Overview

Dataset statistics

Number of variables8
Number of observations399
Missing cells18
Missing cells (%)0.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory25.5 KiB
Average record size in memory65.3 B

Variable types

Numeric1
Text4
Categorical2
DateTime1

Dataset

Description서울특별시 양천구 공공건축물 현황 데이터 입니다.연번, 상호명, 주소, 연면적, 주용도 등 정보를 포함하고 있습니다.
Author서울특별시 양천구
URLhttps://www.data.go.kr/data/15112766/fileData.do

Alerts

연번 is highly overall correlated with 관리부서High correlation
주용도 is highly overall correlated with 관리부서High correlation
관리부서 is highly overall correlated with 연번 and 1 other fieldsHigh correlation
사용승인일 has 16 (4.0%) missing valuesMissing
연번 has unique valuesUnique

Reproduction

Analysis started2024-04-06 08:53:52.783360
Analysis finished2024-04-06 08:53:54.243214
Duration1.46 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct399
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean200
Minimum1
Maximum399
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.6 KiB
2024-04-06T17:53:54.408900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile20.9
Q1100.5
median200
Q3299.5
95-th percentile379.1
Maximum399
Range398
Interquartile range (IQR)199

Descriptive statistics

Standard deviation115.32563
Coefficient of variation (CV)0.57662813
Kurtosis-1.2
Mean200
Median Absolute Deviation (MAD)100
Skewness0
Sum79800
Variance13300
MonotonicityStrictly increasing
2024-04-06T17:53:54.683419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.3%
264 1
 
0.3%
274 1
 
0.3%
273 1
 
0.3%
272 1
 
0.3%
271 1
 
0.3%
270 1
 
0.3%
269 1
 
0.3%
268 1
 
0.3%
267 1
 
0.3%
Other values (389) 389
97.5%
ValueCountFrequency (%)
1 1
0.3%
2 1
0.3%
3 1
0.3%
4 1
0.3%
5 1
0.3%
6 1
0.3%
7 1
0.3%
8 1
0.3%
9 1
0.3%
10 1
0.3%
ValueCountFrequency (%)
399 1
0.3%
398 1
0.3%
397 1
0.3%
396 1
0.3%
395 1
0.3%
394 1
0.3%
393 1
0.3%
392 1
0.3%
391 1
0.3%
390 1
0.3%

상호명(건물명)
Text

Distinct364
Distinct (%)91.2%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
2024-04-06T17:53:55.116265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length19
Mean length9.245614
Min length3

Characters and Unicode

Total characters3689
Distinct characters275
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique329 ?
Unique (%)82.5%

Sample

1st row신월청소년문화센터
2nd row신월여의도지하도로 관리사무소
3rd row갈산공공도서관
4th row갈산문화예술센터
5th row강서수도사업소
ValueCountFrequency (%)
공중화장실 8
 
1.6%
및 6
 
1.2%
책쉼터 5
 
1.0%
구립 5
 
1.0%
관리사무소 5
 
1.0%
양천공원 4
 
0.8%
파리공원 4
 
0.8%
공영주차장 4
 
0.8%
양천구 3
 
0.6%
야외무대 3
 
0.6%
Other values (394) 442
90.4%
2024-04-06T17:53:56.348825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 126
 
3.4%
신 121
 
3.3%
서 109
 
3.0%
90
 
2.4%
구 81
 
2.2%
학 81
 
2.2%
관 79
 
2.1%
목 74
 
2.0%
정 67
 
1.8%
공 66
 
1.8%
Other values (265) 2795
75.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3384
91.7%
Space Separator 90
 
2.4%
Decimal Number 89
 
2.4%
Open Punctuation 60
 
1.6%
Close Punctuation 60
 
1.6%
Other Punctuation 5
 
0.1%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 126
 
3.7%
신 121
 
3.6%
서 109
 
3.2%
구 81
 
2.4%
학 81
 
2.4%
관 79
 
2.3%
목 74
 
2.2%
정 67
 
2.0%
공 66
 
2.0%
소 65
 
1.9%
Other values (248) 2515
74.3%
Decimal Number
ValueCountFrequency (%)
3 24
27.0%
2 19
21.3%
1 17
19.1%
4 11
12.4%
7 6
 
6.7%
5 5
 
5.6%
6 4
 
4.5%
9 2
 
2.2%
0 1
 
1.1%
Other Punctuation
ValueCountFrequency (%)
· 2
40.0%
& 1
20.0%
. 1
20.0%
, 1
20.0%
Space Separator
ValueCountFrequency (%)
90
100.0%
Open Punctuation
ValueCountFrequency (%)
( 60
100.0%
Close Punctuation
ValueCountFrequency (%)
) 60
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3384
91.7%
Common 305
 
8.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 126
 
3.7%
신 121
 
3.6%
서 109
 
3.2%
구 81
 
2.4%
학 81
 
2.4%
관 79
 
2.3%
목 74
 
2.2%
정 67
 
2.0%
공 66
 
2.0%
소 65
 
1.9%
Other values (248) 2515
74.3%
Common
ValueCountFrequency (%)
90
29.5%
( 60
19.7%
) 60
19.7%
3 24
 
7.9%
2 19
 
6.2%
1 17
 
5.6%
4 11
 
3.6%
7 6
 
2.0%
5 5
 
1.6%
6 4
 
1.3%
Other values (7) 9
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3384
91.7%
ASCII 303
 
8.2%
None 2
 
0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 126
 
3.7%
신 121
 
3.6%
서 109
 
3.2%
구 81
 
2.4%
학 81
 
2.4%
관 79
 
2.3%
목 74
 
2.2%
정 67
 
2.0%
공 66
 
2.0%
소 65
 
1.9%
Other values (248) 2515
74.3%
ASCII
ValueCountFrequency (%)
90
29.7%
( 60
19.8%
) 60
19.8%
3 24
 
7.9%
2 19
 
6.3%
1 17
 
5.6%
4 11
 
3.6%
7 6
 
2.0%
5 5
 
1.7%
6 4
 
1.3%
Other values (6) 7
 
2.3%
None
ValueCountFrequency (%)
· 2
100.0%

도로명주소
Text

Distinct265
Distinct (%)66.8%
Missing2
Missing (%)0.5%
Memory size3.2 KiB
2024-04-06T17:53:56.861532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length24
Mean length17.843829
Min length6

Characters and Unicode

Total characters7084
Distinct characters62
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique188 ?
Unique (%)47.4%

Sample

1st row서울특별시 양천구 가로공원로 86
2nd row서울특별시 양천구 국회대로 248-1
3rd row서울특별시 양천구 목동남로4길 48-6
4th row서울특별시 양천구 목동남로 106
5th row서울특별시 양천구 목동동로 155
ValueCountFrequency (%)
양천구 353
23.5%
서울특별시 335
22.3%
목동동로 32
 
2.1%
목동서로 28
 
1.9%
지양로 23
 
1.5%
서울 18
 
1.2%
41 15
 
1.0%
139 14
 
0.9%
목동로3길 14
 
0.9%
20 12
 
0.8%
Other values (288) 659
43.8%
2024-04-06T17:53:57.791403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1107
15.6%
로 404
 
5.7%
양 398
 
5.6%
서 389
 
5.5%
천 365
 
5.2%
구 353
 
5.0%
울 353
 
5.0%
시 335
 
4.7%
특 335
 
4.7%
별 335
 
4.7%
Other values (52) 2710
38.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4616
65.2%
Decimal Number 1294
 
18.3%
Space Separator 1107
 
15.6%
Dash Punctuation 57
 
0.8%
Close Punctuation 5
 
0.1%
Open Punctuation 5
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 404
 
8.8%
양 398
 
8.6%
서 389
 
8.4%
천 365
 
7.9%
구 353
 
7.6%
울 353
 
7.6%
시 335
 
7.3%
특 335
 
7.3%
별 335
 
7.3%
동 213
 
4.6%
Other values (38) 1136
24.6%
Decimal Number
ValueCountFrequency (%)
1 274
21.2%
3 214
16.5%
2 140
10.8%
5 116
9.0%
4 108
 
8.3%
0 103
 
8.0%
6 102
 
7.9%
7 84
 
6.5%
9 77
 
6.0%
8 76
 
5.9%
Space Separator
ValueCountFrequency (%)
1107
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 57
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4616
65.2%
Common 2468
34.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 404
 
8.8%
양 398
 
8.6%
서 389
 
8.4%
천 365
 
7.9%
구 353
 
7.6%
울 353
 
7.6%
시 335
 
7.3%
특 335
 
7.3%
별 335
 
7.3%
동 213
 
4.6%
Other values (38) 1136
24.6%
Common
ValueCountFrequency (%)
1107
44.9%
1 274
 
11.1%
3 214
 
8.7%
2 140
 
5.7%
5 116
 
4.7%
4 108
 
4.4%
0 103
 
4.2%
6 102
 
4.1%
7 84
 
3.4%
9 77
 
3.1%
Other values (4) 143
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4616
65.2%
ASCII 2468
34.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1107
44.9%
1 274
 
11.1%
3 214
 
8.7%
2 140
 
5.7%
5 116
 
4.7%
4 108
 
4.4%
0 103
 
4.2%
6 102
 
4.1%
7 84
 
3.4%
9 77
 
3.1%
Other values (4) 143
 
5.8%
Hangul
ValueCountFrequency (%)
로 404
 
8.8%
양 398
 
8.6%
서 389
 
8.4%
천 365
 
7.9%
구 353
 
7.6%
울 353
 
7.6%
시 335
 
7.3%
특 335
 
7.3%
별 335
 
7.3%
동 213
 
4.6%
Other values (38) 1136
24.6%

지번주소
Text

Distinct246
Distinct (%)61.7%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
2024-04-06T17:53:58.682018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length21
Mean length18.498747
Min length13

Characters and Unicode

Total characters7381
Distinct characters30
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

Unique156 ?
Unique (%)39.1%

Sample

1st row서울특별시 양천구 신월동 150-3
2nd row서울특별시 양천구 신정동 1060
3rd row서울특별시 양천구 신정동 337-9
4th row서울특별시 양천구 신정동 1325-3
5th row서울특별시 양천구 신정동 319-15
ValueCountFrequency (%)
양천구 399
25.0%
서울특별시 380
23.8%
신정동 159
 
10.0%
신월동 124
 
7.8%
목동 115
 
7.2%
서울 19
 
1.2%
331-1 14
 
0.9%
276 13
 
0.8%
900 9
 
0.6%
987 8
 
0.5%
Other values (225) 357
22.4%
2024-04-06T17:54:00.005279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1206
16.3%
서 399
 
5.4%
양 399
 
5.4%
천 399
 
5.4%
구 399
 
5.4%
동 399
 
5.4%
울 399
 
5.4%
1 386
 
5.2%
특 380
 
5.1%
별 380
 
5.1%
Other values (20) 2635
35.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4221
57.2%
Decimal Number 1648
 
22.3%
Space Separator 1206
 
16.3%
Dash Punctuation 306
 
4.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 399
9.5%
양 399
9.5%
천 399
9.5%
구 399
9.5%
동 399
9.5%
울 399
9.5%
특 380
9.0%
별 380
9.0%
시 380
9.0%
신 283
6.7%
Other values (8) 404
9.6%
Decimal Number
ValueCountFrequency (%)
1 386
23.4%
2 207
12.6%
3 193
11.7%
9 182
11.0%
0 133
 
8.1%
7 127
 
7.7%
5 125
 
7.6%
6 112
 
6.8%
4 101
 
6.1%
8 82
 
5.0%
Space Separator
ValueCountFrequency (%)
1206
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 306
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4221
57.2%
Common 3160
42.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 399
9.5%
양 399
9.5%
천 399
9.5%
구 399
9.5%
동 399
9.5%
울 399
9.5%
특 380
9.0%
별 380
9.0%
시 380
9.0%
신 283
6.7%
Other values (8) 404
9.6%
Common
ValueCountFrequency (%)
1206
38.2%
1 386
 
12.2%
- 306
 
9.7%
2 207
 
6.6%
3 193
 
6.1%
9 182
 
5.8%
0 133
 
4.2%
7 127
 
4.0%
5 125
 
4.0%
6 112
 
3.5%
Other values (2) 183
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4221
57.2%
ASCII 3160
42.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1206
38.2%
1 386
 
12.2%
- 306
 
9.7%
2 207
 
6.6%
3 193
 
6.1%
9 182
 
5.8%
0 133
 
4.2%
7 127
 
4.0%
5 125
 
4.0%
6 112
 
3.5%
Other values (2) 183
 
5.8%
Hangul
ValueCountFrequency (%)
서 399
9.5%
양 399
9.5%
천 399
9.5%
구 399
9.5%
동 399
9.5%
울 399
9.5%
특 380
9.0%
별 380
9.0%
시 380
9.0%
신 283
6.7%
Other values (8) 404
9.6%

연면적(제곱미터)
Text

Distinct343
Distinct (%)86.0%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
2024-04-06T17:54:00.749973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length6.1779449
Min length2

Characters and Unicode

Total characters2465
Distinct characters13
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique293 ?
Unique (%)73.4%

Sample

1st row1949.4
2nd row725.26
3rd row2639.31
4th row5295.88
5th row4106.35
ValueCountFrequency (%)
70.38 4
 
1.0%
94.68 4
 
1.0%
99.78 4
 
1.0%
538.15 2
 
0.5%
76.2 2
 
0.5%
5480.65 2
 
0.5%
5666.36 2
 
0.5%
3809.93 2
 
0.5%
548.94 2
 
0.5%
82.89 2
 
0.5%
Other values (318) 373
93.5%
2024-04-06T17:54:01.805749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 363
14.7%
3 254
10.3%
2 249
10.1%
1 240
9.7%
4 206
8.4%
6 193
7.8%
8 191
7.7%
9 186
7.5%
7 178
7.2%
5 177
7.2%
Other values (3) 228
9.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2020
81.9%
Other Punctuation 379
 
15.4%
Space Separator 66
 
2.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 254
12.6%
2 249
12.3%
1 240
11.9%
4 206
10.2%
6 193
9.6%
8 191
9.5%
9 186
9.2%
7 178
8.8%
5 177
8.8%
0 146
7.2%
Other Punctuation
ValueCountFrequency (%)
. 363
95.8%
, 16
 
4.2%
Space Separator
ValueCountFrequency (%)
66
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2465
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 363
14.7%
3 254
10.3%
2 249
10.1%
1 240
9.7%
4 206
8.4%
6 193
7.8%
8 191
7.7%
9 186
7.5%
7 178
7.2%
5 177
7.2%
Other values (3) 228
9.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2465
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 363
14.7%
3 254
10.3%
2 249
10.1%
1 240
9.7%
4 206
8.4%
6 193
7.8%
8 191
7.7%
9 186
7.5%
7 178
7.2%
5 177
7.2%
Other values (3) 228
9.2%

주용도
Categorical

HIGH CORRELATION 

Distinct39
Distinct (%)9.8%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
노유자시설
85 
제1종근린생활시설
73 
교육연구시설
73 
업무시설
52 
교육연구및복지시설
14 
Other values (34)
102 

Length

Max length20
Median length14
Mean length6.4110276
Min length2

Unique

Unique18 ?
Unique (%)4.5%

Sample

1st row교육연구및복지시설
2nd row공공업무시설(사무소)
3rd row교육연구시설
4th row문화및집회시설
5th row업무시설

Common Values

ValueCountFrequency (%)
노유자시설 85
21.3%
제1종근린생활시설 73
18.3%
교육연구시설 73
18.3%
업무시설 52
13.0%
교육연구및복지시설 14
 
3.5%
자동차관련시설 13
 
3.3%
제2종근린생활시설 11
 
2.8%
관광휴게시설 11
 
2.8%
공공용시설 7
 
1.8%
공공업무시설 7
 
1.8%
Other values (29) 53
13.3%

Length

2024-04-06T17:54:02.091252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
노유자시설 87
21.3%
교육연구시설 73
17.8%
제1종근린생활시설 73
17.8%
업무시설 52
12.7%
교육연구및복지시설 14
 
3.4%
자동차관련시설 13
 
3.2%
제2종근린생활시설 11
 
2.7%
관광휴게시설 11
 
2.7%
공공업무시설 8
 
2.0%
공공용시설 7
 
1.7%
Other values (31) 60
14.7%

사용승인일
Date

MISSING 

Distinct247
Distinct (%)64.5%
Missing16
Missing (%)4.0%
Memory size3.2 KiB
Minimum1978-12-30 00:00:00
Maximum2023-10-31 00:00:00
2024-04-06T17:54:02.346547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:54:02.607421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

관리부서
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
건축과
246 
어르신복지과
47 
교육지원과
32 
보육정책과
 
24
자치행정과
 
20
Other values (9)
30 

Length

Max length6
Median length3
Mean length3.8746867
Min length3

Unique

Unique5 ?
Unique (%)1.3%

Sample

1st row가족정책과
2nd row건설관리과
3rd row건축과
4th row건축과
5th row건축과

Common Values

ValueCountFrequency (%)
건축과 246
61.7%
어르신복지과 47
 
11.8%
교육지원과 32
 
8.0%
보육정책과 24
 
6.0%
자치행정과 20
 
5.0%
공원녹지과 19
 
4.8%
보건지소 2
 
0.5%
자립지원과 2
 
0.5%
주차관리과 2
 
0.5%
가족정책과 1
 
0.3%
Other values (4) 4
 
1.0%

Length

2024-04-06T17:54:02.898154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
건축과 246
61.7%
어르신복지과 47
 
11.8%
교육지원과 32
 
8.0%
보육정책과 24
 
6.0%
자치행정과 20
 
5.0%
공원녹지과 19
 
4.8%
보건지소 2
 
0.5%
자립지원과 2
 
0.5%
주차관리과 2
 
0.5%
가족정책과 1
 
0.3%
Other values (4) 4
 
1.0%

Interactions

2024-04-06T17:53:53.418457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-06T17:54:03.060193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번주용도관리부서
연번1.0000.7490.822
주용도0.7491.0000.945
관리부서0.8220.9451.000
2024-04-06T17:54:03.246903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
주용도관리부서
주용도1.0000.636
관리부서0.6361.000
2024-04-06T17:54:03.439928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번주용도관리부서
연번1.0000.3550.513
주용도0.3551.0000.636
관리부서0.5130.6361.000

Missing values

2024-04-06T17:53:53.712760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-06T17:53:53.958855image/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.
2024-04-06T17:53:54.145510image/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

연번상호명(건물명)도로명주소지번주소연면적(제곱미터)주용도사용승인일관리부서
01신월청소년문화센터서울특별시 양천구 가로공원로 86서울특별시 양천구 신월동 150-31949.4교육연구및복지시설2002-06-17가족정책과
12신월여의도지하도로 관리사무소서울특별시 양천구 국회대로 248-1서울특별시 양천구 신정동 1060725.26공공업무시설(사무소)2021-04-14건설관리과
23갈산공공도서관서울특별시 양천구 목동남로4길 48-6서울특별시 양천구 신정동 337-92639.31교육연구시설2014-05-29건축과
34갈산문화예술센터서울특별시 양천구 목동남로 106서울특별시 양천구 신정동 1325-35295.88문화및집회시설2022-01-28건축과
45강서수도사업소서울특별시 양천구 목동동로 155서울특별시 양천구 신정동 319-154106.35업무시설1992-05-14건축과
56강신중학교서울특별시 양천구 남부순환로 604서울특별시 양천구 신월동 101010031.44교육연구시설1989-05-04건축과
67건강힐링문화관서울특별시 양천구 남부순환로83길 54서울특별시 양천구 신월동 1077-26999.84업무시설2021-05-17건축과
78경창시장고객지원센터서울특별시 양천구 오목로9길 31서울특별시 양천구 신월동 447-8258.96제2종근린생활시설2011-10-21건축과
89계남근린공원 노인정서울특별시 양천구 중앙로25길 33서울특별시 양천구 신정동 521164.28노유자시설1995-06-21건축과
910계남근린공원공중화장실서울특별시 양천구 중앙로 153서울특별시 양천구 신정동 62197.68제1종근린생활시설2010-05-12건축과
연번상호명(건물명)도로명주소지번주소연면적(제곱미터)주용도사용승인일관리부서
389390신월7동복합청사서울특별시 양천구 지양로14길 17서울특별시 양천구 신월동 915-45,295.70공공업무시설2023-10-31자치행정과
390391신정1동주민센터서울특별시 양천구 중앙로32길 1서울특별시 양천구 신정동 1051-112,055.47공공업무시설2003-07-16자치행정과
391392신정2동주민센터서울특별시 양천구 목동동로 154서울특별시 양천구 신정동 329-9858.08제1종근린생활시설1989-12-20자치행정과
392393신정3동주민센터서울특별시 양천구 중앙로 209서울특별시 양천구 신정동 12631,993.33제1종근린생활시설2000-12-23자치행정과
393394신정4동주민센터서울특별시 양천구 오목로34길 5서울특별시 양천구 신정동 949-52,416.49공공업무시설2016-01-11자치행정과
394395신정6동주민센터서울특별시 양천구 목동서로 353서울특별시 양천구 신정동 322-91,096.31제1종근린생활시설1989-10-31자치행정과
395396신정7동주민센터서울특별시 양천구 목동동로 37서울특별시 양천구 신정동 324-5876.39제1종근린생활시설1989-12-27자치행정과
396397양천나눔누리타운서울특별시 양천구 중앙로 250서울특별시 양천구 신정동 1032-121800.94업무시설2015-07-13자치행정과
397398목동타운홀서울특별시 양천구 목동동로 345서울특별시 양천구 목동 950-21633.06사무실 임대2006-10-24주차관리과
398399양천사회단체봉사센터서울특별시 양천구 목동서로 131서울특별시 양천구 목동 905-29684.51사무실 임대2010-02-02주차관리과