Overview

Dataset statistics

Number of variables9
Number of observations157
Missing cells155
Missing cells (%)11.0%
Duplicate rows1
Duplicate rows (%)0.6%
Total size in memory11.3 KiB
Average record size in memory73.8 B

Variable types

Categorical5
Text3
Numeric1

Dataset

Description서울특별시 양천구에 소재한 실외운동기구현황(실외운동기구설치주소, 설치기구종류, 설치장소, 관리횟수, 담당부서 등)입니다.
Author서울특별시 양천구
URLhttps://www.data.go.kr/data/15037938/fileData.do

Alerts

기관명 has constant value ""Constant
설치업체 운동기구 관리 횟수 has constant value ""Constant
고장난 실외운동기구 has constant value ""Constant
Dataset has 1 (0.6%) duplicate rowsDuplicates
담당부서 is highly overall correlated with 최근5년간설치현황High correlation
최근5년간설치현황 is highly overall correlated with 담당부서High correlation
담당부서 is highly imbalanced (90.2%)Imbalance
최근5년간설치현황 is highly imbalanced (73.0%)Imbalance
설치기구종류 has 155 (98.7%) missing valuesMissing

Reproduction

Analysis started2024-03-16 04:11:05.150195
Analysis finished2024-03-16 04:11:17.672764
Duration12.52 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기관명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
서울특별시 양천구
157 

Length

Max length9
Median length9
Mean length9
Min length9

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row서울특별시 양천구
2nd row서울특별시 양천구
3rd row서울특별시 양천구
4th row서울특별시 양천구
5th row서울특별시 양천구

Common Values

ValueCountFrequency (%)
서울특별시 양천구 157
100.0%

Length

2024-03-16T13:11:17.932150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-16T13:11:18.227059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서울특별시 157
50.0%
양천구 157
50.0%

실외운동기구설치주소
Text

Distinct145
Distinct (%)92.4%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
2024-03-16T13:11:18.647239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length25
Mean length19.853503
Min length16

Characters and Unicode

Total characters3117
Distinct characters65
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

Unique140 ?
Unique (%)89.2%

Sample

1st row서울특별시 양천구 신정동 1319-2
2nd row서울특별시 양천구 안양천로 711
3rd row서울특별시 양천구 목2동 산7-1 외 41
4th row서울특별시 양천구 신정7동 산73-4 외 40
5th row서울특별시 양천구 신정3동 44-2 외 65
ValueCountFrequency (%)
서울특별시 157
24.8%
양천구 157
24.8%
신정3동 21
 
3.3%
목2동 15
 
2.4%
신정4동 12
 
1.9%
신정7동 9
 
1.4%
신월동 9
 
1.4%
목5동 8
 
1.3%
목동 6
 
0.9%
신정1동 6
 
0.9%
Other values (166) 233
36.8%
2024-03-16T13:11:19.334306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
476
 
15.3%
양 160
 
5.1%
구 159
 
5.1%
천 159
 
5.1%
동 158
 
5.1%
별 158
 
5.1%
울 157
 
5.0%
서 157
 
5.0%
시 157
 
5.0%
특 157
 
5.0%
Other values (55) 1219
39.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1775
56.9%
Decimal Number 731
23.5%
Space Separator 476
 
15.3%
Dash Punctuation 111
 
3.6%
Open Punctuation 12
 
0.4%
Close Punctuation 12
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
양 160
9.0%
구 159
9.0%
천 159
9.0%
동 158
8.9%
별 158
8.9%
울 157
8.8%
서 157
8.8%
시 157
8.8%
특 157
8.8%
신 111
6.3%
Other values (41) 242
13.6%
Decimal Number
ValueCountFrequency (%)
1 153
20.9%
2 125
17.1%
3 93
12.7%
0 61
 
8.3%
4 60
 
8.2%
7 57
 
7.8%
9 56
 
7.7%
5 53
 
7.3%
6 47
 
6.4%
8 26
 
3.6%
Space Separator
ValueCountFrequency (%)
476
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 111
100.0%
Open Punctuation
ValueCountFrequency (%)
( 12
100.0%
Close Punctuation
ValueCountFrequency (%)
) 12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1775
56.9%
Common 1342
43.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
양 160
9.0%
구 159
9.0%
천 159
9.0%
동 158
8.9%
별 158
8.9%
울 157
8.8%
서 157
8.8%
시 157
8.8%
특 157
8.8%
신 111
6.3%
Other values (41) 242
13.6%
Common
ValueCountFrequency (%)
476
35.5%
1 153
 
11.4%
2 125
 
9.3%
- 111
 
8.3%
3 93
 
6.9%
0 61
 
4.5%
4 60
 
4.5%
7 57
 
4.2%
9 56
 
4.2%
5 53
 
3.9%
Other values (4) 97
 
7.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1775
56.9%
ASCII 1342
43.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
476
35.5%
1 153
 
11.4%
2 125
 
9.3%
- 111
 
8.3%
3 93
 
6.9%
0 61
 
4.5%
4 60
 
4.5%
7 57
 
4.2%
9 56
 
4.2%
5 53
 
3.9%
Other values (4) 97
 
7.2%
Hangul
ValueCountFrequency (%)
양 160
9.0%
구 159
9.0%
천 159
9.0%
동 158
8.9%
별 158
8.9%
울 157
8.8%
서 157
8.8%
시 157
8.8%
특 157
8.8%
신 111
6.3%
Other values (41) 242
13.6%

설치기구종류
Text

MISSING 

Distinct2
Distinct (%)100.0%
Missing155
Missing (%)98.7%
Memory size1.4 KiB
2024-03-16T13:11:19.706712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length42
Median length36.5
Mean length36.5
Min length31

Characters and Unicode

Total characters73
Distinct characters37
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st row터닝암+마사지롤+복합형+파워싸이클+크로스워킹+스카이워킹+사이드스윙+레그프레스
2nd row워킹트레이너머신+트위스트머신+스윙워커머신+오버헤드풀리머신
ValueCountFrequency (%)
터닝암+마사지롤+복합형+파워싸이클+크로스워킹+스카이워킹+사이드스윙+레그프레스 1
50.0%
워킹트레이너머신+트위스트머신+스윙워커머신+오버헤드풀리머신 1
50.0%
2024-03-16T13:11:20.507734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
+ 10
 
13.7%
스 6
 
8.2%
워 5
 
6.8%
머 4
 
5.5%
이 4
 
5.5%
신 4
 
5.5%
트 3
 
4.1%
레 3
 
4.1%
킹 3
 
4.1%
드 2
 
2.7%
Other values (27) 29
39.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 63
86.3%
Math Symbol 10
 
13.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
스 6
 
9.5%
워 5
 
7.9%
머 4
 
6.3%
이 4
 
6.3%
신 4
 
6.3%
트 3
 
4.8%
레 3
 
4.8%
킹 3
 
4.8%
드 2
 
3.2%
사 2
 
3.2%
Other values (26) 27
42.9%
Math Symbol
ValueCountFrequency (%)
+ 10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 63
86.3%
Common 10
 
13.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
스 6
 
9.5%
워 5
 
7.9%
머 4
 
6.3%
이 4
 
6.3%
신 4
 
6.3%
트 3
 
4.8%
레 3
 
4.8%
킹 3
 
4.8%
드 2
 
3.2%
사 2
 
3.2%
Other values (26) 27
42.9%
Common
ValueCountFrequency (%)
+ 10
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 63
86.3%
ASCII 10
 
13.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
+ 10
100.0%
Hangul
ValueCountFrequency (%)
스 6
 
9.5%
워 5
 
7.9%
머 4
 
6.3%
이 4
 
6.3%
신 4
 
6.3%
트 3
 
4.8%
레 3
 
4.8%
킹 3
 
4.8%
드 2
 
3.2%
사 2
 
3.2%
Other values (26) 27
42.9%

설치기구개수
Real number (ℝ)

Distinct28
Distinct (%)17.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.6305732
Minimum1
Maximum112
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2024-03-16T13:11:20.752999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q14
median5
Q39
95-th percentile23.2
Maximum112
Range111
Interquartile range (IQR)5

Descriptive statistics

Standard deviation11.84531
Coefficient of variation (CV)1.3724824
Kurtosis41.643401
Mean8.6305732
Median Absolute Deviation (MAD)2
Skewness5.6850319
Sum1355
Variance140.31137
MonotonicityNot monotonic
2024-03-16T13:11:21.014038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
4 30
19.1%
3 29
18.5%
5 16
10.2%
6 14
8.9%
8 11
 
7.0%
7 11
 
7.0%
10 7
 
4.5%
2 5
 
3.2%
9 4
 
2.5%
12 4
 
2.5%
Other values (18) 26
16.6%
ValueCountFrequency (%)
1 1
 
0.6%
2 5
 
3.2%
3 29
18.5%
4 30
19.1%
5 16
10.2%
6 14
8.9%
7 11
 
7.0%
8 11
 
7.0%
9 4
 
2.5%
10 7
 
4.5%
ValueCountFrequency (%)
112 1
0.6%
69 1
0.6%
51 1
0.6%
35 1
0.6%
33 1
0.6%
30 1
0.6%
27 1
0.6%
24 1
0.6%
23 1
0.6%
22 1
0.6%

담당부서
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
공원녹지과
155 
문화체육과
 
2

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row문화체육과
2nd row문화체육과
3rd row공원녹지과
4th row공원녹지과
5th row공원녹지과

Common Values

ValueCountFrequency (%)
공원녹지과 155
98.7%
문화체육과 2
 
1.3%

Length

2024-03-16T13:11:21.184331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-16T13:11:21.315842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
공원녹지과 155
98.7%
문화체육과 2
 
1.3%

최근5년간설치현황
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct10
Distinct (%)6.4%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
<NA>
138 
2022 설치
 
4
2020 설치
 
3
2021 설치
 
3
2018 설치
 
2
Other values (5)
 
7

Length

Max length12
Median length4
Mean length4.388535
Min length4

Unique

Unique3 ?
Unique (%)1.9%

Sample

1st row2017 설치
2nd row2018 설치
3rd row2022 설치
4th row<NA>
5th row2022 설치

Common Values

ValueCountFrequency (%)
<NA> 138
87.9%
2022 설치 4
 
2.5%
2020 설치 3
 
1.9%
2021 설치 3
 
1.9%
2018 설치 2
 
1.3%
2024 설치 2
 
1.3%
2023 설치 2
 
1.3%
2017 설치 1
 
0.6%
2024설치 1
 
0.6%
2018,2023 설치 1
 
0.6%

Length

2024-03-16T13:11:21.496403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-16T13:11:21.710074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 138
78.9%
설치 18
 
10.3%
2022 4
 
2.3%
2020 3
 
1.7%
2021 3
 
1.7%
2018 2
 
1.1%
2024 2
 
1.1%
2023 2
 
1.1%
2017 1
 
0.6%
2024설치 1
 
0.6%

설치업체 운동기구 관리 횟수
Categorical

CONSTANT 

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
수시점검
157 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
수시점검 157
100.0%

Length

2024-03-16T13:11:21.887258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-16T13:11:22.021351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
수시점검 157
100.0%

고장난 실외운동기구
Categorical

CONSTANT 

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
없음
157 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
없음 157
100.0%

Length

2024-03-16T13:11:22.181582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-16T13:11:22.343045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
없음 157
100.0%

장소
Text

Distinct140
Distinct (%)89.2%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
2024-03-16T13:11:22.640154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length21
Mean length11.22293
Min length4

Characters and Unicode

Total characters1762
Distinct characters171
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique134 ?
Unique (%)85.4%

Sample

1st row연의체육공원
2nd row해누리체육공원
3rd row용왕산근린공원
4th row갈산근린공원
5th row계남근린공원
ValueCountFrequency (%)
근린공원 51
 
17.5%
및 18
 
6.2%
시설녹지 18
 
6.2%
등 18
 
6.2%
안양천생태공원 12
 
4.1%
옆(시 6
 
2.1%
운동장 3
 
1.0%
소공원(마을마당 3
 
1.0%
화장실 2
 
0.7%
공공공지(마을마당 2
 
0.7%
Other values (152) 158
54.3%
2024-03-16T13:11:23.167799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
공 165
 
9.4%
원 160
 
9.1%
린 142
 
8.1%
134
 
7.6%
이 75
 
4.3%
( 73
 
4.1%
) 73
 
4.1%
어 71
 
4.0%
근 70
 
4.0%
시 54
 
3.1%
Other values (161) 745
42.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1481
84.1%
Space Separator 134
 
7.6%
Open Punctuation 73
 
4.1%
Close Punctuation 73
 
4.1%
Decimal Number 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
공 165
 
11.1%
원 160
 
10.8%
린 142
 
9.6%
이 75
 
5.1%
어 71
 
4.8%
근 70
 
4.7%
시 54
 
3.6%
마 45
 
3.0%
지 34
 
2.3%
당 23
 
1.6%
Other values (157) 642
43.3%
Space Separator
ValueCountFrequency (%)
134
100.0%
Open Punctuation
ValueCountFrequency (%)
( 73
100.0%
Close Punctuation
ValueCountFrequency (%)
) 73
100.0%
Decimal Number
ValueCountFrequency (%)
2 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1481
84.1%
Common 281
 
15.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
공 165
 
11.1%
원 160
 
10.8%
린 142
 
9.6%
이 75
 
5.1%
어 71
 
4.8%
근 70
 
4.7%
시 54
 
3.6%
마 45
 
3.0%
지 34
 
2.3%
당 23
 
1.6%
Other values (157) 642
43.3%
Common
ValueCountFrequency (%)
134
47.7%
( 73
26.0%
) 73
26.0%
2 1
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1481
84.1%
ASCII 281
 
15.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
공 165
 
11.1%
원 160
 
10.8%
린 142
 
9.6%
이 75
 
5.1%
어 71
 
4.8%
근 70
 
4.7%
시 54
 
3.6%
마 45
 
3.0%
지 34
 
2.3%
당 23
 
1.6%
Other values (157) 642
43.3%
ASCII
ValueCountFrequency (%)
134
47.7%
( 73
26.0%
) 73
26.0%
2 1
 
0.4%

Interactions

2024-03-16T13:11:16.865996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-16T13:11:23.318670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
설치기구종류설치기구개수담당부서최근5년간설치현황
설치기구종류1.000NaNNaN0.000
설치기구개수NaN1.0000.0000.000
담당부서NaN0.0001.0000.708
최근5년간설치현황0.0000.0000.7081.000
2024-03-16T13:11:23.453836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
담당부서최근5년간설치현황
담당부서1.0000.541
최근5년간설치현황0.5411.000
2024-03-16T13:11:23.553132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
설치기구개수담당부서최근5년간설치현황
설치기구개수1.0000.0000.000
담당부서0.0001.0000.541
최근5년간설치현황0.0000.5411.000

Missing values

2024-03-16T13:11:17.115865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-16T13:11:17.388416image/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

기관명실외운동기구설치주소설치기구종류설치기구개수담당부서최근5년간설치현황설치업체 운동기구 관리 횟수고장난 실외운동기구장소
0서울특별시 양천구서울특별시 양천구 신정동 1319-2터닝암+마사지롤+복합형+파워싸이클+크로스워킹+스카이워킹+사이드스윙+레그프레스8문화체육과2017 설치수시점검없음연의체육공원
1서울특별시 양천구서울특별시 양천구 안양천로 711워킹트레이너머신+트위스트머신+스윙워커머신+오버헤드풀리머신4문화체육과2018 설치수시점검없음해누리체육공원
2서울특별시 양천구서울특별시 양천구 목2동 산7-1 외 41<NA>69공원녹지과2022 설치수시점검없음용왕산근린공원
3서울특별시 양천구서울특별시 양천구 신정7동 산73-4 외 40<NA>35공원녹지과<NA>수시점검없음갈산근린공원
4서울특별시 양천구서울특별시 양천구 신정3동 44-2 외 65<NA>112공원녹지과2022 설치수시점검없음계남근린공원
5서울특별시 양천구서울특별시 양천구 신월동 산85-4<NA>51공원녹지과2024 설치수시점검없음온수근린공원
6서울특별시 양천구서울특별시 양천구 목1동 925-1<NA>4공원녹지과<NA>수시점검없음무지개어린이공원
7서울특별시 양천구서울특별시 양천구 목3동 712-1<NA>3공원녹지과2020 설치수시점검없음무궁화어린이공원
8서울특별시 양천구서울특별시 양천구 목3동 611-1<NA>3공원녹지과<NA>수시점검없음한두어린이공원
9서울특별시 양천구서울특별시 양천구 목3동 646-23<NA>8공원녹지과<NA>수시점검없음백석어린이공원
기관명실외운동기구설치주소설치기구종류설치기구개수담당부서최근5년간설치현황설치업체 운동기구 관리 횟수고장난 실외운동기구장소
147서울특별시 양천구서울특별시 양천구 신정6동(별마루축구장)<NA>10공원녹지과2018 설치수시점검없음안양천생태공원
148서울특별시 양천구서울특별시 양천구 신정2동(해마루축구장)<NA>5공원녹지과<NA>수시점검없음안양천생태공원
149서울특별시 양천구서울특별시 양천구 신정2동(피크닉광장제방)<NA>19공원녹지과<NA>수시점검없음안양천생태공원
150서울특별시 양천구서울특별시 양천구 신정2동(오목교 하부)<NA>8공원녹지과2021 설치수시점검없음안양천생태공원
151서울특별시 양천구서울특별시 양천구 신정 2동(실개천생태공원)<NA>4공원녹지과<NA>수시점검없음안양천생태공원
152서울특별시 양천구서울특별시 양천구 목1동(목동운동장 제방)<NA>7공원녹지과<NA>수시점검없음안양천생태공원
153서울특별시 양천구서울특별시 양천구 목1동(양정중 맞은편 제방)<NA>8공원녹지과<NA>수시점검없음안양천생태공원
154서울특별시 양천구서울특별시 양천구 목5동(양평교 하부)<NA>14공원녹지과2022 설치수시점검없음안양천생태공원
155서울특별시 양천구서울특별시 양천구 목5동(목마공원 맞은편 제방)<NA>7공원녹지과<NA>수시점검없음안양천생태공원
156서울특별시 양천구서울특별시 양천구 목5동(식약청 맞은편 제방)<NA>7공원녹지과<NA>수시점검없음안양천생태공원

Duplicate rows

Most frequently occurring

기관명실외운동기구설치주소설치기구종류설치기구개수담당부서최근5년간설치현황설치업체 운동기구 관리 횟수고장난 실외운동기구장소# duplicates
0서울특별시 양천구서울특별시 양천구 목2동 202-36<NA>6공원녹지과<NA>수시점검없음법무단지 화장실 옆(시 근린공원)2