
 
5.5%
2 4
 
4.4%
Space Separator
ValueCountFrequency (%)
25
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 119
51.5%
Hangul 112
48.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 26
23.2%
번 11
 
9.8%
길 11
 
9.8%
부 8
 
7.1%
평 7
 
6.2%
장 5
 
4.5%
마 4
 
3.6%
흥 3
 
2.7%
북 2
 
1.8%
화 2
 
1.8%
Other values (26) 33
29.5%
Common
ValueCountFrequency (%)
25
21.0%
1 16
13.4%
4 14
11.8%
3 13
10.9%
9 12
10.1%
6 8
 
6.7%
5 7
 
5.9%
8 6
 
5.0%
0 6
 
5.0%
7 5
 
4.2%
Other values (2) 7
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 119
51.5%
Hangul 112
48.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
로 26
23.2%
번 11
 
9.8%
길 11
 
9.8%
부 8
 
7.1%
평 7
 
6.2%
장 5
 
4.5%
마 4
 
3.6%
흥 3
 
2.7%
북 2
 
1.8%
화 2
 
1.8%
Other values (26) 33
29.5%
ASCII
ValueCountFrequency (%)
25
21.0%
1 16
13.4%
4 14
11.8%
3 13
10.9%
9 12
10.1%
6 8
 
6.7%
5 7
 
5.9%
8 6
 
5.0%
0 6
 
5.0%
7 5
 
4.2%
Other values (2) 7
 
5.9%

소재지 지번주소
Text

UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size348.0 B
2023-12-13T02:38:17.714442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length13
Mean length9.6296296
Min length7

Characters and Unicode

Total characters260
Distinct characters29
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

Unique27 ?
Unique (%)100.0%

Sample

1st row부평동 542-18
2nd row부평동 440-1
3rd row부평동 136-33
4th row부평동 171
5th row부개1동 357
ValueCountFrequency (%)
산곡동 6
 
10.3%
부평동 5
 
8.6%
부개동 4
 
6.9%
갈산동 3
 
5.2%
청천동 2
 
3.4%
부개1동 2
 
3.4%
인천 2
 
3.4%
부평구 2
 
3.4%
411번지 1
 
1.7%
1호 1
 
1.7%
Other values (30) 30
51.7%
2023-12-13T02:38:18.044552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
31
 
11.9%
동 27
 
10.4%
1 26
 
10.0%
- 21
 
8.1%
3 16
 
6.2%
부 14
 
5.4%
2 13
 
5.0%
산 11
 
4.2%
0 11
 
4.2%
5 9
 
3.5%
Other values (19) 81
31.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 111
42.7%
Other Letter 97
37.3%
Space Separator 31
 
11.9%
Dash Punctuation 21
 
8.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 27
27.8%
부 14
14.4%
산 11
11.3%
평 8
 
8.2%
곡 6
 
6.2%
개 6
 
6.2%
천 4
 
4.1%
갈 3
 
3.1%
구 3
 
3.1%
십 2
 
2.1%
Other values (7) 13
13.4%
Decimal Number
ValueCountFrequency (%)
1 26
23.4%
3 16
14.4%
2 13
11.7%
0 11
9.9%
5 9
 
8.1%
6 9
 
8.1%
4 8
 
7.2%
8 7
 
6.3%
7 7
 
6.3%
9 5
 
4.5%
Space Separator
ValueCountFrequency (%)
31
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 21
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 163
62.7%
Hangul 97
37.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 27
27.8%
부 14
14.4%
산 11
11.3%
평 8
 
8.2%
곡 6
 
6.2%
개 6
 
6.2%
천 4
 
4.1%
갈 3
 
3.1%
구 3
 
3.1%
십 2
 
2.1%
Other values (7) 13
13.4%
Common
ValueCountFrequency (%)
31
19.0%
1 26
16.0%
- 21
12.9%
3 16
9.8%
2 13
8.0%
0 11
 
6.7%
5 9
 
5.5%
6 9
 
5.5%
4 8
 
4.9%
8 7
 
4.3%
Other values (2) 12
 
7.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 163
62.7%
Hangul 97
37.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
31
19.0%
1 26
16.0%
- 21
12.9%
3 16
9.8%
2 13
8.0%
0 11
 
6.7%
5 9
 
5.5%
6 9
 
5.5%
4 8
 
4.9%
8 7
 
4.3%
Other values (2) 12
 
7.4%
Hangul
ValueCountFrequency (%)
동 27
27.8%
부 14
14.4%
산 11
11.3%
평 8
 
8.2%
곡 6
 
6.2%
개 6
 
6.2%
천 4
 
4.1%
갈 3
 
3.1%
구 3
 
3.1%
십 2
 
2.1%
Other values (7) 13
13.4%

Unnamed: 7
Categorical

IMBALANCE 

Distinct2
Distinct (%)7.4%
Missing0
Missing (%)0.0%
Memory size348.0 B
<NA>
26 
0
 
1

Length

Max length4
Median length4
Mean length3.8888889
Min length1

Unique

Unique1 ?
Unique (%)3.7%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row0

Common Values

ValueCountFrequency (%)
<NA> 26
96.3%
0 1
 
3.7%

Length

2023-12-13T02:38:18.166229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:38:18.259355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 26
96.3%
0 1
 
3.7%

Interactions

2023-12-13T02:38:15.085694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T02:38:18.320354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번읍면동시설종류민방위 비상급수시설 명소재지 도로명 주소소재지 지번주소
연번1.0000.8970.7861.0001.0001.000
읍면동0.8971.0000.8901.0001.0001.000
시설종류0.7860.8901.0001.0001.0001.000
민방위 비상급수시설 명1.0001.0001.0001.0001.0001.000
소재지 도로명 주소1.0001.0001.0001.0001.0001.000
소재지 지번주소1.0001.0001.0001.0001.0001.000
2023-12-13T02:38:18.417215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시설종류Unnamed: 7
시설종류1.000NaN
Unnamed: 7NaN1.000
2023-12-13T02:38:18.546227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번시설종류Unnamed: 7
연번1.0000.558NaN
시설종류0.5581.000NaN
Unnamed: 7NaNNaN1.000

Missing values

2023-12-13T02:38:15.218987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T02:38:15.358319image/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

연번읍면동시설종류개방유무민방위 비상급수시설 명소재지 도로명 주소소재지 지번주소Unnamed: 7
01부평1동정부지원개방부평서초부평문화로53번길 19부평동 542-18<NA>
12부평4동정부지원개방부평동초부평대로88번길 19부평동 440-1<NA>
23부평4동정부지원개방부평중부흥북로57번길 10부평동 136-33<NA>
34부평5동정부지원개방부흥초부흥로366번길 34부평동 171<NA>
45부개1동정부지원개방부개초마분로 9부개1동 3570
56부개2동정부지원개방부평동중수변로107번길 4부개동 120-6<NA>
67부개2동정부지원개방부평여중부평문화로 193-1부개동 120-21<NA>
78부개3동정부지원개방비둘기아파트부흥로385번길 30부개동 65-1<NA>
89부개3동정부지원개방부광초충선로 104부개동 69-6<NA>
910갈산1동정부지원개방갈산근린공원부평대로296번길71갈산동 166-1<NA>
연번읍면동시설종류개방유무민방위 비상급수시설 명소재지 도로명 주소소재지 지번주소Unnamed: 7
1718산곡3동공공용개방길경세차장마장로204번길 10산곡동 335-2<NA>
1819산곡4동공공용개방수랜드사우나원적로 434산곡동 137-13<NA>
1920청천2동공공용개방㈜동서식품새벌로 55청천동 411번지 1호<NA>
2021청천2동공공용개방지에스칼텍스안남로 399청천동 400번지 6호<NA>
2122갈산1동공공용개방명품카센터평천로 349인천 부평구 갈산동 80<NA>
2223갈산1동공공용개방에이짐피트니스평천로 314인천 부평구 갈산동 172<NA>
2324삼산1동공공용개방천제연불가마부평북로 448삼산동 389-2<NA>
2425부개1동공공용개방㈜BL수변로 4-9부개1동 304-8<NA>
2526일신동공공용개방보브프라자주유소무네미로 471구산동 93-1<NA>
2627십정1동공공용개방향나무집함봉로36번길19-6십정동 29-7<NA>