Overview

Dataset statistics

Number of variables9
Number of observations106
Missing cells27
Missing cells (%)2.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.9 KiB
Average record size in memory76.2 B

Variable types

Numeric3
Text3
Categorical2
DateTime1

Dataset

Description충청남도 논산시 소하천현황(소하천번호, 소하천명, 수계명, 지정일자, 고시번호, 구간(시점), 구간(종점), 총연장, 유역면적) 공공데이터 입니다.
Author충청남도
URLhttps://alldam.chungnam.go.kr/index.chungnam?menuCd=DOM_000000201001001001&st=&cds=&orgCd=&apiType=&isOpen=Y&pageIndex=375&beforeMenuCd=DOM_000000201001001000&publicdatapk=15066947

Alerts

소하천번호 is highly overall correlated with 수계명High correlation
총연장 is highly overall correlated with 유역면적High correlation
유역면적 is highly overall correlated with 총연장High correlation
수계명 is highly overall correlated with 소하천번호High correlation
지정일자 has 27 (25.5%) missing valuesMissing
소하천번호 has unique valuesUnique
소하천명 has unique valuesUnique
구간(시점) has unique valuesUnique

Reproduction

Analysis started2024-01-09 23:19:54.123757
Analysis finished2024-01-09 23:19:55.349715
Duration1.23 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

소하천번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct106
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean57.801887
Minimum1
Maximum112
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-01-10T08:19:55.413475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6.25
Q129.25
median59.5
Q385.75
95-th percentile106.75
Maximum112
Range111
Interquartile range (IQR)56.5

Descriptive statistics

Standard deviation32.733388
Coefficient of variation (CV)0.5663031
Kurtosis-1.2124371
Mean57.801887
Median Absolute Deviation (MAD)28.5
Skewness-0.075020362
Sum6127
Variance1071.4747
MonotonicityStrictly increasing
2024-01-10T08:19:55.529545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.9%
87 1
 
0.9%
85 1
 
0.9%
84 1
 
0.9%
83 1
 
0.9%
82 1
 
0.9%
81 1
 
0.9%
80 1
 
0.9%
79 1
 
0.9%
78 1
 
0.9%
Other values (96) 96
90.6%
ValueCountFrequency (%)
1 1
0.9%
2 1
0.9%
3 1
0.9%
4 1
0.9%
5 1
0.9%
6 1
0.9%
7 1
0.9%
8 1
0.9%
9 1
0.9%
10 1
0.9%
ValueCountFrequency (%)
112 1
0.9%
111 1
0.9%
110 1
0.9%
109 1
0.9%
108 1
0.9%
107 1
0.9%
106 1
0.9%
105 1
0.9%
104 1
0.9%
103 1
0.9%

소하천명
Text

UNIQUE 

Distinct106
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size980.0 B
2024-01-10T08:19:55.800924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length3
Mean length3.1415094
Min length2

Characters and Unicode

Total characters333
Distinct characters124
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique106 ?
Unique (%)100.0%

Sample

1st row대흥천
2nd row동산천
3rd row소룡천
4th row양지천
5th row안심천
ValueCountFrequency (%)
대흥천 1
 
0.9%
고산천 1
 
0.9%
버팽이천 1
 
0.9%
절천 1
 
0.9%
제비천 1
 
0.9%
개설천 1
 
0.9%
백촌천 1
 
0.9%
대목천 1
 
0.9%
독방골천 1
 
0.9%
만목천 1
 
0.9%
Other values (96) 96
90.6%
2024-01-10T08:19:56.183076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
천 106
31.8%
골 9
 
2.7%
정 8
 
2.4%
용 7
 
2.1%
동 6
 
1.8%
산 6
 
1.8%
곡 5
 
1.5%
고 5
 
1.5%
사 5
 
1.5%
수 4
 
1.2%
Other values (114) 172
51.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 331
99.4%
Decimal Number 2
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
천 106
32.0%
골 9
 
2.7%
정 8
 
2.4%
용 7
 
2.1%
동 6
 
1.8%
산 6
 
1.8%
곡 5
 
1.5%
고 5
 
1.5%
사 5
 
1.5%
수 4
 
1.2%
Other values (112) 170
51.4%
Decimal Number
ValueCountFrequency (%)
1 1
50.0%
2 1
50.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 331
99.4%
Common 2
 
0.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
천 106
32.0%
골 9
 
2.7%
정 8
 
2.4%
용 7
 
2.1%
동 6
 
1.8%
산 6
 
1.8%
곡 5
 
1.5%
고 5
 
1.5%
사 5
 
1.5%
수 4
 
1.2%
Other values (112) 170
51.4%
Common
ValueCountFrequency (%)
1 1
50.0%
2 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 331
99.4%
ASCII 2
 
0.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
천 106
32.0%
골 9
 
2.7%
정 8
 
2.4%
용 7
 
2.1%
동 6
 
1.8%
산 6
 
1.8%
곡 5
 
1.5%
고 5
 
1.5%
사 5
 
1.5%
수 4
 
1.2%
Other values (112) 170
51.4%
ASCII
ValueCountFrequency (%)
1 1
50.0%
2 1
50.0%

수계명
Categorical

HIGH CORRELATION 

Distinct38
Distinct (%)35.8%
Missing0
Missing (%)0.0%
Memory size980.0 B
주천
10 
갑천
10 
연산천
7 
양지천
 
6
노성천
 
6
Other values (33)
67 

Length

Max length5
Median length3
Mean length2.8396226
Min length2

Unique

Unique16 ?
Unique (%)15.1%

Sample

1st row논산천
2nd row방축천
3rd row마산천
4th row마산천
5th row마산천

Common Values

ValueCountFrequency (%)
주천 10
 
9.4%
갑천 10
 
9.4%
연산천 7
 
6.6%
양지천 6
 
5.7%
노성천 6
 
5.7%
왕덕천 5
 
4.7%
장성천 5
 
4.7%
시묘천 4
 
3.8%
마산천 4
 
3.8%
탑정지 4
 
3.8%
Other values (28) 45
42.5%

Length

2024-01-10T08:19:56.312710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
주천 10
 
9.4%
갑천 10
 
9.4%
연산천 7
 
6.6%
양지천 6
 
5.7%
노성천 6
 
5.7%
왕덕천 5
 
4.7%
장성천 5
 
4.7%
시묘천 4
 
3.8%
마산천 4
 
3.8%
탑정지 4
 
3.8%
Other values (28) 45
42.5%

지정일자
Date

MISSING 

Distinct3
Distinct (%)3.8%
Missing27
Missing (%)25.5%
Memory size980.0 B
Minimum1996-04-24 00:00:00
Maximum1998-07-31 00:00:00
2024-01-10T08:19:56.406851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:19:56.489651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=3)

고시번호
Categorical

Distinct4
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Memory size980.0 B
96-73
41 
97-452
37 
미등록
27 
98-257
 
1

Length

Max length6
Median length5
Mean length4.8490566
Min length3

Unique

Unique1 ?
Unique (%)0.9%

Sample

1st row96-73
2nd row96-73
3rd row96-73
4th row96-73
5th row96-73

Common Values

ValueCountFrequency (%)
96-73 41
38.7%
97-452 37
34.9%
미등록 27
25.5%
98-257 1
 
0.9%

Length

2024-01-10T08:19:56.590123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T08:19:56.679251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
96-73 41
38.7%
97-452 37
34.9%
미등록 27
25.5%
98-257 1
 
0.9%

구간(시점)
Text

UNIQUE 

Distinct106
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size980.0 B
2024-01-10T08:19:56.995999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length23
Mean length20.792453
Min length18

Characters and Unicode

Total characters2204
Distinct characters100
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

Unique106 ?
Unique (%)100.0%

Sample

1st row충청남도 논산시 강경읍 산양리 372-1
2nd row충청남도 논산시 연무읍 동산리 44
3rd row충청남도 논산시 소룡면 371-1
4th row충청남도 논산시 연무읍 양지리 237
5th row충청남도 논산시 연무읍 안심리 28
ValueCountFrequency (%)
충청남도 106
19.7%
논산시 106
19.7%
연산면 33
 
6.1%
양촌면 22
 
4.1%
상월면 18
 
3.3%
산 10
 
1.9%
연무읍 9
 
1.7%
가야곡면 8
 
1.5%
노성면 8
 
1.5%
상도리 5
 
0.9%
Other values (176) 214
39.7%
2024-01-10T08:19:57.425847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
433
19.6%
산 161
 
7.3%
도 112
 
5.1%
시 108
 
4.9%
충 107
 
4.9%
청 107
 
4.9%
남 107
 
4.9%
논 106
 
4.8%
리 105
 
4.8%
면 96
 
4.4%
Other values (90) 762
34.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1391
63.1%
Space Separator 433
 
19.6%
Decimal Number 331
 
15.0%
Dash Punctuation 49
 
2.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 161
11.6%
도 112
 
8.1%
시 108
 
7.8%
충 107
 
7.7%
청 107
 
7.7%
남 107
 
7.7%
논 106
 
7.6%
리 105
 
7.5%
면 96
 
6.9%
연 42
 
3.0%
Other values (78) 340
24.4%
Decimal Number
ValueCountFrequency (%)
1 70
21.1%
3 52
15.7%
2 47
14.2%
4 36
10.9%
7 32
9.7%
5 27
 
8.2%
6 19
 
5.7%
8 19
 
5.7%
9 15
 
4.5%
0 14
 
4.2%
Space Separator
ValueCountFrequency (%)
433
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 49
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1391
63.1%
Common 813
36.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 161
11.6%
도 112
 
8.1%
시 108
 
7.8%
충 107
 
7.7%
청 107
 
7.7%
남 107
 
7.7%
논 106
 
7.6%
리 105
 
7.5%
면 96
 
6.9%
연 42
 
3.0%
Other values (78) 340
24.4%
Common
ValueCountFrequency (%)
433
53.3%
1 70
 
8.6%
3 52
 
6.4%
- 49
 
6.0%
2 47
 
5.8%
4 36
 
4.4%
7 32
 
3.9%
5 27
 
3.3%
6 19
 
2.3%
8 19
 
2.3%
Other values (2) 29
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1391
63.1%
ASCII 813
36.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
433
53.3%
1 70
 
8.6%
3 52
 
6.4%
- 49
 
6.0%
2 47
 
5.8%
4 36
 
4.4%
7 32
 
3.9%
5 27
 
3.3%
6 19
 
2.3%
8 19
 
2.3%
Other values (2) 29
 
3.6%
Hangul
ValueCountFrequency (%)
산 161
11.6%
도 112
 
8.1%
시 108
 
7.8%
충 107
 
7.7%
청 107
 
7.7%
남 107
 
7.7%
논 106
 
7.6%
리 105
 
7.5%
면 96
 
6.9%
연 42
 
3.0%
Other values (78) 340
24.4%

구간(종점)
Text

Distinct105
Distinct (%)99.1%
Missing0
Missing (%)0.0%
Memory size980.0 B
2024-01-10T08:19:57.758070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length23
Mean length21.245283
Min length18

Characters and Unicode

Total characters2252
Distinct characters105
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

Unique104 ?
Unique (%)98.1%

Sample

1st row충청남도 논산시 강경읍 서창리 117
2nd row충청남도 논산시 연무읍 죽본리 751-1
3rd row충청남도 논산시 소룡읍 440-1
4th row충청남도 논산시 연무읍 마산리 704-79
5th row충청남도 논산시 연무읍 안심리 1011-16
ValueCountFrequency (%)
충청남도 106
20.0%
논산시 106
20.0%
연산면 33
 
6.2%
양촌면 23
 
4.3%
상월면 19
 
3.6%
연무읍 8
 
1.5%
가야곡면 7
 
1.3%
노성면 5
 
0.9%
석종리 5
 
0.9%
반암리 3
 
0.6%
Other values (177) 214
40.5%
2024-01-10T08:19:58.198827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
423
18.8%
산 154
 
6.8%
도 110
 
4.9%
시 108
 
4.8%
충 107
 
4.8%
남 107
 
4.8%
논 106
 
4.7%
청 106
 
4.7%
리 105
 
4.7%
면 96
 
4.3%
Other values (95) 830
36.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1380
61.3%
Space Separator 423
 
18.8%
Decimal Number 379
 
16.8%
Dash Punctuation 70
 
3.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 154
11.2%
도 110
 
8.0%
시 108
 
7.8%
충 107
 
7.8%
남 107
 
7.8%
논 106
 
7.7%
청 106
 
7.7%
리 105
 
7.6%
면 96
 
7.0%
연 42
 
3.0%
Other values (83) 339
24.6%
Decimal Number
ValueCountFrequency (%)
1 82
21.6%
3 50
13.2%
4 48
12.7%
2 47
12.4%
5 36
9.5%
6 29
 
7.7%
7 26
 
6.9%
8 24
 
6.3%
0 20
 
5.3%
9 17
 
4.5%
Space Separator
ValueCountFrequency (%)
423
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 70
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1380
61.3%
Common 872
38.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 154
11.2%
도 110
 
8.0%
시 108
 
7.8%
충 107
 
7.8%
남 107
 
7.8%
논 106
 
7.7%
청 106
 
7.7%
리 105
 
7.6%
면 96
 
7.0%
연 42
 
3.0%
Other values (83) 339
24.6%
Common
ValueCountFrequency (%)
423
48.5%
1 82
 
9.4%
- 70
 
8.0%
3 50
 
5.7%
4 48
 
5.5%
2 47
 
5.4%
5 36
 
4.1%
6 29
 
3.3%
7 26
 
3.0%
8 24
 
2.8%
Other values (2) 37
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1380
61.3%
ASCII 872
38.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
423
48.5%
1 82
 
9.4%
- 70
 
8.0%
3 50
 
5.7%
4 48
 
5.5%
2 47
 
5.4%
5 36
 
4.1%
6 29
 
3.3%
7 26
 
3.0%
8 24
 
2.8%
Other values (2) 37
 
4.2%
Hangul
ValueCountFrequency (%)
산 154
11.2%
도 110
 
8.0%
시 108
 
7.8%
충 107
 
7.8%
남 107
 
7.8%
논 106
 
7.7%
청 106
 
7.7%
리 105
 
7.6%
면 96
 
7.0%
연 42
 
3.0%
Other values (83) 339
24.6%

총연장
Real number (ℝ)

HIGH CORRELATION 

Distinct103
Distinct (%)97.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1510.8774
Minimum500
Maximum4769
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-01-10T08:19:58.323697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum500
5-th percentile626.25
Q1931
median1163
Q31835
95-th percentile3398.25
Maximum4769
Range4269
Interquartile range (IQR)904

Descriptive statistics

Standard deviation839.93176
Coefficient of variation (CV)0.55592319
Kurtosis1.9223346
Mean1510.8774
Median Absolute Deviation (MAD)371.5
Skewness1.4288489
Sum160153
Variance705485.37
MonotonicityNot monotonic
2024-01-10T08:19:58.431939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1100 2
 
1.9%
850 2
 
1.9%
1540 2
 
1.9%
1450 1
 
0.9%
1117 1
 
0.9%
1122 1
 
0.9%
616 1
 
0.9%
600 1
 
0.9%
2225 1
 
0.9%
1712 1
 
0.9%
Other values (93) 93
87.7%
ValueCountFrequency (%)
500 1
0.9%
525 1
0.9%
580 1
0.9%
600 1
0.9%
616 1
0.9%
625 1
0.9%
630 1
0.9%
671 1
0.9%
682 1
0.9%
688 1
0.9%
ValueCountFrequency (%)
4769 1
0.9%
3900 1
0.9%
3450 1
0.9%
3448 1
0.9%
3430 1
0.9%
3418 1
0.9%
3339 1
0.9%
3200 1
0.9%
2816 1
0.9%
2793 1
0.9%

유역면적
Real number (ℝ)

HIGH CORRELATION 

Distinct91
Distinct (%)85.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8872642
Minimum0.29
Maximum15.47
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-01-10T08:19:58.546075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.29
5-th percentile0.46
Q10.8125
median1.245
Q32.28
95-th percentile4.8125
Maximum15.47
Range15.18
Interquartile range (IQR)1.4675

Descriptive statistics

Standard deviation1.9389032
Coefficient of variation (CV)1.0273619
Kurtosis22.644861
Mean1.8872642
Median Absolute Deviation (MAD)0.565
Skewness3.8769545
Sum200.05
Variance3.7593458
MonotonicityNot monotonic
2024-01-10T08:19:58.663840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.88 3
 
2.8%
0.89 2
 
1.9%
1.05 2
 
1.9%
0.59 2
 
1.9%
0.61 2
 
1.9%
0.46 2
 
1.9%
0.72 2
 
1.9%
0.97 2
 
1.9%
0.57 2
 
1.9%
3.64 2
 
1.9%
Other values (81) 85
80.2%
ValueCountFrequency (%)
0.29 1
0.9%
0.33 1
0.9%
0.4 1
0.9%
0.41 1
0.9%
0.43 1
0.9%
0.46 2
1.9%
0.55 1
0.9%
0.57 2
1.9%
0.59 2
1.9%
0.61 2
1.9%
ValueCountFrequency (%)
15.47 1
0.9%
6.79 1
0.9%
6.74 1
0.9%
6.02 1
0.9%
5.55 1
0.9%
4.82 1
0.9%
4.79 1
0.9%
4.4 1
0.9%
4.38 1
0.9%
4.26 1
0.9%

Interactions

2024-01-10T08:19:54.939805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:19:54.538922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:19:54.742632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:19:55.027637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:19:54.603869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:19:54.807515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:19:55.091928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:19:54.664833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:19:54.861806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-10T08:19:58.744889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
소하천번호수계명지정일자고시번호총연장유역면적
소하천번호1.0000.9380.4260.4700.0000.194
수계명0.9381.0000.9050.2920.5650.296
지정일자0.4260.9051.0001.0000.3320.498
고시번호0.4700.2921.0001.0000.2220.239
총연장0.0000.5650.3320.2221.0000.867
유역면적0.1940.2960.4980.2390.8671.000
2024-01-10T08:19:58.831087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
고시번호수계명
고시번호1.0000.110
수계명0.1101.000
2024-01-10T08:19:58.903154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
소하천번호총연장유역면적수계명고시번호
소하천번호1.000-0.0460.1460.5800.289
총연장-0.0461.0000.7480.1900.147
유역면적0.1460.7481.0000.0930.153
수계명0.5800.1900.0931.0000.110
고시번호0.2890.1470.1530.1101.000

Missing values

2024-01-10T08:19:55.196433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-10T08:19:55.304676image/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

소하천번호소하천명수계명지정일자고시번호구간(시점)구간(종점)총연장유역면적
01대흥천논산천1996-04-2496-73충청남도 논산시 강경읍 산양리 372-1충청남도 논산시 강경읍 서창리 11714500.89
12동산천방축천1996-04-2496-73충청남도 논산시 연무읍 동산리 44충청남도 논산시 연무읍 죽본리 751-139006.74
23소룡천마산천1996-04-2496-73충청남도 논산시 소룡면 371-1충청남도 논산시 소룡읍 440-16981.06
34양지천마산천1996-04-2496-73충청남도 논산시 연무읍 양지리 237충청남도 논산시 연무읍 마산리 704-7924234.38
45안심천마산천1996-04-2496-73충청남도 논산시 연무읍 안심리 28충청남도 논산시 연무읍 안심리 1011-1622682.04
56모내천마산천1996-04-2496-73충청남도 논산시 연무읍 안심리 407-5충청남도 논산시 은진면 토양리 152-117782.52
67당산천양지천1997-12-3196-73충청남도 논산시 연무읍 양지리 332충청남도 논산시 연무읍 양지리 225-68940.7
78도장천양지천1997-12-3197-452충청남도 논산시 연무읍 고내리 284충청남도 논산시 연무읍 고내리 33810760.7
89죽평천양지천1997-12-3197-452충청남도 논산시 연무읍 죽평리 15-2충청남도 논산시 연무읍 금곡리 174-134502.17
910작은용뎅이천큰용뎅이천<NA>미등록충청남도 논산시 연무읍 양지리 135충청남도 논산시 연무읍 양지리 494-109960.68
소하천번호소하천명수계명지정일자고시번호구간(시점)구간(종점)총연장유역면적
96103육곡천왕암천1996-04-2496-73충청남도 논산시 가야곡면 육곡리 19-7충청남도 논산시 가야곡면 육곡리 337-418420.97
97104용댕천탑정지1996-04-2496-73충청남도 논산시 가야곡면 등리 135-1충청남도 논산시 가야곡면 등리 1-212732.77
98105두월천왕암천1996-04-2496-73충청남도 논산시 가야곡면 두월리 517충청남도 논산시 가야곡면 두월리 573-611070.92
99106평촌천탑정지1997-12-3197-452충청남도 논산시 가야곡면 목곡리 403충청남도 논산시 가야곡면 산노리 449-325004.4
100107고당천탑정지1997-12-3197-452충청남도 논산시 가야곡면 등리 565-1충청남도 논산시 가야곡면 종연리 59-210650.96
101108말목천삼전천<NA>미등록충청남도 논산시 가야곡면 삼전리 847충청남도 논산시 가야곡면 삼전리 3339000.94
102109부수골천시묘천<NA>미등록충청남도 논산시 은진면 시묘리 339충청남도 논산시 은진면 시묘리 645-211650.88
103110황골천시묘천<NA>미등록충청남도 논산시 은진면 시묘리 115충청남도 논산시 은진면 시묘리 718-117232.39
104111용화천시묘천1998-07-3198-257충청남도 논산시 은진면 교촌리 482충청남도 논산시 채운면 용화리 97-1434304.79
105112섬밭천시묘천<NA>미등록충청남도 논산시 노성면 죽림리 115-1충청남도 논산시 노성면 장구리 380-3024853.36