Overview

Dataset statistics

Number of variables17
Number of observations9170
Missing cells20912
Missing cells (%)13.4%
Duplicate rows8
Duplicate rows (%)0.1%
Total size in memory1.2 MiB
Average record size in memory141.0 B

Variable types

Text4
Categorical7
Numeric5
DateTime1

Dataset

Description대전광역시 도로관리시스템을 통해 신청되었던 도로 굴착 허가 위치 목록입니다.(지리정보를 포함한 데이터입니다.)
Author대전광역시
URLhttps://www.data.go.kr/data/15110273/fileData.do

Alerts

지형지물부호 has constant value ""Constant
Dataset has 8 (0.1%) duplicate rowsDuplicates
변경일자 is highly overall correlated with 관리번호 and 7 other fieldsHigh correlation
공사유형코드 is highly overall correlated with 허가일련번호 and 3 other fieldsHigh correlation
관리번호 is highly overall correlated with 점용시작일 and 3 other fieldsHigh correlation
허가일련번호 is highly overall correlated with 포장종류 and 4 other fieldsHigh correlation
일시점용_굴착폭 is highly overall correlated with 일시점용_굴착길이 and 4 other fieldsHigh correlation
일시점용_굴착길이 is highly overall correlated with 일시점용_굴착폭 and 2 other fieldsHigh correlation
일시점용_굴착면적 is highly overall correlated with 일시점용_굴착폭 and 4 other fieldsHigh correlation
포장종류 is highly overall correlated with 허가일련번호 and 3 other fieldsHigh correlation
점용시작일 is highly overall correlated with 관리번호 and 7 other fieldsHigh correlation
점용종료일 is highly overall correlated with 관리번호 and 7 other fieldsHigh correlation
차도보도구분 is highly overall correlated with 관리번호 and 3 other fieldsHigh correlation
점용시작일 is highly imbalanced (97.3%)Imbalance
점용종료일 is highly imbalanced (97.5%)Imbalance
변경일자 is highly imbalanced (98.8%)Imbalance
공사유형코드 is highly imbalanced (51.6%)Imbalance
허가일련번호 has 3279 (35.8%) missing valuesMissing
굴착시작일 has 4083 (44.5%) missing valuesMissing
굴착종료일 has 6258 (68.2%) missing valuesMissing
복구일자 has 7292 (79.5%) missing valuesMissing
허가일련번호 is highly skewed (γ1 = -76.63665851)Skewed
일시점용_굴착폭 has 2305 (25.1%) zerosZeros
일시점용_굴착길이 has 2304 (25.1%) zerosZeros
일시점용_굴착면적 has 2305 (25.1%) zerosZeros

Reproduction

Analysis started2023-12-12 08:31:39.788746
Analysis finished2023-12-12 08:31:46.728200
Duration6.94 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Distinct8648
Distinct (%)94.3%
Missing0
Missing (%)0.0%
Memory size71.8 KiB
2023-12-12T17:31:46.964264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length1024
Median length1001
Mean length239.74678
Min length118

Characters and Unicode

Total characters2198478
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

Unique8141 ?
Unique (%)88.8%

Sample

1st rowMULTIPOLYGON (((231133.883642406 417810.675354974,231111.880942371 417691.543954788,231109.520857628 417691.979845213,231131.523557596 417811.111245032,231136.347057694 417837.22764557,231150.187057829 417912.162546292,231152.547142172 417911.726653708,231138.707142307 417836.791754434,231133.883642406 417810.675354974)))
2nd rowMULTIPOLYGON (((230767.624239422 417408.890850993,230751.877338797 417360.704449078,230749.596061204 417361.449950924,230765.34296058 417409.636349009,230769.47335945 417422.275745556,230771.589160089 417428.75034751,230793.763661101 417496.605550608,230796.279360969 417504.303750204,230798.560639031 417503.558249796,230796.044938901 417495.860049393,230773.870439915 417428.00485249,230771.754640552 417421.53025445,230767.624239422 417408.890850993)))
3rd rowMULTIPOLYGON (((227389.977145541 416046.092835968,227388.058745543 416043.500135969,227386.129454459 416044.927664032,227388.04785446 416047.520364035,227389.977145541 416046.092835968)))
4th rowMULTIPOLYGON (((230502.993401332 417400.449617021,230502.219598669 417398.177782983,230471.354998669 417408.690482982,230472.128801332 417410.962317022,230502.993401332 417400.449617021)))
5th rowMULTIPOLYGON (((230196.555211559 417849.439200684,230194.179388441 417849.099399321,230192.764788442 417858.989999318,230195.14061156 417859.329800684,230196.555211559 417849.439200684)))
ValueCountFrequency (%)
multipolygon 9170
 
11.6%
413699.6592,230879.4023 4
 
< 0.1%
414839.701900002,230658.258000001 4
 
< 0.1%
418768.358900001,234082.8084 4
 
< 0.1%
418762.358900001,234080.8084 4
 
< 0.1%
414013.954800002,231068.656300001 4
 
< 0.1%
414015.954800002,231070.656300001 4
 
< 0.1%
416368.299200002,234578.5009 4
 
< 0.1%
416366.299200002,234577.000900001 4
 
< 0.1%
412130.640700003,230160.470800002 4
 
< 0.1%
Other values (64397) 69650
88.3%
2023-12-12T17:31:47.526438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 226260
10.3%
4 222768
10.1%
1 206686
9.4%
3 203025
9.2%
0 159799
 
7.3%
7 156401
 
7.1%
9 155135
 
7.1%
5 154248
 
7.0%
8 153786
 
7.0%
6 153621
 
7.0%
Other values (15) 406749
18.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1791729
81.5%
Other Punctuation 172398
 
7.8%
Uppercase Letter 110040
 
5.0%
Space Separator 69686
 
3.2%
Open Punctuation 27511
 
1.3%
Close Punctuation 27114
 
1.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 226260
12.6%
4 222768
12.4%
1 206686
11.5%
3 203025
11.3%
0 159799
8.9%
7 156401
8.7%
9 155135
8.7%
5 154248
8.6%
8 153786
8.6%
6 153621
8.6%
Uppercase Letter
ValueCountFrequency (%)
O 18340
16.7%
L 18340
16.7%
U 9170
8.3%
N 9170
8.3%
G 9170
8.3%
Y 9170
8.3%
P 9170
8.3%
I 9170
8.3%
T 9170
8.3%
M 9170
8.3%
Other Punctuation
ValueCountFrequency (%)
. 121033
70.2%
, 51365
29.8%
Space Separator
ValueCountFrequency (%)
69686
100.0%
Open Punctuation
ValueCountFrequency (%)
( 27511
100.0%
Close Punctuation
ValueCountFrequency (%)
) 27114
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2088438
95.0%
Latin 110040
 
5.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 226260
10.8%
4 222768
10.7%
1 206686
9.9%
3 203025
9.7%
0 159799
7.7%
7 156401
7.5%
9 155135
7.4%
5 154248
7.4%
8 153786
7.4%
6 153621
7.4%
Other values (5) 296709
14.2%
Latin
ValueCountFrequency (%)
O 18340
16.7%
L 18340
16.7%
U 9170
8.3%
N 9170
8.3%
G 9170
8.3%
Y 9170
8.3%
P 9170
8.3%
I 9170
8.3%
T 9170
8.3%
M 9170
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2198478
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 226260
10.3%
4 222768
10.1%
1 206686
9.4%
3 203025
9.2%
0 159799
 
7.3%
7 156401
 
7.1%
9 155135
 
7.1%
5 154248
 
7.0%
8 153786
 
7.0%
6 153621
 
7.0%
Other values (15) 406749
18.5%

지형지물부호
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size71.8 KiB
굴착허가위치
9170 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
굴착허가위치 9170
100.0%

Length

2023-12-12T17:31:47.699360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T17:31:47.803457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
굴착허가위치 9170
100.0%

관리번호
Real number (ℝ)

HIGH CORRELATION 

Distinct9159
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4587.1953
Minimum0
Maximum9179
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size80.7 KiB
2023-12-12T17:31:47.952661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile458.45
Q12292.25
median4584.5
Q36883.75
95-th percentile8719.55
Maximum9179
Range9179
Interquartile range (IQR)4591.5

Descriptive statistics

Standard deviation2650.4557
Coefficient of variation (CV)0.57779439
Kurtosis-1.2003043
Mean4587.1953
Median Absolute Deviation (MAD)2296
Skewness0.0015154467
Sum42064581
Variance7024915.4
MonotonicityNot monotonic
2023-12-12T17:31:48.120936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6916 4
 
< 0.1%
7853 3
 
< 0.1%
6017 2
 
< 0.1%
5893 2
 
< 0.1%
9051 2
 
< 0.1%
6007 2
 
< 0.1%
5963 2
 
< 0.1%
6019 2
 
< 0.1%
6125 1
 
< 0.1%
6124 1
 
< 0.1%
Other values (9149) 9149
99.8%
ValueCountFrequency (%)
0 1
< 0.1%
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
ValueCountFrequency (%)
9179 1
< 0.1%
9178 1
< 0.1%
9177 1
< 0.1%
9176 1
< 0.1%
9175 1
< 0.1%
9174 1
< 0.1%
9173 1
< 0.1%
9172 1
< 0.1%
9171 1
< 0.1%
9170 1
< 0.1%

허가일련번호
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct2972
Distinct (%)50.4%
Missing3279
Missing (%)35.8%
Infinite0
Infinite (%)0.0%
Mean2.0138488 × 109
Minimum0
Maximum2.015214 × 109
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size80.7 KiB
2023-12-12T17:31:48.275126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.013013 × 109
Q12.0131145 × 109
median2.014088 × 109
Q32.015046 × 109
95-th percentile2.015173 × 109
Maximum2.015214 × 109
Range2.015214 × 109
Interquartile range (IQR)1931504

Descriptive statistics

Standard deviation26255805
Coefficient of variation (CV)0.013037625
Kurtosis5879.1095
Mean2.0138488 × 109
Median Absolute Deviation (MAD)963993
Skewness-76.636659
Sum1.1863583 × 1013
Variance6.8936727 × 1014
MonotonicityNot monotonic
2023-12-12T17:31:48.450374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2013116001 46
 
0.5%
2014105001 34
 
0.4%
2013096001 28
 
0.3%
2014023001 23
 
0.3%
2013110001 22
 
0.2%
2013103001 22
 
0.2%
2015044001 16
 
0.2%
2015033003 16
 
0.2%
2015009001 16
 
0.2%
2014024001 14
 
0.2%
Other values (2962) 5654
61.7%
(Missing) 3279
35.8%
ValueCountFrequency (%)
0 1
 
< 0.1%
2013001001 2
 
< 0.1%
2013002001 4
 
< 0.1%
2013002002 2
 
< 0.1%
2013002003 2
 
< 0.1%
2013002004 2
 
< 0.1%
2013002005 13
0.1%
2013003001 4
 
< 0.1%
2013003002 2
 
< 0.1%
2013003003 2
 
< 0.1%
ValueCountFrequency (%)
2015214001 2
< 0.1%
2015213002 1
< 0.1%
2015213001 2
< 0.1%
2015212002 2
< 0.1%
2015212001 2
< 0.1%
2015211001 2
< 0.1%
2015210001 2
< 0.1%
2015209001 2
< 0.1%
2015208001 2
< 0.1%
2015207028 2
< 0.1%
Distinct134
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size71.8 KiB
2023-12-12T17:31:48.825799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length13
Mean length11.311668
Min length8

Characters and Unicode

Total characters103728
Distinct characters115
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)0.1%

Sample

1st row대전광역시 유성구 궁동
2nd row대전광역시 유성구 궁동
3rd row대전광역시 유성구 계산동
4th row대전광역시 유성구 죽동
5th row대전광역시 유성구 죽동
ValueCountFrequency (%)
대전광역시 9170
36.4%
서구 2816
 
11.2%
중구 2108
 
8.4%
유성구 2091
 
8.3%
동구 1178
 
4.7%
대덕구 977
 
3.9%
봉명동 331
 
1.3%
유천동 281
 
1.1%
구암동 254
 
1.0%
선화동 236
 
0.9%
Other values (126) 5764
22.9%
2023-12-12T17:31:49.348320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
16036
15.5%
10800
10.4%
9445
9.1%
9297
9.0%
9170
8.8%
9170
8.8%
9170
8.8%
8044
7.8%
2834
 
2.7%
2374
 
2.3%
Other values (105) 17388
16.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 87692
84.5%
Space Separator 16036
 
15.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
10800
12.3%
9445
10.8%
9297
10.6%
9170
10.5%
9170
10.5%
9170
10.5%
8044
9.2%
2834
 
3.2%
2374
 
2.7%
2372
 
2.7%
Other values (104) 15016
17.1%
Space Separator
ValueCountFrequency (%)
16036
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 87692
84.5%
Common 16036
 
15.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
10800
12.3%
9445
10.8%
9297
10.6%
9170
10.5%
9170
10.5%
9170
10.5%
8044
9.2%
2834
 
3.2%
2374
 
2.7%
2372
 
2.7%
Other values (104) 15016
17.1%
Common
ValueCountFrequency (%)
16036
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 87692
84.5%
ASCII 16036
 
15.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
16036
100.0%
Hangul
ValueCountFrequency (%)
10800
12.3%
9445
10.8%
9297
10.6%
9170
10.5%
9170
10.5%
9170
10.5%
8044
9.2%
2834
 
3.2%
2374
 
2.7%
2372
 
2.7%
Other values (104) 15016
17.1%

굴착시작일
Date

MISSING 

Distinct636
Distinct (%)12.5%
Missing4083
Missing (%)44.5%
Memory size71.8 KiB
Minimum2013-03-11 00:00:00
Maximum2022-09-20 00:00:00
2023-12-12T17:31:49.515819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:31:49.672309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

굴착종료일
Text

MISSING 

Distinct444
Distinct (%)15.2%
Missing6258
Missing (%)68.2%
Memory size71.8 KiB
2023-12-12T17:31:49.963987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters29120
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique152 ?
Unique (%)5.2%

Sample

1st row2016-01-04
2nd row2016-01-04
3rd row2015-12-02
4th row2014-08-24
5th row2014-08-24
ValueCountFrequency (%)
2020-11-30 175
 
6.0%
2015-12-21 62
 
2.1%
2020-06-29 59
 
2.0%
2013-11-03 50
 
1.7%
2020-07-25 49
 
1.7%
2020-05-20 45
 
1.5%
2020-03-28 45
 
1.5%
2013-12-19 44
 
1.5%
2013-11-30 43
 
1.5%
2013-10-19 39
 
1.3%
Other values (434) 2301
79.0%
2023-12-12T17:31:50.431457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 6687
23.0%
- 5824
20.0%
2 5295
18.2%
1 4964
17.0%
3 1518
 
5.2%
5 1218
 
4.2%
7 835
 
2.9%
8 775
 
2.7%
4 726
 
2.5%
6 651
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23296
80.0%
Dash Punctuation 5824
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 6687
28.7%
2 5295
22.7%
1 4964
21.3%
3 1518
 
6.5%
5 1218
 
5.2%
7 835
 
3.6%
8 775
 
3.3%
4 726
 
3.1%
6 651
 
2.8%
9 627
 
2.7%
Dash Punctuation
ValueCountFrequency (%)
- 5824
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 29120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 6687
23.0%
- 5824
20.0%
2 5295
18.2%
1 4964
17.0%
3 1518
 
5.2%
5 1218
 
4.2%
7 835
 
2.9%
8 775
 
2.7%
4 726
 
2.5%
6 651
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 29120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 6687
23.0%
- 5824
20.0%
2 5295
18.2%
1 4964
17.0%
3 1518
 
5.2%
5 1218
 
4.2%
7 835
 
2.9%
8 775
 
2.7%
4 726
 
2.5%
6 651
 
2.2%

포장종류
Categorical

HIGH CORRELATION 

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size71.8 KiB
<NA>
3783 
아스팔트콘크리트
1950 
화강석
1627 
기타
1351 
고압블록
 
203
Other values (7)
 
256

Length

Max length8
Median length6
Mean length4.3533261
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 3783
41.3%
아스팔트콘크리트 1950
21.3%
화강석 1627
17.7%
기타 1351
 
14.7%
고압블록 203
 
2.2%
투수콘 154
 
1.7%
블록 34
 
0.4%
AC 26
 
0.3%
일반사각블록 17
 
0.2%
콘크리트 13
 
0.1%
Other values (2) 12
 
0.1%

Length

2023-12-12T17:31:50.606425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 3783
41.3%
아스팔트콘크리트 1950
21.3%
화강석 1627
17.7%
기타 1351
 
14.7%
고압블록 203
 
2.2%
투수콘 154
 
1.7%
블록 34
 
0.4%
ac 26
 
0.3%
일반사각블록 17
 
0.2%
콘크리트 13
 
0.1%
Other values (2) 12
 
0.1%

일시점용_굴착폭
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct38
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.93489967
Minimum0
Maximum30
Zeros2305
Zeros (%)25.1%
Negative0
Negative (%)0.0%
Memory size80.7 KiB
2023-12-12T17:31:50.775496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.2
Q31.2
95-th percentile2
Maximum30
Range30
Interquartile range (IQR)1.2

Descriptive statistics

Standard deviation0.74924957
Coefficient of variation (CV)0.80142243
Kurtosis265.93461
Mean0.93489967
Median Absolute Deviation (MAD)0.2
Skewness8.1734498
Sum8573.03
Variance0.56137491
MonotonicityNot monotonic
2023-12-12T17:31:51.053405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
1.2 4296
46.8%
0.0 2305
25.1%
1.0 1837
20.0%
2.0 408
 
4.4%
2.5 144
 
1.6%
0.5 58
 
0.6%
3.0 19
 
0.2%
1.5 13
 
0.1%
1.7 8
 
0.1%
8.0 8
 
0.1%
Other values (28) 74
 
0.8%
ValueCountFrequency (%)
0.0 2305
25.1%
0.3 2
 
< 0.1%
0.5 58
 
0.6%
0.6 3
 
< 0.1%
0.63 2
 
< 0.1%
0.7 2
 
< 0.1%
0.8 4
 
< 0.1%
0.9 5
 
0.1%
1.0 1837
20.0%
1.1 5
 
0.1%
ValueCountFrequency (%)
30.0 1
 
< 0.1%
12.0 1
 
< 0.1%
10.0 2
 
< 0.1%
9.0 1
 
< 0.1%
8.0 8
0.1%
7.0 3
 
< 0.1%
6.0 2
 
< 0.1%
5.0 2
 
< 0.1%
4.0 7
0.1%
3.6 2
 
< 0.1%

일시점용_굴착길이
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct445
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean66.962009
Minimum0
Maximum4000
Zeros2304
Zeros (%)25.1%
Negative0
Negative (%)0.0%
Memory size80.7 KiB
2023-12-12T17:31:51.217878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q38
95-th percentile387
Maximum4000
Range4000
Interquartile range (IQR)8

Descriptive statistics

Standard deviation257.0054
Coefficient of variation (CV)3.8380779
Kurtosis49.404095
Mean66.962009
Median Absolute Deviation (MAD)2
Skewness6.4441881
Sum614041.62
Variance66051.778
MonotonicityNot monotonic
2023-12-12T17:31:51.383539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 2304
25.1%
1.0 1909
20.8%
2.0 912
 
9.9%
3.0 448
 
4.9%
5.0 359
 
3.9%
6.0 325
 
3.5%
4.0 284
 
3.1%
7.0 273
 
3.0%
8.0 158
 
1.7%
10.0 115
 
1.3%
Other values (435) 2083
22.7%
ValueCountFrequency (%)
0.0 2304
25.1%
1.0 1909
20.8%
1.5 4
 
< 0.1%
1.7 1
 
< 0.1%
2.0 912
 
9.9%
2.2 1
 
< 0.1%
2.5 1
 
< 0.1%
3.0 448
 
4.9%
3.5 4
 
< 0.1%
3.7 1
 
< 0.1%
ValueCountFrequency (%)
4000.0 1
 
< 0.1%
2690.0 7
 
0.1%
2315.0 33
0.4%
2308.0 1
 
< 0.1%
2300.0 1
 
< 0.1%
2250.0 2
 
< 0.1%
2117.0 1
 
< 0.1%
2110.0 1
 
< 0.1%
2043.0 5
 
0.1%
2040.0 3
 
< 0.1%

일시점용_굴착면적
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct549
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean98.648249
Minimum0
Maximum11665.5
Zeros2305
Zeros (%)25.1%
Negative0
Negative (%)0.0%
Memory size80.7 KiB
2023-12-12T17:31:51.556398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2.4
Q39.6
95-th percentile465.6
Maximum11665.5
Range11665.5
Interquartile range (IQR)9.6

Descriptive statistics

Standard deviation445.76039
Coefficient of variation (CV)4.5186853
Kurtosis112.28689
Mean98.648249
Median Absolute Deviation (MAD)2.4
Skewness8.9345589
Sum904604.44
Variance198702.33
MonotonicityNot monotonic
2023-12-12T17:31:51.715824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 2305
25.1%
1.0 1636
17.8%
2.4 648
 
7.1%
3.6 396
 
4.3%
6.0 358
 
3.9%
7.2 296
 
3.2%
1.2 268
 
2.9%
8.4 258
 
2.8%
4.0 257
 
2.8%
4.8 253
 
2.8%
Other values (539) 2495
27.2%
ValueCountFrequency (%)
0.0 2305
25.1%
0.18 1
 
< 0.1%
1.0 1636
17.8%
1.2 268
 
2.9%
1.5 2
 
< 0.1%
1.6 1
 
< 0.1%
1.75 2
 
< 0.1%
2.0 32
 
0.3%
2.04 1
 
< 0.1%
2.1 1
 
< 0.1%
ValueCountFrequency (%)
11665.5 1
 
< 0.1%
6200.0 3
 
< 0.1%
4800.0 1
 
< 0.1%
4630.0 33
0.4%
4074.5 8
 
0.1%
3749.88 1
 
< 0.1%
3228.0 7
 
0.1%
3174.25 9
 
0.1%
2849.7 5
 
0.1%
2700.0 2
 
< 0.1%

점용시작일
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct16
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size71.8 KiB
<NA>
9083 
2014-03-10
 
18
2014-03-19
 
15
2014-04-30
 
11
2014-05-21
 
10
Other values (11)
 
33

Length

Max length10
Median length4
Mean length4.0569248
Min length4

Unique

Unique5 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 9083
99.1%
2014-03-10 18
 
0.2%
2014-03-19 15
 
0.2%
2014-04-30 11
 
0.1%
2014-05-21 10
 
0.1%
2014-03-28 8
 
0.1%
2014-04-17 5
 
0.1%
2014-04-21 5
 
0.1%
2014-06-10 5
 
0.1%
2014-06-02 3
 
< 0.1%
Other values (6) 7
 
0.1%

Length

2023-12-12T17:31:51.863111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 9083
99.1%
2014-03-10 18
 
0.2%
2014-03-19 15
 
0.2%
2014-04-30 11
 
0.1%
2014-05-21 10
 
0.1%
2014-03-28 8
 
0.1%
2014-04-17 5
 
0.1%
2014-04-21 5
 
0.1%
2014-06-10 5
 
0.1%
2014-06-02 3
 
< 0.1%
Other values (6) 7
 
0.1%

점용종료일
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct19
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size71.8 KiB
<NA>
9088 
2014-10-27
 
17
2014-05-09
 
13
2014-11-03
 
11
2016-03-15
 
8
Other values (14)
 
33

Length

Max length10
Median length4
Mean length4.0536532
Min length4

Unique

Unique8 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 9088
99.1%
2014-10-27 17
 
0.2%
2014-05-09 13
 
0.1%
2014-11-03 11
 
0.1%
2016-03-15 8
 
0.1%
2014-06-16 7
 
0.1%
2014-08-27 5
 
0.1%
2014-04-24 4
 
< 0.1%
2014-06-10 4
 
< 0.1%
2014-07-01 3
 
< 0.1%
Other values (9) 10
 
0.1%

Length

2023-12-12T17:31:51.988680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 9088
99.1%
2014-10-27 17
 
0.2%
2014-05-09 13
 
0.1%
2014-11-03 11
 
0.1%
2016-03-15 8
 
0.1%
2014-06-16 7
 
0.1%
2014-08-27 5
 
0.1%
2014-04-24 4
 
< 0.1%
2014-06-10 4
 
< 0.1%
2014-07-01 3
 
< 0.1%
Other values (9) 10
 
0.1%

변경일자
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size71.8 KiB
<NA>
9138 
2014-11-12
 
14
2015-05-21
 
9
2014-09-03
 
2
2014-12-01
 
1
Other values (6)
 
6

Length

Max length10
Median length4
Mean length4.0209378
Min length4

Unique

Unique7 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 9138
99.7%
2014-11-12 14
 
0.2%
2015-05-21 9
 
0.1%
2014-09-03 2
 
< 0.1%
2014-12-01 1
 
< 0.1%
2014-10-20 1
 
< 0.1%
2013-07-04 1
 
< 0.1%
2014-05-23 1
 
< 0.1%
2014-08-29 1
 
< 0.1%
2015-06-12 1
 
< 0.1%

Length

2023-12-12T17:31:52.138958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 9138
99.7%
2014-11-12 14
 
0.2%
2015-05-21 9
 
0.1%
2014-09-03 2
 
< 0.1%
2014-12-01 1
 
< 0.1%
2014-10-20 1
 
< 0.1%
2013-07-04 1
 
< 0.1%
2014-05-23 1
 
< 0.1%
2014-08-29 1
 
< 0.1%
2015-06-12 1
 
< 0.1%

복구일자
Text

MISSING 

Distinct294
Distinct (%)15.7%
Missing7292
Missing (%)79.5%
Memory size71.8 KiB
2023-12-12T17:31:52.496725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters18780
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique102 ?
Unique (%)5.4%

Sample

1st row2016-01-04
2nd row2016-01-04
3rd row2015-12-02
4th row2014-08-24
5th row2014-08-24
ValueCountFrequency (%)
2015-12-21 69
 
3.7%
2013-12-27 67
 
3.6%
2013-11-03 50
 
2.7%
2013-12-19 44
 
2.3%
2015-11-23 41
 
2.2%
2013-10-19 39
 
2.1%
2014-04-23 36
 
1.9%
2014-08-24 34
 
1.8%
2014-10-27 33
 
1.8%
2014-12-20 33
 
1.8%
Other values (284) 1432
76.3%
2023-12-12T17:31:53.457274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 3782
20.1%
- 3756
20.0%
0 3662
19.5%
2 3270
17.4%
3 1214
 
6.5%
5 932
 
5.0%
4 652
 
3.5%
7 408
 
2.2%
8 394
 
2.1%
6 384
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15024
80.0%
Dash Punctuation 3756
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 3782
25.2%
0 3662
24.4%
2 3270
21.8%
3 1214
 
8.1%
5 932
 
6.2%
4 652
 
4.3%
7 408
 
2.7%
8 394
 
2.6%
6 384
 
2.6%
9 326
 
2.2%
Dash Punctuation
ValueCountFrequency (%)
- 3756
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 18780
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 3782
20.1%
- 3756
20.0%
0 3662
19.5%
2 3270
17.4%
3 1214
 
6.5%
5 932
 
5.0%
4 652
 
3.5%
7 408
 
2.2%
8 394
 
2.1%
6 384
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 18780
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 3782
20.1%
- 3756
20.0%
0 3662
19.5%
2 3270
17.4%
3 1214
 
6.5%
5 932
 
5.0%
4 652
 
3.5%
7 408
 
2.2%
8 394
 
2.1%
6 384
 
2.0%

공사유형코드
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size71.8 KiB
<NA>
5581 
미분류
1938 
소규모
917 
기타
624 
보수
 
42
Other values (5)
 
68

Length

Max length4
Median length4
Mean length3.5285714
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row기타
2nd row기타
3rd row소규모
4th row기타
5th row기타

Common Values

ValueCountFrequency (%)
<NA> 5581
60.9%
미분류 1938
 
21.1%
소규모 917
 
10.0%
기타 624
 
6.8%
보수 42
 
0.5%
개량 27
 
0.3%
교체 16
 
0.2%
이설 15
 
0.2%
신설 8
 
0.1%
긴급 2
 
< 0.1%

Length

2023-12-12T17:31:53.643707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T17:31:53.807213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 5581
60.9%
미분류 1938
 
21.1%
소규모 917
 
10.0%
기타 624
 
6.8%
보수 42
 
0.5%
개량 27
 
0.3%
교체 16
 
0.2%
이설 15
 
0.2%
신설 8
 
0.1%
긴급 2
 
< 0.1%

차도보도구분
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size71.8 KiB
차도
3340 
<NA>
2301 
기타
1721 
미분류
1170 
보도
638 

Length

Max length4
Median length2
Mean length2.6294438
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
차도 3340
36.4%
<NA> 2301
25.1%
기타 1721
18.8%
미분류 1170
 
12.8%
보도 638
 
7.0%

Length

2023-12-12T17:31:53.999854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T17:31:54.167034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
차도 3340
36.4%
na 2301
25.1%
기타 1721
18.8%
미분류 1170
 
12.8%
보도 638
 
7.0%

Interactions

2023-12-12T17:31:45.249703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:31:42.273959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:31:42.857607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:31:43.538014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:31:44.192634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:31:45.366482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:31:42.375582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:31:42.969246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:31:43.646562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:31:44.634416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:31:45.508605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:31:42.521164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:31:43.130944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:31:43.791152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:31:44.811981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:31:45.630225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:31:42.625878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:31:43.265973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:31:43.912927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:31:44.966071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:31:45.764569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:31:42.752207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:31:43.409502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:31:44.063022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:31:45.103024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T17:31:54.285886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관리번호허가일련번호포장종류일시점용_굴착폭일시점용_굴착길이일시점용_굴착면적점용시작일점용종료일변경일자공사유형코드차도보도구분
관리번호1.0000.0000.6980.0870.2310.174NaNNaN1.0000.6050.815
허가일련번호0.0001.000NaN0.0000.0000.000NaNNaNNaNNaN0.039
포장종류0.698NaN1.0000.0420.2140.1190.8120.8850.8040.1200.667
일시점용_굴착폭0.0870.0000.0421.0000.0650.512NaNNaNNaN0.0790.052
일시점용_굴착길이0.2310.0000.2140.0651.0000.8350.7540.7391.0000.3370.331
일시점용_굴착면적0.1740.0000.1190.5120.8351.0000.9400.9021.0000.2350.154
점용시작일NaNNaN0.812NaN0.7540.9401.0000.997NaN0.8930.801
점용종료일NaNNaN0.885NaN0.7390.9020.9971.000NaN1.0000.976
변경일자1.000NaN0.804NaN1.0001.000NaNNaN1.0001.0001.000
공사유형코드0.605NaN0.1200.0790.3370.2350.8931.0001.0001.0000.605
차도보도구분0.8150.0390.6670.0520.3310.1540.8010.9761.0000.6051.000
2023-12-12T17:31:54.470969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
차도보도구분점용종료일변경일자공사유형코드포장종류점용시작일
차도보도구분1.0000.8340.8560.4350.4730.560
점용종료일0.8341.000NaN0.8940.6440.899
변경일자0.856NaN1.0000.9200.603NaN
공사유형코드0.4350.8940.9201.0000.0590.799
포장종류0.4730.6440.6030.0591.0000.574
점용시작일0.5600.899NaN0.7990.5741.000
2023-12-12T17:31:54.649003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관리번호허가일련번호일시점용_굴착폭일시점용_굴착길이일시점용_굴착면적포장종류점용시작일점용종료일변경일자공사유형코드차도보도구분
관리번호1.000-0.1800.056-0.023-0.0270.4101.0001.0000.8860.3520.682
허가일련번호-0.1801.000-0.294-0.328-0.3271.0001.0001.0001.0001.0000.026
일시점용_굴착폭0.056-0.2941.0000.8160.8460.0191.0001.0001.0000.0000.036
일시점용_굴착길이-0.023-0.3280.8161.0000.9950.1020.4450.3580.8710.1720.153
일시점용_굴착면적-0.027-0.3270.8460.9951.0000.0580.6790.6070.8560.1260.106
포장종류0.4101.0000.0190.1020.0581.0000.5740.6440.6030.0590.473
점용시작일1.0001.0001.0000.4450.6790.5741.0000.8990.0000.7990.560
점용종료일1.0001.0001.0000.3580.6070.6440.8991.0000.0000.8940.834
변경일자0.8861.0001.0000.8710.8560.6030.0000.0001.0000.9200.856
공사유형코드0.3521.0000.0000.1720.1260.0590.7990.8940.9201.0000.435
차도보도구분0.6820.0260.0360.1530.1060.4730.5600.8340.8560.4351.000

Missing values

2023-12-12T17:31:45.988894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T17:31:46.351637image/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.
2023-12-12T17:31:46.578688image/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

지리정보(WKT)지형지물부호관리번호허가일련번호행정읍면동굴착시작일굴착종료일포장종류일시점용_굴착폭일시점용_굴착길이일시점용_굴착면적점용시작일점용종료일변경일자복구일자공사유형코드차도보도구분
0MULTIPOLYGON (((231133.883642406 417810.675354974,231111.880942371 417691.543954788,231109.520857628 417691.979845213,231131.523557596 417811.111245032,231136.347057694 417837.22764557,231150.187057829 417912.162546292,231152.547142172 417911.726653708,231138.707142307 417836.791754434,231133.883642406 417810.675354974)))굴착허가위치12015159001대전광역시 유성구 궁동2015-11-072016-01-04기타1.2643.0773.7<NA><NA><NA>2016-01-04기타미분류
1MULTIPOLYGON (((230767.624239422 417408.890850993,230751.877338797 417360.704449078,230749.596061204 417361.449950924,230765.34296058 417409.636349009,230769.47335945 417422.275745556,230771.589160089 417428.75034751,230793.763661101 417496.605550608,230796.279360969 417504.303750204,230798.560639031 417503.558249796,230796.044938901 417495.860049393,230773.870439915 417428.00485249,230771.754640552 417421.53025445,230767.624239422 417408.890850993)))굴착허가위치22015159001대전광역시 유성구 궁동2015-11-072016-01-04기타1.2643.0773.7<NA><NA><NA>2016-01-04기타미분류
2MULTIPOLYGON (((227389.977145541 416046.092835968,227388.058745543 416043.500135969,227386.129454459 416044.927664032,227388.04785446 416047.520364035,227389.977145541 416046.092835968)))굴착허가위치32015160001대전광역시 유성구 계산동2015-12-012015-12-02기타1.25.06.0<NA><NA><NA>2015-12-02소규모미분류
3MULTIPOLYGON (((230502.993401332 417400.449617021,230502.219598669 417398.177782983,230471.354998669 417408.690482982,230472.128801332 417410.962317022,230502.993401332 417400.449617021)))굴착허가위치42014023001대전광역시 유성구 죽동2014-04-252014-08-24기타1.2556.0667.2<NA><NA><NA>2014-08-24기타미분류
4MULTIPOLYGON (((230196.555211559 417849.439200684,230194.179388441 417849.099399321,230192.764788442 417858.989999318,230195.14061156 417859.329800684,230196.555211559 417849.439200684)))굴착허가위치52014023001대전광역시 유성구 죽동2014-04-252014-08-24기타1.2556.0667.2<NA><NA><NA>2014-08-24기타미분류
5MULTIPOLYGON (((230176.931864933 417905.479985174,230174.585935067 417904.973414829,230173.705435066 417909.051014829,230176.051364933 417909.557585174,230176.931864933 417905.479985174)))굴착허가위치62014023001대전광역시 유성구 죽동2014-04-252014-08-24기타1.2556.0667.2<NA><NA><NA>2014-08-24기타미분류
6MULTIPOLYGON (((230364.963385377 417448.658666143,230364.192814622 417446.38573386,230347.424214624 417452.070633858,230348.19478538 417454.343566142,230364.963385377 417448.658666143)))굴착허가위치72014023001대전광역시 유성구 죽동2014-04-252014-08-24기타1.2556.0667.2<NA><NA><NA>2014-08-24기타미분류
7MULTIPOLYGON (((230054.980848439 417543.108661419,230054.318951561 417540.801738583,230048.32385156 417542.521838581,230048.98574844 417544.828761421,230054.980848439 417543.108661419)))굴착허가위치82014023001대전광역시 유성구 죽동2014-04-252014-08-24기타1.2556.0667.2<NA><NA><NA>2014-08-24기타미분류
8MULTIPOLYGON (((228554.897012735 420991.443417188,228552.835587265 420990.214382814,228536.017687266 421018.422582814,228538.079112736 421019.651617188,228554.897012735 420991.443417188)))굴착허가위치92014023001대전광역시 유성구 죽동2014-04-252014-08-24기타1.2556.0667.2<NA><NA><NA>2014-08-24기타미분류
9MULTIPOLYGON (((232588.552359439 418636.193728499,232497.45335944 418584.600428501,232496.270640561 418586.688771503,232587.369640561 418638.282071505,232588.552359439 418636.193728499)))굴착허가위치102014024001대전광역시 유성구 신성동2014-05-072014-10-23기타1.2229.0274.8<NA><NA><NA>2014-10-23기타미분류
지리정보(WKT)지형지물부호관리번호허가일련번호행정읍면동굴착시작일굴착종료일포장종류일시점용_굴착폭일시점용_굴착길이일시점용_굴착면적점용시작일점용종료일변경일자복구일자공사유형코드차도보도구분
9160MULTIPOLYGON (((240024.096229425 416109.431135367,240026.3741903 416109.945111726,240029.443310101 416103.903235541,240027.673335882 416102.885736257,240024.096229425 416109.431135367)))굴착허가위치9170<NA>대전광역시 동구 가양동2022-09-01<NA>화강석1.01.01.0<NA><NA><NA><NA><NA>차도
9161MULTIPOLYGON (((239836.889240424 417083.569910645,239845.508495031 417083.103673255,239844.509947858 417079.819706603,239836.397454853 417080.034168123,239836.889240424 417083.569910645)))굴착허가위치9171<NA>대전광역시 동구 가양동2022-09-01<NA>화강석1.01.01.0<NA><NA><NA><NA><NA>차도
9162MULTIPOLYGON (((240434.028068137 417364.244136671,240436.317507524 417362.739815563,240434.815912323 417358.444344336,240432.023247016 417359.440521704,240434.028068137 417364.244136671)))굴착허가위치9172<NA>대전광역시 동구 가양동2022-09-01<NA>화강석1.01.01.0<NA><NA><NA><NA><NA>차도
9163MULTIPOLYGON (((240444.038619242 417362.216702031,240446.830060338 417360.966427241,240443.568273162 417354.138554701,240440.772074571 417355.89221701,240444.038619242 417362.216702031)))굴착허가위치9173<NA>대전광역시 동구 가양동2022-09-01<NA>화강석1.01.01.0<NA><NA><NA><NA><NA>차도
9164MULTIPOLYGON (((242141.381923935 409243.793290546,242145.184594349 409244.315581118,242145.981567973 409236.996578316,242143.192239071 409236.728732432,242141.381923935 409243.793290546)))굴착허가위치9174<NA>대전광역시 동구 낭월동2022-09-01<NA>화강석1.01.01.0<NA><NA><NA><NA><NA>차도
9165MULTIPOLYGON (((242063.032055778 409092.091192345,242071.132589496 409095.4151508,242072.670262531 409092.392135898,242064.06370033 409088.561858404,242063.032055778 409092.091192345)))굴착허가위치9175<NA>대전광역시 동구 낭월동2022-09-01<NA>화강석1.01.01.0<NA><NA><NA><NA><NA>차도
9166MULTIPOLYGON (((241512.131904817 414015.26147175,241519.985994176 414016.308601393,241520.508661268 414013.282352134,241512.909865383 414011.732855524,241512.131904817 414015.26147175)))굴착허가위치9176<NA>대전광역시 동구 용운동2022-09-01<NA>화강석1.01.01.0<NA><NA><NA><NA><NA>차도
9167MULTIPOLYGON (((240112.190336941 415411.459585001,240113.445346897 415413.987727852,240119.040000726 415410.227660321,240118.035527896 415408.204185383,240112.190336941 415411.459585001)))굴착허가위치9177<NA>대전광역시 동구 자양동2022-09-01<NA>화강석1.01.01.0<NA><NA><NA><NA><NA>차도
9168MULTIPOLYGON (((241456.838472914 413503.032042086,241459.87576202 413504.056176482,241462.189284402 413497.755167333,241459.911907168 413496.482842856,241456.838472914 413503.032042086)))굴착허가위치9178<NA>대전광역시 동구 판암동2022-09-01<NA>화강석1.01.01.0<NA><NA><NA><NA><NA>차도
9169MULTIPOLYGON (((239955.223018085 414383.981158165,239962.841487406 414381.239390831,239962.603689716 414377.9578955,239954.227659891 414379.938433069,239955.223018085 414383.981158165)))굴착허가위치9179<NA>대전광역시 동구 대동2022-09-20<NA>화강석1.01.01.0<NA><NA><NA><NA><NA>보도

Duplicate rows

Most frequently occurring

지리정보(WKT)지형지물부호관리번호허가일련번호행정읍면동굴착시작일굴착종료일포장종류일시점용_굴착폭일시점용_굴착길이일시점용_굴착면적점용시작일점용종료일변경일자복구일자공사유형코드차도보도구분# duplicates
2MULTIPOLYGON (((235906.172274085 413288.189105741,235908.578973402 413288.678699139,235908.819841492 413288.67969474,235911.009465092 413283.412638776,235908.604746154 413282.443406344,235906.172274085 413288.189105741)))굴착허가위치6916<NA>대전광역시 중구 유천동2020-08-072020-08-26<NA>1.22.02.4<NA><NA><NA><NA><NA>차도4
0MULTIPOLYGON (((226468.453506578 418372.989549133,226508.713934194 418369.469368979,226508.912958295 418367.360793535,226468.26619573 418371.263277245,226468.453506578 418372.989549133)))굴착허가위치7853<NA>대전광역시 유성구 갑동2021-05-18<NA>화강석1.01.01.0<NA><NA><NA><NA><NA>차도3
1MULTIPOLYGON (((229509.677653355 416829.120609354,229511.949163955 416831.650222742,229518.809828105 416826.375668017,229516.792857893 416823.341103792,229509.677653355 416829.120609354)))굴착허가위치9051<NA>대전광역시 유성구 구암동2022-07-19<NA>화강석1.01.01.0<NA><NA><NA><NA><NA>차도2
3MULTIPOLYGON (((237439.614177038 416228.79832645,237443.931794515 416232.652842836,237444.904250529 416230.499350352,237441.306925569 416227.127426311,237439.614177038 416228.79832645)))굴착허가위치6007<NA>대전광역시 동구 삼성동2017-07-172017-08-06<NA>1.22.02.4<NA><NA><NA><NA><NA>차도2
4MULTIPOLYGON (((238379.627605801 415189.654412391,238385.393409361 415192.796938685,238386.611270629 415189.685286944,238382.293542187 415185.82984384,238379.627605801 415189.654412391)))굴착허가위치6017<NA>대전광역시 동구 삼성동2017-07-282017-08-17<NA>2.02.04.0<NA><NA><NA><NA><NA>차도2
5MULTIPOLYGON (((238761.678972781 413535.130390889,238761.438110895 413535.129316134,238763.099532207 413540.652531119,238771.061887924 413537.570438766,238769.160679346 413531.806332482,238761.678972781 413535.130390889)))굴착허가위치5893<NA>대전광역시 중구 부사동2017-01-182017-02-06<NA>1.25.06.0<NA><NA><NA><NA><NA>보도2
6MULTIPOLYGON (((239057.193675743 414241.707046255,239060.799842622 414243.162054494,239062.990074089 414238.136140062,239059.62474878 414236.682213203,239057.193675743 414241.707046255)))굴착허가위치6019<NA>대전광역시 동구 인동2017-07-282017-08-17<NA>2.03.06.0<NA><NA><NA><NA><NA>차도2
7MULTIPOLYGON (((241423.627538846 413449.373452836,241427.471085994 413451.550171661,241432.566995157 413443.660374405,241428.723445741 413441.483651316,241423.627538846 413449.373452836)))굴착허가위치5963<NA>대전광역시 동구 판암동2017-04-052017-06-30<NA>1.33.84.9<NA><NA><NA><NA><NA>보도2