Overview

Dataset statistics

Number of variables8
Number of observations178
Missing cells12
Missing cells (%)0.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory11.6 KiB
Average record size in memory66.7 B

Variable types

Numeric2
Categorical1
DateTime2
Text3

Dataset

Description인천광역시 부평구 숙박업 현황(업종명,신고일자,업소명,도로명주소,지번주소,소재지전화,영업자시작일) 을 제공하는 데이터입니다.
Author인천광역시 부평구
URLhttps://data.incheon.go.kr/findData/publicDataDetail?dataId=3045126&srcSe=7661IVAWM27C61E190

Alerts

연번 is highly overall correlated with 업종명High correlation
업종명 is highly overall correlated with 연번High correlation
업종명 is highly imbalanced (54.5%)Imbalance
소재지전화 has 12 (6.7%) missing valuesMissing
연번 has unique valuesUnique
영업소주소(도로명) has unique valuesUnique

Reproduction

Analysis started2024-01-28 12:22:58.539509
Analysis finished2024-01-28 12:22:59.245179
Duration0.71 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct178
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean89.5
Minimum1
Maximum178
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2024-01-28T21:22:59.299587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9.85
Q145.25
median89.5
Q3133.75
95-th percentile169.15
Maximum178
Range177
Interquartile range (IQR)88.5

Descriptive statistics

Standard deviation51.528309
Coefficient of variation (CV)0.5757353
Kurtosis-1.2
Mean89.5
Median Absolute Deviation (MAD)44.5
Skewness0
Sum15931
Variance2655.1667
MonotonicityStrictly increasing
2024-01-28T21:22:59.406754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.6%
135 1
 
0.6%
115 1
 
0.6%
116 1
 
0.6%
117 1
 
0.6%
118 1
 
0.6%
119 1
 
0.6%
120 1
 
0.6%
121 1
 
0.6%
122 1
 
0.6%
Other values (168) 168
94.4%
ValueCountFrequency (%)
1 1
0.6%
2 1
0.6%
3 1
0.6%
4 1
0.6%
5 1
0.6%
6 1
0.6%
7 1
0.6%
8 1
0.6%
9 1
0.6%
10 1
0.6%
ValueCountFrequency (%)
178 1
0.6%
177 1
0.6%
176 1
0.6%
175 1
0.6%
174 1
0.6%
173 1
0.6%
172 1
0.6%
171 1
0.6%
170 1
0.6%
169 1
0.6%

업종명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
숙박업(일반)
161 
숙박업(생활)
17 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row숙박업(일반)
2nd row숙박업(일반)
3rd row숙박업(일반)
4th row숙박업(일반)
5th row숙박업(일반)

Common Values

ValueCountFrequency (%)
숙박업(일반) 161
90.4%
숙박업(생활) 17
 
9.6%

Length

2024-01-28T21:22:59.511476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-28T21:22:59.591039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
숙박업(일반 161
90.4%
숙박업(생활 17
 
9.6%

신고일자
Date

Distinct170
Distinct (%)95.5%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
Minimum1967-11-10 00:00:00
Maximum2022-05-27 00:00:00
2024-01-28T21:22:59.677081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:22:59.785285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

업소명
Text

Distinct176
Distinct (%)98.9%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
2024-01-28T21:22:59.993546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length17
Mean length5.1685393
Min length2

Characters and Unicode

Total characters920
Distinct characters236
Distinct categories8 ?
Distinct scripts4 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique174 ?
Unique (%)97.8%

Sample

1st row백운여관
2nd row씨마모텔
3rd row동신여관
4th row호텔아미(Amy)부평문화의거리점
5th row킹모텔
ValueCountFrequency (%)
리치모텔 2
 
1.1%
호텔두루와 2
 
1.1%
아이엠티(imt)호텔 1
 
0.6%
화이트캐슬 1
 
0.6%
부평스퀘어 1
 
0.6%
보보스모텔 1
 
0.6%
제니스호텔부평역 1
 
0.6%
호텔벨루스 1
 
0.6%
칼튼호텔 1
 
0.6%
버스 1
 
0.6%
Other values (166) 166
93.3%
2024-01-28T21:23:00.303315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
텔 97
 
10.5%
모 47
 
5.1%
호 47
 
5.1%
여 44
 
4.8%
스 27
 
2.9%
인 24
 
2.6%
관 24
 
2.6%
이 23
 
2.5%
숙 20
 
2.2%
) 15
 
1.6%
Other values (226) 552
60.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 817
88.8%
Uppercase Letter 50
 
5.4%
Close Punctuation 15
 
1.6%
Open Punctuation 15
 
1.6%
Decimal Number 11
 
1.2%
Lowercase Letter 6
 
0.7%
Other Punctuation 5
 
0.5%
Dash Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
텔 97
 
11.9%
모 47
 
5.8%
호 47
 
5.8%
여 44
 
5.4%
스 27
 
3.3%
인 24
 
2.9%
관 24
 
2.9%
이 23
 
2.8%
숙 20
 
2.4%
부 13
 
1.6%
Other values (189) 451
55.2%
Uppercase Letter
ValueCountFrequency (%)
O 6
 
12.0%
T 5
 
10.0%
E 4
 
8.0%
A 4
 
8.0%
R 3
 
6.0%
L 3
 
6.0%
U 3
 
6.0%
N 2
 
4.0%
J 2
 
4.0%
W 2
 
4.0%
Other values (11) 16
32.0%
Lowercase Letter
ValueCountFrequency (%)
y 1
16.7%
m 1
16.7%
t 1
16.7%
g 1
16.7%
e 1
16.7%
a 1
16.7%
Decimal Number
ValueCountFrequency (%)
2 5
45.5%
5 2
 
18.2%
1 2
 
18.2%
4 1
 
9.1%
3 1
 
9.1%
Other Punctuation
ValueCountFrequency (%)
. 3
60.0%
& 2
40.0%
Close Punctuation
ValueCountFrequency (%)
) 15
100.0%
Open Punctuation
ValueCountFrequency (%)
( 15
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 816
88.7%
Latin 56
 
6.1%
Common 47
 
5.1%
Han 1
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
텔 97
 
11.9%
모 47
 
5.8%
호 47
 
5.8%
여 44
 
5.4%
스 27
 
3.3%
인 24
 
2.9%
관 24
 
2.9%
이 23
 
2.8%
숙 20
 
2.5%
부 13
 
1.6%
Other values (188) 450
55.1%
Latin
ValueCountFrequency (%)
O 6
 
10.7%
T 5
 
8.9%
E 4
 
7.1%
A 4
 
7.1%
R 3
 
5.4%
L 3
 
5.4%
U 3
 
5.4%
N 2
 
3.6%
J 2
 
3.6%
W 2
 
3.6%
Other values (17) 22
39.3%
Common
ValueCountFrequency (%)
) 15
31.9%
( 15
31.9%
2 5
 
10.6%
. 3
 
6.4%
& 2
 
4.3%
5 2
 
4.3%
1 2
 
4.3%
- 1
 
2.1%
4 1
 
2.1%
3 1
 
2.1%
Han
ValueCountFrequency (%)
休 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 816
88.7%
ASCII 103
 
11.2%
CJK 1
 
0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
텔 97
 
11.9%
모 47
 
5.8%
호 47
 
5.8%
여 44
 
5.4%
스 27
 
3.3%
인 24
 
2.9%
관 24
 
2.9%
이 23
 
2.8%
숙 20
 
2.5%
부 13
 
1.6%
Other values (188) 450
55.1%
ASCII
ValueCountFrequency (%)
) 15
 
14.6%
( 15
 
14.6%
O 6
 
5.8%
T 5
 
4.9%
2 5
 
4.9%
E 4
 
3.9%
A 4
 
3.9%
. 3
 
2.9%
R 3
 
2.9%
L 3
 
2.9%
Other values (27) 40
38.8%
CJK
ValueCountFrequency (%)
休 1
100.0%

영업소주소(도로명)
Text

UNIQUE 

Distinct178
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
2024-01-28T21:23:00.505854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length35
Median length30
Mean length23.55618
Min length18

Characters and Unicode

Total characters4193
Distinct characters81
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

Unique178 ?
Unique (%)100.0%

Sample

1st row인천광역시부평구평천로306번길7(갈산동)
2nd row인천광역시부평구경원대로1377번길9-6(부평동)
3rd row인천광역시부평구평천로357번길37(갈산동)
4th row인천광역시부평구부평문화로72번길10(부평동)
5th row인천광역시부평구평천로287번길13(갈산동)
ValueCountFrequency (%)
인천광역시부평구평천로306번길7(갈산동 1
 
0.6%
인천광역시부평구시장로12번길28(부평동 1
 
0.6%
인천광역시부평구동암광장로8번길25(십정동 1
 
0.6%
인천광역시부평구대정로82번길19(부평동 1
 
0.6%
인천광역시부평구시장로12번길31(부평동 1
 
0.6%
인천광역시부평구부평대로17번길20(부평동 1
 
0.6%
인천광역시부평구부평대로17번길23(부평동 1
 
0.6%
인천광역시부평구경원대로1427-1(부평동 1
 
0.6%
인천광역시부평구부흥로294번길19(부평동 1
 
0.6%
인천광역시부평구경원대로1367번길26-3(부평동 1
 
0.6%
Other values (168) 168
94.4%
2024-01-28T21:23:00.817034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
평 338
 
8.1%
부 333
 
7.9%
광 214
 
5.1%
천 203
 
4.8%
1 202
 
4.8%
동 200
 
4.8%
시 190
 
4.5%
인 179
 
4.3%
( 178
 
4.2%
역 178
 
4.2%
Other values (71) 1978
47.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2976
71.0%
Decimal Number 799
 
19.1%
Open Punctuation 178
 
4.2%
Close Punctuation 178
 
4.2%
Dash Punctuation 45
 
1.1%
Other Punctuation 15
 
0.4%
Math Symbol 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
평 338
11.4%
부 333
11.2%
광 214
 
7.2%
천 203
 
6.8%
동 200
 
6.7%
시 190
 
6.4%
인 179
 
6.0%
역 178
 
6.0%
구 178
 
6.0%
로 178
 
6.0%
Other values (56) 785
26.4%
Decimal Number
ValueCountFrequency (%)
1 202
25.3%
2 106
13.3%
3 89
11.1%
7 82
10.3%
4 78
 
9.8%
6 72
 
9.0%
8 57
 
7.1%
0 41
 
5.1%
9 40
 
5.0%
5 32
 
4.0%
Open Punctuation
ValueCountFrequency (%)
( 178
100.0%
Close Punctuation
ValueCountFrequency (%)
) 178
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 45
100.0%
Other Punctuation
ValueCountFrequency (%)
, 15
100.0%
Math Symbol
ValueCountFrequency (%)
~ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2976
71.0%
Common 1217
29.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
평 338
11.4%
부 333
11.2%
광 214
 
7.2%
천 203
 
6.8%
동 200
 
6.7%
시 190
 
6.4%
인 179
 
6.0%
역 178
 
6.0%
구 178
 
6.0%
로 178
 
6.0%
Other values (56) 785
26.4%
Common
ValueCountFrequency (%)
1 202
16.6%
( 178
14.6%
) 178
14.6%
2 106
8.7%
3 89
7.3%
7 82
6.7%
4 78
 
6.4%
6 72
 
5.9%
8 57
 
4.7%
- 45
 
3.7%
Other values (5) 130
10.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2976
71.0%
ASCII 1217
29.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
평 338
11.4%
부 333
11.2%
광 214
 
7.2%
천 203
 
6.8%
동 200
 
6.7%
시 190
 
6.4%
인 179
 
6.0%
역 178
 
6.0%
구 178
 
6.0%
로 178
 
6.0%
Other values (56) 785
26.4%
ASCII
ValueCountFrequency (%)
1 202
16.6%
( 178
14.6%
) 178
14.6%
2 106
8.7%
3 89
7.3%
7 82
6.7%
4 78
 
6.4%
6 72
 
5.9%
8 57
 
4.7%
- 45
 
3.7%
Other values (5) 130
10.7%

소재지전화
Text

MISSING 

Distinct164
Distinct (%)98.8%
Missing12
Missing (%)6.7%
Memory size1.5 KiB
2024-01-28T21:23:01.015070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.006024
Min length12

Characters and Unicode

Total characters1993
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique162 ?
Unique (%)97.6%

Sample

1st row032-502-6389
2nd row032-361-3265
3rd row032-517-1440
4th row032-508-5510
5th row032-503-7512
ValueCountFrequency (%)
032-362-4500 2
 
1.2%
032-421-0381 2
 
1.2%
032-519-8808 1
 
0.6%
032-521-3108 1
 
0.6%
032-424-6046 1
 
0.6%
032-502-6389 1
 
0.6%
032-423-3567 1
 
0.6%
032-428-4780 1
 
0.6%
032-433-1374 1
 
0.6%
032-439-4497 1
 
0.6%
Other values (154) 154
92.8%
2024-01-28T21:23:01.333899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 332
16.7%
2 315
15.8%
0 296
14.9%
3 284
14.2%
5 213
10.7%
1 146
7.3%
4 109
 
5.5%
8 81
 
4.1%
7 81
 
4.1%
6 71
 
3.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1661
83.3%
Dash Punctuation 332
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 315
19.0%
0 296
17.8%
3 284
17.1%
5 213
12.8%
1 146
8.8%
4 109
 
6.6%
8 81
 
4.9%
7 81
 
4.9%
6 71
 
4.3%
9 65
 
3.9%
Dash Punctuation
ValueCountFrequency (%)
- 332
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1993
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 332
16.7%
2 315
15.8%
0 296
14.9%
3 284
14.2%
5 213
10.7%
1 146
7.3%
4 109
 
5.5%
8 81
 
4.1%
7 81
 
4.1%
6 71
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1993
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 332
16.7%
2 315
15.8%
0 296
14.9%
3 284
14.2%
5 213
10.7%
1 146
7.3%
4 109
 
5.5%
8 81
 
4.1%
7 81
 
4.1%
6 71
 
3.6%

영업자시작일
Date

Distinct169
Distinct (%)94.9%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
Minimum1967-11-10 00:00:00
Maximum2023-05-22 00:00:00
2024-01-28T21:23:01.445875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:23:01.549023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

객실수
Real number (ℝ)

Distinct41
Distinct (%)23.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.146067
Minimum5
Maximum512
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2024-01-28T21:23:01.661132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile10
Q117
median24
Q332
95-th percentile45
Maximum512
Range507
Interquartile range (IQR)15

Descriptive statistics

Standard deviation37.889238
Coefficient of variation (CV)1.3461645
Kurtosis152.39676
Mean28.146067
Median Absolute Deviation (MAD)7
Skewness11.894379
Sum5010
Variance1435.5944
MonotonicityNot monotonic
2024-01-28T21:23:01.774830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
30 14
 
7.9%
21 12
 
6.7%
17 11
 
6.2%
20 9
 
5.1%
16 8
 
4.5%
29 6
 
3.4%
28 6
 
3.4%
13 6
 
3.4%
40 6
 
3.4%
27 6
 
3.4%
Other values (31) 94
52.8%
ValueCountFrequency (%)
5 1
 
0.6%
7 1
 
0.6%
9 4
 
2.2%
10 5
2.8%
12 2
 
1.1%
13 6
3.4%
14 4
 
2.2%
15 4
 
2.2%
16 8
4.5%
17 11
6.2%
ValueCountFrequency (%)
512 1
0.6%
56 1
0.6%
54 1
0.6%
51 1
0.6%
50 2
1.1%
48 2
1.1%
45 2
1.1%
43 2
1.1%
42 2
1.1%
41 1
0.6%

Interactions

2024-01-28T21:22:58.968313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:22:58.830041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:22:59.032770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:22:58.900254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-28T21:23:01.846146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번업종명객실수
연번1.0000.9990.062
업종명0.9991.0000.000
객실수0.0620.0001.000
2024-01-28T21:23:01.910328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번객실수업종명
연번1.0000.3030.945
객실수0.3031.0000.000
업종명0.9450.0001.000

Missing values

2024-01-28T21:22:59.116111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-28T21:22:59.208484image/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

연번업종명신고일자업소명영업소주소(도로명)소재지전화영업자시작일객실수
01숙박업(일반)1984-09-04백운여관인천광역시부평구평천로306번길7(갈산동)032-502-63892006-11-2014
12숙박업(일반)1989-11-08씨마모텔인천광역시부평구경원대로1377번길9-6(부평동)032-361-32652021-05-2121
23숙박업(일반)1989-09-23동신여관인천광역시부평구평천로357번길37(갈산동)032-517-14401989-09-2313
34숙박업(일반)2000-12-18호텔아미(Amy)부평문화의거리점인천광역시부평구부평문화로72번길10(부평동)032-508-55102023-04-2020
45숙박업(일반)2000-11-04킹모텔인천광역시부평구평천로287번길13(갈산동)032-503-75122019-06-1317
56숙박업(일반)1989-01-16금강인천광역시부평구장제로91번길22(부평동)032-524-12272020-12-0219
67숙박업(일반)1988-08-05갤러리아호텔인천광역시부평구대정로90번길24(부평동)032-503-48481999-12-2140
78숙박업(일반)1988-12-05아이디여관인천광역시부평구대정로82번길26(부평동)032-517-67482006-09-1321
89숙박업(일반)1985-07-16백마여인숙인천광역시부평구광장로4번길19(부평동)032-514-88842008-09-2910
910숙박업(일반)1987-11-16휴(休)스테이인천광역시부평구시장로20번길20(부평동)032-522-88762018-06-2020
연번업종명신고일자업소명영업소주소(도로명)소재지전화영업자시작일객실수
168169숙박업(생활)2012-09-14리안인천광역시부평구대정로36번길15(부평동)<NA>2013-03-0741
169170숙박업(생활)2013-02-07다원인천광역시부평구부평문화로79번길22(부평동)<NA>2015-07-3035
170171숙박업(생활)2013-06-17하이온인천광역시부평구부평문화로79번길25(부평동)<NA>2016-08-2251
171172숙박업(생활)2013-07-02대운유스텔인천광역시부평구경원대로1367번길26-6(부평동)<NA>2013-07-0239
172173숙박업(생활)2013-07-02쌍암유스텔인천광역시부평구경원대로1367번길26-8(부평동)<NA>2013-07-0239
173174숙박업(생활)2013-10-04프레스티지샵원리빙텔인천광역시부평구경원대로1377번길29-5(부평동)<NA>2013-10-0429
174175숙박업(생활)2015-06-24꿈에그린빌인천광역시부평구광장로30번길33(부평동,1-4층)032-511-51122015-06-2424
175176숙박업(생활)2017-02-28청우인천광역시부평구부평대로17번길22,2~4층(부평동)<NA>2017-02-2820
176177숙박업(생활)2018-03-27블루험인천광역시부평구경원대로1347번길38-6,블루험(부평동)070-4800-49712018-03-2710
177178숙박업(생활)2019-10-14에코인천광역시부평구대정로82번길6,에코라이프힐(부평동)<NA>2019-10-1445