Overview

Dataset statistics

Number of variables9
Number of observations461
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory36.1 KiB
Average record size in memory80.3 B

Variable types

Categorical3
Numeric5
Text1

Dataset

Description경상남도 도로대장전산화 시스템 데이터의 중장기개방계획에 따른 데이터입니다. 시스템 상에서의 각 도로의 유로도로, 터널 등의 정보를 가지고 있으며, 도로대장의 동영상정보 데이터를 포함하고있습니다.
Author경상남도
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15091949

Alerts

이력코드 has constant value ""Constant
노선번호 is highly overall correlated with 파일명 and 1 other fieldsHigh correlation
파일명 is highly overall correlated with 노선번호 and 1 other fieldsHigh correlation
시점 이정 is highly overall correlated with 종점 이정 and 1 other fieldsHigh correlation
종점 이정 is highly overall correlated with 시점 이정 and 1 other fieldsHigh correlation
도로종류 is highly overall correlated with 노선번호 and 1 other fieldsHigh correlation
위치_방향 is highly overall correlated with 시점 이정 and 1 other fieldsHigh correlation
시점 이정 has 211 (45.8%) zerosZeros
종점 이정 has 206 (44.7%) zerosZeros

Reproduction

Analysis started2023-12-10 23:45:13.078480
Analysis finished2023-12-10 23:45:16.715555
Duration3.64 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

도로종류
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
1504
378 
1507
66 
1638
 
17

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1507
2nd row1507
3rd row1507
4th row1507
5th row1507

Common Values

ValueCountFrequency (%)
1504 378
82.0%
1507 66
 
14.3%
1638 17
 
3.7%

Length

2023-12-11T08:45:16.774924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:45:16.861195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1504 378
82.0%
1507 66
 
14.3%
1638 17
 
3.7%

노선번호
Real number (ℝ)

HIGH CORRELATION 

Distinct42
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean888.42516
Minimum30
Maximum1099
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2023-12-11T08:45:16.995635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile58
Q11002
median1018
Q31034
95-th percentile1089
Maximum1099
Range1069
Interquartile range (IQR)32

Descriptive statistics

Standard deviation342.92273
Coefficient of variation (CV)0.38598944
Kurtosis2.1265057
Mean888.42516
Median Absolute Deviation (MAD)16
Skewness-2.0139328
Sum409564
Variance117596
MonotonicityNot monotonic
2023-12-11T08:45:17.157759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
60 28
 
6.1%
1001 26
 
5.6%
1021 25
 
5.4%
1002 24
 
5.2%
1034 22
 
4.8%
1022 18
 
3.9%
1018 17
 
3.7%
1007 16
 
3.5%
1077 16
 
3.5%
1089 15
 
3.3%
Other values (32) 254
55.1%
ValueCountFrequency (%)
30 10
 
2.2%
37 10
 
2.2%
58 4
 
0.9%
60 28
6.1%
67 6
 
1.3%
69 8
 
1.7%
907 6
 
1.3%
1001 26
5.6%
1002 24
5.2%
1003 14
3.0%
ValueCountFrequency (%)
1099 10
2.2%
1089 15
3.3%
1084 14
3.0%
1080 10
2.2%
1077 16
3.5%
1051 4
 
0.9%
1049 6
 
1.3%
1047 6
 
1.3%
1042 4
 
0.9%
1041 10
2.2%

구간번호
Real number (ℝ)

Distinct16
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.2039046
Minimum1
Maximum16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2023-12-11T08:45:17.260765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q37
95-th percentile13
Maximum16
Range15
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.6815737
Coefficient of variation (CV)0.70746372
Kurtosis0.1194192
Mean5.2039046
Median Absolute Deviation (MAD)2
Skewness0.90241473
Sum2399
Variance13.553985
MonotonicityNot monotonic
2023-12-11T08:45:17.390500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
1 75
16.3%
4 57
12.4%
2 56
12.1%
3 52
11.3%
5 46
10.0%
7 38
8.2%
6 28
 
6.1%
9 26
 
5.6%
8 21
 
4.6%
11 14
 
3.0%
Other values (6) 48
10.4%
ValueCountFrequency (%)
1 75
16.3%
2 56
12.1%
3 52
11.3%
4 57
12.4%
5 46
10.0%
6 28
 
6.1%
7 38
8.2%
8 21
 
4.6%
9 26
 
5.6%
10 12
 
2.6%
ValueCountFrequency (%)
16 4
 
0.9%
15 4
 
0.9%
14 10
 
2.2%
13 8
 
1.7%
12 10
 
2.2%
11 14
 
3.0%
10 12
 
2.6%
9 26
5.6%
8 21
4.6%
7 38
8.2%

이력코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
0
461 

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 (%)
0 461
100.0%

Length

2023-12-11T08:45:17.540929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:45:17.639559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 461
100.0%

파일명
Real number (ℝ)

HIGH CORRELATION 

Distinct458
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean888342.05
Minimum30011
Maximum1099072
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2023-12-11T08:45:17.796639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum30011
5-th percentile58032
Q11002102
median1018073
Q31034141
95-th percentile1089011
Maximum1099072
Range1069061
Interquartile range (IQR)32039

Descriptive statistics

Standard deviation342852.25
Coefficient of variation (CV)0.38594621
Kurtosis2.127131
Mean888342.05
Median Absolute Deviation (MAD)16002
Skewness-2.014221
Sum4.0952568 × 108
Variance1.1754766 × 1011
MonotonicityNot monotonic
2023-12-11T08:45:17.980931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1021091 2
 
0.4%
1021051 2
 
0.4%
1021092 2
 
0.4%
60072 1
 
0.2%
37011 1
 
0.2%
67092 1
 
0.2%
67091 1
 
0.2%
67032 1
 
0.2%
67031 1
 
0.2%
67012 1
 
0.2%
Other values (448) 448
97.2%
ValueCountFrequency (%)
30011 1
0.2%
30012 1
0.2%
30031 1
0.2%
30032 1
0.2%
30041 1
0.2%
30042 1
0.2%
30061 1
0.2%
30062 1
0.2%
30071 1
0.2%
30072 1
0.2%
ValueCountFrequency (%)
1099072 1
0.2%
1099071 1
0.2%
1099054 1
0.2%
1099053 1
0.2%
1099052 1
0.2%
1099051 1
0.2%
1099042 1
0.2%
1099041 1
0.2%
1099022 1
0.2%
1099021 1
0.2%

위치_방향
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
0
242 
1
219 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 242
52.5%
1 219
47.5%

Length

2023-12-11T08:45:18.135151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:45:18.325727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 242
52.5%
1 219
47.5%

시점 이정
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct238
Distinct (%)51.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4883.2169
Minimum0
Maximum28903
Zeros211
Zeros (%)45.8%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2023-12-11T08:45:18.445382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2656
Q39500
95-th percentile14658
Maximum28903
Range28903
Interquartile range (IQR)9500

Descriptive statistics

Standard deviation5539.1846
Coefficient of variation (CV)1.134331
Kurtosis-0.015090013
Mean4883.2169
Median Absolute Deviation (MAD)2656
Skewness0.83565552
Sum2251163
Variance30682566
MonotonicityNot monotonic
2023-12-11T08:45:18.594424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 211
45.8%
3390 2
 
0.4%
8300 2
 
0.4%
11040 2
 
0.4%
8989 2
 
0.4%
12020 2
 
0.4%
9844 2
 
0.4%
2656 2
 
0.4%
1776 2
 
0.4%
12180 2
 
0.4%
Other values (228) 232
50.3%
ValueCountFrequency (%)
0 211
45.8%
664 1
 
0.2%
700 1
 
0.2%
797 2
 
0.4%
940 1
 
0.2%
1580 1
 
0.2%
1653 1
 
0.2%
1776 2
 
0.4%
1800 1
 
0.2%
2000 1
 
0.2%
ValueCountFrequency (%)
28903 1
0.2%
22100 1
0.2%
21200 1
0.2%
21000 1
0.2%
20560 1
0.2%
19380 1
0.2%
18700 1
0.2%
17263 1
0.2%
17260 1
0.2%
16550 1
0.2%

종점 이정
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct240
Distinct (%)52.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5021.3839
Minimum0
Maximum28903
Zeros206
Zeros (%)44.7%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2023-12-11T08:45:18.771395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3310
Q39692
95-th percentile14760
Maximum28903
Range28903
Interquartile range (IQR)9692

Descriptive statistics

Standard deviation5580.5927
Coefficient of variation (CV)1.1113655
Kurtosis-0.1145424
Mean5021.3839
Median Absolute Deviation (MAD)3310
Skewness0.79226162
Sum2314858
Variance31143015
MonotonicityNot monotonic
2023-12-11T08:45:18.945094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 206
44.7%
7260 2
 
0.4%
9500 2
 
0.4%
8300 2
 
0.4%
12020 2
 
0.4%
8989 2
 
0.4%
5500 2
 
0.4%
11040 2
 
0.4%
9844 2
 
0.4%
2656 2
 
0.4%
Other values (230) 237
51.4%
ValueCountFrequency (%)
0 206
44.7%
664 1
 
0.2%
700 1
 
0.2%
797 2
 
0.4%
940 1
 
0.2%
1580 1
 
0.2%
1653 1
 
0.2%
1776 2
 
0.4%
1800 1
 
0.2%
2000 1
 
0.2%
ValueCountFrequency (%)
28903 1
0.2%
22100 1
0.2%
21200 1
0.2%
21000 1
0.2%
20560 1
0.2%
19380 1
0.2%
18700 1
0.2%
17440 1
0.2%
17263 1
0.2%
16550 1
0.2%

비고
Text

Distinct453
Distinct (%)98.3%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
2023-12-11T08:45:19.222656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length58
Median length46
Mean length35.542299
Min length10

Characters and Unicode

Total characters16385
Distinct characters247
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique445 ?
Unique (%)96.5%

Sample

1st row(하행)합천군 삼가면(국도33호분기)~합천군 가회면(지방도1089호분기,장대삼거리)
2nd row(상행)합천군 쌍백면(지방도1041호분기)~합천군 쌍백면(대곡리,미개통이전)
3rd row(상행)의령군 궁유면(청계리,미개통이후)~의령군 궁유면(지방도1011분기)
4th row(하행)의령군 궁유면(지방도1011분기)~의령군 궁유면(청계리,미개통이후)
5th row(하행)합천군 쌍백면(대곡리,미개통이전)~합천군 쌍백면(지방도1041호분기)
ValueCountFrequency (%)
89
 
4.6%
거제시 34
 
1.8%
하행 32
 
1.7%
상행 31
 
1.6%
국도 29
 
1.5%
앞 25
 
1.3%
창녕군 24
 
1.2%
양산시 22
 
1.1%
밀양시 20
 
1.0%
합천군 18
 
0.9%
Other values (789) 1606
83.2%
2023-12-11T08:45:19.686951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1477
 
9.0%
( 1030
 
6.3%
) 1028
 
6.3%
면 664
 
4.1%
리 528
 
3.2%
군 464
 
2.8%
~ 454
 
2.8%
행 452
 
2.8%
도 389
 
2.4%
기 362
 
2.2%
Other values (237) 9537
58.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 11226
68.5%
Space Separator 1477
 
9.0%
Decimal Number 1034
 
6.3%
Open Punctuation 1030
 
6.3%
Close Punctuation 1028
 
6.3%
Math Symbol 456
 
2.8%
Other Punctuation 123
 
0.8%
Uppercase Letter 8
 
< 0.1%
Lowercase Letter 2
 
< 0.1%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
면 664
 
5.9%
리 528
 
4.7%
군 464
 
4.1%
행 452
 
4.0%
도 389
 
3.5%
기 362
 
3.2%
분 354
 
3.2%
시 329
 
2.9%
거 319
 
2.8%
하 316
 
2.8%
Other values (214) 7049
62.8%
Decimal Number
ValueCountFrequency (%)
0 257
24.9%
1 216
20.9%
2 115
11.1%
3 109
10.5%
7 82
 
7.9%
4 69
 
6.7%
9 67
 
6.5%
5 51
 
4.9%
6 47
 
4.5%
8 21
 
2.0%
Uppercase Letter
ValueCountFrequency (%)
C 2
25.0%
I 2
25.0%
S 2
25.0%
G 2
25.0%
Math Symbol
ValueCountFrequency (%)
~ 454
99.6%
+ 2
 
0.4%
Other Punctuation
ValueCountFrequency (%)
, 121
98.4%
@ 2
 
1.6%
Space Separator
ValueCountFrequency (%)
1477
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1030
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1028
100.0%
Lowercase Letter
ValueCountFrequency (%)
m 2
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 11226
68.5%
Common 5149
31.4%
Latin 10
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
면 664
 
5.9%
리 528
 
4.7%
군 464
 
4.1%
행 452
 
4.0%
도 389
 
3.5%
기 362
 
3.2%
분 354
 
3.2%
시 329
 
2.9%
거 319
 
2.8%
하 316
 
2.8%
Other values (214) 7049
62.8%
Common
ValueCountFrequency (%)
1477
28.7%
( 1030
20.0%
) 1028
20.0%
~ 454
 
8.8%
0 257
 
5.0%
1 216
 
4.2%
, 121
 
2.3%
2 115
 
2.2%
3 109
 
2.1%
7 82
 
1.6%
Other values (8) 260
 
5.0%
Latin
ValueCountFrequency (%)
m 2
20.0%
C 2
20.0%
I 2
20.0%
S 2
20.0%
G 2
20.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 11226
68.5%
ASCII 5159
31.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1477
28.6%
( 1030
20.0%
) 1028
19.9%
~ 454
 
8.8%
0 257
 
5.0%
1 216
 
4.2%
, 121
 
2.3%
2 115
 
2.2%
3 109
 
2.1%
7 82
 
1.6%
Other values (13) 270
 
5.2%
Hangul
ValueCountFrequency (%)
면 664
 
5.9%
리 528
 
4.7%
군 464
 
4.1%
행 452
 
4.0%
도 389
 
3.5%
기 362
 
3.2%
분 354
 
3.2%
시 329
 
2.9%
거 319
 
2.8%
하 316
 
2.8%
Other values (214) 7049
62.8%

Interactions

2023-12-11T08:45:15.757384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:45:13.556575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:45:14.153064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:45:14.705033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:45:15.252677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:45:15.843355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:45:13.658535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:45:14.260137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:45:14.799549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:45:15.370816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:45:15.933344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:45:13.795797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:45:14.365500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:45:14.919502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:45:15.494467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:45:16.029059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:45:13.926581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:45:14.481451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:45:15.022804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:45:15.579017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:45:16.115366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:45:14.041585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:45:14.590110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:45:15.130551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:45:15.666516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T08:45:19.796424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
도로종류노선번호구간번호파일명위치_방향시점 이정종점 이정
도로종류1.0000.9420.1090.9420.0000.2140.360
노선번호0.9421.0000.1611.0000.0000.0000.000
구간번호0.1090.1611.0000.1640.0000.0000.000
파일명0.9421.0000.1641.0000.0000.0000.000
위치_방향0.0000.0000.0000.0001.0000.7860.810
시점 이정0.2140.0000.0000.0000.7861.0000.648
종점 이정0.3600.0000.0000.0000.8100.6481.000
2023-12-11T08:45:19.907085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
도로종류위치_방향
도로종류1.0000.000
위치_방향0.0001.000
2023-12-11T08:45:20.001832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
노선번호구간번호파일명시점 이정종점 이정도로종류위치_방향
노선번호1.000-0.0760.9980.0000.0280.7060.000
구간번호-0.0761.000-0.0480.0540.0550.0930.000
파일명0.998-0.0481.0000.0050.0260.7060.000
시점 이정0.0000.0540.0051.000-0.6560.0950.801
종점 이정0.0280.0550.026-0.6561.0000.1690.828
도로종류0.7060.0930.7060.0950.1691.0000.000
위치_방향0.0000.0000.0000.8010.8280.0001.000

Missing values

2023-12-11T08:45:16.519467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T08:45:16.668519image/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

도로종류노선번호구간번호이력코드파일명위치_방향시점 이정종점 이정비고
01507607060072189140(하행)합천군 삼가면(국도33호분기)~합천군 가회면(지방도1089호분기,장대삼거리)
11507609060091004980(상행)합천군 쌍백면(지방도1041호분기)~합천군 쌍백면(대곡리,미개통이전)
21507609060092069809104(상행)의령군 궁유면(청계리,미개통이후)~의령군 궁유면(지방도1011분기)
31507609060093191046980(하행)의령군 궁유면(지방도1011분기)~의령군 궁유면(청계리,미개통이후)
41507609060094149800(하행)합천군 쌍백면(대곡리,미개통이전)~합천군 쌍백면(지방도1041호분기)
515076010060101009823(상행)의령군 궁유면(지방도1011분기)~의령군 부림면(국도22호분기)
615076010060102198230(하행)의령군 부림면(국도20호분기)~의령군 궁유면(지방도1011분기)
715076012060121002840(상행)의령군 부림면(국도20호분기)~의령군 부림면(경산리 박진마을,미개통이전)
81507601206012201114014210(상행)함안군 대산면(장암리,미개통이후)~함안군 대산면(지방도1021호분기,부목삼거리)
91507601206012311421011140(하행)함안군 대산면(지방도1021호분기,부목삼거리)~함안군 대산면(장암리,미개통이후)
도로종류노선번호구간번호이력코드파일명위치_방향시점 이정종점 이정비고
451150410992010990210014990(상행)거창 남하 국도24분기~거창 가조 마상사거리
452150410992010990221149900(하행)거창 가조 마상사거리~거창 남하 국도24분기
453150410994010990410011500(상행)거창 가조 장기삼거리~거창 가북 회남삼거리
454150410994010990421115000(하행)거창 가북 회남삼거리~거창 가조 장기삼거리
45515041099501099051004168(상행)거창 가북 회남삼거리~가창 가북 심방마을경로
45615041099501099053141680(하행)거창 웅양 도계~거창 우양 강천삼거리
457150410995010990520974013720(상행)거창 웅양~거창 웅양 우량마을 입구
458150410995010990540137209740(하행)거창 웅양 우량마을 입구~거창 웅양
45915041099701099071004576(상행)거창 우양 강천삼거리~거창 웅양 도계
46015041099701099072145760(하행)거창 웅양 도계~거창 우양 강천삼거리