Overview

Dataset statistics

Number of variables9
Number of observations48
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.6 KiB
Average record size in memory77.8 B

Variable types

Numeric3
Text2
Categorical4

Dataset

Description파주시 주요 하천 및 수변 산책로 등 집중호우 시 통제기준에 따라 상황에 맞게 자동음성 안내(경보)방송을 전파하여 시민의 생명과 재산피해를 최소화하기 위한 시설
URLhttps://www.data.go.kr/data/15100337/fileData.do

Alerts

측기검정일자 is highly overall correlated with 순번 and 1 other fieldsHigh correlation
설치일자 is highly overall correlated with 순번 and 1 other fieldsHigh correlation
순번 is highly overall correlated with 경도 and 3 other fieldsHigh correlation
위도 is highly overall correlated with 경도 and 2 other fieldsHigh correlation
경도 is highly overall correlated with 순번 and 2 other fieldsHigh correlation
지구명 is highly overall correlated with 위도 and 1 other fieldsHigh correlation
측기 is highly overall correlated with 순번 and 1 other fieldsHigh correlation
순번 has unique valuesUnique
관측소명 has unique valuesUnique
상세위치 has unique valuesUnique
위도 has unique valuesUnique
경도 has unique valuesUnique

Reproduction

Analysis started2023-12-12 21:18:46.986370
Analysis finished2023-12-12 21:18:48.490957
Duration1.5 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct48
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.5
Minimum1
Maximum48
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size564.0 B
2023-12-13T06:18:48.559584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.35
Q112.75
median24.5
Q336.25
95-th percentile45.65
Maximum48
Range47
Interquartile range (IQR)23.5

Descriptive statistics

Standard deviation14
Coefficient of variation (CV)0.57142857
Kurtosis-1.2
Mean24.5
Median Absolute Deviation (MAD)12
Skewness0
Sum1176
Variance196
MonotonicityStrictly increasing
2023-12-13T06:18:48.709785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
1 1
 
2.1%
26 1
 
2.1%
28 1
 
2.1%
29 1
 
2.1%
30 1
 
2.1%
31 1
 
2.1%
32 1
 
2.1%
33 1
 
2.1%
34 1
 
2.1%
35 1
 
2.1%
Other values (38) 38
79.2%
ValueCountFrequency (%)
1 1
2.1%
2 1
2.1%
3 1
2.1%
4 1
2.1%
5 1
2.1%
6 1
2.1%
7 1
2.1%
8 1
2.1%
9 1
2.1%
10 1
2.1%
ValueCountFrequency (%)
48 1
2.1%
47 1
2.1%
46 1
2.1%
45 1
2.1%
44 1
2.1%
43 1
2.1%
42 1
2.1%
41 1
2.1%
40 1
2.1%
39 1
2.1%

관측소명
Text

UNIQUE 

Distinct48
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size516.0 B
2023-12-13T06:18:49.007355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length10
Mean length5.1041667
Min length3

Characters and Unicode

Total characters245
Distinct characters121
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

Unique48 ?
Unique (%)100.0%

Sample

1st row애룡교
2nd row연풍2교
3rd row설원교
4th row웅담새마을교
5th row웅비교
ValueCountFrequency (%)
옆 2
 
3.2%
배수펌프장 2
 
3.2%
애룡교 1
 
1.6%
연풍2교 1
 
1.6%
금촌기존펌프장 1
 
1.6%
도마산교 1
 
1.6%
토파즈 1
 
1.6%
아파트 1
 
1.6%
배드민턴장 1
 
1.6%
건너편 1
 
1.6%
Other values (50) 50
80.6%
2023-12-13T06:18:49.492766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
교 20
 
8.2%
14
 
5.7%
장 8
 
3.3%
지 8
 
3.3%
곡 7
 
2.9%
산 7
 
2.9%
배 5
 
2.0%
천 5
 
2.0%
원 5
 
2.0%
문 4
 
1.6%
Other values (111) 162
66.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 225
91.8%
Space Separator 14
 
5.7%
Decimal Number 4
 
1.6%
Close Punctuation 1
 
0.4%
Open Punctuation 1
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
교 20
 
8.9%
장 8
 
3.6%
지 8
 
3.6%
곡 7
 
3.1%
산 7
 
3.1%
배 5
 
2.2%
천 5
 
2.2%
원 5
 
2.2%
문 4
 
1.8%
수 4
 
1.8%
Other values (106) 152
67.6%
Decimal Number
ValueCountFrequency (%)
2 3
75.0%
7 1
 
25.0%
Space Separator
ValueCountFrequency (%)
14
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 225
91.8%
Common 20
 
8.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
교 20
 
8.9%
장 8
 
3.6%
지 8
 
3.6%
곡 7
 
3.1%
산 7
 
3.1%
배 5
 
2.2%
천 5
 
2.2%
원 5
 
2.2%
문 4
 
1.8%
수 4
 
1.8%
Other values (106) 152
67.6%
Common
ValueCountFrequency (%)
14
70.0%
2 3
 
15.0%
) 1
 
5.0%
( 1
 
5.0%
7 1
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 225
91.8%
ASCII 20
 
8.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
교 20
 
8.9%
장 8
 
3.6%
지 8
 
3.6%
곡 7
 
3.1%
산 7
 
3.1%
배 5
 
2.2%
천 5
 
2.2%
원 5
 
2.2%
문 4
 
1.8%
수 4
 
1.8%
Other values (106) 152
67.6%
ASCII
ValueCountFrequency (%)
14
70.0%
2 3
 
15.0%
) 1
 
5.0%
( 1
 
5.0%
7 1
 
5.0%

지구명
Categorical

HIGH CORRELATION 

Distinct15
Distinct (%)31.2%
Missing0
Missing (%)0.0%
Memory size516.0 B
적성
10 
법원
6 
광탄
6 
파평
6 
문산
4 
Other values (10)
16 

Length

Max length4
Median length2
Mean length2.125
Min length2

Unique

Unique5 ?
Unique (%)10.4%

Sample

1st row파주
2nd row파주
3rd row법원
4th row법원
5th row법원

Common Values

ValueCountFrequency (%)
적성 10
20.8%
법원 6
12.5%
광탄 6
12.5%
파평 6
12.5%
문산 4
 
8.3%
교하 3
 
6.2%
파주 2
 
4.2%
조리 2
 
4.2%
금촌 2
 
4.2%
광탄면 2
 
4.2%
Other values (5) 5
10.4%

Length

2023-12-13T06:18:49.650004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
적성 10
20.8%
법원 6
12.5%
광탄 6
12.5%
파평 6
12.5%
문산 4
 
8.3%
교하 3
 
6.2%
파주 2
 
4.2%
조리 2
 
4.2%
금촌 2
 
4.2%
광탄면 2
 
4.2%
Other values (5) 5
10.4%

상세위치
Text

UNIQUE 

Distinct48
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size516.0 B
2023-12-13T06:18:49.962268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length13
Mean length12.083333
Min length7

Characters and Unicode

Total characters580
Distinct characters72
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

Unique48 ?
Unique (%)100.0%

Sample

1st row파주읍 연풍리 7-3
2nd row파주읍 연풍리 372-42
3rd row법원읍 직천리 667-3
4th row법원읍 웅담리 308-10
5th row법원읍 웅담리 395-3
ValueCountFrequency (%)
적성면 10
 
7.4%
파평면 7
 
5.1%
광탄면 7
 
5.1%
법원읍 6
 
4.4%
설마리 5
 
3.7%
마장리 4
 
2.9%
선유리 3
 
2.2%
문산읍 3
 
2.2%
율곡리 3
 
2.2%
파주읍 2
 
1.5%
Other values (78) 86
63.2%
2023-12-13T06:18:50.458449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
88
 
15.2%
리 42
 
7.2%
- 42
 
7.2%
3 30
 
5.2%
1 28
 
4.8%
2 25
 
4.3%
4 25
 
4.3%
면 25
 
4.3%
8 16
 
2.8%
7 15
 
2.6%
Other values (62) 244
42.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 270
46.6%
Decimal Number 180
31.0%
Space Separator 88
 
15.2%
Dash Punctuation 42
 
7.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 42
 
15.6%
면 25
 
9.3%
읍 14
 
5.2%
산 12
 
4.4%
마 11
 
4.1%
파 11
 
4.1%
성 10
 
3.7%
동 10
 
3.7%
적 10
 
3.7%
장 7
 
2.6%
Other values (50) 118
43.7%
Decimal Number
ValueCountFrequency (%)
3 30
16.7%
1 28
15.6%
2 25
13.9%
4 25
13.9%
8 16
8.9%
7 15
8.3%
6 12
 
6.7%
0 12
 
6.7%
9 10
 
5.6%
5 7
 
3.9%
Space Separator
ValueCountFrequency (%)
88
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 42
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 310
53.4%
Hangul 270
46.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 42
 
15.6%
면 25
 
9.3%
읍 14
 
5.2%
산 12
 
4.4%
마 11
 
4.1%
파 11
 
4.1%
성 10
 
3.7%
동 10
 
3.7%
적 10
 
3.7%
장 7
 
2.6%
Other values (50) 118
43.7%
Common
ValueCountFrequency (%)
88
28.4%
- 42
13.5%
3 30
 
9.7%
1 28
 
9.0%
2 25
 
8.1%
4 25
 
8.1%
8 16
 
5.2%
7 15
 
4.8%
6 12
 
3.9%
0 12
 
3.9%
Other values (2) 17
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 310
53.4%
Hangul 270
46.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
88
28.4%
- 42
13.5%
3 30
 
9.7%
1 28
 
9.0%
2 25
 
8.1%
4 25
 
8.1%
8 16
 
5.2%
7 15
 
4.8%
6 12
 
3.9%
0 12
 
3.9%
Other values (2) 17
 
5.5%
Hangul
ValueCountFrequency (%)
리 42
 
15.6%
면 25
 
9.3%
읍 14
 
5.2%
산 12
 
4.4%
마 11
 
4.1%
파 11
 
4.1%
성 10
 
3.7%
동 10
 
3.7%
적 10
 
3.7%
장 7
 
2.6%
Other values (50) 118
43.7%

설치일자
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)29.2%
Missing0
Missing (%)0.0%
Memory size516.0 B
2012-06-01
15 
2021-08-13
11 
2022-08-25
4 
2020-06-01
3 
2012-07-01
2 
Other values (9)
13 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique5 ?
Unique (%)10.4%

Sample

1st row2012-06-01
2nd row2012-06-01
3rd row2012-06-01
4th row2012-06-01
5th row2012-06-01

Common Values

ValueCountFrequency (%)
2012-06-01 15
31.2%
2021-08-13 11
22.9%
2022-08-25 4
 
8.3%
2020-06-01 3
 
6.2%
2012-07-01 2
 
4.2%
2013-04-01 2
 
4.2%
2015-05-01 2
 
4.2%
2017-03-01 2
 
4.2%
2019-05-01 2
 
4.2%
2013-08-01 1
 
2.1%
Other values (4) 4
 
8.3%

Length

2023-12-13T06:18:50.627419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2012-06-01 15
31.2%
2021-08-13 11
22.9%
2022-08-25 4
 
8.3%
2020-06-01 3
 
6.2%
2012-07-01 2
 
4.2%
2013-04-01 2
 
4.2%
2015-05-01 2
 
4.2%
2017-03-01 2
 
4.2%
2019-05-01 2
 
4.2%
2013-08-01 1
 
2.1%
Other values (4) 4
 
8.3%

측기검정일자
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)20.8%
Missing0
Missing (%)0.0%
Memory size516.0 B
2015-11-01
17 
2021-08-13
11 
2016-04-01
5 
2022-07-08
4 
2020-06-01
3 
Other values (5)
8 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique2 ?
Unique (%)4.2%

Sample

1st row2015-11-01
2nd row2015-11-01
3rd row2015-11-01
4th row2015-11-01
5th row2015-11-01

Common Values

ValueCountFrequency (%)
2015-11-01 17
35.4%
2021-08-13 11
22.9%
2016-04-01 5
 
10.4%
2022-07-08 4
 
8.3%
2020-06-01 3
 
6.2%
2015-05-01 2
 
4.2%
2017-03-01 2
 
4.2%
2019-05-01 2
 
4.2%
2017-08-01 1
 
2.1%
2021-11-19 1
 
2.1%

Length

2023-12-13T06:18:51.061998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:18:51.209968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2015-11-01 17
35.4%
2021-08-13 11
22.9%
2016-04-01 5
 
10.4%
2022-07-08 4
 
8.3%
2020-06-01 3
 
6.2%
2015-05-01 2
 
4.2%
2017-03-01 2
 
4.2%
2019-05-01 2
 
4.2%
2017-08-01 1
 
2.1%
2021-11-19 1
 
2.1%

측기
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size516.0 B
우량
41 
우량+수위
7 

Length

Max length5
Median length2
Mean length2.4375
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row우량+수위
2nd row우량
3rd row우량+수위
4th row우량+수위
5th row우량+수위

Common Values

ValueCountFrequency (%)
우량 41
85.4%
우량+수위 7
 
14.6%

Length

2023-12-13T06:18:51.372021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:18:51.480015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
우량 41
85.4%
우량+수위 7
 
14.6%

위도
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct48
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.849694
Minimum37.731729
Maximum37.975322
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size564.0 B
2023-12-13T06:18:51.604995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.731729
5-th percentile37.743418
Q137.775063
median37.855465
Q337.926087
95-th percentile37.946817
Maximum37.975322
Range0.243593
Interquartile range (IQR)0.15102369

Descriptive statistics

Standard deviation0.075159731
Coefficient of variation (CV)0.0019857421
Kurtosis-1.4970028
Mean37.849694
Median Absolute Deviation (MAD)0.0756505
Skewness-0.039398265
Sum1816.7853
Variance0.0056489852
MonotonicityNot monotonic
2023-12-13T06:18:51.770597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
37.829398 1
 
2.1%
37.924527 1
 
2.1%
37.755278 1
 
2.1%
37.751035 1
 
2.1%
37.86331 1
 
2.1%
37.76495 1
 
2.1%
37.80161 1
 
2.1%
37.73531 1
 
2.1%
37.76306 1
 
2.1%
37.76388 1
 
2.1%
Other values (38) 38
79.2%
ValueCountFrequency (%)
37.731729 1
2.1%
37.73531 1
2.1%
37.74169 1
2.1%
37.746626 1
2.1%
37.751035 1
2.1%
37.755278 1
2.1%
37.76306 1
2.1%
37.76388 1
2.1%
37.76495 1
2.1%
37.771212 1
2.1%
ValueCountFrequency (%)
37.975322 1
2.1%
37.956699 1
2.1%
37.947342 1
2.1%
37.945843 1
2.1%
37.941402 1
2.1%
37.939087 1
2.1%
37.938728 1
2.1%
37.936863 1
2.1%
37.936059 1
2.1%
37.931449 1
2.1%

경도
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct48
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.85137
Minimum126.72064
Maximum126.94652
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size564.0 B
2023-12-13T06:18:51.944796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.72064
5-th percentile126.74408
Q1126.81145
median126.85455
Q3126.89924
95-th percentile126.9445
Maximum126.94652
Range0.22588
Interquartile range (IQR)0.08778075

Descriptive statistics

Standard deviation0.063925317
Coefficient of variation (CV)0.00050393871
Kurtosis-0.92044159
Mean126.85137
Median Absolute Deviation (MAD)0.044906
Skewness-0.25424955
Sum6088.8659
Variance0.0040864462
MonotonicityNot monotonic
2023-12-13T06:18:52.107753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
126.850925 1
 
2.1%
126.829898 1
 
2.1%
126.733055 1
 
2.1%
126.720636 1
 
2.1%
126.8058 1
 
2.1%
126.77032 1
 
2.1%
126.87952 1
 
2.1%
126.82811 1
 
2.1%
126.75632 1
 
2.1%
126.74213 1
 
2.1%
Other values (38) 38
79.2%
ValueCountFrequency (%)
126.720636 1
2.1%
126.733055 1
2.1%
126.74213 1
2.1%
126.74771 1
2.1%
126.75632 1
2.1%
126.76954 1
2.1%
126.77032 1
2.1%
126.772282 1
2.1%
126.77959 1
2.1%
126.79315 1
2.1%
ValueCountFrequency (%)
126.946516 1
2.1%
126.944781 1
2.1%
126.944564 1
2.1%
126.944392 1
2.1%
126.944296 1
2.1%
126.933549 1
2.1%
126.926392 1
2.1%
126.922431 1
2.1%
126.920871 1
2.1%
126.914946 1
2.1%

Interactions

2023-12-13T06:18:47.983065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:18:47.436189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:18:47.707418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:18:48.092542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:18:47.516862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:18:47.807900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:18:48.173198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:18:47.604441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:18:47.901906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T06:18:52.219473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번관측소명지구명상세위치설치일자측기검정일자측기위도경도
순번1.0001.0000.8241.0000.9180.9520.8180.8460.585
관측소명1.0001.0001.0001.0001.0001.0001.0001.0001.000
지구명0.8241.0001.0001.0000.0000.6400.5470.9100.906
상세위치1.0001.0001.0001.0001.0001.0001.0001.0001.000
설치일자0.9181.0000.0001.0001.0001.0000.3120.5400.625
측기검정일자0.9521.0000.6401.0001.0001.0000.4070.5720.683
측기0.8181.0000.5471.0000.3120.4071.0000.7980.422
위도0.8461.0000.9101.0000.5400.5720.7981.0000.742
경도0.5851.0000.9061.0000.6250.6830.4220.7421.000
2023-12-13T06:18:52.374552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
측기검정일자측기설치일자지구명
측기검정일자1.0000.2780.9460.265
측기0.2781.0000.1960.420
설치일자0.9460.1961.0000.000
지구명0.2650.4200.0001.000
2023-12-13T06:18:52.489603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번위도경도지구명설치일자측기검정일자측기
순번1.000-0.125-0.5350.4420.6620.6270.590
위도-0.1251.0000.5880.5890.2230.1960.572
경도-0.5350.5881.0000.5800.2820.2630.288
지구명0.4420.5890.5801.0000.0000.2650.420
설치일자0.6620.2230.2820.0001.0000.9460.196
측기검정일자0.6270.1960.2630.2650.9461.0000.278
측기0.5900.5720.2880.4200.1960.2781.000

Missing values

2023-12-13T06:18:48.287665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T06:18:48.442831image/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

순번관측소명지구명상세위치설치일자측기검정일자측기위도경도
01애룡교파주파주읍 연풍리 7-32012-06-012015-11-01우량+수위37.829398126.850925
12연풍2교파주파주읍 연풍리 372-422012-06-012015-11-01우량37.827487126.835334
23설원교법원법원읍 직천리 667-32012-06-012015-11-01우량+수위37.902524126.914946
34웅담새마을교법원법원읍 웅담리 308-102012-06-012015-11-01우량+수위37.907388126.897088
45웅비교법원법원읍 웅담리 395-32012-06-012015-11-01우량+수위37.913689126.898434
56영장교광탄광탄면 마장리 16-102012-06-012015-11-01우량+수위37.771212126.897673
67중산교광탄광탄면 기산리 5182012-06-012015-11-01우량+수위37.774794126.920871
78가송가든광탄광탄면 영장리 233-72012-06-012015-11-01우량37.775153126.905317
89유일교광탄광탄면 마장리 99-42012-06-012015-11-01우량37.774165126.885335
910마장교광탄광탄면 마장리 408-82012-06-012015-11-01우량37.778926126.878591
순번관측소명지구명상세위치설치일자측기검정일자측기위도경도
3839월롱교월롱월롱면 위전리 1602021-08-132021-08-13우량37.80075126.79315
3940율곡습지파평파평면 율곡리 187-12021-08-132021-08-13우량37.89124126.81334
4041율곡수목원파평파평면 율곡리 6-12021-08-132021-08-13우량37.89822126.82125
4142법원읍 갈곡교 옆법원법원읍 갈곡리 388-42021-08-132021-08-13우량37.84937126.89902
4243운정동 배수펌프장운정와동동 823-1052021-08-132021-08-13우량37.74169126.76954
4344눌노천 합류부파평파평면 눌노리 346-182021-11-192021-11-19우량37.930767126.84574
4445목월교(문산천지구)광탄면광탄면 마장리 237-82022-08-252022-07-08우량37.7886126.87391
4546신산지구광탄면광탄면 신산리 353-22022-08-252022-07-08우량37.785126.84456
4647율곡습지2파평면파평면 율곡리 193-32022-08-252022-07-08우량37.8893126.81496
4748찬우물밑천지구검산동검산동 57-22022-08-252022-07-08우량37.78612126.74771