Overview

Dataset statistics

Number of variables11
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.5 KiB
Average record size in memory97.3 B

Variable types

Numeric6
Text1
Categorical4

Alerts

시도코드 has constant value ""Constant
시도명 has constant value ""Constant
시군구코드 has constant value ""Constant
시군구명 has constant value ""Constant
기울기 is highly overall correlated with y절편 and 3 other fieldsHigh correlation
y절편 is highly overall correlated with 기울기 and 3 other fieldsHigh correlation
10cm 침수심 유발 강우량 is highly overall correlated with 기울기 and 3 other fieldsHigh correlation
20cm 침수심 유발 강우량 is highly overall correlated with 기울기 and 3 other fieldsHigh correlation
50cm 침수심 유발 강우량 is highly overall correlated with 기울기 and 3 other fieldsHigh correlation
아이디 has unique valuesUnique
격자번호 has unique valuesUnique

Reproduction

Analysis started2023-12-10 11:13:29.631161
Analysis finished2023-12-10 11:13:36.198833
Duration6.57 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

아이디
Real number (ℝ)

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8886.25
Minimum8478
Maximum9259
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:13:36.310630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8478
5-th percentile8482.95
Q18683.75
median8879.5
Q39073.25
95-th percentile9175.05
Maximum9259
Range781
Interquartile range (IQR)389.5

Descriptive statistics

Standard deviation226.68072
Coefficient of variation (CV)0.025509154
Kurtosis-1.0983159
Mean8886.25
Median Absolute Deviation (MAD)195
Skewness-0.1743456
Sum888625
Variance51384.149
MonotonicityStrictly increasing
2023-12-10T20:13:36.836404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8478 1
 
1.0%
8978 1
 
1.0%
9073 1
 
1.0%
9072 1
 
1.0%
9071 1
 
1.0%
9070 1
 
1.0%
9069 1
 
1.0%
9068 1
 
1.0%
9067 1
 
1.0%
9066 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
8478 1
1.0%
8479 1
1.0%
8480 1
1.0%
8481 1
1.0%
8482 1
1.0%
8483 1
1.0%
8486 1
1.0%
8577 1
1.0%
8578 1
1.0%
8579 1
1.0%
ValueCountFrequency (%)
9259 1
1.0%
9258 1
1.0%
9257 1
1.0%
9256 1
1.0%
9176 1
1.0%
9175 1
1.0%
9174 1
1.0%
9173 1
1.0%
9172 1
1.0%
9171 1
1.0%

격자번호
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T20:13:37.297953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters600
Distinct characters12
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

Unique100 ?
Unique (%)100.0%

Sample

1st row다라2280
2nd row다라2281
3rd row다라2282
4th row다라2283
5th row다라2284
ValueCountFrequency (%)
다라2280 1
 
1.0%
다라2787 1
 
1.0%
다라2886 1
 
1.0%
다라2885 1
 
1.0%
다라2884 1
 
1.0%
다라2883 1
 
1.0%
다라2882 1
 
1.0%
다라2881 1
 
1.0%
다라2880 1
 
1.0%
다라2879 1
 
1.0%
Other values (90) 90
90.0%
2023-12-10T20:13:38.347681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 114
19.0%
8 102
17.0%
다 100
16.7%
라 100
16.7%
9 43
 
7.2%
7 26
 
4.3%
3 23
 
3.8%
5 21
 
3.5%
4 20
 
3.3%
6 20
 
3.3%
Other values (2) 31
 
5.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 400
66.7%
Other Letter 200
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 114
28.5%
8 102
25.5%
9 43
 
10.8%
7 26
 
6.5%
3 23
 
5.8%
5 21
 
5.2%
4 20
 
5.0%
6 20
 
5.0%
0 18
 
4.5%
1 13
 
3.2%
Other Letter
ValueCountFrequency (%)
다 100
50.0%
라 100
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 400
66.7%
Hangul 200
33.3%

Most frequent character per script

Common
ValueCountFrequency (%)
2 114
28.5%
8 102
25.5%
9 43
 
10.8%
7 26
 
6.5%
3 23
 
5.8%
5 21
 
5.2%
4 20
 
5.0%
6 20
 
5.0%
0 18
 
4.5%
1 13
 
3.2%
Hangul
ValueCountFrequency (%)
다 100
50.0%
라 100
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 400
66.7%
Hangul 200
33.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 114
28.5%
8 102
25.5%
9 43
 
10.8%
7 26
 
6.5%
3 23
 
5.8%
5 21
 
5.2%
4 20
 
5.0%
6 20
 
5.0%
0 18
 
4.5%
1 13
 
3.2%
Hangul
ValueCountFrequency (%)
다 100
50.0%
라 100
50.0%

시도코드
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
29
100 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row29
2nd row29
3rd row29
4th row29
5th row29

Common Values

ValueCountFrequency (%)
29 100
100.0%

Length

2023-12-10T20:13:38.713159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:13:38.885233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
29 100
100.0%

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
광주
100 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
광주 100
100.0%

Length

2023-12-10T20:13:39.070344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:13:39.311689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
광주 100
100.0%

시군구코드
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
29200
100 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row29200
2nd row29200
3rd row29200
4th row29200
5th row29200

Common Values

ValueCountFrequency (%)
29200 100
100.0%

Length

2023-12-10T20:13:39.485367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:13:39.669521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
29200 100
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
광산구
100 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
광산구 100
100.0%

Length

2023-12-10T20:13:39.840084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:13:40.002743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
광산구 100
100.0%

기울기
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.1849
Minimum0.78
Maximum11.43
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:13:40.140825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.78
5-th percentile0.78
Q11.76
median1.76
Q33.15
95-th percentile11.43
Maximum11.43
Range10.65
Interquartile range (IQR)1.39

Descriptive statistics

Standard deviation3.4006323
Coefficient of variation (CV)1.067736
Kurtosis2.1498772
Mean3.1849
Median Absolute Deviation (MAD)0
Skewness1.9676882
Sum318.49
Variance11.5643
MonotonicityNot monotonic
2023-12-10T20:13:40.334530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1.76 55
55.0%
11.43 14
 
14.0%
3.15 12
 
12.0%
1.21 9
 
9.0%
0.78 8
 
8.0%
3.37 2
 
2.0%
ValueCountFrequency (%)
0.78 8
 
8.0%
1.21 9
 
9.0%
1.76 55
55.0%
3.15 12
 
12.0%
3.37 2
 
2.0%
11.43 14
 
14.0%
ValueCountFrequency (%)
11.43 14
 
14.0%
3.37 2
 
2.0%
3.15 12
 
12.0%
1.76 55
55.0%
1.21 9
 
9.0%
0.78 8
 
8.0%

y절편
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-12.2089
Minimum-38.84
Maximum-2.11
Zeros0
Zeros (%)0.0%
Negative100
Negative (%)100.0%
Memory size1.0 KiB
2023-12-10T20:13:40.532610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-38.84
5-th percentile-38.84
Q1-14.89
median-7.3
Q3-7.3
95-th percentile-2.11
Maximum-2.11
Range36.73
Interquartile range (IQR)7.59

Descriptive statistics

Standard deviation11.283434
Coefficient of variation (CV)-0.92419743
Kurtosis1.6592046
Mean-12.2089
Median Absolute Deviation (MAD)0
Skewness-1.7730973
Sum-1220.89
Variance127.31588
MonotonicityNot monotonic
2023-12-10T20:13:40.753165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
-7.3 55
55.0%
-38.84 14
 
14.0%
-14.89 12
 
12.0%
-5.37 9
 
9.0%
-2.11 8
 
8.0%
-15.87 2
 
2.0%
ValueCountFrequency (%)
-38.84 14
 
14.0%
-15.87 2
 
2.0%
-14.89 12
 
12.0%
-7.3 55
55.0%
-5.37 9
 
9.0%
-2.11 8
 
8.0%
ValueCountFrequency (%)
-2.11 8
 
8.0%
-5.37 9
 
9.0%
-7.3 55
55.0%
-14.89 12
 
12.0%
-15.87 2
 
2.0%
-38.84 14
 
14.0%

10cm 침수심 유발 강우량
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.6545
Minimum5.69
Maximum75.46
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:13:40.925810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5.69
5-th percentile5.69
Q110.32
median10.32
Q316.61
95-th percentile75.46
Maximum75.46
Range69.77
Interquartile range (IQR)6.29

Descriptive statistics

Standard deviation22.822099
Coefficient of variation (CV)1.1611641
Kurtosis2.3168901
Mean19.6545
Median Absolute Deviation (MAD)0
Skewness2.0350742
Sum1965.45
Variance520.8482
MonotonicityNot monotonic
2023-12-10T20:13:41.095515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
10.32 55
55.0%
75.46 14
 
14.0%
16.61 12
 
12.0%
6.77 9
 
9.0%
5.69 8
 
8.0%
17.82 2
 
2.0%
ValueCountFrequency (%)
5.69 8
 
8.0%
6.77 9
 
9.0%
10.32 55
55.0%
16.61 12
 
12.0%
17.82 2
 
2.0%
75.46 14
 
14.0%
ValueCountFrequency (%)
75.46 14
 
14.0%
17.82 2
 
2.0%
16.61 12
 
12.0%
10.32 55
55.0%
6.77 9
 
9.0%
5.69 8
 
8.0%

20cm 침수심 유발 강우량
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51.5169
Minimum13.49
Maximum189.76
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:13:41.273874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum13.49
5-th percentile13.49
Q127.94
median27.94
Q348.1
95-th percentile189.76
Maximum189.76
Range176.27
Interquartile range (IQR)20.16

Descriptive statistics

Standard deviation56.800766
Coefficient of variation (CV)1.1025657
Kurtosis2.224138
Mean51.5169
Median Absolute Deviation (MAD)0
Skewness1.9974818
Sum5151.69
Variance3226.3271
MonotonicityNot monotonic
2023-12-10T20:13:41.453294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
27.94 55
55.0%
189.76 14
 
14.0%
48.1 12
 
12.0%
18.91 9
 
9.0%
13.49 8
 
8.0%
51.52 2
 
2.0%
ValueCountFrequency (%)
13.49 8
 
8.0%
18.91 9
 
9.0%
27.94 55
55.0%
48.1 12
 
12.0%
51.52 2
 
2.0%
189.76 14
 
14.0%
ValueCountFrequency (%)
189.76 14
 
14.0%
51.52 2
 
2.0%
48.1 12
 
12.0%
27.94 55
55.0%
18.91 9
 
9.0%
13.49 8
 
8.0%

50cm 침수심 유발 강우량
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean147.1105
Minimum36.89
Maximum532.66
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:13:41.646373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.89
5-th percentile36.89
Q180.81
median80.81
Q3142.59
95-th percentile532.66
Maximum532.66
Range495.77
Interquartile range (IQR)61.78

Descriptive statistics

Standard deviation158.78858
Coefficient of variation (CV)1.0793831
Kurtosis2.1778361
Mean147.1105
Median Absolute Deviation (MAD)0
Skewness1.97885
Sum14711.05
Variance25213.814
MonotonicityNot monotonic
2023-12-10T20:13:41.812969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
80.81 55
55.0%
532.66 14
 
14.0%
142.59 12
 
12.0%
55.32 9
 
9.0%
36.89 8
 
8.0%
152.59 2
 
2.0%
ValueCountFrequency (%)
36.89 8
 
8.0%
55.32 9
 
9.0%
80.81 55
55.0%
142.59 12
 
12.0%
152.59 2
 
2.0%
532.66 14
 
14.0%
ValueCountFrequency (%)
532.66 14
 
14.0%
152.59 2
 
2.0%
142.59 12
 
12.0%
80.81 55
55.0%
55.32 9
 
9.0%
36.89 8
 
8.0%

Interactions

2023-12-10T20:13:34.951710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:13:30.017310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:13:31.229120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:13:32.174634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:13:33.069929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:13:34.014737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:13:35.089734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:13:30.167018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:13:31.427372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:13:32.339616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:13:33.242015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:13:34.188224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:13:35.223588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:13:30.287895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:13:31.586891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:13:32.479421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:13:33.384217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:13:34.334255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:13:35.362833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:13:30.461088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:13:31.747276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:13:32.626823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:13:33.555316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:13:34.494809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:13:35.492697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:13:30.848541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:13:31.893228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:13:32.772915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:13:33.711493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:13:34.656131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:13:35.641730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:13:31.036423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:13:32.039959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:13:32.934999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:13:33.872830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:13:34.807018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T20:13:41.957189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
아이디격자번호기울기y절편10cm 침수심 유발 강우량20cm 침수심 유발 강우량50cm 침수심 유발 강우량
아이디1.0001.0000.7430.6350.7430.5470.743
격자번호1.0001.0001.0001.0001.0001.0001.000
기울기0.7431.0001.0001.0001.0001.0001.000
y절편0.6351.0001.0001.0001.0000.9811.000
10cm 침수심 유발 강우량0.7431.0001.0001.0001.0001.0001.000
20cm 침수심 유발 강우량0.5471.0001.0000.9811.0001.0001.000
50cm 침수심 유발 강우량0.7431.0001.0001.0001.0001.0001.000
2023-12-10T20:13:42.173022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
아이디기울기y절편10cm 침수심 유발 강우량20cm 침수심 유발 강우량50cm 침수심 유발 강우량
아이디1.0000.143-0.1430.1430.1430.143
기울기0.1431.000-1.0001.0001.0001.000
y절편-0.143-1.0001.000-1.000-1.000-1.000
10cm 침수심 유발 강우량0.1431.000-1.0001.0001.0001.000
20cm 침수심 유발 강우량0.1431.000-1.0001.0001.0001.000
50cm 침수심 유발 강우량0.1431.000-1.0001.0001.0001.000

Missing values

2023-12-10T20:13:35.827594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T20:13:36.102215image/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

아이디격자번호시도코드시도명시군구코드시군구명기울기y절편10cm 침수심 유발 강우량20cm 침수심 유발 강우량50cm 침수심 유발 강우량
08478다라228029광주29200광산구3.37-15.8717.8251.52152.59
18479다라228129광주29200광산구0.78-2.115.6913.4936.89
28480다라228229광주29200광산구0.78-2.115.6913.4936.89
38481다라228329광주29200광산구0.78-2.115.6913.4936.89
48482다라228429광주29200광산구0.78-2.115.6913.4936.89
58483다라228529광주29200광산구1.76-7.310.3227.9480.81
68486다라228829광주29200광산구1.76-7.310.3227.9480.81
78577다라238029광주29200광산구3.37-15.8717.8251.52152.59
88578다라238129광주29200광산구0.78-2.115.6913.4936.89
98579다라238229광주29200광산구0.78-2.115.6913.4936.89
아이디격자번호시도코드시도명시군구코드시군구명기울기y절편10cm 침수심 유발 강우량20cm 침수심 유발 강우량50cm 침수심 유발 강우량
909171다라298929광주29200광산구1.21-5.376.7718.9155.32
919172다라299029광주29200광산구1.21-5.376.7718.9155.32
929173다라299129광주29200광산구1.21-5.376.7718.9155.32
939174다라299229광주29200광산구1.21-5.376.7718.9155.32
949175다라299329광주29200광산구1.21-5.376.7718.9155.32
959176다라299429광주29200광산구1.21-5.376.7718.9155.32
969256다라307929광주29200광산구11.43-38.8475.46189.76532.66
979257다라308029광주29200광산구11.43-38.8475.46189.76532.66
989258다라308129광주29200광산구11.43-38.8475.46189.76532.66
999259다라308229광주29200광산구11.43-38.8475.46189.76532.66