Overview

Dataset statistics

Number of variables7
Number of observations196
Missing cells51
Missing cells (%)3.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory11.4 KiB
Average record size in memory59.7 B

Variable types

Text3
Categorical2
Numeric2

Dataset

Description경상남도 김해시 자전거도로 현황 자료로 노선명, 기점, 종점, 구분, 연장, 폭원, 자전거도로폭원의 데이터로 구성되어 있습니다.
Author경상남도 김해시
URLhttps://www.data.go.kr/data/15098401/fileData.do

Alerts

구분 is highly overall correlated with 폭원(m)High correlation
폭원(m) is highly overall correlated with 구분High correlation
폭원(m) is highly imbalanced (63.5%)Imbalance
자전거도로폭원(m) has 51 (26.0%) missing valuesMissing

Reproduction

Analysis started2023-12-12 02:03:30.352514
Analysis finished2023-12-12 02:03:31.510000
Duration1.16 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

노선명
Text

Distinct184
Distinct (%)93.9%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-12-12T11:03:31.881524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length13
Mean length6.377551
Min length3

Characters and Unicode

Total characters1250
Distinct characters128
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

Unique173 ?
Unique (%)88.3%

Sample

1st row김해대로2L
2nd row김해대로2R
3rd row김해대로4L
4th row김해대로4R
5th row생림대로
ValueCountFrequency (%)
김해대로1902번길 3
 
1.4%
삼안로r 2
 
1.0%
도로 2
 
1.0%
홈플러스옆길 2
 
1.0%
삼안로l 2
 
1.0%
젤미1,3,6,7단지 2
 
1.0%
안(동서 2
 
1.0%
안(남북 2
 
1.0%
젤미4,5,8,9단지 2
 
1.0%
진입도로 2
 
1.0%
Other values (179) 187
89.9%
2023-12-12T11:03:32.401400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
로 159
 
12.7%
L 73
 
5.8%
R 69
 
5.5%
길 57
 
4.6%
1 52
 
4.2%
2 43
 
3.4%
동 35
 
2.8%
번 35
 
2.8%
대 34
 
2.7%
하 27
 
2.2%
Other values (118) 666
53.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 871
69.7%
Decimal Number 201
 
16.1%
Uppercase Letter 143
 
11.4%
Other Punctuation 15
 
1.2%
Space Separator 12
 
1.0%
Open Punctuation 4
 
0.3%
Close Punctuation 4
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 159
 
18.3%
길 57
 
6.5%
동 35
 
4.0%
번 35
 
4.0%
대 34
 
3.9%
하 27
 
3.1%
율 23
 
2.6%
해 22
 
2.5%
김 17
 
2.0%
전 16
 
1.8%
Other values (101) 446
51.2%
Decimal Number
ValueCountFrequency (%)
1 52
25.9%
2 43
21.4%
3 26
12.9%
4 20
 
10.0%
9 14
 
7.0%
7 13
 
6.5%
5 11
 
5.5%
6 10
 
5.0%
0 8
 
4.0%
8 4
 
2.0%
Uppercase Letter
ValueCountFrequency (%)
L 73
51.0%
R 69
48.3%
E 1
 
0.7%
Other Punctuation
ValueCountFrequency (%)
, 15
100.0%
Space Separator
ValueCountFrequency (%)
12
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 871
69.7%
Common 236
 
18.9%
Latin 143
 
11.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 159
 
18.3%
길 57
 
6.5%
동 35
 
4.0%
번 35
 
4.0%
대 34
 
3.9%
하 27
 
3.1%
율 23
 
2.6%
해 22
 
2.5%
김 17
 
2.0%
전 16
 
1.8%
Other values (101) 446
51.2%
Common
ValueCountFrequency (%)
1 52
22.0%
2 43
18.2%
3 26
11.0%
4 20
 
8.5%
, 15
 
6.4%
9 14
 
5.9%
7 13
 
5.5%
12
 
5.1%
5 11
 
4.7%
6 10
 
4.2%
Other values (4) 20
 
8.5%
Latin
ValueCountFrequency (%)
L 73
51.0%
R 69
48.3%
E 1
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 871
69.7%
ASCII 379
30.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
로 159
 
18.3%
길 57
 
6.5%
동 35
 
4.0%
번 35
 
4.0%
대 34
 
3.9%
하 27
 
3.1%
율 23
 
2.6%
해 22
 
2.5%
김 17
 
2.0%
전 16
 
1.8%
Other values (101) 446
51.2%
ASCII
ValueCountFrequency (%)
L 73
19.3%
R 69
18.2%
1 52
13.7%
2 43
11.3%
3 26
 
6.9%
4 20
 
5.3%
, 15
 
4.0%
9 14
 
3.7%
7 13
 
3.4%
12
 
3.2%
Other values (7) 42
11.1%

기점
Text

Distinct166
Distinct (%)84.7%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-12-12T11:03:32.793074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length16
Mean length6.7346939
Min length3

Characters and Unicode

Total characters1320
Distinct characters213
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique140 ?
Unique (%)71.4%

Sample

1st row동신아파트앞
2nd row연지2교사거리
3rd row전하교
4th row동김해IC사거리
5th row삼계사거리
ValueCountFrequency (%)
장유동 8
 
3.5%
앞 5
 
2.2%
김해대로합류점 4
 
1.7%
장유로합류점 3
 
1.3%
구산육거리 3
 
1.3%
안동육거리 2
 
0.9%
장유터널 2
 
0.9%
칠산로합류점 2
 
0.9%
대청프라자사거리 2
 
0.9%
무접삼거리 2
 
0.9%
Other values (174) 196
85.6%
2023-12-12T11:03:33.389791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
리 76
 
5.8%
거 75
 
5.7%
로 46
 
3.5%
동 45
 
3.4%
사 44
 
3.3%
삼 42
 
3.2%
교 40
 
3.0%
34
 
2.6%
장 31
 
2.3%
유 28
 
2.1%
Other values (203) 859
65.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1177
89.2%
Decimal Number 87
 
6.6%
Space Separator 34
 
2.6%
Uppercase Letter 14
 
1.1%
Dash Punctuation 4
 
0.3%
Lowercase Letter 2
 
0.2%
Open Punctuation 1
 
0.1%
Close Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 76
 
6.5%
거 75
 
6.4%
로 46
 
3.9%
동 45
 
3.8%
사 44
 
3.7%
삼 42
 
3.6%
교 40
 
3.4%
장 31
 
2.6%
유 28
 
2.4%
점 25
 
2.1%
Other values (183) 725
61.6%
Decimal Number
ValueCountFrequency (%)
1 19
21.8%
2 13
14.9%
3 10
11.5%
8 9
10.3%
0 9
10.3%
4 7
 
8.0%
5 5
 
5.7%
7 5
 
5.7%
6 5
 
5.7%
9 5
 
5.7%
Uppercase Letter
ValueCountFrequency (%)
I 5
35.7%
C 5
35.7%
S 2
 
14.3%
G 1
 
7.1%
K 1
 
7.1%
Space Separator
ValueCountFrequency (%)
34
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1177
89.2%
Common 127
 
9.6%
Latin 16
 
1.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 76
 
6.5%
거 75
 
6.4%
로 46
 
3.9%
동 45
 
3.8%
사 44
 
3.7%
삼 42
 
3.6%
교 40
 
3.4%
장 31
 
2.6%
유 28
 
2.4%
점 25
 
2.1%
Other values (183) 725
61.6%
Common
ValueCountFrequency (%)
34
26.8%
1 19
15.0%
2 13
 
10.2%
3 10
 
7.9%
8 9
 
7.1%
0 9
 
7.1%
4 7
 
5.5%
5 5
 
3.9%
7 5
 
3.9%
6 5
 
3.9%
Other values (4) 11
 
8.7%
Latin
ValueCountFrequency (%)
I 5
31.2%
C 5
31.2%
S 2
 
12.5%
e 2
 
12.5%
G 1
 
6.2%
K 1
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1177
89.2%
ASCII 143
 
10.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
리 76
 
6.5%
거 75
 
6.4%
로 46
 
3.9%
동 45
 
3.8%
사 44
 
3.7%
삼 42
 
3.6%
교 40
 
3.4%
장 31
 
2.6%
유 28
 
2.4%
점 25
 
2.1%
Other values (183) 725
61.6%
ASCII
ValueCountFrequency (%)
34
23.8%
1 19
13.3%
2 13
 
9.1%
3 10
 
7.0%
8 9
 
6.3%
0 9
 
6.3%
4 7
 
4.9%
I 5
 
3.5%
C 5
 
3.5%
5 5
 
3.5%
Other values (10) 27
18.9%

종점
Text

Distinct171
Distinct (%)87.2%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-12-12T11:03:33.772233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length18
Mean length6.9132653
Min length3

Characters and Unicode

Total characters1355
Distinct characters213
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique151 ?
Unique (%)77.0%

Sample

1st row연지2교사거리
2nd row월드가구백화점
3rd row신어교
4th row전하교
5th row프루지오1차삼거리(생림)
ValueCountFrequency (%)
장유동 6
 
2.5%
앞 6
 
2.5%
김해대로합류점 4
 
1.7%
장유로합류점 4
 
1.7%
원메이저 3
 
1.2%
옆 3
 
1.2%
연지교 3
 
1.2%
305동 3
 
1.2%
연지2교사거리 2
 
0.8%
장동로합류점 2
 
0.8%
Other values (183) 204
85.0%
2023-12-12T11:03:34.359816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
리 71
 
5.2%
거 67
 
4.9%
교 48
 
3.5%
45
 
3.3%
로 44
 
3.2%
동 41
 
3.0%
사 40
 
3.0%
장 35
 
2.6%
삼 34
 
2.5%
유 28
 
2.1%
Other values (203) 902
66.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1208
89.2%
Decimal Number 76
 
5.6%
Space Separator 45
 
3.3%
Uppercase Letter 14
 
1.0%
Dash Punctuation 6
 
0.4%
Lowercase Letter 2
 
0.1%
Open Punctuation 2
 
0.1%
Close Punctuation 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 71
 
5.9%
거 67
 
5.5%
교 48
 
4.0%
로 44
 
3.6%
동 41
 
3.4%
사 40
 
3.3%
장 35
 
2.9%
삼 34
 
2.8%
유 28
 
2.3%
차 25
 
2.1%
Other values (183) 775
64.2%
Decimal Number
ValueCountFrequency (%)
2 13
17.1%
3 11
14.5%
1 9
11.8%
4 7
9.2%
0 7
9.2%
5 7
9.2%
7 7
9.2%
8 6
7.9%
9 6
7.9%
6 3
 
3.9%
Uppercase Letter
ValueCountFrequency (%)
I 4
28.6%
C 4
28.6%
S 3
21.4%
K 2
14.3%
G 1
 
7.1%
Space Separator
ValueCountFrequency (%)
45
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 6
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1208
89.2%
Common 131
 
9.7%
Latin 16
 
1.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 71
 
5.9%
거 67
 
5.5%
교 48
 
4.0%
로 44
 
3.6%
동 41
 
3.4%
사 40
 
3.3%
장 35
 
2.9%
삼 34
 
2.8%
유 28
 
2.3%
차 25
 
2.1%
Other values (183) 775
64.2%
Common
ValueCountFrequency (%)
45
34.4%
2 13
 
9.9%
3 11
 
8.4%
1 9
 
6.9%
4 7
 
5.3%
0 7
 
5.3%
5 7
 
5.3%
7 7
 
5.3%
- 6
 
4.6%
8 6
 
4.6%
Other values (4) 13
 
9.9%
Latin
ValueCountFrequency (%)
I 4
25.0%
C 4
25.0%
S 3
18.8%
e 2
12.5%
K 2
12.5%
G 1
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1208
89.2%
ASCII 147
 
10.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
리 71
 
5.9%
거 67
 
5.5%
교 48
 
4.0%
로 44
 
3.6%
동 41
 
3.4%
사 40
 
3.3%
장 35
 
2.9%
삼 34
 
2.8%
유 28
 
2.3%
차 25
 
2.1%
Other values (183) 775
64.2%
ASCII
ValueCountFrequency (%)
45
30.6%
2 13
 
8.8%
3 11
 
7.5%
1 9
 
6.1%
4 7
 
4.8%
0 7
 
4.8%
5 7
 
4.8%
7 7
 
4.8%
- 6
 
4.1%
8 6
 
4.1%
Other values (10) 29
19.7%

구분
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
분리형 겸용도로
103 
비분리형 겸용도로
45 
전용도로
20 
전용차로
18 
우선도로
 
10

Length

Max length9
Median length8
Mean length7.25
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
분리형 겸용도로 103
52.6%
비분리형 겸용도로 45
23.0%
전용도로 20
 
10.2%
전용차로 18
 
9.2%
우선도로 10
 
5.1%

Length

2023-12-12T11:03:34.550470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:03:34.716527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
겸용도로 148
43.0%
분리형 103
29.9%
비분리형 45
 
13.1%
전용도로 20
 
5.8%
전용차로 18
 
5.2%
우선도로 10
 
2.9%

연장(km)
Real number (ℝ)

Distinct127
Distinct (%)64.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2835714
Minimum0.08
Maximum12.36
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-12T11:03:34.921966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.08
5-th percentile0.15
Q10.3575
median0.72
Q31.52
95-th percentile3.69
Maximum12.36
Range12.28
Interquartile range (IQR)1.1625

Descriptive statistics

Standard deviation1.763335
Coefficient of variation (CV)1.3737724
Kurtosis15.505199
Mean1.2835714
Median Absolute Deviation (MAD)0.45
Skewness3.6374679
Sum251.58
Variance3.1093503
MonotonicityNot monotonic
2023-12-12T11:03:35.175255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.32 7
 
3.6%
0.19 5
 
2.6%
0.72 4
 
2.0%
0.56 4
 
2.0%
0.39 4
 
2.0%
0.23 4
 
2.0%
0.22 3
 
1.5%
0.64 3
 
1.5%
0.41 3
 
1.5%
0.97 3
 
1.5%
Other values (117) 156
79.6%
ValueCountFrequency (%)
0.08 1
 
0.5%
0.09 1
 
0.5%
0.11 3
1.5%
0.12 2
 
1.0%
0.13 1
 
0.5%
0.14 1
 
0.5%
0.15 2
 
1.0%
0.17 2
 
1.0%
0.18 1
 
0.5%
0.19 5
2.6%
ValueCountFrequency (%)
12.36 1
0.5%
9.79 2
1.0%
9.16 1
0.5%
7.14 1
0.5%
7.08 2
1.0%
6.35 1
0.5%
4.98 1
0.5%
4.5 1
0.5%
3.42 1
0.5%
3.04 1
0.5%

폭원(m)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
<NA>
164 
1.8
23 
2.0
 
5
1.5
 
2
1.3
 
2

Length

Max length4
Median length4
Mean length3.8367347
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.8
2nd row1.8
3rd row2.0
4th row2.0
5th row1.5

Common Values

ValueCountFrequency (%)
<NA> 164
83.7%
1.8 23
 
11.7%
2.0 5
 
2.6%
1.5 2
 
1.0%
1.3 2
 
1.0%

Length

2023-12-12T11:03:35.379661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:03:35.539754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 164
83.7%
1.8 23
 
11.7%
2.0 5
 
2.6%
1.5 2
 
1.0%
1.3 2
 
1.0%

자전거도로폭원(m)
Real number (ℝ)

MISSING 

Distinct21
Distinct (%)14.5%
Missing51
Missing (%)26.0%
Infinite0
Infinite (%)0.0%
Mean1.8710345
Minimum0.9
Maximum3.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-12T11:03:35.678910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.9
5-th percentile1.12
Q11.5
median2
Q32
95-th percentile2.96
Maximum3.7
Range2.8
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.53592045
Coefficient of variation (CV)0.28643002
Kurtosis0.75426172
Mean1.8710345
Median Absolute Deviation (MAD)0.3
Skewness0.6534325
Sum271.3
Variance0.28721073
MonotonicityNot monotonic
2023-12-12T11:03:35.828610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
2.0 48
24.5%
1.8 18
 
9.2%
1.2 15
 
7.7%
1.5 11
 
5.6%
1.7 10
 
5.1%
2.7 8
 
4.1%
1.3 5
 
2.6%
2.5 5
 
2.6%
0.9 4
 
2.0%
3.1 3
 
1.5%
Other values (11) 18
 
9.2%
(Missing) 51
26.0%
ValueCountFrequency (%)
0.9 4
 
2.0%
1.0 3
 
1.5%
1.1 1
 
0.5%
1.2 15
7.7%
1.3 5
 
2.6%
1.4 2
 
1.0%
1.5 11
5.6%
1.6 2
 
1.0%
1.7 10
5.1%
1.8 18
9.2%
ValueCountFrequency (%)
3.7 1
 
0.5%
3.3 1
 
0.5%
3.2 1
 
0.5%
3.1 3
 
1.5%
3.0 2
 
1.0%
2.8 1
 
0.5%
2.7 8
4.1%
2.5 5
2.6%
2.3 2
 
1.0%
2.1 2
 
1.0%

Interactions

2023-12-12T11:03:31.042241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:03:30.850222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:03:31.138389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:03:30.940551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T11:03:35.940758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분연장(km)폭원(m)자전거도로폭원(m)
구분1.0000.4890.8690.267
연장(km)0.4891.0000.6530.000
폭원(m)0.8690.6531.000NaN
자전거도로폭원(m)0.2670.000NaN1.000
2023-12-12T11:03:36.082711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
폭원(m)구분
폭원(m)1.0000.648
구분0.6481.000
2023-12-12T11:03:36.191630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연장(km)자전거도로폭원(m)구분폭원(m)
연장(km)1.0000.1990.3390.302
자전거도로폭원(m)0.1991.0000.1510.000
구분0.3390.1511.0000.648
폭원(m)0.3020.0000.6481.000

Missing values

2023-12-12T11:03:31.287063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T11:03:31.437260image/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

노선명기점종점구분연장(km)폭원(m)자전거도로폭원(m)
0김해대로2L동신아파트앞연지2교사거리전용도로1.81.8<NA>
1김해대로2R연지2교사거리월드가구백화점전용도로2.31.8<NA>
2김해대로4L전하교신어교전용도로1.952.0<NA>
3김해대로4R동김해IC사거리전하교전용도로2.622.0<NA>
4생림대로삼계사거리프루지오1차삼거리(생림)전용도로0.921.5<NA>
5가야로L삼계사거리이구삼거리전용도로2.981.8<NA>
6가야로R이구삼거리삼계사거리전용도로2.751.8<NA>
7경원로L가야중학교현대3차사거리전용도로0.851.3<NA>
8경원로R현대3차사거리가야중학교전용도로0.851.3<NA>
9경원로55번길거북공원사거리가야웨딩홀전용도로0.392.0<NA>
노선명기점종점구분연장(km)폭원(m)자전거도로폭원(m)
186삼안로195번길L신어중사거리신어중비분리형 겸용도로0.15<NA>2.7
187삼안로195번길R신어중신어중사거리비분리형 겸용도로0.12<NA>2.5
188장유E편한세상 앞 도로삼문로장유e편한세상 아파트비분리형 겸용도로0.32<NA>1.8
189장유로334번길삼문로신장유일동미라주더파크아파트비분리형 겸용도로0.56<NA>2.0
190율하천L관동교(구)관동교비분리형 겸용도로1.81<NA>1.0
191율하천R(구)관동교관동교비분리형 겸용도로1.97<NA>1.0
192대청천L조만강장유IC비분리형 겸용도로0.55<NA>2.0
193대청천R장유IC조만강비분리형 겸용도로0.93<NA>2.0
194낙동강자전거우회길2여차고개삼거리사촌교비분리형 겸용도로1.5<NA><NA>
195골든루트중앙로L남측진입로북측진입로비분리형 겸용도로1.95<NA><NA>