Overview

Dataset statistics

Number of variables7
Number of observations53
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.1 KiB
Average record size in memory59.5 B

Variable types

Text3
Categorical3
Numeric1

Dataset

Description서대문구 자전거 도로 현황(노서명, 기점, 종점, 종류, 연장, 너비, 관리주체)에 대한 데이터를 제공합니다. 자료제공부서 : 교통행정과
Author서울특별시 서대문구
URLhttps://www.data.go.kr/data/15125319/fileData.do

Alerts

연장 is highly overall correlated with 관리주체High correlation
종류 is highly overall correlated with 너비High correlation
너비 is highly overall correlated with 종류 and 1 other fieldsHigh correlation
관리주체 is highly overall correlated with 연장 and 1 other fieldsHigh correlation

Reproduction

Analysis started2023-12-12 19:44:56.487775
Analysis finished2023-12-12 19:44:57.245884
Duration0.76 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

노선명
Text

Distinct50
Distinct (%)94.3%
Missing0
Missing (%)0.0%
Memory size556.0 B
2023-12-13T04:44:57.426635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length14
Mean length8.509434
Min length4

Characters and Unicode

Total characters451
Distinct characters57
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique47 ?
Unique (%)88.7%

Sample

1st row불광천(좌안)
2nd row홍제천(우안)
3rd row홍제천(좌안)
4th row홍제천(좌안)
5th row거북골로(동측)
ValueCountFrequency (%)
홍제천(좌안 2
 
3.8%
거북골로(동측 2
 
3.8%
거북골로(서측 2
 
3.8%
가좌로(동측 1
 
1.9%
북아현로1길(동측 1
 
1.9%
모래내로 1
 
1.9%
북아현로1길(북측 1
 
1.9%
불광천(좌안 1
 
1.9%
거북골로(중로1-1호)(남측 1
 
1.9%
충정로(서측 1
 
1.9%
Other values (40) 40
75.5%
2023-12-13T04:44:57.841613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
( 50
 
11.1%
) 50
 
11.1%
로 48
 
10.6%
측 42
 
9.3%
길 19
 
4.2%
동 19
 
4.2%
서 18
 
4.0%
북 18
 
4.0%
1 16
 
3.5%
연 11
 
2.4%
Other values (47) 160
35.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 312
69.2%
Open Punctuation 50
 
11.1%
Close Punctuation 50
 
11.1%
Decimal Number 33
 
7.3%
Dash Punctuation 4
 
0.9%
Other Punctuation 2
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 48
15.4%
측 42
 
13.5%
길 19
 
6.1%
동 19
 
6.1%
서 18
 
5.8%
북 18
 
5.8%
연 11
 
3.5%
현 7
 
2.2%
골 7
 
2.2%
거 7
 
2.2%
Other values (37) 116
37.2%
Decimal Number
ValueCountFrequency (%)
1 16
48.5%
5 7
21.2%
2 6
 
18.2%
9 2
 
6.1%
3 1
 
3.0%
7 1
 
3.0%
Open Punctuation
ValueCountFrequency (%)
( 50
100.0%
Close Punctuation
ValueCountFrequency (%)
) 50
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%
Other Punctuation
ValueCountFrequency (%)
, 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 312
69.2%
Common 139
30.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 48
15.4%
측 42
 
13.5%
길 19
 
6.1%
동 19
 
6.1%
서 18
 
5.8%
북 18
 
5.8%
연 11
 
3.5%
현 7
 
2.2%
골 7
 
2.2%
거 7
 
2.2%
Other values (37) 116
37.2%
Common
ValueCountFrequency (%)
( 50
36.0%
) 50
36.0%
1 16
 
11.5%
5 7
 
5.0%
2 6
 
4.3%
- 4
 
2.9%
, 2
 
1.4%
9 2
 
1.4%
3 1
 
0.7%
7 1
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 312
69.2%
ASCII 139
30.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
( 50
36.0%
) 50
36.0%
1 16
 
11.5%
5 7
 
5.0%
2 6
 
4.3%
- 4
 
2.9%
, 2
 
1.4%
9 2
 
1.4%
3 1
 
0.7%
7 1
 
0.7%
Hangul
ValueCountFrequency (%)
로 48
15.4%
측 42
 
13.5%
길 19
 
6.1%
동 19
 
6.1%
서 18
 
5.8%
북 18
 
5.8%
연 11
 
3.5%
현 7
 
2.2%
골 7
 
2.2%
거 7
 
2.2%
Other values (37) 116
37.2%

기점
Text

Distinct48
Distinct (%)90.6%
Missing0
Missing (%)0.0%
Memory size556.0 B
2023-12-13T04:44:58.127165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length12
Mean length7.9433962
Min length3

Characters and Unicode

Total characters421
Distinct characters122
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique43 ?
Unique (%)81.1%

Sample

1st row와산교
2nd row홍제3교
3rd row사천교
4th row백련교
5th row중앙근린공원
ValueCountFrequency (%)
교차로 3
 
4.0%
앞 3
 
4.0%
한국스텐다드차타드은행앞 2
 
2.7%
신촌 2
 
2.7%
e편한세상 2
 
2.7%
북아현동 2
 
2.7%
연희동 2
 
2.7%
충정로사거리 2
 
2.7%
중앙근린공원 2
 
2.7%
영광교회 2
 
2.7%
Other values (52) 53
70.7%
2023-12-13T04:44:58.584327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
22
 
5.2%
동 19
 
4.5%
교 17
 
4.0%
1 17
 
4.0%
앞 15
 
3.6%
차 12
 
2.9%
파 11
 
2.6%
0 10
 
2.4%
로 8
 
1.9%
크 8
 
1.9%
Other values (112) 282
67.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 326
77.4%
Decimal Number 46
 
10.9%
Uppercase Letter 23
 
5.5%
Space Separator 22
 
5.2%
Dash Punctuation 2
 
0.5%
Lowercase Letter 2
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 19
 
5.8%
교 17
 
5.2%
앞 15
 
4.6%
차 12
 
3.7%
파 11
 
3.4%
로 8
 
2.5%
크 8
 
2.5%
거 8
 
2.5%
대 8
 
2.5%
리 7
 
2.1%
Other values (97) 213
65.3%
Decimal Number
ValueCountFrequency (%)
1 17
37.0%
0 10
21.7%
2 6
 
13.0%
3 4
 
8.7%
5 3
 
6.5%
8 3
 
6.5%
7 2
 
4.3%
4 1
 
2.2%
Uppercase Letter
ValueCountFrequency (%)
C 8
34.8%
D 7
30.4%
M 7
30.4%
I 1
 
4.3%
Space Separator
ValueCountFrequency (%)
22
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 326
77.4%
Common 70
 
16.6%
Latin 25
 
5.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 19
 
5.8%
교 17
 
5.2%
앞 15
 
4.6%
차 12
 
3.7%
파 11
 
3.4%
로 8
 
2.5%
크 8
 
2.5%
거 8
 
2.5%
대 8
 
2.5%
리 7
 
2.1%
Other values (97) 213
65.3%
Common
ValueCountFrequency (%)
22
31.4%
1 17
24.3%
0 10
14.3%
2 6
 
8.6%
3 4
 
5.7%
5 3
 
4.3%
8 3
 
4.3%
7 2
 
2.9%
- 2
 
2.9%
4 1
 
1.4%
Latin
ValueCountFrequency (%)
C 8
32.0%
D 7
28.0%
M 7
28.0%
e 2
 
8.0%
I 1
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 326
77.4%
ASCII 95
 
22.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
22
23.2%
1 17
17.9%
0 10
10.5%
C 8
 
8.4%
D 7
 
7.4%
M 7
 
7.4%
2 6
 
6.3%
3 4
 
4.2%
5 3
 
3.2%
8 3
 
3.2%
Other values (5) 8
 
8.4%
Hangul
ValueCountFrequency (%)
동 19
 
5.8%
교 17
 
5.2%
앞 15
 
4.6%
차 12
 
3.7%
파 11
 
3.4%
로 8
 
2.5%
크 8
 
2.5%
거 8
 
2.5%
대 8
 
2.5%
리 7
 
2.1%
Other values (97) 213
65.3%

종점
Text

Distinct47
Distinct (%)88.7%
Missing0
Missing (%)0.0%
Memory size556.0 B
2023-12-13T04:44:58.938894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length12
Mean length7.9056604
Min length3

Characters and Unicode

Total characters419
Distinct characters123
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique41 ?
Unique (%)77.4%

Sample

1st row증산교
2nd row사천교
3rd row홍연교
4th row홍은교
5th row북가좌초 사거리
ValueCountFrequency (%)
교차로 3
 
4.0%
앞 3
 
4.0%
한국스텐다드차타드은행앞 2
 
2.7%
dmc파크뷰105동 2
 
2.7%
연희동 2
 
2.7%
북아현동 2
 
2.7%
래미안루센티아아파트 2
 
2.7%
충정로사거리 2
 
2.7%
dmc파크뷰101동 2
 
2.7%
중앙근린공원 2
 
2.7%
Other values (52) 53
70.7%
2023-12-13T04:44:59.433390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
22
 
5.3%
동 21
 
5.0%
1 19
 
4.5%
교 15
 
3.6%
앞 14
 
3.3%
파 12
 
2.9%
아 12
 
2.9%
차 11
 
2.6%
0 10
 
2.4%
로 8
 
1.9%
Other values (113) 275
65.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 322
76.8%
Decimal Number 50
 
11.9%
Space Separator 22
 
5.3%
Uppercase Letter 20
 
4.8%
Dash Punctuation 3
 
0.7%
Lowercase Letter 2
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 21
 
6.5%
교 15
 
4.7%
앞 14
 
4.3%
파 12
 
3.7%
아 12
 
3.7%
차 11
 
3.4%
로 8
 
2.5%
리 7
 
2.2%
거 7
 
2.2%
연 7
 
2.2%
Other values (97) 208
64.6%
Decimal Number
ValueCountFrequency (%)
1 19
38.0%
0 10
20.0%
5 5
 
10.0%
3 4
 
8.0%
2 4
 
8.0%
7 3
 
6.0%
8 2
 
4.0%
9 2
 
4.0%
4 1
 
2.0%
Uppercase Letter
ValueCountFrequency (%)
C 7
35.0%
M 6
30.0%
D 6
30.0%
I 1
 
5.0%
Space Separator
ValueCountFrequency (%)
22
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 322
76.8%
Common 75
 
17.9%
Latin 22
 
5.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 21
 
6.5%
교 15
 
4.7%
앞 14
 
4.3%
파 12
 
3.7%
아 12
 
3.7%
차 11
 
3.4%
로 8
 
2.5%
리 7
 
2.2%
거 7
 
2.2%
연 7
 
2.2%
Other values (97) 208
64.6%
Common
ValueCountFrequency (%)
22
29.3%
1 19
25.3%
0 10
13.3%
5 5
 
6.7%
3 4
 
5.3%
2 4
 
5.3%
7 3
 
4.0%
- 3
 
4.0%
8 2
 
2.7%
9 2
 
2.7%
Latin
ValueCountFrequency (%)
C 7
31.8%
M 6
27.3%
D 6
27.3%
e 2
 
9.1%
I 1
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 322
76.8%
ASCII 97
 
23.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
22
22.7%
1 19
19.6%
0 10
10.3%
C 7
 
7.2%
M 6
 
6.2%
D 6
 
6.2%
5 5
 
5.2%
3 4
 
4.1%
2 4
 
4.1%
7 3
 
3.1%
Other values (6) 11
11.3%
Hangul
ValueCountFrequency (%)
동 21
 
6.5%
교 15
 
4.7%
앞 14
 
4.3%
파 12
 
3.7%
아 12
 
3.7%
차 11
 
3.4%
로 8
 
2.5%
리 7
 
2.2%
거 7
 
2.2%
연 7
 
2.2%
Other values (97) 208
64.6%

종류
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)9.4%
Missing0
Missing (%)0.0%
Memory size556.0 B
자전거우선도로
26 
자전거보행자겸용도로(분리형)
16 
자전거전용차로
4 
자전거보행자겸용도로(비분리형)
4 
자전거전용도로
3 

Length

Max length16
Median length7
Mean length10.09434
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row자전거전용도로
2nd row자전거보행자겸용도로(분리형)
3rd row자전거보행자겸용도로(분리형)
4th row자전거보행자겸용도로(분리형)
5th row자전거전용차로

Common Values

ValueCountFrequency (%)
자전거우선도로 26
49.1%
자전거보행자겸용도로(분리형) 16
30.2%
자전거전용차로 4
 
7.5%
자전거보행자겸용도로(비분리형) 4
 
7.5%
자전거전용도로 3
 
5.7%

Length

2023-12-13T04:44:59.623221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:44:59.777986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
자전거우선도로 26
49.1%
자전거보행자겸용도로(분리형 16
30.2%
자전거전용차로 4
 
7.5%
자전거보행자겸용도로(비분리형 4
 
7.5%
자전거전용도로 3
 
5.7%

연장
Real number (ℝ)

HIGH CORRELATION 

Distinct18
Distinct (%)34.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.76773585
Minimum0.1
Maximum3.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size609.0 B
2023-12-13T04:45:00.006430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.16
Q10.3
median0.4
Q30.8
95-th percentile2.6
Maximum3.3
Range3.2
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.81754944
Coefficient of variation (CV)1.0648838
Kurtosis3.3116522
Mean0.76773585
Median Absolute Deviation (MAD)0.2
Skewness1.9973943
Sum40.69
Variance0.66838708
MonotonicityNot monotonic
2023-12-13T04:45:00.218695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0.4 8
15.1%
0.2 6
11.3%
0.5 6
11.3%
0.3 6
11.3%
2.2 3
 
5.7%
0.6 3
 
5.7%
0.1 3
 
5.7%
0.8 3
 
5.7%
0.35 3
 
5.7%
1.1 2
 
3.8%
Other values (8) 10
18.9%
ValueCountFrequency (%)
0.1 3
 
5.7%
0.2 6
11.3%
0.27 1
 
1.9%
0.3 6
11.3%
0.32 1
 
1.9%
0.35 3
 
5.7%
0.4 8
15.1%
0.5 6
11.3%
0.6 3
 
5.7%
0.75 1
 
1.9%
ValueCountFrequency (%)
3.3 2
3.8%
3.2 1
 
1.9%
2.2 3
5.7%
2.0 1
 
1.9%
1.4 1
 
1.9%
1.3 2
3.8%
1.1 2
3.8%
0.8 3
5.7%
0.75 1
 
1.9%
0.6 3
5.7%

너비
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)15.1%
Missing0
Missing (%)0.0%
Memory size556.0 B
차로공유
26 
2
16 
3
3 
2.4
 
2
1.7
 
2
Other values (3)
4 

Length

Max length4
Median length3
Mean length2.6981132
Min length1

Unique

Unique2 ?
Unique (%)3.8%

Sample

1st row3
2nd row3.7
3rd row2.4
4th row2.4
5th row2

Common Values

ValueCountFrequency (%)
차로공유 26
49.1%
2 16
30.2%
3 3
 
5.7%
2.4 2
 
3.8%
1.7 2
 
3.8%
4 2
 
3.8%
3.7 1
 
1.9%
3.3 1
 
1.9%

Length

2023-12-13T04:45:00.445268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:45:00.622058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
차로공유 26
49.1%
2 16
30.2%
3 3
 
5.7%
2.4 2
 
3.8%
1.7 2
 
3.8%
4 2
 
3.8%
3.7 1
 
1.9%
3.3 1
 
1.9%

관리주체
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)5.7%
Missing0
Missing (%)0.0%
Memory size556.0 B
서대문구 교통행정과
39 
서부도로사업소
10 
서대문구 치수과
4 

Length

Max length10
Median length10
Mean length9.2830189
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row서대문구 치수과
2nd row서대문구 치수과
3rd row서대문구 치수과
4th row서대문구 치수과
5th row서대문구 교통행정과

Common Values

ValueCountFrequency (%)
서대문구 교통행정과 39
73.6%
서부도로사업소 10
 
18.9%
서대문구 치수과 4
 
7.5%

Length

2023-12-13T04:45:00.816318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:45:00.975585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서대문구 43
44.8%
교통행정과 39
40.6%
서부도로사업소 10
 
10.4%
치수과 4
 
4.2%

Interactions

2023-12-13T04:44:56.955680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T04:45:01.096403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
노선명기점종점종류연장너비관리주체
노선명1.0000.9580.9520.0000.0001.0001.000
기점0.9581.0000.8660.8280.9600.9781.000
종점0.9520.8661.0000.6130.9600.9321.000
종류0.0000.8280.6131.0000.4320.7540.415
연장0.0000.9600.9600.4321.0000.8570.764
너비1.0000.9780.9320.7540.8571.0000.762
관리주체1.0001.0001.0000.4150.7640.7621.000
2023-12-13T04:45:01.618916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
종류너비관리주체
종류1.0000.5740.336
너비0.5741.0000.634
관리주체0.3360.6341.000
2023-12-13T04:45:01.737350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연장종류너비관리주체
연장1.0000.2690.4520.637
종류0.2691.0000.5740.336
너비0.4520.5741.0000.634
관리주체0.6370.3360.6341.000

Missing values

2023-12-13T04:44:57.079767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T04:44:57.197532image/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

노선명기점종점종류연장너비관리주체
0불광천(좌안)와산교증산교자전거전용도로1.43서대문구 치수과
1홍제천(우안)홍제3교사천교자전거보행자겸용도로(분리형)3.23.7서대문구 치수과
2홍제천(좌안)사천교홍연교자전거보행자겸용도로(분리형)2.22.4서대문구 치수과
3홍제천(좌안)백련교홍은교자전거보행자겸용도로(분리형)2.02.4서대문구 치수과
4거북골로(동측)중앙근린공원북가좌초 사거리자전거전용차로0.52서대문구 교통행정과
5거북골로(서측)북가좌초 사거리중앙근린공원자전거전용차로0.52서대문구 교통행정과
6가재울미래로(서측)DMC파크뷰101동DMC파크뷰105동자전거전용차로0.42서대문구 교통행정과
7가재울미래로(동측)DMC파크뷰105동DMC파크뷰101동자전거전용차로0.42서대문구 교통행정과
8연희로(동측)연희교차로서대문소방서 앞자전거우선도로1.3차로공유서부도로사업소
9연희로(서측)서대문소방서 앞연희교차로자전거우선도로1.3차로공유서부도로사업소
노선명기점종점종류연장너비관리주체
43가재울미래로(중로2-1호)DMC파크뷰130동DMC파크뷰105동자전거보행자겸용도로(분리형)0.43서대문구 교통행정과
44북아현로e편한세상 신촌 401동아현역자전거보행자겸용도로(분리형)0.12서대문구 교통행정과
45거북골로(서측)명지대학교 정문래미안루센티아아파트 107동자전거보행자겸용도로(분리형)0.752서대문구 교통행정과
46거북골로(동측)모래내교회래미안루센티아아파트 111동자전거보행자겸용도로(분리형)0.272서대문구 교통행정과
47북아현로19길(서측)북아현동 187-18북아현동 199-3자전거전용도로0.322서대문구 교통행정과
48북아현로19길(동측)북아현동 251-285북아현동 187-18자전거전용도로0.352서대문구 교통행정과
49가좌로(동측)현대교통충암고앞 구경계자전거보행자겸용도로(비분리형)1.14서대문구 교통행정과
50가좌로(서측)충암고앞 구경계현대교통자전거보행자겸용도로(비분리형)1.14서대문구 교통행정과
51모래내로홍제센트럴아이파크 101동홍제동 155-5자전거보행자겸용도로(비분리형)0.33서대문구 교통행정과
52홍제내길홍제자전거주차장남양아파트 103동자전거보행자겸용도로(비분리형)0.42서대문구 교통행정과