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 11:12:46.382063
Analysis finished2023-12-10 11:12:51.494667
Duration5.11 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%
Mean30400.66
Minimum29988
Maximum30583
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:12:51.588427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum29988
5-th percentile30173.55
Q130287.75
median30389.5
Q330489.25
95-th percentile30578.05
Maximum30583
Range595
Interquartile range (IQR)201.5

Descriptive statistics

Standard deviation142.85765
Coefficient of variation (CV)0.0046991627
Kurtosis-0.24919742
Mean30400.66
Median Absolute Deviation (MAD)101
Skewness-0.63675629
Sum3040066
Variance20408.307
MonotonicityStrictly increasing
2023-12-10T20:12:51.781095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29988 1
 
1.0%
30479 1
 
1.0%
30489 1
 
1.0%
30488 1
 
1.0%
30487 1
 
1.0%
30486 1
 
1.0%
30485 1
 
1.0%
30484 1
 
1.0%
30483 1
 
1.0%
30482 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
29988 1
1.0%
30086 1
1.0%
30087 1
1.0%
30088 1
1.0%
30089 1
1.0%
30178 1
1.0%
30179 1
1.0%
30181 1
1.0%
30186 1
1.0%
30187 1
1.0%
ValueCountFrequency (%)
30583 1
1.0%
30582 1
1.0%
30581 1
1.0%
30580 1
1.0%
30579 1
1.0%
30578 1
1.0%
30577 1
1.0%
30576 1
1.0%
30575 1
1.0%
30574 1
1.0%

격자번호
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T20:12:52.191582image/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 row다바6656
2nd row다바6754
3rd row다바6755
4th row다바6756
5th row다바6757
ValueCountFrequency (%)
다바6656 1
 
1.0%
다바7145 1
 
1.0%
다바7156 1
 
1.0%
다바7155 1
 
1.0%
다바7154 1
 
1.0%
다바7153 1
 
1.0%
다바7152 1
 
1.0%
다바7151 1
 
1.0%
다바7150 1
 
1.0%
다바7149 1
 
1.0%
Other values (90) 90
90.0%
2023-12-10T20:12:52.735039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
다 100
16.7%
바 100
16.7%
7 88
14.7%
5 55
9.2%
6 44
7.3%
4 41
6.8%
3 33
 
5.5%
2 32
 
5.3%
1 30
 
5.0%
0 29
 
4.8%
Other values (2) 48
8.0%

Most occurring categories

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

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
7 88
22.0%
5 55
13.8%
6 44
11.0%
4 41
10.2%
3 33
 
8.2%
2 32
 
8.0%
1 30
 
7.5%
0 29
 
7.2%
9 27
 
6.8%
8 21
 
5.2%
Other Letter
ValueCountFrequency (%)
다 100
50.0%
바 100
50.0%

Most occurring scripts

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

Most frequent character per script

Common
ValueCountFrequency (%)
7 88
22.0%
5 55
13.8%
6 44
11.0%
4 41
10.2%
3 33
 
8.2%
2 32
 
8.0%
1 30
 
7.5%
0 29
 
7.2%
9 27
 
6.8%
8 21
 
5.2%
Hangul
ValueCountFrequency (%)
다 100
50.0%
바 100
50.0%

Most occurring blocks

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

Most frequent character per block

Hangul
ValueCountFrequency (%)
다 100
50.0%
바 100
50.0%
ASCII
ValueCountFrequency (%)
7 88
22.0%
5 55
13.8%
6 44
11.0%
4 41
10.2%
3 33
 
8.2%
2 32
 
8.0%
1 30
 
7.5%
0 29
 
7.2%
9 27
 
6.8%
8 21
 
5.2%

시도코드
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
36
100 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row36
2nd row36
3rd row36
4th row36
5th row36

Common Values

ValueCountFrequency (%)
36 100
100.0%

Length

2023-12-10T20:12:52.909312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:12:53.033635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
36 100
100.0%

시도명
Categorical

CONSTANT 

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

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
세종 100
100.0%

Length

2023-12-10T20:12:53.143449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:12:53.246080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
세종 100
100.0%

시군구코드
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
36110
100 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row36110
2nd row36110
3rd row36110
4th row36110
5th row36110

Common Values

ValueCountFrequency (%)
36110 100
100.0%

Length

2023-12-10T20:12:53.351611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:12:53.457347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
36110 100
100.0%

시군구명
Categorical

CONSTANT 

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

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
세종특별자치시 100
100.0%

Length

2023-12-10T20:12:53.562640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:12:53.686729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
세종특별자치시 100
100.0%

기울기
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.5384
Minimum3.22
Maximum17.99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:12:53.799119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.22
5-th percentile3.22
Q13.22
median4.58
Q310.115
95-th percentile17.99
Maximum17.99
Range14.77
Interquartile range (IQR)6.895

Descriptive statistics

Standard deviation6.1013247
Coefficient of variation (CV)0.80936601
Kurtosis-0.6743093
Mean7.5384
Median Absolute Deviation (MAD)1.36
Skewness1.1317149
Sum753.84
Variance37.226163
MonotonicityNot monotonic
2023-12-10T20:12:53.921153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4.58 42
42.0%
3.22 29
29.0%
17.99 25
25.0%
3.55 2
 
2.0%
7.49 1
 
1.0%
3.76 1
 
1.0%
ValueCountFrequency (%)
3.22 29
29.0%
3.55 2
 
2.0%
3.76 1
 
1.0%
4.58 42
42.0%
7.49 1
 
1.0%
17.99 25
25.0%
ValueCountFrequency (%)
17.99 25
25.0%
7.49 1
 
1.0%
4.58 42
42.0%
3.76 1
 
1.0%
3.55 2
 
2.0%
3.22 29
29.0%

y절편
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-31.5434
Minimum-75.15
Maximum-13.11
Zeros0
Zeros (%)0.0%
Negative100
Negative (%)100.0%
Memory size1.0 KiB
2023-12-10T20:12:54.060928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-75.15
5-th percentile-75.15
Q1-47.4075
median-19.18
Q3-13.11
95-th percentile-13.11
Maximum-13.11
Range62.04
Interquartile range (IQR)34.2975

Descriptive statistics

Standard deviation25.51989
Coefficient of variation (CV)-0.80904057
Kurtosis-0.6928742
Mean-31.5434
Median Absolute Deviation (MAD)6.07
Skewness-1.1183122
Sum-3154.34
Variance651.26479
MonotonicityNot monotonic
2023-12-10T20:12:54.203350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
-19.18 42
42.0%
-13.11 29
29.0%
-75.15 25
25.0%
-18.08 2
 
2.0%
-38.16 1
 
1.0%
-15.52 1
 
1.0%
ValueCountFrequency (%)
-75.15 25
25.0%
-38.16 1
 
1.0%
-19.18 42
42.0%
-18.08 2
 
2.0%
-15.52 1
 
1.0%
-13.11 29
29.0%
ValueCountFrequency (%)
-13.11 29
29.0%
-15.52 1
 
1.0%
-18.08 2
 
2.0%
-19.18 42
42.0%
-38.16 1
 
1.0%
-75.15 25
25.0%

10cm 침수심 유발 강우량
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.8467
Minimum17.46
Maximum104.75
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:12:54.345616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum17.46
5-th percentile19.11
Q119.11
median26.62
Q353.7275
95-th percentile104.75
Maximum104.75
Range87.29
Interquartile range (IQR)34.6175

Descriptive statistics

Standard deviation35.513237
Coefficient of variation (CV)0.80994094
Kurtosis-0.66603394
Mean43.8467
Median Absolute Deviation (MAD)7.51
Skewness1.1383126
Sum4384.67
Variance1261.19
MonotonicityNot monotonic
2023-12-10T20:12:54.486265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
26.62 42
42.0%
19.11 29
29.0%
104.75 25
25.0%
17.46 2
 
2.0%
36.72 1
 
1.0%
22.05 1
 
1.0%
ValueCountFrequency (%)
17.46 2
 
2.0%
19.11 29
29.0%
22.05 1
 
1.0%
26.62 42
42.0%
36.72 1
 
1.0%
104.75 25
25.0%
ValueCountFrequency (%)
104.75 25
25.0%
36.72 1
 
1.0%
26.62 42
42.0%
22.05 1
 
1.0%
19.11 29
29.0%
17.46 2
 
2.0%

20cm 침수심 유발 강우량
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean119.2396
Minimum51.34
Maximum284.65
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:12:54.641212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum51.34
5-th percentile51.34
Q151.34
median72.42
Q3154.87
95-th percentile284.65
Maximum284.65
Range233.31
Interquartile range (IQR)103.53

Descriptive statistics

Standard deviation96.51363
Coefficient of variation (CV)0.80940921
Kurtosis-0.67070306
Mean119.2396
Median Absolute Deviation (MAD)21.08
Skewness1.1345794
Sum11923.96
Variance9314.8808
MonotonicityNot monotonic
2023-12-10T20:12:54.818475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
72.42 42
42.0%
51.34 29
29.0%
284.65 25
25.0%
52.99 2
 
2.0%
111.61 1
 
1.0%
59.62 1
 
1.0%
ValueCountFrequency (%)
51.34 29
29.0%
52.99 2
 
2.0%
59.62 1
 
1.0%
72.42 42
42.0%
111.61 1
 
1.0%
284.65 25
25.0%
ValueCountFrequency (%)
284.65 25
25.0%
111.61 1
 
1.0%
72.42 42
42.0%
59.62 1
 
1.0%
52.99 2
 
2.0%
51.34 29
29.0%

50cm 침수심 유발 강우량
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean345.4083
Minimum148.01
Maximum824.35
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:12:54.987518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum148.01
5-th percentile148.01
Q1148.01
median209.81
Q3458.275
95-th percentile824.35
Maximum824.35
Range676.34
Interquartile range (IQR)310.265

Descriptive statistics

Standard deviation279.53764
Coefficient of variation (CV)0.80929625
Kurtosis-0.67292884
Mean345.4083
Median Absolute Deviation (MAD)61.8
Skewness1.1328336
Sum34540.83
Variance78141.293
MonotonicityNot monotonic
2023-12-10T20:12:55.122428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
209.81 42
42.0%
148.01 29
29.0%
824.35 25
25.0%
159.6 2
 
2.0%
336.25 1
 
1.0%
172.32 1
 
1.0%
ValueCountFrequency (%)
148.01 29
29.0%
159.6 2
 
2.0%
172.32 1
 
1.0%
209.81 42
42.0%
336.25 1
 
1.0%
824.35 25
25.0%
ValueCountFrequency (%)
824.35 25
25.0%
336.25 1
 
1.0%
209.81 42
42.0%
172.32 1
 
1.0%
159.6 2
 
2.0%
148.01 29
29.0%

Interactions

2023-12-10T20:12:50.419766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:46.686605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:47.418067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:48.156714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:48.855027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:49.498646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:50.534140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:46.846743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:47.525811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:48.265458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:48.962135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:49.605196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:50.652655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:46.971834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:47.627780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:48.387894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:49.086546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:49.708338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:50.756056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:47.088082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:47.766288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:48.508128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:49.186139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:49.809189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:50.850702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:47.201213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:47.895707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:48.634779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:49.287319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:50.194074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:50.941334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:47.307981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:48.033352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:48.745724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:49.397169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:50.293576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T20:12:55.250378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
아이디격자번호기울기y절편10cm 침수심 유발 강우량20cm 침수심 유발 강우량50cm 침수심 유발 강우량
아이디1.0001.0000.3690.3690.6770.3690.345
격자번호1.0001.0001.0001.0001.0001.0001.000
기울기0.3691.0001.0001.0001.0001.0001.000
y절편0.3691.0001.0001.0001.0001.0001.000
10cm 침수심 유발 강우량0.6771.0001.0001.0001.0001.0001.000
20cm 침수심 유발 강우량0.3691.0001.0001.0001.0001.0001.000
50cm 침수심 유발 강우량0.3451.0001.0001.0001.0001.0001.000
2023-12-10T20:12:55.400789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
아이디기울기y절편10cm 침수심 유발 강우량20cm 침수심 유발 강우량50cm 침수심 유발 강우량
아이디1.0000.472-0.4700.4710.4720.472
기울기0.4721.000-1.0000.9881.0001.000
y절편-0.470-1.0001.000-0.987-1.000-1.000
10cm 침수심 유발 강우량0.4710.988-0.9871.0000.9880.988
20cm 침수심 유발 강우량0.4721.000-1.0000.9881.0001.000
50cm 침수심 유발 강우량0.4721.000-1.0000.9881.0001.000

Missing values

2023-12-10T20:12:51.171936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T20:12:51.415499image/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
029988다바665636세종36110세종특별자치시3.22-13.1119.1151.34148.01
130086다바675436세종36110세종특별자치시3.22-13.1119.1151.34148.01
230087다바675536세종36110세종특별자치시3.22-13.1119.1151.34148.01
330088다바675636세종36110세종특별자치시3.22-13.1119.1151.34148.01
430089다바675736세종36110세종특별자치시3.22-13.1119.1151.34148.01
530178다바684636세종36110세종특별자치시4.58-19.1826.6272.42209.81
630179다바684736세종36110세종특별자치시4.58-19.1826.6272.42209.81
730181다바684936세종36110세종특별자치시4.58-19.1826.6272.42209.81
830186다바685436세종36110세종특별자치시3.22-13.1119.1151.34148.01
930187다바685536세종36110세종특별자치시3.22-13.1119.1151.34148.01
아이디격자번호시도코드시도명시군구코드시군구명기울기y절편10cm 침수심 유발 강우량20cm 침수심 유발 강우량50cm 침수심 유발 강우량
9030574다바724236세종36110세종특별자치시3.76-15.5222.0559.62172.32
9130575다바724336세종36110세종특별자치시4.58-19.1826.6272.42209.81
9230576다바724436세종36110세종특별자치시4.58-19.1826.6272.42209.81
9330577다바724536세종36110세종특별자치시4.58-19.1826.6272.42209.81
9430578다바724636세종36110세종특별자치시4.58-19.1826.6272.42209.81
9530579다바724736세종36110세종특별자치시4.58-19.1826.6272.42209.81
9630580다바724836세종36110세종특별자치시4.58-19.1826.6272.42209.81
9730581다바724936세종36110세종특별자치시4.58-19.1826.6272.42209.81
9830582다바725036세종36110세종특별자치시4.58-19.1826.6272.42209.81
9930583다바725136세종36110세종특별자치시4.58-19.1826.6272.42209.81