Overview

Dataset statistics

Number of variables16
Number of observations768
Missing cells26
Missing cells (%)0.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory98.4 KiB
Average record size in memory131.2 B

Variable types

Text4
Categorical9
Numeric3

Dataset

Description관리번호,자치구,와이파이명,도로명주소,상세주소,설치위치(층),설치유형,설치기관,서비스구분,망종류,설치년도,실내외구분,wifi접속환경,X좌표,Y좌표,작업일자
Author용산구
URLhttps://data.seoul.go.kr/dataList/OA-20891/S/1/datasetView.do

Alerts

자치구 has constant value ""Constant
실내외구분 is highly overall correlated with 설치년도 and 6 other fieldsHigh correlation
설치유형 is highly overall correlated with 설치년도 and 7 other fieldsHigh correlation
설치위치(층) is highly overall correlated with 설치유형 and 5 other fieldsHigh correlation
설치기관 is highly overall correlated with 설치년도 and 7 other fieldsHigh correlation
서비스구분 is highly overall correlated with 설치년도 and 7 other fieldsHigh correlation
망종류 is highly overall correlated with 설치년도 and 6 other fieldsHigh correlation
작업일자 is highly overall correlated with 설치년도 and 7 other fieldsHigh correlation
wifi접속환경 is highly overall correlated with 설치년도 and 7 other fieldsHigh correlation
설치년도 is highly overall correlated with 설치유형 and 6 other fieldsHigh correlation
X좌표 is highly overall correlated with wifi접속환경High correlation
설치위치(층) is highly imbalanced (64.5%)Imbalance
wifi접속환경 is highly imbalanced (82.3%)Imbalance
도로명주소 has 21 (2.7%) missing valuesMissing
관리번호 has unique valuesUnique

Reproduction

Analysis started2024-05-18 01:59:43.244156
Analysis finished2024-05-18 01:59:50.144804
Duration6.9 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

관리번호
Text

UNIQUE 

Distinct768
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size6.1 KiB
2024-05-18T10:59:50.669780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length8
Mean length8.0416667
Min length7

Characters and Unicode

Total characters6176
Distinct characters23
Distinct categories4 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique768 ?
Unique (%)100.0%

Sample

1st rowBS101022
2nd rowBS101023
3rd rowBS101024
4th rowBS101025
5th rowBS101026
ValueCountFrequency (%)
bs101022 1
 
0.1%
bs101023 1
 
0.1%
서울-0467 1
 
0.1%
서울-0455 1
 
0.1%
서울-0456 1
 
0.1%
서울-0457 1
 
0.1%
서울-0458 1
 
0.1%
서울-0462 1
 
0.1%
서울-0463 1
 
0.1%
서울-0464 1
 
0.1%
Other values (758) 758
98.7%
2024-05-18T10:59:51.837672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1026
16.6%
1 853
13.8%
2 397
 
6.4%
- 357
 
5.8%
4 315
 
5.1%
3 312
 
5.1%
6 307
 
5.0%
서 303
 
4.9%
울 303
 
4.9%
7 284
 
4.6%
Other values (13) 1719
27.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4159
67.3%
Uppercase Letter 951
 
15.4%
Other Letter 709
 
11.5%
Dash Punctuation 357
 
5.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1026
24.7%
1 853
20.5%
2 397
 
9.5%
4 315
 
7.6%
3 312
 
7.5%
6 307
 
7.4%
7 284
 
6.8%
5 252
 
6.1%
9 236
 
5.7%
8 177
 
4.3%
Uppercase Letter
ValueCountFrequency (%)
S 258
27.1%
W 222
23.3%
F 213
22.4%
Y 130
13.7%
B 107
11.3%
N 9
 
0.9%
H 6
 
0.6%
G 4
 
0.4%
T 2
 
0.2%
Other Letter
ValueCountFrequency (%)
서 303
42.7%
울 303
42.7%
차 103
 
14.5%
Dash Punctuation
ValueCountFrequency (%)
- 357
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4516
73.1%
Latin 951
 
15.4%
Hangul 709
 
11.5%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1026
22.7%
1 853
18.9%
2 397
 
8.8%
- 357
 
7.9%
4 315
 
7.0%
3 312
 
6.9%
6 307
 
6.8%
7 284
 
6.3%
5 252
 
5.6%
9 236
 
5.2%
Latin
ValueCountFrequency (%)
S 258
27.1%
W 222
23.3%
F 213
22.4%
Y 130
13.7%
B 107
11.3%
N 9
 
0.9%
H 6
 
0.6%
G 4
 
0.4%
T 2
 
0.2%
Hangul
ValueCountFrequency (%)
서 303
42.7%
울 303
42.7%
차 103
 
14.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5467
88.5%
Hangul 709
 
11.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1026
18.8%
1 853
15.6%
2 397
 
7.3%
- 357
 
6.5%
4 315
 
5.8%
3 312
 
5.7%
6 307
 
5.6%
7 284
 
5.2%
S 258
 
4.7%
5 252
 
4.6%
Other values (10) 1106
20.2%
Hangul
ValueCountFrequency (%)
서 303
42.7%
울 303
42.7%
차 103
 
14.5%

자치구
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size6.1 KiB
용산구
768 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
용산구 768
100.0%

Length

2024-05-18T10:59:52.296137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T10:59:52.686237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
용산구 768
100.0%

와이파이명
Text

Distinct169
Distinct (%)22.0%
Missing0
Missing (%)0.0%
Memory size6.1 KiB
2024-05-18T10:59:53.196440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length20
Mean length7.8606771
Min length3

Characters and Unicode

Total characters6037
Distinct characters230
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

Unique59 ?
Unique (%)7.7%

Sample

1st row버스정류소_KT용산지사
2nd row버스정류소_KT용산지사
3rd row버스정류소_갈월동
4th row버스정류소_갈월동
5th row버스정류소_남산예술원
ValueCountFrequency (%)
용산구청 56
 
6.9%
이촌한강공원 36
 
4.5%
이태원역 35
 
4.3%
꿈나무종합타운 33
 
4.1%
일대 25
 
3.1%
숙명여대 24
 
3.0%
이태원 22
 
2.7%
경리단및해방촌길 22
 
2.7%
갈월종합사회복지관 21
 
2.6%
용산청소년센터 21
 
2.6%
Other values (162) 512
63.4%
2024-05-18T10:59:54.304589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
용 262
 
4.3%
산 222
 
3.7%
원 201
 
3.3%
센 171
 
2.8%
터 162
 
2.7%
소 158
 
2.6%
이 153
 
2.5%
스 151
 
2.5%
동 127
 
2.1%
구 126
 
2.1%
Other values (220) 4304
71.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5770
95.6%
Connector Punctuation 107
 
1.8%
Decimal Number 58
 
1.0%
Space Separator 39
 
0.6%
Other Punctuation 29
 
0.5%
Open Punctuation 13
 
0.2%
Close Punctuation 13
 
0.2%
Uppercase Letter 8
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
용 262
 
4.5%
산 222
 
3.8%
원 201
 
3.5%
센 171
 
3.0%
터 162
 
2.8%
소 158
 
2.7%
이 153
 
2.7%
스 151
 
2.6%
동 127
 
2.2%
구 126
 
2.2%
Other values (204) 4037
70.0%
Decimal Number
ValueCountFrequency (%)
1 28
48.3%
2 22
37.9%
3 3
 
5.2%
9 2
 
3.4%
5 2
 
3.4%
4 1
 
1.7%
Uppercase Letter
ValueCountFrequency (%)
G 2
25.0%
T 2
25.0%
L 2
25.0%
K 2
25.0%
Other Punctuation
ValueCountFrequency (%)
. 27
93.1%
, 2
 
6.9%
Connector Punctuation
ValueCountFrequency (%)
_ 107
100.0%
Space Separator
ValueCountFrequency (%)
39
100.0%
Open Punctuation
ValueCountFrequency (%)
( 13
100.0%
Close Punctuation
ValueCountFrequency (%)
) 13
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5770
95.6%
Common 259
 
4.3%
Latin 8
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
용 262
 
4.5%
산 222
 
3.8%
원 201
 
3.5%
센 171
 
3.0%
터 162
 
2.8%
소 158
 
2.7%
이 153
 
2.7%
스 151
 
2.6%
동 127
 
2.2%
구 126
 
2.2%
Other values (204) 4037
70.0%
Common
ValueCountFrequency (%)
_ 107
41.3%
39
 
15.1%
1 28
 
10.8%
. 27
 
10.4%
2 22
 
8.5%
( 13
 
5.0%
) 13
 
5.0%
3 3
 
1.2%
9 2
 
0.8%
5 2
 
0.8%
Other values (2) 3
 
1.2%
Latin
ValueCountFrequency (%)
G 2
25.0%
T 2
25.0%
L 2
25.0%
K 2
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5770
95.6%
ASCII 267
 
4.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
용 262
 
4.5%
산 222
 
3.8%
원 201
 
3.5%
센 171
 
3.0%
터 162
 
2.8%
소 158
 
2.7%
이 153
 
2.7%
스 151
 
2.6%
동 127
 
2.2%
구 126
 
2.2%
Other values (204) 4037
70.0%
ASCII
ValueCountFrequency (%)
_ 107
40.1%
39
 
14.6%
1 28
 
10.5%
. 27
 
10.1%
2 22
 
8.2%
( 13
 
4.9%
) 13
 
4.9%
3 3
 
1.1%
9 2
 
0.7%
5 2
 
0.7%
Other values (6) 11
 
4.1%

도로명주소
Text

MISSING 

Distinct273
Distinct (%)36.5%
Missing21
Missing (%)2.7%
Memory size6.1 KiB
2024-05-18T10:59:54.984772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length49
Median length35
Mean length14.954485
Min length4

Characters and Unicode

Total characters11171
Distinct characters167
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

Unique147 ?
Unique (%)19.7%

Sample

1st row한강대로
2nd row한강대로
3rd row한강대로
4th row한강대로
5th row한남동 726-374
ValueCountFrequency (%)
용산구 308
 
13.7%
서울특별시 276
 
12.2%
이태원동 88
 
3.9%
34-87 71
 
3.1%
25 35
 
1.6%
62 32
 
1.4%
이촌로72길 31
 
1.4%
이태원로 28
 
1.2%
서울시 27
 
1.2%
원효로 25
 
1.1%
Other values (397) 1335
59.2%
2024-05-18T10:59:56.381794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1509
 
13.5%
1 596
 
5.3%
2 514
 
4.6%
로 464
 
4.2%
- 439
 
3.9%
서 386
 
3.5%
3 369
 
3.3%
용 351
 
3.1%
산 351
 
3.1%
동 342
 
3.1%
Other values (157) 5850
52.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5901
52.8%
Decimal Number 3128
28.0%
Space Separator 1509
 
13.5%
Dash Punctuation 439
 
3.9%
Open Punctuation 73
 
0.7%
Close Punctuation 72
 
0.6%
Uppercase Letter 25
 
0.2%
Other Punctuation 24
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 464
 
7.9%
서 386
 
6.5%
용 351
 
5.9%
산 351
 
5.9%
동 342
 
5.8%
구 317
 
5.4%
울 307
 
5.2%
시 305
 
5.2%
별 278
 
4.7%
특 276
 
4.7%
Other values (135) 2524
42.8%
Decimal Number
ValueCountFrequency (%)
1 596
19.1%
2 514
16.4%
3 369
11.8%
7 329
10.5%
4 289
9.2%
5 250
8.0%
6 233
 
7.4%
9 192
 
6.1%
8 190
 
6.1%
0 166
 
5.3%
Uppercase Letter
ValueCountFrequency (%)
C 8
32.0%
V 4
16.0%
T 4
16.0%
K 3
 
12.0%
E 3
 
12.0%
B 3
 
12.0%
Other Punctuation
ValueCountFrequency (%)
, 15
62.5%
. 9
37.5%
Space Separator
ValueCountFrequency (%)
1509
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 439
100.0%
Open Punctuation
ValueCountFrequency (%)
( 73
100.0%
Close Punctuation
ValueCountFrequency (%)
) 72
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5901
52.8%
Common 5245
47.0%
Latin 25
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 464
 
7.9%
서 386
 
6.5%
용 351
 
5.9%
산 351
 
5.9%
동 342
 
5.8%
구 317
 
5.4%
울 307
 
5.2%
시 305
 
5.2%
별 278
 
4.7%
특 276
 
4.7%
Other values (135) 2524
42.8%
Common
ValueCountFrequency (%)
1509
28.8%
1 596
 
11.4%
2 514
 
9.8%
- 439
 
8.4%
3 369
 
7.0%
7 329
 
6.3%
4 289
 
5.5%
5 250
 
4.8%
6 233
 
4.4%
9 192
 
3.7%
Other values (6) 525
 
10.0%
Latin
ValueCountFrequency (%)
C 8
32.0%
V 4
16.0%
T 4
16.0%
K 3
 
12.0%
E 3
 
12.0%
B 3
 
12.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5901
52.8%
ASCII 5270
47.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1509
28.6%
1 596
 
11.3%
2 514
 
9.8%
- 439
 
8.3%
3 369
 
7.0%
7 329
 
6.2%
4 289
 
5.5%
5 250
 
4.7%
6 233
 
4.4%
9 192
 
3.6%
Other values (12) 550
 
10.4%
Hangul
ValueCountFrequency (%)
로 464
 
7.9%
서 386
 
6.5%
용 351
 
5.9%
산 351
 
5.9%
동 342
 
5.8%
구 317
 
5.4%
울 307
 
5.2%
시 305
 
5.2%
별 278
 
4.7%
특 276
 
4.7%
Other values (135) 2524
42.8%

상세주소
Text

Distinct664
Distinct (%)87.0%
Missing5
Missing (%)0.7%
Memory size6.1 KiB
2024-05-18T10:59:57.108880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length47
Median length33
Mean length13.096986
Min length2

Characters and Unicode

Total characters9993
Distinct characters335
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique604 ?
Unique (%)79.2%

Sample

1st row03-005
2nd row03-006
3rd row03-011
4th row03-012
5th row03-159
ValueCountFrequency (%)
용산구청 71
 
3.7%
일대 56
 
2.9%
복도 47
 
2.4%
내 46
 
2.4%
앞 36
 
1.9%
이태원역 35
 
1.8%
주민센터 35
 
1.8%
1층 28
 
1.4%
2층 25
 
1.3%
꿈나무종합타운 24
 
1.2%
Other values (661) 1531
79.2%
2024-05-18T10:59:58.832492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1175
 
11.8%
1 476
 
4.8%
) 317
 
3.2%
( 317
 
3.2%
3 317
 
3.2%
2 311
 
3.1%
- 282
 
2.8%
0 223
 
2.2%
옥 206
 
2.1%
층 194
 
1.9%
Other values (325) 6175
61.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5511
55.1%
Decimal Number 1958
 
19.6%
Space Separator 1175
 
11.8%
Close Punctuation 317
 
3.2%
Open Punctuation 317
 
3.2%
Dash Punctuation 282
 
2.8%
Uppercase Letter 258
 
2.6%
Connector Punctuation 96
 
1.0%
Lowercase Letter 62
 
0.6%
Other Punctuation 16
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
옥 206
 
3.7%
층 194
 
3.5%
용 162
 
2.9%
외 137
 
2.5%
내 134
 
2.4%
산 133
 
2.4%
실 129
 
2.3%
대 124
 
2.3%
구 124
 
2.3%
동 118
 
2.1%
Other values (282) 4050
73.5%
Uppercase Letter
ValueCountFrequency (%)
F 117
45.3%
C 43
 
16.7%
B 21
 
8.1%
T 20
 
7.8%
V 20
 
7.8%
O 7
 
2.7%
S 7
 
2.7%
E 5
 
1.9%
K 5
 
1.9%
P 5
 
1.9%
Other values (6) 8
 
3.1%
Decimal Number
ValueCountFrequency (%)
1 476
24.3%
3 317
16.2%
2 311
15.9%
0 223
11.4%
4 139
 
7.1%
5 139
 
7.1%
9 99
 
5.1%
8 94
 
4.8%
7 84
 
4.3%
6 76
 
3.9%
Lowercase Letter
ValueCountFrequency (%)
c 28
45.2%
t 13
21.0%
v 11
 
17.7%
a 6
 
9.7%
d 2
 
3.2%
g 2
 
3.2%
Other Punctuation
ValueCountFrequency (%)
, 9
56.2%
. 2
 
12.5%
& 2
 
12.5%
; 2
 
12.5%
/ 1
 
6.2%
Space Separator
ValueCountFrequency (%)
1175
100.0%
Close Punctuation
ValueCountFrequency (%)
) 317
100.0%
Open Punctuation
ValueCountFrequency (%)
( 317
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 282
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 96
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5511
55.1%
Common 4162
41.6%
Latin 320
 
3.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
옥 206
 
3.7%
층 194
 
3.5%
용 162
 
2.9%
외 137
 
2.5%
내 134
 
2.4%
산 133
 
2.4%
실 129
 
2.3%
대 124
 
2.3%
구 124
 
2.3%
동 118
 
2.1%
Other values (282) 4050
73.5%
Latin
ValueCountFrequency (%)
F 117
36.6%
C 43
 
13.4%
c 28
 
8.8%
B 21
 
6.6%
T 20
 
6.2%
V 20
 
6.2%
t 13
 
4.1%
v 11
 
3.4%
O 7
 
2.2%
S 7
 
2.2%
Other values (12) 33
 
10.3%
Common
ValueCountFrequency (%)
1175
28.2%
1 476
11.4%
) 317
 
7.6%
( 317
 
7.6%
3 317
 
7.6%
2 311
 
7.5%
- 282
 
6.8%
0 223
 
5.4%
4 139
 
3.3%
5 139
 
3.3%
Other values (11) 466
 
11.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5509
55.1%
ASCII 4482
44.9%
Compat Jamo 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1175
26.2%
1 476
10.6%
) 317
 
7.1%
( 317
 
7.1%
3 317
 
7.1%
2 311
 
6.9%
- 282
 
6.3%
0 223
 
5.0%
4 139
 
3.1%
5 139
 
3.1%
Other values (33) 786
17.5%
Hangul
ValueCountFrequency (%)
옥 206
 
3.7%
층 194
 
3.5%
용 162
 
2.9%
외 137
 
2.5%
내 134
 
2.4%
산 133
 
2.4%
실 129
 
2.3%
대 124
 
2.3%
구 124
 
2.3%
동 118
 
2.1%
Other values (280) 4048
73.5%
Compat Jamo
ValueCountFrequency (%)
ㅡ 1
50.0%
ㄱ 1
50.0%

설치위치(층)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct22
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size6.1 KiB
<NA>
605 
1F
 
29
2F
 
24
3F
 
11
B1F
 
10
Other values (17)
89 

Length

Max length4
Median length4
Mean length3.6223958
Min length1

Unique

Unique2 ?
Unique (%)0.3%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 605
78.8%
1F 29
 
3.8%
2F 24
 
3.1%
3F 11
 
1.4%
B1F 10
 
1.3%
4F 10
 
1.3%
B2F 9
 
1.2%
5F 9
 
1.2%
지하1층 8
 
1.0%
3층 7
 
0.9%
Other values (12) 46
 
6.0%

Length

2024-05-18T10:59:59.917824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 605
78.8%
1f 29
 
3.8%
2f 24
 
3.1%
3f 11
 
1.4%
b1f 10
 
1.3%
4f 10
 
1.3%
b2f 9
 
1.2%
5f 9
 
1.2%
지하1층 8
 
1.0%
3층 7
 
0.9%
Other values (12) 46
 
6.0%

설치유형
Categorical

HIGH CORRELATION 

Distinct20
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size6.1 KiB
1. 주요거리
150 
5-1. 버스정류소(국비)
86 
7-2-3. 공공 - 동주민센터
84 
6-4. 복지 - 아동청소년
64 
7-2-1. 공공 - 구청사 및 별관
59 
Other values (15)
325 

Length

Max length21
Median length17
Mean length12.332031
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5-1. 버스정류소(국비)
2nd row5-1. 버스정류소(국비)
3rd row5-1. 버스정류소(국비)
4th row5-1. 버스정류소(국비)
5th row5-1. 버스정류소(국비)

Common Values

ValueCountFrequency (%)
1. 주요거리 150
19.5%
5-1. 버스정류소(국비) 86
11.2%
7-2-3. 공공 - 동주민센터 84
10.9%
6-4. 복지 - 아동청소년 64
8.3%
7-2-1. 공공 - 구청사 및 별관 59
 
7.7%
3. 공원(하천) 59
 
7.7%
6-1. 복지 - 사회 56
 
7.3%
2. 전통시장 49
 
6.4%
4. 문화관광 34
 
4.4%
6-3. 복지 - 장애인 22
 
2.9%
Other values (10) 105
13.7%

Length

2024-05-18T11:00:00.536377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
363
 
15.0%
공공 199
 
8.2%
복지 164
 
6.8%
1 150
 
6.2%
주요거리 150
 
6.2%
5-1 86
 
3.6%
버스정류소(국비 86
 
3.6%
7-2-3 84
 
3.5%
동주민센터 84
 
3.5%
및 78
 
3.2%
Other values (35) 974
40.3%

설치기관
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size6.1 KiB
서울시(AP)
222 
디지털뉴딜(KT)
177 
자치구
130 
디지털뉴딜(LG U+)
126 
버스정류소(국비)
86 
Other values (3)
27 

Length

Max length12
Median length9
Mean length7.890625
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row버스정류소(국비)
2nd row버스정류소(국비)
3rd row버스정류소(국비)
4th row버스정류소(국비)
5th row버스정류소(국비)

Common Values

ValueCountFrequency (%)
서울시(AP) 222
28.9%
디지털뉴딜(KT) 177
23.0%
자치구 130
16.9%
디지털뉴딜(LG U+) 126
16.4%
버스정류소(국비) 86
 
11.2%
버스정류소(시비) 21
 
2.7%
서울시(LTE) 4
 
0.5%
서울시(공유기) 2
 
0.3%

Length

2024-05-18T11:00:01.102776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T11:00:01.565609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서울시(ap 222
24.8%
디지털뉴딜(kt 177
19.8%
자치구 130
14.5%
디지털뉴딜(lg 126
14.1%
u 126
14.1%
버스정류소(국비 86
 
9.6%
버스정류소(시비 21
 
2.3%
서울시(lte 4
 
0.4%
서울시(공유기 2
 
0.2%

서비스구분
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size6.1 KiB
공공WiFi
449 
과기부WiFi(핫플레이스)
134 
과기부WiFi
86 
과기부WiFi(복지시설)
66 
<NA>
 
33

Length

Max length14
Median length6
Mean length8.0234375
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
공공WiFi 449
58.5%
과기부WiFi(핫플레이스) 134
 
17.4%
과기부WiFi 86
 
11.2%
과기부WiFi(복지시설) 66
 
8.6%
<NA> 33
 
4.3%

Length

2024-05-18T11:00:02.043066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T11:00:02.424724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
공공wifi 449
58.5%
과기부wifi(핫플레이스 134
 
17.4%
과기부wifi 86
 
11.2%
과기부wifi(복지시설 66
 
8.6%
na 33
 
4.3%

망종류
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size6.1 KiB
인터넷망_뉴딜용
303 
자가망_U무선망
258 
임대망
186 
<NA>
 
21

Length

Max length8
Median length8
Mean length6.6796875
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
인터넷망_뉴딜용 303
39.5%
자가망_U무선망 258
33.6%
임대망 186
24.2%
<NA> 21
 
2.7%

Length

2024-05-18T11:00:02.988456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T11:00:03.344534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
인터넷망_뉴딜용 303
39.5%
자가망_u무선망 258
33.6%
임대망 186
24.2%
na 21
 
2.7%

설치년도
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2020.5924
Minimum2017
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.9 KiB
2024-05-18T11:00:03.725216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2017
5-th percentile2017
Q12019
median2022
Q32022
95-th percentile2023
Maximum2023
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.1286633
Coefficient of variation (CV)0.0010534847
Kurtosis-0.95431003
Mean2020.5924
Median Absolute Deviation (MAD)1
Skewness-0.7320663
Sum1551815
Variance4.5312076
MonotonicityNot monotonic
2024-05-18T11:00:04.235739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2022 278
36.2%
2017 161
21.0%
2023 112
14.6%
2021 109
 
14.2%
2019 59
 
7.7%
2020 47
 
6.1%
2018 2
 
0.3%
ValueCountFrequency (%)
2017 161
21.0%
2018 2
 
0.3%
2019 59
 
7.7%
2020 47
 
6.1%
2021 109
 
14.2%
2022 278
36.2%
2023 112
14.6%
ValueCountFrequency (%)
2023 112
14.6%
2022 278
36.2%
2021 109
 
14.2%
2020 47
 
6.1%
2019 59
 
7.7%
2018 2
 
0.3%
2017 161
21.0%

실내외구분
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size6.1 KiB
실내
396 
실외
372 

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 (%)
실내 396
51.6%
실외 372
48.4%

Length

2024-05-18T11:00:04.645941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T11:00:04.988214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
실내 396
51.6%
실외 372
48.4%

wifi접속환경
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size6.1 KiB
<NA>
717 
보안접속 임시적용(머큐리 Proxy 서버 개발중)
 
41
10G 백홀, WIFI6E
 
8
H149
 
1
H150
 
1

Length

Max length27
Median length4
Mean length5.3320312
Min length4

Unique

Unique2 ?
Unique (%)0.3%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 717
93.4%
보안접속 임시적용(머큐리 Proxy 서버 개발중) 41
 
5.3%
10G 백홀, WIFI6E 8
 
1.0%
H149 1
 
0.1%
H150 1
 
0.1%

Length

2024-05-18T11:00:05.308586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T11:00:05.617103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 717
75.6%
보안접속 41
 
4.3%
임시적용(머큐리 41
 
4.3%
proxy 41
 
4.3%
서버 41
 
4.3%
개발중 41
 
4.3%
10g 8
 
0.8%
백홀 8
 
0.8%
wifi6e 8
 
0.8%
h149 1
 
0.1%

X좌표
Real number (ℝ)

HIGH CORRELATION 

Distinct340
Distinct (%)44.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.534913
Minimum37.390636
Maximum37.554432
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.9 KiB
2024-05-18T11:00:06.080930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.390636
5-th percentile37.520344
Q137.530336
median37.534777
Q337.541008
95-th percentile37.54865
Maximum37.554432
Range0.163796
Interquartile range (IQR)0.01067175

Descriptive statistics

Standard deviation0.010325618
Coefficient of variation (CV)0.0002750937
Kurtosis48.428437
Mean37.534913
Median Absolute Deviation (MAD)0.00589
Skewness-3.589438
Sum28826.813
Variance0.00010661839
MonotonicityNot monotonic
2024-05-18T11:00:06.776291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.53235 71
 
9.2%
37.517822 27
 
3.5%
37.53883 24
 
3.1%
37.52126 21
 
2.7%
37.52581 19
 
2.5%
37.547302 12
 
1.6%
37.545773 11
 
1.4%
37.545666 10
 
1.3%
37.541225 9
 
1.2%
37.54538 8
 
1.0%
Other values (330) 556
72.4%
ValueCountFrequency (%)
37.390636 1
 
0.1%
37.51517 1
 
0.1%
37.517048 1
 
0.1%
37.5171 1
 
0.1%
37.51775 1
 
0.1%
37.517822 27
3.5%
37.517853 1
 
0.1%
37.518497 1
 
0.1%
37.51905 1
 
0.1%
37.519054 1
 
0.1%
ValueCountFrequency (%)
37.554432 4
0.5%
37.55394 2
 
0.3%
37.55392 1
 
0.1%
37.55356 1
 
0.1%
37.55337 3
0.4%
37.553078 1
 
0.1%
37.553 1
 
0.1%
37.552963 6
0.8%
37.552734 5
0.7%
37.551464 1
 
0.1%

Y좌표
Real number (ℝ)

Distinct327
Distinct (%)42.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.97769
Minimum126.94656
Maximum127.1174
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.9 KiB
2024-05-18T11:00:07.387635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.94656
5-th percentile126.95699
Q1126.96652
median126.97378
Q3126.98792
95-th percentile127.0019
Maximum127.1174
Range0.170844
Interquartile range (IQR)0.0213975

Descriptive statistics

Standard deviation0.015110418
Coefficient of variation (CV)0.00011900057
Kurtosis8.2300927
Mean126.97769
Median Absolute Deviation (MAD)0.011265
Skewness1.2359106
Sum97518.868
Variance0.00022832474
MonotonicityNot monotonic
2024-05-18T11:00:07.873648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.987915 71
 
9.2%
126.97085 30
 
3.9%
126.96536 29
 
3.8%
126.97331 21
 
2.7%
126.96763 20
 
2.6%
126.96157 12
 
1.6%
126.97482 11
 
1.4%
126.974846 10
 
1.3%
126.96302 9
 
1.2%
127.00472 8
 
1.0%
Other values (317) 547
71.2%
ValueCountFrequency (%)
126.946556 1
 
0.1%
126.94824 1
 
0.1%
126.94931 3
0.4%
126.94932 1
 
0.1%
126.95083 1
 
0.1%
126.95149 1
 
0.1%
126.95206 4
0.5%
126.95309 1
 
0.1%
126.95351 1
 
0.1%
126.95435 1
 
0.1%
ValueCountFrequency (%)
127.1174 1
 
0.1%
127.009315 1
 
0.1%
127.00767 1
 
0.1%
127.00739 3
0.4%
127.00671 1
 
0.1%
127.00655 3
0.4%
127.00653 2
0.3%
127.00631 3
0.4%
127.00629 2
0.3%
127.006065 1
 
0.1%

작업일자
Categorical

HIGH CORRELATION 

Distinct13
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size6.1 KiB
2024-05-17 11:13:01.0
139 
2024-05-17 11:13:02.0
115 
2024-05-17 11:12:59.0
109 
2024-05-17 11:13:00.0
83 
2024-05-17 11:12:52.0
58 
Other values (8)
264 

Length

Max length21
Median length21
Mean length21
Min length21

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2024-05-17 11:12:52.0
2nd row2024-05-17 11:12:52.0
3rd row2024-05-17 11:12:52.0
4th row2024-05-17 11:12:52.0
5th row2024-05-17 11:12:52.0

Common Values

ValueCountFrequency (%)
2024-05-17 11:13:01.0 139
18.1%
2024-05-17 11:13:02.0 115
15.0%
2024-05-17 11:12:59.0 109
14.2%
2024-05-17 11:13:00.0 83
10.8%
2024-05-17 11:12:52.0 58
7.6%
2024-05-17 11:13:03.0 58
7.6%
2024-05-17 11:13:05.0 52
 
6.8%
2024-05-17 11:13:06.0 51
 
6.6%
2024-05-17 11:12:53.0 28
 
3.6%
2024-05-17 11:13:04.0 27
 
3.5%
Other values (3) 48
 
6.2%

Length

2024-05-18T11:00:08.426315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2024-05-17 768
50.0%
11:13:01.0 139
 
9.0%
11:13:02.0 115
 
7.5%
11:12:59.0 109
 
7.1%
11:13:00.0 83
 
5.4%
11:12:52.0 58
 
3.8%
11:13:03.0 58
 
3.8%
11:13:05.0 52
 
3.4%
11:13:06.0 51
 
3.3%
11:12:53.0 28
 
1.8%
Other values (4) 75
 
4.9%

Interactions

2024-05-18T10:59:47.764588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:59:45.972906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:59:46.855821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:59:48.056157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:59:46.266222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:59:47.150104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:59:48.413517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:59:46.550919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:59:47.410187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-18T11:00:08.879025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
설치위치(층)설치유형설치기관서비스구분망종류설치년도실내외구분wifi접속환경X좌표Y좌표작업일자
설치위치(층)1.0000.8931.000NaN1.0000.6031.000NaN0.5730.6881.000
설치유형0.8931.0000.9440.9310.8910.8971.0000.9790.7220.7140.936
설치기관1.0000.9441.0000.9790.8750.8600.8311.0000.6030.3300.965
서비스구분NaN0.9310.9791.0000.6680.7940.8761.0000.2500.3550.960
망종류1.0000.8910.8750.6681.0000.9740.2341.0000.1370.2340.999
설치년도0.6030.8970.8600.7940.9741.0000.8731.0000.2470.2080.937
실내외구분1.0001.0000.8310.8760.2340.8731.0001.0000.2190.0000.777
wifi접속환경NaN0.9791.0001.0001.0001.0001.0001.0001.0000.5080.979
X좌표0.5730.7220.6030.2500.1370.2470.2191.0001.0000.6820.556
Y좌표0.6880.7140.3300.3550.2340.2080.0000.5080.6821.0000.470
작업일자1.0000.9360.9650.9600.9990.9370.7770.9790.5560.4701.000
2024-05-18T11:00:09.350740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
실내외구분설치유형설치위치(층)설치기관서비스구분망종류작업일자wifi접속환경
실내외구분1.0000.9760.9390.6480.6800.3820.7410.979
설치유형0.9761.0000.5950.7440.7980.7540.6780.803
설치위치(층)0.9390.5951.0000.9391.0000.9390.939NaN
설치기관0.6480.7440.9391.0000.8050.8620.8840.990
서비스구분0.6800.7981.0000.8051.0000.6960.9040.979
망종류0.3820.7540.9390.8620.6961.0000.9500.979
작업일자0.7410.6780.9390.8840.9040.9501.0000.803
wifi접속환경0.9790.803NaN0.9900.9790.9790.8031.000
2024-05-18T11:00:09.679626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
설치년도X좌표Y좌표설치위치(층)설치유형설치기관서비스구분망종류실내외구분wifi접속환경작업일자
설치년도1.000-0.0300.0130.4730.6210.6110.6640.8870.6570.9790.727
X좌표-0.0301.000-0.1150.3260.4190.3040.2390.1300.1450.9790.356
Y좌표0.013-0.1151.0000.4000.3890.2090.1450.1800.0000.3360.276
설치위치(층)0.4730.3260.4001.0000.5950.9391.0000.9390.9390.0000.939
설치유형0.6210.4190.3890.5951.0000.7440.7980.7540.9760.8030.678
설치기관0.6110.3040.2090.9390.7441.0000.8050.8620.6480.9900.884
서비스구분0.6640.2390.1451.0000.7980.8051.0000.6960.6800.9790.904
망종류0.8870.1300.1800.9390.7540.8620.6961.0000.3820.9790.950
실내외구분0.6570.1450.0000.9390.9760.6480.6800.3821.0000.9790.741
wifi접속환경0.9790.9790.3360.0000.8030.9900.9790.9790.9791.0000.803
작업일자0.7270.3560.2760.9390.6780.8840.9040.9500.7410.8031.000

Missing values

2024-05-18T10:59:48.841892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-18T10:59:49.485510image/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.
2024-05-18T10:59:49.902581image/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

관리번호자치구와이파이명도로명주소상세주소설치위치(층)설치유형설치기관서비스구분망종류설치년도실내외구분wifi접속환경X좌표Y좌표작업일자
0BS101022용산구버스정류소_KT용산지사한강대로03-005<NA>5-1. 버스정류소(국비)버스정류소(국비)과기부WiFi임대망2021실외<NA>37.531498126.970242024-05-17 11:12:52.0
1BS101023용산구버스정류소_KT용산지사한강대로03-006<NA>5-1. 버스정류소(국비)버스정류소(국비)과기부WiFi임대망2021실외<NA>37.53205126.9705662024-05-17 11:12:52.0
2BS101024용산구버스정류소_갈월동한강대로03-011<NA>5-1. 버스정류소(국비)버스정류소(국비)과기부WiFi임대망2021실외<NA>37.550976126.972242024-05-17 11:12:52.0
3BS101025용산구버스정류소_갈월동한강대로03-012<NA>5-1. 버스정류소(국비)버스정류소(국비)과기부WiFi임대망2021실외<NA>37.549534126.971712024-05-17 11:12:52.0
4BS101026용산구버스정류소_남산예술원한남동 726-37403-159<NA>5-1. 버스정류소(국비)버스정류소(국비)과기부WiFi임대망2021실외<NA>37.544437127.002042024-05-17 11:12:52.0
5BS101027용산구버스정류소_남산체육관이태원동 260-38103-173<NA>5-1. 버스정류소(국비)버스정류소(국비)과기부WiFi임대망2021실외<NA>37.542706126.992792024-05-17 11:12:52.0
6BS101028용산구버스정류소_남산체육관소월로 28003-174<NA>5-1. 버스정류소(국비)버스정류소(국비)과기부WiFi임대망2021실외<NA>37.542503126.992922024-05-17 11:12:52.0
7BS101029용산구버스정류소_남영동주민센터두텁바위로 2703-206<NA>5-1. 버스정류소(국비)버스정류소(국비)과기부WiFi임대망2021실외<NA>37.54575126.976752024-05-17 11:12:52.0
8BS101030용산구버스정류소_남영동주민센터용산동1가 1-403-207<NA>5-1. 버스정류소(국비)버스정류소(국비)과기부WiFi임대망2021실외<NA>37.54536126.975062024-05-17 11:12:52.0
9BS101031용산구버스정류소_남영역갈월동 100-503-130<NA>5-1. 버스정류소(국비)버스정류소(국비)과기부WiFi임대망2021실외<NA>37.540813126.971032024-05-17 11:12:52.0
관리번호자치구와이파이명도로명주소상세주소설치위치(층)설치유형설치기관서비스구분망종류설치년도실내외구분wifi접속환경X좌표Y좌표작업일자
758서울5차-0826용산구신계역사공원서울특별시 용산구 신계동 55(CCTV) 공원5-28(3)-3. 공원(하천)디지털뉴딜(LG U+)<NA>인터넷망_뉴딜용2023실외<NA>37.53587126.966052024-05-17 11:13:06.0
759서울5차-0827용산구신계역사공원서울특별시 용산구 신계동 55(CCTV) 공원5-28(4)-3. 공원(하천)디지털뉴딜(LG U+)<NA>인터넷망_뉴딜용2023실외<NA>37.535927126.966722024-05-17 11:13:06.0
760서울5차-0828용산구신계역사공원서울특별시 용산구 신계동 55(CCTV) 공원5-28(5)-3. 공원(하천)디지털뉴딜(LG U+)<NA>인터넷망_뉴딜용2023실외<NA>37.5359126.967142024-05-17 11:13:06.0
761서울5차-0829용산구신계역사공원서울특별시 용산구 신계동 55(CCTV) 공원5-28(6)-3. 공원(하천)디지털뉴딜(LG U+)<NA>인터넷망_뉴딜용2023실외<NA>37.535034126.967022024-05-17 11:13:06.0
762서울5차-0830용산구시립용산노인종합복지관서울특별시 용산구 독서당로11길 16로비지하1층6-1. 복지 - 사회디지털뉴딜(LG U+)<NA>인터넷망_뉴딜용2023실내<NA>37.531376127.006552024-05-17 11:13:06.0
763서울5차-0830-1용산구시립용산노인종합복지관서울특별시 용산구 독서당로11길 16주차장 앞지하1층6-1. 복지 - 사회디지털뉴딜(LG U+)<NA>인터넷망_뉴딜용2023실내<NA>37.531376127.006552024-05-17 11:13:06.0
764서울5차-0830-2용산구시립용산노인종합복지관서울특별시 용산구 독서당로11길 16로비2층6-1. 복지 - 사회디지털뉴딜(LG U+)<NA>인터넷망_뉴딜용2023실내<NA>37.531376127.006552024-05-17 11:13:06.0
765서울5차-0831용산구시립용산노인종합복지관서울특별시 용산구 독서당로11길 16로비3층6-1. 복지 - 사회디지털뉴딜(LG U+)<NA>인터넷망_뉴딜용2023실내<NA>37.53137127.006312024-05-17 11:13:06.0
766서울5차-0831-1용산구시립용산노인종합복지관서울특별시 용산구 독서당로11길 16로비4층6-1. 복지 - 사회디지털뉴딜(LG U+)<NA>인터넷망_뉴딜용2023실내<NA>37.53137127.006312024-05-17 11:13:06.0
767서울5차-0831-2용산구시립용산노인종합복지관서울특별시 용산구 독서당로11길 16옥상 출입구 좌측천장5층6-1. 복지 - 사회디지털뉴딜(LG U+)<NA>인터넷망_뉴딜용2023실내<NA>37.53137127.006312024-05-17 11:13:06.0