Overview

Dataset statistics

Number of variables15
Number of observations49
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.3 KiB
Average record size in memory132.7 B

Variable types

Text3
Categorical4
Numeric8

Dataset

Description대전광역시 도로관리시스템에 등재된 도로 터널 현황입니다. 데이터에 지리정보(Geometry), 위치(동), 터널명, 연장 등이 포함되어있습니다.
Author대전광역시
URLhttps://www.data.go.kr/data/15110448/fileData.do

Alerts

지형지물부호 has constant value ""Constant
대장초기화여부 has constant value ""Constant
관리번호 is highly overall correlated with 행정읍면동High correlation
도엽번호 is highly overall correlated with 일반제원_터널높이High correlation
총연장 is highly overall correlated with 총면적High correlation
총면적 is highly overall correlated with 총연장High correlation
폭원_계 is highly overall correlated with 폭원_차도 and 1 other fieldsHigh correlation
폭원_차도 is highly overall correlated with 폭원_계 and 1 other fieldsHigh correlation
왕복차로수 is highly overall correlated with 폭원_계 and 1 other fieldsHigh correlation
행정읍면동 is highly overall correlated with 관리번호High correlation
일반제원_터널높이 is highly overall correlated with 도엽번호High correlation
지리정보(WKT) has unique valuesUnique
관리번호 has unique valuesUnique
총면적 has unique valuesUnique
왕복차로수 has 6 (12.2%) zerosZeros

Reproduction

Analysis started2023-12-12 21:58:34.680702
Analysis finished2023-12-12 21:58:41.872318
Duration7.19 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

지리정보(WKT)
Text

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size524.0 B
2023-12-13T06:58:42.040341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length1024
Median length525
Mean length454.91837
Min length182

Characters and Unicode

Total characters22291
Distinct characters25
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique49 ?
Unique (%)100.0%

Sample

1st rowMULTIPOLYGON (((233075.466602563 409908.323785327,233307.460102563 409811.430085327,233330.610102563 409799.820085327,233327.250102563 409792.510085327,233298.680102563 409802.410085327,233045.25908852 409908.074936904,232706.720092773 410049.660095215,232674.910095215 410063.854125977,232678.252075195 410073.304077148,232711.780090332 410060.220092773,233075.466602563 409908.323785327)))
2nd rowMULTIPOLYGON (((232997.815693164 409908.063196802,233296.630102563 409783.510085327,233324.270102563 409770.170085327,233320.260102563 409762.400085327,233289.800102563 409773.680085327,232967.653302563 409907.961985327,232693.989074707 410023.118103027,232681.937072754 410028.542114258,232685.518127441 410037.74407959,232703.546081543 410029.639099121,232997.815693164 409908.063196802)))
3rd rowMULTIPOLYGON (((233895.850102563 408047.110085327,233886.610102563 408044.000085327,233886.022102563 408053.855085327,233885.426102563 408063.848085327,233883.320129395 408070.220092773,233894.130126953 408070.260070801,233894.483102563 408068.891085327,233895.106102563 408058.975085327,233895.850102563 408047.110085327)))
4th rowMULTIPOLYGON (((233884.798078089 408223.241806802,233876.136321264 408219.629609162,233869.689945798 408327.730160578,233878.610093488 408327.120135625,233878.93009517 408316.760174259,233884.798078089 408223.241806802)))
5th rowMULTIPOLYGON (((233847.510025925 408323.52010184,233866.983254476 408024.802065831,233854.661759056 408024.754123973,233839.650109713 408320.219933044,233839.210112636 408327.1200011,233847.27005574 408328.610031286,233847.510025925 408323.52010184)))
ValueCountFrequency (%)
multipolygon 49
 
6.7%
411197.125988162,231197.969051122 1
 
0.1%
412543.108097751,230861.748174268 1
 
0.1%
412544.934122819,230891.283108465 1
 
0.1%
412527.616964834,230886.640094164 1
 
0.1%
412516.74698718 1
 
0.1%
231313.608962301 1
 
0.1%
411221.205070668,231311.696119166 1
 
0.1%
411196.075959834,231206.513006258 1
 
0.1%
411197.433972109,231204.721038649 1
 
0.1%
Other values (674) 674
92.1%
2023-12-13T06:58:42.438925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 2669
12.0%
0 2527
11.3%
3 2195
9.8%
1 2065
9.3%
4 1953
8.8%
5 1643
7.4%
7 1510
6.8%
9 1485
6.7%
6 1433
6.4%
8 1411
6.3%
Other values (15) 3400
15.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 18891
84.7%
Other Punctuation 1853
 
8.3%
Space Separator 683
 
3.1%
Uppercase Letter 588
 
2.6%
Open Punctuation 147
 
0.7%
Close Punctuation 129
 
0.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 2669
14.1%
0 2527
13.4%
3 2195
11.6%
1 2065
10.9%
4 1953
10.3%
5 1643
8.7%
7 1510
8.0%
9 1485
7.9%
6 1433
7.6%
8 1411
7.5%
Uppercase Letter
ValueCountFrequency (%)
O 98
16.7%
L 98
16.7%
U 49
8.3%
N 49
8.3%
G 49
8.3%
Y 49
8.3%
P 49
8.3%
I 49
8.3%
T 49
8.3%
M 49
8.3%
Other Punctuation
ValueCountFrequency (%)
. 1268
68.4%
, 585
31.6%
Space Separator
ValueCountFrequency (%)
683
100.0%
Open Punctuation
ValueCountFrequency (%)
( 147
100.0%
Close Punctuation
ValueCountFrequency (%)
) 129
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 21703
97.4%
Latin 588
 
2.6%

Most frequent character per script

Common
ValueCountFrequency (%)
2 2669
12.3%
0 2527
11.6%
3 2195
10.1%
1 2065
9.5%
4 1953
9.0%
5 1643
7.6%
7 1510
7.0%
9 1485
6.8%
6 1433
6.6%
8 1411
6.5%
Other values (5) 2812
13.0%
Latin
ValueCountFrequency (%)
O 98
16.7%
L 98
16.7%
U 49
8.3%
N 49
8.3%
G 49
8.3%
Y 49
8.3%
P 49
8.3%
I 49
8.3%
T 49
8.3%
M 49
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 22291
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 2669
12.0%
0 2527
11.3%
3 2195
9.8%
1 2065
9.3%
4 1953
8.8%
5 1643
7.4%
7 1510
6.8%
9 1485
6.7%
6 1433
6.4%
8 1411
6.3%
Other values (15) 3400
15.3%

지형지물부호
Categorical

CONSTANT 

Distinct1
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size524.0 B
터널
49 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
터널 49
100.0%

Length

2023-12-13T06:58:42.575784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:58:42.664064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
터널 49
100.0%

관리번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25
Minimum1
Maximum49
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-13T06:58:42.755660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.4
Q113
median25
Q337
95-th percentile46.6
Maximum49
Range48
Interquartile range (IQR)24

Descriptive statistics

Standard deviation14.28869
Coefficient of variation (CV)0.57154761
Kurtosis-1.2
Mean25
Median Absolute Deviation (MAD)12
Skewness0
Sum1225
Variance204.16667
MonotonicityStrictly increasing
2023-12-13T06:58:42.884730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
1 1
 
2.0%
38 1
 
2.0%
28 1
 
2.0%
29 1
 
2.0%
30 1
 
2.0%
31 1
 
2.0%
32 1
 
2.0%
33 1
 
2.0%
34 1
 
2.0%
35 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
1 1
2.0%
2 1
2.0%
3 1
2.0%
4 1
2.0%
5 1
2.0%
6 1
2.0%
7 1
2.0%
8 1
2.0%
9 1
2.0%
10 1
2.0%
ValueCountFrequency (%)
49 1
2.0%
48 1
2.0%
47 1
2.0%
46 1
2.0%
45 1
2.0%
44 1
2.0%
43 1
2.0%
42 1
2.0%
41 1
2.0%
40 1
2.0%

행정읍면동
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)10.2%
Missing0
Missing (%)0.0%
Memory size524.0 B
대전광역시 동구
20 
대전광역시 유성구
12 
대전광역시 중구
8 
대전광역시 서구
8 
대전광역시 대덕구
 
1

Length

Max length10
Median length9
Mean length9.2653061
Min length9

Unique

Unique1 ?
Unique (%)2.0%

Sample

1st row대전광역시 중구
2nd row대전광역시 중구
3rd row대전광역시 중구
4th row대전광역시 중구
5th row대전광역시 중구

Common Values

ValueCountFrequency (%)
대전광역시 동구 20
40.8%
대전광역시 유성구 12
24.5%
대전광역시 중구 8
 
16.3%
대전광역시 서구 8
 
16.3%
대전광역시 대덕구 1
 
2.0%

Length

2023-12-13T06:58:42.996944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:58:43.097128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
대전광역시 49
50.0%
동구 20
20.4%
유성구 12
 
12.2%
중구 8
 
8.2%
서구 8
 
8.2%
대덕구 1
 
1.0%

도엽번호
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)51.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36710691
Minimum36710015
Maximum36714029
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-13T06:58:43.194476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36710015
5-th percentile36710025
Q136710059
median36710074
Q336710096
95-th percentile36714010
Maximum36714029
Range4014
Interquartile range (IQR)37

Descriptive statistics

Standard deviation1391.0296
Coefficient of variation (CV)3.7891677 × 10-5
Kurtosis2.2159927
Mean36710691
Median Absolute Deviation (MAD)20
Skewness1.994916
Sum1.7988238 × 109
Variance1934963.4
MonotonicityNot monotonic
2023-12-13T06:58:43.313045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
36714010 5
 
10.2%
36710069 4
 
8.2%
36710059 4
 
8.2%
36710084 4
 
8.2%
36710096 3
 
6.1%
36710025 2
 
4.1%
36711061 2
 
4.1%
36710065 2
 
4.1%
36710085 2
 
4.1%
36710015 2
 
4.1%
Other values (15) 19
38.8%
ValueCountFrequency (%)
36710015 2
4.1%
36710025 2
4.1%
36710026 1
 
2.0%
36710043 1
 
2.0%
36710046 2
4.1%
36710053 2
4.1%
36710057 1
 
2.0%
36710059 4
8.2%
36710065 2
4.1%
36710069 4
8.2%
ValueCountFrequency (%)
36714029 1
 
2.0%
36714010 5
10.2%
36714009 1
 
2.0%
36711061 2
 
4.1%
36711051 1
 
2.0%
36710100 1
 
2.0%
36710096 3
6.1%
36710094 1
 
2.0%
36710088 2
 
4.1%
36710086 1
 
2.0%

도로구간번호
Real number (ℝ)

Distinct36
Distinct (%)73.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26510.224
Minimum2551
Maximum56310
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-13T06:58:43.446445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2551
5-th percentile13430
Q113960
median19980
Q339910
95-th percentile47714
Maximum56310
Range53759
Interquartile range (IQR)25950

Descriptive statistics

Standard deviation13933.112
Coefficient of variation (CV)0.52557501
Kurtosis-1.3508764
Mean26510.224
Median Absolute Deviation (MAD)6550
Skewness0.34313101
Sum1299001
Variance1.941316 × 108
MonotonicityNot monotonic
2023-12-13T06:58:43.568410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
13460 3
 
6.1%
13960 3
 
6.1%
13780 2
 
4.1%
42860 2
 
4.1%
37120 2
 
4.1%
33330 2
 
4.1%
13430 2
 
4.1%
14050 2
 
4.1%
14060 2
 
4.1%
43400 2
 
4.1%
Other values (26) 27
55.1%
ValueCountFrequency (%)
2551 1
 
2.0%
13050 1
 
2.0%
13430 2
4.1%
13460 3
6.1%
13670 1
 
2.0%
13680 1
 
2.0%
13780 2
4.1%
13960 3
6.1%
14050 2
4.1%
14060 2
4.1%
ValueCountFrequency (%)
56310 1
2.0%
48190 1
2.0%
48170 1
2.0%
47030 1
2.0%
43400 2
4.1%
42960 1
2.0%
42860 2
4.1%
42500 1
2.0%
42490 1
2.0%
42000 1
2.0%

터널명
Text

Distinct31
Distinct (%)63.3%
Missing0
Missing (%)0.0%
Memory size524.0 B
2023-12-13T06:58:43.772148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length4
Mean length4.4081633
Min length2

Characters and Unicode

Total characters216
Distinct characters62
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

Unique16 ?
Unique (%)32.7%

Sample

1st row안영1터널
2nd row안영1터널
3rd row샛고개굴길
4th row샛고개굴길
5th row샛고개굴길
ValueCountFrequency (%)
대전터널 4
 
8.2%
샛고개굴길 3
 
6.1%
구완터널 2
 
4.1%
안영1터널 2
 
4.1%
구봉터널 2
 
4.1%
증약터널 2
 
4.1%
가양비래터널 2
 
4.1%
금산터널 2
 
4.1%
용운터널 2
 
4.1%
도솔터널 2
 
4.1%
Other values (21) 26
53.1%
2023-12-13T06:58:44.068123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
터 44
20.4%
널 44
20.4%
대 7
 
3.2%
1 6
 
2.8%
구 5
 
2.3%
전 4
 
1.9%
봉 4
 
1.9%
덕 4
 
1.9%
가 3
 
1.4%
안 3
 
1.4%
Other values (52) 92
42.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 205
94.9%
Decimal Number 7
 
3.2%
Close Punctuation 2
 
0.9%
Open Punctuation 2
 
0.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
터 44
21.5%
널 44
21.5%
대 7
 
3.4%
구 5
 
2.4%
전 4
 
2.0%
봉 4
 
2.0%
덕 4
 
2.0%
가 3
 
1.5%
안 3
 
1.5%
영 3
 
1.5%
Other values (48) 84
41.0%
Decimal Number
ValueCountFrequency (%)
1 6
85.7%
2 1
 
14.3%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 205
94.9%
Common 11
 
5.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
터 44
21.5%
널 44
21.5%
대 7
 
3.4%
구 5
 
2.4%
전 4
 
2.0%
봉 4
 
2.0%
덕 4
 
2.0%
가 3
 
1.5%
안 3
 
1.5%
영 3
 
1.5%
Other values (48) 84
41.0%
Common
ValueCountFrequency (%)
1 6
54.5%
) 2
 
18.2%
( 2
 
18.2%
2 1
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 205
94.9%
ASCII 11
 
5.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
터 44
21.5%
널 44
21.5%
대 7
 
3.4%
구 5
 
2.4%
전 4
 
2.0%
봉 4
 
2.0%
덕 4
 
2.0%
가 3
 
1.5%
안 3
 
1.5%
영 3
 
1.5%
Other values (48) 84
41.0%
ASCII
ValueCountFrequency (%)
1 6
54.5%
) 2
 
18.2%
( 2
 
18.2%
2 1
 
9.1%

총연장
Real number (ℝ)

HIGH CORRELATION 

Distinct48
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean567.15041
Minimum27
Maximum2465.77
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-13T06:58:44.205787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum27
5-th percentile84.546
Q1205
median518
Q3755
95-th percentile1150.314
Maximum2465.77
Range2438.77
Interquartile range (IQR)550

Descriptive statistics

Standard deviation502.20827
Coefficient of variation (CV)0.88549397
Kurtosis6.6509908
Mean567.15041
Median Absolute Deviation (MAD)287.3
Skewness2.1879919
Sum27790.37
Variance252213.15
MonotonicityNot monotonic
2023-12-13T06:58:44.407767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
755.0 2
 
4.1%
707.14 1
 
2.0%
248.14 1
 
2.0%
522.43 1
 
2.0%
205.0 1
 
2.0%
658.23 1
 
2.0%
701.28 1
 
2.0%
813.54 1
 
2.0%
819.6 1
 
2.0%
452.2 1
 
2.0%
Other values (38) 38
77.6%
ValueCountFrequency (%)
27.0 1
2.0%
50.46 1
2.0%
74.01 1
2.0%
100.35 1
2.0%
102.39 1
2.0%
103.0 1
2.0%
109.1 1
2.0%
114.39 1
2.0%
115.24 1
2.0%
175.0 1
2.0%
ValueCountFrequency (%)
2465.77 1
2.0%
2449.62 1
2.0%
1208.09 1
2.0%
1063.65 1
2.0%
1057.6 1
2.0%
997.0 1
2.0%
819.6 1
2.0%
813.54 1
2.0%
809.96 1
2.0%
797.0 1
2.0%

총면적
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6454.1516
Minimum237.2
Maximum25892.76
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-13T06:58:44.546584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum237.2
5-th percentile986.916
Q11717.59
median4543.18
Q38835.53
95-th percentile16292.288
Maximum25892.76
Range25655.56
Interquartile range (IQR)7117.94

Descriptive statistics

Standard deviation5951.7451
Coefficient of variation (CV)0.92215761
Kurtosis2.5806091
Mean6454.1516
Median Absolute Deviation (MAD)3435.11
Skewness1.5236013
Sum316253.43
Variance35423269
MonotonicityNot monotonic
2023-12-13T06:58:44.684200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
8244.85 1
 
2.0%
2423.08 1
 
2.0%
4582.37 1
 
2.0%
3998.08 1
 
2.0%
8184.55 1
 
2.0%
8670.17 1
 
2.0%
8105.89 1
 
2.0%
8188.72 1
 
2.0%
4403.61 1
 
2.0%
4543.18 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
237.2 1
2.0%
821.31 1
2.0%
925.8 1
2.0%
1078.59 1
2.0%
1084.69 1
2.0%
1108.07 1
2.0%
1267.69 1
2.0%
1286.53 1
2.0%
1368.51 1
2.0%
1559.83 1
2.0%
ValueCountFrequency (%)
25892.76 1
2.0%
25151.14 1
2.0%
17035.24 1
2.0%
15177.86 1
2.0%
14869.78 1
2.0%
14146.28 1
2.0%
12251.79 1
2.0%
11487.77 1
2.0%
10625.05 1
2.0%
10389.79 1
2.0%

일반제원_터널높이
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)10.2%
Missing0
Missing (%)0.0%
Memory size524.0 B
0.0
30 
4.5
11 
7.0
4 
5.0
 
3
8.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique1 ?
Unique (%)2.0%

Sample

1st row4.5
2nd row4.5
3rd row4.5
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 30
61.2%
4.5 11
 
22.4%
7.0 4
 
8.2%
5.0 3
 
6.1%
8.0 1
 
2.0%

Length

2023-12-13T06:58:44.801592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:58:44.916844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 30
61.2%
4.5 11
 
22.4%
7.0 4
 
8.2%
5.0 3
 
6.1%
8.0 1
 
2.0%

폭원_계
Real number (ℝ)

HIGH CORRELATION 

Distinct47
Distinct (%)95.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.781633
Minimum6.19
Maximum40.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-13T06:58:45.049778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6.19
5-th percentile9.28
Q110.32
median13.09
Q315.79
95-th percentile29.542
Maximum40.6
Range34.41
Interquartile range (IQR)5.47

Descriptive statistics

Standard deviation7.0402247
Coefficient of variation (CV)0.47628194
Kurtosis4.4491688
Mean14.781633
Median Absolute Deviation (MAD)2.77
Skewness2.0476984
Sum724.3
Variance49.564764
MonotonicityNot monotonic
2023-12-13T06:58:45.177367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
13.6 2
 
4.1%
11.16 2
 
4.1%
13.59 1
 
2.0%
10.93 1
 
2.0%
26.53 1
 
2.0%
9.31 1
 
2.0%
20.15 1
 
2.0%
13.22 1
 
2.0%
12.93 1
 
2.0%
11.76 1
 
2.0%
Other values (37) 37
75.5%
ValueCountFrequency (%)
6.19 1
2.0%
6.36 1
2.0%
9.26 1
2.0%
9.31 1
2.0%
9.5 1
2.0%
9.53 1
2.0%
9.76 1
2.0%
9.91 1
2.0%
9.98 1
2.0%
10.0 1
2.0%
ValueCountFrequency (%)
40.6 1
2.0%
36.16 1
2.0%
31.55 1
2.0%
26.53 1
2.0%
25.62 1
2.0%
24.85 1
2.0%
20.15 1
2.0%
19.55 1
2.0%
17.41 1
2.0%
16.91 1
2.0%

폭원_차도
Real number (ℝ)

HIGH CORRELATION 

Distinct47
Distinct (%)95.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.781633
Minimum6.19
Maximum40.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-13T06:58:45.305675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6.19
5-th percentile9.28
Q110.32
median13.09
Q315.79
95-th percentile29.542
Maximum40.6
Range34.41
Interquartile range (IQR)5.47

Descriptive statistics

Standard deviation7.0402247
Coefficient of variation (CV)0.47628194
Kurtosis4.4491688
Mean14.781633
Median Absolute Deviation (MAD)2.77
Skewness2.0476984
Sum724.3
Variance49.564764
MonotonicityNot monotonic
2023-12-13T06:58:45.428646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
13.6 2
 
4.1%
11.16 2
 
4.1%
13.59 1
 
2.0%
10.93 1
 
2.0%
26.53 1
 
2.0%
9.31 1
 
2.0%
20.15 1
 
2.0%
13.22 1
 
2.0%
12.93 1
 
2.0%
11.76 1
 
2.0%
Other values (37) 37
75.5%
ValueCountFrequency (%)
6.19 1
2.0%
6.36 1
2.0%
9.26 1
2.0%
9.31 1
2.0%
9.5 1
2.0%
9.53 1
2.0%
9.76 1
2.0%
9.91 1
2.0%
9.98 1
2.0%
10.0 1
2.0%
ValueCountFrequency (%)
40.6 1
2.0%
36.16 1
2.0%
31.55 1
2.0%
26.53 1
2.0%
25.62 1
2.0%
24.85 1
2.0%
20.15 1
2.0%
19.55 1
2.0%
17.41 1
2.0%
16.91 1
2.0%

왕복차로수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)16.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3469388
Minimum0
Maximum8
Zeros6
Zeros (%)12.2%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-13T06:58:45.548885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median2
Q33
95-th percentile5.6
Maximum8
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.6400079
Coefficient of variation (CV)0.69878597
Kurtosis2.2579557
Mean2.3469388
Median Absolute Deviation (MAD)1
Skewness1.1550861
Sum115
Variance2.6896259
MonotonicityNot monotonic
2023-12-13T06:58:45.653607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
2 23
46.9%
4 6
 
12.2%
0 6
 
12.2%
1 5
 
10.2%
3 5
 
10.2%
6 2
 
4.1%
5 1
 
2.0%
8 1
 
2.0%
ValueCountFrequency (%)
0 6
 
12.2%
1 5
 
10.2%
2 23
46.9%
3 5
 
10.2%
4 6
 
12.2%
5 1
 
2.0%
6 2
 
4.1%
8 1
 
2.0%
ValueCountFrequency (%)
8 1
 
2.0%
6 2
 
4.1%
5 1
 
2.0%
4 6
 
12.2%
3 5
 
10.2%
2 23
46.9%
1 5
 
10.2%
0 6
 
12.2%

라벨
Text

Distinct31
Distinct (%)63.3%
Missing0
Missing (%)0.0%
Memory size524.0 B
2023-12-13T06:58:45.829210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length4
Mean length4.4897959
Min length4

Characters and Unicode

Total characters220
Distinct characters64
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

Unique16 ?
Unique (%)32.7%

Sample

1st row안영1터널
2nd row안영1터널
3rd row샛고개굴길
4th row샛고개굴길
5th row샛고개굴길
ValueCountFrequency (%)
대전터널 4
 
8.2%
샛고개굴길 3
 
6.1%
구완터널 2
 
4.1%
안영1터널 2
 
4.1%
구봉터널 2
 
4.1%
증약터널 2
 
4.1%
가양비래터널 2
 
4.1%
금산터널 2
 
4.1%
용운터널 2
 
4.1%
도솔터널 2
 
4.1%
Other values (21) 26
53.1%
2023-12-13T06:58:46.117571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
터 44
20.0%
널 44
20.0%
대 7
 
3.2%
1 6
 
2.7%
구 5
 
2.3%
전 4
 
1.8%
봉 4
 
1.8%
덕 4
 
1.8%
안 3
 
1.4%
영 3
 
1.4%
Other values (54) 96
43.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 209
95.0%
Decimal Number 7
 
3.2%
Close Punctuation 2
 
0.9%
Open Punctuation 2
 
0.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
터 44
21.1%
널 44
21.1%
대 7
 
3.3%
구 5
 
2.4%
전 4
 
1.9%
봉 4
 
1.9%
덕 4
 
1.9%
안 3
 
1.4%
영 3
 
1.4%
곡 3
 
1.4%
Other values (50) 88
42.1%
Decimal Number
ValueCountFrequency (%)
1 6
85.7%
2 1
 
14.3%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 209
95.0%
Common 11
 
5.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
터 44
21.1%
널 44
21.1%
대 7
 
3.3%
구 5
 
2.4%
전 4
 
1.9%
봉 4
 
1.9%
덕 4
 
1.9%
안 3
 
1.4%
영 3
 
1.4%
곡 3
 
1.4%
Other values (50) 88
42.1%
Common
ValueCountFrequency (%)
1 6
54.5%
) 2
 
18.2%
( 2
 
18.2%
2 1
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 209
95.0%
ASCII 11
 
5.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
터 44
21.1%
널 44
21.1%
대 7
 
3.3%
구 5
 
2.4%
전 4
 
1.9%
봉 4
 
1.9%
덕 4
 
1.9%
안 3
 
1.4%
영 3
 
1.4%
곡 3
 
1.4%
Other values (50) 88
42.1%
ASCII
ValueCountFrequency (%)
1 6
54.5%
) 2
 
18.2%
( 2
 
18.2%
2 1
 
9.1%

대장초기화여부
Categorical

CONSTANT 

Distinct1
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size524.0 B
1
49 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1 49
100.0%

Length

2023-12-13T06:58:46.225489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:58:46.307808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 49
100.0%

Interactions

2023-12-13T06:58:40.585328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:58:35.412045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:58:36.281547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:58:37.110936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:58:37.842613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:58:38.504981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:58:39.222528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:58:39.925520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:58:40.658054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:58:35.488169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:58:36.392296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:58:37.196506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:58:37.914925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:58:38.580267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:58:39.298235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:58:39.998066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:58:40.997293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:58:35.576460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:58:36.519226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:58:37.285964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:58:38.028089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:58:38.668617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:58:39.379928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:58:40.080279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:58:41.073567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:58:35.690249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:58:36.616805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:58:37.357071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:58:38.102844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:58:38.765207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:58:39.467850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:58:40.153132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:58:41.167591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:58:35.800567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:58:36.710793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:58:37.450814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:58:38.182341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:58:38.864652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:58:39.567671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:58:40.229705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:58:41.242768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:58:35.981913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:58:36.822442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:58:37.540826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:58:38.255976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:58:38.953612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:58:39.647687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:58:40.315952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:58:41.327086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:58:36.082437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:58:36.919394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:58:37.640472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:58:38.337224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:58:39.029681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:58:39.730676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:58:40.394911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:58:41.409872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:58:36.183954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:58:37.012373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:58:37.744776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:58:38.422831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:58:39.116670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:58:39.826901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:58:40.484878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T06:58:46.371356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지리정보(WKT)관리번호행정읍면동도엽번호도로구간번호터널명총연장총면적일반제원_터널높이폭원_계폭원_차도왕복차로수라벨
지리정보(WKT)1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
관리번호1.0001.0000.9590.6820.3990.9640.5370.6190.6860.2480.2480.7150.964
행정읍면동1.0000.9591.0000.3950.4310.9940.3600.4650.0000.0000.0000.3810.994
도엽번호1.0000.6820.3951.0000.3451.0000.0000.0000.5970.0000.0000.5441.000
도로구간번호1.0000.3990.4310.3451.0000.9750.6220.3840.0000.0000.0000.3200.975
터널명1.0000.9640.9941.0000.9751.0000.9680.9160.7920.9180.9180.9521.000
총연장1.0000.5370.3600.0000.6220.9681.0000.8370.3250.2740.2740.4540.968
총면적1.0000.6190.4650.0000.3840.9160.8371.0000.2820.3820.3820.7770.916
일반제원_터널높이1.0000.6860.0000.5970.0000.7920.3250.2821.0000.3390.3390.0000.792
폭원_계1.0000.2480.0000.0000.0000.9180.2740.3820.3391.0001.0000.7410.918
폭원_차도1.0000.2480.0000.0000.0000.9180.2740.3820.3391.0001.0000.7410.918
왕복차로수1.0000.7150.3810.5440.3200.9520.4540.7770.0000.7410.7411.0000.952
라벨1.0000.9640.9941.0000.9751.0000.9680.9160.7920.9180.9180.9521.000
2023-12-13T06:58:46.817712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일반제원_터널높이행정읍면동
일반제원_터널높이1.0000.000
행정읍면동0.0001.000
2023-12-13T06:58:46.899770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관리번호도엽번호도로구간번호총연장총면적폭원_계폭원_차도왕복차로수행정읍면동일반제원_터널높이
관리번호1.0000.1390.046-0.109-0.1550.0040.004-0.2920.6830.334
도엽번호0.1391.000-0.384-0.036-0.184-0.376-0.376-0.3340.3270.537
도로구간번호0.046-0.3841.000-0.240-0.268-0.053-0.0530.0630.2200.000
총연장-0.109-0.036-0.2401.0000.8420.0780.0780.0630.2460.220
총면적-0.155-0.184-0.2680.8421.0000.3890.3890.4440.2940.135
폭원_계0.004-0.376-0.0530.0780.3891.0001.0000.5410.0000.185
폭원_차도0.004-0.376-0.0530.0780.3891.0001.0000.5410.0000.185
왕복차로수-0.292-0.3340.0630.0630.4440.5410.5411.0000.2300.000
행정읍면동0.6830.3270.2200.2460.2940.0000.0000.2301.0000.000
일반제원_터널높이0.3340.5370.0000.2200.1350.1850.1850.0000.0001.000

Missing values

2023-12-13T06:58:41.573924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T06:58:41.804330image/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

지리정보(WKT)지형지물부호관리번호행정읍면동도엽번호도로구간번호터널명총연장총면적일반제원_터널높이폭원_계폭원_차도왕복차로수라벨대장초기화여부
0MULTIPOLYGON (((233075.466602563 409908.323785327,233307.460102563 409811.430085327,233330.610102563 409799.820085327,233327.250102563 409792.510085327,233298.680102563 409802.410085327,233045.25908852 409908.074936904,232706.720092773 410049.660095215,232674.910095215 410063.854125977,232678.252075195 410073.304077148,232711.780090332 410060.220092773,233075.466602563 409908.323785327)))터널1대전광역시 중구3671008513780안영1터널707.148244.854.513.5913.595안영1터널1
1MULTIPOLYGON (((232997.815693164 409908.063196802,233296.630102563 409783.510085327,233324.270102563 409770.170085327,233320.260102563 409762.400085327,233289.800102563 409773.680085327,232967.653302563 409907.961985327,232693.989074707 410023.118103027,232681.937072754 410028.542114258,232685.518127441 410037.74407959,232703.546081543 410029.639099121,232997.815693164 409908.063196802)))터널2대전광역시 중구3671008513780안영1터널691.637711.454.511.0811.082안영1터널1
2MULTIPOLYGON (((233895.850102563 408047.110085327,233886.610102563 408044.000085327,233886.022102563 408053.855085327,233885.426102563 408063.848085327,233883.320129395 408070.220092773,233894.130126953 408070.260070801,233894.483102563 408068.891085327,233895.106102563 408058.975085327,233895.850102563 408047.110085327)))터널3대전광역시 중구3671009633600샛고개굴길27.0237.24.59.769.762샛고개굴길1
3MULTIPOLYGON (((233884.798078089 408223.241806802,233876.136321264 408219.629609162,233869.689945798 408327.730160578,233878.610093488 408327.120135625,233878.93009517 408316.760174259,233884.798078089 408223.241806802)))터널4대전광역시 중구3671009616670샛고개굴길109.1925.80.09.59.52샛고개굴길1
4MULTIPOLYGON (((233847.510025925 408323.52010184,233866.983254476 408024.802065831,233854.661759056 408024.754123973,233839.650109713 408320.219933044,233839.210112636 408327.1200011,233847.27005574 408328.610031286,233847.510025925 408323.52010184)))터널5대전광역시 중구3671009616670샛고개굴길102.39821.310.09.269.262샛고개굴길1
5MULTIPOLYGON (((239116.360078263 409013.910012591,239116.040090827 408999.040140952,238500.820035274 409005.209951151,238500.950151738 409020.310131934,239116.360078263 409013.910012591)))터널6대전광역시 중구3671008813460구완터널615.599221.930.014.6114.612구완터널1
6MULTIPOLYGON (((239117.000119509 409051.890114009,239116.669981258 409037.00998552,238501.150161755 409044.620043491,238501.279995106 409058.689978724,239117.000119509 409051.890114009)))터널7대전광역시 중구3671008813460구완터널616.918912.780.013.0913.092구완터널1
7MULTIPOLYGON (((234927.280103597 409509.600135761,234916.020129997 409488.400156295,234840.430017382 409519.750143244,234761.270026136 409549.620064872,234742.730003557 409554.910017375,234689.780151838 409565.38015271,234630.109981247 409571.870096789,234575.530086242 409574.97007018,234336.1700163 409573.799958154,234336.440104184 409604.799972806,234401.720039525 409600.830066623,234563.159980423 409598.260152066,234592.100040765 409597.270123277,234656.899939205 409592.380144574,234702.869967425 409586.180002996,234744.339994645 409576.780065024,234791.329990626 409563.310083259,234927.280103597 409509.600135761)))터널8대전광역시 중구3671008613460안영2터널589.641667.30.024.8524.851안영2터널1
8MULTIPOLYGON (((223697.760102563 413677.690085327,223680.750102563 413605.350085327,223671.480102563 413611.390085327,223687.460102563 413679.990085327,223704.560102563 413755.460085327,223729.630102563 413840.360085327,223757.610102563 413911.100085327,223810.970102563 414015.990085327,223823.940102563 414044.780085327,223837.210102563 414076.550085327,223843.120102563 414103.690085327,223844.420102563 414118.580085327,223844.130102563 414140.110085327,223727.888702563 416021.685685327,223737.318902563 416039.844885327,223854.680102563 414140.140085327,223854.980102563 414118.150085327,223853.580102563 414102.110085327,223847.320102563 414073.360085327,223833.620102563 414040.580085327,223820.500102563 414011.430085327,223767.240102563 413906.750085327,223739.620102563 413836.910085327,223714.780102563 413752.790085327,223697.760102563 413677.690085327)))터널9대전광역시 유성구3671007115320계룡1터널2465.7725892.764.512.6412.642계룡1터널1
9MULTIPOLYGON (((223732.010102563 413748.300085327,223715.120102563 413673.760085327,223705.120102563 413676.190085327,223722.060102563 413750.900085327,223746.670102563 413834.240085327,223775.450102563 413895.630085327,223836.520102563 414003.300085327,223850.090102563 414033.430085327,223864.520102563 414067.950085327,223871.360102563 414099.400085327,223872.940102563 414117.410085327,223872.630102563 414140.180085327,223753.067502563 416070.171285327,223762.152502563 416087.665285327,223883.100102563 414140.210085327,223883.220102563 414116.990085327,223881.550102563 414097.860085327,223874.370102563 414064.850085327,223859.520102563 414029.340085327,223845.700102563 413998.640085327,223784.590102563 413890.900085327,223756.310102563 413830.580085327,223732.010102563 413748.300085327)))터널10대전광역시 유성구3671007115310계룡1터널2449.6225151.144.512.2512.252계룡1터널1
지리정보(WKT)지형지물부호관리번호행정읍면동도엽번호도로구간번호터널명총연장총면적일반제원_터널높이폭원_계폭원_차도왕복차로수라벨대장초기화여부
39MULTIPOLYGON (((241560.770111506 415851.940148178,241550.399968029 415846.84012858,241409.090006362 415952.85013464,241415.299928803 415962.7501346,241560.770111506 415851.940148178)))터널40대전광역시 동구3671006925830용운터널176.691754.070.011.5511.552용운터널1
40MULTIPOLYGON (((242062.160163979 416775.18011023,242052.030012583 416762.210094225,241810.090902801 416982.239320426,241452.239094689 417310.525122641,241464.05594326 417322.068037697,241836.929996812 416982.369943177,241836.933314201 416982.366858987,242062.160163979 416775.18011023)))터널41대전광역시 동구3671006913960대전터널809.9611487.775.016.9116.914대전터널1
41MULTIPOLYGON (((242041.82001718 416749.590017955,242032.400004132 416736.15006953,241760.589315422 416982.000779046,241439.654083972 417269.204101349,241451.619057704 417280.890043728,241785.430098335 416982.119964968,242041.82001718 416749.590017955)))터널42대전광역시 동구3671006913960대전터널797.010389.795.017.4117.413대전터널1
42MULTIPOLYGON (((236879.190031549 418195.54015288,236867.589984717 418192.729954849,236822.590125447 418287.890080435,236816.550172679 418296.970140968,236830.069971083 418298.169977415,236832.980027038 418293.760134502,236878.690053169 418197.08002828,236879.190031549 418195.54015288)))터널43대전광역시 대덕구3671005742000양지터널114.391368.510.015.7915.792양지터널1
43MULTIPOLYGON (((246825.685717977 417445.500103895,246817.781293308 417447.832963633,246808.218241358 417451.436005875,246792.409417024 417456.10176022,246773.66814461 417460.020802338,246754.561808599 417463.572451716,246732.639126203 417465.568031293,246733.029304625 417474.603071588,246749.549515708 417474.143882282,246771.032787638 417472.249625978,246795.101191942 417467.445657504,246812.011149295 417462.785855941,246829.482551191 417456.119062743,246825.685717977 417445.500103895)))터널44대전광역시 동구3671105148170대덕터널230.71078.590.010.010.00대덕터널1
44MULTIPOLYGON (((246391.710919657 416867.393979117,246366.162835733 416858.752168975,246335.529975909 416879.050122709,246257.200071104 416932.290154212,246207.070105828 416961.520022125,246181.774018557 416973.892074004,246187.612969915 416990.489959522,246220.784049996 416974.147078031,246265.290138503 416952.50006783,246343.620045199 416899.260037598,246391.710919657 416867.393979117)))터널45대전광역시 동구3671106114050증약터널755.04642.020.013.613.60증약터널1
45MULTIPOLYGON (((246320.061739663 416843.158165797,246316.877172764 416842.081001314,246296.880015436 416855.330066054,246218.550115373 416908.57009734,246183.480004243 416929.979990307,246152.778040422 416946.974925252,246159.148031378 416963.030117111,246196.721170276 416945.695958992,246232.930027333 416924.200116074,246311.260129642 416870.960086329,246342.299265273 416850.680157018,246320.061739663 416843.158165797)))터널46대전광역시 동구3671106114050증약터널755.04198.120.013.613.60증약터널1
46MULTIPOLYGON (((233774.394058478 425611.116275257,233765.18576597 425610.075148021,233753.960806074 425608.397920618,233746.26733586 425607.237074442,233732.403544543 425602.659666663,233711.736473166 425595.165850411,233694.360713283 425585.046269737,233678.501701283 425574.304371721,233670.251581972 425586.460202539,233695.424050834 425603.646412944,233724.153375531 425614.815407136,233749.48811784 425622.704387474,233776.61504265 425624.066724896,233774.394058478 425611.116275257)))터널47대전광역시 유성구3671002613050덕진터널103.01559.830.015.015.00덕진터널1
47MULTIPOLYGON (((240658.420456553 400232.38586841,240654.073298122 400221.496559239,240524.087407036 400330.942757062,240520.550123087 400333.903115954,240528.548964222 400342.720145095,240542.996234507 400331.033012225,240658.420456553 400232.38586841)))터널48대전광역시 동구3671402948190추부터널322.122022.064.511.7211.720추부터널1
48MULTIPOLYGON (((241912.336984907 404515.962130976,242047.196008113 404620.912984481,242053.207267822 404612.766424344,241917.604087719 404507.316305964,241912.336984907 404515.962130976)))터널49대전광역시 동구3671400918150상소터널175.01728.670.09.919.910상소터널1