Overview

Dataset statistics

Number of variables8
Number of observations105
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.9 KiB
Average record size in memory67.3 B

Variable types

Text3
Numeric2
Categorical2
DateTime1

Dataset

Description파주시 자전거도로에 대한 데이터로서 노선명(술이홀로, 적성산단로 등), 기점, 종점, 연장(Km), 자전거도로폭원(m), 보도폭원(m), 자전거도로구분(분리형, 비분리형) 등의 정보를 제공합니다.
Author경기도 파주시
URLhttps://www.data.go.kr/data/15038627/fileData.do

Alerts

기준일자 has constant value ""Constant
자전거도로구분 is highly overall correlated with 연장(km) and 1 other fieldsHigh correlation
보도폭원(m) is highly overall correlated with 연장(km) and 2 other fieldsHigh correlation
연장(km) is highly overall correlated with 보도폭원(m) and 1 other fieldsHigh correlation
자전거도로폭원(m) is highly overall correlated with 보도폭원(m)High correlation
보도폭원(m) is highly imbalanced (51.6%)Imbalance
자전거도로구분 is highly imbalanced (80.0%)Imbalance

Reproduction

Analysis started2023-12-12 19:13:00.341483
Analysis finished2023-12-12 19:13:02.285629
Duration1.94 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

노선명
Text

Distinct103
Distinct (%)98.1%
Missing0
Missing (%)0.0%
Memory size972.0 B
2023-12-13T04:13:02.503044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length10
Mean length5.4095238
Min length3

Characters and Unicode

Total characters568
Distinct characters128
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique101 ?
Unique (%)96.2%

Sample

1st row술이홀로
2nd row적성산단로
3rd row적성산단1로
4th row적성산단2로
5th row당동2로
ValueCountFrequency (%)
술이홀로 2
 
1.9%
경의로(1 2
 
1.9%
책향기로(2 1
 
0.9%
미래로602번길 1
 
0.9%
가람로21번길 1
 
0.9%
가람로 1
 
0.9%
미래로 1
 
0.9%
해솔로 1
 
0.9%
가재울로 1
 
0.9%
청암로17번길 1
 
0.9%
Other values (94) 94
88.7%
2023-12-13T04:13:02.914234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
로 95
 
16.7%
( 48
 
8.5%
) 48
 
8.5%
1 32
 
5.6%
2 23
 
4.0%
길 16
 
2.8%
3 11
 
1.9%
번 11
 
1.9%
가 10
 
1.8%
리 9
 
1.6%
Other values (118) 265
46.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 389
68.5%
Decimal Number 82
 
14.4%
Open Punctuation 48
 
8.5%
Close Punctuation 48
 
8.5%
Space Separator 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 95
24.4%
길 16
 
4.1%
번 11
 
2.8%
가 10
 
2.6%
리 9
 
2.3%
산 9
 
2.3%
람 7
 
1.8%
천 6
 
1.5%
암 6
 
1.5%
문 5
 
1.3%
Other values (106) 215
55.3%
Decimal Number
ValueCountFrequency (%)
1 32
39.0%
2 23
28.0%
3 11
 
13.4%
0 4
 
4.9%
4 4
 
4.9%
6 3
 
3.7%
5 2
 
2.4%
8 2
 
2.4%
7 1
 
1.2%
Open Punctuation
ValueCountFrequency (%)
( 48
100.0%
Close Punctuation
ValueCountFrequency (%)
) 48
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 389
68.5%
Common 179
31.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 95
24.4%
길 16
 
4.1%
번 11
 
2.8%
가 10
 
2.6%
리 9
 
2.3%
산 9
 
2.3%
람 7
 
1.8%
천 6
 
1.5%
암 6
 
1.5%
문 5
 
1.3%
Other values (106) 215
55.3%
Common
ValueCountFrequency (%)
( 48
26.8%
) 48
26.8%
1 32
17.9%
2 23
12.8%
3 11
 
6.1%
0 4
 
2.2%
4 4
 
2.2%
6 3
 
1.7%
5 2
 
1.1%
8 2
 
1.1%
Other values (2) 2
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 389
68.5%
ASCII 179
31.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
로 95
24.4%
길 16
 
4.1%
번 11
 
2.8%
가 10
 
2.6%
리 9
 
2.3%
산 9
 
2.3%
람 7
 
1.8%
천 6
 
1.5%
암 6
 
1.5%
문 5
 
1.3%
Other values (106) 215
55.3%
ASCII
ValueCountFrequency (%)
( 48
26.8%
) 48
26.8%
1 32
17.9%
2 23
12.8%
3 11
 
6.1%
0 4
 
2.2%
4 4
 
2.2%
6 3
 
1.7%
5 2
 
1.1%
8 2
 
1.1%
Other values (2) 2
 
1.1%

기점
Text

Distinct91
Distinct (%)86.7%
Missing0
Missing (%)0.0%
Memory size972.0 B
2023-12-13T04:13:03.176696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length12
Mean length8.1809524
Min length3

Characters and Unicode

Total characters859
Distinct characters164
Distinct categories4 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique81 ?
Unique (%)77.1%

Sample

1st row 장남교 남측
2nd row적성일반산업단지 내
3rd row적성일반산업단지 내
4th row적성일반산업단지 내
5th row 당동1교
ValueCountFrequency (%)
앞 11
 
6.1%
남측 8
 
4.4%
북동측 8
 
4.4%
내 7
 
3.9%
북측 7
 
3.9%
북서측 7
 
3.9%
남동측 6
 
3.3%
교차로 4
 
2.2%
선유일반사업단지 4
 
2.2%
동측 4
 
2.2%
Other values (97) 115
63.5%
2023-12-13T04:13:03.568025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
84
 
9.8%
교 43
 
5.0%
측 43
 
5.0%
지 33
 
3.8%
동 27
 
3.1%
단 24
 
2.8%
북 23
 
2.7%
산 21
 
2.4%
로 21
 
2.4%
리 18
 
2.1%
Other values (154) 522
60.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 735
85.6%
Space Separator 84
 
9.8%
Decimal Number 30
 
3.5%
Uppercase Letter 10
 
1.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
교 43
 
5.9%
측 43
 
5.9%
지 33
 
4.5%
동 27
 
3.7%
단 24
 
3.3%
북 23
 
3.1%
산 21
 
2.9%
로 21
 
2.9%
리 18
 
2.4%
차 18
 
2.4%
Other values (140) 464
63.1%
Decimal Number
ValueCountFrequency (%)
1 16
53.3%
2 4
 
13.3%
3 3
 
10.0%
5 2
 
6.7%
4 2
 
6.7%
6 1
 
3.3%
7 1
 
3.3%
9 1
 
3.3%
Uppercase Letter
ValueCountFrequency (%)
L 4
40.0%
H 2
20.0%
G 2
20.0%
T 1
 
10.0%
K 1
 
10.0%
Space Separator
ValueCountFrequency (%)
84
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 735
85.6%
Common 114
 
13.3%
Latin 10
 
1.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
교 43
 
5.9%
측 43
 
5.9%
지 33
 
4.5%
동 27
 
3.7%
단 24
 
3.3%
북 23
 
3.1%
산 21
 
2.9%
로 21
 
2.9%
리 18
 
2.4%
차 18
 
2.4%
Other values (140) 464
63.1%
Common
ValueCountFrequency (%)
84
73.7%
1 16
 
14.0%
2 4
 
3.5%
3 3
 
2.6%
5 2
 
1.8%
4 2
 
1.8%
6 1
 
0.9%
7 1
 
0.9%
9 1
 
0.9%
Latin
ValueCountFrequency (%)
L 4
40.0%
H 2
20.0%
G 2
20.0%
T 1
 
10.0%
K 1
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 735
85.6%
ASCII 124
 
14.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
84
67.7%
1 16
 
12.9%
2 4
 
3.2%
L 4
 
3.2%
3 3
 
2.4%
5 2
 
1.6%
4 2
 
1.6%
H 2
 
1.6%
G 2
 
1.6%
6 1
 
0.8%
Other values (4) 4
 
3.2%
Hangul
ValueCountFrequency (%)
교 43
 
5.9%
측 43
 
5.9%
지 33
 
4.5%
동 27
 
3.7%
단 24
 
3.3%
북 23
 
3.1%
산 21
 
2.9%
로 21
 
2.9%
리 18
 
2.4%
차 18
 
2.4%
Other values (140) 464
63.1%

종점
Text

Distinct94
Distinct (%)89.5%
Missing0
Missing (%)0.0%
Memory size972.0 B
2023-12-13T04:13:03.804046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length14
Mean length8.2190476
Min length3

Characters and Unicode

Total characters863
Distinct characters172
Distinct categories4 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique86 ?
Unique (%)81.9%

Sample

1st row 적성교차로 동측
2nd row적성일반산업단지 내
3rd row적성일반산업단지 내
4th row적성일반산업단지 내
5th row 파주힐스테이트1차 앞
ValueCountFrequency (%)
앞 13
 
7.1%
북측 11
 
6.0%
서측 7
 
3.8%
내 7
 
3.8%
남측 6
 
3.3%
교차로 5
 
2.7%
남동측 5
 
2.7%
선유일반사업단지 4
 
2.2%
북서측 3
 
1.6%
적성일반산업단지 3
 
1.6%
Other values (107) 118
64.8%
2023-12-13T04:13:04.186779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
83
 
9.6%
교 48
 
5.6%
측 39
 
4.5%
지 28
 
3.2%
로 27
 
3.1%
리 26
 
3.0%
차 25
 
2.9%
동 21
 
2.4%
북 21
 
2.4%
단 20
 
2.3%
Other values (162) 525
60.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 749
86.8%
Space Separator 83
 
9.6%
Decimal Number 23
 
2.7%
Uppercase Letter 8
 
0.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
교 48
 
6.4%
측 39
 
5.2%
지 28
 
3.7%
로 27
 
3.6%
리 26
 
3.5%
차 25
 
3.3%
동 21
 
2.8%
북 21
 
2.8%
단 20
 
2.7%
앞 15
 
2.0%
Other values (148) 479
64.0%
Decimal Number
ValueCountFrequency (%)
1 10
43.5%
3 3
 
13.0%
8 2
 
8.7%
2 2
 
8.7%
4 2
 
8.7%
7 1
 
4.3%
9 1
 
4.3%
6 1
 
4.3%
5 1
 
4.3%
Uppercase Letter
ValueCountFrequency (%)
L 3
37.5%
C 2
25.0%
D 2
25.0%
G 1
 
12.5%
Space Separator
ValueCountFrequency (%)
83
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 749
86.8%
Common 106
 
12.3%
Latin 8
 
0.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
교 48
 
6.4%
측 39
 
5.2%
지 28
 
3.7%
로 27
 
3.6%
리 26
 
3.5%
차 25
 
3.3%
동 21
 
2.8%
북 21
 
2.8%
단 20
 
2.7%
앞 15
 
2.0%
Other values (148) 479
64.0%
Common
ValueCountFrequency (%)
83
78.3%
1 10
 
9.4%
3 3
 
2.8%
8 2
 
1.9%
2 2
 
1.9%
4 2
 
1.9%
7 1
 
0.9%
9 1
 
0.9%
6 1
 
0.9%
5 1
 
0.9%
Latin
ValueCountFrequency (%)
L 3
37.5%
C 2
25.0%
D 2
25.0%
G 1
 
12.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 749
86.8%
ASCII 114
 
13.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
83
72.8%
1 10
 
8.8%
3 3
 
2.6%
L 3
 
2.6%
8 2
 
1.8%
2 2
 
1.8%
C 2
 
1.8%
D 2
 
1.8%
4 2
 
1.8%
7 1
 
0.9%
Other values (4) 4
 
3.5%
Hangul
ValueCountFrequency (%)
교 48
 
6.4%
측 39
 
5.2%
지 28
 
3.7%
로 27
 
3.6%
리 26
 
3.5%
차 25
 
3.3%
동 21
 
2.8%
북 21
 
2.8%
단 20
 
2.7%
앞 15
 
2.0%
Other values (148) 479
64.0%

연장(km)
Real number (ℝ)

HIGH CORRELATION 

Distinct37
Distinct (%)35.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.472381
Minimum0.1
Maximum75.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-13T04:13:04.318884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.3
Q10.5
median1.1
Q31.9
95-th percentile6
Maximum75.3
Range75.2
Interquartile range (IQR)1.4

Descriptive statistics

Standard deviation7.5087064
Coefficient of variation (CV)3.0370346
Kurtosis87.145708
Mean2.472381
Median Absolute Deviation (MAD)0.6
Skewness8.9999941
Sum259.6
Variance56.380672
MonotonicityNot monotonic
2023-12-13T04:13:04.439533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
0.4 9
 
8.6%
1.2 8
 
7.6%
0.5 7
 
6.7%
0.3 7
 
6.7%
0.7 6
 
5.7%
0.8 5
 
4.8%
1.1 5
 
4.8%
1.5 5
 
4.8%
0.9 4
 
3.8%
2.3 4
 
3.8%
Other values (27) 45
42.9%
ValueCountFrequency (%)
0.1 2
 
1.9%
0.2 3
 
2.9%
0.3 7
6.7%
0.4 9
8.6%
0.5 7
6.7%
0.6 2
 
1.9%
0.7 6
5.7%
0.8 5
4.8%
0.9 4
3.8%
1.0 3
 
2.9%
ValueCountFrequency (%)
75.3 1
 
1.0%
13.8 1
 
1.0%
10.8 1
 
1.0%
10.0 1
 
1.0%
9.0 1
 
1.0%
6.1 1
 
1.0%
5.6 1
 
1.0%
4.6 1
 
1.0%
4.3 1
 
1.0%
4.0 3
2.9%

자전거도로폭원(m)
Real number (ℝ)

HIGH CORRELATION 

Distinct12
Distinct (%)11.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.96
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-13T04:13:04.536699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.5
Q11.5
median1.8
Q32
95-th percentile3
Maximum5
Range4
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.60536067
Coefficient of variation (CV)0.30885748
Kurtosis5.2919746
Mean1.96
Median Absolute Deviation (MAD)0.3
Skewness1.8579918
Sum205.8
Variance0.36646154
MonotonicityNot monotonic
2023-12-13T04:13:04.633983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1.5 41
39.0%
2.0 32
30.5%
1.8 10
 
9.5%
3.0 10
 
9.5%
2.8 4
 
3.8%
2.4 2
 
1.9%
2.5 1
 
1.0%
3.2 1
 
1.0%
3.5 1
 
1.0%
1.1 1
 
1.0%
Other values (2) 2
 
1.9%
ValueCountFrequency (%)
1.0 1
 
1.0%
1.1 1
 
1.0%
1.5 41
39.0%
1.8 10
 
9.5%
2.0 32
30.5%
2.4 2
 
1.9%
2.5 1
 
1.0%
2.8 4
 
3.8%
3.0 10
 
9.5%
3.2 1
 
1.0%
ValueCountFrequency (%)
5.0 1
 
1.0%
3.5 1
 
1.0%
3.2 1
 
1.0%
3.0 10
 
9.5%
2.8 4
 
3.8%
2.5 1
 
1.0%
2.4 2
 
1.9%
2.0 32
30.5%
1.8 10
 
9.5%
1.5 41
39.0%

보도폭원(m)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size972.0 B
1.5~2
94 
<NA>
11 

Length

Max length5
Median length5
Mean length4.8952381
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row1.5~2
3rd row1.5~2
4th row1.5~2
5th row1.5~2

Common Values

ValueCountFrequency (%)
1.5~2 94
89.5%
<NA> 11
 
10.5%

Length

2023-12-13T04:13:04.745535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:13:04.828622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1.5~2 94
89.5%
na 11
 
10.5%

자전거도로구분
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Memory size972.0 B
분리형 겸용도로
99 
비분리형 겸용도로
 
3
자전거 전용차로
 
2
자전거 전용도로
 
1

Length

Max length9
Median length8
Mean length8.0285714
Min length8

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row비분리형 겸용도로
2nd row분리형 겸용도로
3rd row분리형 겸용도로
4th row분리형 겸용도로
5th row분리형 겸용도로

Common Values

ValueCountFrequency (%)
분리형 겸용도로 99
94.3%
비분리형 겸용도로 3
 
2.9%
자전거 전용차로 2
 
1.9%
자전거 전용도로 1
 
1.0%

Length

2023-12-13T04:13:04.937805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:13:05.120800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
겸용도로 102
48.6%
분리형 99
47.1%
비분리형 3
 
1.4%
자전거 3
 
1.4%
전용차로 2
 
1.0%
전용도로 1
 
0.5%

기준일자
Date

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size972.0 B
Minimum2021-09-30 00:00:00
Maximum2021-09-30 00:00:00
2023-12-13T04:13:05.216063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:13:05.305199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-13T04:13:01.756067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:13:01.504047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:13:01.909422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:13:01.618676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T04:13:05.386058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기점종점연장(km)자전거도로폭원(m)자전거도로구분
기점1.0000.9980.8470.9601.000
종점0.9981.0000.7390.0000.782
연장(km)0.8470.7391.0000.3860.668
자전거도로폭원(m)0.9600.0000.3861.0000.637
자전거도로구분1.0000.7820.6680.6371.000
2023-12-13T04:13:05.486666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
자전거도로구분보도폭원(m)
자전거도로구분1.0001.000
보도폭원(m)1.0001.000
2023-12-13T04:13:05.571750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연장(km)자전거도로폭원(m)보도폭원(m)자전거도로구분
연장(km)1.0000.0511.0000.694
자전거도로폭원(m)0.0511.0001.0000.376
보도폭원(m)1.0001.0001.0001.000
자전거도로구분0.6940.3761.0001.000

Missing values

2023-12-13T04:13:02.086637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T04:13:02.237311image/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

노선명기점종점연장(km)자전거도로폭원(m)보도폭원(m)자전거도로구분기준일자
0술이홀로장남교 남측적성교차로 동측0.81.5<NA>비분리형 겸용도로2021-09-30
1적성산단로적성일반산업단지 내적성일반산업단지 내1.41.51.5~2분리형 겸용도로2021-09-30
2적성산단1로적성일반산업단지 내적성일반산업단지 내1.21.51.5~2분리형 겸용도로2021-09-30
3적성산단2로적성일반산업단지 내적성일반산업단지 내9.01.51.5~2분리형 겸용도로2021-09-30
4당동2로당동1교파주힐스테이트1차 앞1.21.51.5~2분리형 겸용도로2021-09-30
5방촌로(1)홈플러스 파주문산점문산북중학교1.12.01.5~2분리형 겸용도로2021-09-30
6방촌로(2)북부공원임월교교차로1.72.01.5~2분리형 겸용도로2021-09-30
7방촌로(3)당동일반산업단지 남동측문산당동3단지 앞0.21.51.5~2분리형 겸용도로2021-09-30
8당동1로문산세성1차 앞하동사거리0.51.51.5~2분리형 겸용도로2021-09-30
9휴암로(1)방촌교차로내포2리교차로1.42.8<NA>비분리형 겸용도로2021-09-30
노선명기점종점연장(km)자전거도로폭원(m)보도폭원(m)자전거도로구분기준일자
95동패로동패고등학교 북서측한울마을5단지 북서측1.31.51.5~2분리형 겸용도로2021-09-30
96한울로동패고등학교 북서측새암공원 서측1.22.41.5~2분리형 겸용도로2021-09-30
97대로3류(동패동)휴먼시아4단지 북동측이마트 운정점 남서측0.31.51.5~2분리형 겸용도로2021-09-30
98미래로408번길운정건강공원 남동측한빛중학교 정문0.61.51.5~2분리형 겸용도로2021-09-30
99한빛로한빛마을2단지 북동측휴먼시아8단지 북서측1.01.51.5~2분리형 겸용도로2021-09-30
100운정호수공원야당역 남측금촌지구 교차로10.85.01.5~2분리형 겸용도로2021-09-30
101기곡길공릉지주차장 서측장곡교 북측0.81.51.5~2분리형 겸용도로2021-09-30
102소리천변교하동야당동4.61.51.5~2분리형 겸용도로2021-09-30
103문산천변임월교백석리10.02.01.5~2분리형 겸용도로2021-09-30
104평화누리 자전거도로파주출판도시휴게소적성 두지리75.31.5<NA>자전거 전용도로2021-09-30