Overview

Dataset statistics

Number of variables8
Number of observations111
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.4 KiB
Average record size in memory68.2 B

Variable types

Numeric3
Categorical2
Text2
DateTime1

Dataset

Description인천광역시 남동구 화물자동차 운송주선 업체 현황에 대한 데이터로(시군구명, 업체명, 인허가일자, 소재지주소, 위도, 경도)등을 제공합니다.
Author인천광역시 남동구
URLhttps://data.incheon.go.kr/findData/publicDataDetail?dataId=15039153&srcSe=7661IVAWM27C61E190

Alerts

시군구명 has constant value ""Constant
데이터기준일자 has constant value ""Constant
연번 has unique valuesUnique

Reproduction

Analysis started2024-01-28 13:34:36.313581
Analysis finished2024-01-28 13:34:37.412801
Duration1.1 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

UNIQUE 

Distinct111
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56
Minimum1
Maximum111
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-01-28T22:34:37.475603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6.5
Q128.5
median56
Q383.5
95-th percentile105.5
Maximum111
Range110
Interquartile range (IQR)55

Descriptive statistics

Standard deviation32.186954
Coefficient of variation (CV)0.57476703
Kurtosis-1.2
Mean56
Median Absolute Deviation (MAD)28
Skewness0
Sum6216
Variance1036
MonotonicityStrictly increasing
2024-01-28T22:34:37.593006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.9%
2 1
 
0.9%
83 1
 
0.9%
82 1
 
0.9%
81 1
 
0.9%
80 1
 
0.9%
79 1
 
0.9%
78 1
 
0.9%
77 1
 
0.9%
76 1
 
0.9%
Other values (101) 101
91.0%
ValueCountFrequency (%)
1 1
0.9%
2 1
0.9%
3 1
0.9%
4 1
0.9%
5 1
0.9%
6 1
0.9%
7 1
0.9%
8 1
0.9%
9 1
0.9%
10 1
0.9%
ValueCountFrequency (%)
111 1
0.9%
110 1
0.9%
109 1
0.9%
108 1
0.9%
107 1
0.9%
106 1
0.9%
105 1
0.9%
104 1
0.9%
103 1
0.9%
102 1
0.9%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size1020.0 B
인천광역시 남동구
111 

Length

Max length9
Median length9
Mean length9
Min length9

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row인천광역시 남동구
2nd row인천광역시 남동구
3rd row인천광역시 남동구
4th row인천광역시 남동구
5th row인천광역시 남동구

Common Values

ValueCountFrequency (%)
인천광역시 남동구 111
100.0%

Length

2024-01-28T22:34:37.712048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-28T22:34:37.792526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
인천광역시 111
50.0%
남동구 111
50.0%

업체명
Text

Distinct110
Distinct (%)99.1%
Missing0
Missing (%)0.0%
Memory size1020.0 B
2024-01-28T22:34:38.005092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length10
Mean length6.2612613
Min length2

Characters and Unicode

Total characters695
Distinct characters165
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique109 ?
Unique (%)98.2%

Sample

1st row신성익스프레스
2nd row종합화물
3rd row논현화물
4th row가고파이삿짐
5th row행복익스프레스
ValueCountFrequency (%)
sk익스프레스 2
 
1.7%
이사의 1
 
0.9%
인천퀵익스프레스 1
 
0.9%
㈜세광종합물류 1
 
0.9%
좋은날익스프레스 1
 
0.9%
한양퀵 1
 
0.9%
뉴페이스 1
 
0.9%
로젠이사 1
 
0.9%
오봉산화물 1
 
0.9%
인천남부점 1
 
0.9%
Other values (106) 106
90.6%
2024-01-28T22:34:38.369532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
스 84
 
12.1%
물 43
 
6.2%
프 32
 
4.6%
익 30
 
4.3%
레 30
 
4.3%
류 27
 
3.9%
이 22
 
3.2%
㈜ 19
 
2.7%
로 17
 
2.4%
화 17
 
2.4%
Other values (155) 374
53.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 633
91.1%
Other Symbol 19
 
2.7%
Uppercase Letter 17
 
2.4%
Close Punctuation 6
 
0.9%
Open Punctuation 6
 
0.9%
Space Separator 6
 
0.9%
Decimal Number 4
 
0.6%
Other Punctuation 4
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
스 84
 
13.3%
물 43
 
6.8%
프 32
 
5.1%
익 30
 
4.7%
레 30
 
4.7%
류 27
 
4.3%
이 22
 
3.5%
로 17
 
2.7%
화 17
 
2.7%
지 13
 
2.1%
Other values (139) 318
50.2%
Uppercase Letter
ValueCountFrequency (%)
K 5
29.4%
S 3
17.6%
B 3
17.6%
G 2
 
11.8%
D 1
 
5.9%
L 1
 
5.9%
T 1
 
5.9%
N 1
 
5.9%
Decimal Number
ValueCountFrequency (%)
2 2
50.0%
4 2
50.0%
Other Punctuation
ValueCountFrequency (%)
& 2
50.0%
. 2
50.0%
Other Symbol
ValueCountFrequency (%)
㈜ 19
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6
100.0%
Space Separator
ValueCountFrequency (%)
6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 652
93.8%
Common 26
 
3.7%
Latin 17
 
2.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
스 84
 
12.9%
물 43
 
6.6%
프 32
 
4.9%
익 30
 
4.6%
레 30
 
4.6%
류 27
 
4.1%
이 22
 
3.4%
㈜ 19
 
2.9%
로 17
 
2.6%
화 17
 
2.6%
Other values (140) 331
50.8%
Latin
ValueCountFrequency (%)
K 5
29.4%
S 3
17.6%
B 3
17.6%
G 2
 
11.8%
D 1
 
5.9%
L 1
 
5.9%
T 1
 
5.9%
N 1
 
5.9%
Common
ValueCountFrequency (%)
) 6
23.1%
( 6
23.1%
6
23.1%
2 2
 
7.7%
& 2
 
7.7%
4 2
 
7.7%
. 2
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 633
91.1%
ASCII 43
 
6.2%
None 19
 
2.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
스 84
 
13.3%
물 43
 
6.8%
프 32
 
5.1%
익 30
 
4.7%
레 30
 
4.7%
류 27
 
4.3%
이 22
 
3.5%
로 17
 
2.7%
화 17
 
2.7%
지 13
 
2.1%
Other values (139) 318
50.2%
None
ValueCountFrequency (%)
㈜ 19
100.0%
ASCII
ValueCountFrequency (%)
) 6
14.0%
( 6
14.0%
6
14.0%
K 5
11.6%
S 3
7.0%
B 3
7.0%
2 2
 
4.7%
G 2
 
4.7%
& 2
 
4.7%
4 2
 
4.7%
Other values (5) 6
14.0%

인허가일자
Date

Distinct104
Distinct (%)93.7%
Missing0
Missing (%)0.0%
Memory size1020.0 B
Minimum1983-06-22 00:00:00
Maximum2019-06-25 00:00:00
2024-01-28T22:34:38.504727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T22:34:38.632922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

소재지주소
Text

Distinct108
Distinct (%)97.3%
Missing0
Missing (%)0.0%
Memory size1020.0 B
2024-01-28T22:34:38.911550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length49
Median length38
Mean length28.783784
Min length17

Characters and Unicode

Total characters3195
Distinct characters153
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique105 ?
Unique (%)94.6%

Sample

1st row인천광역시 남동구 석산로9번길 70, 1층(간석동)
2nd row인천광역시 남동구 고잔동 632-5 승민빌딩 505호
3rd row인천광역시 남동구 논현동 442-3, c동 1층
4th row인천광역시 남동구 수현로 103. 2층
5th row인천광역시 남동구 남동대로897번길 81
ValueCountFrequency (%)
인천광역시 111
 
19.9%
남동구 111
 
19.9%
은청로 6
 
1.1%
1층 6
 
1.1%
승민빌딩 4
 
0.7%
2층 4
 
0.7%
석정로 4
 
0.7%
고잔동 3
 
0.5%
호구포로 3
 
0.5%
은청로4-7 3
 
0.5%
Other values (262) 304
54.4%
2024-01-28T22:34:39.318753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
450
 
14.1%
동 204
 
6.4%
남 142
 
4.4%
구 139
 
4.4%
1 130
 
4.1%
인 117
 
3.7%
시 116
 
3.6%
천 116
 
3.6%
광 113
 
3.5%
역 113
 
3.5%
Other values (143) 1555
48.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1845
57.7%
Decimal Number 652
 
20.4%
Space Separator 450
 
14.1%
Other Punctuation 98
 
3.1%
Dash Punctuation 48
 
1.5%
Open Punctuation 46
 
1.4%
Close Punctuation 46
 
1.4%
Uppercase Letter 9
 
0.3%
Lowercase Letter 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 204
 
11.1%
남 142
 
7.7%
구 139
 
7.5%
인 117
 
6.3%
시 116
 
6.3%
천 116
 
6.3%
광 113
 
6.1%
역 113
 
6.1%
로 98
 
5.3%
호 67
 
3.6%
Other values (123) 620
33.6%
Decimal Number
ValueCountFrequency (%)
1 130
19.9%
3 89
13.7%
2 78
12.0%
0 72
11.0%
7 64
9.8%
4 61
9.4%
5 54
8.3%
6 40
 
6.1%
9 37
 
5.7%
8 27
 
4.1%
Uppercase Letter
ValueCountFrequency (%)
B 5
55.6%
A 3
33.3%
D 1
 
11.1%
Other Punctuation
ValueCountFrequency (%)
, 92
93.9%
. 6
 
6.1%
Space Separator
ValueCountFrequency (%)
450
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 48
100.0%
Open Punctuation
ValueCountFrequency (%)
( 46
100.0%
Close Punctuation
ValueCountFrequency (%)
) 46
100.0%
Lowercase Letter
ValueCountFrequency (%)
c 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1845
57.7%
Common 1340
41.9%
Latin 10
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 204
 
11.1%
남 142
 
7.7%
구 139
 
7.5%
인 117
 
6.3%
시 116
 
6.3%
천 116
 
6.3%
광 113
 
6.1%
역 113
 
6.1%
로 98
 
5.3%
호 67
 
3.6%
Other values (123) 620
33.6%
Common
ValueCountFrequency (%)
450
33.6%
1 130
 
9.7%
, 92
 
6.9%
3 89
 
6.6%
2 78
 
5.8%
0 72
 
5.4%
7 64
 
4.8%
4 61
 
4.6%
5 54
 
4.0%
- 48
 
3.6%
Other values (6) 202
15.1%
Latin
ValueCountFrequency (%)
B 5
50.0%
A 3
30.0%
c 1
 
10.0%
D 1
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1845
57.7%
ASCII 1350
42.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
450
33.3%
1 130
 
9.6%
, 92
 
6.8%
3 89
 
6.6%
2 78
 
5.8%
0 72
 
5.3%
7 64
 
4.7%
4 61
 
4.5%
5 54
 
4.0%
- 48
 
3.6%
Other values (10) 212
15.7%
Hangul
ValueCountFrequency (%)
동 204
 
11.1%
남 142
 
7.7%
구 139
 
7.5%
인 117
 
6.3%
시 116
 
6.3%
천 116
 
6.3%
광 113
 
6.1%
역 113
 
6.1%
로 98
 
5.3%
호 67
 
3.6%
Other values (123) 620
33.6%

위도
Real number (ℝ)

Distinct93
Distinct (%)83.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.432191
Minimum37.384455
Maximum37.477843
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-01-28T22:34:39.449135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.384455
5-th percentile37.398235
Q137.40695
median37.434462
Q337.452519
95-th percentile37.467573
Maximum37.477843
Range0.09338764
Interquartile range (IQR)0.04556857

Descriptive statistics

Standard deviation0.025968528
Coefficient of variation (CV)0.00069374854
Kurtosis-1.4172017
Mean37.432191
Median Absolute Deviation (MAD)0.02615118
Skewness-0.0729387
Sum4154.9732
Variance0.00067436445
MonotonicityNot monotonic
2024-01-28T22:34:39.570316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.40695006 6
 
5.4%
37.40619329 4
 
3.6%
37.40662462 3
 
2.7%
37.45032054 2
 
1.8%
37.40042095 2
 
1.8%
37.44695549 2
 
1.8%
37.44286938 2
 
1.8%
37.40957313 2
 
1.8%
37.40831075 2
 
1.8%
37.40699409 2
 
1.8%
Other values (83) 84
75.7%
ValueCountFrequency (%)
37.38445546 1
0.9%
37.38460526 1
0.9%
37.38599905 1
0.9%
37.39034508 1
0.9%
37.39451097 1
0.9%
37.39803769 1
0.9%
37.39843293 1
0.9%
37.3997019 1
0.9%
37.39975986 1
0.9%
37.4001824 1
0.9%
ValueCountFrequency (%)
37.4778431 1
0.9%
37.4771234 1
0.9%
37.47660127 1
0.9%
37.46797598 1
0.9%
37.46784725 2
1.8%
37.46729823 1
0.9%
37.46718205 1
0.9%
37.46680468 1
0.9%
37.46632835 1
0.9%
37.46554839 1
0.9%

경도
Real number (ℝ)

Distinct93
Distinct (%)83.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.71326
Minimum126.6933
Maximum126.75626
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-01-28T22:34:39.716409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.6933
5-th percentile126.6934
Q1126.69928
median126.7107
Q3126.72568
95-th percentile126.74053
Maximum126.75626
Range0.0629532
Interquartile range (IQR)0.0264021

Descriptive statistics

Standard deviation0.015966715
Coefficient of variation (CV)0.00012600666
Kurtosis-0.41678477
Mean126.71326
Median Absolute Deviation (MAD)0.0125811
Skewness0.63434795
Sum14065.172
Variance0.00025493598
MonotonicityNot monotonic
2024-01-28T22:34:40.183336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.6933996 6
 
5.4%
126.694226 4
 
3.6%
126.6933036 3
 
2.7%
126.7512913 2
 
1.8%
126.7076646 2
 
1.8%
126.7339432 2
 
1.8%
126.7402838 2
 
1.8%
126.6951275 2
 
1.8%
126.695861 2
 
1.8%
126.700673 2
 
1.8%
Other values (83) 84
75.7%
ValueCountFrequency (%)
126.6933036 3
2.7%
126.6933996 6
5.4%
126.694226 4
3.6%
126.6942321 1
 
0.9%
126.6947163 1
 
0.9%
126.6951275 2
 
1.8%
126.695273 1
 
0.9%
126.6957223 1
 
0.9%
126.695861 2
 
1.8%
126.6970598 1
 
0.9%
ValueCountFrequency (%)
126.7562568 1
0.9%
126.7512913 2
1.8%
126.7503631 1
0.9%
126.741266 1
0.9%
126.7407199 1
0.9%
126.7403474 1
0.9%
126.7402838 2
1.8%
126.7380563 1
0.9%
126.737205 1
0.9%
126.7356127 1
0.9%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size1020.0 B
2023-03-24
111 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-03-24
2nd row2023-03-24
3rd row2023-03-24
4th row2023-03-24
5th row2023-03-24

Common Values

ValueCountFrequency (%)
2023-03-24 111
100.0%

Length

2024-01-28T22:34:40.316854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-28T22:34:40.407449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-03-24 111
100.0%

Interactions

2024-01-28T22:34:37.009875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T22:34:36.562199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T22:34:36.785969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T22:34:37.088026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T22:34:36.638111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T22:34:36.858391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T22:34:37.164607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T22:34:36.710510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T22:34:36.936990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-28T22:34:40.459627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번위도경도
연번1.0000.3770.000
위도0.3771.0000.672
경도0.0000.6721.000
2024-01-28T22:34:40.546558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번위도경도
연번1.000-0.0550.115
위도-0.0551.0000.086
경도0.1150.0861.000

Missing values

2024-01-28T22:34:37.263909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-28T22:34:37.373610image/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

연번시군구명업체명인허가일자소재지주소위도경도데이터기준일자
01인천광역시 남동구신성익스프레스1993-06-01인천광역시 남동구 석산로9번길 70, 1층(간석동)37.46372126.698712023-03-24
12인천광역시 남동구종합화물1993-07-23인천광역시 남동구 고잔동 632-5 승민빌딩 505호37.406193126.6942262023-03-24
23인천광역시 남동구논현화물1992-12-11인천광역시 남동구 논현동 442-3, c동 1층37.405095126.7033712023-03-24
34인천광역시 남동구가고파이삿짐1994-02-07인천광역시 남동구 수현로 103. 2층37.462715126.740722023-03-24
45인천광역시 남동구행복익스프레스2000-04-18인천광역시 남동구 남동대로897번길 8137.462223126.7034942023-03-24
56인천광역시 남동구성진익스프레스2003-01-15인천광역시 남동구 하촌로35번길 837.451482126.7317082023-03-24
67인천광역시 남동구대신익스프레스1995-04-07인천광역시 남동구 도림동 346-737.413545126.725572023-03-24
78인천광역시 남동구고잔화물1997-03-24인천광역시 남동구 논현고잔로 32, 2층37.385999126.7113222023-03-24
89인천광역시 남동구타이거화물1998-05-12인천광역시 남동구 은청로16-17(고잔동) 승민빌딩 304호37.406193126.6942262023-03-24
910인천광역시 남동구㈜이사방앤일호차1998-08-24인천광역시 남동구 구월로 336,4층(만수동)37.45508126.7278162023-03-24
연번시군구명업체명인허가일자소재지주소위도경도데이터기준일자
101102인천광역시 남동구백마물류1999-02-03인천광역시 남동구 은청로4-7, B동 309호37.40695126.69342023-03-24
102103인천광역시 남동구박군이사2007-11-13인천광역시 남동구 논고개로26837.414415126.7268772023-03-24
103104인천광역시 남동구전국종합운송2003-07-09인천광역시 남동구 호구포로194, 더마크원스테이 703호37.399702126.7086592023-03-24
104105인천광역시 남동구시온2004-07-19인천광역시 남동구 서창남로41, 205호(서창동, 광명프라자)37.423198126.7503632023-03-24
105106인천광역시 남동구서현종합물류㈜1998-04-17인천광역시 남동구 논곡로91번길 5-4, 101호(논현동)37.413652126.7130072023-03-24
106107인천광역시 남동구스타트종합특송2013-03-27인천광역시 남동구 남동서로236번길30,1726호(논현동,논현2차푸르지오시티)37.409573126.6951272023-03-24
107108인천광역시 남동구영&진 익스프레스2001-07-23인천광역시 남동구 호구포로764번길 36(구월동)37.45033126.7209682023-03-24
108109인천광역시 남동구T&D로지스2000-10-25인천광역시 남동구 남동대로733번길 51(구월동)37.448116126.7046082023-03-24
109110인천광역시 남동구대부물류2007-05-18인천광역시 남동구 남동대로915번길25, 308호(간석동, 형진프라자)37.464292126.7065442023-03-24
110111인천광역시 남동구프로로지스 주식회사2006-10-30인천광역시 남동구 소래역남로40, 에이동 1503호(논현동, 에코메트로3차더타워오피스텔)37.400182126.7322682023-03-24