Overview

Dataset statistics

Number of variables13
Number of observations131
Missing cells135
Missing cells (%)7.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory14.8 KiB
Average record size in memory116.0 B

Variable types

Numeric5
Categorical5
DateTime1
Text1
Unsupported1

Dataset

Description경상남도 도로대장전산화 시스템 데이터의 중장기개방계획에 따른 데이터입니다. 시스템 상에서의 각 도로별 시설물 기본정보를 가지고 있으며, 도로대장의 제설시설 데이터를 포함하고있습니다.
Author경상남도
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15091923

Alerts

관리기관 has constant value ""Constant
이력코드 has constant value ""Constant
종류 has constant value ""Constant
식별번호 is highly overall correlated with 관리번호 and 3 other fieldsHigh correlation
관리번호 is highly overall correlated with 식별번호 and 3 other fieldsHigh correlation
노선번호 is highly overall correlated with 식별번호 and 4 other fieldsHigh correlation
구간번호 is highly overall correlated with 식별번호 and 3 other fieldsHigh correlation
위치 is highly overall correlated with 노선번호High correlation
도로종류 is highly overall correlated with 식별번호 and 3 other fieldsHigh correlation
사진 has 3 (2.3%) missing valuesMissing
비고 has 131 (100.0%) missing valuesMissing
식별번호 has unique valuesUnique
비고 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-12-11 00:30:36.478361
Analysis finished2023-12-11 00:30:39.456899
Duration2.98 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

식별번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct131
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean66
Minimum1
Maximum131
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-11T09:30:39.539117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile7.5
Q133.5
median66
Q398.5
95-th percentile124.5
Maximum131
Range130
Interquartile range (IQR)65

Descriptive statistics

Standard deviation37.960506
Coefficient of variation (CV)0.57515918
Kurtosis-1.2
Mean66
Median Absolute Deviation (MAD)33
Skewness0
Sum8646
Variance1441
MonotonicityStrictly increasing
2023-12-11T09:30:39.680269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.8%
84 1
 
0.8%
98 1
 
0.8%
97 1
 
0.8%
96 1
 
0.8%
95 1
 
0.8%
94 1
 
0.8%
93 1
 
0.8%
92 1
 
0.8%
91 1
 
0.8%
Other values (121) 121
92.4%
ValueCountFrequency (%)
1 1
0.8%
2 1
0.8%
3 1
0.8%
4 1
0.8%
5 1
0.8%
6 1
0.8%
7 1
0.8%
8 1
0.8%
9 1
0.8%
10 1
0.8%
ValueCountFrequency (%)
131 1
0.8%
130 1
0.8%
129 1
0.8%
128 1
0.8%
127 1
0.8%
126 1
0.8%
125 1
0.8%
124 1
0.8%
123 1
0.8%
122 1
0.8%

관리번호
Real number (ℝ)

HIGH CORRELATION 

Distinct130
Distinct (%)100.0%
Missing1
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean8114776.5
Minimum370001
Maximum10770015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-11T09:30:39.856263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum370001
5-th percentile690002.45
Q110030001
median10180004
Q310510004
95-th percentile10770009
Maximum10770015
Range10400014
Interquartile range (IQR)480002.5

Descriptive statistics

Standard deviation4097734.1
Coefficient of variation (CV)0.5049719
Kurtosis-0.34249446
Mean8114776.5
Median Absolute Deviation (MAD)329997
Skewness-1.2831677
Sum1.0549209 × 109
Variance1.6791425 × 1013
MonotonicityNot monotonic
2023-12-11T09:30:40.042698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
670001 1
 
0.8%
10770003 1
 
0.8%
10770001 1
 
0.8%
10770000 1
 
0.8%
10510003 1
 
0.8%
10510002 1
 
0.8%
10510001 1
 
0.8%
10510000 1
 
0.8%
10510012 1
 
0.8%
10510020 1
 
0.8%
Other values (120) 120
91.6%
ValueCountFrequency (%)
370001 1
0.8%
600000 1
0.8%
600001 1
0.8%
670001 1
0.8%
690000 1
0.8%
690001 1
0.8%
690002 1
0.8%
690003 1
0.8%
690004 1
0.8%
690005 1
0.8%
ValueCountFrequency (%)
10770015 1
0.8%
10770014 1
0.8%
10770013 1
0.8%
10770012 1
0.8%
10770011 1
0.8%
10770010 1
0.8%
10770009 1
0.8%
10770008 1
0.8%
10770007 1
0.8%
10770006 1
0.8%

관리기관
Categorical

CONSTANT 

Distinct1
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
1683
131 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1683
2nd row1683
3rd row1683
4th row1683
5th row1683

Common Values

ValueCountFrequency (%)
1683 131
100.0%

Length

2023-12-11T09:30:40.178935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:30:40.271411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1683 131
100.0%

도로종류
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
1504
101 
1507
30 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1507
2nd row1507
3rd row1504
4th row1504
5th row1504

Common Values

ValueCountFrequency (%)
1504 101
77.1%
1507 30
 
22.9%

Length

2023-12-11T09:30:40.369938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:30:40.476709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1504 101
77.1%
1507 30
 
22.9%

노선번호
Real number (ℝ)

HIGH CORRELATION 

Distinct19
Distinct (%)14.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean813.29008
Minimum37
Maximum1077
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-11T09:30:40.568199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37
5-th percentile69
Q11003
median1018
Q31051
95-th percentile1077
Maximum1077
Range1040
Interquartile range (IQR)48

Descriptive statistics

Standard deviation408.72169
Coefficient of variation (CV)0.5025534
Kurtosis-0.31143208
Mean813.29008
Median Absolute Deviation (MAD)33
Skewness-1.2949825
Sum106541
Variance167053.42
MonotonicityNot monotonic
2023-12-11T09:30:40.674568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
69 26
19.8%
1051 21
16.0%
1077 16
12.2%
1018 15
11.5%
1042 11
8.4%
1016 8
 
6.1%
1010 8
 
6.1%
1007 6
 
4.6%
1022 6
 
4.6%
1004 2
 
1.5%
Other values (9) 12
9.2%
ValueCountFrequency (%)
37 1
 
0.8%
60 2
 
1.5%
67 1
 
0.8%
69 26
19.8%
1002 2
 
1.5%
1003 2
 
1.5%
1004 2
 
1.5%
1007 6
 
4.6%
1008 1
 
0.8%
1010 8
 
6.1%
ValueCountFrequency (%)
1077 16
12.2%
1051 21
16.0%
1049 1
 
0.8%
1042 11
8.4%
1022 6
 
4.6%
1020 1
 
0.8%
1018 15
11.5%
1016 8
 
6.1%
1011 1
 
0.8%
1010 8
 
6.1%

구간번호
Real number (ℝ)

HIGH CORRELATION 

Distinct9
Distinct (%)6.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.5572519
Minimum1
Maximum15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-11T09:30:40.771802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median3
Q36
95-th percentile7
Maximum15
Range14
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.8960459
Coefficient of variation (CV)0.81412448
Kurtosis1.8897459
Mean3.5572519
Median Absolute Deviation (MAD)2
Skewness1.2110275
Sum466
Variance8.3870816
MonotonicityNot monotonic
2023-12-11T09:30:40.871164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 51
38.9%
6 20
 
15.3%
7 18
 
13.7%
3 15
 
11.5%
2 14
 
10.7%
4 5
 
3.8%
9 4
 
3.1%
15 2
 
1.5%
5 2
 
1.5%
ValueCountFrequency (%)
1 51
38.9%
2 14
 
10.7%
3 15
 
11.5%
4 5
 
3.8%
5 2
 
1.5%
6 20
 
15.3%
7 18
 
13.7%
9 4
 
3.1%
15 2
 
1.5%
ValueCountFrequency (%)
15 2
 
1.5%
9 4
 
3.1%
7 18
 
13.7%
6 20
 
15.3%
5 2
 
1.5%
4 5
 
3.8%
3 15
 
11.5%
2 14
 
10.7%
1 51
38.9%

이력코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
0
131 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 131
100.0%

Length

2023-12-11T09:30:40.996069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:30:41.087939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 131
100.0%

위치
Real number (ℝ)

HIGH CORRELATION 

Distinct130
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.072458
Minimum0.018
Maximum14.253
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-11T09:30:41.194913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.018
5-th percentile0.425
Q12.356
median3.991
Q37.895
95-th percentile11.669
Maximum14.253
Range14.235
Interquartile range (IQR)5.539

Descriptive statistics

Standard deviation3.6594782
Coefficient of variation (CV)0.7214408
Kurtosis-0.60593959
Mean5.072458
Median Absolute Deviation (MAD)2.314
Skewness0.67544352
Sum664.492
Variance13.391781
MonotonicityNot monotonic
2023-12-11T09:30:41.584900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.075 2
 
1.5%
1.901 1
 
0.8%
0.388 1
 
0.8%
0.071 1
 
0.8%
0.018 1
 
0.8%
1.505 1
 
0.8%
1.445 1
 
0.8%
1.345 1
 
0.8%
1.235 1
 
0.8%
2.475 1
 
0.8%
Other values (120) 120
91.6%
ValueCountFrequency (%)
0.018 1
0.8%
0.071 1
0.8%
0.16 1
0.8%
0.162 1
0.8%
0.17 1
0.8%
0.313 1
0.8%
0.388 1
0.8%
0.462 1
0.8%
0.478 1
0.8%
0.564 1
0.8%
ValueCountFrequency (%)
14.253 1
0.8%
13.885 1
0.8%
12.91 1
0.8%
12.895 1
0.8%
12.628 1
0.8%
11.823 1
0.8%
11.701 1
0.8%
11.637 1
0.8%
11.602 1
0.8%
11.51 1
0.8%

위치_방향
Categorical

Distinct2
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
0
70 
1
61 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 70
53.4%
1 61
46.6%

Length

2023-12-11T09:30:41.731352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:30:41.840540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 70
53.4%
1 61
46.6%

종류
Categorical

CONSTANT 

Distinct1
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
4801
131 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4801
2nd row4801
3rd row4801
4th row4801
5th row4801

Common Values

ValueCountFrequency (%)
4801 131
100.0%

Length

2023-12-11T09:30:41.944077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:30:42.047988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
4801 131
100.0%

설치일자
Date

Distinct2
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
Minimum1900-01-01 00:00:00
Maximum2007-10-12 00:00:00
2023-12-11T09:30:42.133279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:30:42.244854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=2)

사진
Text

MISSING 

Distinct128
Distinct (%)100.0%
Missing3
Missing (%)2.3%
Memory size1.2 KiB
2023-12-11T09:30:42.415400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length13
Mean length13
Min length13

Characters and Unicode

Total characters1664
Distinct characters15
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique128 ?
Unique (%)100.0%

Sample

1st row006703T01901D
2nd row003704T05275D
3rd row101101T01215D
4th row101002T02465U
5th row101002T06064D
ValueCountFrequency (%)
101809t04171d 1
 
0.8%
101101t01215d 1
 
0.8%
105101t01235u 1
 
0.8%
105101t03015u 1
 
0.8%
105101t03075u 1
 
0.8%
105101t03152u 1
 
0.8%
105101t03238u 1
 
0.8%
105101t03294u 1
 
0.8%
105101t02475d 1
 
0.8%
105101t01345u 1
 
0.8%
Other values (118) 118
92.2%
2023-12-11T09:30:42.769209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 497
29.9%
1 277
16.6%
2 115
 
6.9%
T 110
 
6.6%
5 93
 
5.6%
6 88
 
5.3%
7 82
 
4.9%
3 71
 
4.3%
4 70
 
4.2%
9 66
 
4.0%
Other values (5) 195
 
11.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1408
84.6%
Uppercase Letter 256
 
15.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 497
35.3%
1 277
19.7%
2 115
 
8.2%
5 93
 
6.6%
6 88
 
6.2%
7 82
 
5.8%
3 71
 
5.0%
4 70
 
5.0%
9 66
 
4.7%
8 49
 
3.5%
Uppercase Letter
ValueCountFrequency (%)
T 110
43.0%
D 64
25.0%
U 64
25.0%
S 16
 
6.2%
M 2
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
Common 1408
84.6%
Latin 256
 
15.4%

Most frequent character per script

Common
ValueCountFrequency (%)
0 497
35.3%
1 277
19.7%
2 115
 
8.2%
5 93
 
6.6%
6 88
 
6.2%
7 82
 
5.8%
3 71
 
5.0%
4 70
 
5.0%
9 66
 
4.7%
8 49
 
3.5%
Latin
ValueCountFrequency (%)
T 110
43.0%
D 64
25.0%
U 64
25.0%
S 16
 
6.2%
M 2
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1664
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 497
29.9%
1 277
16.6%
2 115
 
6.9%
T 110
 
6.6%
5 93
 
5.6%
6 88
 
5.3%
7 82
 
4.9%
3 71
 
4.3%
4 70
 
4.2%
9 66
 
4.0%
Other values (5) 195
 
11.7%

비고
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing131
Missing (%)100.0%
Memory size1.3 KiB

Interactions

2023-12-11T09:30:38.635610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:30:36.824301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:30:37.275984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:30:37.704728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:30:38.148603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:30:38.745074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:30:36.912596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:30:37.371356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:30:37.791800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:30:38.229935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:30:38.849140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:30:37.002823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:30:37.449820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:30:37.869021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:30:38.343635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:30:38.930556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:30:37.098782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:30:37.534641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:30:37.946192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:30:38.434075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:30:39.017416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:30:37.185663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:30:37.620374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:30:38.052383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:30:38.518494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T09:30:42.891526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
식별번호관리번호도로종류노선번호구간번호위치위치_방향설치일자
식별번호1.0000.9640.9550.9550.7300.8170.3000.688
관리번호0.9641.0000.9990.9990.8090.6390.2570.768
도로종류0.9550.9991.0000.9990.7920.6270.2820.757
노선번호0.9550.9990.9991.0000.7920.6270.2820.757
구간번호0.7300.8090.7920.7921.0000.5450.1300.429
위치0.8170.6390.6270.6270.5451.0000.2940.519
위치_방향0.3000.2570.2820.2820.1300.2941.0000.000
설치일자0.6880.7680.7570.7570.4290.5190.0001.000
2023-12-11T09:30:43.026303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위치_방향도로종류
위치_방향1.0000.182
도로종류0.1821.000
2023-12-11T09:30:43.122108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
식별번호관리번호노선번호구간번호위치도로종류위치_방향
식별번호1.0000.7460.752-0.503-0.2460.8180.266
관리번호0.7461.0000.992-0.648-0.4750.9780.178
노선번호0.7520.9921.000-0.676-0.5190.9780.182
구간번호-0.503-0.648-0.6761.0000.3510.8420.135
위치-0.246-0.475-0.5190.3511.0000.4700.218
도로종류0.8180.9780.9780.8420.4701.0000.182
위치_방향0.2660.1780.1820.1350.2180.1821.000

Missing values

2023-12-11T09:30:39.139061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T09:30:39.300232image/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.
2023-12-11T09:30:39.412680image/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

식별번호관리번호관리기관도로종류노선번호구간번호이력코드위치위치_방향종류설치일자사진비고
016700011683150767301.901148011900-01-01006703T01901D<NA>
123700011683150737405.275148011900-01-01003704T05275D<NA>
2310200001168315041020307.84148011900-01-01<NA><NA>
3410110001168315041011101.215148011900-01-01101101T01215D<NA>
4510100001168315041010202.465048011900-01-01101002T02465U<NA>
5610100004168315041010206.064148011900-01-01101002T06064D<NA>
6710100002168315041010204.223048011900-01-01101002T04223U<NA>
7810100003168315041010204.327048011900-01-01101002T04327U<NA>
8910100005168315041010207.54048011900-01-01101002T07540D<NA>
91010100006168315041010209.242048011900-01-01101002T09242U<NA>
식별번호관리번호관리기관도로종류노선번호구간번호이력코드위치위치_방향종류설치일자사진비고
121122104200061683150410421011.602148011900-01-01104201T11602D<NA>
122123104200011683150410421010.43048011900-01-01104201T10430U<NA>
123124104200021683150410421010.83148011900-01-01104201T10830D<NA>
124125104200031683150410421011.144148011900-01-01104201T11144D<NA>
125126104200041683150410421011.27148011900-01-01104201T11270D<NA>
12612710420008168315041042204.433148011900-01-01104202T04433D<NA>
12712810420009168315041042204.55148011900-01-01104202T04550D<NA>
12812910420010168315041042206.34048011900-01-01104202T06340U<NA>
12913010420011168315041042206.422148011900-01-01104202T06422D<NA>
13013110420007168315041042203.991048011900-01-01104202T03991U<NA>