Overview

Dataset statistics

Number of variables10
Number of observations10000
Missing cells534
Missing cells (%)0.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory927.7 KiB
Average record size in memory95.0 B

Variable types

Numeric5
Categorical5

Dataset

Description한국부동산원(구.한국감정원)에서 제공하는 전국주택가격동향조사 통계를 조회할 수 있는 서비스로 월간동향으로 구성되어있습니다. 해당 서비스에서는 충남에 대한 월별, 지역, 주택유형의 주택가격지수 정보를 제공합니다.
Author충청남도
URLhttps://alldam.chungnam.go.kr/bigdata/collect/view.chungnam?menuCd=DOM_000000201001001000&apiIdx=2540

Alerts

지역구분 레벨 is highly overall correlated with 번호 and 3 other fieldsHigh correlation
지역명 is highly overall correlated with 번호 and 3 other fieldsHigh correlation
번호 is highly overall correlated with 지역코드 and 3 other fieldsHigh correlation
지역코드 is highly overall correlated with 번호 and 3 other fieldsHigh correlation
조사일자 is highly overall correlated with 주택유형구분 and 1 other fieldsHigh correlation
정렬순서 is highly overall correlated with 번호 and 3 other fieldsHigh correlation
주택유형구분 is highly overall correlated with 조사일자 and 1 other fieldsHigh correlation
월간구분 is highly overall correlated with 조사일자 and 1 other fieldsHigh correlation
지수 has 534 (5.3%) missing valuesMissing
번호 has unique valuesUnique

Reproduction

Analysis started2024-01-09 23:01:34.487677
Analysis finished2024-01-09 23:01:38.648083
Duration4.16 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18094.847
Minimum2
Maximum36210
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T08:01:38.711953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile1760.85
Q19039.5
median18049
Q327053.25
95-th percentile34455.15
Maximum36210
Range36208
Interquartile range (IQR)18013.75

Descriptive statistics

Standard deviation10491.315
Coefficient of variation (CV)0.57979572
Kurtosis-1.2041132
Mean18094.847
Median Absolute Deviation (MAD)9005.5
Skewness-0.0033490038
Sum1.8094847 × 108
Variance1.1006769 × 108
MonotonicityNot monotonic
2024-01-10T08:01:38.848866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19607 1
 
< 0.1%
32193 1
 
< 0.1%
25390 1
 
< 0.1%
730 1
 
< 0.1%
35548 1
 
< 0.1%
28236 1
 
< 0.1%
18316 1
 
< 0.1%
2418 1
 
< 0.1%
13382 1
 
< 0.1%
5113 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
2 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
8 1
< 0.1%
10 1
< 0.1%
19 1
< 0.1%
29 1
< 0.1%
30 1
< 0.1%
32 1
< 0.1%
34 1
< 0.1%
ValueCountFrequency (%)
36210 1
< 0.1%
36208 1
< 0.1%
36202 1
< 0.1%
36201 1
< 0.1%
36195 1
< 0.1%
36190 1
< 0.1%
36189 1
< 0.1%
36182 1
< 0.1%
36178 1
< 0.1%
36170 1
< 0.1%

지역코드
Real number (ℝ)

HIGH CORRELATION 

Distinct13
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44253.961
Minimum44000
Maximum44810
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T08:01:38.969268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum44000
5-th percentile44000
Q144131
median44200
Q344250
95-th percentile44810
Maximum44810
Range810
Interquartile range (IQR)119

Descriptive statistics

Standard deviation239.51987
Coefficient of variation (CV)0.0054123939
Kurtosis1.2645671
Mean44253.961
Median Absolute Deviation (MAD)67
Skewness1.5806071
Sum4.4253961 × 108
Variance57369.766
MonotonicityNot monotonic
2024-01-10T08:01:39.377212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
44000 1294
12.9%
44250 778
 
7.8%
44810 754
 
7.5%
44150 753
 
7.5%
44180 741
 
7.4%
44130 723
 
7.2%
44133 722
 
7.2%
44270 718
 
7.2%
44230 715
 
7.1%
44210 706
 
7.1%
Other values (3) 2096
21.0%
ValueCountFrequency (%)
44000 1294
12.9%
44130 723
7.2%
44131 692
6.9%
44133 722
7.2%
44150 753
7.5%
44180 741
7.4%
44200 704
7.0%
44210 706
7.1%
44230 715
7.1%
44250 778
7.8%
ValueCountFrequency (%)
44810 754
7.5%
44800 700
7.0%
44270 718
7.2%
44250 778
7.8%
44230 715
7.1%
44210 706
7.1%
44200 704
7.0%
44180 741
7.4%
44150 753
7.5%
44133 722
7.2%

지역명
Categorical

HIGH CORRELATION 

Distinct13
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
충남
1294 
계룡시
778 
예산군
754 
공주시
753 
보령시
741 
Other values (8)
5680 

Length

Max length3
Median length3
Mean length2.8706
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row아산시
2nd row충남
3rd row계룡시
4th row아산시
5th row서북구

Common Values

ValueCountFrequency (%)
충남 1294
12.9%
계룡시 778
 
7.8%
예산군 754
 
7.5%
공주시 753
 
7.5%
보령시 741
 
7.4%
천안시 723
 
7.2%
서북구 722
 
7.2%
당진시 718
 
7.2%
논산시 715
 
7.1%
서산시 706
 
7.1%
Other values (3) 2096
21.0%

Length

2024-01-10T08:01:39.486634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
충남 1294
12.9%
계룡시 778
 
7.8%
예산군 754
 
7.5%
공주시 753
 
7.5%
보령시 741
 
7.4%
천안시 723
 
7.2%
서북구 722
 
7.2%
당진시 718
 
7.2%
논산시 715
 
7.1%
서산시 706
 
7.1%
Other values (3) 2096
21.0%

조사일자
Real number (ℝ)

HIGH CORRELATION 

Distinct754
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7817419.9
Minimum200311
Maximum20220711
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T08:01:39.598033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum200311
5-th percentile200606.95
Q1201512
median202006
Q320151123
95-th percentile20210208
Maximum20220711
Range20020400
Interquartile range (IQR)19949611

Descriptive statistics

Standard deviation9699690.3
Coefficient of variation (CV)1.240779
Kurtosis-1.7617829
Mean7817419.9
Median Absolute Deviation (MAD)1004
Skewness0.48842068
Sum7.8174199 × 1010
Variance9.4083991 × 1013
MonotonicityNot monotonic
2024-01-10T08:01:39.739636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
202206 68
 
0.7%
202002 60
 
0.6%
201902 58
 
0.6%
201507 57
 
0.6%
201804 57
 
0.6%
202112 57
 
0.6%
202007 55
 
0.5%
202102 55
 
0.5%
201705 55
 
0.5%
201510 55
 
0.5%
Other values (744) 9423
94.2%
ValueCountFrequency (%)
200311 18
0.2%
200312 15
0.1%
200401 9
0.1%
200402 13
0.1%
200403 16
0.2%
200404 16
0.2%
200405 20
0.2%
200406 16
0.2%
200407 20
0.2%
200408 16
0.2%
ValueCountFrequency (%)
20220711 5
0.1%
20220704 8
0.1%
20220627 11
0.1%
20220620 6
0.1%
20220613 9
0.1%
20220606 6
0.1%
20220530 5
0.1%
20220523 6
0.1%
20220516 10
0.1%
20220509 5
0.1%

주택유형구분
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
6646 
0
2839 
3
 
269
7
 
246

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row7
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 6646
66.5%
0 2839
28.4%
3 269
 
2.7%
7 246
 
2.5%

Length

2024-01-10T08:01:39.863409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T08:01:39.952855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 6646
66.5%
0 2839
28.4%
3 269
 
2.7%
7 246
 
2.5%

매매전세월세구분
Categorical

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
S
3657 
D
3643 
R3
666 
R4
660 
R2
660 
Other values (2)
714 

Length

Max length2
Median length1
Mean length1.2622
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowS
2nd rowR4
3rd rowS
4th rowD
5th rowD

Common Values

ValueCountFrequency (%)
S 3657
36.6%
D 3643
36.4%
R3 666
 
6.7%
R4 660
 
6.6%
R2 660
 
6.6%
R1 636
 
6.4%
T 78
 
0.8%

Length

2024-01-10T08:01:40.050498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T08:01:40.155969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
s 3657
36.6%
d 3643
36.4%
r3 666
 
6.7%
r4 660
 
6.6%
r2 660
 
6.6%
r1 636
 
6.4%
t 78
 
0.8%

월간구분
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
M
6186 
W
3814 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowW
2nd rowM
3rd rowW
4th rowW
5th rowW

Common Values

ValueCountFrequency (%)
M 6186
61.9%
W 3814
38.1%

Length

2024-01-10T08:01:40.269999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T08:01:40.367022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
m 6186
61.9%
w 3814
38.1%

지수
Real number (ℝ)

MISSING 

Distinct8984
Distinct (%)94.9%
Missing534
Missing (%)5.3%
Infinite0
Infinite (%)0.0%
Mean94.731039
Minimum41.694088
Maximum123.80539
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T08:01:40.472974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum41.694088
5-th percentile73.112898
Q190.072517
median97.231246
Q3100.98259
95-th percentile108.47067
Maximum123.80539
Range82.111298
Interquartile range (IQR)10.910069

Descriptive statistics

Standard deviation10.936135
Coefficient of variation (CV)0.11544406
Kurtosis2.7264961
Mean94.731039
Median Absolute Deviation (MAD)5.0380573
Skewness-1.2212485
Sum896724.02
Variance119.59906
MonotonicityNot monotonic
2024-01-10T08:01:40.614788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100.0 56
 
0.6%
118.728790977002 6
 
0.1%
104.033772480644 6
 
0.1%
85.4781251970879 6
 
0.1%
94.9950332537265 6
 
0.1%
84.9231318136641 5
 
0.1%
92.7389406857231 5
 
0.1%
85.85028498191 4
 
< 0.1%
84.9070307078406 4
 
< 0.1%
117.659192308071 4
 
< 0.1%
Other values (8974) 9364
93.6%
(Missing) 534
 
5.3%
ValueCountFrequency (%)
41.6940875070385 1
 
< 0.1%
41.7509905349993 1
 
< 0.1%
42.2166409618452 1
 
< 0.1%
44.7675246381993 1
 
< 0.1%
44.8388981578502 1
 
< 0.1%
45.8652997865119 1
 
< 0.1%
47.2459238594427 1
 
< 0.1%
47.7301136003097 3
< 0.1%
47.8057359873382 1
 
< 0.1%
48.1595589174307 2
< 0.1%
ValueCountFrequency (%)
123.805385294374 2
< 0.1%
123.71644418484 3
< 0.1%
123.675627101741 1
 
< 0.1%
123.588632916375 1
 
< 0.1%
123.499140315954 2
< 0.1%
123.453406038672 1
 
< 0.1%
123.099417055811 1
 
< 0.1%
123.007312325618 1
 
< 0.1%
122.928273712809 1
 
< 0.1%
122.863017436267 2
< 0.1%

지역구분 레벨
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
7292 
2
1414 
0
1294 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row1
5th row2

Common Values

ValueCountFrequency (%)
1 7292
72.9%
2 1414
 
14.1%
0 1294
 
12.9%

Length

2024-01-10T08:01:40.733812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T08:01:40.827775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 7292
72.9%
2 1414
 
14.1%
0 1294
 
12.9%

정렬순서
Real number (ℝ)

HIGH CORRELATION 

Distinct13
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean183.896
Minimum178
Maximum191
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T08:01:40.931270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum178
5-th percentile178
Q1180
median184
Q3187
95-th percentile191
Maximum191
Range13
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.1986363
Coefficient of variation (CV)0.02283158
Kurtosis-1.1856179
Mean183.896
Median Absolute Deviation (MAD)3
Skewness0.18738204
Sum1838960
Variance17.628547
MonotonicityNot monotonic
2024-01-10T08:01:41.063687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
178 1294
12.9%
187 778
 
7.8%
190 754
 
7.5%
182 753
 
7.5%
183 741
 
7.4%
179 723
 
7.2%
181 722
 
7.2%
191 718
 
7.2%
186 715
 
7.1%
185 706
 
7.1%
Other values (3) 2096
21.0%
ValueCountFrequency (%)
178 1294
12.9%
179 723
7.2%
180 692
6.9%
181 722
7.2%
182 753
7.5%
183 741
7.4%
184 704
7.0%
185 706
7.1%
186 715
7.1%
187 778
7.8%
ValueCountFrequency (%)
191 718
7.2%
190 754
7.5%
189 700
7.0%
187 778
7.8%
186 715
7.1%
185 706
7.1%
184 704
7.0%
183 741
7.4%
182 753
7.5%
181 722
7.2%

Interactions

2024-01-10T08:01:37.859867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:01:35.642393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:01:36.185732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:01:36.734180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:01:37.315997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:01:37.986194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:01:35.738894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:01:36.296830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:01:36.845688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:01:37.438407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:01:38.113548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:01:35.842765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:01:36.417731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:01:36.961740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:01:37.537246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:01:38.241307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:01:35.944949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:01:36.532087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:01:37.074862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:01:37.642294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:01:38.327818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:01:36.043617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:01:36.623901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:01:37.195232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:01:37.747408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-10T08:01:41.166580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호지역코드지역명조사일자주택유형구분매매전세월세구분월간구분지수지역구분 레벨정렬순서
번호1.0000.9440.9650.1390.4890.1730.1390.4540.9240.978
지역코드0.9441.0001.0000.0000.0000.0000.0000.3870.8300.880
지역명0.9651.0001.0000.1230.5420.1970.1230.4981.0001.000
조사일자0.1390.0000.1231.0000.7660.4461.0000.4240.0690.000
주택유형구분0.4890.0000.5420.7661.0000.2780.7660.2670.4340.000
매매전세월세구분0.1730.0000.1970.4460.2781.0000.4450.4050.2390.000
월간구분0.1390.0000.1231.0000.7660.4451.0000.4240.0690.000
지수0.4540.3870.4980.4240.2670.4050.4241.0000.2220.400
지역구분 레벨0.9240.8301.0000.0690.4340.2390.0690.2221.0001.000
정렬순서0.9780.8801.0000.0000.0000.0000.0000.4001.0001.000
2024-01-10T08:01:41.289528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
매매전세월세구분주택유형구분지역구분 레벨지역명월간구분
매매전세월세구분1.0000.1940.1650.0930.477
주택유형구분0.1941.0000.4270.3470.558
지역구분 레벨0.1650.4271.0000.9990.115
지역명0.0930.3470.9991.0000.115
월간구분0.4770.5580.1150.1151.000
2024-01-10T08:01:41.392005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호지역코드조사일자지수정렬순서지역명주택유형구분매매전세월세구분월간구분지역구분 레벨
번호1.0000.9970.0540.0640.9830.8610.3130.0880.1060.894
지역코드0.9971.0000.0550.0630.9861.0000.3480.1160.1140.834
조사일자0.0540.0551.0000.2050.0550.1150.5580.4771.0000.115
지수0.0640.0630.2051.0000.0880.2320.1620.2190.3260.135
정렬순서0.9830.9860.0550.0881.0001.0000.2660.0710.0970.888
지역명0.8611.0000.1150.2321.0001.0000.3470.0930.1150.999
주택유형구분0.3130.3480.5580.1620.2660.3471.0000.1940.5580.427
매매전세월세구분0.0880.1160.4770.2190.0710.0930.1941.0000.4770.165
월간구분0.1060.1141.0000.3260.0970.1150.5580.4771.0000.115
지역구분 레벨0.8940.8340.1150.1350.8880.9990.4270.1650.1151.000

Missing values

2024-01-10T08:01:38.451720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-10T08:01:38.587944image/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

번호지역코드지역명조사일자주택유형구분매매전세월세구분월간구분지수지역구분 레벨정렬순서
196061960744200아산시201902251SW90.0722651184
2593259444000충남2020087R4M99.8208930178
260342603544250계룡시201609121SW81.086051187
201342013544200아산시201710091DW91.7185741184
118321183344133서북구202108161DW101.5609542181
346063460744810예산군201210221SW94.9950331190
83783844000충남2019010R3M99.3218630178
320403204144800홍성군2017120R3M97.5986421189
1563156444000충남2011113DM95.0109050178
8215821644131동남구201303111SW98.1106022180
번호지역코드지역명조사일자주택유형구분매매전세월세구분월간구분지수지역구분 레벨정렬순서
189841898544200아산시2007021DM51.682461184
247362473744230논산시201611211DW94.8252881186
114221142344133서북구2020011DM86.1465472181
5369537044130천안시201205141DW74.5889441179
124251242644133서북구201802121SW87.5539792181
260682606944250계룡시2014090SM86.38281187
190401904144200아산시201806041DW87.8956681184
105831058444133서북구2010091DM59.4992792181
257032570444250계룡시201512071SW81.630161187
119151191644133서북구201710301SW89.5128932181