Overview

Dataset statistics

Number of variables10
Number of observations28
Missing cells26
Missing cells (%)9.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.5 KiB
Average record size in memory90.7 B

Variable types

Numeric3
Text2
DateTime2
Categorical3

Dataset

Description인천광역시 미추홀구 관교동 이행보증보험증권관리대장에 대한 데이터로 연번, 지번주소, 증권번호, 증권발급일,1년치금액, 수령일, 좌표값을 제공합니다.
URLhttps://www.data.go.kr/data/15072186/fileData.do

Alerts

위도 is highly overall correlated with 경도High correlation
경도 is highly overall correlated with 위도High correlation
1년치 금액 is highly imbalanced (77.8%)Imbalance
2년치 금액 is highly imbalanced (77.8%)Imbalance
10년치 금액 is highly imbalanced (77.8%)Imbalance
증권발급일 has 9 (32.1%) missing valuesMissing
수령일 has 17 (60.7%) missing valuesMissing
연번 has unique valuesUnique
지번주소 has unique valuesUnique
증권번호 has unique valuesUnique

Reproduction

Analysis started2023-12-12 11:32:05.274787
Analysis finished2023-12-12 11:32:07.934278
Duration2.66 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

UNIQUE 

Distinct28
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.5
Minimum1
Maximum28
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-12T20:32:08.043437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.35
Q17.75
median14.5
Q321.25
95-th percentile26.65
Maximum28
Range27
Interquartile range (IQR)13.5

Descriptive statistics

Standard deviation8.2259751
Coefficient of variation (CV)0.56730863
Kurtosis-1.2
Mean14.5
Median Absolute Deviation (MAD)7
Skewness0
Sum406
Variance67.666667
MonotonicityStrictly increasing
2023-12-12T20:32:08.257223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
1 1
 
3.6%
16 1
 
3.6%
28 1
 
3.6%
27 1
 
3.6%
26 1
 
3.6%
25 1
 
3.6%
24 1
 
3.6%
23 1
 
3.6%
22 1
 
3.6%
21 1
 
3.6%
Other values (18) 18
64.3%
ValueCountFrequency (%)
1 1
3.6%
2 1
3.6%
3 1
3.6%
4 1
3.6%
5 1
3.6%
6 1
3.6%
7 1
3.6%
8 1
3.6%
9 1
3.6%
10 1
3.6%
ValueCountFrequency (%)
28 1
3.6%
27 1
3.6%
26 1
3.6%
25 1
3.6%
24 1
3.6%
23 1
3.6%
22 1
3.6%
21 1
3.6%
20 1
3.6%
19 1
3.6%

지번주소
Text

UNIQUE 

Distinct28
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size356.0 B
2023-12-12T20:32:08.605027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length34
Median length31
Mean length21.678571
Min length18

Characters and Unicode

Total characters607
Distinct characters36
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique28 ?
Unique (%)100.0%

Sample

1st row인천광역시 미추홀구 관교동 394-45
2nd row인천광역시 미추홀구 관교동 394-6
3rd row인천광역시 미추홀구 관교동 394-41
4th row인천광역시 미추홀구 관교동 317-18
5th row인천광역시 미추홀구 관교동 394-24
ValueCountFrequency (%)
인천광역시 28
24.6%
미추홀구 28
24.6%
관교동 27
23.7%
324-12 1
 
0.9%
448-6 1
 
0.9%
451-33 1
 
0.9%
325-2 1
 
0.9%
관교동451-31 1
 
0.9%
451-8 1
 
0.9%
304-3 1
 
0.9%
Other values (24) 24
21.1%
2023-12-12T20:32:09.149570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
86
 
14.2%
- 29
 
4.8%
3 29
 
4.8%
인 28
 
4.6%
구 28
 
4.6%
천 28
 
4.6%
교 28
 
4.6%
관 28
 
4.6%
동 28
 
4.6%
홀 28
 
4.6%
Other values (26) 267
44.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 341
56.2%
Decimal Number 133
 
21.9%
Space Separator 86
 
14.2%
Dash Punctuation 29
 
4.8%
Uppercase Letter 13
 
2.1%
Other Punctuation 3
 
0.5%
Open Punctuation 1
 
0.2%
Close Punctuation 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
인 28
8.2%
구 28
8.2%
천 28
8.2%
교 28
8.2%
관 28
8.2%
동 28
8.2%
홀 28
8.2%
추 28
8.2%
미 28
8.2%
시 28
8.2%
Other values (7) 61
17.9%
Decimal Number
ValueCountFrequency (%)
3 29
21.8%
4 27
20.3%
1 21
15.8%
2 15
11.3%
5 13
9.8%
7 8
 
6.0%
8 7
 
5.3%
9 5
 
3.8%
0 5
 
3.8%
6 3
 
2.3%
Uppercase Letter
ValueCountFrequency (%)
L 6
46.2%
T 3
23.1%
B 3
23.1%
O 1
 
7.7%
Space Separator
ValueCountFrequency (%)
86
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 29
100.0%
Other Punctuation
ValueCountFrequency (%)
, 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 341
56.2%
Common 253
41.7%
Latin 13
 
2.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
인 28
8.2%
구 28
8.2%
천 28
8.2%
교 28
8.2%
관 28
8.2%
동 28
8.2%
홀 28
8.2%
추 28
8.2%
미 28
8.2%
시 28
8.2%
Other values (7) 61
17.9%
Common
ValueCountFrequency (%)
86
34.0%
- 29
 
11.5%
3 29
 
11.5%
4 27
 
10.7%
1 21
 
8.3%
2 15
 
5.9%
5 13
 
5.1%
7 8
 
3.2%
8 7
 
2.8%
9 5
 
2.0%
Other values (5) 13
 
5.1%
Latin
ValueCountFrequency (%)
L 6
46.2%
T 3
23.1%
B 3
23.1%
O 1
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 341
56.2%
ASCII 266
43.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
86
32.3%
- 29
 
10.9%
3 29
 
10.9%
4 27
 
10.2%
1 21
 
7.9%
2 15
 
5.6%
5 13
 
4.9%
7 8
 
3.0%
8 7
 
2.6%
L 6
 
2.3%
Other values (9) 25
 
9.4%
Hangul
ValueCountFrequency (%)
인 28
8.2%
구 28
8.2%
천 28
8.2%
교 28
8.2%
관 28
8.2%
동 28
8.2%
홀 28
8.2%
추 28
8.2%
미 28
8.2%
시 28
8.2%
Other values (7) 61
17.9%

증권번호
Text

UNIQUE 

Distinct28
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size356.0 B
2023-12-12T20:32:09.492514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length22
Mean length19.5
Min length10

Characters and Unicode

Total characters546
Distinct characters15
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

Unique28 ?
Unique (%)100.0%

Sample

1st row310-023-200100013733-37
2nd row310-023-200100022013-17
3rd row310-023-200200013530-34
4th row310-023-200200013669-73
5th row310-023-200200019421-25
ValueCountFrequency (%)
310-023-200100013733-37 1
 
2.9%
100-000-2012 1
 
2.9%
317-023-96003545 1
 
2.9%
319-023-199700006577 1
 
2.9%
100-000-200804121024 1
 
2.9%
100-000-200900537395 1
 
2.9%
100-000-201001711442 1
 
2.9%
100-000-201004515408 1
 
2.9%
0294 1
 
2.9%
310-023-200100022013-17 1
 
2.9%
Other values (24) 24
70.6%
2023-12-12T20:32:10.062426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 186
34.1%
2 69
 
12.6%
3 60
 
11.0%
- 58
 
10.6%
1 54
 
9.9%
9 33
 
6.0%
4 18
 
3.3%
5 17
 
3.1%
7 16
 
2.9%
6 13
 
2.4%
Other values (5) 22
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 474
86.8%
Dash Punctuation 58
 
10.6%
Other Letter 8
 
1.5%
Space Separator 6
 
1.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 186
39.2%
2 69
 
14.6%
3 60
 
12.7%
1 54
 
11.4%
9 33
 
7.0%
4 18
 
3.8%
5 17
 
3.6%
7 16
 
3.4%
6 13
 
2.7%
8 8
 
1.7%
Other Letter
ValueCountFrequency (%)
인 3
37.5%
하 3
37.5%
동 2
25.0%
Dash Punctuation
ValueCountFrequency (%)
- 58
100.0%
Space Separator
ValueCountFrequency (%)
6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 538
98.5%
Hangul 8
 
1.5%

Most frequent character per script

Common
ValueCountFrequency (%)
0 186
34.6%
2 69
 
12.8%
3 60
 
11.2%
- 58
 
10.8%
1 54
 
10.0%
9 33
 
6.1%
4 18
 
3.3%
5 17
 
3.2%
7 16
 
3.0%
6 13
 
2.4%
Other values (2) 14
 
2.6%
Hangul
ValueCountFrequency (%)
인 3
37.5%
하 3
37.5%
동 2
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 538
98.5%
Hangul 8
 
1.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 186
34.6%
2 69
 
12.8%
3 60
 
11.2%
- 58
 
10.8%
1 54
 
10.0%
9 33
 
6.1%
4 18
 
3.3%
5 17
 
3.2%
7 16
 
3.0%
6 13
 
2.4%
Other values (2) 14
 
2.6%
Hangul
ValueCountFrequency (%)
인 3
37.5%
하 3
37.5%
동 2
25.0%

증권발급일
Date

MISSING 

Distinct18
Distinct (%)94.7%
Missing9
Missing (%)32.1%
Memory size356.0 B
Minimum2001-08-06 00:00:00
Maximum2022-11-30 00:00:00
2023-12-12T20:32:10.270257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:32:10.495606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)

1년치 금액
Categorical

IMBALANCE 

Distinct2
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Memory size356.0 B
<NA>
27 
1742160
 
1

Length

Max length7
Median length4
Mean length4.1071429
Min length4

Unique

Unique1 ?
Unique (%)3.6%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 27
96.4%
1742160 1
 
3.6%

Length

2023-12-12T20:32:10.729925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T20:32:10.914936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 27
96.4%
1742160 1
 
3.6%

2년치 금액
Categorical

IMBALANCE 

Distinct2
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Memory size356.0 B
<NA>
27 
2931790
 
1

Length

Max length7
Median length4
Mean length4.1071429
Min length4

Unique

Unique1 ?
Unique (%)3.6%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 27
96.4%
2931790 1
 
3.6%

Length

2023-12-12T20:32:11.091142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T20:32:11.275023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 27
96.4%
2931790 1
 
3.6%

10년치 금액
Categorical

IMBALANCE 

Distinct2
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Memory size356.0 B
<NA>
27 
3909050
 
1

Length

Max length7
Median length4
Mean length4.1071429
Min length4

Unique

Unique1 ?
Unique (%)3.6%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 27
96.4%
3909050 1
 
3.6%

Length

2023-12-12T20:32:11.436826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T20:32:11.599429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 27
96.4%
3909050 1
 
3.6%

수령일
Date

MISSING 

Distinct11
Distinct (%)100.0%
Missing17
Missing (%)60.7%
Memory size356.0 B
Minimum2004-04-13 00:00:00
Maximum2013-06-26 00:00:00
2023-12-12T20:32:11.742732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:32:11.896883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)

위도
Real number (ℝ)

HIGH CORRELATION 

Distinct20
Distinct (%)71.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.444481
Minimum37.441503
Maximum37.447639
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-12T20:32:12.092247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.441503
5-th percentile37.441891
Q137.44265
median37.445359
Q337.445889
95-th percentile37.44759
Maximum37.447639
Range0.00613623
Interquartile range (IQR)0.0032388875

Descriptive statistics

Standard deviation0.0020714603
Coefficient of variation (CV)5.5320843 × 10-5
Kurtosis-1.6597216
Mean37.444481
Median Absolute Deviation (MAD)0.00226949
Skewness0.046076361
Sum1048.4455
Variance4.2909478 × 10-6
MonotonicityNot monotonic
2023-12-12T20:32:12.291292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
37.44265 9
32.1%
37.44681026 1
 
3.6%
37.4467025669623 1
 
3.6%
37.44560851 1
 
3.6%
37.44571855 1
 
3.6%
37.4463584 1
 
3.6%
37.44588987 1
 
3.6%
37.44588856 1
 
3.6%
37.44587623 1
 
3.6%
37.44510865 1
 
3.6%
Other values (10) 10
35.7%
ValueCountFrequency (%)
37.44150292 1
 
3.6%
37.44186298 1
 
3.6%
37.44194395 1
 
3.6%
37.44206108 1
 
3.6%
37.44265 9
32.1%
37.44510865 1
 
3.6%
37.44560851 1
 
3.6%
37.44571855 1
 
3.6%
37.44575844 1
 
3.6%
37.44586239 1
 
3.6%
ValueCountFrequency (%)
37.44763915 1
3.6%
37.44761699 1
3.6%
37.44754056 1
3.6%
37.44681026 1
3.6%
37.4467025669623 1
3.6%
37.4463584 1
3.6%
37.44588987 1
3.6%
37.44588856 1
3.6%
37.44588192 1
3.6%
37.44587623 1
3.6%

경도
Real number (ℝ)

HIGH CORRELATION 

Distinct20
Distinct (%)71.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.69198
Minimum126.68705
Maximum126.69441
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-12T20:32:12.497995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.68705
5-th percentile126.68721
Q1126.68895
median126.69307
Q3126.69441
95-th percentile126.69441
Maximum126.69441
Range0.0073643
Interquartile range (IQR)0.005464725

Descriptive statistics

Standard deviation0.0027425387
Coefficient of variation (CV)2.1647295 × 10-5
Kurtosis-1.0385621
Mean126.69198
Median Absolute Deviation (MAD)0.0013415
Skewness-0.80194333
Sum3547.3755
Variance7.5215188 × 10-6
MonotonicityNot monotonic
2023-12-12T20:32:12.697476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
126.694411 9
32.1%
126.6918928 1
 
3.6%
126.692023587596 1
 
3.6%
126.6890806 1
 
3.6%
126.6885433 1
 
3.6%
126.6923765 1
 
3.6%
126.6883188 1
 
3.6%
126.687925 1
 
3.6%
126.6881688 1
 
3.6%
126.6922895 1
 
3.6%
Other values (10) 10
35.7%
ValueCountFrequency (%)
126.6870467 1
3.6%
126.6871582 1
3.6%
126.6873061 1
3.6%
126.687925 1
3.6%
126.6881688 1
3.6%
126.6883188 1
3.6%
126.6885433 1
3.6%
126.6890806 1
3.6%
126.6918928 1
3.6%
126.692023587596 1
3.6%
ValueCountFrequency (%)
126.694411 9
32.1%
126.694065 1
 
3.6%
126.693752 1
 
3.6%
126.6935888 1
 
3.6%
126.6935489 1
 
3.6%
126.6935083 1
 
3.6%
126.6926307 1
 
3.6%
126.6925386 1
 
3.6%
126.6923765 1
 
3.6%
126.6922895 1
 
3.6%

Interactions

2023-12-12T20:32:06.806133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:32:05.815365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:32:06.311963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:32:06.969474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:32:05.970198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:32:06.460747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:32:07.132143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:32:06.144162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:32:06.630973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T20:32:12.848129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번지번주소증권번호증권발급일수령일위도경도
연번1.0001.0001.0001.0001.0000.7770.698
지번주소1.0001.0001.0001.0001.0001.0001.000
증권번호1.0001.0001.0001.0001.0001.0001.000
증권발급일1.0001.0001.0001.0001.0001.0001.000
수령일1.0001.0001.0001.0001.0001.0001.000
위도0.7771.0001.0001.0001.0001.0000.957
경도0.6981.0001.0001.0001.0000.9571.000
2023-12-12T20:32:13.014760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
10년치 금액1년치 금액2년치 금액
10년치 금액1.000NaNNaN
1년치 금액NaN1.000NaN
2년치 금액NaNNaN1.000
2023-12-12T20:32:13.683204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번위도경도1년치 금액2년치 금액10년치 금액
연번1.0000.277-0.132NaNNaNNaN
위도0.2771.000-0.613NaNNaNNaN
경도-0.132-0.6131.000NaNNaNNaN
1년치 금액NaNNaNNaN1.0000.0000.000
2년치 금액NaNNaNNaN0.0001.000NaN
10년치 금액NaNNaNNaN0.000NaN1.000

Missing values

2023-12-12T20:32:07.373629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T20:32:07.611374image/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.
2023-12-12T20:32:07.818977image/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

연번지번주소증권번호증권발급일1년치 금액2년치 금액10년치 금액수령일위도경도
01인천광역시 미추홀구 관교동 394-45310-023-200100013733-372001-08-06<NA><NA><NA><NA>37.441863126.693589
12인천광역시 미추홀구 관교동 394-6310-023-200100022013-172001-11-05<NA><NA><NA><NA>37.441944126.694065
23인천광역시 미추홀구 관교동 394-41310-023-200200013530-342002-05-27<NA><NA><NA><NA>37.441503126.693549
34인천광역시 미추홀구 관교동 317-18310-023-200200013669-732002-06-15<NA><NA><NA><NA>37.447639126.693508
45인천광역시 미추홀구 관교동 394-24310-023-200200019421-252002-07-29<NA><NA><NA><NA>37.442061126.693752
56인천광역시 미추홀구 관교동 451-38310-023-200200020826-302002-08-21<NA><NA><NA><NA>37.445758126.687306
67인천광역시 미추홀구 관교동 317-33310-023-2002000266482002-11-22<NA><NA><NA>2004-04-1337.447617126.692539
78인천광역시 미추홀구 관교동 451-2310-023-2002000019292002-11-23<NA><NA><NA>2004-07-1837.445882126.687047
89인천광역시 미추홀구 관교동 451-92335-023-2002000019242002-11-23<NA><NA><NA>2004-08-2437.445862126.687158
910인천광역시 미추홀구 관교동 317-34310-023-2003000002642003-01-13<NA><NA><NA>2004-04-2037.447541126.692631
연번지번주소증권번호증권발급일1년치 금액2년치 금액10년치 금액수령일위도경도
1819인천광역시 미추홀구 관교동 474-7310-023-199900008517<NA><NA><NA><NA><NA>37.445109126.69229
1920인천광역시 미추홀구 관교동 26BL-7LOT317-023-96003545<NA><NA><NA><NA><NA>37.44265126.694411
2021인천광역시 미추홀구 관교동 54BL 1-2LT319-023-199700006577<NA><NA><NA><NA><NA>37.44265126.694411
2122인천광역시 미추홀구 관교동 451-30100-000-2008041210242008-11-19<NA><NA><NA>2009-12-0437.445876126.688169
2223인천광역시 미추홀구 관교동 451-8100-000-2009005373952009-02-16<NA><NA><NA>2010-01-2537.445889126.687925
2324인천광역시 미추홀구 관교동451-31100-000-2010017114422010-05-04<NA><NA><NA>2010-05-3137.44589126.688319
2425인천광역시 미추홀구 관교동 325-2100-000-2010045154082010-12-15<NA><NA><NA>2012-02-2237.446358126.692376
2526인천광역시 미추홀구 관교동 451-33100-000-2012 0294 51492012-07-27<NA><NA><NA>2013-06-2637.445719126.688543
2627인천광역시 미추홀구 관교동 448-6100-000-2014 0417 35982014-10-271742160<NA><NA><NA>37.445609126.689081
2728인천광역시 미추홀구 관교동 324-12 외2필지(8세대)100-000-2022 0513 97902022-11-30<NA>29317903909050<NA>37.446703126.692024