Overview

Dataset statistics

Number of variables11
Number of observations200
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory19.1 KiB
Average record size in memory97.7 B

Variable types

DateTime1
Numeric6
Categorical4

Alerts

경도값 has constant value ""Constant
측정주소 has constant value ""Constant
위도값 has constant value ""Constant
온도값 is highly overall correlated with 습도값High correlation
이산화탄소농도값 is highly overall correlated with 배지전기전도값 and 2 other fieldsHigh correlation
토양온도값 is highly overall correlated with 토양수분값 and 1 other fieldsHigh correlation
배지전기전도값 is highly overall correlated with 이산화탄소농도값 and 3 other fieldsHigh correlation
액상전기전도값 is highly overall correlated with 이산화탄소농도값 and 2 other fieldsHigh correlation
토양수분값 is highly overall correlated with 토양온도값 and 2 other fieldsHigh correlation
습도값 is highly overall correlated with 온도값 and 5 other fieldsHigh correlation
측정일시 has unique valuesUnique

Reproduction

Analysis started2023-12-10 06:35:50.160784
Analysis finished2023-12-10 06:35:57.834810
Duration7.67 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

측정일시
Date

UNIQUE 

Distinct200
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
Minimum2021-01-01 00:00:00
Maximum2021-01-01 16:35:00
2023-12-10T15:35:57.950392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:35:58.176814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

온도값
Real number (ℝ)

HIGH CORRELATION 

Distinct125
Distinct (%)62.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-200.499
Minimum-999
Maximum18.91
Zeros0
Zeros (%)0.0%
Negative42
Negative (%)21.0%
Memory size1.9 KiB
2023-12-10T15:35:58.421695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-999
5-th percentile-999
Q18.4675
median8.98
Q314.27
95-th percentile17.2085
Maximum18.91
Range1017.91
Interquartile range (IQR)5.8025

Descriptive statistics

Standard deviation412.73517
Coefficient of variation (CV)-2.0585398
Kurtosis0.058803357
Mean-200.499
Median Absolute Deviation (MAD)4.375
Skewness-1.4345703
Sum-40099.8
Variance170350.32
MonotonicityNot monotonic
2023-12-10T15:35:58.675417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-999.0 42
 
21.0%
16.25 4
 
2.0%
8.73 3
 
1.5%
8.66 3
 
1.5%
8.64 3
 
1.5%
8.47 3
 
1.5%
15.22 2
 
1.0%
8.55 2
 
1.0%
8.86 2
 
1.0%
16.48 2
 
1.0%
Other values (115) 134
67.0%
ValueCountFrequency (%)
-999.0 42
21.0%
8.25 1
 
0.5%
8.36 1
 
0.5%
8.38 1
 
0.5%
8.4 1
 
0.5%
8.43 1
 
0.5%
8.44 2
 
1.0%
8.46 1
 
0.5%
8.47 3
 
1.5%
8.51 2
 
1.0%
ValueCountFrequency (%)
18.91 1
0.5%
18.27 1
0.5%
17.89 2
1.0%
17.68 1
0.5%
17.49 2
1.0%
17.47 1
0.5%
17.37 2
1.0%
17.2 1
0.5%
17.18 1
0.5%
16.98 1
0.5%

경도값
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
127.9644
200 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row127.9644
2nd row127.9644
3rd row127.9644
4th row127.9644
5th row127.9644

Common Values

ValueCountFrequency (%)
127.9644 200
100.0%

Length

2023-12-10T15:35:58.912114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:35:59.097832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
127.9644 200
100.0%

습도값
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
100
158 
-999
42 

Length

Max length4
Median length3
Mean length3.21
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row100
2nd row100
3rd row100
4th row100
5th row100

Common Values

ValueCountFrequency (%)
100 158
79.0%
-999 42
 
21.0%

Length

2023-12-10T15:35:59.281593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:35:59.463238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
100 158
79.0%
999 42
 
21.0%

이산화탄소농도값
Real number (ℝ)

HIGH CORRELATION 

Distinct118
Distinct (%)59.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean494.195
Minimum-999
Maximum1020
Zeros0
Zeros (%)0.0%
Negative42
Negative (%)21.0%
Memory size1.9 KiB
2023-12-10T15:35:59.654351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-999
5-th percentile-999
Q1598
median871
Q3969.75
95-th percentile1009
Maximum1020
Range2019
Interquartile range (IQR)371.75

Descriptive statistics

Standard deviation778.41595
Coefficient of variation (CV)1.575119
Kurtosis-0.023310631
Mean494.195
Median Absolute Deviation (MAD)106.5
Skewness-1.375613
Sum98839
Variance605931.38
MonotonicityNot monotonic
2023-12-10T15:35:59.941036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-999 42
 
21.0%
986 4
 
2.0%
999 3
 
1.5%
981 3
 
1.5%
858 3
 
1.5%
906 3
 
1.5%
871 2
 
1.0%
1012 2
 
1.0%
968 2
 
1.0%
1009 2
 
1.0%
Other values (108) 134
67.0%
ValueCountFrequency (%)
-999 42
21.0%
561 2
 
1.0%
563 2
 
1.0%
569 1
 
0.5%
583 2
 
1.0%
598 2
 
1.0%
613 2
 
1.0%
650 2
 
1.0%
775 1
 
0.5%
781 2
 
1.0%
ValueCountFrequency (%)
1020 1
0.5%
1019 1
0.5%
1018 1
0.5%
1017 1
0.5%
1014 2
1.0%
1012 2
1.0%
1010 1
0.5%
1009 2
1.0%
1008 2
1.0%
1007 2
1.0%

토양온도값
Real number (ℝ)

HIGH CORRELATION 

Distinct15
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-197.3555
Minimum-999
Maximum16.8
Zeros0
Zeros (%)0.0%
Negative42
Negative (%)21.0%
Memory size1.9 KiB
2023-12-10T15:36:00.230763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-999
5-th percentile-999
Q115
median15.5
Q316
95-th percentile16.7
Maximum16.8
Range1015.8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation414.34932
Coefficient of variation (CV)-2.0995073
Kurtosis0.059038885
Mean-197.3555
Median Absolute Deviation (MAD)0.5
Skewness-1.434762
Sum-39471.1
Variance171685.36
MonotonicityNot monotonic
2023-12-10T15:36:00.503856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
-999.0 42
21.0%
15.4 24
12.0%
16.0 22
11.0%
15.7 19
9.5%
15.5 19
9.5%
15.8 14
 
7.0%
15.2 11
 
5.5%
16.3 9
 
4.5%
15.0 8
 
4.0%
16.2 7
 
3.5%
Other values (5) 25
12.5%
ValueCountFrequency (%)
-999.0 42
21.0%
14.7 2
 
1.0%
14.9 5
 
2.5%
15.0 8
 
4.0%
15.2 11
 
5.5%
15.4 24
12.0%
15.5 19
9.5%
15.7 19
9.5%
15.8 14
 
7.0%
16.0 22
11.0%
ValueCountFrequency (%)
16.8 7
 
3.5%
16.7 5
 
2.5%
16.5 6
 
3.0%
16.3 9
 
4.5%
16.2 7
 
3.5%
16.0 22
11.0%
15.8 14
7.0%
15.7 19
9.5%
15.5 19
9.5%
15.4 24
12.0%

배지전기전도값
Real number (ℝ)

HIGH CORRELATION 

Distinct13
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-209.223
Minimum-999
Maximum0.77
Zeros0
Zeros (%)0.0%
Negative42
Negative (%)21.0%
Memory size1.9 KiB
2023-12-10T15:36:00.699734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-999
5-th percentile-999
Q10.69
median0.705
Q30.73
95-th percentile0.75
Maximum0.77
Range999.77
Interquartile range (IQR)0.04

Descriptive statistics

Standard deviation408.21508
Coefficient of variation (CV)-1.9511004
Kurtosis0.059044088
Mean-209.223
Median Absolute Deviation (MAD)0.025
Skewness-1.4347662
Sum-41844.6
Variance166639.55
MonotonicityNot monotonic
2023-12-10T15:36:00.928030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
-999.0 42
21.0%
0.7 32
16.0%
0.71 21
10.5%
0.73 21
10.5%
0.74 20
10.0%
0.69 20
10.0%
0.75 18
9.0%
0.72 15
 
7.5%
0.68 3
 
1.5%
0.77 3
 
1.5%
Other values (3) 5
 
2.5%
ValueCountFrequency (%)
-999.0 42
21.0%
0.65 1
 
0.5%
0.67 2
 
1.0%
0.68 3
 
1.5%
0.69 20
10.0%
0.7 32
16.0%
0.71 21
10.5%
0.72 15
 
7.5%
0.73 21
10.5%
0.74 20
10.0%
ValueCountFrequency (%)
0.77 3
 
1.5%
0.76 2
 
1.0%
0.75 18
9.0%
0.74 20
10.0%
0.73 21
10.5%
0.72 15
7.5%
0.71 21
10.5%
0.7 32
16.0%
0.69 20
10.0%
0.68 3
 
1.5%

액상전기전도값
Real number (ℝ)

HIGH CORRELATION 

Distinct72
Distinct (%)36.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-205.31515
Minimum-999
Maximum7.39
Zeros0
Zeros (%)0.0%
Negative42
Negative (%)21.0%
Memory size1.9 KiB
2023-12-10T15:36:01.192925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-999
5-th percentile-999
Q14.9225
median5.495
Q35.81
95-th percentile6.52
Maximum7.39
Range1006.39
Interquartile range (IQR)0.8875

Descriptive statistics

Standard deviation410.23522
Coefficient of variation (CV)-1.9980757
Kurtosis0.059038066
Mean-205.31515
Median Absolute Deviation (MAD)0.475
Skewness-1.4347613
Sum-41063.03
Variance168292.93
MonotonicityNot monotonic
2023-12-10T15:36:01.540288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-999.0 42
21.0%
5.8 13
 
6.5%
5.88 8
 
4.0%
5.76 7
 
3.5%
5.72 5
 
2.5%
5.49 5
 
2.5%
5.53 5
 
2.5%
5.38 5
 
2.5%
5.6 4
 
2.0%
5.56 4
 
2.0%
Other values (62) 102
51.0%
ValueCountFrequency (%)
-999.0 42
21.0%
3.79 1
 
0.5%
4.42 1
 
0.5%
4.63 1
 
0.5%
4.64 1
 
0.5%
4.75 1
 
0.5%
4.89 2
 
1.0%
4.9 1
 
0.5%
4.93 3
 
1.5%
4.94 1
 
0.5%
ValueCountFrequency (%)
7.39 1
0.5%
7.0 1
0.5%
6.9 1
0.5%
6.78 1
0.5%
6.73 1
0.5%
6.68 1
0.5%
6.63 1
0.5%
6.58 1
0.5%
6.54 1
0.5%
6.52 2
1.0%

토양수분값
Real number (ℝ)

HIGH CORRELATION 

Distinct54
Distinct (%)27.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-191.6225
Minimum-999
Maximum28.3
Zeros0
Zeros (%)0.0%
Negative42
Negative (%)21.0%
Memory size1.9 KiB
2023-12-10T15:36:01.833655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-999
5-th percentile-999
Q120.7
median22.7
Q323.5
95-th percentile25.105
Maximum28.3
Range1027.3
Interquartile range (IQR)2.8

Descriptive statistics

Standard deviation417.31418
Coefficient of variation (CV)-2.1777932
Kurtosis0.059003232
Mean-191.6225
Median Absolute Deviation (MAD)1.1
Skewness-1.4347331
Sum-38324.5
Variance174151.13
MonotonicityNot monotonic
2023-12-10T15:36:02.050696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-999.0 42
21.0%
23.0 10
 
5.0%
23.2 9
 
4.5%
22.8 8
 
4.0%
23.7 7
 
3.5%
23.5 7
 
3.5%
21.8 7
 
3.5%
22.0 6
 
3.0%
23.3 6
 
3.0%
23.1 6
 
3.0%
Other values (44) 92
46.0%
ValueCountFrequency (%)
-999.0 42
21.0%
19.2 1
 
0.5%
19.7 1
 
0.5%
20.3 2
 
1.0%
20.4 1
 
0.5%
20.6 2
 
1.0%
20.7 3
 
1.5%
20.8 1
 
0.5%
20.9 2
 
1.0%
21.0 1
 
0.5%
ValueCountFrequency (%)
28.3 1
 
0.5%
26.4 1
 
0.5%
26.1 1
 
0.5%
25.9 1
 
0.5%
25.6 1
 
0.5%
25.5 1
 
0.5%
25.3 1
 
0.5%
25.2 3
1.5%
25.1 3
1.5%
25.0 2
1.0%

측정주소
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
경상북도 상주시 모동면 신천리 220-8
200 

Length

Max length22
Median length22
Mean length22
Min length22

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row경상북도 상주시 모동면 신천리 220-8
2nd row경상북도 상주시 모동면 신천리 220-8
3rd row경상북도 상주시 모동면 신천리 220-8
4th row경상북도 상주시 모동면 신천리 220-8
5th row경상북도 상주시 모동면 신천리 220-8

Common Values

ValueCountFrequency (%)
경상북도 상주시 모동면 신천리 220-8 200
100.0%

Length

2023-12-10T15:36:02.313966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:36:02.534728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경상북도 200
20.0%
상주시 200
20.0%
모동면 200
20.0%
신천리 200
20.0%
220-8 200
20.0%

위도값
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
36.2965
200 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row36.2965
2nd row36.2965
3rd row36.2965
4th row36.2965
5th row36.2965

Common Values

ValueCountFrequency (%)
36.2965 200
100.0%

Length

2023-12-10T15:36:02.679389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:36:02.826433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
36.2965 200
100.0%

Interactions

2023-12-10T15:35:56.110109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:35:50.683059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:35:51.824858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:35:52.882095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:35:53.995775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:35:55.188509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:35:56.255347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:35:50.882089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:35:52.004954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:35:53.010631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:35:54.252276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:35:55.363241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:35:56.438721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:35:51.051259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:35:52.189254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:35:53.186411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:35:54.436421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:35:55.509380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:35:56.581828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:35:51.244959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:35:52.395700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:35:53.327795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:35:54.588405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:35:55.683446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:35:56.708977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:35:51.416440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:35:52.563126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:35:53.523823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:35:54.726747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:35:55.863856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:35:56.860755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:35:51.617367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:35:52.711001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:35:53.802411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:35:54.996405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:35:55.980966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T15:36:02.932856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
온도값습도값이산화탄소농도값토양온도값배지전기전도값액상전기전도값토양수분값
온도값1.000NaNNaNNaNNaNNaNNaN
습도값NaN1.000NaNNaNNaNNaNNaN
이산화탄소농도값NaNNaN1.000NaNNaNNaNNaN
토양온도값NaNNaNNaN1.000NaNNaNNaN
배지전기전도값NaNNaNNaNNaN1.000NaNNaN
액상전기전도값NaNNaNNaNNaNNaN1.000NaN
토양수분값NaNNaNNaNNaNNaNNaN1.000
2023-12-10T15:36:03.126183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
온도값이산화탄소농도값토양온도값배지전기전도값액상전기전도값토양수분값습도값
온도값1.0000.4980.4610.4400.4860.4900.985
이산화탄소농도값0.4981.0000.0520.5710.5880.4620.995
토양온도값0.4610.0521.0000.4470.3530.6120.985
배지전기전도값0.4400.5710.4471.0000.5320.6410.985
액상전기전도값0.4860.5880.3530.5321.0000.0410.985
토양수분값0.4900.4620.6120.6410.0411.0000.985
습도값0.9850.9950.9850.9850.9850.9851.000

Missing values

2023-12-10T15:35:57.451831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T15:35:57.725497image/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

측정일시온도값경도값습도값이산화탄소농도값토양온도값배지전기전도값액상전기전도값토양수분값측정주소위도값
02021-01-01 0:008.88127.964410077516.50.715.7222.6경상북도 상주시 모동면 신천리 220-836.2965
12021-01-01 0:058.7127.964410078516.70.75.822.2경상북도 상주시 모동면 신천리 220-836.2965
22021-01-01 0:108.6127.964410078816.50.74.9724.5경상북도 상주시 모동면 신천리 220-836.2965
32021-01-01 0:158.66127.964410078816.50.744.925.6경상북도 상주시 모동면 신천리 220-836.2965
42021-01-01 0:208.64127.964410080316.50.75.822.2경상북도 상주시 모동면 신천리 220-836.2965
52021-01-01 0:258.47127.964410079916.30.755.1225.1경상북도 상주시 모동면 신천리 220-836.2965
62021-01-01 0:309.74127.964410080516.30.715.4523.3경상북도 상주시 모동면 신천리 220-836.2965
72021-01-01 0:3510.09127.964410079416.50.746.022.5경상북도 상주시 모동면 신천리 220-836.2965
82021-01-01 0:409.85127.964410080516.20.74.9424.6경상북도 상주시 모동면 신천리 220-836.2965
92021-01-01 0:459.58127.964410080916.30.715.0124.6경상북도 상주시 모동면 신천리 220-836.2965
측정일시온도값경도값습도값이산화탄소농도값토양온도값배지전기전도값액상전기전도값토양수분값측정주소위도값
1902021-01-01 15:50-999.0127.9644-999-999-999.0-999.0-999.0-999.0경상북도 상주시 모동면 신천리 220-836.2965
1912021-01-01 15:55-999.0127.9644-999-999-999.0-999.0-999.0-999.0경상북도 상주시 모동면 신천리 220-836.2965
1922021-01-01 16:00-999.0127.9644-999-999-999.0-999.0-999.0-999.0경상북도 상주시 모동면 신천리 220-836.2965
1932021-01-01 16:05-999.0127.9644-999-999-999.0-999.0-999.0-999.0경상북도 상주시 모동면 신천리 220-836.2965
1942021-01-01 16:1015.14127.964410056116.80.724.8925.2경상북도 상주시 모동면 신천리 220-836.2965
1952021-01-01 16:1515.14127.964410056116.80.724.8925.2경상북도 상주시 모동면 신천리 220-836.2965
1962021-01-01 16:20-999.0127.9644-999-999-999.0-999.0-999.0-999.0경상북도 상주시 모동면 신천리 220-836.2965
1972021-01-01 16:2515.01127.964410056316.80.75.3823.3경상북도 상주시 모동면 신천리 220-836.2965
1982021-01-01 16:3015.01127.964410056316.80.75.3823.3경상북도 상주시 모동면 신천리 220-836.2965
1992021-01-01 16:3514.17127.964410056916.80.75.4523.1경상북도 상주시 모동면 신천리 220-836.2965