Overview

Dataset statistics

Number of variables15
Number of observations7939
Missing cells7
Missing cells (%)< 0.1%
Duplicate rows162
Duplicate rows (%)2.0%
Total size in memory977.0 KiB
Average record size in memory126.0 B

Variable types

Text5
Categorical6
Numeric3
DateTime1

Dataset

Description경상남도 밀양시 도로구간 현황입니다
Author경상남도 밀양시
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15092045

Alerts

Dataset has 162 (2.0%) duplicate rowsDuplicates
도로폭 is highly overall correlated with 광역도로구분코드 and 5 other fieldsHigh correlation
도로길이 is highly overall correlated with 광역도로구분코드 and 5 other fieldsHigh correlation
이동사유코드 is highly overall correlated with 부여일자 and 1 other fieldsHigh correlation
광역도로구분코드 is highly overall correlated with 도로폭 and 3 other fieldsHigh correlation
도로위계기능구분 is highly overall correlated with 도로폭 and 4 other fieldsHigh correlation
도로구간종속구분 is highly overall correlated with 도로폭 and 1 other fieldsHigh correlation
기초간격 is highly overall correlated with 도로폭 and 2 other fieldsHigh correlation
부여일자 is highly overall correlated with 도로폭 and 4 other fieldsHigh correlation
이동사유 is highly overall correlated with 도로폭 and 4 other fieldsHigh correlation
광역도로구분코드 is highly imbalanced (88.3%)Imbalance
도로위계기능구분 is highly imbalanced (66.0%)Imbalance
기초간격 is highly imbalanced (87.0%)Imbalance
부여일자 is highly imbalanced (62.9%)Imbalance
이동사유 is highly imbalanced (82.0%)Imbalance
도로폭 is highly skewed (γ1 = 89.09430239)Skewed
도로길이 is highly skewed (γ1 = 88.53638477)Skewed

Reproduction

Analysis started2023-12-11 00:54:50.515768
Analysis finished2023-12-11 00:54:53.298171
Duration2.78 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

도로명
Text

Distinct986
Distinct (%)12.4%
Missing1
Missing (%)< 0.1%
Memory size62.2 KiB
2023-12-11T09:54:53.483890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length4.0312421
Min length3

Characters and Unicode

Total characters32000
Distinct characters214
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

Unique62 ?
Unique (%)0.8%

Sample

1st row무릉3길
2nd row국전1안길
3rd row무릉2길
4th row아불3길
5th row시전3길
ValueCountFrequency (%)
상동로 71
 
0.9%
표충로 65
 
0.8%
중앙로 62
 
0.8%
산내로 49
 
0.6%
밀양대로 48
 
0.6%
단장로 48
 
0.6%
삼랑진로 47
 
0.6%
사명로 45
 
0.6%
산외로 43
 
0.5%
외평로 42
 
0.5%
Other values (976) 7418
93.4%
2023-12-11T09:54:53.887836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
길 6246
 
19.5%
로 2080
 
6.5%
1 1514
 
4.7%
2 1443
 
4.5%
산 841
 
2.6%
3 820
 
2.6%
안 715
 
2.2%
곡 608
 
1.9%
동 602
 
1.9%
중 487
 
1.5%
Other values (204) 16644
52.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 27413
85.7%
Decimal Number 4587
 
14.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
길 6246
22.8%
로 2080
 
7.6%
산 841
 
3.1%
안 715
 
2.6%
곡 608
 
2.2%
동 602
 
2.2%
중 487
 
1.8%
앙 439
 
1.6%
남 402
 
1.5%
전 359
 
1.3%
Other values (194) 14634
53.4%
Decimal Number
ValueCountFrequency (%)
1 1514
33.0%
2 1443
31.5%
3 820
17.9%
4 447
 
9.7%
5 186
 
4.1%
6 80
 
1.7%
7 56
 
1.2%
8 19
 
0.4%
0 12
 
0.3%
9 10
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Hangul 27413
85.7%
Common 4587
 
14.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
길 6246
22.8%
로 2080
 
7.6%
산 841
 
3.1%
안 715
 
2.6%
곡 608
 
2.2%
동 602
 
2.2%
중 487
 
1.8%
앙 439
 
1.6%
남 402
 
1.5%
전 359
 
1.3%
Other values (194) 14634
53.4%
Common
ValueCountFrequency (%)
1 1514
33.0%
2 1443
31.5%
3 820
17.9%
4 447
 
9.7%
5 186
 
4.1%
6 80
 
1.7%
7 56
 
1.2%
8 19
 
0.4%
0 12
 
0.3%
9 10
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 27413
85.7%
ASCII 4587
 
14.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
길 6246
22.8%
로 2080
 
7.6%
산 841
 
3.1%
안 715
 
2.6%
곡 608
 
2.2%
동 602
 
2.2%
중 487
 
1.8%
앙 439
 
1.6%
남 402
 
1.5%
전 359
 
1.3%
Other values (194) 14634
53.4%
ASCII
ValueCountFrequency (%)
1 1514
33.0%
2 1443
31.5%
3 820
17.9%
4 447
 
9.7%
5 186
 
4.1%
6 80
 
1.7%
7 56
 
1.2%
8 19
 
0.4%
0 12
 
0.3%
9 10
 
0.2%

영문도로명
Text

Distinct986
Distinct (%)12.4%
Missing1
Missing (%)< 0.1%
Memory size62.2 KiB
2023-12-11T09:54:54.171459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length23
Mean length12.779164
Min length7

Characters and Unicode

Total characters101441
Distinct characters53
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique62 ?
Unique (%)0.8%

Sample

1st rowMureung 3-gil
2nd rowGukjeon 1an-gil
3rd rowMureung 2-gil
4th rowAbul 3-gil
5th rowSijeon 3-gil
ValueCountFrequency (%)
2-gil 1352
 
10.9%
1-gil 1335
 
10.7%
3-gil 758
 
6.1%
4-gil 421
 
3.4%
5-gil 155
 
1.2%
1an-gil 114
 
0.9%
yongpyeong-ro 92
 
0.7%
geumsan 84
 
0.7%
sammunjungang-ro 81
 
0.7%
6-gil 80
 
0.6%
Other values (493) 7976
64.1%
2023-12-11T09:54:54.658307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
g 12653
12.5%
n 9729
 
9.6%
o 8917
 
8.8%
- 8249
 
8.1%
i 8061
 
7.9%
l 6999
 
6.9%
a 6852
 
6.8%
e 5485
 
5.4%
4510
 
4.4%
r 2740
 
2.7%
Other values (43) 27246
26.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 76156
75.1%
Dash Punctuation 8249
 
8.1%
Uppercase Letter 7939
 
7.8%
Decimal Number 4587
 
4.5%
Space Separator 4510
 
4.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
g 12653
16.6%
n 9729
12.8%
o 8917
11.7%
i 8061
10.6%
l 6999
9.2%
a 6852
9.0%
e 5485
7.2%
r 2740
 
3.6%
u 2504
 
3.3%
m 2327
 
3.1%
Other values (12) 9889
13.0%
Uppercase Letter
ValueCountFrequency (%)
S 1849
23.3%
G 1027
12.9%
D 698
 
8.8%
Y 679
 
8.6%
M 672
 
8.5%
B 550
 
6.9%
C 343
 
4.3%
N 331
 
4.2%
H 315
 
4.0%
J 283
 
3.6%
Other values (9) 1192
15.0%
Decimal Number
ValueCountFrequency (%)
1 1514
33.0%
2 1443
31.5%
3 820
17.9%
4 447
 
9.7%
5 186
 
4.1%
6 80
 
1.7%
7 56
 
1.2%
8 19
 
0.4%
0 12
 
0.3%
9 10
 
0.2%
Dash Punctuation
ValueCountFrequency (%)
- 8249
100.0%
Space Separator
ValueCountFrequency (%)
4510
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 84095
82.9%
Common 17346
 
17.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
g 12653
15.0%
n 9729
11.6%
o 8917
10.6%
i 8061
9.6%
l 6999
 
8.3%
a 6852
 
8.1%
e 5485
 
6.5%
r 2740
 
3.3%
u 2504
 
3.0%
m 2327
 
2.8%
Other values (31) 17828
21.2%
Common
ValueCountFrequency (%)
- 8249
47.6%
4510
26.0%
1 1514
 
8.7%
2 1443
 
8.3%
3 820
 
4.7%
4 447
 
2.6%
5 186
 
1.1%
6 80
 
0.5%
7 56
 
0.3%
8 19
 
0.1%
Other values (2) 22
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 101441
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
g 12653
12.5%
n 9729
 
9.6%
o 8917
 
8.8%
- 8249
 
8.1%
i 8061
 
7.9%
l 6999
 
6.9%
a 6852
 
6.8%
e 5485
 
5.4%
4510
 
4.4%
r 2740
 
2.7%
Other values (43) 27246
26.9%

광역도로구분코드
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size62.2 KiB
3
7648 
2
 
283
1
 
7
<NA>
 
1

Length

Max length4
Median length1
Mean length1.0003779
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row3
2nd row3
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3 7648
96.3%
2 283
 
3.6%
1 7
 
0.1%
<NA> 1
 
< 0.1%

Length

2023-12-11T09:54:54.793245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:54:54.890750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3 7648
96.3%
2 283
 
3.6%
1 7
 
0.1%
na 1
 
< 0.1%

도로위계기능구분
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size62.2 KiB
4
6246 
3
1643 
2
 
48
1
 
1
<NA>
 
1

Length

Max length4
Median length1
Mean length1.0003779
Min length1

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row4
2nd row4
3rd row4
4th row4
5th row4

Common Values

ValueCountFrequency (%)
4 6246
78.7%
3 1643
 
20.7%
2 48
 
0.6%
1 1
 
< 0.1%
<NA> 1
 
< 0.1%

Length

2023-12-11T09:54:55.008009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:54:55.106422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
4 6246
78.7%
3 1643
 
20.7%
2 48
 
0.6%
1 1
 
< 0.1%
na 1
 
< 0.1%

도로구간종속구분
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size62.2 KiB
1
5597 
2
1355 
0
986 
<NA>
 
1

Length

Max length4
Median length1
Mean length1.0003779
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row2
2nd row0
3rd row2
4th row0
5th row0

Common Values

ValueCountFrequency (%)
1 5597
70.5%
2 1355
 
17.1%
0 986
 
12.4%
<NA> 1
 
< 0.1%

Length

2023-12-11T09:54:55.210153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:54:55.328329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 5597
70.5%
2 1355
 
17.1%
0 986
 
12.4%
na 1
 
< 0.1%

기점
Text

Distinct1196
Distinct (%)15.1%
Missing1
Missing (%)< 0.1%
Memory size62.2 KiB
2023-12-11T09:54:55.576913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length23
Mean length12.542832
Min length7

Characters and Unicode

Total characters99565
Distinct characters154
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique378 ?
Unique (%)4.8%

Sample

1st row단장면 무릉리1548
2nd row단장면 국전리1937
3rd row단장면 무릉리1548
4th row단장면 범도리 1531-13
5th row단장면 구천리2364
ValueCountFrequency (%)
삼랑진읍 866
 
4.2%
상남면 761
 
3.7%
무안면 650
 
3.2%
단장면 635
 
3.1%
산내면 634
 
3.1%
부북면 627
 
3.1%
하남읍 625
 
3.1%
초동면 541
 
2.6%
산외면 480
 
2.3%
상동면 449
 
2.2%
Other values (1326) 14191
69.4%
2023-12-11T09:54:56.029380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
13450
 
13.5%
1 7151
 
7.2%
리 6879
 
6.9%
- 6047
 
6.1%
면 5183
 
5.2%
2 4379
 
4.4%
3 3266
 
3.3%
4 3104
 
3.1%
동 2534
 
2.5%
6 2459
 
2.5%
Other values (144) 45113
45.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 47588
47.8%
Decimal Number 32046
32.2%
Space Separator 13450
 
13.5%
Dash Punctuation 6047
 
6.1%
Open Punctuation 214
 
0.2%
Close Punctuation 214
 
0.2%
Uppercase Letter 6
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 6879
 
14.5%
면 5183
 
10.9%
동 2534
 
5.3%
산 2427
 
5.1%
남 1708
 
3.6%
읍 1529
 
3.2%
삼 1508
 
3.2%
상 1241
 
2.6%
내 1086
 
2.3%
랑 1051
 
2.2%
Other values (128) 22442
47.2%
Decimal Number
ValueCountFrequency (%)
1 7151
22.3%
2 4379
13.7%
3 3266
10.2%
4 3104
9.7%
6 2459
 
7.7%
5 2452
 
7.7%
7 2380
 
7.4%
8 2363
 
7.4%
0 2356
 
7.4%
9 2136
 
6.7%
Uppercase Letter
ValueCountFrequency (%)
I 3
50.0%
C 3
50.0%
Space Separator
ValueCountFrequency (%)
13450
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 6047
100.0%
Open Punctuation
ValueCountFrequency (%)
( 214
100.0%
Close Punctuation
ValueCountFrequency (%)
) 214
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 51971
52.2%
Hangul 47588
47.8%
Latin 6
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 6879
 
14.5%
면 5183
 
10.9%
동 2534
 
5.3%
산 2427
 
5.1%
남 1708
 
3.6%
읍 1529
 
3.2%
삼 1508
 
3.2%
상 1241
 
2.6%
내 1086
 
2.3%
랑 1051
 
2.2%
Other values (128) 22442
47.2%
Common
ValueCountFrequency (%)
13450
25.9%
1 7151
13.8%
- 6047
11.6%
2 4379
 
8.4%
3 3266
 
6.3%
4 3104
 
6.0%
6 2459
 
4.7%
5 2452
 
4.7%
7 2380
 
4.6%
8 2363
 
4.5%
Other values (4) 4920
 
9.5%
Latin
ValueCountFrequency (%)
I 3
50.0%
C 3
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 51977
52.2%
Hangul 47588
47.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
13450
25.9%
1 7151
13.8%
- 6047
11.6%
2 4379
 
8.4%
3 3266
 
6.3%
4 3104
 
6.0%
6 2459
 
4.7%
5 2452
 
4.7%
7 2380
 
4.6%
8 2363
 
4.5%
Other values (6) 4926
 
9.5%
Hangul
ValueCountFrequency (%)
리 6879
 
14.5%
면 5183
 
10.9%
동 2534
 
5.3%
산 2427
 
5.1%
남 1708
 
3.6%
읍 1529
 
3.2%
삼 1508
 
3.2%
상 1241
 
2.6%
내 1086
 
2.3%
랑 1051
 
2.2%
Other values (128) 22442
47.2%

종점
Text

Distinct1332
Distinct (%)16.8%
Missing1
Missing (%)< 0.1%
Memory size62.2 KiB
2023-12-11T09:54:56.392366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length21
Mean length12.046989
Min length6

Characters and Unicode

Total characters95629
Distinct characters150
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique454 ?
Unique (%)5.7%

Sample

1st row단장면 무릉리 1436-1
2nd row단장면 국전리1909
3rd row단장면 무릉리10
4th row단장면 범도리652-1
5th row단장면 구천리2364
ValueCountFrequency (%)
상남면 836
 
4.1%
단장면 742
 
3.6%
삼랑진읍 728
 
3.6%
부북면 689
 
3.4%
무안면 683
 
3.3%
산내면 658
 
3.2%
하남읍 576
 
2.8%
상동면 547
 
2.7%
초동면 485
 
2.4%
산외면 351
 
1.7%
Other values (1489) 14138
69.2%
2023-12-11T09:54:56.981021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
13579
 
14.2%
리 6860
 
7.2%
1 5997
 
6.3%
면 5385
 
5.6%
- 4384
 
4.6%
2 4001
 
4.2%
산 3086
 
3.2%
4 2987
 
3.1%
3 2854
 
3.0%
동 2724
 
2.8%
Other values (140) 43772
45.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 47811
50.0%
Decimal Number 29410
30.8%
Space Separator 13579
 
14.2%
Dash Punctuation 4384
 
4.6%
Open Punctuation 198
 
0.2%
Close Punctuation 198
 
0.2%
Other Punctuation 47
 
< 0.1%
Uppercase Letter 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 6860
 
14.3%
면 5385
 
11.3%
산 3086
 
6.5%
동 2724
 
5.7%
남 1654
 
3.5%
상 1425
 
3.0%
삼 1313
 
2.7%
읍 1306
 
2.7%
내 1085
 
2.3%
안 897
 
1.9%
Other values (123) 22076
46.2%
Decimal Number
ValueCountFrequency (%)
1 5997
20.4%
2 4001
13.6%
4 2987
10.2%
3 2854
9.7%
5 2480
8.4%
6 2448
8.3%
0 2291
 
7.8%
7 2188
 
7.4%
9 2102
 
7.1%
8 2062
 
7.0%
Uppercase Letter
ValueCountFrequency (%)
I 1
50.0%
C 1
50.0%
Space Separator
ValueCountFrequency (%)
13579
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4384
100.0%
Open Punctuation
ValueCountFrequency (%)
( 198
100.0%
Close Punctuation
ValueCountFrequency (%)
) 198
100.0%
Other Punctuation
ValueCountFrequency (%)
· 47
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 47816
50.0%
Hangul 47811
50.0%
Latin 2
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 6860
 
14.3%
면 5385
 
11.3%
산 3086
 
6.5%
동 2724
 
5.7%
남 1654
 
3.5%
상 1425
 
3.0%
삼 1313
 
2.7%
읍 1306
 
2.7%
내 1085
 
2.3%
안 897
 
1.9%
Other values (123) 22076
46.2%
Common
ValueCountFrequency (%)
13579
28.4%
1 5997
12.5%
- 4384
 
9.2%
2 4001
 
8.4%
4 2987
 
6.2%
3 2854
 
6.0%
5 2480
 
5.2%
6 2448
 
5.1%
0 2291
 
4.8%
7 2188
 
4.6%
Other values (5) 4607
 
9.6%
Latin
ValueCountFrequency (%)
I 1
50.0%
C 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 47811
50.0%
ASCII 47771
50.0%
None 47
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
13579
28.4%
1 5997
12.6%
- 4384
 
9.2%
2 4001
 
8.4%
4 2987
 
6.3%
3 2854
 
6.0%
5 2480
 
5.2%
6 2448
 
5.1%
0 2291
 
4.8%
7 2188
 
4.6%
Other values (6) 4562
 
9.5%
Hangul
ValueCountFrequency (%)
리 6860
 
14.3%
면 5385
 
11.3%
산 3086
 
6.5%
동 2724
 
5.7%
남 1654
 
3.5%
상 1425
 
3.0%
삼 1313
 
2.7%
읍 1306
 
2.7%
내 1085
 
2.3%
안 897
 
1.9%
Other values (123) 22076
46.2%
None
ValueCountFrequency (%)
· 47
100.0%

도로폭
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct174
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.9607177
Minimum1
Maximum39539.069
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size69.9 KiB
2023-12-11T09:54:57.162915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q13
median4
Q36
95-th percentile10
Maximum39539.069
Range39538.069
Interquartile range (IQR)3

Descriptive statistics

Standard deviation443.71061
Coefficient of variation (CV)44.546048
Kurtosis7938.1962
Mean9.9607177
Median Absolute Deviation (MAD)1
Skewness89.094302
Sum79078.138
Variance196879.1
MonotonicityNot monotonic
2023-12-11T09:54:57.328413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.0 3016
38.0%
4.0 1531
19.3%
5.0 745
 
9.4%
6.0 588
 
7.4%
8.0 355
 
4.5%
7.0 342
 
4.3%
9.0 288
 
3.6%
10.0 251
 
3.2%
2.0 215
 
2.7%
20.0 111
 
1.4%
Other values (164) 497
 
6.3%
ValueCountFrequency (%)
1.0 21
0.3%
1.1 1
 
< 0.1%
1.271 1
 
< 0.1%
1.274 1
 
< 0.1%
1.296 1
 
< 0.1%
1.6 1
 
< 0.1%
1.636 1
 
< 0.1%
1.795 1
 
< 0.1%
1.8 1
 
< 0.1%
1.9 1
 
< 0.1%
ValueCountFrequency (%)
39539.069 1
 
< 0.1%
40.0 1
 
< 0.1%
27.0 1
 
< 0.1%
25.0 5
 
0.1%
20.0 111
1.4%
18.0 6
 
0.1%
17.0 12
 
0.2%
16.018 1
 
< 0.1%
15.0 58
0.7%
14.0 4
 
0.1%

도로길이
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct1559
Distinct (%)19.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2297.6472
Minimum3
Maximum9120510.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size69.9 KiB
2023-12-11T09:54:57.754690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile17
Q139
median89
Q3253
95-th percentile2678.2
Maximum9120510.4
Range9120507.4
Interquartile range (IQR)214

Descriptive statistics

Standard deviation102568.27
Coefficient of variation (CV)44.640566
Kurtosis7871.1751
Mean2297.6472
Median Absolute Deviation (MAD)61
Skewness88.536385
Sum18241021
Variance1.052025 × 1010
MonotonicityNot monotonic
2023-12-11T09:54:57.899435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20.0 157
 
2.0%
40.0 101
 
1.3%
39.0 98
 
1.2%
19.0 96
 
1.2%
30.0 86
 
1.1%
34.0 78
 
1.0%
18.0 76
 
1.0%
29.0 71
 
0.9%
23.0 71
 
0.9%
33.0 70
 
0.9%
Other values (1549) 7035
88.6%
ValueCountFrequency (%)
3.0 1
 
< 0.1%
3.25 1
 
< 0.1%
4.0 3
< 0.1%
4.915 1
 
< 0.1%
5.0 3
< 0.1%
5.438 1
 
< 0.1%
6.0 4
0.1%
6.6 1
 
< 0.1%
6.622 1
 
< 0.1%
6.982 1
 
< 0.1%
ValueCountFrequency (%)
9120510.43 1
 
< 0.1%
370850.0 1
 
< 0.1%
54025.0 46
0.6%
36224.0 38
0.5%
23800.0 5
 
0.1%
20904.0 2
 
< 0.1%
20636.0 4
 
0.1%
17398.0 4
 
0.1%
17187.0 47
0.6%
16733.0 1
 
< 0.1%

기초간격
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size62.2 KiB
20
7268 
10
 
641
15
 
13
5
 
6
18
 
3
Other values (6)
 
8

Length

Max length4
Median length2
Mean length1.9993702
Min length1

Unique

Unique4 ?
Unique (%)0.1%

Sample

1st row20
2nd row20
3rd row20
4th row20
5th row20

Common Values

ValueCountFrequency (%)
20 7268
91.5%
10 641
 
8.1%
15 13
 
0.2%
5 6
 
0.1%
18 3
 
< 0.1%
19 2
 
< 0.1%
9 2
 
< 0.1%
17 1
 
< 0.1%
2000 1
 
< 0.1%
1
 
< 0.1%

Length

2023-12-11T09:54:58.035051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
20 7268
91.6%
10 641
 
8.1%
15 13
 
0.2%
5 6
 
0.1%
18 3
 
< 0.1%
19 2
 
< 0.1%
9 2
 
< 0.1%
17 1
 
< 0.1%
2000 1
 
< 0.1%
na 1
 
< 0.1%

부여사유
Text

Distinct641
Distinct (%)8.1%
Missing1
Missing (%)< 0.1%
Memory size62.2 KiB
2023-12-11T09:54:58.283152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length68
Median length49
Mean length27.785714
Min length7

Characters and Unicode

Total characters220563
Distinct characters324
Distinct categories10 ?
Distinct scripts2 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32 ?
Unique (%)0.4%

Sample

1st row행정구역명(법정리:무릉리) 및 국전로에서 분기된 순서 반영
2nd row행정구역명(법정리:국전리) 및 국전로에서 분기된 순서, 도로구간의 지역내 위치성 반영
3rd row행정구역명(법정리:무릉리) 및 국전로에서 분기된 순서 반영
4th row행정구역명(행정리:아불리) 및 표충로에서 분기된 순서 반영
5th row행정구역명(행정리:시전리) 및 표충로에서 분기된 순서 반영
ValueCountFrequency (%)
반영 5729
 
14.2%
및 3474
 
8.6%
순서 3139
 
7.8%
분기된 2862
 
7.1%
분기되는 1355
 
3.3%
도로 1228
 
3.0%
시작지점에서부터 1081
 
2.7%
번째로 1071
 
2.6%
위치성 689
 
1.7%
도로구간의 684
 
1.7%
Other values (728) 19141
47.3%
2023-12-11T09:54:58.689214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
32526
 
14.7%
정 10523
 
4.8%
리 9998
 
4.5%
서 7710
 
3.5%
로 7511
 
3.4%
명 7106
 
3.2%
행 6873
 
3.1%
역 6501
 
2.9%
) 6465
 
2.9%
( 6465
 
2.9%
Other values (314) 118885
53.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 168325
76.3%
Space Separator 32526
 
14.7%
Close Punctuation 6700
 
3.0%
Open Punctuation 6700
 
3.0%
Other Punctuation 5777
 
2.6%
Dash Punctuation 289
 
0.1%
Decimal Number 141
 
0.1%
Math Symbol 67
 
< 0.1%
Final Punctuation 19
 
< 0.1%
Initial Punctuation 19
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
정 10523
 
6.3%
리 9998
 
5.9%
서 7710
 
4.6%
로 7511
 
4.5%
명 7106
 
4.2%
행 6873
 
4.1%
역 6501
 
3.9%
구 6430
 
3.8%
영 6183
 
3.7%
반 6175
 
3.7%
Other values (297) 93315
55.4%
Other Punctuation
ValueCountFrequency (%)
: 5122
88.7%
, 644
 
11.1%
' 6
 
0.1%
· 5
 
0.1%
Decimal Number
ValueCountFrequency (%)
3 60
42.6%
2 33
23.4%
1 26
18.4%
4 22
 
15.6%
Close Punctuation
ValueCountFrequency (%)
) 6465
96.5%
」 235
 
3.5%
Open Punctuation
ValueCountFrequency (%)
( 6465
96.5%
「 235
 
3.5%
Space Separator
ValueCountFrequency (%)
32526
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 289
100.0%
Math Symbol
ValueCountFrequency (%)
+ 67
100.0%
Final Punctuation
ValueCountFrequency (%)
’ 19
100.0%
Initial Punctuation
ValueCountFrequency (%)
‘ 19
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 168325
76.3%
Common 52238
 
23.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
정 10523
 
6.3%
리 9998
 
5.9%
서 7710
 
4.6%
로 7511
 
4.5%
명 7106
 
4.2%
행 6873
 
4.1%
역 6501
 
3.9%
구 6430
 
3.8%
영 6183
 
3.7%
반 6175
 
3.7%
Other values (297) 93315
55.4%
Common
ValueCountFrequency (%)
32526
62.3%
) 6465
 
12.4%
( 6465
 
12.4%
: 5122
 
9.8%
, 644
 
1.2%
- 289
 
0.6%
「 235
 
0.4%
」 235
 
0.4%
+ 67
 
0.1%
3 60
 
0.1%
Other values (7) 130
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 168325
76.3%
ASCII 51725
 
23.5%
None 475
 
0.2%
Punctuation 38
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
32526
62.9%
) 6465
 
12.5%
( 6465
 
12.5%
: 5122
 
9.9%
, 644
 
1.2%
- 289
 
0.6%
+ 67
 
0.1%
3 60
 
0.1%
2 33
 
0.1%
1 26
 
0.1%
Other values (2) 28
 
0.1%
Hangul
ValueCountFrequency (%)
정 10523
 
6.3%
리 9998
 
5.9%
서 7710
 
4.6%
로 7511
 
4.5%
명 7106
 
4.2%
행 6873
 
4.1%
역 6501
 
3.9%
구 6430
 
3.8%
영 6183
 
3.7%
반 6175
 
3.7%
Other values (297) 93315
55.4%
None
ValueCountFrequency (%)
「 235
49.5%
」 235
49.5%
· 5
 
1.1%
Punctuation
ValueCountFrequency (%)
’ 19
50.0%
‘ 19
50.0%

부여일자
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct20
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size62.2 KiB
-07-28
4959 
-06-25
2113 
2009-07-28
 
235
-06-05
 
223
2009-06-25
 
114
Other values (15)
 
295

Length

Max length10
Median length6
Mean length6.1836503
Min length4

Unique

Unique4 ?
Unique (%)0.1%

Sample

1st row2009-07-28
2nd row2009-07-28
3rd row2009-07-28
4th row2009-07-28
5th row2009-07-28

Common Values

ValueCountFrequency (%)
-07-28 4959
62.5%
-06-25 2113
26.6%
2009-07-28 235
 
3.0%
-06-05 223
 
2.8%
2009-06-25 114
 
1.4%
-07-10 102
 
1.3%
-12-28 38
 
0.5%
-11-22 32
 
0.4%
-06-28 31
 
0.4%
-09-30 28
 
0.4%
Other values (10) 64
 
0.8%

Length

2023-12-11T09:54:58.830109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
07-28 4959
62.5%
06-25 2113
26.6%
2009-07-28 235
 
3.0%
06-05 223
 
2.8%
2009-06-25 114
 
1.4%
07-10 102
 
1.3%
12-28 38
 
0.5%
11-22 32
 
0.4%
06-28 31
 
0.4%
09-30 28
 
0.4%
Other values (10) 64
 
0.8%

이동사유코드
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)0.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean83.90388
Minimum1
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size69.9 KiB
2023-12-11T09:54:58.942489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile12
Q190
median90
Q390
95-th percentile90
Maximum99
Range98
Interquartile range (IQR)0

Descriptive statistics

Standard deviation21.389132
Coefficient of variation (CV)0.25492423
Kurtosis7.4400409
Mean83.90388
Median Absolute Deviation (MAD)0
Skewness-3.0594829
Sum666029
Variance457.49498
MonotonicityNot monotonic
2023-12-11T09:54:59.048860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
90 7105
89.5%
12 626
 
7.9%
99 188
 
2.4%
3 8
 
0.1%
71 6
 
0.1%
1 5
 
0.1%
(Missing) 1
 
< 0.1%
ValueCountFrequency (%)
1 5
 
0.1%
3 8
 
0.1%
12 626
 
7.9%
71 6
 
0.1%
90 7105
89.5%
99 188
 
2.4%
ValueCountFrequency (%)
99 188
 
2.4%
90 7105
89.5%
71 6
 
0.1%
12 626
 
7.9%
3 8
 
0.1%
1 5
 
0.1%

이동사유
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct15
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size62.2 KiB
광역도로 정비 및 부여사유, 부여방식 정비
7099 
 
311
도로구간변경
 
267
직권수정(속성변경)
 
186
행안부 광역도로 시도 이관에 따른 도로구간 변경
 
47
Other values (10)
 
29

Length

Max length27
Median length23
Mean length21.263887
Min length1

Unique

Unique6 ?
Unique (%)0.1%

Sample

1st row광역도로 정비 및 부여사유, 부여방식 정비
2nd row광역도로 정비 및 부여사유, 부여방식 정비
3rd row광역도로 정비 및 부여사유, 부여방식 정비
4th row광역도로 정비 및 부여사유, 부여방식 정비
5th row광역도로 정비 및 부여사유, 부여방식 정비

Common Values

ValueCountFrequency (%)
광역도로 정비 및 부여사유, 부여방식 정비 7099
89.4%
311
 
3.9%
도로구간변경 267
 
3.4%
직권수정(속성변경) 186
 
2.3%
행안부 광역도로 시도 이관에 따른 도로구간 변경 47
 
0.6%
자연마을명(오시골) 오기 부여 8
 
0.1%
도로선형 위치정확도 개선사업에 의한 도로구간 변경 6
 
0.1%
광역도로의 시군구별 도로명코드 불일치에 따른 정비 6
 
0.1%
하남산업단지 조성 3
 
< 0.1%
3 1
 
< 0.1%
Other values (5) 5
 
0.1%

Length

2023-12-11T09:54:59.166534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
정비 14205
32.7%
광역도로 7146
16.4%
및 7100
16.3%
부여사유 7099
16.3%
부여방식 7099
16.3%
도로구간변경 267
 
0.6%
직권수정(속성변경 186
 
0.4%
따른 53
 
0.1%
도로구간 53
 
0.1%
변경 53
 
0.1%
Other values (29) 234
 
0.5%

이동일자
Date

Distinct385
Distinct (%)4.9%
Missing1
Missing (%)< 0.1%
Memory size62.2 KiB
Minimum2014-10-31 00:00:00
Maximum2020-07-13 00:00:00
2023-12-11T09:54:59.308629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:54:59.449491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Interactions

2023-12-11T09:54:52.469388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:54:51.885490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:54:52.179703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:54:52.576077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:54:51.985828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:54:52.291191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:54:52.668229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:54:52.071850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:54:52.372978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T09:54:59.540895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
광역도로구분코드도로위계기능구분도로구간종속구분도로폭도로길이기초간격부여일자이동사유코드이동사유
광역도로구분코드1.0000.4560.128NaNNaN0.4100.8490.1670.715
도로위계기능구분0.4561.0000.087NaNNaN0.7670.8090.3980.789
도로구간종속구분0.1280.0871.000NaNNaN0.1360.0950.1150.208
도로폭NaNNaNNaN1.0000.707NaNNaNNaNNaN
도로길이NaNNaNNaN0.7071.000NaNNaNNaNNaN
기초간격0.4100.7670.136NaNNaN1.0000.2590.1330.130
부여일자0.8490.8090.095NaNNaN0.2591.0000.7630.854
이동사유코드0.1670.3980.115NaNNaN0.1330.7631.0001.000
이동사유0.7150.7890.208NaNNaN0.1300.8541.0001.000
2023-12-11T09:54:59.677676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
도로구간종속구분광역도로구분코드부여일자도로위계기능구분이동사유기초간격
도로구간종속구분1.0000.0390.0490.0820.1170.081
광역도로구분코드0.0391.0000.6920.4510.5380.270
부여일자0.0490.6921.0000.5940.4900.100
도로위계기능구분0.0820.4510.5941.0000.5830.580
이동사유0.1170.5380.4900.5831.0000.053
기초간격0.0810.2700.1000.5800.0531.000
2023-12-11T09:54:59.795270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
도로폭도로길이이동사유코드광역도로구분코드도로위계기능구분도로구간종속구분기초간격부여일자이동사유
도로폭1.0000.1030.0761.0001.0001.0001.0001.0001.000
도로길이0.1031.000-0.1401.0001.0001.0001.0001.0001.000
이동사유코드0.076-0.1401.0000.1580.1640.1090.0800.5350.999
광역도로구분코드1.0001.0000.1581.0000.4510.0390.2700.6920.538
도로위계기능구분1.0001.0000.1640.4511.0000.0820.5800.5940.583
도로구간종속구분1.0001.0000.1090.0390.0821.0000.0810.0490.117
기초간격1.0001.0000.0800.2700.5800.0811.0000.1000.053
부여일자1.0001.0000.5350.6920.5940.0490.1001.0000.490
이동사유1.0001.0000.9990.5380.5830.1170.0530.4901.000

Missing values

2023-12-11T09:54:52.803134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T09:54:52.993013image/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.
2023-12-11T09:54:53.157850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

도로명영문도로명광역도로구분코드도로위계기능구분도로구간종속구분기점종점도로폭도로길이기초간격부여사유부여일자이동사유코드이동사유이동일자
0무릉3길Mureung 3-gil342단장면 무릉리1548단장면 무릉리 1436-13.039.020행정구역명(법정리:무릉리) 및 국전로에서 분기된 순서 반영2009-07-2890광역도로 정비 및 부여사유, 부여방식 정비2014-10-31
1국전1안길Gukjeon 1an-gil340단장면 국전리1937단장면 국전리19093.0319.020행정구역명(법정리:국전리) 및 국전로에서 분기된 순서, 도로구간의 지역내 위치성 반영2009-07-2890광역도로 정비 및 부여사유, 부여방식 정비2014-10-31
2무릉2길Mureung 2-gil342단장면 무릉리1548단장면 무릉리103.057.020행정구역명(법정리:무릉리) 및 국전로에서 분기된 순서 반영2009-07-2890광역도로 정비 및 부여사유, 부여방식 정비2014-10-31
3아불3길Abul 3-gil340단장면 범도리 1531-13단장면 범도리652-14.0596.020행정구역명(행정리:아불리) 및 표충로에서 분기된 순서 반영2009-07-2890광역도로 정비 및 부여사유, 부여방식 정비2014-10-31
4시전3길Sijeon 3-gil340단장면 구천리2364단장면 구천리23643.0297.020행정구역명(행정리:시전리) 및 표충로에서 분기된 순서 반영2009-07-2890광역도로 정비 및 부여사유, 부여방식 정비2014-10-31
5국전로Gukjeon-ro331단장면 태룡리 401-2단장면 국전리 19543.0627.020행정구역명(법정리:국전리) 반영2009-06-2590광역도로 정비 및 부여사유, 부여방식 정비2014-10-31
6태동길Taedong-gil342단장면 태룡리493-12단장면 태룡리산 753.040.020행정구역명(행정리:태동리) 반영2009-07-2890광역도로 정비 및 부여사유, 부여방식 정비2014-10-31
7태동길Taedong-gil342단장면 태룡리493-12단장면 태룡리산 753.059.020행정구역명(행정리:태동리) 반영2009-07-2890광역도로 정비 및 부여사유, 부여방식 정비2014-10-31
8용회길Yonghoe-gil342단장면 단장리86-4단장면 태룡리336.0862.020자연마을명(용회동) 반영2009-07-28122014-12-01
9수산중앙로Susanjungang-ro332하남읍 수산리 843-3하남읍 양동리 611-6917.093.020수산리 중심을 관통하는 도로로 행정구역명(법정리) 반영2009-06-2590광역도로 정비 및 부여사유, 부여방식 정비2014-10-31
도로명영문도로명광역도로구분코드도로위계기능구분도로구간종속구분기점종점도로폭도로길이기초간격부여사유부여일자이동사유코드이동사유이동일자
7929감운로Gamun-ro331부북면 운전리 1160-0부북면 오례리 612-03.0134.53620행정구역명(법정리) 조합(감천리-운전리간 연결도로)-06-2512도로구간변경2018-08-23
7930사포로Sapo-ro331부북면 전사포리 491-2부북면 전사포리 434-113.0182.21420도로구간 내 주요시설물인 사포지방산업단지의 명칭 반영-06-2512도로구간변경2018-12-11
7931무안로Muan-ro331무안면 마흘리 455-6무안면 마흘리 454-33.098.66920행정구역명(읍면동:무안면) 반영-06-2512도로구간변경2018-06-05
7932검세길Geomse-gil341삼랑진읍 검세리 575-123삼랑진읍 검세리 500-15.0268.04120행정구역명(법정리:검세리) 반영-07-2812도로구간변경2020-06-17
7933안인로Anin-ro331상동면 안인리 68-7상동면 안인리 75-13.0109.74320행정구역명(법정리:안인리) 반영-06-2512도로구간변경2018-05-11
7934하양지길Hayangji-gil341산내면 삼양리 2311-0산내면 삼양리 2297-03.0112.19820행정구역명(행정리:하양리) 반영-07-2812도로구간변경2018-05-11
7935표충로Pyochung-ro331단장면 구천리 44-0단장면 구천리 산1-02.5299.31820문화재명 반영(「표충사」로 가는 단장면 중심도로)-06-2512도로구간변경2018-05-11
7936외평로Oepyeong-ro331상남면 동산리 224-1상남면 동산리 10-223.0625.96420행정구역명(법정리) 조합(외산리-평촌리간 연결도로)-06-2512도로구간변경2018-10-02
7937백안1길Baegan 1-gil341무안면 마흘리 1249-0무안면 마흘리 산11-64.082.56820행정구역명(행정리:백안리) 및 무안로에서 분기된 순서 반영-07-2812도로구간변경2019-07-11
7938<NA><NA><NA><NA><NA><NA><NA>39539.0699120510.43<NA><NA><NA><NA><NA><NA>

Duplicate rows

Most frequently occurring

도로명영문도로명광역도로구분코드도로위계기능구분도로구간종속구분기점종점도로폭도로길이기초간격부여사유부여일자이동사유코드이동사유이동일자# duplicates
74삼랑진로Samnangjin-ro231삼랑진읍 삼랑리 구 삼랑진교 기점(마사로 분기)가곡동 멍에실로·중앙로삼거리 (중앙로 연결)9.017187.020행정구역명(삼랑진읍) 반영-06-0590광역도로 정비 및 부여사유, 부여방식 정비2014-10-3133
143창밀로Changmil-ro231창녕읍 송현리 솔터사거리(화왕산로 분기)내이동 신촌오거리 (밀양대로 분기)10.036224.020행정구역명(창녕+밀양) 활용-06-0590광역도로 정비 및 부여사유, 부여방식 정비2014-10-3116
151초하로Choha-ro231창녕군 부곡면 학포리 학포삼거리(구산학로에서 분기)대산면 일동리 구수산교시점삼거리(대산북로 연결)9.08331.020행정구역명(초동면+하남읍) 반영-06-0590광역도로 정비 및 부여사유, 부여방식 정비2014-10-3116
160행곡로Haenggok-ro231삼랑진읍 안태리행곡리진입로(천태로 분기)양산 원동면 용당리 천태호 (행곡로 종점)9.08787.020행정구역명(삼랑진 행곡리) 활용-06-0590광역도로 정비 및 부여사유, 부여방식 정비2014-10-3116
63사명로Samyeong-ro231부곡면 수다리 인교삼거리 (온천로 분기)무안면 동산리 동산삼거리(창밀로 분기)9.013478.020표충비각, 사명대사 생가 등-06-0590광역도로 정비 및 부여사유, 부여방식 정비2014-10-3114
77삼문중앙로Sammunjungang-ro331삼문동 104-3삼문동 15-4920.0606.020행정구역명(읍면동:삼문동) 및 도로구간의 지역내 위치성 반영-06-2590광역도로 정비 및 부여사유, 부여방식 정비2014-10-3113
10고례로Gorye-ro231양산시원동면 대리고점사거리 (원동로 분기)단장면 범도리 1531-139.013269.020행정구역명(법정리:고례리) 반영-06-0599직권수정(속성변경)2014-10-3111
147천태로Cheontae-ro231삼랑진읍 삼랑리 송지사거리(삼랑진로 분기)원동면 원리 삼거리(원동로 연결)9.015481.020천태산, 천태호 등 지명 활용-06-0590광역도로 정비 및 부여사유, 부여방식 정비2014-10-3111
54백산로Baeksan-ro331하남읍 양동리 629-2상남면 외산리 227-43.991320.020행정구역명(법정리:백산리) 반영-06-2590광역도로 정비 및 부여사유, 부여방식 정비2014-10-319
64사명로Samyeong-ro231부곡면 수다리 인교삼거리 (온천로 분기)무안면 동산리 동산삼거리(창밀로 분기)9.013478.020표충비각, 사명대사 생가 등2009-06-0590광역도로 정비 및 부여사유, 부여방식 정비2014-10-318