Overview

Dataset statistics

Number of variables7
Number of observations47
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.9 KiB
Average record size in memory62.8 B

Variable types

Numeric4
Text3

Dataset

Description파일 다운로드
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-1168/S/1/datasetView.do

Alerts

강우량계 코드 is highly overall correlated with 구청 코드 and 2 other fieldsHigh correlation
구청 코드 is highly overall correlated with 강우량계 코드 and 2 other fieldsHigh correlation
설치위치(X좌표) is highly overall correlated with 강우량계 코드 and 1 other fieldsHigh correlation
설치위치(Y좌표) is highly overall correlated with 강우량계 코드 and 1 other fieldsHigh correlation
강우량계 코드 has unique valuesUnique
강우량계명 has unique valuesUnique
주소 has unique valuesUnique
설치위치(X좌표) has unique valuesUnique
설치위치(Y좌표) has unique valuesUnique

Reproduction

Analysis started2024-05-04 03:55:27.858800
Analysis finished2024-05-04 03:55:34.545983
Duration6.69 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

강우량계 코드
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct47
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1335.617
Minimum101
Maximum2502
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size555.0 B
2024-05-04T03:55:34.928897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum101
5-th percentile132.4
Q1751.5
median1303
Q31951.5
95-th percentile2401.7
Maximum2502
Range2401
Interquartile range (IQR)1200

Descriptive statistics

Standard deviation733.09521
Coefficient of variation (CV)0.5488813
Kurtosis-1.1475673
Mean1335.617
Median Absolute Deviation (MAD)601
Skewness-0.10041629
Sum62774
Variance537428.59
MonotonicityStrictly increasing
2024-05-04T03:55:35.668249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
101 1
 
2.1%
102 1
 
2.1%
1502 1
 
2.1%
1601 1
 
2.1%
1602 1
 
2.1%
1701 1
 
2.1%
1702 1
 
2.1%
1801 1
 
2.1%
1802 1
 
2.1%
1901 1
 
2.1%
Other values (37) 37
78.7%
ValueCountFrequency (%)
101 1
2.1%
102 1
2.1%
103 1
2.1%
201 1
2.1%
202 1
2.1%
401 1
2.1%
402 1
2.1%
501 1
2.1%
601 1
2.1%
602 1
2.1%
ValueCountFrequency (%)
2502 1
2.1%
2501 1
2.1%
2402 1
2.1%
2401 1
2.1%
2302 1
2.1%
2301 1
2.1%
2202 1
2.1%
2201 1
2.1%
2102 1
2.1%
2101 1
2.1%

강우량계명
Text

UNIQUE 

Distinct47
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size508.0 B
2024-05-04T03:55:36.401082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length3.787234
Min length3

Characters and Unicode

Total characters178
Distinct characters69
Distinct categories3 ?
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 row강남구청
2nd row세곡동
3rd row개포2동
4th row강동구청
5th row고덕2동
ValueCountFrequency (%)
강남구청 1
 
2.1%
서대문구청 1
 
2.1%
봉원p 1
 
2.1%
용산구청 1
 
2.1%
한남p 1
 
2.1%
강서구청 1
 
2.1%
공항동p 1
 
2.1%
양천구청 1
 
2.1%
목동p 1
 
2.1%
영등포구청 1
 
2.1%
Other values (37) 37
78.7%
2024-05-04T03:55:37.629070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
구 25
 
14.0%
청 24
 
13.5%
동 16
 
9.0%
P 13
 
7.3%
2 6
 
3.4%
포 4
 
2.2%
서 4
 
2.2%
강 4
 
2.2%
문 3
 
1.7%
산 3
 
1.7%
Other values (59) 76
42.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 157
88.2%
Uppercase Letter 13
 
7.3%
Decimal Number 8
 
4.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 25
 
15.9%
청 24
 
15.3%
동 16
 
10.2%
포 4
 
2.5%
서 4
 
2.5%
강 4
 
2.5%
문 3
 
1.9%
산 3
 
1.9%
천 3
 
1.9%
마 2
 
1.3%
Other values (56) 69
43.9%
Decimal Number
ValueCountFrequency (%)
2 6
75.0%
1 2
 
25.0%
Uppercase Letter
ValueCountFrequency (%)
P 13
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 157
88.2%
Latin 13
 
7.3%
Common 8
 
4.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 25
 
15.9%
청 24
 
15.3%
동 16
 
10.2%
포 4
 
2.5%
서 4
 
2.5%
강 4
 
2.5%
문 3
 
1.9%
산 3
 
1.9%
천 3
 
1.9%
마 2
 
1.3%
Other values (56) 69
43.9%
Common
ValueCountFrequency (%)
2 6
75.0%
1 2
 
25.0%
Latin
ValueCountFrequency (%)
P 13
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 157
88.2%
ASCII 21
 
11.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
구 25
 
15.9%
청 24
 
15.3%
동 16
 
10.2%
포 4
 
2.5%
서 4
 
2.5%
강 4
 
2.5%
문 3
 
1.9%
산 3
 
1.9%
천 3
 
1.9%
마 2
 
1.3%
Other values (56) 69
43.9%
ASCII
ValueCountFrequency (%)
P 13
61.9%
2 6
28.6%
1 2
 
9.5%

구청 코드
Real number (ℝ)

HIGH CORRELATION 

Distinct24
Distinct (%)51.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean113.34043
Minimum101
Maximum125
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size555.0 B
2024-05-04T03:55:38.031250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum101
5-th percentile101.3
Q1107.5
median113
Q3119.5
95-th percentile124
Maximum125
Range24
Interquartile range (IQR)12

Descriptive statistics

Standard deviation7.331518
Coefficient of variation (CV)0.064685817
Kurtosis-1.1473764
Mean113.34043
Median Absolute Deviation (MAD)6
Skewness-0.10042617
Sum5327
Variance53.751156
MonotonicityIncreasing
2024-05-04T03:55:38.424529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
101 3
 
6.4%
113 3
 
6.4%
121 2
 
4.3%
120 2
 
4.3%
119 2
 
4.3%
118 2
 
4.3%
117 2
 
4.3%
116 2
 
4.3%
115 2
 
4.3%
102 2
 
4.3%
Other values (14) 25
53.2%
ValueCountFrequency (%)
101 3
6.4%
102 2
4.3%
104 2
4.3%
105 1
 
2.1%
106 2
4.3%
107 2
4.3%
108 2
4.3%
109 2
4.3%
110 2
4.3%
111 2
4.3%
ValueCountFrequency (%)
125 2
4.3%
124 2
4.3%
123 2
4.3%
122 2
4.3%
121 2
4.3%
120 2
4.3%
119 2
4.3%
118 2
4.3%
117 2
4.3%
116 2
4.3%

구청명
Text

Distinct24
Distinct (%)51.1%
Missing0
Missing (%)0.0%
Memory size508.0 B
2024-05-04T03:55:38.818048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.0638298
Min length2

Characters and Unicode

Total characters144
Distinct characters34
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)6.4%

Sample

1st row강남구
2nd row강남구
3rd row강남구
4th row강동구
5th row강동구
ValueCountFrequency (%)
강남구 3
 
6.4%
은평구 3
 
6.4%
송파구 2
 
4.3%
마포구 2
 
4.3%
서초구 2
 
4.3%
관악구 2
 
4.3%
금천구 2
 
4.3%
동작구 2
 
4.3%
구로구 2
 
4.3%
영등포구 2
 
4.3%
Other values (14) 25
53.2%
2024-05-04T03:55:39.684341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
구 49
34.0%
강 8
 
5.6%
동 8
 
5.6%
서 5
 
3.5%
성 4
 
2.8%
포 4
 
2.8%
천 4
 
2.8%
중 4
 
2.8%
로 4
 
2.8%
대 3
 
2.1%
Other values (24) 51
35.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 144
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 49
34.0%
강 8
 
5.6%
동 8
 
5.6%
서 5
 
3.5%
성 4
 
2.8%
포 4
 
2.8%
천 4
 
2.8%
중 4
 
2.8%
로 4
 
2.8%
대 3
 
2.1%
Other values (24) 51
35.4%

Most occurring scripts

ValueCountFrequency (%)
Hangul 144
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 49
34.0%
강 8
 
5.6%
동 8
 
5.6%
서 5
 
3.5%
성 4
 
2.8%
포 4
 
2.8%
천 4
 
2.8%
중 4
 
2.8%
로 4
 
2.8%
대 3
 
2.1%
Other values (24) 51
35.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 144
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
구 49
34.0%
강 8
 
5.6%
동 8
 
5.6%
서 5
 
3.5%
성 4
 
2.8%
포 4
 
2.8%
천 4
 
2.8%
중 4
 
2.8%
로 4
 
2.8%
대 3
 
2.1%
Other values (24) 51
35.4%

주소
Text

UNIQUE 

Distinct47
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size508.0 B
2024-05-04T03:55:40.353545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length34
Median length29
Mean length26.085106
Min length20

Characters and Unicode

Total characters1226
Distinct characters120
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

Unique47 ?
Unique (%)100.0%

Sample

1st row서울특별시 강남구 삼성동 16-1 강남구청 별관
2nd row서울특별시 강남구 율현동 278-1 세곡동사무소
3rd row서울특별시 강남구 개포동 182-1 개포2동 사무소
4th row서울특별시 강동구 성내동 강동구청 540
5th row서울특별시 강동구 고덕2동 226-4 고덕2동사무소
ValueCountFrequency (%)
서울특별시 47
 
19.5%
은평구 3
 
1.2%
강남구 3
 
1.2%
사무소 3
 
1.2%
영등포구 2
 
0.8%
양천구 2
 
0.8%
금천구 2
 
0.8%
관악구 2
 
0.8%
종로구 2
 
0.8%
상계동 2
 
0.8%
Other values (158) 173
71.8%
2024-05-04T03:55:41.675367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
195
 
15.9%
구 76
 
6.2%
동 68
 
5.5%
서 56
 
4.6%
별 50
 
4.1%
시 48
 
3.9%
특 47
 
3.8%
울 47
 
3.8%
1 43
 
3.5%
2 37
 
3.0%
Other values (110) 559
45.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 792
64.6%
Decimal Number 199
 
16.2%
Space Separator 195
 
15.9%
Dash Punctuation 30
 
2.4%
Close Punctuation 5
 
0.4%
Open Punctuation 5
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 76
 
9.6%
동 68
 
8.6%
서 56
 
7.1%
별 50
 
6.3%
시 48
 
6.1%
특 47
 
5.9%
울 47
 
5.9%
청 25
 
3.2%
장 13
 
1.6%
펌 13
 
1.6%
Other values (96) 349
44.1%
Decimal Number
ValueCountFrequency (%)
1 43
21.6%
2 37
18.6%
6 20
10.1%
7 17
 
8.5%
5 17
 
8.5%
3 16
 
8.0%
4 16
 
8.0%
8 12
 
6.0%
0 11
 
5.5%
9 10
 
5.0%
Space Separator
ValueCountFrequency (%)
195
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 30
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 792
64.6%
Common 434
35.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 76
 
9.6%
동 68
 
8.6%
서 56
 
7.1%
별 50
 
6.3%
시 48
 
6.1%
특 47
 
5.9%
울 47
 
5.9%
청 25
 
3.2%
장 13
 
1.6%
펌 13
 
1.6%
Other values (96) 349
44.1%
Common
ValueCountFrequency (%)
195
44.9%
1 43
 
9.9%
2 37
 
8.5%
- 30
 
6.9%
6 20
 
4.6%
7 17
 
3.9%
5 17
 
3.9%
3 16
 
3.7%
4 16
 
3.7%
8 12
 
2.8%
Other values (4) 31
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 792
64.6%
ASCII 434
35.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
195
44.9%
1 43
 
9.9%
2 37
 
8.5%
- 30
 
6.9%
6 20
 
4.6%
7 17
 
3.9%
5 17
 
3.9%
3 16
 
3.7%
4 16
 
3.7%
8 12
 
2.8%
Other values (4) 31
 
7.1%
Hangul
ValueCountFrequency (%)
구 76
 
9.6%
동 68
 
8.6%
서 56
 
7.1%
별 50
 
6.3%
시 48
 
6.1%
특 47
 
5.9%
울 47
 
5.9%
청 25
 
3.2%
장 13
 
1.6%
펌 13
 
1.6%
Other values (96) 349
44.1%

설치위치(X좌표)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct47
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44991775
Minimum43972750
Maximum46446690
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size555.0 B
2024-05-04T03:55:42.274413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum43972750
5-th percentile44188760
Q144540090
median44936460
Q345340870
95-th percentile46112893
Maximum46446690
Range2473940
Interquartile range (IQR)800780

Descriptive statistics

Standard deviation600138.68
Coefficient of variation (CV)0.013338853
Kurtosis-0.24188808
Mean44991775
Median Absolute Deviation (MAD)411550
Skewness0.50603557
Sum2.1146134 × 109
Variance3.6016643 × 1011
MonotonicityNot monotonic
2024-05-04T03:55:42.850958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
44645510 1
 
2.1%
44107740 1
 
2.1%
44936460 1
 
2.1%
44812500 1
 
2.1%
44774390 1
 
2.1%
45016190 1
 
2.1%
45081270 1
 
2.1%
44640470 1
 
2.1%
44760150 1
 
2.1%
44742410 1
 
2.1%
Other values (37) 37
78.7%
ValueCountFrequency (%)
43972750 1
2.1%
44107740 1
2.1%
44180249 1
2.1%
44208620 1
2.1%
44270030 1
2.1%
44281840 1
2.1%
44337240 1
2.1%
44349540 1
2.1%
44398740 1
2.1%
44417470 1
2.1%
ValueCountFrequency (%)
46446690 1
2.1%
46322960 1
2.1%
46161610 1
2.1%
45999220 1
2.1%
45823380 1
2.1%
45634240 1
2.1%
45624950 1
2.1%
45590180 1
2.1%
45478040 1
2.1%
45463610 1
2.1%

설치위치(Y좌표)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct47
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19892204
Minimum18381700
Maximum21452360
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size555.0 B
2024-05-04T03:55:43.417964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum18381700
5-th percentile18758050
Q119166020
median19949280
Q320453920
95-th percentile21049301
Maximum21452360
Range3070660
Interquartile range (IQR)1287900

Descriptive statistics

Standard deviation779185.14
Coefficient of variation (CV)0.039170377
Kurtosis-0.99604225
Mean19892204
Median Absolute Deviation (MAD)653960
Skewness0.024462615
Sum9.349336 × 108
Variance6.0712948 × 1011
MonotonicityNot monotonic
2024-05-04T03:55:44.009893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
20419810 1
 
2.1%
20946730 1
 
2.1%
19382650 1
 
2.1%
19912110 1
 
2.1%
20068210 1
 
2.1%
18671560 1
 
2.1%
18381700 1
 
2.1%
18819580 1
 
2.1%
18919870 1
 
2.1%
19080280 1
 
2.1%
Other values (37) 37
78.7%
ValueCountFrequency (%)
18381700 1
2.1%
18671560 1
2.1%
18731680 1
2.1%
18819580 1
2.1%
18919870 1
2.1%
18926290 1
2.1%
19006520 1
2.1%
19075530 1
2.1%
19080280 1
2.1%
19092950 1
2.1%
ValueCountFrequency (%)
21452360 1
2.1%
21313980 1
2.1%
21093260 1
2.1%
20946730 1
2.1%
20937880 1
2.1%
20820370 1
2.1%
20728680 1
2.1%
20706810 1
2.1%
20610870 1
2.1%
20603240 1
2.1%

Interactions

2024-05-04T03:55:32.247105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:55:28.634063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:55:29.764532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:55:30.878857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:55:32.495559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:55:28.871218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:55:30.013656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:55:31.192332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:55:32.791380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:55:29.108452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:55:30.254449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:55:31.583888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:55:33.060546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:55:29.393693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:55:30.562517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:55:31.913742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-04T03:55:44.424320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
강우량계 코드강우량계명구청 코드구청명주소설치위치(X좌표)설치위치(Y좌표)
강우량계 코드1.0001.0000.9990.9951.0000.8080.719
강우량계명1.0001.0001.0001.0001.0001.0001.000
구청 코드0.9991.0001.0001.0001.0000.7810.696
구청명0.9951.0001.0001.0001.0000.7880.734
주소1.0001.0001.0001.0001.0001.0001.000
설치위치(X좌표)0.8081.0000.7810.7881.0001.0000.000
설치위치(Y좌표)0.7191.0000.6960.7341.0000.0001.000
2024-05-04T03:55:44.871466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
강우량계 코드구청 코드설치위치(X좌표)설치위치(Y좌표)
강우량계 코드1.0000.999-0.603-0.553
구청 코드0.9991.000-0.601-0.553
설치위치(X좌표)-0.603-0.6011.0000.240
설치위치(Y좌표)-0.553-0.5530.2401.000

Missing values

2024-05-04T03:55:33.642575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-04T03:55:34.210913image/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

강우량계 코드강우량계명구청 코드구청명주소설치위치(X좌표)설치위치(Y좌표)
0101강남구청101강남구서울특별시 강남구 삼성동 16-1 강남구청 별관4464551020419810
1102세곡동101강남구서울특별시 강남구 율현동 278-1 세곡동사무소4410774020946730
2103개포2동101강남구서울특별시 강남구 개포동 182-1 개포2동 사무소4433724020603240
3201강동구청102강동구서울특별시 강동구 성내동 강동구청 5404478514021093260
4202고덕2동102강동구서울특별시 강동구 고덕2동 226-4 고덕2동사무소4512372021452360
5401노원구청104노원구서울특별시 도봉구 방학동 720 도봉구청4632296020415540
6402상계1동104노원구서울특별시 노원구 상계동 701-1 노원구청4616161020499990
7501강북구청105강북구서울특별시 노원구 상계동 1259 상계1동 사무소4644669020484150
8601성북구청106성북구서울특별시 강북구 수유동 95(강북구청 별관)4599922020224820
9602상월곡동106성북구서울특별시 성북구 보문동 보문로 168 성북구청(별관)4542031020183300
강우량계 코드강우량계명구청 코드구청명주소설치위치(X좌표)설치위치(Y좌표)
372101동작구청121동작구서울특별시 동작구 노량진동 47-2 동작구청4458700019464780
382102흑석P121동작구서울특별시 동작구 흑석동 114번지 흑석빗물펌프장4455527019677120
392201금천구청122금천구서울특별시 금천구 시흥동 1020 금천구청4397275019075530
402202가산2P122금천구서울특별시 금천구 가산동 550-22 가산1펌프장4418024918926290
412301관악구청123관악구서울특별시 관악구 봉천4동 1570-1 관악구청4420862019573270
422302신림P123관악구서울특별시 관악구 신림8동 1649 신림빗물펌프장4428184019188710
432401서초구청124서초구서울특별시 서초구 서초동 1376-3 서초구청4427003020288210
442402반포P124서초구서울특별시 서초구 반포2동 15-2 반포펌프장4445638019949280
452501송파구청125송파구서울특별시 송파구 신천동 29-5(올림픽로 326) 송파구청4461311020937880
462502마천2동125송파구서울특별시 송파구 마천2동 127-1 마천 2동 사무소4441747021313980