Overview

Dataset statistics

Number of variables16
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory13.8 KiB
Average record size in memory141.3 B

Variable types

Numeric9
Categorical4
Text3

Alerts

도로종류 has constant value ""Constant
측정일 has constant value ""Constant
측정시간 has constant value ""Constant
좌표위치경도((°)) is highly overall correlated with co((g/km))High correlation
co((g/km)) is highly overall correlated with 좌표위치경도((°)) and 4 other fieldsHigh correlation
nox((g/km)) is highly overall correlated with co((g/km)) and 3 other fieldsHigh correlation
hc((g/km)) is highly overall correlated with co((g/km)) and 3 other fieldsHigh correlation
pm((g/km)) is highly overall correlated with co((g/km)) and 3 other fieldsHigh correlation
co2((g/km)) is highly overall correlated with co((g/km)) and 3 other fieldsHigh correlation
기본키 has unique valuesUnique
co((g/km)) has unique valuesUnique
nox((g/km)) has unique valuesUnique
hc((g/km)) has unique valuesUnique
pm((g/km)) has unique valuesUnique
co2((g/km)) has unique valuesUnique

Reproduction

Analysis started2023-12-10 12:41:54.236060
Analysis finished2023-12-10 12:42:03.127299
Duration8.89 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기본키
Real number (ℝ)

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.5
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:42:03.213234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5.95
Q125.75
median50.5
Q375.25
95-th percentile95.05
Maximum100
Range99
Interquartile range (IQR)49.5

Descriptive statistics

Standard deviation29.011492
Coefficient of variation (CV)0.57448499
Kurtosis-1.2
Mean50.5
Median Absolute Deviation (MAD)25
Skewness0
Sum5050
Variance841.66667
MonotonicityStrictly increasing
2023-12-10T21:42:03.403571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.0%
65 1
 
1.0%
75 1
 
1.0%
74 1
 
1.0%
73 1
 
1.0%
72 1
 
1.0%
71 1
 
1.0%
70 1
 
1.0%
69 1
 
1.0%
68 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
1 1
1.0%
2 1
1.0%
3 1
1.0%
4 1
1.0%
5 1
1.0%
6 1
1.0%
7 1
1.0%
8 1
1.0%
9 1
1.0%
10 1
1.0%
ValueCountFrequency (%)
100 1
1.0%
99 1
1.0%
98 1
1.0%
97 1
1.0%
96 1
1.0%
95 1
1.0%
94 1
1.0%
93 1
1.0%
92 1
1.0%
91 1
1.0%

도로종류
Categorical

CONSTANT 

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

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
건기연 100
100.0%

Length

2023-12-10T21:42:03.549599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:42:03.653888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
건기연 100
100.0%

지점
Text

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T21:42:03.907795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length8
Min length7

Characters and Unicode

Total characters800
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row[0216-2]
2nd row[0216-2]
3rd row[0218-1]
4th row[0218-1]
5th row[0220-2]
ValueCountFrequency (%)
0216-2 2
 
2.0%
5802-0 2
 
2.0%
00101-2 2
 
2.0%
2422-0 2
 
2.0%
2502-0 2
 
2.0%
3101-6 2
 
2.0%
3301-4 2
 
2.0%
3302-2 2
 
2.0%
3304-2 2
 
2.0%
3305-0 2
 
2.0%
Other values (40) 80
80.0%
2023-12-10T21:42:04.347548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 158
19.8%
[ 100
12.5%
] 100
12.5%
- 98
12.2%
1 92
11.5%
2 70
8.8%
3 50
 
6.2%
4 40
 
5.0%
7 28
 
3.5%
5 24
 
3.0%
Other values (3) 40
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 502
62.7%
Open Punctuation 100
 
12.5%
Close Punctuation 100
 
12.5%
Dash Punctuation 98
 
12.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 158
31.5%
1 92
18.3%
2 70
13.9%
3 50
 
10.0%
4 40
 
8.0%
7 28
 
5.6%
5 24
 
4.8%
9 20
 
4.0%
6 10
 
2.0%
8 10
 
2.0%
Open Punctuation
ValueCountFrequency (%)
[ 100
100.0%
Close Punctuation
ValueCountFrequency (%)
] 100
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 98
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 158
19.8%
[ 100
12.5%
] 100
12.5%
- 98
12.2%
1 92
11.5%
2 70
8.8%
3 50
 
6.2%
4 40
 
5.0%
7 28
 
3.5%
5 24
 
3.0%
Other values (3) 40
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 158
19.8%
[ 100
12.5%
] 100
12.5%
- 98
12.2%
1 92
11.5%
2 70
8.8%
3 50
 
6.2%
4 40
 
5.0%
7 28
 
3.5%
5 24
 
3.0%
Other values (3) 40
 
5.0%

방향
Categorical

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
1
50 
2
50 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row1
4th row2
5th row1

Common Values

ValueCountFrequency (%)
1 50
50.0%
2 50
50.0%

Length

2023-12-10T21:42:04.545853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:42:04.645190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 50
50.0%
2 50
50.0%

측정구간
Text

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T21:42:04.915036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length5
Mean length5.3
Min length5

Characters and Unicode

Total characters530
Distinct characters88
Distinct categories3 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row북천-완사
2nd row북천-완사
3rd row진주-사봉
4th row진주-사봉
5th row일반성-진북
ValueCountFrequency (%)
북천-완사 2
 
2.0%
진영-김해 2
 
2.0%
노포jct-양산jct 2
 
2.0%
가산-남기 2
 
2.0%
창원-대산 2
 
2.0%
서생-온산 2
 
2.0%
상리-고성 2
 
2.0%
대암-송계 2
 
2.0%
쌍백-합천 2
 
2.0%
합천-쌍림 2
 
2.0%
Other values (40) 80
80.0%
2023-12-10T21:42:05.316525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 100
 
18.9%
산 32
 
6.0%
남 18
 
3.4%
양 18
 
3.4%
천 16
 
3.0%
창 14
 
2.6%
성 12
 
2.3%
진 12
 
2.3%
C 10
 
1.9%
정 8
 
1.5%
Other values (78) 290
54.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 404
76.2%
Dash Punctuation 100
 
18.9%
Uppercase Letter 26
 
4.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 32
 
7.9%
남 18
 
4.5%
양 18
 
4.5%
천 16
 
4.0%
창 14
 
3.5%
성 12
 
3.0%
진 12
 
3.0%
정 8
 
2.0%
하 8
 
2.0%
녕 8
 
2.0%
Other values (73) 258
63.9%
Uppercase Letter
ValueCountFrequency (%)
C 10
38.5%
J 6
23.1%
T 6
23.1%
I 4
 
15.4%
Dash Punctuation
ValueCountFrequency (%)
- 100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 404
76.2%
Common 100
 
18.9%
Latin 26
 
4.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 32
 
7.9%
남 18
 
4.5%
양 18
 
4.5%
천 16
 
4.0%
창 14
 
3.5%
성 12
 
3.0%
진 12
 
3.0%
정 8
 
2.0%
하 8
 
2.0%
녕 8
 
2.0%
Other values (73) 258
63.9%
Latin
ValueCountFrequency (%)
C 10
38.5%
J 6
23.1%
T 6
23.1%
I 4
 
15.4%
Common
ValueCountFrequency (%)
- 100
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 404
76.2%
ASCII 126
 
23.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 100
79.4%
C 10
 
7.9%
J 6
 
4.8%
T 6
 
4.8%
I 4
 
3.2%
Hangul
ValueCountFrequency (%)
산 32
 
7.9%
남 18
 
4.5%
양 18
 
4.5%
천 16
 
4.0%
창 14
 
3.5%
성 12
 
3.0%
진 12
 
3.0%
정 8
 
2.0%
하 8
 
2.0%
녕 8
 
2.0%
Other values (73) 258
63.9%

연장((km))
Real number (ℝ)

Distinct43
Distinct (%)43.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.67
Minimum2.7
Maximum20.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:42:05.510139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.7
5-th percentile3.3
Q15.2
median7.75
Q311
95-th percentile17.6
Maximum20.1
Range17.4
Interquartile range (IQR)5.8

Descriptive statistics

Standard deviation4.3182605
Coefficient of variation (CV)0.49806926
Kurtosis0.32825521
Mean8.67
Median Absolute Deviation (MAD)2.8
Skewness0.89779419
Sum867
Variance18.647374
MonotonicityNot monotonic
2023-12-10T21:42:05.712092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
5.2 4
 
4.0%
10.2 4
 
4.0%
6.8 4
 
4.0%
17.2 4
 
4.0%
11.2 4
 
4.0%
7.9 4
 
4.0%
10.5 4
 
4.0%
4.7 2
 
2.0%
3.3 2
 
2.0%
5.7 2
 
2.0%
Other values (33) 66
66.0%
ValueCountFrequency (%)
2.7 2
2.0%
3.0 2
2.0%
3.3 2
2.0%
3.5 2
2.0%
3.8 2
2.0%
3.9 2
2.0%
4.1 2
2.0%
4.2 2
2.0%
4.7 2
2.0%
4.8 2
2.0%
ValueCountFrequency (%)
20.1 2
2.0%
19.3 2
2.0%
17.6 2
2.0%
17.2 4
4.0%
13.5 2
2.0%
13.2 2
2.0%
12.2 2
2.0%
11.9 2
2.0%
11.2 4
4.0%
11.1 2
2.0%

측정일
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
20210601
100 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20210601
2nd row20210601
3rd row20210601
4th row20210601
5th row20210601

Common Values

ValueCountFrequency (%)
20210601 100
100.0%

Length

2023-12-10T21:42:05.878591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:42:05.997996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20210601 100
100.0%

측정시간
Categorical

CONSTANT 

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

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 100
100.0%

Length

2023-12-10T21:42:06.126119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:42:06.234751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 100
100.0%

좌표위치위도((°))
Real number (ℝ)

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.289759
Minimum34.86496
Maximum35.72809
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:42:06.378078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum34.86496
5-th percentile34.89878
Q135.10632
median35.306605
Q335.51777
95-th percentile35.62274
Maximum35.72809
Range0.86313
Interquartile range (IQR)0.41145

Descriptive statistics

Standard deviation0.2471748
Coefficient of variation (CV)0.007004151
Kurtosis-1.0941769
Mean35.289759
Median Absolute Deviation (MAD)0.20909
Skewness-0.19565486
Sum3528.9759
Variance0.061095382
MonotonicityNot monotonic
2023-12-10T21:42:06.521162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.12031 2
 
2.0%
34.98771 2
 
2.0%
35.32823 2
 
2.0%
35.39903 2
 
2.0%
34.97974 2
 
2.0%
35.28102 2
 
2.0%
35.51362 2
 
2.0%
35.62274 2
 
2.0%
35.31018 2
 
2.0%
35.52321 2
 
2.0%
Other values (40) 80
80.0%
ValueCountFrequency (%)
34.86496 2
2.0%
34.87506 2
2.0%
34.89878 2
2.0%
34.90043 2
2.0%
34.90831 2
2.0%
34.92571 2
2.0%
34.93132 2
2.0%
34.95217 2
2.0%
34.97974 2
2.0%
34.98771 2
2.0%
ValueCountFrequency (%)
35.72809 2
2.0%
35.65095 2
2.0%
35.62274 2
2.0%
35.61557 2
2.0%
35.61463 2
2.0%
35.60563 2
2.0%
35.58349 2
2.0%
35.57918 2
2.0%
35.57341 2
2.0%
35.55894 2
2.0%

좌표위치경도((°))
Real number (ℝ)

HIGH CORRELATION 

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.40958
Minimum127.78878
Maximum129.33586
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:42:06.669070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum127.78878
5-th percentile127.80269
Q1128.00958
median128.3277
Q3128.72421
95-th percentile129.24056
Maximum129.33586
Range1.54708
Interquartile range (IQR)0.71463

Descriptive statistics

Standard deviation0.46780703
Coefficient of variation (CV)0.0036430851
Kurtosis-0.91693938
Mean128.40958
Median Absolute Deviation (MAD)0.33558
Skewness0.54843134
Sum12840.958
Variance0.21884342
MonotonicityNot monotonic
2023-12-10T21:42:06.841820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.97011 2
 
2.0%
127.80269 2
 
2.0%
128.71274 2
 
2.0%
129.33586 2
 
2.0%
128.2752 2
 
2.0%
128.08474 2
 
2.0%
128.17297 2
 
2.0%
128.19963 2
 
2.0%
129.02747 2
 
2.0%
129.09913 2
 
2.0%
Other values (40) 80
80.0%
ValueCountFrequency (%)
127.78878 2
2.0%
127.78997 2
2.0%
127.80269 2
2.0%
127.83555 2
2.0%
127.86341 2
2.0%
127.86709 2
2.0%
127.89437 2
2.0%
127.90225 2
2.0%
127.95798 2
2.0%
127.97011 2
2.0%
ValueCountFrequency (%)
129.33586 2
2.0%
129.28105 2
2.0%
129.24056 2
2.0%
129.2158 2
2.0%
129.17553 2
2.0%
129.14403 2
2.0%
129.12842 2
2.0%
129.09913 2
2.0%
129.07556 2
2.0%
129.02747 2
2.0%

co((g/km))
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5576.6956
Minimum239.53
Maximum28117.04
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:42:06.998184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum239.53
5-th percentile618.7825
Q12040.9075
median3762.55
Q37755.0225
95-th percentile18170.918
Maximum28117.04
Range27877.51
Interquartile range (IQR)5714.115

Descriptive statistics

Standard deviation5438.4117
Coefficient of variation (CV)0.97520325
Kurtosis3.9841351
Mean5576.6956
Median Absolute Deviation (MAD)2219.14
Skewness1.8962211
Sum557669.56
Variance29576322
MonotonicityNot monotonic
2023-12-10T21:42:07.123096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1716.36 1
 
1.0%
4421.69 1
 
1.0%
2699.12 1
 
1.0%
7633.51 1
 
1.0%
7875.68 1
 
1.0%
4304.59 1
 
1.0%
5210.1 1
 
1.0%
10389.34 1
 
1.0%
10724.98 1
 
1.0%
3628.36 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
239.53 1
1.0%
248.26 1
1.0%
317.15 1
1.0%
338.59 1
1.0%
590.33 1
1.0%
620.28 1
1.0%
632.58 1
1.0%
640.05 1
1.0%
977.21 1
1.0%
1042.24 1
1.0%
ValueCountFrequency (%)
28117.04 1
1.0%
24655.38 1
1.0%
20866.32 1
1.0%
19115.24 1
1.0%
18709.34 1
1.0%
18142.58 1
1.0%
16082.9 1
1.0%
14761.17 1
1.0%
12680.56 1
1.0%
12285.19 1
1.0%

nox((g/km))
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6707.1269
Minimum229.68
Maximum49227.02
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:42:07.245529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum229.68
5-th percentile559.7815
Q12128.3925
median4695.13
Q38188.1625
95-th percentile17507.338
Maximum49227.02
Range48997.34
Interquartile range (IQR)6059.77

Descriptive statistics

Standard deviation8475.165
Coefficient of variation (CV)1.2636059
Kurtosis13.043344
Mean6707.1269
Median Absolute Deviation (MAD)2786.065
Skewness3.3711031
Sum670712.69
Variance71828423
MonotonicityNot monotonic
2023-12-10T21:42:07.387061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1258.59 1
 
1.0%
8074.07 1
 
1.0%
3112.54 1
 
1.0%
9152.23 1
 
1.0%
9835.15 1
 
1.0%
3638.32 1
 
1.0%
5300.83 1
 
1.0%
10055.15 1
 
1.0%
11256.4 1
 
1.0%
5326.47 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
229.68 1
1.0%
232.78 1
1.0%
235.01 1
1.0%
264.98 1
1.0%
520.1 1
1.0%
561.87 1
1.0%
563.49 1
1.0%
600.84 1
1.0%
958.05 1
1.0%
979.46 1
1.0%
ValueCountFrequency (%)
49227.02 1
1.0%
47035.91 1
1.0%
38358.81 1
1.0%
36231.07 1
1.0%
18532.17 1
1.0%
17453.4 1
1.0%
15546.88 1
1.0%
14477.75 1
1.0%
13327.22 1
1.0%
12610.72 1
1.0%

hc((g/km))
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean806.1287
Minimum32.22
Maximum5104
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:42:07.519919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum32.22
5-th percentile75.048
Q1287.51
median575.475
Q31015.6125
95-th percentile2174.662
Maximum5104
Range5071.78
Interquartile range (IQR)728.1025

Descriptive statistics

Standard deviation898.83535
Coefficient of variation (CV)1.1150023
Kurtosis10.032197
Mean806.1287
Median Absolute Deviation (MAD)317.625
Skewness2.9071327
Sum80612.87
Variance807904.98
MonotonicityNot monotonic
2023-12-10T21:42:07.665930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
186.65 1
 
1.0%
845.17 1
 
1.0%
455.64 1
 
1.0%
1211.87 1
 
1.0%
1317.61 1
 
1.0%
505.06 1
 
1.0%
687.3 1
 
1.0%
1286.36 1
 
1.0%
1329.94 1
 
1.0%
566.13 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
32.22 1
1.0%
32.28 1
1.0%
33.32 1
1.0%
35.47 1
1.0%
70.07 1
1.0%
75.31 1
1.0%
75.83 1
1.0%
77.63 1
1.0%
128.24 1
1.0%
133.91 1
1.0%
ValueCountFrequency (%)
5104.0 1
1.0%
4729.07 1
1.0%
4174.12 1
1.0%
3721.18 1
1.0%
2367.36 1
1.0%
2164.52 1
1.0%
1857.56 1
1.0%
1758.2 1
1.0%
1738.99 1
1.0%
1673.11 1
1.0%

pm((g/km))
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean421.1943
Minimum22.15
Maximum2998.19
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:42:07.886912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum22.15
5-th percentile43.869
Q1132.085
median319.58
Q3490.3325
95-th percentile1040.148
Maximum2998.19
Range2976.04
Interquartile range (IQR)358.2475

Descriptive statistics

Standard deviation513.88648
Coefficient of variation (CV)1.2200699
Kurtosis12.966003
Mean421.1943
Median Absolute Deviation (MAD)184.685
Skewness3.3638479
Sum42119.43
Variance264079.32
MonotonicityNot monotonic
2023-12-10T21:42:08.029120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
82.5 1
 
1.0%
489.78 1
 
1.0%
222.66 1
 
1.0%
572.57 1
 
1.0%
642.4 1
 
1.0%
217.22 1
 
1.0%
328.25 1
 
1.0%
615.33 1
 
1.0%
748.37 1
 
1.0%
344.05 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
22.15 1
1.0%
22.34 1
1.0%
27.32 1
1.0%
27.79 1
1.0%
42.52 1
1.0%
43.94 1
1.0%
45.54 1
1.0%
53.51 1
1.0%
72.28 1
1.0%
73.39 1
1.0%
ValueCountFrequency (%)
2998.19 1
1.0%
2864.1 1
1.0%
2298.1 1
1.0%
2245.58 1
1.0%
1118.77 1
1.0%
1036.01 1
1.0%
1014.85 1
1.0%
1000.19 1
1.0%
911.52 1
1.0%
748.37 1
1.0%

co2((g/km))
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1438022.4
Minimum59358.64
Maximum7626384.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:42:08.429115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum59358.64
5-th percentile155769.67
Q1489347.65
median975071.66
Q31901960.6
95-th percentile4575162.7
Maximum7626384.7
Range7567026.1
Interquartile range (IQR)1412613

Descriptive statistics

Standard deviation1458342.6
Coefficient of variation (CV)1.0141306
Kurtosis5.0288741
Mean1438022.4
Median Absolute Deviation (MAD)561220.64
Skewness2.0789732
Sum1.4380224 × 108
Variance2.126763 × 1012
MonotonicityNot monotonic
2023-12-10T21:42:08.572874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
440976.2 1
 
1.0%
1205038.6 1
 
1.0%
619882.72 1
 
1.0%
1846950.25 1
 
1.0%
1883482.62 1
 
1.0%
1090274.9 1
 
1.0%
1323137.65 1
 
1.0%
2570420.15 1
 
1.0%
2725460.64 1
 
1.0%
932550.92 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
59358.64 1
1.0%
62028.74 1
1.0%
83353.4 1
1.0%
88588.14 1
1.0%
149554.97 1
1.0%
156096.76 1
1.0%
159788.23 1
1.0%
161765.63 1
1.0%
225340.82 1
1.0%
232079.5 1
1.0%
ValueCountFrequency (%)
7626384.71 1
1.0%
7117391.25 1
1.0%
5527678.58 1
1.0%
5346646.18 1
1.0%
4682116.96 1
1.0%
4569533.48 1
1.0%
4162210.3 1
1.0%
3824936.65 1
1.0%
3178410.94 1
1.0%
3111665.74 1
1.0%

주소
Text

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T21:42:08.901963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length11
Mean length10.86
Min length8

Characters and Unicode

Total characters1086
Distinct characters107
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

Unique0 ?
Unique (%)0.0%

Sample

1st row경남 사천 곤명 작팔
2nd row경남 사천 곤명 작팔
3rd row경남 진주 문산 상문
4th row경남 진주 문산 상문
5th row경남 창원 진전 근곡
ValueCountFrequency (%)
경남 96
24.1%
울주 10
 
2.5%
합천 10
 
2.5%
고성 10
 
2.5%
진주 8
 
2.0%
창원 8
 
2.0%
남해 8
 
2.0%
창녕 6
 
1.5%
산청 6
 
1.5%
부산 4
 
1.0%
Other values (106) 232
58.3%
2023-12-10T21:42:09.408446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
298
27.4%
남 108
 
9.9%
경 96
 
8.8%
산 32
 
2.9%
천 26
 
2.4%
창 20
 
1.8%
고 20
 
1.8%
주 18
 
1.7%
동 14
 
1.3%
대 14
 
1.3%
Other values (97) 440
40.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 788
72.6%
Space Separator 298
 
27.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
남 108
 
13.7%
경 96
 
12.2%
산 32
 
4.1%
천 26
 
3.3%
창 20
 
2.5%
고 20
 
2.5%
주 18
 
2.3%
동 14
 
1.8%
대 14
 
1.8%
양 14
 
1.8%
Other values (96) 426
54.1%
Space Separator
ValueCountFrequency (%)
298
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 788
72.6%
Common 298
 
27.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
남 108
 
13.7%
경 96
 
12.2%
산 32
 
4.1%
천 26
 
3.3%
창 20
 
2.5%
고 20
 
2.5%
주 18
 
2.3%
동 14
 
1.8%
대 14
 
1.8%
양 14
 
1.8%
Other values (96) 426
54.1%
Common
ValueCountFrequency (%)
298
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 788
72.6%
ASCII 298
 
27.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
298
100.0%
Hangul
ValueCountFrequency (%)
남 108
 
13.7%
경 96
 
12.2%
산 32
 
4.1%
천 26
 
3.3%
창 20
 
2.5%
고 20
 
2.5%
주 18
 
2.3%
동 14
 
1.8%
대 14
 
1.8%
양 14
 
1.8%
Other values (96) 426
54.1%

Interactions

2023-12-10T21:42:01.685351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:41:54.754080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:41:55.602158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:41:56.469878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:41:57.473719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:41:58.421288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:41:59.181070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:41:59.976593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:00.885488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:01.787812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:41:54.846046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:41:55.684822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:41:56.559493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:41:57.600758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:41:58.503400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:41:59.251955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:00.081813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:00.976595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:01.883514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:41:54.961772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:41:55.775859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:41:56.661862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:41:57.709619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:41:58.586571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:41:59.331240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:00.167740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:01.075716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:01.969989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:41:55.038182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:41:55.853525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:41:56.728575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:41:57.804147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:41:58.672071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:41:59.425430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:00.244388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:01.155881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:02.089881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:41:55.140362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:41:55.964132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:41:56.807781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:41:57.919369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:41:58.770818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:41:59.515592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:00.363103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:01.245465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:02.179509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:41:55.220055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:41:56.071290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:41:56.878878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:41:58.017718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:41:58.843624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:41:59.590723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:00.491205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:01.333021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:02.258820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:41:55.310798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:41:56.192090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:41:56.960841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:41:58.121985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:41:58.921184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:41:59.674096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:00.582767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:01.419568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:02.351278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:41:55.421265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:41:56.276862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:41:57.285769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:41:58.224145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:41:59.004324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:41:59.757388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:00.663941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:01.507647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:02.443494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:41:55.511441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:41:56.364954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:41:57.361001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:41:58.317749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:41:59.100248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:41:59.836572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:00.775488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:01.591476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T21:42:09.537093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기본키지점방향측정구간연장((km))좌표위치위도((°))좌표위치경도((°))co((g/km))nox((g/km))hc((g/km))pm((g/km))co2((g/km))주소
기본키1.0001.0000.0001.0000.4420.8260.6220.7420.5390.5770.5680.5461.000
지점1.0001.0000.0001.0001.0001.0001.0000.9740.9900.9630.9640.9251.000
방향0.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
측정구간1.0001.0000.0001.0001.0001.0001.0000.9740.9900.9630.9640.9251.000
연장((km))0.4421.0000.0001.0001.0000.6110.6870.3490.5920.4980.5350.5651.000
좌표위치위도((°))0.8261.0000.0001.0000.6111.0000.5670.6510.5420.4460.5080.5011.000
좌표위치경도((°))0.6221.0000.0001.0000.6870.5671.0000.6630.6750.6560.5470.6831.000
co((g/km))0.7420.9740.0000.9740.3490.6510.6631.0000.9160.9520.8910.9570.974
nox((g/km))0.5390.9900.0000.9900.5920.5420.6750.9161.0000.9500.9950.9390.990
hc((g/km))0.5770.9630.0000.9630.4980.4460.6560.9520.9501.0000.9370.9050.963
pm((g/km))0.5680.9640.0000.9640.5350.5080.5470.8910.9950.9371.0000.9350.964
co2((g/km))0.5460.9250.0000.9250.5650.5010.6830.9570.9390.9050.9351.0000.925
주소1.0001.0000.0001.0001.0001.0001.0000.9740.9900.9630.9640.9251.000
2023-12-10T21:42:09.753389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기본키연장((km))좌표위치위도((°))좌표위치경도((°))co((g/km))nox((g/km))hc((g/km))pm((g/km))co2((g/km))방향
기본키1.0000.0430.1210.206-0.113-0.021-0.056-0.022-0.1080.000
연장((km))0.0431.0000.2510.105-0.133-0.197-0.195-0.190-0.1310.000
좌표위치위도((°))0.1210.2511.0000.356-0.0640.0180.0020.016-0.0710.000
좌표위치경도((°))0.2060.1050.3561.0000.5050.4480.4740.4410.4940.000
co((g/km))-0.113-0.133-0.0640.5051.0000.9480.9740.9380.9970.000
nox((g/km))-0.021-0.1970.0180.4480.9481.0000.9910.9920.9520.000
hc((g/km))-0.056-0.1950.0020.4740.9740.9911.0000.9810.9730.000
pm((g/km))-0.022-0.1900.0160.4410.9380.9920.9811.0000.9430.000
co2((g/km))-0.108-0.131-0.0710.4940.9970.9520.9730.9431.0000.000
방향0.0000.0000.0000.0000.0000.0000.0000.0000.0001.000

Missing values

2023-12-10T21:42:02.787281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T21:42:03.041553image/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

기본키도로종류지점방향측정구간연장((km))측정일측정시간좌표위치위도((°))좌표위치경도((°))co((g/km))nox((g/km))hc((g/km))pm((g/km))co2((g/km))주소
01건기연[0216-2]1북천-완사4.220210601035.12031127.970111716.361258.59186.6582.5440976.2경남 사천 곤명 작팔
12건기연[0216-2]2북천-완사4.220210601035.12031127.970112097.421894.81244.98132.39547449.3경남 사천 곤명 작팔
23건기연[0218-1]1진주-사봉3.520210601035.16901128.18737940.378184.81091.26540.611973018.95경남 진주 문산 상문
34건기연[0218-1]2진주-사봉3.520210601035.16901128.18737190.36716.65912.47398.141822586.22경남 진주 문산 상문
45건기연[0220-2]1일반성-진북4.820210601035.10632128.4421811346.5215546.881673.111014.853111665.74경남 창원 진전 근곡
56건기연[0220-2]2일반성-진북4.820210601035.10632128.4421811028.5711388.281381.12714.322931840.27경남 창원 진전 근곡
67건기연[0302-4]1상죽-사천10.220210601034.87506128.009583027.352179.77312.66155.34786415.15경남 남해 창선 동대
78건기연[0302-4]2상죽-사천10.220210601034.87506128.009582827.82096.18296.11165.05734320.36경남 남해 창선 동대
89건기연[0303-1]1용현-정촌6.820210601035.01785128.057759588.948212.071144.74491.992442491.62경남 사천 용현 송지
910건기연[0303-1]2용현-정촌6.820210601035.01785128.0577510161.879218.731246.67565.942590254.22경남 사천 용현 송지
기본키도로종류지점방향측정구간연장((km))측정일측정시간좌표위치위도((°))좌표위치경도((°))co((g/km))nox((g/km))hc((g/km))pm((g/km))co2((g/km))주소
9091건기연[7901-0]1정곡-의령2.720210601035.29567128.2984620.556101.34670.56388.041261088.21경남 함안 군북 월촌
9192건기연[7901-0]2정곡-의령2.720210601035.29567128.2984782.876333.13707.82419.121288829.02경남 함안 군북 월촌
9293건기연[7904-0]1마산-진영5.220210601035.28732128.6117216082.912610.721758.2700.734162210.3경남 창원 북 외감
9394건기연[7904-0]2마산-진영5.220210601035.28732128.6117214761.1711286.121575.91605.753824936.65경남 창원 북 외감
9495건기연[7904-3]1창녕-남지7.920210601035.44323128.56024367.034818.28594.58361.221109174.12경남 창녕 도천 덕곡
9596건기연[7904-3]2창녕-남지7.920210601035.44323128.56023883.953808.35513.12241.76954990.82경남 창녕 도천 덕곡
9697건기연[00101-2]1노포JCT-양산JCT7.320210601035.30603129.0755624655.3836231.073721.182245.587117391.25부산 금정 노포
9798건기연[00101-2]2노포JCT-양산JCT7.320210601035.30603129.0755628117.0438358.814174.122298.17626384.71부산 금정 노포
9899건기연[00106]1언양JCT-활천IC17.220210601035.60563129.1440320866.3249227.025104.02998.195527678.58울산 울주 언양 반곡
99100건기연[00106]2언양JCT-활천IC17.220210601035.60563129.1440319115.2447035.914729.072864.15346646.18울산 울주 언양 반곡