Overview

Dataset statistics

Number of variables9
Number of observations460
Missing cells55
Missing cells (%)1.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory33.8 KiB
Average record size in memory75.3 B

Variable types

Numeric3
Categorical4
Text2

Dataset

Description인천광역시 서구 기계설비 성능점검 실시 대상 건축물 현황에 관한 데이터입니다. 데이터 제공 신청에 의하여 등록된 데이터입니다. 법정동, 도로명주소, 주용도 등의 항목을 제공합니다.
Author인천광역시 서구
URLhttps://www.data.go.kr/data/15124598/fileData.do

Alerts

시군구 has constant value ""Constant
순번 is highly overall correlated with 구분High correlation
본번 is highly overall correlated with 법정동High correlation
구분 is highly overall correlated with 순번High correlation
법정동 is highly overall correlated with 본번High correlation
본번 has 11 (2.4%) missing valuesMissing
부번 has 41 (8.9%) missing valuesMissing
순번 has unique valuesUnique
총연면적 has unique valuesUnique
부번 has 45 (9.8%) zerosZeros

Reproduction

Analysis started2023-12-12 12:42:39.545711
Analysis finished2023-12-12 12:42:41.381937
Duration1.84 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct460
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean230.5
Minimum1
Maximum460
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2023-12-12T21:42:41.468614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile23.95
Q1115.75
median230.5
Q3345.25
95-th percentile437.05
Maximum460
Range459
Interquartile range (IQR)229.5

Descriptive statistics

Standard deviation132.93482
Coefficient of variation (CV)0.57672374
Kurtosis-1.2
Mean230.5
Median Absolute Deviation (MAD)115
Skewness0
Sum106030
Variance17671.667
MonotonicityStrictly increasing
2023-12-12T21:42:41.624061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.2%
318 1
 
0.2%
316 1
 
0.2%
315 1
 
0.2%
314 1
 
0.2%
313 1
 
0.2%
312 1
 
0.2%
311 1
 
0.2%
310 1
 
0.2%
309 1
 
0.2%
Other values (450) 450
97.8%
ValueCountFrequency (%)
1 1
0.2%
2 1
0.2%
3 1
0.2%
4 1
0.2%
5 1
0.2%
6 1
0.2%
7 1
0.2%
8 1
0.2%
9 1
0.2%
10 1
0.2%
ValueCountFrequency (%)
460 1
0.2%
459 1
0.2%
458 1
0.2%
457 1
0.2%
456 1
0.2%
455 1
0.2%
454 1
0.2%
453 1
0.2%
452 1
0.2%
451 1
0.2%

구분
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
1만㎡이상 1.5만㎡미만
157 
1.5만㎡이상 3만㎡미만
114 
500세대 이상 1천세대 미만
72 
3만㎡ 이상
66 
1천세대 이상 2천세대 미만
38 
Other values (3)
 
13

Length

Max length29
Median length13
Mean length12.882609
Min length6

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st row3만㎡ 이상
2nd row3만㎡ 이상
3rd row3만㎡ 이상
4th row3만㎡ 이상
5th row3만㎡ 이상

Common Values

ValueCountFrequency (%)
1만㎡이상 1.5만㎡미만 157
34.1%
1.5만㎡이상 3만㎡미만 114
24.8%
500세대 이상 1천세대 미만 72
15.7%
3만㎡ 이상 66
14.3%
1천세대 이상 2천세대 미만 38
 
8.3%
300세대 이상 500세대 미만(중앙집중, 지역난방) 7
 
1.5%
2천세대 이상 3천세대 미만 5
 
1.1%
3천세대 이상 1
 
0.2%

Length

2023-12-12T21:42:41.775845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:42:41.906241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
이상 189
16.1%
1만㎡이상 157
13.4%
1.5만㎡미만 157
13.4%
미만 115
9.8%
1.5만㎡이상 114
9.7%
3만㎡미만 114
9.7%
1천세대 110
9.4%
500세대 79
6.7%
3만㎡ 66
 
5.6%
2천세대 43
 
3.7%
Other values (4) 27
 
2.3%

시군구
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
인천광역시 서구
460 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
인천광역시 서구 460
100.0%

Length

2023-12-12T21:42:42.069979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:42:42.161650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
인천광역시 460
50.0%
서구 460
50.0%

법정동
Categorical

HIGH CORRELATION 

Distinct19
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
청라동
130 
가좌동
92 
당하동
35 
원당동
33 
가정동
31 
Other values (14)
139 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row경서동
2nd row오류동
3rd row가좌동
4th row백석동
5th row오류동

Common Values

ValueCountFrequency (%)
청라동 130
28.3%
가좌동 92
20.0%
당하동 35
 
7.6%
원당동 33
 
7.2%
가정동 31
 
6.7%
원창동 24
 
5.2%
오류동 21
 
4.6%
마전동 15
 
3.3%
석남동 12
 
2.6%
심곡동 10
 
2.2%
Other values (9) 57
12.4%

Length

2023-12-12T21:42:42.265480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
청라동 130
28.3%
가좌동 92
20.0%
당하동 35
 
7.6%
원당동 33
 
7.2%
가정동 31
 
6.7%
원창동 24
 
5.2%
오류동 21
 
4.6%
마전동 15
 
3.3%
석남동 12
 
2.6%
불로동 10
 
2.2%
Other values (9) 57
12.4%

본번
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct266
Distinct (%)59.2%
Missing11
Missing (%)2.4%
Infinite0
Infinite (%)0.0%
Mean475.26949
Minimum0
Maximum1708
Zeros1
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2023-12-12T21:42:42.412677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile20.4
Q1157
median308
Q3640
95-th percentile1317.2
Maximum1708
Range1708
Interquartile range (IQR)483

Descriptive statistics

Standard deviation426.3497
Coefficient of variation (CV)0.89706936
Kurtosis0.47206916
Mean475.26949
Median Absolute Deviation (MAD)210
Skewness1.146187
Sum213396
Variance181774.06
MonotonicityNot monotonic
2023-12-12T21:42:42.560638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
178 13
 
2.8%
96 10
 
2.2%
162 9
 
2.0%
157 9
 
2.0%
167 8
 
1.7%
152 7
 
1.5%
524 5
 
1.1%
103 5
 
1.1%
396 4
 
0.9%
104 4
 
0.9%
Other values (256) 375
81.5%
(Missing) 11
 
2.4%
ValueCountFrequency (%)
0 1
 
0.2%
1 4
0.9%
2 2
0.4%
3 1
 
0.2%
4 1
 
0.2%
5 2
0.4%
6 1
 
0.2%
7 3
0.7%
8 2
0.4%
9 1
 
0.2%
ValueCountFrequency (%)
1708 1
0.2%
1658 1
0.2%
1656 1
0.2%
1650 1
0.2%
1648 1
0.2%
1644 2
0.4%
1643 1
0.2%
1640 1
0.2%
1638 1
0.2%
1637 1
0.2%

부번
Real number (ℝ)

MISSING  ZEROS 

Distinct71
Distinct (%)16.9%
Missing41
Missing (%)8.9%
Infinite0
Infinite (%)0.0%
Mean23.522673
Minimum0
Maximum1216
Zeros45
Zeros (%)9.8%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2023-12-12T21:42:42.711154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q310
95-th percentile90.2
Maximum1216
Range1216
Interquartile range (IQR)9

Descriptive statistics

Standard deviation95.379606
Coefficient of variation (CV)4.0547945
Kurtosis98.555173
Mean23.522673
Median Absolute Deviation (MAD)2
Skewness9.0574411
Sum9856
Variance9097.2692
MonotonicityNot monotonic
2023-12-12T21:42:42.893934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 112
24.3%
0 45
9.8%
2 44
 
9.6%
3 28
 
6.1%
4 25
 
5.4%
5 18
 
3.9%
6 13
 
2.8%
7 11
 
2.4%
10 8
 
1.7%
16 8
 
1.7%
Other values (61) 107
23.3%
(Missing) 41
 
8.9%
ValueCountFrequency (%)
0 45
9.8%
1 112
24.3%
2 44
 
9.6%
3 28
 
6.1%
4 25
 
5.4%
5 18
 
3.9%
6 13
 
2.8%
7 11
 
2.4%
8 7
 
1.5%
9 4
 
0.9%
ValueCountFrequency (%)
1216 1
0.2%
1095 1
0.2%
446 1
0.2%
438 1
0.2%
401 1
0.2%
379 1
0.2%
353 1
0.2%
299 1
0.2%
270 1
0.2%
244 1
0.2%

도로명주소
Text

Distinct452
Distinct (%)98.9%
Missing3
Missing (%)0.7%
Memory size3.7 KiB
2023-12-12T21:42:43.186235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length14
Mean length8.1706783
Min length5

Characters and Unicode

Total characters3734
Distinct characters139
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique447 ?
Unique (%)97.8%

Sample

1st row경명대로322
2nd row보듬로158
3rd row백범로789
4th row거월로160
5th row가람로14
ValueCountFrequency (%)
검단로 6
 
1.0%
서로3로 5
 
0.9%
청라에메랄드로 5
 
0.9%
이음2로 5
 
0.9%
서곶로 5
 
0.9%
비즈니스로 4
 
0.7%
청라커낼로 4
 
0.7%
33 4
 
0.7%
이음3로 4
 
0.7%
봉오재3로 4
 
0.7%
Other values (480) 534
92.1%
2023-12-12T21:42:43.621097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
로 469
 
12.6%
1 275
 
7.4%
2 214
 
5.7%
3 186
 
5.0%
4 160
 
4.3%
길 153
 
4.1%
번 150
 
4.0%
5 139
 
3.7%
0 137
 
3.7%
7 123
 
3.3%
Other values (129) 1728
46.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2041
54.7%
Decimal Number 1538
41.2%
Space Separator 123
 
3.3%
Dash Punctuation 29
 
0.8%
Close Punctuation 1
 
< 0.1%
Uppercase Letter 1
 
< 0.1%
Open Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 469
23.0%
길 153
 
7.5%
번 150
 
7.3%
라 87
 
4.3%
청 85
 
4.2%
대 58
 
2.8%
봉 54
 
2.6%
가 45
 
2.2%
정 35
 
1.7%
이 31
 
1.5%
Other values (114) 874
42.8%
Decimal Number
ValueCountFrequency (%)
1 275
17.9%
2 214
13.9%
3 186
12.1%
4 160
10.4%
5 139
9.0%
0 137
8.9%
7 123
8.0%
6 117
7.6%
8 111
7.2%
9 76
 
4.9%
Space Separator
ValueCountFrequency (%)
123
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 29
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Uppercase Letter
ValueCountFrequency (%)
C 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2041
54.7%
Common 1692
45.3%
Latin 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 469
23.0%
길 153
 
7.5%
번 150
 
7.3%
라 87
 
4.3%
청 85
 
4.2%
대 58
 
2.8%
봉 54
 
2.6%
가 45
 
2.2%
정 35
 
1.7%
이 31
 
1.5%
Other values (114) 874
42.8%
Common
ValueCountFrequency (%)
1 275
16.3%
2 214
12.6%
3 186
11.0%
4 160
9.5%
5 139
8.2%
0 137
8.1%
7 123
7.3%
123
7.3%
6 117
6.9%
8 111
6.6%
Other values (4) 107
 
6.3%
Latin
ValueCountFrequency (%)
C 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2041
54.7%
ASCII 1693
45.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
로 469
23.0%
길 153
 
7.5%
번 150
 
7.3%
라 87
 
4.3%
청 85
 
4.2%
대 58
 
2.8%
봉 54
 
2.6%
가 45
 
2.2%
정 35
 
1.7%
이 31
 
1.5%
Other values (114) 874
42.8%
ASCII
ValueCountFrequency (%)
1 275
16.2%
2 214
12.6%
3 186
11.0%
4 160
9.5%
5 139
8.2%
0 137
8.1%
7 123
7.3%
123
7.3%
6 117
6.9%
8 111
6.6%
Other values (5) 108
 
6.4%

총연면적
Text

UNIQUE 

Distinct460
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
2023-12-12T21:42:43.983348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length8
Mean length8.276087
Min length5

Characters and Unicode

Total characters3807
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique460 ?
Unique (%)100.0%

Sample

1st row188268.08
2nd row153155.25
3rd row145040.46
4th row141933.99
5th row129123.59
ValueCountFrequency (%)
188268.08 1
 
0.2%
10649.2 1
 
0.2%
10730.69 1
 
0.2%
10749.15 1
 
0.2%
10752.68 1
 
0.2%
10762.74 1
 
0.2%
10779.14 1
 
0.2%
10818.3 1
 
0.2%
10847.1 1
 
0.2%
10882.71 1
 
0.2%
Other values (450) 450
97.8%
2023-12-12T21:42:44.609550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 622
16.3%
. 456
12.0%
2 376
9.9%
4 304
8.0%
5 304
8.0%
3 294
7.7%
6 279
7.3%
9 279
7.3%
0 277
7.3%
7 274
7.2%
Other values (2) 342
9.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3272
85.9%
Other Punctuation 535
 
14.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 622
19.0%
2 376
11.5%
4 304
9.3%
5 304
9.3%
3 294
9.0%
6 279
8.5%
9 279
8.5%
0 277
8.5%
7 274
8.4%
8 263
8.0%
Other Punctuation
ValueCountFrequency (%)
. 456
85.2%
, 79
 
14.8%

Most occurring scripts

ValueCountFrequency (%)
Common 3807
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 622
16.3%
. 456
12.0%
2 376
9.9%
4 304
8.0%
5 304
8.0%
3 294
7.7%
6 279
7.3%
9 279
7.3%
0 277
7.3%
7 274
7.2%
Other values (2) 342
9.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3807
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 622
16.3%
. 456
12.0%
2 376
9.9%
4 304
8.0%
5 304
8.0%
3 294
7.7%
6 279
7.3%
9 279
7.3%
0 277
7.3%
7 274
7.2%
Other values (2) 342
9.0%

주용도
Categorical

Distinct26
Distinct (%)5.7%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
공동주택
123 
공장
97 
교육연구시설
66 
업무시설
58 
제1종근린생활시설
27 
Other values (21)
89 

Length

Max length11
Median length10
Mean length4.6304348
Min length2

Unique

Unique9 ?
Unique (%)2.0%

Sample

1st row공장
2nd row공장
3rd row공장
4th row자원순환관련시설
5th row공장

Common Values

ValueCountFrequency (%)
공동주택 123
26.7%
공장 97
21.1%
교육연구시설 66
14.3%
업무시설 58
12.6%
제1종근린생활시설 27
 
5.9%
제2종근린생활시설 19
 
4.1%
자동차관련시설 17
 
3.7%
판매시설 13
 
2.8%
발전시설 5
 
1.1%
문화및집회시설 5
 
1.1%
Other values (16) 30
 
6.5%

Length

2023-12-12T21:42:44.804799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
공동주택 123
26.7%
공장 97
21.1%
교육연구시설 66
14.3%
업무시설 58
12.6%
제1종근린생활시설 27
 
5.9%
제2종근린생활시설 19
 
4.1%
자동차관련시설 17
 
3.7%
판매시설 13
 
2.8%
발전시설 5
 
1.1%
문화및집회시설 5
 
1.1%
Other values (16) 30
 
6.5%

Interactions

2023-12-12T21:42:40.544978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:42:39.944744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:42:40.224818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:42:40.649260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:42:40.030606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:42:40.333080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:42:40.773323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:42:40.140794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:42:40.439388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T21:42:44.914070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번구분법정동본번부번주용도
순번1.0000.8680.4520.4890.0970.725
구분0.8681.0000.4880.3430.0000.717
법정동0.4520.4881.0000.8940.0000.749
본번0.4890.3430.8941.0000.2300.643
부번0.0970.0000.0000.2301.0000.000
주용도0.7250.7170.7490.6430.0001.000
2023-12-12T21:42:45.058822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
주용도법정동구분
주용도1.0000.3020.377
법정동0.3021.0000.226
구분0.3770.2261.000
2023-12-12T21:42:45.173050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번본번부번구분법정동주용도
순번1.0000.207-0.1680.6590.1850.355
본번0.2071.000-0.2440.1710.6060.292
부번-0.168-0.2441.0000.0000.0000.000
구분0.6590.1710.0001.0000.2260.377
법정동0.1850.6060.0000.2261.0000.302
주용도0.3550.2920.0000.3770.3021.000

Missing values

2023-12-12T21:42:40.944116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T21:42:41.155622image/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.
2023-12-12T21:42:41.300470image/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

순번구분시군구법정동본번부번도로명주소총연면적주용도
013만㎡ 이상인천광역시 서구경서동36380경명대로322188268.08공장
123만㎡ 이상인천광역시 서구오류동16561보듬로158153155.25공장
233만㎡ 이상인천광역시 서구가좌동5713백범로789145040.46공장
343만㎡ 이상인천광역시 서구백석동580거월로160141933.99자원순환관련시설
453만㎡ 이상인천광역시 서구오류동16101가람로14129123.59공장
563만㎡ 이상인천광역시 서구청라동15721청라커낼로260번길27112221.01업무시설
673만㎡ 이상인천광역시 서구연희동8260봉수대로806110153.08문화및집회시설
783만㎡ 이상인천광역시 서구청라동2025파랑로495106190.52공장
893만㎡ 이상인천광역시 서구청라동15710청라커낼로288번길26106012.87업무시설
9103만㎡ 이상인천광역시 서구원창동39221북항로206번길10-20104992.86자동차관련시설
순번구분시군구법정동본번부번도로명주소총연면적주용도
450451500세대 이상 1천세대 미만인천광역시 서구당하동1274<NA>매밭로 130136,703.22공동주택
451452500세대 이상 1천세대 미만인천광역시 서구당하동13111이음2로 90118,277.48공동주택
452453500세대 이상 1천세대 미만인천광역시 서구원당동10823이음3로 221103,207.14공동주택
453454300세대 이상 500세대 미만(중앙집중, 지역난방)인천광역시 서구청라동1164청라라임로 131113,984.01공동주택
454455300세대 이상 500세대 미만(중앙집중, 지역난방)인천광역시 서구청라동16510청라에메랄드로 6558,812.57공동주택
455456300세대 이상 500세대 미만(중앙집중, 지역난방)인천광역시 서구청라동1661청라에메랄드로 112101,849.24공동주택
456457300세대 이상 500세대 미만(중앙집중, 지역난방)인천광역시 서구원당동10652이음4로 9463,390.09공동주택
457458300세대 이상 500세대 미만(중앙집중, 지역난방)인천광역시 서구청라동821비즈니스로 16558,956.38공동주택
458459300세대 이상 500세대 미만(중앙집중, 지역난방)인천광역시 서구청라동10413보석로 3261,559.42공동주택
459460300세대 이상 500세대 미만(중앙집중, 지역난방)인천광역시 서구당하동13051이음2로 8954,962.96공동주택