Overview

Dataset statistics

Number of variables9
Number of observations7901
Missing cells655
Missing cells (%)0.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory602.0 KiB
Average record size in memory78.0 B

Variable types

Numeric6
Categorical2
DateTime1

Dataset

Description인천광역시 부평구 법정동별 본번, 부번, 대지면적, 건축면적, 연면적, 용도별, 사용승인일자에 대한 데이터 항목이 포함된 집합건축물 현황을 제공합니다.
Author인천광역시 부평구
URLhttps://www.data.go.kr/data/15100092/fileData.do

Alerts

건축면적 is highly overall correlated with 연면적High correlation
연면적 is highly overall correlated with 건축면적High correlation
주용도 is highly imbalanced (82.6%)Imbalance
대지면적 has 508 (6.4%) missing valuesMissing
사용승인일자 has 98 (1.2%) missing valuesMissing
대지면적 is highly skewed (γ1 = 41.96618319)Skewed
건축면적 is highly skewed (γ1 = 27.89139419)Skewed
연면적 is highly skewed (γ1 = 88.40657825)Skewed
연번 has unique valuesUnique
부번 has 1018 (12.9%) zerosZeros
대지면적 has 3332 (42.2%) zerosZeros
건축면적 has 263 (3.3%) zerosZeros

Reproduction

Analysis started2023-12-12 05:13:45.839807
Analysis finished2023-12-12 05:13:52.078386
Duration6.24 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

UNIQUE 

Distinct7901
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3951
Minimum1
Maximum7901
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size69.6 KiB
2023-12-12T14:13:52.173301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile396
Q11976
median3951
Q35926
95-th percentile7506
Maximum7901
Range7900
Interquartile range (IQR)3950

Descriptive statistics

Standard deviation2280.9666
Coefficient of variation (CV)0.57731374
Kurtosis-1.2
Mean3951
Median Absolute Deviation (MAD)1975
Skewness0
Sum31216851
Variance5202808.5
MonotonicityStrictly increasing
2023-12-12T14:13:52.335826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
5279 1
 
< 0.1%
5277 1
 
< 0.1%
5276 1
 
< 0.1%
5275 1
 
< 0.1%
5274 1
 
< 0.1%
5273 1
 
< 0.1%
5272 1
 
< 0.1%
5271 1
 
< 0.1%
5270 1
 
< 0.1%
Other values (7891) 7891
99.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
7901 1
< 0.1%
7900 1
< 0.1%
7899 1
< 0.1%
7898 1
< 0.1%
7897 1
< 0.1%
7896 1
< 0.1%
7895 1
< 0.1%
7894 1
< 0.1%
7893 1
< 0.1%
7892 1
< 0.1%

법정동
Categorical

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size61.9 KiB
부평동
2637 
십정동
1078 
부개동
1005 
산곡동
941 
청천동
718 
Other values (4)
1522 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row부평동
2nd row부평동
3rd row청천동
4th row청천동
5th row청천동

Common Values

ValueCountFrequency (%)
부평동 2637
33.4%
십정동 1078
13.6%
부개동 1005
 
12.7%
산곡동 941
 
11.9%
청천동 718
 
9.1%
삼산동 715
 
9.0%
갈산동 590
 
7.5%
일신동 194
 
2.5%
구산동 23
 
0.3%

Length

2023-12-12T14:13:52.493466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T14:13:52.611324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
부평동 2637
33.4%
십정동 1078
13.6%
부개동 1005
 
12.7%
산곡동 941
 
11.9%
청천동 718
 
9.1%
삼산동 715
 
9.0%
갈산동 590
 
7.5%
일신동 194
 
2.5%
구산동 23
 
0.3%

본번
Real number (ℝ)

Distinct606
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean344.44577
Minimum2
Maximum995
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size69.6 KiB
2023-12-12T14:13:52.780441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile28
Q1175
median341
Q3479
95-th percentile760
Maximum995
Range993
Interquartile range (IQR)304

Descriptive statistics

Standard deviation218.46667
Coefficient of variation (CV)0.63425564
Kurtosis-0.31998459
Mean344.44577
Median Absolute Deviation (MAD)159
Skewness0.53437092
Sum2721466
Variance47727.686
MonotonicityNot monotonic
2023-12-12T14:13:52.930113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 117
 
1.5%
760 108
 
1.4%
768 103
 
1.3%
120 102
 
1.3%
182 102
 
1.3%
180 101
 
1.3%
124 95
 
1.2%
756 83
 
1.1%
370 80
 
1.0%
12 77
 
1.0%
Other values (596) 6933
87.7%
ValueCountFrequency (%)
2 1
 
< 0.1%
3 21
 
0.3%
4 2
 
< 0.1%
5 8
 
0.1%
6 10
 
0.1%
7 2
 
< 0.1%
9 7
 
0.1%
10 117
1.5%
11 5
 
0.1%
12 77
1.0%
ValueCountFrequency (%)
995 19
0.2%
954 1
 
< 0.1%
950 1
 
< 0.1%
947 28
0.4%
946 8
 
0.1%
938 29
0.4%
937 10
 
0.1%
904 2
 
< 0.1%
903 1
 
< 0.1%
901 1
 
< 0.1%

부번
Real number (ℝ)

ZEROS 

Distinct523
Distinct (%)6.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51.917099
Minimum0
Maximum1192
Zeros1018
Zeros (%)12.9%
Negative0
Negative (%)0.0%
Memory size69.6 KiB
2023-12-12T14:13:53.079906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median12
Q338
95-th percentile274
Maximum1192
Range1192
Interquartile range (IQR)35

Descriptive statistics

Standard deviation117.37085
Coefficient of variation (CV)2.2607359
Kurtosis23.363899
Mean51.917099
Median Absolute Deviation (MAD)11
Skewness4.3505258
Sum410197
Variance13775.917
MonotonicityNot monotonic
2023-12-12T14:13:53.246405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1018
 
12.9%
1 551
 
7.0%
2 330
 
4.2%
5 324
 
4.1%
3 317
 
4.0%
4 265
 
3.4%
8 252
 
3.2%
6 232
 
2.9%
7 194
 
2.5%
9 183
 
2.3%
Other values (513) 4235
53.6%
ValueCountFrequency (%)
0 1018
12.9%
1 551
7.0%
2 330
 
4.2%
3 317
 
4.0%
4 265
 
3.4%
5 324
 
4.1%
6 232
 
2.9%
7 194
 
2.5%
8 252
 
3.2%
9 183
 
2.3%
ValueCountFrequency (%)
1192 1
< 0.1%
1140 1
< 0.1%
1119 1
< 0.1%
1118 1
< 0.1%
1065 1
< 0.1%
1060 1
< 0.1%
1059 1
< 0.1%
1021 1
< 0.1%
1003 1
< 0.1%
1002 1
< 0.1%

대지면적
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct2192
Distinct (%)29.6%
Missing508
Missing (%)6.4%
Infinite0
Infinite (%)0.0%
Mean827.56704
Minimum0
Maximum501744
Zeros3332
Zeros (%)42.2%
Negative0
Negative (%)0.0%
Memory size69.6 KiB
2023-12-12T14:13:53.388237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median159.2
Q3285.9
95-th percentile832.24
Maximum501744
Range501744
Interquartile range (IQR)285.9

Descriptive statistics

Standard deviation7563.0694
Coefficient of variation (CV)9.1389205
Kurtosis2622.3616
Mean827.56704
Median Absolute Deviation (MAD)159.2
Skewness41.966183
Sum6118203.1
Variance57200018
MonotonicityNot monotonic
2023-12-12T14:13:53.521430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 3332
42.2%
165.0 16
 
0.2%
218.0 15
 
0.2%
240.0 14
 
0.2%
215.0 13
 
0.2%
35646.0 13
 
0.2%
205.0 12
 
0.2%
203.0 12
 
0.2%
166.0 12
 
0.2%
28655.0 12
 
0.2%
Other values (2182) 3942
49.9%
(Missing) 508
 
6.4%
ValueCountFrequency (%)
0.0 3332
42.2%
12.827 1
 
< 0.1%
13.0 1
 
< 0.1%
27.4 1
 
< 0.1%
36.0 1
 
< 0.1%
43.0 1
 
< 0.1%
53.0 1
 
< 0.1%
64.0 1
 
< 0.1%
73.0 1
 
< 0.1%
74.0 1
 
< 0.1%
ValueCountFrequency (%)
501744.0 1
 
< 0.1%
81928.0 1
 
< 0.1%
71775.0 10
0.1%
54451.3 1
 
< 0.1%
54451.2 11
0.1%
44855.9 11
0.1%
35646.0 13
0.2%
35120.0 1
 
< 0.1%
31153.7 5
 
0.1%
30207.8 8
0.1%

건축면적
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct5501
Distinct (%)70.1%
Missing49
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean421.23953
Minimum0
Maximum90353.84
Zeros263
Zeros (%)3.3%
Negative0
Negative (%)0.0%
Memory size69.6 KiB
2023-12-12T14:13:54.026751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile11.48
Q198.27
median147.34
Q3264.7225
95-th percentile811.3555
Maximum90353.84
Range90353.84
Interquartile range (IQR)166.4525

Descriptive statistics

Standard deviation2868.8284
Coefficient of variation (CV)6.8104443
Kurtosis860.56988
Mean421.23953
Median Absolute Deviation (MAD)59.9
Skewness27.891394
Sum3307572.8
Variance8230176.1
MonotonicityNot monotonic
2023-12-12T14:13:54.173599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 263
 
3.3%
83.52 17
 
0.2%
14.973 16
 
0.2%
410.388 16
 
0.2%
116.56 15
 
0.2%
129.62 14
 
0.2%
6337.4176 14
 
0.2%
7820.8 12
 
0.2%
6601.5198 12
 
0.2%
8244.711 11
 
0.1%
Other values (5491) 7462
94.4%
(Missing) 49
 
0.6%
ValueCountFrequency (%)
0.0 263
3.3%
1.0 3
 
< 0.1%
1.39 1
 
< 0.1%
1.44 1
 
< 0.1%
1.8 1
 
< 0.1%
1.98 2
 
< 0.1%
2.0 3
 
< 0.1%
2.16 1
 
< 0.1%
2.22 3
 
< 0.1%
2.25 1
 
< 0.1%
ValueCountFrequency (%)
90353.84 7
0.1%
22609.0162 1
 
< 0.1%
15126.412 10
0.1%
13440.53 1
 
< 0.1%
11782.62 1
 
< 0.1%
11362.5 4
 
0.1%
10612.83 1
 
< 0.1%
10342.56 1
 
< 0.1%
10146.92 1
 
< 0.1%
8757.55 1
 
< 0.1%

연면적
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct6169
Distinct (%)78.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3459.9133
Minimum0
Maximum8745760
Zeros5
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size69.6 KiB
2023-12-12T14:13:54.331879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile90.8
Q1393.41
median610.7
Q31622.89
95-th percentile10609.65
Maximum8745760
Range8745760
Interquartile range (IQR)1229.48

Descriptive statistics

Standard deviation98544.371
Coefficient of variation (CV)28.481746
Kurtosis7843.5502
Mean3459.9133
Median Absolute Deviation (MAD)281.1
Skewness88.406578
Sum27336775
Variance9.710993 × 109
MonotonicityNot monotonic
2023-12-12T14:13:54.519467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
329.76 29
 
0.4%
329.52 22
 
0.3%
9.0 20
 
0.3%
329.04 18
 
0.2%
518.42 17
 
0.2%
328.44 16
 
0.2%
441.84 16
 
0.2%
329.6 15
 
0.2%
329.7 15
 
0.2%
328.8 15
 
0.2%
Other values (6159) 7718
97.7%
ValueCountFrequency (%)
0.0 5
0.1%
1.0 3
< 0.1%
1.39 1
 
< 0.1%
1.44 1
 
< 0.1%
1.8 1
 
< 0.1%
1.98 2
 
< 0.1%
2.0 3
< 0.1%
2.16 1
 
< 0.1%
2.22 3
< 0.1%
2.25 1
 
< 0.1%
ValueCountFrequency (%)
8745760.0 1
< 0.1%
281713.4185 1
< 0.1%
141198.54 1
< 0.1%
130716.53 1
< 0.1%
114100.275 1
< 0.1%
86173.99 1
< 0.1%
74083.95 1
< 0.1%
69181.787 1
< 0.1%
67344.45 1
< 0.1%
66511.03 1
< 0.1%

주용도
Categorical

IMBALANCE 

Distinct19
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size61.9 KiB
공동주택
7047 
업무시설
 
372
제1종근린생활시설
 
209
제2종근린생활시설
 
166
판매시설
 
20
Other values (14)
 
87

Length

Max length9
Median length4
Mean length4.24364
Min length2

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
공동주택 7047
89.2%
업무시설 372
 
4.7%
제1종근린생활시설 209
 
2.6%
제2종근린생활시설 166
 
2.1%
판매시설 20
 
0.3%
공장 19
 
0.2%
교육연구시설 19
 
0.2%
노유자시설 14
 
0.2%
근린생활시설 9
 
0.1%
숙박시설 7
 
0.1%
Other values (9) 19
 
0.2%

Length

2023-12-12T14:13:54.735543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
공동주택 7047
89.2%
업무시설 372
 
4.7%
제1종근린생활시설 209
 
2.6%
제2종근린생활시설 166
 
2.1%
판매시설 20
 
0.3%
공장 19
 
0.2%
교육연구시설 19
 
0.2%
노유자시설 14
 
0.2%
근린생활시설 9
 
0.1%
숙박시설 7
 
0.1%
Other values (9) 19
 
0.2%

사용승인일자
Date

MISSING 

Distinct3237
Distinct (%)41.5%
Missing98
Missing (%)1.2%
Memory size61.9 KiB
Minimum1963-12-26 00:00:00
Maximum2022-03-30 00:00:00
2023-12-12T14:13:54.902677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:13:55.079942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Interactions

2023-12-12T14:13:51.027529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:13:47.279055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:13:47.930170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:13:48.773098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:13:49.556634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:13:50.338219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:13:51.160903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:13:47.407573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:13:48.068368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:13:48.905697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:13:49.686657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:13:50.462584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:13:51.264263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:13:47.504928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:13:48.209998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:13:49.003413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:13:49.793164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:13:50.572454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:13:51.374665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:13:47.607435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:13:48.364988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:13:49.138973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:13:49.908430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:13:50.680755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:13:51.518456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:13:47.711192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:13:48.510741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:13:49.268172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:13:50.038631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:13:50.817461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:13:51.630471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:13:47.825042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:13:48.648578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:13:49.394257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:13:50.204838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:13:50.925741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T14:13:55.225855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번법정동본번부번대지면적건축면적연면적주용도
연번1.0000.3480.3430.1540.1500.0960.0010.254
법정동0.3481.0000.5030.2160.1510.1190.0000.223
본번0.3430.5031.0000.3920.1310.0630.0000.155
부번0.1540.2160.3921.0000.0000.0000.1070.000
대지면적0.1500.1510.1310.0001.0000.3800.0000.000
건축면적0.0960.1190.0630.0000.3801.0000.0000.275
연면적0.0010.0000.0000.1070.0000.0001.0000.000
주용도0.2540.2230.1550.0000.0000.2750.0001.000
2023-12-12T14:13:55.387288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
주용도법정동
주용도1.0000.089
법정동0.0891.000
2023-12-12T14:13:55.515472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번본번부번대지면적건축면적연면적법정동주용도
연번1.0000.0810.0600.3530.0410.0440.1660.098
본번0.0811.000-0.1210.0240.0160.0600.2560.058
부번0.060-0.1211.0000.138-0.179-0.2060.0990.000
대지면적0.3530.0240.1381.0000.4040.3470.0660.000
건축면적0.0410.016-0.1790.4041.0000.8380.0760.153
연면적0.0440.060-0.2060.3470.8381.0000.0000.000
법정동0.1660.2560.0990.0660.0760.0001.0000.089
주용도0.0980.0580.0000.0000.1530.0000.0891.000

Missing values

2023-12-12T14:13:51.750318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T14:13:51.908423image/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.
2023-12-12T14:13:52.017366image/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

연번법정동본번부번대지면적건축면적연면적주용도사용승인일자
01부평동37916302.1190.72779.82공동주택2021-04-28
12부평동15039193.7138.48591.12공동주택2009-02-24
23청천동1901710.0133.2505.14공동주택2002-04-30
34청천동1901710.0124.31477.26공동주택2002-04-30
45청천동190730.0208.99623.73공동주택1979-12-26
56청천동190730.0208.99623.73공동주택1979-12-26
67청천동192150.0112.14448.56공동주택1995-04-21
78청천동192150.0112.14518.09공동주택1995-04-21
89청천동259200.089.56401.07공동주택1994-01-24
910청천동199240.0296.55588.6553공동주택2006-11-15
연번법정동본번부번대지면적건축면적연면적주용도사용승인일자
78917892십정동5681308.4150.5335.2제1종근린생활시설1987-06-10
78927893부평동74713291.0166.93657.85공동주택2022-01-05
78937894일신동558356.2213.33634.78공동주택2022-01-11
78947895청천동17771470.6801.939289.38업무시설2021-12-17
78957896청천동3017869.9521.721945.18공동주택2021-12-09
78967897갈산동94035120.022609.0162281713.4185공장2021-12-15
78977898청천동321613.1296.861838.55업무시설2022-03-23
78987899부개동50280.0258.7183318.228공동주택1998-06-01
78997900부평동5774817.6653.310226.45업무시설2022-03-14
79007901부평동21031153.9902.5412496.12제1종근린생활시설2022-03-14