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
좌표위치위도((°)) is highly overall correlated with nox((g/km))High correlation
co((g/km)) is highly overall correlated with nox((g/km)) and 3 other fieldsHigh correlation
nox((g/km)) is highly overall correlated with 좌표위치위도((°)) and 4 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 13:01:24.360545
Analysis finished2023-12-10 13:01:35.143019
Duration10.78 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
2023-12-10T22:01:35.263236image/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
2023-12-10T22:01:35.466694image/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

2023-12-10T22:01:35.653714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:01:35.786606image/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
2023-12-10T22:01:36.065375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

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[0134-0]
2nd row[0134-0]
3rd row[0141-1]
4th row[0141-1]
5th row[0142-0]
ValueCountFrequency (%)
0134-0 2
 
2.0%
4301-1 2
 
2.0%
4512-1 2
 
2.0%
3804-1 2
 
2.0%
3804-2 2
 
2.0%
3906-1 2
 
2.0%
3906-4 2
 
2.0%
3907-1 2
 
2.0%
3918-2 2
 
2.0%
4205-1 2
 
2.0%
Other values (40) 80
80.0%
2023-12-10T22:01:36.582210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 120
15.0%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
3 86
10.8%
1 68
8.5%
4 62
7.8%
2 54
6.8%
7 26
 
3.2%
8 24
 
3.0%
Other values (3) 60
7.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 500
62.5%
Open Punctuation 100
 
12.5%
Dash Punctuation 100
 
12.5%
Close Punctuation 100
 
12.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 120
24.0%
3 86
17.2%
1 68
13.6%
4 62
12.4%
2 54
10.8%
7 26
 
5.2%
8 24
 
4.8%
6 22
 
4.4%
5 22
 
4.4%
9 16
 
3.2%
Open Punctuation
ValueCountFrequency (%)
[ 100
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 100
100.0%
Close Punctuation
ValueCountFrequency (%)
] 100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 120
15.0%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
3 86
10.8%
1 68
8.5%
4 62
7.8%
2 54
6.8%
7 26
 
3.2%
8 24
 
3.0%
Other values (3) 60
7.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 120
15.0%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
3 86
10.8%
1 68
8.5%
4 62
7.8%
2 54
6.8%
7 26
 
3.2%
8 24
 
3.0%
Other values (3) 60
7.5%

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

2023-12-10T22:01:36.746719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:01:36.859025image/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
2023-12-10T22:01:37.133667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length5
Mean length5.2
Min length4

Characters and Unicode

Total characters520
Distinct characters80
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 row송탄-오산
2nd row송탄-오산
3rd row고양-파주
4th row고양-파주
5th row당동-파평
ValueCountFrequency (%)
송탄-오산 2
 
2.0%
팔탄-양감 2
 
2.0%
화도-청평 2
 
2.0%
죽산교-일죽 2
 
2.0%
안성-죽산 2
 
2.0%
아산만-덕목 2
 
2.0%
발안ic-청북ic 2
 
2.0%
팔탄-비봉 2
 
2.0%
일영-의정부 2
 
2.0%
보라-용인 2
 
2.0%
Other values (40) 80
80.0%
2023-12-10T22:01:37.671613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 100
 
19.2%
평 26
 
5.0%
천 24
 
4.6%
안 14
 
2.7%
주 14
 
2.7%
부 14
 
2.7%
양 14
 
2.7%
성 12
 
2.3%
중 12
 
2.3%
동 10
 
1.9%
Other values (70) 280
53.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 412
79.2%
Dash Punctuation 100
 
19.2%
Uppercase Letter 8
 
1.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
평 26
 
6.3%
천 24
 
5.8%
안 14
 
3.4%
주 14
 
3.4%
부 14
 
3.4%
양 14
 
3.4%
성 12
 
2.9%
중 12
 
2.9%
동 10
 
2.4%
팔 10
 
2.4%
Other values (67) 262
63.6%
Uppercase Letter
ValueCountFrequency (%)
I 4
50.0%
C 4
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 412
79.2%
Common 100
 
19.2%
Latin 8
 
1.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
평 26
 
6.3%
천 24
 
5.8%
안 14
 
3.4%
주 14
 
3.4%
부 14
 
3.4%
양 14
 
3.4%
성 12
 
2.9%
중 12
 
2.9%
동 10
 
2.4%
팔 10
 
2.4%
Other values (67) 262
63.6%
Latin
ValueCountFrequency (%)
I 4
50.0%
C 4
50.0%
Common
ValueCountFrequency (%)
- 100
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 412
79.2%
ASCII 108
 
20.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 100
92.6%
I 4
 
3.7%
C 4
 
3.7%
Hangul
ValueCountFrequency (%)
평 26
 
6.3%
천 24
 
5.8%
안 14
 
3.4%
주 14
 
3.4%
부 14
 
3.4%
양 14
 
3.4%
성 12
 
2.9%
중 12
 
2.9%
동 10
 
2.4%
팔 10
 
2.4%
Other values (67) 262
63.6%

연장((km))
Real number (ℝ)

Distinct45
Distinct (%)45.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.578
Minimum1.5
Maximum27.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:01:37.892847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.5
5-th percentile2.6
Q15.4
median7.45
Q310.9
95-th percentile17.6
Maximum27.5
Range26
Interquartile range (IQR)5.5

Descriptive statistics

Standard deviation4.9511264
Coefficient of variation (CV)0.5771889
Kurtosis2.8423337
Mean8.578
Median Absolute Deviation (MAD)2.8
Skewness1.3468619
Sum857.8
Variance24.513653
MonotonicityNot monotonic
2023-12-10T22:01:38.082717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
6.0 4
 
4.0%
4.8 4
 
4.0%
5.4 4
 
4.0%
10.6 4
 
4.0%
6.1 4
 
4.0%
6.2 2
 
2.0%
5.0 2
 
2.0%
5.6 2
 
2.0%
2.7 2
 
2.0%
27.5 2
 
2.0%
Other values (35) 70
70.0%
ValueCountFrequency (%)
1.5 2
2.0%
2.0 2
2.0%
2.6 2
2.0%
2.7 2
2.0%
2.9 2
2.0%
3.1 2
2.0%
3.4 2
2.0%
4.1 2
2.0%
4.3 2
2.0%
4.8 4
4.0%
ValueCountFrequency (%)
27.5 2
2.0%
19.0 2
2.0%
17.6 2
2.0%
15.5 2
2.0%
15.4 2
2.0%
14.7 2
2.0%
14.6 2
2.0%
12.9 2
2.0%
12.6 2
2.0%
12.1 2
2.0%

측정일
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
20210401
100 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20210401
2nd row20210401
3rd row20210401
4th row20210401
5th row20210401

Common Values

ValueCountFrequency (%)
20210401 100
100.0%

Length

2023-12-10T22:01:38.260008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:01:38.383648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20210401 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

2023-12-10T22:01:38.481894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:01:38.586488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 100
100.0%

좌표위치위도((°))
Real number (ℝ)

HIGH CORRELATION 

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.436322
Minimum36.95627
Maximum38.06264
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:01:38.779040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.95627
5-th percentile36.96033
Q137.16502
median37.380095
Q337.72112
95-th percentile38.0169
Maximum38.06264
Range1.10637
Interquartile range (IQR)0.5561

Descriptive statistics

Standard deviation0.33552787
Coefficient of variation (CV)0.0089626291
Kurtosis-1.0721334
Mean37.436322
Median Absolute Deviation (MAD)0.266565
Skewness0.33734741
Sum3743.6322
Variance0.11257895
MonotonicityNot monotonic
2023-12-10T22:01:39.040740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.09529 2
 
2.0%
37.34137 2
 
2.0%
37.02727 2
 
2.0%
36.95866 2
 
2.0%
37.05786 2
 
2.0%
37.23589 2
 
2.0%
37.71834 2
 
2.0%
37.23653 2
 
2.0%
37.2375 2
 
2.0%
37.29776 2
 
2.0%
Other values (40) 80
80.0%
ValueCountFrequency (%)
36.95627 2
2.0%
36.95866 2
2.0%
36.96033 2
2.0%
36.98521 2
2.0%
37.00613 2
2.0%
37.01644 2
2.0%
37.02727 2
2.0%
37.05786 2
2.0%
37.08149 2
2.0%
37.09529 2
2.0%
ValueCountFrequency (%)
38.06264 2
2.0%
38.06053 2
2.0%
38.0169 2
2.0%
37.99285 2
2.0%
37.96019 2
2.0%
37.93198 2
2.0%
37.91419 2
2.0%
37.87708 2
2.0%
37.83225 2
2.0%
37.76396 2
2.0%

좌표위치경도((°))
Real number (ℝ)

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.22819
Minimum126.77946
Maximum127.74421
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:01:39.214689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.77946
5-th percentile126.83253
Q1127.06364
median127.24601
Q3127.40224
95-th percentile127.62684
Maximum127.74421
Range0.96475
Interquartile range (IQR)0.3386

Descriptive statistics

Standard deviation0.24435108
Coefficient of variation (CV)0.0019205734
Kurtosis-0.77903447
Mean127.22819
Median Absolute Deviation (MAD)0.17778
Skewness0.025180817
Sum12722.819
Variance0.05970745
MonotonicityNot monotonic
2023-12-10T22:01:39.380292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.06364 2
 
2.0%
127.19674 2
 
2.0%
127.3367 2
 
2.0%
126.92256 2
 
2.0%
126.92508 2
 
2.0%
126.88236 2
 
2.0%
126.92818 2
 
2.0%
127.166 2
 
2.0%
127.3107 2
 
2.0%
127.60231 2
 
2.0%
Other values (40) 80
80.0%
ValueCountFrequency (%)
126.77946 2
2.0%
126.82904 2
2.0%
126.83253 2
2.0%
126.85908 2
2.0%
126.88236 2
2.0%
126.88641 2
2.0%
126.91531 2
2.0%
126.92256 2
2.0%
126.92508 2
2.0%
126.92818 2
2.0%
ValueCountFrequency (%)
127.74421 2
2.0%
127.6367 2
2.0%
127.62684 2
2.0%
127.61201 2
2.0%
127.60231 2
2.0%
127.56566 2
2.0%
127.55994 2
2.0%
127.49045 2
2.0%
127.44335 2
2.0%
127.44132 2
2.0%

co((g/km))
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10723.795
Minimum1125.57
Maximum40175.89
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:01:39.592804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1125.57
5-th percentile1905.1415
Q15300.9825
median9868.485
Q314141.757
95-th percentile23883.995
Maximum40175.89
Range39050.32
Interquartile range (IQR)8840.775

Descriptive statistics

Standard deviation7346.838
Coefficient of variation (CV)0.68509686
Kurtosis3.2460511
Mean10723.795
Median Absolute Deviation (MAD)4586.83
Skewness1.4306257
Sum1072379.5
Variance53976028
MonotonicityNot monotonic
2023-12-10T22:01:39.796903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13848.17 1
 
1.0%
5747.04 1
 
1.0%
16018.65 1
 
1.0%
5338.93 1
 
1.0%
5228.77 1
 
1.0%
10970.88 1
 
1.0%
10546.42 1
 
1.0%
12916.91 1
 
1.0%
10873.3 1
 
1.0%
19590.42 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
1125.57 1
1.0%
1310.56 1
1.0%
1354.43 1
1.0%
1383.76 1
1.0%
1885.03 1
1.0%
1906.2 1
1.0%
2146.99 1
1.0%
2206.57 1
1.0%
2269.07 1
1.0%
2348.17 1
1.0%
ValueCountFrequency (%)
40175.89 1
1.0%
38932.65 1
1.0%
25430.0 1
1.0%
24742.35 1
1.0%
24201.01 1
1.0%
23867.31 1
1.0%
22769.46 1
1.0%
21088.65 1
1.0%
20942.25 1
1.0%
20395.83 1
1.0%

nox((g/km))
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12262.353
Minimum949.8
Maximum45267.35
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:01:40.034821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum949.8
5-th percentile2060.7135
Q15014.255
median10988.715
Q315945.27
95-th percentile29107.544
Maximum45267.35
Range44317.55
Interquartile range (IQR)10931.015

Descriptive statistics

Standard deviation8512.3463
Coefficient of variation (CV)0.6941854
Kurtosis1.770744
Mean12262.353
Median Absolute Deviation (MAD)5613.835
Skewness1.1734975
Sum1226235.3
Variance72460039
MonotonicityNot monotonic
2023-12-10T22:01:40.266965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13508.59 1
 
1.0%
4752.36 1
 
1.0%
13625.39 1
 
1.0%
4888.57 1
 
1.0%
4758.15 1
 
1.0%
13168.55 1
 
1.0%
12112.55 1
 
1.0%
17724.45 1
 
1.0%
13421.34 1
 
1.0%
16439.96 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
949.8 1
1.0%
1126.09 1
1.0%
1773.51 1
1.0%
1931.56 1
1.0%
2000.55 1
1.0%
2063.88 1
1.0%
2337.54 1
1.0%
2345.97 1
1.0%
2746.18 1
1.0%
3136.64 1
1.0%
ValueCountFrequency (%)
45267.35 1
1.0%
37540.53 1
1.0%
30035.12 1
1.0%
29866.04 1
1.0%
29404.97 1
1.0%
29091.89 1
1.0%
28128.84 1
1.0%
26646.78 1
1.0%
26235.73 1
1.0%
24225.71 1
1.0%

hc((g/km))
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1540.7111
Minimum128.51
Maximum5727.43
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:01:40.465187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum128.51
5-th percentile272.069
Q1692.1925
median1416.94
Q32018.595
95-th percentile3569.2265
Maximum5727.43
Range5598.92
Interquartile range (IQR)1326.4025

Descriptive statistics

Standard deviation1063.4768
Coefficient of variation (CV)0.69025062
Kurtosis2.4839786
Mean1540.7111
Median Absolute Deviation (MAD)698.415
Skewness1.3197388
Sum154071.11
Variance1130982.9
MonotonicityNot monotonic
2023-12-10T22:01:40.986465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1654.12 1
 
1.0%
677.77 1
 
1.0%
1837.52 1
 
1.0%
607.0 1
 
1.0%
555.37 1
 
1.0%
1634.06 1
 
1.0%
1499.57 1
 
1.0%
2173.41 1
 
1.0%
1603.64 1
 
1.0%
2386.22 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
128.51 1
1.0%
147.07 1
1.0%
249.04 1
1.0%
249.16 1
1.0%
263.12 1
1.0%
272.54 1
1.0%
273.05 1
1.0%
310.91 1
1.0%
312.5 1
1.0%
342.89 1
1.0%
ValueCountFrequency (%)
5727.43 1
1.0%
5150.82 1
1.0%
4017.08 1
1.0%
3665.1 1
1.0%
3602.03 1
1.0%
3567.5 1
1.0%
3566.01 1
1.0%
3460.1 1
1.0%
3074.52 1
1.0%
2795.15 1
1.0%

pm((g/km))
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean817.3481
Minimum72.4
Maximum3176.09
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:01:41.202009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum72.4
5-th percentile142.331
Q1352.72
median759.29
Q31116.1425
95-th percentile1975.7745
Maximum3176.09
Range3103.69
Interquartile range (IQR)763.4225

Descriptive statistics

Standard deviation579.54936
Coefficient of variation (CV)0.70906063
Kurtosis2.1797155
Mean817.3481
Median Absolute Deviation (MAD)394.16
Skewness1.2785934
Sum81734.81
Variance335877.46
MonotonicityNot monotonic
2023-12-10T22:01:41.378182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
789.27 1
 
1.0%
345.97 1
 
1.0%
847.65 1
 
1.0%
292.46 1
 
1.0%
244.72 1
 
1.0%
887.69 1
 
1.0%
818.86 1
 
1.0%
1233.43 1
 
1.0%
845.8 1
 
1.0%
918.22 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
72.4 1
1.0%
83.83 1
1.0%
101.4 1
1.0%
118.21 1
1.0%
128.1 1
1.0%
143.08 1
1.0%
146.79 1
1.0%
157.0 1
1.0%
181.32 1
1.0%
202.7 1
1.0%
ValueCountFrequency (%)
3176.09 1
1.0%
2319.72 1
1.0%
2251.85 1
1.0%
2044.59 1
1.0%
2008.35 1
1.0%
1974.06 1
1.0%
1946.88 1
1.0%
1862.72 1
1.0%
1715.58 1
1.0%
1686.3 1
1.0%

co2((g/km))
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2680846.5
Minimum286883.59
Maximum10298834
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:01:41.548677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum286883.59
5-th percentile467116.44
Q11339127.8
median2456195.3
Q33609480.6
95-th percentile5726816.7
Maximum10298834
Range10011950
Interquartile range (IQR)2270352.8

Descriptive statistics

Standard deviation1855502.4
Coefficient of variation (CV)0.69213304
Kurtosis3.4341542
Mean2680846.5
Median Absolute Deviation (MAD)1122217.5
Skewness1.4550888
Sum2.6808465 × 108
Variance3.4428893 × 1012
MonotonicityNot monotonic
2023-12-10T22:01:41.770046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3490573.41 1
 
1.0%
1455981.61 1
 
1.0%
4074116.25 1
 
1.0%
1342598.97 1
 
1.0%
1333631.14 1
 
1.0%
2729693.83 1
 
1.0%
2605230.67 1
 
1.0%
3244239.54 1
 
1.0%
2815878.75 1
 
1.0%
4646321.48 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
286883.59 1
1.0%
301996.13 1
1.0%
328141.87 1
1.0%
334721.34 1
1.0%
454729.39 1
1.0%
467768.39 1
1.0%
530586.19 1
1.0%
558843.6 1
1.0%
577800.88 1
1.0%
588481.71 1
1.0%
ValueCountFrequency (%)
10298833.5 1
1.0%
9788218.3 1
1.0%
6562852.93 1
1.0%
6154002.39 1
1.0%
5797383.66 1
1.0%
5723102.7 1
1.0%
5469478.16 1
1.0%
5330959.99 1
1.0%
5316764.85 1
1.0%
5110048.4 1
1.0%

주소
Text

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T22:01:42.165843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length11
Mean length10.7
Min length8

Characters and Unicode

Total characters1070
Distinct characters105
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 row경기 평택 진위 신
2nd row경기 평택 진위 신
3rd row경기 파주 조리 장곡
4th row경기 파주 조리 장곡
5th row경기 파주 문산 당동
ValueCountFrequency (%)
경기 100
25.6%
평택 14
 
3.6%
용인 12
 
3.1%
가평 10
 
2.6%
광주 10
 
2.6%
포천 8
 
2.1%
여주 8
 
2.1%
파주 6
 
1.5%
화성 6
 
1.5%
안성 6
 
1.5%
Other values (94) 210
53.8%
2023-12-10T22:01:42.757049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
290
27.1%
기 102
 
9.5%
경 100
 
9.3%
평 38
 
3.6%
주 30
 
2.8%
성 24
 
2.2%
천 18
 
1.7%
양 18
 
1.7%
가 18
 
1.7%
포 16
 
1.5%
Other values (95) 416
38.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 780
72.9%
Space Separator 290
 
27.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
기 102
 
13.1%
경 100
 
12.8%
평 38
 
4.9%
주 30
 
3.8%
성 24
 
3.1%
천 18
 
2.3%
양 18
 
2.3%
가 18
 
2.3%
포 16
 
2.1%
용 16
 
2.1%
Other values (94) 400
51.3%
Space Separator
ValueCountFrequency (%)
290
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 780
72.9%
Common 290
 
27.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
기 102
 
13.1%
경 100
 
12.8%
평 38
 
4.9%
주 30
 
3.8%
성 24
 
3.1%
천 18
 
2.3%
양 18
 
2.3%
가 18
 
2.3%
포 16
 
2.1%
용 16
 
2.1%
Other values (94) 400
51.3%
Common
ValueCountFrequency (%)
290
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 780
72.9%
ASCII 290
 
27.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
290
100.0%
Hangul
ValueCountFrequency (%)
기 102
 
13.1%
경 100
 
12.8%
평 38
 
4.9%
주 30
 
3.8%
성 24
 
3.1%
천 18
 
2.3%
양 18
 
2.3%
가 18
 
2.3%
포 16
 
2.1%
용 16
 
2.1%
Other values (94) 400
51.3%

Interactions

2023-12-10T22:01:33.529063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:25.021540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:26.238643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:27.170520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:28.174473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:28.973580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:29.883464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:30.965170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:32.087644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:33.642939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:25.122859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:26.350451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:27.257024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:28.261857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:29.066195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:29.970698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:31.076597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:32.210206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:33.770015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:25.212844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:26.457389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:27.367161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:28.362050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:29.142542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:30.160877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:31.206972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:32.332500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:33.878814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:25.304422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:26.568155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:27.474073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:28.453298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:29.237108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:30.320168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:31.336623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:32.446844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:33.995775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:25.386771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:26.660949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:27.584580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:28.544984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:29.335436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:30.418421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:31.459890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:32.592900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:34.131993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:25.494910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:26.778892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:27.724127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:28.641497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:29.441688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:30.531862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:31.591923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:33.046578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:34.266276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:25.595461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:26.871678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:27.834837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:28.714689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:29.532228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:30.634252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:31.713640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:33.149923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:34.386901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:26.055899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:26.979762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:27.939328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:28.797126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:29.700018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:30.759119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:31.848719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:33.268947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:34.519778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:26.142843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:27.063294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:28.067992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:28.888779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:29.790425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:30.872709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:31.982105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:33.391940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T22:01:42.913346image/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
기본키1.0001.0000.0001.0000.5980.8380.8340.4790.5130.5230.4560.4661.000
지점1.0001.0000.0001.0001.0001.0001.0000.9700.9510.9210.9050.9801.000
방향0.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
측정구간1.0001.0000.0001.0001.0001.0001.0000.9700.9510.9210.9050.9801.000
연장((km))0.5981.0000.0001.0001.0000.5700.6250.6230.2560.1980.0000.5861.000
좌표위치위도((°))0.8381.0000.0001.0000.5701.0000.8320.4860.5120.5180.4320.4621.000
좌표위치경도((°))0.8341.0000.0001.0000.6250.8321.0000.4240.3590.3190.3110.4291.000
co((g/km))0.4790.9700.0000.9700.6230.4860.4241.0000.8370.8720.7660.9940.970
nox((g/km))0.5130.9510.0000.9510.2560.5120.3590.8371.0000.9930.9840.8280.951
hc((g/km))0.5230.9210.0000.9210.1980.5180.3190.8720.9931.0000.9760.8440.921
pm((g/km))0.4560.9050.0000.9050.0000.4320.3110.7660.9840.9761.0000.7910.905
co2((g/km))0.4660.9800.0000.9800.5860.4620.4290.9940.8280.8440.7911.0000.980
주소1.0001.0000.0001.0001.0001.0001.0000.9700.9510.9210.9050.9801.000
2023-12-10T22:01:43.127513image/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
기본키1.000-0.259-0.1180.0210.004-0.016-0.0180.0240.0090.000
연장((km))-0.2591.0000.2460.027-0.053-0.064-0.059-0.088-0.0390.000
좌표위치위도((°))-0.1180.2461.0000.123-0.410-0.503-0.478-0.486-0.4190.000
좌표위치경도((°))0.0210.0270.1231.000-0.237-0.291-0.275-0.261-0.2570.000
co((g/km))0.004-0.053-0.410-0.2371.0000.9290.9480.9170.9950.000
nox((g/km))-0.016-0.064-0.503-0.2910.9291.0000.9930.9880.9340.000
hc((g/km))-0.018-0.059-0.478-0.2750.9480.9931.0000.9810.9490.000
pm((g/km))0.024-0.088-0.486-0.2610.9170.9880.9811.0000.9220.000
co2((g/km))0.009-0.039-0.419-0.2570.9950.9340.9490.9221.0000.000
방향0.0000.0000.0000.0000.0000.0000.0000.0000.0001.000

Missing values

2023-12-10T22:01:34.715307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T22:01:35.002017image/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[0134-0]1송탄-오산6.220210401037.09529127.0636413848.1713508.591654.12789.273490573.41경기 평택 진위 신
12건기연[0134-0]2송탄-오산6.220210401037.09529127.0636413857.613508.751635.35759.333489441.09경기 평택 진위 신
23건기연[0141-1]1고양-파주4.820210401037.73328126.8325312865.4313357.991771.14966.722974085.95경기 파주 조리 장곡
34건기연[0141-1]2고양-파주4.820210401037.73328126.832538830.789738.291204.13661.162185059.64경기 파주 조리 장곡
45건기연[0142-0]1당동-파평14.720210401037.87708126.779462146.992063.88273.05128.1530586.19경기 파주 문산 당동
56건기연[0142-0]2당동-파평14.720210401037.87708126.779462395.292345.97312.5146.79588481.71경기 파주 문산 당동
67건기연[0328-2]1이천-장호원9.620210401037.19066127.559945605.336960.15914.52554.431366642.75경기 여주 가남 심석
78건기연[0328-2]2이천-장호원9.620210401037.19066127.559945417.665932.21809.97363.91317564.69경기 여주 가남 심석
89건기연[0330-1]1이천-광주15.520210401037.31793127.4276312704.4216207.132020.141126.653186627.08경기 이천 신둔 수하
910건기연[0330-1]2이천-광주15.520210401037.31793127.4276313003.3515670.262123.491112.643113922.93경기 이천 신둔 수하
기본키도로종류지점방향측정구간연장((km))측정일측정시간좌표위치위도((°))좌표위치경도((°))co((g/km))nox((g/km))hc((g/km))pm((g/km))co2((g/km))주소
9091건기연[4506-2]1장서-천5.420210401037.16502127.2056720395.8328128.843460.11946.885110048.4경기 용인 이동 덕성
9192건기연[4506-2]2장서-천5.420210401037.16502127.2056721088.6530035.123567.52251.855330959.99경기 용인 이동 덕성
9293건기연[4508-0]1포곡-광주3.120210401037.34342127.250539550.7410918.861324.65818.192361151.0경기 용인 모현 왕산
9394건기연[4508-0]2포곡-광주3.120210401037.34342127.2505310343.3911252.381382.41891.512571343.45경기 용인 모현 왕산
9495건기연[4509-0]1광주-팔당19.020210401037.4815127.281015320.314069.41568.62284.951363360.84경기 광주 남종 삼성
9596건기연[4509-0]2광주-팔당19.020210401037.4815127.281014829.43644.31517.89253.671244732.91경기 광주 남종 삼성
9697건기연[4512-1]1화도-청평8.320210401037.68735127.379979718.1810871.621315.12732.82466096.94경기 가평 청평 대성
9798건기연[4512-1]2화도-청평8.320210401037.68735127.379979791.239483.491210.79661.632483002.5경기 가평 청평 대성
9899건기연[4606-2]1청평-가평2.920210401037.76396127.443357783.228305.371111.56574.611899509.29경기 가평 청평 상천
99100건기연[4606-2]2청평-가평2.920210401037.76396127.443357776.587833.031096.95557.641882409.47경기 가평 청평 상천