Overview

Dataset statistics

Number of variables11
Number of observations69
Missing cells28
Missing cells (%)3.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.3 KiB
Average record size in memory93.9 B

Variable types

Categorical7
Text1
Numeric3

Dataset

Description서울특별시 영등포구 실외 흡연실 위치정보(설치위치 위경도 등)제동데이터: 위치(건물 단위 상세), 위도, 경도, 관리자 등
Author서울특별시 영등포구
URLhttps://www.data.go.kr/data/15069051/fileData.do

Alerts

자치구 has constant value ""Constant
설치 위치 has constant value ""Constant
관리여부 has constant value ""Constant
설치 주체 is highly overall correlated with 규모(제곱미터) and 2 other fieldsHigh correlation
설치일 is highly overall correlated with 위도 and 4 other fieldsHigh correlation
시설형태 is highly overall correlated with 설치일High correlation
관리 is highly overall correlated with 규모(제곱미터) and 2 other fieldsHigh correlation
위도 is highly overall correlated with 설치일High correlation
경도 is highly overall correlated with 설치일High correlation
규모(제곱미터) is highly overall correlated with 설치 주체 and 1 other fieldsHigh correlation
설치일 is highly imbalanced (55.1%)Imbalance
설치 주체 is highly imbalanced (52.5%)Imbalance
관리 is highly imbalanced (57.4%)Imbalance
규모(제곱미터) has 28 (40.6%) missing valuesMissing
시설 구분 has unique valuesUnique

Reproduction

Analysis started2023-12-16 15:46:12.049126
Analysis finished2023-12-16 15:46:19.444142
Duration7.4 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

자치구
Categorical

CONSTANT 

Distinct1
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size684.0 B
영등포구
69 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
영등포구 69
100.0%

Length

2023-12-16T15:46:19.873793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-16T15:46:20.349497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
영등포구 69
100.0%

시설 구분
Text

UNIQUE 

Distinct69
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size684.0 B
2023-12-16T15:46:21.642435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length12
Mean length7.0289855
Min length3

Characters and Unicode

Total characters485
Distinct characters165
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique69 ?
Unique (%)100.0%

Sample

1st row한강프리젠
2nd row프리가
3rd row에이스 테크노타워
4th row고려빌딩
5th row리버타워오피스텔
ValueCountFrequency (%)
앞 5
 
5.4%
여의도 3
 
3.3%
한국거래소 2
 
2.2%
아이에스비즈타워 2
 
2.2%
상희익스콘벤터타워 2
 
2.2%
한강프리젠 1
 
1.1%
양평우림보보카운티 1
 
1.1%
삼성 1
 
1.1%
가든빌딩 1
 
1.1%
홍우빌딩 1
 
1.1%
Other values (73) 73
79.3%
2023-12-16T15:46:23.746746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
23
 
4.7%
빌 23
 
4.7%
딩 21
 
4.3%
스 16
 
3.3%
타 15
 
3.1%
이 13
 
2.7%
워 13
 
2.7%
한 10
 
2.1%
리 9
 
1.9%
크 7
 
1.4%
Other values (155) 335
69.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 432
89.1%
Space Separator 23
 
4.7%
Uppercase Letter 13
 
2.7%
Decimal Number 9
 
1.9%
Lowercase Letter 6
 
1.2%
Other Symbol 1
 
0.2%
Other Punctuation 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
빌 23
 
5.3%
딩 21
 
4.9%
스 16
 
3.7%
타 15
 
3.5%
이 13
 
3.0%
워 13
 
3.0%
한 10
 
2.3%
리 9
 
2.1%
크 7
 
1.6%
우 7
 
1.6%
Other values (132) 298
69.0%
Uppercase Letter
ValueCountFrequency (%)
B 2
15.4%
H 1
7.7%
N 1
7.7%
A 1
7.7%
J 1
7.7%
L 1
7.7%
V 1
7.7%
K 1
7.7%
S 1
7.7%
G 1
7.7%
Other values (2) 2
15.4%
Lowercase Letter
ValueCountFrequency (%)
e 2
33.3%
r 1
16.7%
t 1
16.7%
c 1
16.7%
n 1
16.7%
Decimal Number
ValueCountFrequency (%)
2 4
44.4%
1 4
44.4%
3 1
 
11.1%
Space Separator
ValueCountFrequency (%)
23
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 1
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 433
89.3%
Common 33
 
6.8%
Latin 19
 
3.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
빌 23
 
5.3%
딩 21
 
4.8%
스 16
 
3.7%
타 15
 
3.5%
이 13
 
3.0%
워 13
 
3.0%
한 10
 
2.3%
리 9
 
2.1%
크 7
 
1.6%
우 7
 
1.6%
Other values (133) 299
69.1%
Latin
ValueCountFrequency (%)
e 2
 
10.5%
B 2
 
10.5%
H 1
 
5.3%
N 1
 
5.3%
A 1
 
5.3%
J 1
 
5.3%
r 1
 
5.3%
t 1
 
5.3%
L 1
 
5.3%
c 1
 
5.3%
Other values (7) 7
36.8%
Common
ValueCountFrequency (%)
23
69.7%
2 4
 
12.1%
1 4
 
12.1%
, 1
 
3.0%
3 1
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 432
89.1%
ASCII 52
 
10.7%
None 1
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
23
44.2%
2 4
 
7.7%
1 4
 
7.7%
e 2
 
3.8%
B 2
 
3.8%
H 1
 
1.9%
N 1
 
1.9%
, 1
 
1.9%
3 1
 
1.9%
A 1
 
1.9%
Other values (12) 12
23.1%
Hangul
ValueCountFrequency (%)
빌 23
 
5.3%
딩 21
 
4.9%
스 16
 
3.7%
타 15
 
3.5%
이 13
 
3.0%
워 13
 
3.0%
한 10
 
2.3%
리 9
 
2.1%
크 7
 
1.6%
우 7
 
1.6%
Other values (132) 298
69.0%
None
ValueCountFrequency (%)
㈜ 1
100.0%

시설형태
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size684.0 B
개방형
41 
완전개방형
28 

Length

Max length5
Median length3
Mean length3.8115942
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row완전개방형
2nd row완전개방형
3rd row완전개방형
4th row개방형
5th row개방형

Common Values

ValueCountFrequency (%)
개방형 41
59.4%
완전개방형 28
40.6%

Length

2023-12-16T15:46:24.561918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-16T15:46:25.102844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
개방형 41
59.4%
완전개방형 28
40.6%

설치 위치
Categorical

CONSTANT 

Distinct1
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size684.0 B
실외
69 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
실외 69
100.0%

Length

2023-12-16T15:46:25.482879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-16T15:46:25.976216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
실외 69
100.0%

위도
Real number (ℝ)

HIGH CORRELATION 

Distinct60
Distinct (%)87.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.447291
Minimum32.5273
Maximum37.5398
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size753.0 B
2023-12-16T15:46:27.090065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum32.5273
5-th percentile37.50352
Q137.5193
median37.5225
Q337.5271
95-th percentile37.53598
Maximum37.5398
Range5.0125
Interquartile range (IQR)0.0078

Descriptive statistics

Standard deviation0.60159171
Coefficient of variation (CV)0.016065026
Kurtosis68.720345
Mean37.447291
Median Absolute Deviation (MAD)0.0034
Skewness-8.2823411
Sum2583.8631
Variance0.36191258
MonotonicityNot monotonic
2023-12-16T15:46:27.927114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.5256 2
 
2.9%
37.515 2
 
2.9%
37.5193 2
 
2.9%
37.5322 2
 
2.9%
37.5289 2
 
2.9%
37.5202 2
 
2.9%
37.5191 2
 
2.9%
37.5208 2
 
2.9%
37.5225 2
 
2.9%
32.5273 1
 
1.4%
Other values (50) 50
72.5%
ValueCountFrequency (%)
32.5273 1
1.4%
37.3133 1
1.4%
37.491 1
1.4%
37.5004 1
1.4%
37.5082 1
1.4%
37.5097 1
1.4%
37.5102 1
1.4%
37.5121 1
1.4%
37.5125 1
1.4%
37.5144 1
1.4%
ValueCountFrequency (%)
37.5398 1
1.4%
37.5396 1
1.4%
37.5388 1
1.4%
37.5361 1
1.4%
37.5358 1
1.4%
37.5322 2
2.9%
37.5318 1
1.4%
37.5307 1
1.4%
37.5306 1
1.4%
37.53 1
1.4%

경도
Real number (ℝ)

HIGH CORRELATION 

Distinct59
Distinct (%)85.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.90436
Minimum126.5541
Maximum126.9386
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size753.0 B
2023-12-16T15:46:28.799108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.5541
5-th percentile126.88738
Q1126.8959
median126.9042
Q3126.9238
95-th percentile126.93078
Maximum126.9386
Range0.3845
Interquartile range (IQR)0.0279

Descriptive statistics

Standard deviation0.045585405
Coefficient of variation (CV)0.0003592107
Kurtosis52.88428
Mean126.90436
Median Absolute Deviation (MAD)0.0155
Skewness-6.824597
Sum8756.401
Variance0.0020780291
MonotonicityNot monotonic
2023-12-16T15:46:29.481243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.8986 3
 
4.3%
126.9274 2
 
2.9%
126.9314 2
 
2.9%
126.9268 2
 
2.9%
126.9238 2
 
2.9%
126.9 2
 
2.9%
126.9226 2
 
2.9%
126.8982 2
 
2.9%
126.9292 2
 
2.9%
126.8826 1
 
1.4%
Other values (49) 49
71.0%
ValueCountFrequency (%)
126.5541 1
1.4%
126.8826 1
1.4%
126.8864 1
1.4%
126.8871 1
1.4%
126.8878 1
1.4%
126.8881 1
1.4%
126.8889 1
1.4%
126.8891 1
1.4%
126.8893 1
1.4%
126.8912 1
1.4%
ValueCountFrequency (%)
126.9386 1
1.4%
126.9314 2
2.9%
126.9309 1
1.4%
126.9306 1
1.4%
126.9301 1
1.4%
126.9294 1
1.4%
126.9292 2
2.9%
126.9274 2
2.9%
126.9269 1
1.4%
126.9268 2
2.9%

규모(제곱미터)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct29
Distinct (%)70.7%
Missing28
Missing (%)40.6%
Infinite0
Infinite (%)0.0%
Mean105.7561
Minimum2
Maximum1180
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size753.0 B
2023-12-16T15:46:30.292766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q110
median17
Q350
95-th percentile551
Maximum1180
Range1178
Interquartile range (IQR)40

Descriptive statistics

Standard deviation253.62312
Coefficient of variation (CV)2.3981891
Kurtosis11.623233
Mean105.7561
Median Absolute Deviation (MAD)12
Skewness3.4174107
Sum4336
Variance64324.689
MonotonicityNot monotonic
2023-12-16T15:46:30.830925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
10 6
 
8.7%
3 3
 
4.3%
5 2
 
2.9%
50 2
 
2.9%
19 2
 
2.9%
20 2
 
2.9%
17 2
 
2.9%
13 1
 
1.4%
4 1
 
1.4%
90 1
 
1.4%
Other values (19) 19
27.5%
(Missing) 28
40.6%
ValueCountFrequency (%)
2 1
 
1.4%
3 3
4.3%
4 1
 
1.4%
5 2
 
2.9%
7 1
 
1.4%
8 1
 
1.4%
10 6
8.7%
11 1
 
1.4%
12 1
 
1.4%
13 1
 
1.4%
ValueCountFrequency (%)
1180 1
1.4%
1033 1
1.4%
551 1
1.4%
330 1
1.4%
314 1
1.4%
165 1
1.4%
90 1
1.4%
86 1
1.4%
60 1
1.4%
50 2
2.9%

설치일
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Memory size684.0 B
<NA>
56 
2020
 
5
2019
 
4
2021
 
3
2023
 
1

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique1 ?
Unique (%)1.4%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 56
81.2%
2020 5
 
7.2%
2019 4
 
5.8%
2021 3
 
4.3%
2023 1
 
1.4%

Length

2023-12-16T15:46:31.802200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-16T15:46:32.638251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 56
81.2%
2020 5
 
7.2%
2019 4
 
5.8%
2021 3
 
4.3%
2023 1
 
1.4%

설치 주체
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)5.8%
Missing0
Missing (%)0.0%
Memory size684.0 B
건물관리자
55 
한국일보-영등포구
10 
시설관리자
 
3
영등포구
 
1

Length

Max length9
Median length5
Mean length5.5652174
Min length4

Unique

Unique1 ?
Unique (%)1.4%

Sample

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

Common Values

ValueCountFrequency (%)
건물관리자 55
79.7%
한국일보-영등포구 10
 
14.5%
시설관리자 3
 
4.3%
영등포구 1
 
1.4%

Length

2023-12-16T15:46:33.122564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-16T15:46:33.646859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
건물관리자 55
79.7%
한국일보-영등포구 10
 
14.5%
시설관리자 3
 
4.3%
영등포구 1
 
1.4%

관리여부
Categorical

CONSTANT 

Distinct1
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size684.0 B
여
69 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
여 69
100.0%

Length

2023-12-16T15:46:34.556482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-16T15:46:35.016683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
여 69
100.0%

관리
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Memory size684.0 B
건물관리자
55 
한국일보-영등포구
10 
시설관리자
 
2
영등포구청
 
1
시장상인회
 
1

Length

Max length9
Median length5
Mean length5.5797101
Min length5

Unique

Unique2 ?
Unique (%)2.9%

Sample

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

Common Values

ValueCountFrequency (%)
건물관리자 55
79.7%
한국일보-영등포구 10
 
14.5%
시설관리자 2
 
2.9%
영등포구청 1
 
1.4%
시장상인회 1
 
1.4%

Length

2023-12-16T15:46:35.718102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-16T15:46:36.218287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
건물관리자 55
79.7%
한국일보-영등포구 10
 
14.5%
시설관리자 2
 
2.9%
영등포구청 1
 
1.4%
시장상인회 1
 
1.4%

Interactions

2023-12-16T15:46:16.459587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:46:13.865025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:46:15.114856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:46:16.875967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:46:14.222966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:46:15.542342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:46:17.302646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:46:14.709759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:46:16.010043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-16T15:46:36.708172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시설 구분시설형태위도경도규모(제곱미터)설치일설치 주체관리
시설 구분1.0001.0001.0001.0001.0001.0001.0001.000
시설형태1.0001.0000.0000.0830.000NaN0.5410.287
위도1.0000.0001.0000.0000.000NaN0.0000.000
경도1.0000.0830.0001.0000.0000.6580.2680.288
규모(제곱미터)1.0000.0000.0000.0001.000NaNNaNNaN
설치일1.000NaNNaN0.658NaN1.0000.8820.882
설치 주체1.0000.5410.0000.268NaN0.8821.0001.000
관리1.0000.2870.0000.288NaN0.8821.0001.000
2023-12-16T15:46:37.492603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
설치 주체설치일시설형태관리
설치 주체1.0000.5370.3630.992
설치일0.5371.0001.0000.537
시설형태0.3631.0001.0000.342
관리0.9920.5370.3421.000
2023-12-16T15:46:38.724620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위도경도규모(제곱미터)시설형태설치일설치 주체관리
위도1.000-0.063-0.2310.0001.0000.0000.000
경도-0.0631.0000.2720.1350.6400.2530.221
규모(제곱미터)-0.2310.2721.0000.0000.0001.0001.000
시설형태0.0000.1350.0001.0001.0000.3630.342
설치일1.0000.6400.0001.0001.0000.5370.537
설치 주체0.0000.2531.0000.3630.5371.0000.992
관리0.0000.2211.0000.3420.5370.9921.000

Missing values

2023-12-16T15:46:17.887350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-16T15:46:18.969352image/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

자치구시설 구분시설형태설치 위치위도경도규모(제곱미터)설치일설치 주체관리여부관리
0영등포구한강프리젠완전개방형실외37.5398126.893710<NA>건물관리자여건물관리자
1영등포구프리가완전개방형실외37.5125126.923810<NA>건물관리자여건물관리자
2영등포구에이스 테크노타워완전개방형실외37.515126.895960<NA>건물관리자여건물관리자
3영등포구고려빌딩개방형실외37.5193126.9314330<NA>건물관리자여건물관리자
4영등포구리버타워오피스텔개방형실외37.5195126.938617<NA>건물관리자여건물관리자
5영등포구금강빌딩완전개방형실외37.5181126.92082<NA>건물관리자여건물관리자
6영등포구두일빌딩완전개방형실외37.5193126.9306165<NA>건물관리자여건물관리자
7영등포구영등포아트자이아파트개방형실외37.5102126.899611<NA>건물관리자여건물관리자
8영등포구데시잉루브완전개방형실외37.5322126.9<NA><NA>건물관리자여건물관리자
9영등포구금산빌딩완전개방형실외37.5282126.916550<NA>건물관리자여건물관리자
자치구시설 구분시설형태설치 위치위도경도규모(제곱미터)설치일설치 주체관리여부관리
59영등포구여의도 오투타워 앞개방형실외37.5231126.9229<NA>2019한국일보-영등포구여한국일보-영등포구
60영등포구여의도 한화손해빌딩 앞개방형실외37.5237126.9221<NA>2020한국일보-영등포구여한국일보-영등포구
61영등포구한국교직원공제회관 앞개방형실외37.5222126.9253<NA>2020한국일보-영등포구여한국일보-영등포구
62영등포구여의도역3번출구 뒤쪽개방형실외37.5225126.9238<NA>2020한국일보-영등포구여한국일보-영등포구
63영등포구상희익스콘벤터타워 1개방형실외37.5271126.9194<NA>2020한국일보-영등포구여한국일보-영등포구
64영등포구상희익스콘벤터타워 2개방형실외37.5272126.9197<NA>2020한국일보-영등포구여한국일보-영등포구
65영등포구한국투자증권 앞개방형실외37.5225126.9226<NA>2021한국일보-영등포구여한국일보-영등포구
66영등포구한국거래소 앞1개방형실외37.5228126.9268<NA>2021한국일보-영등포구여한국일보-영등포구
67영등포구한국거래소 앞2개방형실외37.5227126.9268<NA>2021한국일보-영등포구여한국일보-영등포구
68영등포구파크원개방형실외37.3133126.5541<NA>2023건물관리자여건물관리자