Overview

Dataset statistics

Number of variables9
Number of observations655
Missing cells3
Missing cells (%)0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory48.7 KiB
Average record size in memory76.2 B

Variable types

Text2
Categorical4
Numeric3

Dataset

Description인천광역시 연수구 원도심과 송도에 설치되어 있는 가로등 분전함 관련정보를 보내 드립니다. (도로조명 관리번호, 설치개수, 소재지도로명주소 설치년도, 관리기관전화번호, 관리기관명, 데이터기준일자)
Author인천광역시
URLhttps://www.incheon.go.kr/data/DATA010201/view?docId=15059623

Alerts

설치개수 has constant value ""Constant
관리기관전화번호 has constant value ""Constant
관리기관명 has constant value ""Constant
데이터기준일자 has constant value ""Constant
위도 is highly overall correlated with 설치년도High correlation
경도 is highly overall correlated with 설치년도High correlation
설치년도 is highly overall correlated with 위도 and 1 other fieldsHigh correlation
도로조명 관리번호 has unique valuesUnique

Reproduction

Analysis started2024-01-28 16:12:53.635456
Analysis finished2024-01-28 16:12:54.810334
Duration1.17 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

도로조명 관리번호
Text

UNIQUE 

Distinct655
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size5.2 KiB
2024-01-29T01:12:55.001420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length10
Mean length6.4366412
Min length4

Characters and Unicode

Total characters4216
Distinct characters159
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

Unique655 ?
Unique (%)100.0%

Sample

1st row105고가교1
2nd row105고가교2
3rd row간도장1
4th row간도장2
5th row경원대로525번길1
ValueCountFrequency (%)
경원로 6
 
0.9%
승기천 4
 
0.6%
105고가교1 1
 
0.2%
3_lp_38 1
 
0.2%
3_lp_31 1
 
0.2%
3_lp_32 1
 
0.2%
3_lp_33 1
 
0.2%
3_lp_34 1
 
0.2%
3_lp_35 1
 
0.2%
3_lp_36 1
 
0.2%
Other values (648) 648
97.3%
2024-01-29T01:12:55.349352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
_ 669
15.9%
1 398
 
9.4%
L 331
 
7.9%
P 330
 
7.8%
2 190
 
4.5%
3 176
 
4.2%
0 175
 
4.2%
5 167
 
4.0%
로 150
 
3.6%
길 136
 
3.2%
Other values (149) 1494
35.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1493
35.4%
Other Letter 1213
28.8%
Uppercase Letter 822
19.5%
Connector Punctuation 669
15.9%
Space Separator 11
 
0.3%
Dash Punctuation 4
 
0.1%
Close Punctuation 2
 
< 0.1%
Open Punctuation 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 150
 
12.4%
길 136
 
11.2%
원 42
 
3.5%
대 36
 
3.0%
재 29
 
2.4%
경 28
 
2.3%
암 23
 
1.9%
함 22
 
1.8%
류 21
 
1.7%
비 21
 
1.7%
Other values (125) 705
58.1%
Decimal Number
ValueCountFrequency (%)
1 398
26.7%
2 190
12.7%
3 176
11.8%
0 175
11.7%
5 167
11.2%
4 123
 
8.2%
7 102
 
6.8%
6 69
 
4.6%
8 51
 
3.4%
9 42
 
2.8%
Uppercase Letter
ValueCountFrequency (%)
L 331
40.3%
P 330
40.1%
N 41
 
5.0%
R 26
 
3.2%
A 26
 
3.2%
D 25
 
3.0%
C 16
 
1.9%
S 16
 
1.9%
B 11
 
1.3%
Connector Punctuation
ValueCountFrequency (%)
_ 669
100.0%
Space Separator
ValueCountFrequency (%)
11
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2181
51.7%
Hangul 1213
28.8%
Latin 822
 
19.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 150
 
12.4%
길 136
 
11.2%
원 42
 
3.5%
대 36
 
3.0%
재 29
 
2.4%
경 28
 
2.3%
암 23
 
1.9%
함 22
 
1.8%
류 21
 
1.7%
비 21
 
1.7%
Other values (125) 705
58.1%
Common
ValueCountFrequency (%)
_ 669
30.7%
1 398
18.2%
2 190
 
8.7%
3 176
 
8.1%
0 175
 
8.0%
5 167
 
7.7%
4 123
 
5.6%
7 102
 
4.7%
6 69
 
3.2%
8 51
 
2.3%
Other values (5) 61
 
2.8%
Latin
ValueCountFrequency (%)
L 331
40.3%
P 330
40.1%
N 41
 
5.0%
R 26
 
3.2%
A 26
 
3.2%
D 25
 
3.0%
C 16
 
1.9%
S 16
 
1.9%
B 11
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3003
71.2%
Hangul 1213
28.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
_ 669
22.3%
1 398
13.3%
L 331
11.0%
P 330
11.0%
2 190
 
6.3%
3 176
 
5.9%
0 175
 
5.8%
5 167
 
5.6%
4 123
 
4.1%
7 102
 
3.4%
Other values (14) 342
11.4%
Hangul
ValueCountFrequency (%)
로 150
 
12.4%
길 136
 
11.2%
원 42
 
3.5%
대 36
 
3.0%
재 29
 
2.4%
경 28
 
2.3%
암 23
 
1.9%
함 22
 
1.8%
류 21
 
1.7%
비 21
 
1.7%
Other values (125) 705
58.1%

설치개수
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size5.2 KiB
1
655 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1 655
100.0%

Length

2024-01-29T01:12:55.456669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-29T01:12:55.525303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 655
100.0%

소재지도로명주소
Text

Distinct462
Distinct (%)70.6%
Missing1
Missing (%)0.2%
Memory size5.2 KiB
2024-01-29T01:12:55.672805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length29
Median length26
Mean length18.345566
Min length7

Characters and Unicode

Total characters11998
Distinct characters76
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

Unique352 ?
Unique (%)53.8%

Sample

1st row인천 연수구 연수동 647
2nd row인천 연수구 연수동 647
3rd row인천 연수구 선학동 140-10
4th row인천 연수구 선학동 120-48
5th row인천 연수구 선학동 149-1
ValueCountFrequency (%)
연수구 648
25.0%
인천광역시 364
14.1%
인천 263
 
10.2%
송도2동 133
 
5.1%
송도3동 101
 
3.9%
동춘동 83
 
3.2%
연수동 72
 
2.8%
송도4동 66
 
2.5%
옥련동 65
 
2.5%
송도1동 65
 
2.5%
Other values (455) 730
28.2%
2024-01-29T01:12:55.952404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1936
16.1%
동 738
 
6.2%
연 720
 
6.0%
수 720
 
6.0%
구 653
 
5.4%
인 638
 
5.3%
천 638
 
5.3%
1 514
 
4.3%
2 427
 
3.6%
송 424
 
3.5%
Other values (66) 4590
38.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 7082
59.0%
Decimal Number 2669
 
22.2%
Space Separator 1936
 
16.1%
Dash Punctuation 311
 
2.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 738
10.4%
연 720
10.2%
수 720
10.2%
구 653
9.2%
인 638
9.0%
천 638
9.0%
송 424
 
6.0%
도 422
 
6.0%
시 389
 
5.5%
광 364
 
5.1%
Other values (54) 1376
19.4%
Decimal Number
ValueCountFrequency (%)
1 514
19.3%
2 427
16.0%
3 313
11.7%
4 298
11.2%
9 213
8.0%
5 211
7.9%
6 202
 
7.6%
0 189
 
7.1%
8 188
 
7.0%
7 114
 
4.3%
Space Separator
ValueCountFrequency (%)
1936
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 311
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 7082
59.0%
Common 4916
41.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 738
10.4%
연 720
10.2%
수 720
10.2%
구 653
9.2%
인 638
9.0%
천 638
9.0%
송 424
 
6.0%
도 422
 
6.0%
시 389
 
5.5%
광 364
 
5.1%
Other values (54) 1376
19.4%
Common
ValueCountFrequency (%)
1936
39.4%
1 514
 
10.5%
2 427
 
8.7%
3 313
 
6.4%
- 311
 
6.3%
4 298
 
6.1%
9 213
 
4.3%
5 211
 
4.3%
6 202
 
4.1%
0 189
 
3.8%
Other values (2) 302
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 7082
59.0%
ASCII 4916
41.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1936
39.4%
1 514
 
10.5%
2 427
 
8.7%
3 313
 
6.4%
- 311
 
6.3%
4 298
 
6.1%
9 213
 
4.3%
5 211
 
4.3%
6 202
 
4.1%
0 189
 
3.8%
Other values (2) 302
 
6.1%
Hangul
ValueCountFrequency (%)
동 738
10.4%
연 720
10.2%
수 720
10.2%
구 653
9.2%
인 638
9.0%
천 638
9.0%
송 424
 
6.0%
도 422
 
6.0%
시 389
 
5.5%
광 364
 
5.1%
Other values (54) 1376
19.4%

위도
Real number (ℝ)

HIGH CORRELATION 

Distinct335
Distinct (%)51.2%
Missing1
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean37.400846
Minimum37.356965
Maximum37.441937
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2024-01-29T01:12:56.064590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.356965
5-th percentile37.371722
Q137.386892
median37.399572
Q337.417755
95-th percentile37.428729
Maximum37.441937
Range0.084972
Interquartile range (IQR)0.0308625

Descriptive statistics

Standard deviation0.018745738
Coefficient of variation (CV)0.0005012116
Kurtosis-0.9891199
Mean37.400846
Median Absolute Deviation (MAD)0.013837
Skewness-0.030786344
Sum24460.153
Variance0.00035140269
MonotonicityNot monotonic
2024-01-29T01:12:56.171397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.371722 73
 
11.1%
37.386892 59
 
9.0%
37.391384 23
 
3.5%
37.399572 11
 
1.7%
37.371963 8
 
1.2%
37.41781 7
 
1.1%
37.384878 6
 
0.9%
37.398175 6
 
0.9%
37.368195 5
 
0.8%
37.382168 5
 
0.8%
Other values (325) 451
68.9%
ValueCountFrequency (%)
37.356965 2
 
0.3%
37.368195 5
 
0.8%
37.371482 3
 
0.5%
37.371722 73
11.1%
37.371963 8
 
1.2%
37.372751 2
 
0.3%
37.373018 1
 
0.2%
37.373381 5
 
0.8%
37.373721 1
 
0.2%
37.37508 1
 
0.2%
ValueCountFrequency (%)
37.441937 1
 
0.2%
37.438846 1
 
0.2%
37.438701 1
 
0.2%
37.437473 3
0.5%
37.436316 1
 
0.2%
37.435914 1
 
0.2%
37.43528 1
 
0.2%
37.435183 1
 
0.2%
37.434866 1
 
0.2%
37.434049 1
 
0.2%

경도
Real number (ℝ)

HIGH CORRELATION 

Distinct335
Distinct (%)51.2%
Missing1
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean126.6572
Minimum126.62019
Maximum126.70566
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2024-01-29T01:12:56.272368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.62019
5-th percentile126.6311
Q1126.6414
median126.65588
Q3126.66928
95-th percentile126.69527
Maximum126.70566
Range0.085473
Interquartile range (IQR)0.0278785

Descriptive statistics

Standard deviation0.018488187
Coefficient of variation (CV)0.00014597028
Kurtosis-0.29969427
Mean126.6572
Median Absolute Deviation (MAD)0.014346
Skewness0.48095609
Sum82833.809
Variance0.00034181306
MonotonicityNot monotonic
2024-01-29T01:12:56.377187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.659938 73
 
11.1%
126.639671 59
 
9.0%
126.651617 23
 
3.5%
126.620189 11
 
1.7%
126.677224 8
 
1.2%
126.650369 7
 
1.1%
126.658734 6
 
0.9%
126.673087 6
 
0.9%
126.660533 5
 
0.8%
126.662019 5
 
0.8%
Other values (325) 451
68.9%
ValueCountFrequency (%)
126.620189 11
1.7%
126.625233 1
 
0.2%
126.626879 1
 
0.2%
126.62716 1
 
0.2%
126.627219 1
 
0.2%
126.628127 1
 
0.2%
126.628424 1
 
0.2%
126.628621 1
 
0.2%
126.628936 1
 
0.2%
126.629232 2
 
0.3%
ValueCountFrequency (%)
126.705662 1
0.2%
126.704956 1
0.2%
126.703018 1
0.2%
126.702726 1
0.2%
126.702281 2
0.3%
126.701931 1
0.2%
126.701226 1
0.2%
126.701202 1
0.2%
126.700635 1
0.2%
126.700113 1
0.2%

설치년도
Real number (ℝ)

HIGH CORRELATION 

Distinct10
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2010.3344
Minimum2005
Maximum2021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2024-01-29T01:12:56.471179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2005
5-th percentile2005
Q12007
median2010
Q32012
95-th percentile2020
Maximum2021
Range16
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.8610771
Coefficient of variation (CV)0.0019206144
Kurtosis0.74685266
Mean2010.3344
Median Absolute Deviation (MAD)3
Skewness1.092654
Sum1316769
Variance14.907916
MonotonicityNot monotonic
2024-01-29T01:12:56.553927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
2007 213
32.5%
2010 116
17.7%
2011 106
16.2%
2012 104
15.9%
2005 35
 
5.3%
2020 32
 
4.9%
2018 20
 
3.1%
2017 15
 
2.3%
2019 10
 
1.5%
2021 4
 
0.6%
ValueCountFrequency (%)
2005 35
 
5.3%
2007 213
32.5%
2010 116
17.7%
2011 106
16.2%
2012 104
15.9%
2017 15
 
2.3%
2018 20
 
3.1%
2019 10
 
1.5%
2020 32
 
4.9%
2021 4
 
0.6%
ValueCountFrequency (%)
2021 4
 
0.6%
2020 32
 
4.9%
2019 10
 
1.5%
2018 20
 
3.1%
2017 15
 
2.3%
2012 104
15.9%
2011 106
16.2%
2010 116
17.7%
2007 213
32.5%
2005 35
 
5.3%

관리기관전화번호
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size5.2 KiB
032-749-8564
655 

Length

Max length12
Median length12
Mean length12
Min length12

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row032-749-8564
2nd row032-749-8564
3rd row032-749-8564
4th row032-749-8564
5th row032-749-8564

Common Values

ValueCountFrequency (%)
032-749-8564 655
100.0%

Length

2024-01-29T01:12:56.644572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-29T01:12:56.713612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
032-749-8564 655
100.0%

관리기관명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size5.2 KiB
인천광역시 연수구청
655 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
인천광역시 연수구청 655
100.0%

Length

2024-01-29T01:12:56.786744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-29T01:12:56.863516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
인천광역시 655
50.0%
연수구청 655
50.0%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size5.2 KiB
2023-06-01
655 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-06-01
2nd row2023-06-01
3rd row2023-06-01
4th row2023-06-01
5th row2023-06-01

Common Values

ValueCountFrequency (%)
2023-06-01 655
100.0%

Length

2024-01-29T01:12:56.942506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-29T01:12:57.018243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-06-01 655
100.0%

Interactions

2024-01-29T01:12:54.340037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:12:53.900922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:12:54.136290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:12:54.407955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:12:53.983057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:12:54.201420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:12:54.480285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:12:54.060150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:12:54.264847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-29T01:12:57.300029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위도경도설치년도
위도1.0000.8460.659
경도0.8461.0000.648
설치년도0.6590.6481.000
2024-01-29T01:12:57.380720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위도경도설치년도
위도1.0000.2870.654
경도0.2871.0000.596
설치년도0.6540.5961.000

Missing values

2024-01-29T01:12:54.579984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-29T01:12:54.680342image/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.
2024-01-29T01:12:54.766849image/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

도로조명 관리번호설치개수소재지도로명주소위도경도설치년도관리기관전화번호관리기관명데이터기준일자
0105고가교11인천 연수구 연수동 64737.41285126.6884942011032-749-8564인천광역시 연수구청2023-06-01
1105고가교21인천 연수구 연수동 64737.41285126.6884942011032-749-8564인천광역시 연수구청2023-06-01
2간도장11인천 연수구 선학동 140-1037.43164126.7006352019032-749-8564인천광역시 연수구청2023-06-01
3간도장21인천 연수구 선학동 120-4837.432634126.6983212020032-749-8564인천광역시 연수구청2023-06-01
4경원대로525번길11인천 연수구 선학동 149-137.431615126.697682020032-749-8564인천광역시 연수구청2023-06-01
5경원로 보행등11인천 연수구 동춘동 95837.398175126.6730872012032-749-8564인천광역시 연수구청2023-06-01
6경원로 보행등21인천 연수구 동춘동 92937.401677126.6745252012032-749-8564인천광역시 연수구청2023-06-01
7경원로 보행등31연수동 63537.409927126.6830152012032-749-8564인천광역시 연수구청2023-06-01
8경원로 보행등41인천 연수구 연수동 63437.41179126.6850762012032-749-8564인천광역시 연수구청2023-06-01
9경원로 보행등51인천 연수구 연수동 582-137.416298126.69032012032-749-8564인천광역시 연수구청2023-06-01
도로조명 관리번호설치개수소재지도로명주소위도경도설치년도관리기관전화번호관리기관명데이터기준일자
645어LP_141인천광역시 연수구 송도2동 컨벤시아대로130번길5837.396197126.6415332007032-749-8564인천광역시 연수구청2023-06-01
646어민_LP_011인천광역시 연수구 송도2동 22-1937.39298126.6448972007032-749-8564인천광역시 연수구청2023-06-01
647테크노LP_011인천광역시 연수구 송도2동 994-2237.403225126.6416382005032-749-8564인천광역시 연수구청2023-06-01
648테크노LP_021인천광역시 연수구 송도2동 갯벌로15637.383958126.6503832005032-749-8564인천광역시 연수구청2023-06-01
649테크노LP_031인천광역시 연수구 송도2동 994-6937.399572126.6201892005032-749-8564인천광역시 연수구청2023-06-01
650통합분전함1_LP_621인천광역시 연수구 송도2동 컨벤시아대로16037.392164126.6391972007032-749-8564인천광역시 연수구청2023-06-01
651통합분전함1_LP_851인천광역시 연수구 송도2동 6-137.391384126.6516172007032-749-8564인천광역시 연수구청2023-06-01
652통합분전함LP_241인천광역시 연수구 송도2동 해돋이로10737.391085126.648042007032-749-8564인천광역시 연수구청2023-06-01
653통합분전함LP_251인천광역시 연수구 송도2동 1028-237.395808126.6491652007032-749-8564인천광역시 연수구청2023-06-01
654통합분전함LP_261인천광역시 연수구 송도2동 컨벤시아대로42번길2037.401513126.647252007032-749-8564인천광역시 연수구청2023-06-01