Overview

Dataset statistics

Number of variables9
Number of observations25
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.0 KiB
Average record size in memory80.3 B

Variable types

Numeric3
Categorical2
Text3
DateTime1

Dataset

Description홍성군내 소재한 자전거도로 현황으로 읍면, 노선명, 시점, 종점 자전거 도로 유형, 길이, 너비, 데이터 기준일 등을 제공합니다.
Author충청남도
URLhttps://alldam.chungnam.go.kr/index.chungnam?menuCd=DOM_000000201001001001&st=&cds=&orgCd=&apiType=&isOpen=Y&pageIndex=433&beforeMenuCd=DOM_000000201001001000&publicdatapk=3073589

Alerts

데이터기준일자 has constant value ""Constant
연번 is highly overall correlated with 읍면High correlation
연장(km) is highly overall correlated with 읍면High correlation
자전거도로너비(m) is highly overall correlated with 자전거도로 유형High correlation
읍면 is highly overall correlated with 연번 and 2 other fieldsHigh correlation
자전거도로 유형 is highly overall correlated with 자전거도로너비(m) and 1 other fieldsHigh correlation
연번 has unique valuesUnique
노선명 has unique valuesUnique

Reproduction

Analysis started2024-01-09 22:41:27.733916
Analysis finished2024-01-09 22:41:28.812432
Duration1.08 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13
Minimum1
Maximum25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2024-01-10T07:41:28.860625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.2
Q17
median13
Q319
95-th percentile23.8
Maximum25
Range24
Interquartile range (IQR)12

Descriptive statistics

Standard deviation7.3598007
Coefficient of variation (CV)0.56613852
Kurtosis-1.2
Mean13
Median Absolute Deviation (MAD)6
Skewness0
Sum325
Variance54.166667
MonotonicityStrictly increasing
2024-01-10T07:41:28.985315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
1 1
 
4.0%
2 1
 
4.0%
25 1
 
4.0%
24 1
 
4.0%
23 1
 
4.0%
22 1
 
4.0%
21 1
 
4.0%
20 1
 
4.0%
19 1
 
4.0%
18 1
 
4.0%
Other values (15) 15
60.0%
ValueCountFrequency (%)
1 1
4.0%
2 1
4.0%
3 1
4.0%
4 1
4.0%
5 1
4.0%
6 1
4.0%
7 1
4.0%
8 1
4.0%
9 1
4.0%
10 1
4.0%
ValueCountFrequency (%)
25 1
4.0%
24 1
4.0%
23 1
4.0%
22 1
4.0%
21 1
4.0%
20 1
4.0%
19 1
4.0%
18 1
4.0%
17 1
4.0%
16 1
4.0%

읍면
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)16.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
홍성읍
15 
광천읍
7 
서부면
2 
홍북읍
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique1 ?
Unique (%)4.0%

Sample

1st row서부면
2nd row서부면
3rd row홍성읍
4th row홍성읍
5th row홍성읍

Common Values

ValueCountFrequency (%)
홍성읍 15
60.0%
광천읍 7
28.0%
서부면 2
 
8.0%
홍북읍 1
 
4.0%

Length

2024-01-10T07:41:29.107379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T07:41:29.194636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
홍성읍 15
60.0%
광천읍 7
28.0%
서부면 2
 
8.0%
홍북읍 1
 
4.0%

노선명
Text

UNIQUE 

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
2024-01-10T07:41:29.336921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length6.08
Min length5

Characters and Unicode

Total characters152
Distinct characters33
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

Unique25 ?
Unique (%)100.0%

Sample

1st row 남당항로
2nd row 남당항로2
3rd row 충서로
4th row 터미널로
5th row 역전로
ValueCountFrequency (%)
남당항로 1
 
4.0%
월산로1l 1
 
4.0%
광천천로2 1
 
4.0%
광천천로 1
 
4.0%
광남로 1
 
4.0%
담산로 1
 
4.0%
광천천로1 1
 
4.0%
북동부로l 1
 
4.0%
북동부로r 1
 
4.0%
충서로1l 1
 
4.0%
Other values (15) 15
60.0%
2024-01-10T07:41:29.632248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
50
32.9%
로 25
16.4%
천 6
 
3.9%
1 6
 
3.9%
남 6
 
3.9%
R 5
 
3.3%
산 5
 
3.3%
L 5
 
3.3%
월 4
 
2.6%
광 4
 
2.6%
Other values (23) 36
23.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 84
55.3%
Space Separator 50
32.9%
Uppercase Letter 10
 
6.6%
Decimal Number 8
 
5.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 25
29.8%
천 6
 
7.1%
남 6
 
7.1%
산 5
 
6.0%
월 4
 
4.8%
광 4
 
4.8%
충 4
 
4.8%
장 3
 
3.6%
서 3
 
3.6%
당 2
 
2.4%
Other values (18) 22
26.2%
Decimal Number
ValueCountFrequency (%)
1 6
75.0%
2 2
 
25.0%
Uppercase Letter
ValueCountFrequency (%)
R 5
50.0%
L 5
50.0%
Space Separator
ValueCountFrequency (%)
50
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 84
55.3%
Common 58
38.2%
Latin 10
 
6.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 25
29.8%
천 6
 
7.1%
남 6
 
7.1%
산 5
 
6.0%
월 4
 
4.8%
광 4
 
4.8%
충 4
 
4.8%
장 3
 
3.6%
서 3
 
3.6%
당 2
 
2.4%
Other values (18) 22
26.2%
Common
ValueCountFrequency (%)
50
86.2%
1 6
 
10.3%
2 2
 
3.4%
Latin
ValueCountFrequency (%)
R 5
50.0%
L 5
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 84
55.3%
ASCII 68
44.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
50
73.5%
1 6
 
8.8%
R 5
 
7.4%
L 5
 
7.4%
2 2
 
2.9%
Hangul
ValueCountFrequency (%)
로 25
29.8%
천 6
 
7.1%
남 6
 
7.1%
산 5
 
6.0%
월 4
 
4.8%
광 4
 
4.8%
충 4
 
4.8%
장 3
 
3.6%
서 3
 
3.6%
당 2
 
2.4%
Other values (18) 22
26.2%

시점
Text

Distinct24
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
2024-01-10T07:41:29.795796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length12
Mean length8.32
Min length5

Characters and Unicode

Total characters208
Distinct characters79
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique23 ?
Unique (%)92.0%

Sample

1st row 서부 풍섬
2nd row 서부 꽃섬
3rd row 홍주종합경기장
4th row 홍성버스터미널 입구
5th row 홍성역앞사거리
ValueCountFrequency (%)
월산택지지구 2
 
6.9%
서부 2
 
6.9%
향군회관 1
 
3.4%
홍성초교 1
 
3.4%
지혜상회 1
 
3.4%
전시관 1
 
3.4%
광천토굴새우젓 1
 
3.4%
광남초등학교 1
 
3.4%
현대예식장 1
 
3.4%
광천교 1
 
3.4%
Other values (17) 17
58.6%
2024-01-10T07:41:30.063718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
54
26.0%
지 7
 
3.4%
교 6
 
2.9%
부 5
 
2.4%
광 5
 
2.4%
회 5
 
2.4%
서 4
 
1.9%
홍 4
 
1.9%
천 4
 
1.9%
구 4
 
1.9%
Other values (69) 110
52.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 150
72.1%
Space Separator 54
 
26.0%
Close Punctuation 2
 
1.0%
Open Punctuation 2
 
1.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 7
 
4.7%
교 6
 
4.0%
부 5
 
3.3%
광 5
 
3.3%
회 5
 
3.3%
서 4
 
2.7%
홍 4
 
2.7%
천 4
 
2.7%
구 4
 
2.7%
장 3
 
2.0%
Other values (66) 103
68.7%
Space Separator
ValueCountFrequency (%)
54
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 150
72.1%
Common 58
 
27.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 7
 
4.7%
교 6
 
4.0%
부 5
 
3.3%
광 5
 
3.3%
회 5
 
3.3%
서 4
 
2.7%
홍 4
 
2.7%
천 4
 
2.7%
구 4
 
2.7%
장 3
 
2.0%
Other values (66) 103
68.7%
Common
ValueCountFrequency (%)
54
93.1%
) 2
 
3.4%
( 2
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 150
72.1%
ASCII 58
 
27.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
54
93.1%
) 2
 
3.4%
( 2
 
3.4%
Hangul
ValueCountFrequency (%)
지 7
 
4.7%
교 6
 
4.0%
부 5
 
3.3%
광 5
 
3.3%
회 5
 
3.3%
서 4
 
2.7%
홍 4
 
2.7%
천 4
 
2.7%
구 4
 
2.7%
장 3
 
2.0%
Other values (66) 103
68.7%

종점
Text

Distinct22
Distinct (%)88.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
2024-01-10T07:41:30.219152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length12
Mean length8.12
Min length5

Characters and Unicode

Total characters203
Distinct characters78
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

Unique20 ?
Unique (%)80.0%

Sample

1st row 서부 어사교
2nd row 홍성 방조제
3rd row 대교1교차로
4th row 롯데마트입구
5th row 국도29호(청양통)
ValueCountFrequency (%)
옹암교 3
 
11.1%
월산택지지구 2
 
7.4%
어사교 1
 
3.7%
서부 1
 
3.7%
부영아파트 1
 
3.7%
은광교회 1
 
3.7%
상담주차장 1
 
3.7%
광천북동부우회도로(시점 1
 
3.7%
광천북동부우회도로(종점 1
 
3.7%
소향주유소 1
 
3.7%
Other values (14) 14
51.9%
2024-01-10T07:41:30.472167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
52
25.6%
교 9
 
4.4%
지 6
 
3.0%
회 5
 
2.5%
트 4
 
2.0%
구 4
 
2.0%
부 4
 
2.0%
로 4
 
2.0%
도 4
 
2.0%
) 3
 
1.5%
Other values (68) 108
53.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 142
70.0%
Space Separator 52
 
25.6%
Close Punctuation 3
 
1.5%
Open Punctuation 3
 
1.5%
Decimal Number 3
 
1.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
교 9
 
6.3%
지 6
 
4.2%
회 5
 
3.5%
트 4
 
2.8%
구 4
 
2.8%
부 4
 
2.8%
로 4
 
2.8%
도 4
 
2.8%
아 3
 
2.1%
파 3
 
2.1%
Other values (62) 96
67.6%
Decimal Number
ValueCountFrequency (%)
1 1
33.3%
2 1
33.3%
9 1
33.3%
Space Separator
ValueCountFrequency (%)
52
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 142
70.0%
Common 61
30.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
교 9
 
6.3%
지 6
 
4.2%
회 5
 
3.5%
트 4
 
2.8%
구 4
 
2.8%
부 4
 
2.8%
로 4
 
2.8%
도 4
 
2.8%
아 3
 
2.1%
파 3
 
2.1%
Other values (62) 96
67.6%
Common
ValueCountFrequency (%)
52
85.2%
) 3
 
4.9%
( 3
 
4.9%
1 1
 
1.6%
2 1
 
1.6%
9 1
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 142
70.0%
ASCII 61
30.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
52
85.2%
) 3
 
4.9%
( 3
 
4.9%
1 1
 
1.6%
2 1
 
1.6%
9 1
 
1.6%
Hangul
ValueCountFrequency (%)
교 9
 
6.3%
지 6
 
4.2%
회 5
 
3.5%
트 4
 
2.8%
구 4
 
2.8%
부 4
 
2.8%
로 4
 
2.8%
도 4
 
2.8%
아 3
 
2.1%
파 3
 
2.1%
Other values (62) 96
67.6%

자전거도로 유형
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
자전거보행자겸용도로
22 
자전거 전용도로
3 

Length

Max length10
Median length10
Mean length9.76
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row자전거 전용도로
2nd row자전거 전용도로
3rd row자전거보행자겸용도로
4th row자전거보행자겸용도로
5th row자전거보행자겸용도로

Common Values

ValueCountFrequency (%)
자전거보행자겸용도로 22
88.0%
자전거 전용도로 3
 
12.0%

Length

2024-01-10T07:41:30.591379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T07:41:30.683332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
자전거보행자겸용도로 22
78.6%
자전거 3
 
10.7%
전용도로 3
 
10.7%

연장(km)
Real number (ℝ)

HIGH CORRELATION 

Distinct17
Distinct (%)68.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.94
Minimum0.2
Maximum13.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2024-01-10T07:41:30.760964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile0.22
Q10.6
median1.6
Q31.8
95-th percentile3.96
Maximum13.3
Range13.1
Interquartile range (IQR)1.2

Descriptive statistics

Standard deviation2.5996795
Coefficient of variation (CV)1.340041
Kurtosis16.20523
Mean1.94
Median Absolute Deviation (MAD)0.9
Skewness3.7437251
Sum48.5
Variance6.7583333
MonotonicityNot monotonic
2024-01-10T07:41:30.858877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
1.8 5
20.0%
0.3 2
 
8.0%
0.2 2
 
8.0%
1.7 2
 
8.0%
0.7 2
 
8.0%
1.2 1
 
4.0%
13.3 1
 
4.0%
0.4 1
 
4.0%
1.4 1
 
4.0%
3.6 1
 
4.0%
Other values (7) 7
28.0%
ValueCountFrequency (%)
0.2 2
8.0%
0.3 2
8.0%
0.4 1
4.0%
0.5 1
4.0%
0.6 1
4.0%
0.7 2
8.0%
1.1 1
4.0%
1.2 1
4.0%
1.4 1
4.0%
1.6 1
4.0%
ValueCountFrequency (%)
13.3 1
 
4.0%
4.0 1
 
4.0%
3.8 1
 
4.0%
3.6 1
 
4.0%
2.2 1
 
4.0%
1.8 5
20.0%
1.7 2
 
8.0%
1.6 1
 
4.0%
1.4 1
 
4.0%
1.2 1
 
4.0%

자전거도로너비(m)
Real number (ℝ)

HIGH CORRELATION 

Distinct13
Distinct (%)52.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.588
Minimum0.9
Maximum3.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2024-01-10T07:41:30.951606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.9
5-th percentile0.92
Q11.2
median1.3
Q31.8
95-th percentile2.76
Maximum3.6
Range2.7
Interquartile range (IQR)0.6

Descriptive statistics

Standard deviation0.67473452
Coefficient of variation (CV)0.42489579
Kurtosis2.0473527
Mean1.588
Median Absolute Deviation (MAD)0.2
Skewness1.5401696
Sum39.7
Variance0.45526667
MonotonicityNot monotonic
2024-01-10T07:41:31.271659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
1.3 7
28.0%
1.1 3
12.0%
1.2 2
 
8.0%
1.8 2
 
8.0%
0.9 2
 
8.0%
1.4 2
 
8.0%
2.3 1
 
4.0%
2.0 1
 
4.0%
2.6 1
 
4.0%
2.8 1
 
4.0%
Other values (3) 3
12.0%
ValueCountFrequency (%)
0.9 2
 
8.0%
1.0 1
 
4.0%
1.1 3
12.0%
1.2 2
 
8.0%
1.3 7
28.0%
1.4 2
 
8.0%
1.8 2
 
8.0%
2.0 1
 
4.0%
2.3 1
 
4.0%
2.4 1
 
4.0%
ValueCountFrequency (%)
3.6 1
 
4.0%
2.8 1
 
4.0%
2.6 1
 
4.0%
2.4 1
 
4.0%
2.3 1
 
4.0%
2.0 1
 
4.0%
1.8 2
 
8.0%
1.4 2
 
8.0%
1.3 7
28.0%
1.2 2
 
8.0%

데이터기준일자
Date

CONSTANT 

Distinct1
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
Minimum2023-09-27 00:00:00
Maximum2023-09-27 00:00:00
2024-01-10T07:41:31.345253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:41:31.412627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2024-01-10T07:41:28.437377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:41:28.031951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:41:28.235283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:41:28.503523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:41:28.093513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:41:28.305916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:41:28.570564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:41:28.165643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:41:28.371501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-10T07:41:31.474475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번읍면노선명시점종점자전거도로 유형연장(km)자전거도로너비(m)
연번1.0000.8101.0001.0000.9230.5940.6160.508
읍면0.8101.0001.0001.0001.0000.9480.8950.851
노선명1.0001.0001.0001.0001.0001.0001.0001.000
시점1.0001.0001.0001.0001.0001.0001.0001.000
종점0.9231.0001.0001.0001.0001.0001.0000.984
자전거도로 유형0.5940.9481.0001.0001.0001.0000.0000.974
연장(km)0.6160.8951.0001.0001.0000.0001.0000.745
자전거도로너비(m)0.5080.8511.0001.0000.9840.9740.7451.000
2024-01-10T07:41:31.569236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
읍면자전거도로 유형
읍면1.0000.758
자전거도로 유형0.7581.000
2024-01-10T07:41:31.674972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번연장(km)자전거도로너비(m)읍면자전거도로 유형
연번1.000-0.082-0.1180.5290.354
연장(km)-0.0821.0000.1780.5740.000
자전거도로너비(m)-0.1180.1781.0000.4630.736
읍면0.5290.5740.4631.0000.758
자전거도로 유형0.3540.0000.7360.7581.000

Missing values

2024-01-10T07:41:28.663520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-10T07:41:28.770294image/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

연번읍면노선명시점종점자전거도로 유형연장(km)자전거도로너비(m)데이터기준일자
01서부면남당항로서부 풍섬서부 어사교자전거 전용도로3.82.32023-09-27
12서부면남당항로2서부 꽃섬홍성 방조제자전거 전용도로2.22.02023-09-27
23홍성읍충서로홍주종합경기장대교1교차로자전거보행자겸용도로4.02.62023-09-27
34홍성읍터미널로홍성버스터미널 입구롯데마트입구자전거보행자겸용도로0.21.12023-09-27
45홍성읍역전로홍성역앞사거리국도29호(청양통)자전거보행자겸용도로1.61.12023-09-27
56홍성읍충절로오관교홍성중학교자전거보행자겸용도로0.52.82023-09-27
67홍성읍남장로R경찰서회전교차로주공아파트자전거보행자겸용도로1.81.32023-09-27
78홍성읍남장로L주공아파트경찰서회전교차로자전거보행자겸용도로1.81.32023-09-27
89홍성읍남장로1남장택지개발지구내남장택지개발지구내자전거보행자겸용도로1.11.32023-09-27
910홍성읍옥암로옥암교코오롱아파트자전거보행자겸용도로0.21.22023-09-27
연번읍면노선명시점종점자전거도로 유형연장(km)자전거도로너비(m)데이터기준일자
1516홍성읍충서로1R소향주유소경찰서삼거리자전거보행자겸용도로1.80.92023-09-27
1617홍성읍충서로1L경찰서삼거리소향주유소자전거보행자겸용도로1.80.92023-09-27
1718광천읍북동부로R광천북동부우회도로(시점)광천북동부우회도로(종점)자전거보행자겸용도로1.21.42023-09-27
1819광천읍북동부로L광천북동부우회도로(종점)광천북동부우회도로(시점)자전거보행자겸용도로1.81.42023-09-27
1920광천읍광천천로1광천교옹암교자전거보행자겸용도로0.61.12023-09-27
2021광천읍담산로현대예식장상담주차장자전거보행자겸용도로3.61.32023-09-27
2122광천읍광남로광남초등학교옹암교자전거보행자겸용도로0.71.02023-09-27
2223광천읍광천천로광천토굴새우젓 전시관옹암교자전거보행자겸용도로1.41.32023-09-27
2324광천읍광천천로2지혜상회은광교회자전거 전용도로0.43.62023-09-27
2425홍북읍내포로내포신도시내포신도시자전거보행자겸용도로13.32.42023-09-27