Overview

Dataset statistics

Number of variables8
Number of observations382
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory26.2 KiB
Average record size in memory70.3 B

Variable types

Numeric4
Text2
Categorical2

Dataset

Description10년간(2013년부터 2023년까지) 지적재조사 진행과정에서 일필지 경계결정시 타 토지소유자와 협의를 한 지번 목록들을 보여줍니다.
Author공공데이터포털
URLhttps://www.data.go.kr/data/15121975/fileData.do

Alerts

토지소유자수 is highly overall correlated with 동의자수High correlation
동의자수 is highly overall correlated with 토지소유자수High correlation
시군구코드 is highly overall correlated with 지번코드High correlation
기준년도 is highly overall correlated with 사업지구번호High correlation
사업지구번호 is highly overall correlated with 기준년도High correlation
지번코드 is highly overall correlated with 시군구코드High correlation
동의자수 is highly imbalanced (60.3%)Imbalance
토지소유자수 is highly imbalanced (60.3%)Imbalance
지번코드 has unique valuesUnique

Reproduction

Analysis started2024-04-17 10:17:21.686408
Analysis finished2024-04-17 10:17:23.362815
Duration1.68 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군구코드
Real number (ℝ)

HIGH CORRELATION 

Distinct54
Distinct (%)14.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40314.029
Minimum11140
Maximum51830
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2024-04-17T19:17:23.434760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11140
5-th percentile26350
Q141171
median41650
Q345190
95-th percentile48820
Maximum51830
Range40690
Interquartile range (IQR)4019

Descriptive statistics

Standard deviation8138.0486
Coefficient of variation (CV)0.20186642
Kurtosis0.19523961
Mean40314.029
Median Absolute Deviation (MAD)3540
Skewness-1.0894031
Sum15399959
Variance66227836
MonotonicityNot monotonic
2024-04-17T19:17:23.579591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
45180 57
14.9%
41650 35
 
9.2%
45190 27
 
7.1%
41310 26
 
6.8%
48220 19
 
5.0%
41171 18
 
4.7%
27290 16
 
4.2%
41190 16
 
4.2%
26350 13
 
3.4%
28110 13
 
3.4%
Other values (44) 142
37.2%
ValueCountFrequency (%)
11140 2
 
0.5%
11380 1
 
0.3%
26140 2
 
0.5%
26230 11
2.9%
26290 3
 
0.8%
26350 13
3.4%
26380 10
2.6%
26410 1
 
0.3%
26500 5
 
1.3%
26530 2
 
0.5%
ValueCountFrequency (%)
51830 2
 
0.5%
51230 4
 
1.0%
50130 1
 
0.3%
48890 8
2.1%
48860 4
 
1.0%
48820 6
 
1.6%
48740 1
 
0.3%
48720 1
 
0.3%
48250 7
 
1.8%
48220 19
5.0%

기준년도
Real number (ℝ)

HIGH CORRELATION 

Distinct12
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2018.2199
Minimum2012
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2024-04-17T19:17:23.687966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2012
5-th percentile2014
Q12017
median2018
Q32020
95-th percentile2022
Maximum2023
Range11
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.428791
Coefficient of variation (CV)0.0012034323
Kurtosis-0.50339728
Mean2018.2199
Median Absolute Deviation (MAD)1
Skewness0.0057595129
Sum770960
Variance5.8990257
MonotonicityNot monotonic
2024-04-17T19:17:23.784788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
2017 95
24.9%
2018 61
16.0%
2019 53
13.9%
2021 39
10.2%
2022 36
 
9.4%
2014 30
 
7.9%
2016 21
 
5.5%
2020 19
 
5.0%
2023 11
 
2.9%
2015 10
 
2.6%
Other values (2) 7
 
1.8%
ValueCountFrequency (%)
2012 1
 
0.3%
2013 6
 
1.6%
2014 30
 
7.9%
2015 10
 
2.6%
2016 21
 
5.5%
2017 95
24.9%
2018 61
16.0%
2019 53
13.9%
2020 19
 
5.0%
2021 39
10.2%
ValueCountFrequency (%)
2023 11
 
2.9%
2022 36
 
9.4%
2021 39
10.2%
2020 19
 
5.0%
2019 53
13.9%
2018 61
16.0%
2017 95
24.9%
2016 21
 
5.5%
2015 10
 
2.6%
2014 30
 
7.9%

시군구명
Text

Distinct54
Distinct (%)14.1%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
2024-04-17T19:17:23.954674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length8
Mean length8.2696335
Min length7

Characters and Unicode

Total characters3159
Distinct characters77
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

Unique18 ?
Unique (%)4.7%

Sample

1st row부산광역시 해운대구
2nd row부산광역시 해운대구
3rd row부산광역시 해운대구
4th row부산광역시 해운대구
5th row부산광역시 해운대구
ValueCountFrequency (%)
경기도 113
 
14.3%
전라북도 86
 
10.9%
정읍시 57
 
7.2%
경상남도 47
 
5.9%
부산광역시 47
 
5.9%
포천시 35
 
4.4%
남원시 27
 
3.4%
구리시 26
 
3.3%
대구광역시 20
 
2.5%
경상북도 20
 
2.5%
Other values (61) 312
39.5%
2024-04-17T19:17:24.213829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
408
 
12.9%
시 350
 
11.1%
도 293
 
9.3%
경 181
 
5.7%
구 157
 
5.0%
북 118
 
3.7%
기 113
 
3.6%
남 97
 
3.1%
라 94
 
3.0%
전 94
 
3.0%
Other values (67) 1254
39.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2751
87.1%
Space Separator 408
 
12.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 350
 
12.7%
도 293
 
10.7%
경 181
 
6.6%
구 157
 
5.7%
북 118
 
4.3%
기 113
 
4.1%
남 97
 
3.5%
라 94
 
3.4%
전 94
 
3.4%
광 90
 
3.3%
Other values (66) 1164
42.3%
Space Separator
ValueCountFrequency (%)
408
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2751
87.1%
Common 408
 
12.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 350
 
12.7%
도 293
 
10.7%
경 181
 
6.6%
구 157
 
5.7%
북 118
 
4.3%
기 113
 
4.1%
남 97
 
3.5%
라 94
 
3.4%
전 94
 
3.4%
광 90
 
3.3%
Other values (66) 1164
42.3%
Common
ValueCountFrequency (%)
408
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2751
87.1%
ASCII 408
 
12.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
408
100.0%
Hangul
ValueCountFrequency (%)
시 350
 
12.7%
도 293
 
10.7%
경 181
 
6.6%
구 157
 
5.7%
북 118
 
4.3%
기 113
 
4.1%
남 97
 
3.5%
라 94
 
3.4%
전 94
 
3.4%
광 90
 
3.3%
Other values (66) 1164
42.3%

사업지구명
Text

Distinct78
Distinct (%)20.4%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
2024-04-17T19:17:24.386899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length15
Mean length5.578534
Min length4

Characters and Unicode

Total characters2131
Distinct characters131
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26 ?
Unique (%)6.8%

Sample

1st row중동1지구
2nd row중동1지구
3rd row중동1지구
4th row중동1지구
5th row중동1지구
ValueCountFrequency (%)
신평지구 28
 
6.6%
1지구 19
 
4.4%
신태인1지구 19
 
4.4%
박달1동행정복지센터3지구 18
 
4.2%
풍월1지구 18
 
4.2%
신당1지구 16
 
3.7%
작동2지구 16
 
3.7%
인창 14
 
3.3%
북성1지구 13
 
3.0%
요천지구 12
 
2.8%
Other values (81) 254
59.5%
2024-04-17T19:17:24.658710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
지 401
18.8%
구 382
17.9%
1 166
 
7.8%
신 92
 
4.3%
80
 
3.8%
동 59
 
2.8%
2 50
 
2.3%
정 38
 
1.8%
인 33
 
1.5%
산 31
 
1.5%
Other values (121) 799
37.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1797
84.3%
Decimal Number 245
 
11.5%
Space Separator 80
 
3.8%
Other Punctuation 7
 
0.3%
Dash Punctuation 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 401
22.3%
구 382
21.3%
신 92
 
5.1%
동 59
 
3.3%
정 38
 
2.1%
인 33
 
1.8%
산 31
 
1.7%
천 28
 
1.6%
평 28
 
1.6%
태 24
 
1.3%
Other values (112) 681
37.9%
Decimal Number
ValueCountFrequency (%)
1 166
67.8%
2 50
 
20.4%
3 20
 
8.2%
9 4
 
1.6%
5 3
 
1.2%
0 2
 
0.8%
Space Separator
ValueCountFrequency (%)
80
100.0%
Other Punctuation
ValueCountFrequency (%)
· 7
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1797
84.3%
Common 334
 
15.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 401
22.3%
구 382
21.3%
신 92
 
5.1%
동 59
 
3.3%
정 38
 
2.1%
인 33
 
1.8%
산 31
 
1.7%
천 28
 
1.6%
평 28
 
1.6%
태 24
 
1.3%
Other values (112) 681
37.9%
Common
ValueCountFrequency (%)
1 166
49.7%
80
24.0%
2 50
 
15.0%
3 20
 
6.0%
· 7
 
2.1%
9 4
 
1.2%
5 3
 
0.9%
- 2
 
0.6%
0 2
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1797
84.3%
ASCII 327
 
15.3%
None 7
 
0.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
지 401
22.3%
구 382
21.3%
신 92
 
5.1%
동 59
 
3.3%
정 38
 
2.1%
인 33
 
1.8%
산 31
 
1.7%
천 28
 
1.6%
평 28
 
1.6%
태 24
 
1.3%
Other values (112) 681
37.9%
ASCII
ValueCountFrequency (%)
1 166
50.8%
80
24.5%
2 50
 
15.3%
3 20
 
6.1%
9 4
 
1.2%
5 3
 
0.9%
- 2
 
0.6%
0 2
 
0.6%
None
ValueCountFrequency (%)
· 7
100.0%

사업지구번호
Real number (ℝ)

HIGH CORRELATION 

Distinct78
Distinct (%)20.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3476.3665
Minimum312
Maximum9956
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2024-04-17T19:17:24.773295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum312
5-th percentile892
Q12043
median2739
Q34261
95-th percentile8061
Maximum9956
Range9644
Interquartile range (IQR)2218

Descriptive statistics

Standard deviation2320.3458
Coefficient of variation (CV)0.66746293
Kurtosis0.12442316
Mean3476.3665
Median Absolute Deviation (MAD)981
Skewness1.0669122
Sum1327972
Variance5384004.4
MonotonicityNot monotonic
2024-04-17T19:17:24.882986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
892 28
 
7.3%
2690 19
 
5.0%
2043 18
 
4.7%
3721 18
 
4.7%
8061 16
 
4.2%
2138 16
 
4.2%
8020 14
 
3.7%
3254 13
 
3.4%
2739 12
 
3.1%
2062 12
 
3.1%
Other values (68) 216
56.5%
ValueCountFrequency (%)
312 5
 
1.3%
447 1
 
0.3%
668 1
 
0.3%
789 1
 
0.3%
814 1
 
0.3%
892 28
7.3%
902 7
 
1.8%
935 1
 
0.3%
1001 1
 
0.3%
1178 1
 
0.3%
ValueCountFrequency (%)
9956 1
 
0.3%
9738 3
 
0.8%
9246 1
 
0.3%
9046 6
 
1.6%
8061 16
4.2%
8020 14
3.7%
7994 2
 
0.5%
7812 2
 
0.5%
7653 2
 
0.5%
7060 3
 
0.8%

지번코드
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct382
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.0218521 × 1018
Minimum1.1140162 × 1018
Maximum5.183034 × 1018
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2024-04-17T19:17:25.001452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.1140162 × 1018
5-th percentile2.6350103 × 1018
Q14.1171103 × 1018
median4.165034 × 1018
Q34.51904 × 1018
95-th percentile4.882034 × 1018
Maximum5.183034 × 1018
Range4.0690178 × 1018
Interquartile range (IQR)4.0192973 × 1017

Descriptive statistics

Standard deviation8.0621313 × 1017
Coefficient of variation (CV)0.20045817
Kurtosis0.22825902
Mean4.0218521 × 1018
Median Absolute Deviation (MAD)3.5398444 × 1017
Skewness-1.1108128
Sum5.2677416 × 1018
Variance6.4997961 × 1035
MonotonicityNot monotonic
2024-04-17T19:17:25.137609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2635010600117200000 1
 
0.3%
4723025041107930066 1
 
0.3%
2623010200103740041 1
 
0.3%
2623010200103750001 1
 
0.3%
2623010200103750002 1
 
0.3%
2623010200103750010 1
 
0.3%
2623010200103750011 1
 
0.3%
2623010200103780010 1
 
0.3%
2623010200103780031 1
 
0.3%
4513011200102270004 1
 
0.3%
Other values (372) 372
97.4%
ValueCountFrequency (%)
1114016200103400001 1
0.3%
1114016200103400147 1
0.3%
1138010900102370002 1
0.3%
2614012400105590003 1
0.3%
2614012400105610007 1
0.3%
2623010200103720045 1
0.3%
2623010200103720048 1
0.3%
2623010200103740027 1
0.3%
2623010200103740041 1
0.3%
2623010200103750001 1
0.3%
ValueCountFrequency (%)
5183034038105790000 1
0.3%
5183034038105780003 1
0.3%
5013025321123030002 1
0.3%
4889035029102700000 1
0.3%
4889035029102140000 1
0.3%
4889032021102690000 1
0.3%
4889032021101850003 1
0.3%
4889032021101780000 1
0.3%
4889025021106810004 1
0.3%
4889025021106770000 1
0.3%

동의자수
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
1
311 
2
50 
3
 
11
4
 
9
5
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)0.3%

Sample

1st row2
2nd row1
3rd row1
4th row2
5th row2

Common Values

ValueCountFrequency (%)
1 311
81.4%
2 50
 
13.1%
3 11
 
2.9%
4 9
 
2.4%
5 1
 
0.3%

Length

2024-04-17T19:17:25.247558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T19:17:25.327588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 311
81.4%
2 50
 
13.1%
3 11
 
2.9%
4 9
 
2.4%
5 1
 
0.3%

토지소유자수
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
1
311 
2
50 
3
 
11
4
 
9
5
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)0.3%

Sample

1st row2
2nd row1
3rd row1
4th row2
5th row2

Common Values

ValueCountFrequency (%)
1 311
81.4%
2 50
 
13.1%
3 11
 
2.9%
4 9
 
2.4%
5 1
 
0.3%

Length

2024-04-17T19:17:25.416312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T19:17:25.497535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 311
81.4%
2 50
 
13.1%
3 11
 
2.9%
4 9
 
2.4%
5 1
 
0.3%

Interactions

2024-04-17T19:17:22.888844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T19:17:22.007869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T19:17:22.316140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T19:17:22.611428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T19:17:22.969764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T19:17:22.084578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T19:17:22.407175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T19:17:22.682860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T19:17:23.044204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T19:17:22.155805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T19:17:22.474758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T19:17:22.748617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T19:17:23.116124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T19:17:22.223243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T19:17:22.536759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T19:17:22.809355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-17T19:17:25.562536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구코드기준년도시군구명사업지구명사업지구번호지번코드동의자수토지소유자수
시군구코드1.0000.7721.0001.0000.6771.0000.1580.158
기준년도0.7721.0000.9581.0000.9800.6140.2870.287
시군구명1.0000.9581.0001.0000.9781.0000.5740.574
사업지구명1.0001.0001.0001.0001.0001.0000.7020.702
사업지구번호0.6770.9800.9781.0001.0000.6710.2080.208
지번코드1.0000.6141.0001.0000.6711.0000.1610.161
동의자수0.1580.2870.5740.7020.2080.1611.0001.000
토지소유자수0.1580.2870.5740.7020.2080.1611.0001.000
2024-04-17T19:17:25.650625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
토지소유자수동의자수
토지소유자수1.0001.000
동의자수1.0001.000
2024-04-17T19:17:25.726027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구코드기준년도사업지구번호지번코드동의자수토지소유자수
시군구코드1.000-0.275-0.3060.9840.1390.139
기준년도-0.2751.0000.987-0.3040.1180.118
사업지구번호-0.3060.9871.000-0.3370.0820.082
지번코드0.984-0.304-0.3371.0000.1400.140
동의자수0.1390.1180.0820.1401.0001.000
토지소유자수0.1390.1180.0820.1401.0001.000

Missing values

2024-04-17T19:17:23.223768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-17T19:17:23.325431image/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

시군구코드기준년도시군구명사업지구명사업지구번호지번코드동의자수토지소유자수
0263502013부산광역시 해운대구중동1지구312263501060011720000022
1263502013부산광역시 해운대구중동1지구312263501060011653000011
2263502013부산광역시 해운대구중동1지구312263501060011648000011
3263502013부산광역시 해운대구중동1지구312263501060011376002022
4263502013부산광역시 해운대구중동1지구312263501060011376000322
5472302013경상북도 영천시완전지구447472303202810587000011
6437602015충청북도 괴산군주진지구1178437603302310546000011
7487402016경상남도 창녕군수다지구1310487404203010976000222
8451802015전라북도 정읍시신촌지구789451803102110010000311
9457202016전라북도 진안군주천면 주양지구1496457204002110481000111
시군구코드기준년도시군구명사업지구명사업지구번호지번코드동의자수토지소유자수
372471702017경상북도 안동시정산2지구2114471704103411139000011
373471702017경상북도 안동시정산2지구2114471704103411138000011
374471702017경상북도 안동시정산2지구2114471704103411137000033
375471702017경상북도 안동시정산2지구2114471704103411120000011
376471702017경상북도 안동시정산2지구2114471704103411119000011
377471702017경상북도 안동시정산2지구2114471704103411116000111
378471702017경상북도 안동시정산2지구2114471704103411115000011
379471702017경상북도 안동시정산2지구2114471704103411107000011
380471702017경상북도 안동시정산2지구2114471704103411094000411
381471702017경상북도 안동시정산2지구2114471704103411094000311