Overview

Dataset statistics

Number of variables10
Number of observations97
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.2 KiB
Average record size in memory86.4 B

Variable types

Numeric2
Categorical6
Text2

Dataset

Description인천광역시 부평구 현수막 지정 게시대 현황 데이터는 게시대 설치년도, 주소, 행정동, 규격, 총 면수 등에 대한 데이터를 제공합니다.
URLhttps://www.data.go.kr/data/15102682/fileData.do

Alerts

규격 has constant value ""Constant
연번 is highly overall correlated with 행정동High correlation
상업면 is highly overall correlated with 행정면 and 2 other fieldsHigh correlation
설치연도 is highly overall correlated with 총면수(관리)High correlation
행정동 is highly overall correlated with 연번High correlation
행정면 is highly overall correlated with 상업면 and 1 other fieldsHigh correlation
주민센터 is highly overall correlated with 상업면 and 1 other fieldsHigh correlation
총면수(관리) is highly overall correlated with 상업면 and 1 other fieldsHigh correlation
주민센터 is highly imbalanced (58.1%)Imbalance
총면수(관리) is highly imbalanced (62.7%)Imbalance
연번 has unique valuesUnique
위치 has unique valuesUnique
상업면 has 19 (19.6%) zerosZeros

Reproduction

Analysis started2023-12-12 11:40:04.070057
Analysis finished2023-12-12 11:40:06.632222
Duration2.56 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct97
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49
Minimum1
Maximum97
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1005.0 B
2023-12-12T20:40:06.763158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5.8
Q125
median49
Q373
95-th percentile92.2
Maximum97
Range96
Interquartile range (IQR)48

Descriptive statistics

Standard deviation28.145456
Coefficient of variation (CV)0.57439705
Kurtosis-1.2
Mean49
Median Absolute Deviation (MAD)24
Skewness0
Sum4753
Variance792.16667
MonotonicityStrictly increasing
2023-12-12T20:40:07.022255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.0%
74 1
 
1.0%
72 1
 
1.0%
71 1
 
1.0%
70 1
 
1.0%
69 1
 
1.0%
68 1
 
1.0%
67 1
 
1.0%
66 1
 
1.0%
65 1
 
1.0%
Other values (87) 87
89.7%
ValueCountFrequency (%)
1 1
1.0%
2 1
1.0%
3 1
1.0%
4 1
1.0%
5 1
1.0%
6 1
1.0%
7 1
1.0%
8 1
1.0%
9 1
1.0%
10 1
1.0%
ValueCountFrequency (%)
97 1
1.0%
96 1
1.0%
95 1
1.0%
94 1
1.0%
93 1
1.0%
92 1
1.0%
91 1
1.0%
90 1
1.0%
89 1
1.0%
88 1
1.0%

설치연도
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)10.3%
Missing0
Missing (%)0.0%
Memory size908.0 B
2017-12-01
19 
2015-05-01
18 
2015-11-01
14 
2018-12-01
11 
2014-03-01
10 
Other values (5)
25 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2015-05-01
2nd row2013-07-01
3rd row2014-03-01
4th row2016-10-01
5th row2017-12-01

Common Values

ValueCountFrequency (%)
2017-12-01 19
19.6%
2015-05-01 18
18.6%
2015-11-01 14
14.4%
2018-12-01 11
11.3%
2014-03-01 10
10.3%
2013-07-01 9
9.3%
2016-10-01 7
 
7.2%
2012-12-01 4
 
4.1%
2001-07-12 3
 
3.1%
2020-08-01 2
 
2.1%

Length

2023-12-12T20:40:07.243222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T20:40:07.456249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2017-12-01 19
19.6%
2015-05-01 18
18.6%
2015-11-01 14
14.4%
2018-12-01 11
11.3%
2014-03-01 10
10.3%
2013-07-01 9
9.3%
2016-10-01 7
 
7.2%
2012-12-01 4
 
4.1%
2001-07-12 3
 
3.1%
2020-08-01 2
 
2.1%

주소
Text

Distinct79
Distinct (%)81.4%
Missing0
Missing (%)0.0%
Memory size908.0 B
2023-12-12T20:40:07.949534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length7.8556701
Min length3

Characters and Unicode

Total characters762
Distinct characters69
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

Unique61 ?
Unique (%)62.9%

Sample

1st row평천로 334
2nd row부평북로 273
3rd row부평북로 273
4th row굴포로 81
5th row주부토로 254
ValueCountFrequency (%)
경원대로 6
 
3.0%
굴포로 6
 
3.0%
부영로 6
 
3.0%
충선로 6
 
3.0%
주부토로 5
 
2.5%
평천로 4
 
2.0%
부평대로 4
 
2.0%
장제로 3
 
1.5%
경인로 3
 
1.5%
안남로 3
 
1.5%
Other values (104) 151
76.6%
2023-12-12T20:40:08.635699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
100
 
13.1%
로 91
 
11.9%
1 78
 
10.2%
2 43
 
5.6%
3 39
 
5.1%
5 28
 
3.7%
부 25
 
3.3%
7 23
 
3.0%
4 22
 
2.9%
6 21
 
2.8%
Other values (59) 292
38.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 357
46.9%
Decimal Number 296
38.8%
Space Separator 100
 
13.1%
Dash Punctuation 9
 
1.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 91
25.5%
부 25
 
7.0%
길 18
 
5.0%
번 14
 
3.9%
평 13
 
3.6%
동 11
 
3.1%
대 10
 
2.8%
경 9
 
2.5%
주 9
 
2.5%
남 8
 
2.2%
Other values (47) 149
41.7%
Decimal Number
ValueCountFrequency (%)
1 78
26.4%
2 43
14.5%
3 39
13.2%
5 28
 
9.5%
7 23
 
7.8%
4 22
 
7.4%
6 21
 
7.1%
8 15
 
5.1%
9 15
 
5.1%
0 12
 
4.1%
Space Separator
ValueCountFrequency (%)
100
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 405
53.1%
Hangul 357
46.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 91
25.5%
부 25
 
7.0%
길 18
 
5.0%
번 14
 
3.9%
평 13
 
3.6%
동 11
 
3.1%
대 10
 
2.8%
경 9
 
2.5%
주 9
 
2.5%
남 8
 
2.2%
Other values (47) 149
41.7%
Common
ValueCountFrequency (%)
100
24.7%
1 78
19.3%
2 43
10.6%
3 39
 
9.6%
5 28
 
6.9%
7 23
 
5.7%
4 22
 
5.4%
6 21
 
5.2%
8 15
 
3.7%
9 15
 
3.7%
Other values (2) 21
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 405
53.1%
Hangul 357
46.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
100
24.7%
1 78
19.3%
2 43
10.6%
3 39
 
9.6%
5 28
 
6.9%
7 23
 
5.7%
4 22
 
5.4%
6 21
 
5.2%
8 15
 
3.7%
9 15
 
3.7%
Other values (2) 21
 
5.2%
Hangul
ValueCountFrequency (%)
로 91
25.5%
부 25
 
7.0%
길 18
 
5.0%
번 14
 
3.9%
평 13
 
3.6%
동 11
 
3.1%
대 10
 
2.8%
경 9
 
2.5%
주 9
 
2.5%
남 8
 
2.2%
Other values (47) 149
41.7%

위치
Text

UNIQUE 

Distinct97
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size908.0 B
2023-12-12T20:40:08.997319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length21
Mean length12.835052
Min length5

Characters and Unicode

Total characters1245
Distinct characters161
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique97 ?
Unique (%)100.0%

Sample

1st row상꾸지공원 앞
2nd row부평IC(우)
3rd row부평IC(좌)
4th row[2단]갈산사거리 갈산타운 109동 앞
5th row갈산1동 주민센터 주차장
ValueCountFrequency (%)
학교 22
 
9.2%
앞 14
 
5.8%
주민센터 9
 
3.8%
옆 6
 
2.5%
주차장 6
 
2.5%
맞은편 6
 
2.5%
사거리 5
 
2.1%
우 4
 
1.7%
부개주공 3
 
1.2%
삼거리 3
 
1.2%
Other values (137) 162
67.5%
2023-12-12T20:40:09.664091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
147
 
11.8%
) 78
 
6.3%
( 51
 
4.1%
리 42
 
3.4%
교 41
 
3.3%
학 36
 
2.9%
거 36
 
2.9%
사 30
 
2.4%
산 29
 
2.3%
우 27
 
2.2%
Other values (151) 728
58.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 898
72.1%
Space Separator 147
 
11.8%
Close Punctuation 85
 
6.8%
Open Punctuation 58
 
4.7%
Decimal Number 46
 
3.7%
Uppercase Letter 10
 
0.8%
Other Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 42
 
4.7%
교 41
 
4.6%
학 36
 
4.0%
거 36
 
4.0%
사 30
 
3.3%
산 29
 
3.2%
우 27
 
3.0%
주 24
 
2.7%
부 23
 
2.6%
동 23
 
2.6%
Other values (131) 587
65.4%
Decimal Number
ValueCountFrequency (%)
1 18
39.1%
2 14
30.4%
3 3
 
6.5%
0 3
 
6.5%
9 3
 
6.5%
7 3
 
6.5%
5 1
 
2.2%
4 1
 
2.2%
Uppercase Letter
ValueCountFrequency (%)
M 2
20.0%
G 2
20.0%
I 2
20.0%
C 2
20.0%
L 1
10.0%
H 1
10.0%
Close Punctuation
ValueCountFrequency (%)
) 78
91.8%
] 7
 
8.2%
Open Punctuation
ValueCountFrequency (%)
( 51
87.9%
[ 7
 
12.1%
Space Separator
ValueCountFrequency (%)
147
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 898
72.1%
Common 337
 
27.1%
Latin 10
 
0.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 42
 
4.7%
교 41
 
4.6%
학 36
 
4.0%
거 36
 
4.0%
사 30
 
3.3%
산 29
 
3.2%
우 27
 
3.0%
주 24
 
2.7%
부 23
 
2.6%
동 23
 
2.6%
Other values (131) 587
65.4%
Common
ValueCountFrequency (%)
147
43.6%
) 78
23.1%
( 51
 
15.1%
1 18
 
5.3%
2 14
 
4.2%
] 7
 
2.1%
[ 7
 
2.1%
3 3
 
0.9%
0 3
 
0.9%
9 3
 
0.9%
Other values (4) 6
 
1.8%
Latin
ValueCountFrequency (%)
M 2
20.0%
G 2
20.0%
I 2
20.0%
C 2
20.0%
L 1
10.0%
H 1
10.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 898
72.1%
ASCII 347
 
27.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
147
42.4%
) 78
22.5%
( 51
 
14.7%
1 18
 
5.2%
2 14
 
4.0%
] 7
 
2.0%
[ 7
 
2.0%
3 3
 
0.9%
0 3
 
0.9%
9 3
 
0.9%
Other values (10) 16
 
4.6%
Hangul
ValueCountFrequency (%)
리 42
 
4.7%
교 41
 
4.6%
학 36
 
4.0%
거 36
 
4.0%
사 30
 
3.3%
산 29
 
3.2%
우 27
 
3.0%
주 24
 
2.7%
부 23
 
2.6%
동 23
 
2.6%
Other values (131) 587
65.4%

행정동
Categorical

HIGH CORRELATION 

Distinct27
Distinct (%)27.8%
Missing0
Missing (%)0.0%
Memory size908.0 B
삼산동
12 
청천2동
9 
십정1동
8 
부개3동
7 
부평3동
7 
Other values (22)
54 

Length

Max length4
Median length4
Mean length3.7628866
Min length3

Unique

Unique9 ?
Unique (%)9.3%

Sample

1st row갈산1동
2nd row갈산1동
3rd row갈산1동
4th row갈산동
5th row갈산1동

Common Values

ValueCountFrequency (%)
삼산동 12
 
12.4%
청천2동 9
 
9.3%
십정1동 8
 
8.2%
부개3동 7
 
7.2%
부평3동 7
 
7.2%
산곡4동 6
 
6.2%
부평2동 5
 
5.2%
부평4동 5
 
5.2%
삼산2동 4
 
4.1%
부평6동 4
 
4.1%
Other values (17) 30
30.9%

Length

2023-12-12T20:40:09.920043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
삼산동 12
 
12.4%
청천2동 9
 
9.3%
십정1동 8
 
8.2%
부개3동 7
 
7.2%
부평3동 7
 
7.2%
산곡4동 6
 
6.2%
부평2동 5
 
5.2%
부평4동 5
 
5.2%
삼산2동 4
 
4.1%
부평6동 4
 
4.1%
Other values (17) 30
30.9%

규격
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size908.0 B
600X70
97 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row600X70
2nd row600X70
3rd row600X70
4th row600X70
5th row600X70

Common Values

ValueCountFrequency (%)
600X70 97
100.0%

Length

2023-12-12T20:40:10.146574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T20:40:10.309364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
600x70 97
100.0%

상업면
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.0412371
Minimum0
Maximum6
Zeros19
Zeros (%)19.6%
Negative0
Negative (%)0.0%
Memory size1005.0 B
2023-12-12T20:40:10.447492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q36
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.3448415
Coefficient of variation (CV)0.58022864
Kurtosis-0.95325855
Mean4.0412371
Median Absolute Deviation (MAD)1
Skewness-0.8573928
Sum392
Variance5.4982818
MonotonicityNot monotonic
2023-12-12T20:40:10.641169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
6 38
39.2%
5 27
27.8%
0 19
19.6%
2 11
 
11.3%
4 1
 
1.0%
3 1
 
1.0%
ValueCountFrequency (%)
0 19
19.6%
2 11
 
11.3%
3 1
 
1.0%
4 1
 
1.0%
5 27
27.8%
6 38
39.2%
ValueCountFrequency (%)
6 38
39.2%
5 27
27.8%
4 1
 
1.0%
3 1
 
1.0%
2 11
 
11.3%
0 19
19.6%

행정면
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Memory size908.0 B
0
53 
1
28 
3
15 
2
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row0
2nd row1
3rd row1
4th row0
5th row3

Common Values

ValueCountFrequency (%)
0 53
54.6%
1 28
28.9%
3 15
 
15.5%
2 1
 
1.0%

Length

2023-12-12T20:40:10.872782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T20:40:11.145297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 53
54.6%
1 28
28.9%
3 15
 
15.5%
2 1
 
1.0%

주민센터
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Memory size908.0 B
0
77 
3
15 
6
 
3
4
 
1
2
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique2 ?
Unique (%)2.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row3

Common Values

ValueCountFrequency (%)
0 77
79.4%
3 15
 
15.5%
6 3
 
3.1%
4 1
 
1.0%
2 1
 
1.0%

Length

2023-12-12T20:40:11.358587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T20:40:11.518790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 77
79.4%
3 15
 
15.5%
6 3
 
3.1%
4 1
 
1.0%
2 1
 
1.0%

총면수(관리)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size908.0 B
6
85 
2
11 
4
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row6
2nd row6
3rd row6
4th row2
5th row6

Common Values

ValueCountFrequency (%)
6 85
87.6%
2 11
 
11.3%
4 1
 
1.0%

Length

2023-12-12T20:40:11.737126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T20:40:11.907680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
6 85
87.6%
2 11
 
11.3%
4 1
 
1.0%

Interactions

2023-12-12T20:40:05.908009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:40:05.582047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:40:06.045424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:40:05.740878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T20:40:12.035475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번설치연도주소위치행정동상업면행정면주민센터총면수(관리)
연번1.0000.6360.9961.0000.9430.3010.2810.0000.341
설치연도0.6361.0000.8631.0000.8230.7380.6650.7120.714
주소0.9960.8631.0001.0000.9990.8680.9750.8331.000
위치1.0001.0001.0001.0001.0001.0001.0001.0001.000
행정동0.9430.8230.9991.0001.0000.4350.5670.7460.688
상업면0.3010.7380.8681.0000.4351.0000.8830.7991.000
행정면0.2810.6650.9751.0000.5670.8831.0000.8420.178
주민센터0.0000.7120.8331.0000.7460.7990.8421.0000.000
총면수(관리)0.3410.7141.0001.0000.6881.0000.1780.0001.000
2023-12-12T20:40:12.236449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정면설치연도행정동주민센터총면수(관리)
행정면1.0000.4490.2850.8120.167
설치연도0.4491.0000.4220.3620.551
행정동0.2850.4221.0000.4130.363
주민센터0.8120.3620.4131.0000.000
총면수(관리)0.1670.5510.3630.0001.000
2023-12-12T20:40:12.420145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번상업면설치연도행정동행정면주민센터총면수(관리)
연번1.0000.1080.2400.6500.1630.0000.207
상업면0.1081.0000.4940.1740.7510.6840.984
설치연도0.2400.4941.0000.4220.4490.3620.551
행정동0.6500.1740.4221.0000.2850.4130.363
행정면0.1630.7510.4490.2851.0000.8120.167
주민센터0.0000.6840.3620.4130.8121.0000.000
총면수(관리)0.2070.9840.5510.3630.1670.0001.000

Missing values

2023-12-12T20:40:06.271343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T20:40:06.528881image/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

연번설치연도주소위치행정동규격상업면행정면주민센터총면수(관리)
012015-05-01평천로 334상꾸지공원 앞갈산1동600X706006
122013-07-01부평북로 273부평IC(우)갈산1동600X705106
232014-03-01부평북로 273부평IC(좌)갈산1동600X705106
342016-10-01굴포로 81[2단]갈산사거리 갈산타운 109동 앞갈산동600X702002
452017-12-01주부토로 254갈산1동 주민센터 주차장갈산1동600X700336
562017-12-01굴포로 5부평세관 앞 한국지엠사거리갈산2동600X706006
672020-08-01주부토로 173갈산2동 행정복지센터 주차장갈산2동600X700336
782013-07-01장제로 234신복사거리부개3동600X705106
892012-12-01길주남로 143부개주공 1단지 앞(삼산체육관 맞은편)(우)부개3동600X706006
9102015-11-01길주남로 143부개주공 1단지 앞(삼산체육관 맞은편)(좌)부개3동600X706006
연번설치연도주소위치행정동규격상업면행정면주민센터총면수(관리)
87882014-03-01부평대로 233GM대우 정문 옆청천2동600X705106
88892014-03-01안남로 274학교) 산곡사거리(금호아파트 앞)청천2동600X706006
89902012-12-01부평대로 233새마을금고 청천본점 옆청천2동600X706006
90912014-03-01세월천로 16GM대우 서문 맞은편청천2동600X706006
91922014-03-01평천로 187수출공단 오거리청천2동600X706006
92932017-12-01청천동 125-1나비공원 공영주차장 (좌)(1)청천1동600X700336
93942017-12-01청천동 125-1나비공원 공영주차장 (좌)(2)청천2동600X706006
94952017-12-01청안로 8인향아파트 101동 맞은편청천1동600X706006
95962017-12-01청천178-17청천2동 주민센터 주차장청천2동600X700336
96972017-12-01부평북로 99새벼리사거리청천1동600X706006