Overview

Dataset statistics

Number of variables8
Number of observations21
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.5 KiB
Average record size in memory73.3 B

Variable types

Categorical2
Text3
Numeric3

Dataset

Description인천광역시_재난대응용 배수펌프장 시설현황에 대한 데이터로 펌프장의 위치, 규모, 토출량, 배수유역 등을 제공합니다.
Author인천광역시
URLhttps://data.incheon.go.kr/findData/publicDataDetail?dataId=15021458&srcSe=7661IVAWM27C61E190

Alerts

설치목적 has constant value ""Constant
토출량(분) is highly overall correlated with 배수유역High correlation
배수유역 is highly overall correlated with 토출량(분) and 1 other fieldsHigh correlation
구분 is highly overall correlated with 배수유역High correlation

Reproduction

Analysis started2024-03-18 05:27:30.516175
Analysis finished2024-03-18 05:27:32.001807
Duration1.49 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Memory size300.0 B
서구
6 
남동구
5 
미추홀구
3 
부평구
3 
동구
2 
Other values (2)
2 

Length

Max length4
Median length3
Mean length2.7619048
Min length2

Unique

Unique2 ?
Unique (%)9.5%

Sample

1st row동구
2nd row동구
3rd row미추홀구
4th row미추홀구
5th row미추홀구

Common Values

ValueCountFrequency (%)
서구 6
28.6%
남동구 5
23.8%
미추홀구 3
14.3%
부평구 3
14.3%
동구 2
 
9.5%
계양구 1
 
4.8%
강화군 1
 
4.8%

Length

2024-03-18T14:27:32.070134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-18T14:27:32.184335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서구 6
28.6%
남동구 5
23.8%
미추홀구 3
14.3%
부평구 3
14.3%
동구 2
 
9.5%
계양구 1
 
4.8%
강화군 1
 
4.8%

명칭
Text

Distinct20
Distinct (%)95.2%
Missing0
Missing (%)0.0%
Memory size300.0 B
2024-03-18T14:27:32.349289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length6.3333333
Min length5

Characters and Unicode

Total characters133
Distinct characters40
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19 ?
Unique (%)90.5%

Sample

1st row인천교매립지 펌프장
2nd row인천교매립지 펌프장
3rd row용현펌프장
4th row백운펌프장
5th row학익펌프장
ValueCountFrequency (%)
인천교매립지 2
 
8.7%
펌프장 2
 
8.7%
삼산(1)펌프장 1
 
4.3%
백석펌프장 1
 
4.3%
왕길펌프장 1
 
4.3%
경서펌프장 1
 
4.3%
가정펌프장 1
 
4.3%
검단펌프장 1
 
4.3%
가좌펌프장 1
 
4.3%
서운펌프장 1
 
4.3%
Other values (11) 11
47.8%
2024-03-18T14:27:32.646602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
펌 21
15.8%
프 21
15.8%
장 21
15.8%
11
 
8.3%
서 4
 
3.0%
산 3
 
2.3%
천 3
 
2.3%
정 2
 
1.5%
( 2
 
1.5%
2 2
 
1.5%
Other values (30) 43
32.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 114
85.7%
Space Separator 11
 
8.3%
Decimal Number 4
 
3.0%
Open Punctuation 2
 
1.5%
Close Punctuation 2
 
1.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
펌 21
18.4%
프 21
18.4%
장 21
18.4%
서 4
 
3.5%
산 3
 
2.6%
천 3
 
2.6%
정 2
 
1.8%
창 2
 
1.8%
가 2
 
1.8%
인 2
 
1.8%
Other values (25) 33
28.9%
Decimal Number
ValueCountFrequency (%)
2 2
50.0%
1 2
50.0%
Space Separator
ValueCountFrequency (%)
11
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 114
85.7%
Common 19
 
14.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
펌 21
18.4%
프 21
18.4%
장 21
18.4%
서 4
 
3.5%
산 3
 
2.6%
천 3
 
2.6%
정 2
 
1.8%
창 2
 
1.8%
가 2
 
1.8%
인 2
 
1.8%
Other values (25) 33
28.9%
Common
ValueCountFrequency (%)
11
57.9%
( 2
 
10.5%
2 2
 
10.5%
1 2
 
10.5%
) 2
 
10.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 114
85.7%
ASCII 19
 
14.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
펌 21
18.4%
프 21
18.4%
장 21
18.4%
서 4
 
3.5%
산 3
 
2.6%
천 3
 
2.6%
정 2
 
1.8%
창 2
 
1.8%
가 2
 
1.8%
인 2
 
1.8%
Other values (25) 33
28.9%
ASCII
ValueCountFrequency (%)
11
57.9%
( 2
 
10.5%
2 2
 
10.5%
1 2
 
10.5%
) 2
 
10.5%

위치
Text

Distinct20
Distinct (%)95.2%
Missing0
Missing (%)0.0%
Memory size300.0 B
2024-03-18T14:27:32.848134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length22
Mean length19.238095
Min length11

Characters and Unicode

Total characters404
Distinct characters76
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19 ?
Unique (%)90.5%

Sample

1st row중봉대로 113-134(송현동 147)
2nd row중봉대로 113-134(송현동 147)
3rd row아암대로 43(용현동 574-10)
4th row아암대로 135(용현동 627-76)
5th row아암대로 338(학익동 729)
ValueCountFrequency (%)
아암대로 4
 
6.2%
서구 3
 
4.7%
중봉대로 2
 
3.1%
113-134(송현동 2
 
3.1%
147 2
 
3.1%
89(선두리 1
 
1.6%
1665-1 1
 
1.6%
19(삼산동 1
 
1.6%
393-2 1
 
1.6%
계양구 1
 
1.6%
Other values (46) 46
71.9%
2024-03-18T14:27:33.203925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
48
 
11.9%
1 37
 
9.2%
동 21
 
5.2%
4 19
 
4.7%
3 19
 
4.7%
) 17
 
4.2%
로 17
 
4.2%
( 17
 
4.2%
- 15
 
3.7%
6 14
 
3.5%
Other values (66) 180
44.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 158
39.1%
Decimal Number 149
36.9%
Space Separator 48
 
11.9%
Close Punctuation 17
 
4.2%
Open Punctuation 17
 
4.2%
Dash Punctuation 15
 
3.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 21
 
13.3%
로 17
 
10.8%
서 9
 
5.7%
길 7
 
4.4%
대 7
 
4.4%
현 6
 
3.8%
번 6
 
3.8%
구 5
 
3.2%
암 4
 
2.5%
아 4
 
2.5%
Other values (52) 72
45.6%
Decimal Number
ValueCountFrequency (%)
1 37
24.8%
4 19
12.8%
3 19
12.8%
6 14
 
9.4%
7 14
 
9.4%
2 13
 
8.7%
0 10
 
6.7%
5 9
 
6.0%
8 7
 
4.7%
9 7
 
4.7%
Space Separator
ValueCountFrequency (%)
48
100.0%
Close Punctuation
ValueCountFrequency (%)
) 17
100.0%
Open Punctuation
ValueCountFrequency (%)
( 17
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 15
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 246
60.9%
Hangul 158
39.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 21
 
13.3%
로 17
 
10.8%
서 9
 
5.7%
길 7
 
4.4%
대 7
 
4.4%
현 6
 
3.8%
번 6
 
3.8%
구 5
 
3.2%
암 4
 
2.5%
아 4
 
2.5%
Other values (52) 72
45.6%
Common
ValueCountFrequency (%)
48
19.5%
1 37
15.0%
4 19
 
7.7%
3 19
 
7.7%
) 17
 
6.9%
( 17
 
6.9%
- 15
 
6.1%
6 14
 
5.7%
7 14
 
5.7%
2 13
 
5.3%
Other values (4) 33
13.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 246
60.9%
Hangul 158
39.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
48
19.5%
1 37
15.0%
4 19
 
7.7%
3 19
 
7.7%
) 17
 
6.9%
( 17
 
6.9%
- 15
 
6.1%
6 14
 
5.7%
7 14
 
5.7%
2 13
 
5.3%
Other values (4) 33
13.4%
Hangul
ValueCountFrequency (%)
동 21
 
13.3%
로 17
 
10.8%
서 9
 
5.7%
길 7
 
4.4%
대 7
 
4.4%
현 6
 
3.8%
번 6
 
3.8%
구 5
 
3.2%
암 4
 
2.5%
아 4
 
2.5%
Other values (52) 72
45.6%

규모 및 대수
Text

Distinct20
Distinct (%)95.2%
Missing0
Missing (%)0.0%
Memory size300.0 B
2024-03-18T14:27:33.386924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length8
Mean length11.285714
Min length7

Characters and Unicode

Total characters237
Distinct characters15
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19 ?
Unique (%)90.5%

Sample

1st row600HP×5대, 1350HP×7대
2nd row600HP×5대, 1350HP×7대
3rd row310HP×4대
4th row150HP×3대
5th row1000HP×7대
ValueCountFrequency (%)
600hp×5대 2
 
6.5%
1350hp×7대 2
 
6.5%
750hp×7대 1
 
3.2%
80hp×4대 1
 
3.2%
113hp×3대 1
 
3.2%
260hp×3대 1
 
3.2%
280hp×2대 1
 
3.2%
300hp×3대 1
 
3.2%
75hp×3대 1
 
3.2%
420hp×4대 1
 
3.2%
Other values (19) 19
61.3%
2024-03-18T14:27:33.686674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 31
13.1%
H 28
11.8%
P 28
11.8%
대 28
11.8%
× 25
10.5%
1 19
8.0%
5 15
6.3%
3 13
5.5%
10
 
4.2%
2 10
 
4.2%
Other values (5) 30
12.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 111
46.8%
Uppercase Letter 56
23.6%
Other Letter 28
 
11.8%
Math Symbol 25
 
10.5%
Space Separator 10
 
4.2%
Other Punctuation 7
 
3.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 31
27.9%
1 19
17.1%
5 15
13.5%
3 13
11.7%
2 10
 
9.0%
4 9
 
8.1%
7 8
 
7.2%
6 4
 
3.6%
8 2
 
1.8%
Uppercase Letter
ValueCountFrequency (%)
H 28
50.0%
P 28
50.0%
Other Letter
ValueCountFrequency (%)
대 28
100.0%
Math Symbol
ValueCountFrequency (%)
× 25
100.0%
Space Separator
ValueCountFrequency (%)
10
100.0%
Other Punctuation
ValueCountFrequency (%)
, 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 153
64.6%
Latin 56
 
23.6%
Hangul 28
 
11.8%

Most frequent character per script

Common
ValueCountFrequency (%)
0 31
20.3%
× 25
16.3%
1 19
12.4%
5 15
9.8%
3 13
8.5%
10
 
6.5%
2 10
 
6.5%
4 9
 
5.9%
7 8
 
5.2%
, 7
 
4.6%
Other values (2) 6
 
3.9%
Latin
ValueCountFrequency (%)
H 28
50.0%
P 28
50.0%
Hangul
ValueCountFrequency (%)
대 28
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 184
77.6%
Hangul 28
 
11.8%
None 25
 
10.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 31
16.8%
H 28
15.2%
P 28
15.2%
1 19
10.3%
5 15
8.2%
3 13
7.1%
10
 
5.4%
2 10
 
5.4%
4 9
 
4.9%
7 8
 
4.3%
Other values (3) 13
7.1%
Hangul
ValueCountFrequency (%)
대 28
100.0%
None
ValueCountFrequency (%)
× 25
100.0%

토출량(분)
Real number (ℝ)

HIGH CORRELATION 

Distinct19
Distinct (%)90.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1294.0476
Minimum60
Maximum5700
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2024-03-18T14:27:33.790661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum60
5-th percentile65
Q1180
median400
Q31250
95-th percentile5700
Maximum5700
Range5640
Interquartile range (IQR)1070

Descriptive statistics

Standard deviation1795.5334
Coefficient of variation (CV)1.3875327
Kurtosis1.811821
Mean1294.0476
Median Absolute Deviation (MAD)310
Skewness1.6996361
Sum27175
Variance3223940.3
MonotonicityNot monotonic
2024-03-18T14:27:33.880703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
5700 2
 
9.5%
180 2
 
9.5%
210 1
 
4.8%
90 1
 
4.8%
240 1
 
4.8%
619 1
 
4.8%
60 1
 
4.8%
360 1
 
4.8%
152 1
 
4.8%
480 1
 
4.8%
Other values (9) 9
42.9%
ValueCountFrequency (%)
60 1
4.8%
65 1
4.8%
90 1
4.8%
152 1
4.8%
180 2
9.5%
210 1
4.8%
240 1
4.8%
340 1
4.8%
360 1
4.8%
400 1
4.8%
ValueCountFrequency (%)
5700 2
9.5%
3640 1
4.8%
3269 1
4.8%
2550 1
4.8%
1250 1
4.8%
920 1
4.8%
770 1
4.8%
619 1
4.8%
480 1
4.8%
400 1
4.8%

배수유역
Real number (ℝ)

HIGH CORRELATION 

Distinct19
Distinct (%)90.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean715.18095
Minimum2.9
Maximum3980
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2024-03-18T14:27:33.989510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.9
5-th percentile3.4
Q147
median67
Q3450
95-th percentile3100
Maximum3980
Range3977.1
Interquartile range (IQR)403

Descriptive statistics

Standard deviation1219.7491
Coefficient of variation (CV)1.7055112
Kurtosis2.0501561
Mean715.18095
Median Absolute Deviation (MAD)63.6
Skewness1.8014916
Sum15018.8
Variance1487787.9
MonotonicityNot monotonic
2024-03-18T14:27:34.102266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
3100.0 2
 
9.5%
3.4 2
 
9.5%
11.0 1
 
4.8%
1654.0 1
 
4.8%
60.0 1
 
4.8%
47.0 1
 
4.8%
339.0 1
 
4.8%
2.9 1
 
4.8%
300.0 1
 
4.8%
59.0 1
 
4.8%
Other values (9) 9
42.9%
ValueCountFrequency (%)
2.9 1
4.8%
3.4 2
9.5%
10.0 1
4.8%
11.0 1
4.8%
47.0 1
4.8%
53.0 1
4.8%
54.8 1
4.8%
59.0 1
4.8%
60.0 1
4.8%
67.0 1
4.8%
ValueCountFrequency (%)
3980.0 1
4.8%
3100.0 2
9.5%
1654.0 1
4.8%
1499.0 1
4.8%
450.0 1
4.8%
339.0 1
4.8%
300.0 1
4.8%
136.7 1
4.8%
88.6 1
4.8%
67.0 1
4.8%

설치목적
Categorical

CONSTANT 

Distinct1
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Memory size300.0 B
빗물 배수펌프
21 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row빗물 배수펌프
2nd row빗물 배수펌프
3rd row빗물 배수펌프
4th row빗물 배수펌프
5th row빗물 배수펌프

Common Values

ValueCountFrequency (%)
빗물 배수펌프 21
100.0%

Length

2024-03-18T14:27:34.250772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-18T14:27:34.340472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
빗물 21
50.0%
배수펌프 21
50.0%

설치년도
Real number (ℝ)

Distinct13
Distinct (%)61.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2003.5714
Minimum1987
Maximum2018
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2024-03-18T14:27:34.433054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1987
5-th percentile1988
Q11993
median2004
Q32015
95-th percentile2018
Maximum2018
Range31
Interquartile range (IQR)22

Descriptive statistics

Standard deviation11.478551
Coefficient of variation (CV)0.0057290453
Kurtosis-1.5958472
Mean2003.5714
Median Absolute Deviation (MAD)11
Skewness-0.20862088
Sum42075
Variance131.75714
MonotonicityNot monotonic
2024-03-18T14:27:34.525255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
2015 5
23.8%
1988 3
14.3%
2001 2
 
9.5%
2018 2
 
9.5%
1990 1
 
4.8%
1994 1
 
4.8%
2004 1
 
4.8%
2007 1
 
4.8%
1993 1
 
4.8%
2000 1
 
4.8%
Other values (3) 3
14.3%
ValueCountFrequency (%)
1987 1
 
4.8%
1988 3
14.3%
1990 1
 
4.8%
1993 1
 
4.8%
1994 1
 
4.8%
2000 1
 
4.8%
2001 2
9.5%
2004 1
 
4.8%
2007 1
 
4.8%
2009 1
 
4.8%
ValueCountFrequency (%)
2018 2
 
9.5%
2015 5
23.8%
2014 1
 
4.8%
2009 1
 
4.8%
2007 1
 
4.8%
2004 1
 
4.8%
2001 2
 
9.5%
2000 1
 
4.8%
1994 1
 
4.8%
1993 1
 
4.8%

Interactions

2024-03-18T14:27:31.324351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:27:30.877738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:27:31.103495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:27:31.393050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:27:30.960834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:27:31.174538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:27:31.474477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:27:31.033991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:27:31.253025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-18T14:27:34.610383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분명칭위치규모 및 대수토출량(분)배수유역설치년도
구분1.0001.0001.0001.0000.7630.7760.715
명칭1.0001.0001.0001.0001.0001.0000.797
위치1.0001.0001.0001.0001.0001.0000.797
규모 및 대수1.0001.0001.0001.0001.0001.0000.797
토출량(분)0.7631.0001.0001.0001.0000.9270.707
배수유역0.7761.0001.0001.0000.9271.0000.836
설치년도0.7150.7970.7970.7970.7070.8361.000
2024-03-18T14:27:34.710191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
토출량(분)배수유역설치년도구분
토출량(분)1.0000.680-0.2950.335
배수유역0.6801.000-0.3810.571
설치년도-0.295-0.3811.0000.404
구분0.3350.5710.4041.000

Missing values

2024-03-18T14:27:31.823977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-18T14:27:31.947681image/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

구분명칭위치규모 및 대수토출량(분)배수유역설치목적설치년도
0동구인천교매립지 펌프장중봉대로 113-134(송현동 147)600HP×5대, 1350HP×7대57003100.0빗물 배수펌프1990
1동구인천교매립지 펌프장중봉대로 113-134(송현동 147)600HP×5대, 1350HP×7대57003100.0빗물 배수펌프1994
2미추홀구용현펌프장아암대로 43(용현동 574-10)310HP×4대77067.0빗물 배수펌프1988
3미추홀구백운펌프장아암대로 135(용현동 627-76)150HP×3대18053.0빗물 배수펌프2001
4미추홀구학익펌프장아암대로 338(학익동 729)1000HP×7대36401499.0빗물 배수펌프2004
5남동구구월펌프장문화서로23번길 52(구월동 1341-10)150HP 3대, 112HP 1대34010.0빗물 배수펌프1988
6남동구남동펌프장아암대로 1039(고잔동 711)450HP 7대25503980.0빗물 배수펌프1988
7남동구소래펌프장앵고개로941번길 8-4(논현동 668-4)50HP×4대653.4빗물 배수펌프2007
8남동구서창1펌프장서창방산로133번길 94(서창동 747)200HP×4대40054.8빗물 배수펌프2015
9남동구서창2펌프장서창남순환로 124-46(서창동 634)620HP×4대92088.6빗물 배수펌프2015
구분명칭위치규모 및 대수토출량(분)배수유역설치목적설치년도
11부평구삼산(1)펌프장체육관로 161(삼산동 450-1)750HP×7대3269450.0빗물 배수펌프2001
12부평구삼산(2)펌프장영성동로 19(삼산동 393-2)100HP×3대21011.0빗물 배수펌프2000
13계양구서운펌프장계양구 서운동 176210HP×3대48059.0빗물 배수펌프2018
14서구가좌펌프장가재울로6번길 2(가좌동 550)210HP×1대, 150HP×1대, 15HP×2대152300.0빗물 배수펌프1987
15서구검단펌프장원당대로157번길 8(오류동 1665-1)420HP×4대3603.4빗물 배수펌프2014
16서구가정펌프장새오개로111번길 7(신현동 307-14)75HP×3대602.9빗물 배수펌프2018
17서구경서펌프장서구 오류동 1608300HP×3대, 280HP×2대, 260HP×3대619339.0빗물 배수펌프2015
18서구왕길펌프장서구 왕길동 64-308113HP×3대18047.0빗물 배수펌프2015
19서구백석펌프장서구 백석동 152-222외 1필지80HP×4대24060.0빗물 배수펌프2015
20강화군길정천펌프장보리고개로 89(선두리 1210-1)170HP×5대901654.0빗물 배수펌프2009