Overview

Dataset statistics

Number of variables7
Number of observations10000
Missing cells2731
Missing cells (%)3.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory634.8 KiB
Average record size in memory65.0 B

Variable types

Numeric1
Categorical2
Text4

Dataset

Description원격탐사에 활용되는 법정동코드 정보입니다. 법정동 코드 시도, 시군구, 읍면동, 리, 조합주소, 간략시도 정보를 포함합니다.
Author통계청
URLhttps://www.data.go.kr/data/15086652/fileData.do

Alerts

간략시도 is highly overall correlated with 법정동코드 and 1 other fieldsHigh correlation
시도 is highly overall correlated with 법정동코드 and 1 other fieldsHigh correlation
법정동코드 is highly overall correlated with 시도 and 1 other fieldsHigh correlation
읍면동 has 124 (1.2%) missing valuesMissing
리 has 2600 (26.0%) missing valuesMissing
법정동코드 has unique valuesUnique
주소조합 has unique valuesUnique

Reproduction

Analysis started2023-12-12 20:52:22.861715
Analysis finished2023-12-12 20:52:24.178053
Duration1.32 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

법정동코드
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.3413068 × 109
Minimum1.1110101 × 109
Maximum5.013032 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T05:52:24.251745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.1110101 × 109
5-th percentile2.771034 × 109
Q14.275034 × 109
median4.5190116 × 109
Q34.717038 × 109
95-th percentile4.882039 × 109
Maximum5.013032 × 109
Range3.9020219 × 109
Interquartile range (IQR)4.4200401 × 108

Descriptive statistics

Standard deviation7.0474815 × 108
Coefficient of variation (CV)0.16233549
Kurtosis9.104138
Mean4.3413068 × 109
Median Absolute Deviation (MAD)2.0788057 × 108
Skewness-2.8885021
Sum4.3413068 × 1013
Variance4.9666996 × 1017
MonotonicityNot monotonic
2023-12-13T05:52:24.394275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4376032026 1
 
< 0.1%
4575037032 1
 
< 0.1%
4784034000 1
 
< 0.1%
1174010500 1
 
< 0.1%
4889041022 1
 
< 0.1%
4423040029 1
 
< 0.1%
4418036032 1
 
< 0.1%
4717040026 1
 
< 0.1%
4689025600 1
 
< 0.1%
4213035023 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
1111010100 1
< 0.1%
1111010300 1
< 0.1%
1111010500 1
< 0.1%
1111010600 1
< 0.1%
1111010700 1
< 0.1%
1111011000 1
< 0.1%
1111011400 1
< 0.1%
1111011700 1
< 0.1%
1111011800 1
< 0.1%
1111011900 1
< 0.1%
ValueCountFrequency (%)
5013032026 1
< 0.1%
5013032024 1
< 0.1%
5013032021 1
< 0.1%
5013031029 1
< 0.1%
5013031028 1
< 0.1%
5013031027 1
< 0.1%
5013031024 1
< 0.1%
5013031001 1
< 0.1%
5013025931 1
< 0.1%
5013025930 1
< 0.1%

시도
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
경상북도
1650 
전라남도
1451 
경상남도
1166 
경기도
1094 
충청남도
1086 
Other values (12)
3553 

Length

Max length7
Median length4
Mean length3.9598
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row충청북도
2nd row충청남도
3rd row경상남도
4th row전라남도
5th row경상북도

Common Values

ValueCountFrequency (%)
경상북도 1650
16.5%
전라남도 1451
14.5%
경상남도 1166
11.7%
경기도 1094
10.9%
충청남도 1086
10.9%
전라북도 921
9.2%
충청북도 776
7.8%
강원도 757
7.6%
서울특별시 246
 
2.5%
대구광역시 145
 
1.5%
Other values (7) 708
7.1%

Length

2023-12-13T05:52:24.554169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경상북도 1650
16.5%
전라남도 1451
14.5%
경상남도 1166
11.7%
경기도 1094
10.9%
충청남도 1086
10.9%
전라북도 921
9.2%
충청북도 776
7.8%
강원도 757
7.6%
서울특별시 246
 
2.5%
대구광역시 145
 
1.5%
Other values (7) 708
7.1%

시군구
Text

Distinct235
Distinct (%)2.4%
Missing7
Missing (%)0.1%
Memory size156.2 KiB
2023-12-13T05:52:24.862404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length3
Mean length3.3218253
Min length2

Characters and Unicode

Total characters33195
Distinct characters149
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

Unique11 ?
Unique (%)0.1%

Sample

1st row괴산군
2nd row서천군
3rd row사천시
4th row영광군
5th row예천군
ValueCountFrequency (%)
청주시 167
 
1.6%
창원시 164
 
1.5%
포항시 133
 
1.2%
북구 133
 
1.2%
중구 133
 
1.2%
고성군 121
 
1.1%
안동시 118
 
1.1%
경주시 113
 
1.0%
순천시 106
 
1.0%
상주시 106
 
1.0%
Other values (231) 9480
88.0%
2023-12-13T05:52:25.317557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
군 4833
 
14.6%
시 4573
 
13.8%
구 1663
 
5.0%
주 1394
 
4.2%
천 1251
 
3.8%
성 1028
 
3.1%
산 991
 
3.0%
781
 
2.4%
양 752
 
2.3%
안 657
 
2.0%
Other values (139) 15272
46.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 32414
97.6%
Space Separator 781
 
2.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
군 4833
 
14.9%
시 4573
 
14.1%
구 1663
 
5.1%
주 1394
 
4.3%
천 1251
 
3.9%
성 1028
 
3.2%
산 991
 
3.1%
양 752
 
2.3%
안 657
 
2.0%
창 566
 
1.7%
Other values (138) 14706
45.4%
Space Separator
ValueCountFrequency (%)
781
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 32414
97.6%
Common 781
 
2.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
군 4833
 
14.9%
시 4573
 
14.1%
구 1663
 
5.1%
주 1394
 
4.3%
천 1251
 
3.9%
성 1028
 
3.2%
산 991
 
3.1%
양 752
 
2.3%
안 657
 
2.0%
창 566
 
1.7%
Other values (138) 14706
45.4%
Common
ValueCountFrequency (%)
781
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 32414
97.6%
ASCII 781
 
2.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
군 4833
 
14.9%
시 4573
 
14.1%
구 1663
 
5.1%
주 1394
 
4.3%
천 1251
 
3.9%
성 1028
 
3.2%
산 991
 
3.1%
양 752
 
2.3%
안 657
 
2.0%
창 566
 
1.7%
Other values (138) 14706
45.4%
ASCII
ValueCountFrequency (%)
781
100.0%

읍면동
Text

MISSING 

Distinct2742
Distinct (%)27.8%
Missing124
Missing (%)1.2%
Memory size156.2 KiB
2023-12-13T05:52:25.730748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length3
Mean length3.0213649
Min length2

Characters and Unicode

Total characters29839
Distinct characters341
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

Unique1369 ?
Unique (%)13.9%

Sample

1st row장연면
2nd row종천면
3rd row용현면
4th row영광읍
5th row개포면
ValueCountFrequency (%)
남면 85
 
0.9%
서면 50
 
0.5%
북면 49
 
0.5%
동면 35
 
0.4%
금성면 30
 
0.3%
화산면 29
 
0.3%
성산면 27
 
0.3%
옥산면 26
 
0.3%
봉산면 25
 
0.3%
북이면 22
 
0.2%
Other values (2732) 9498
96.2%
2023-12-13T05:52:26.375914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
면 6647
22.3%
동 2262
 
7.6%
읍 1468
 
4.9%
산 928
 
3.1%
남 521
 
1.7%
성 505
 
1.7%
천 471
 
1.6%
양 403
 
1.4%
가 385
 
1.3%
북 378
 
1.3%
Other values (331) 15871
53.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 29628
99.3%
Decimal Number 211
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
면 6647
22.4%
동 2262
 
7.6%
읍 1468
 
5.0%
산 928
 
3.1%
남 521
 
1.8%
성 505
 
1.7%
천 471
 
1.6%
양 403
 
1.4%
가 385
 
1.3%
북 378
 
1.3%
Other values (324) 15660
52.9%
Decimal Number
ValueCountFrequency (%)
1 65
30.8%
2 61
28.9%
3 48
22.7%
5 14
 
6.6%
4 14
 
6.6%
6 7
 
3.3%
7 2
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
Hangul 29628
99.3%
Common 211
 
0.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
면 6647
22.4%
동 2262
 
7.6%
읍 1468
 
5.0%
산 928
 
3.1%
남 521
 
1.8%
성 505
 
1.7%
천 471
 
1.6%
양 403
 
1.4%
가 385
 
1.3%
북 378
 
1.3%
Other values (324) 15660
52.9%
Common
ValueCountFrequency (%)
1 65
30.8%
2 61
28.9%
3 48
22.7%
5 14
 
6.6%
4 14
 
6.6%
6 7
 
3.3%
7 2
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 29628
99.3%
ASCII 211
 
0.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
면 6647
22.4%
동 2262
 
7.6%
읍 1468
 
5.0%
산 928
 
3.1%
남 521
 
1.8%
성 505
 
1.7%
천 471
 
1.6%
양 403
 
1.4%
가 385
 
1.3%
북 378
 
1.3%
Other values (324) 15660
52.9%
ASCII
ValueCountFrequency (%)
1 65
30.8%
2 61
28.9%
3 48
22.7%
5 14
 
6.6%
4 14
 
6.6%
6 7
 
3.3%
7 2
 
0.9%

리
Text

MISSING 

Distinct4320
Distinct (%)58.4%
Missing2600
Missing (%)26.0%
Memory size156.2 KiB
2023-12-13T05:52:26.809334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length3
Mean length2.9906757
Min length2

Characters and Unicode

Total characters22131
Distinct characters360
Distinct categories4 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3081 ?
Unique (%)41.6%

Sample

1st row장암리
2nd row도만리
3rd row온정리
4th row송림리
5th row우감리
ValueCountFrequency (%)
대곡리 26
 
0.4%
신흥리 25
 
0.3%
덕산리 25
 
0.3%
금곡리 24
 
0.3%
용산리 20
 
0.3%
용암리 18
 
0.2%
남산리 17
 
0.2%
오산리 16
 
0.2%
용두리 16
 
0.2%
화산리 16
 
0.2%
Other values (4310) 7197
97.3%
2023-12-13T05:52:27.354572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
리 7426
33.6%
산 650
 
2.9%
곡 464
 
2.1%
신 357
 
1.6%
동 341
 
1.5%
대 320
 
1.4%
천 305
 
1.4%
정 282
 
1.3%
성 261
 
1.2%
용 251
 
1.1%
Other values (350) 11474
51.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 22124
> 99.9%
Decimal Number 3
 
< 0.1%
Close Punctuation 2
 
< 0.1%
Open Punctuation 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 7426
33.6%
산 650
 
2.9%
곡 464
 
2.1%
신 357
 
1.6%
동 341
 
1.5%
대 320
 
1.4%
천 305
 
1.4%
정 282
 
1.3%
성 261
 
1.2%
용 251
 
1.1%
Other values (346) 11467
51.8%
Decimal Number
ValueCountFrequency (%)
1 2
66.7%
2 1
33.3%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 22120
> 99.9%
Common 7
 
< 0.1%
Han 4
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 7426
33.6%
산 650
 
2.9%
곡 464
 
2.1%
신 357
 
1.6%
동 341
 
1.5%
대 320
 
1.4%
천 305
 
1.4%
정 282
 
1.3%
성 261
 
1.2%
용 251
 
1.1%
Other values (342) 11463
51.8%
Common
ValueCountFrequency (%)
1 2
28.6%
) 2
28.6%
( 2
28.6%
2 1
14.3%
Han
ValueCountFrequency (%)
平 1
25.0%
沙 1
25.0%
基 1
25.0%
岩 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 22120
> 99.9%
ASCII 7
 
< 0.1%
CJK 4
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
리 7426
33.6%
산 650
 
2.9%
곡 464
 
2.1%
신 357
 
1.6%
동 341
 
1.5%
대 320
 
1.4%
천 305
 
1.4%
정 282
 
1.3%
성 261
 
1.2%
용 251
 
1.1%
Other values (342) 11463
51.8%
ASCII
ValueCountFrequency (%)
1 2
28.6%
) 2
28.6%
( 2
28.6%
2 1
14.3%
CJK
ValueCountFrequency (%)
平 1
25.0%
沙 1
25.0%
基 1
25.0%
岩 1
25.0%

주소조합
Text

UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-13T05:52:27.748771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length16
Mean length15.4763
Min length6

Characters and Unicode

Total characters154763
Distinct characters389
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10000 ?
Unique (%)100.0%

Sample

1st row충청북도 괴산군 장연면 장암리
2nd row충청남도 서천군 종천면 도만리
3rd row경상남도 사천시 용현면 온정리
4th row전라남도 영광군 영광읍 송림리
5th row경상북도 예천군 개포면 우감리
ValueCountFrequency (%)
경상북도 1650
 
4.3%
전라남도 1451
 
3.8%
경상남도 1166
 
3.1%
경기도 1094
 
2.9%
충청남도 1086
 
2.9%
전라북도 921
 
2.4%
충청북도 776
 
2.0%
강원도 757
 
2.0%
서울특별시 246
 
0.6%
청주시 167
 
0.4%
Other values (7309) 28736
75.5%
2023-12-13T05:52:28.325339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
30781
19.9%
도 9646
 
6.2%
리 7498
 
4.8%
면 6653
 
4.3%
시 5632
 
3.6%
군 4971
 
3.2%
남 4824
 
3.1%
경 4250
 
2.7%
북 3976
 
2.6%
상 3366
 
2.2%
Other values (379) 73166
47.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 123764
80.0%
Space Separator 30781
 
19.9%
Decimal Number 214
 
0.1%
Open Punctuation 2
 
< 0.1%
Close Punctuation 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 9646
 
7.8%
리 7498
 
6.1%
면 6653
 
5.4%
시 5632
 
4.6%
군 4971
 
4.0%
남 4824
 
3.9%
경 4250
 
3.4%
북 3976
 
3.2%
상 3366
 
2.7%
동 3050
 
2.5%
Other values (369) 69898
56.5%
Decimal Number
ValueCountFrequency (%)
1 67
31.3%
2 62
29.0%
3 48
22.4%
4 14
 
6.5%
5 14
 
6.5%
6 7
 
3.3%
7 2
 
0.9%
Space Separator
ValueCountFrequency (%)
30781
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 123760
80.0%
Common 30999
 
20.0%
Han 4
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 9646
 
7.8%
리 7498
 
6.1%
면 6653
 
5.4%
시 5632
 
4.6%
군 4971
 
4.0%
남 4824
 
3.9%
경 4250
 
3.4%
북 3976
 
3.2%
상 3366
 
2.7%
동 3050
 
2.5%
Other values (365) 69894
56.5%
Common
ValueCountFrequency (%)
30781
99.3%
1 67
 
0.2%
2 62
 
0.2%
3 48
 
0.2%
4 14
 
< 0.1%
5 14
 
< 0.1%
6 7
 
< 0.1%
( 2
 
< 0.1%
) 2
 
< 0.1%
7 2
 
< 0.1%
Han
ValueCountFrequency (%)
平 1
25.0%
沙 1
25.0%
岩 1
25.0%
基 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 123760
80.0%
ASCII 30999
 
20.0%
CJK 4
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
30781
99.3%
1 67
 
0.2%
2 62
 
0.2%
3 48
 
0.2%
4 14
 
< 0.1%
5 14
 
< 0.1%
6 7
 
< 0.1%
( 2
 
< 0.1%
) 2
 
< 0.1%
7 2
 
< 0.1%
Hangul
ValueCountFrequency (%)
도 9646
 
7.8%
리 7498
 
6.1%
면 6653
 
5.4%
시 5632
 
4.6%
군 4971
 
4.0%
남 4824
 
3.9%
경 4250
 
3.4%
북 3976
 
3.2%
상 3366
 
2.7%
동 3050
 
2.5%
Other values (365) 69894
56.5%
CJK
ValueCountFrequency (%)
平 1
25.0%
沙 1
25.0%
岩 1
25.0%
基 1
25.0%

간략시도
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
경북
1650 
전남
1451 
경남
1166 
경기
1094 
충남
1086 
Other values (12)
3553 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row충북
2nd row충남
3rd row경남
4th row전남
5th row경북

Common Values

ValueCountFrequency (%)
경북 1650
16.5%
전남 1451
14.5%
경남 1166
11.7%
경기 1094
10.9%
충남 1086
10.9%
전북 921
9.2%
충북 776
7.8%
강원 757
7.6%
서울 246
 
2.5%
대구 145
 
1.5%
Other values (7) 708
7.1%

Length

2023-12-13T05:52:28.511567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경북 1650
16.5%
전남 1451
14.5%
경남 1166
11.7%
경기 1094
10.9%
충남 1086
10.9%
전북 921
9.2%
충북 776
7.8%
강원 757
7.6%
서울 246
 
2.5%
대구 145
 
1.5%
Other values (7) 708
7.1%

Interactions

2023-12-13T05:52:23.764559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T05:52:28.592489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동코드시도간략시도
법정동코드1.0000.9950.995
시도0.9951.0001.000
간략시도0.9951.0001.000
2023-12-13T05:52:28.692574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
간략시도시도
간략시도1.0001.000
시도1.0001.000
2023-12-13T05:52:28.798141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동코드시도간략시도
법정동코드1.0000.9780.978
시도0.9781.0001.000
간략시도0.9781.0001.000

Missing values

2023-12-13T05:52:23.916709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T05:52:24.030478image/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.
2023-12-13T05:52:24.124031image/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

법정동코드시도시군구읍면동리주소조합간략시도
36684376032026충청북도괴산군장연면장암리충청북도 괴산군 장연면 장암리충북
10534477039028충청남도서천군종천면도만리충청남도 서천군 종천면 도만리충남
116404824033025경상남도사천시용현면온정리경상남도 사천시 용현면 온정리경남
159874687025038전라남도영광군영광읍송림리전라남도 영광군 영광읍 송림리전남
86974790039025경상북도예천군개포면우감리경상북도 예천군 개포면 우감리경북
176654873032029경상남도함안군군북면사도리경상남도 함안군 군북면 사도리경남
123541129010300서울특별시성북구돈암동<NA>서울특별시 성북구 돈암동서울
184304678032021전라남도보성군미력면도개리전라남도 보성군 미력면 도개리전남
91894613025025전라남도여수시돌산읍죽포리전라남도 여수시 돌산읍 죽포리전남
175954889036031경상남도합천군초계면신촌리경상남도 합천군 초계면 신촌리경남
법정동코드시도시군구읍면동리주소조합간략시도
189844283025027강원도양양군양양읍송암리강원도 양양군 양양읍 송암리강원
33404282025300강원도고성군거진읍<NA>강원도 고성군 거진읍강원
17534421039038충청남도서산시해미면기지리충청남도 서산시 해미면 기지리충남
64804777032046경상북도영덕군남정면남정리경상북도 영덕군 남정면 남정리경북
16664211040028강원도춘천시남산면서천리강원도 춘천시 남산면 서천리강원
127144311425325충청북도청주시 청원구오창읍모정리충청북도 청주시 청원구 오창읍 모정리충북
94864717039024경상북도안동시길안면금곡리경상북도 안동시 길안면 금곡리경북
103104574031025전라북도장수군산서면하월리전라북도 장수군 산서면 하월리전북
190904719032021경상북도구미시옥성면초곡리경상북도 구미시 옥성면 초곡리경북
82113020011800대전광역시유성구갑동<NA>대전광역시 유성구 갑동대전