Overview

Dataset statistics

Number of variables8
Number of observations33
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.3 KiB
Average record size in memory71.0 B

Variable types

Numeric3
Text3
Categorical2

Dataset

Description부산광역시 남구 내 존재하는 공중화장실 현황에 대한 데이터로 화장실명, 소재지, 변기 수(남), 변기 수(여), 면적, 처리방법, 설치연도(개선연도) 자료를 제공합니다.
URLhttps://www.data.go.kr/data/15084024/fileData.do

Alerts

변기수(여) is highly overall correlated with 면적(제곱미터) and 1 other fieldsHigh correlation
면적(제곱미터) is highly overall correlated with 변기수(여) and 2 other fieldsHigh correlation
변기수(남) is highly overall correlated with 변기수(여) and 1 other fieldsHigh correlation
처리방법 is highly overall correlated with 면적(제곱미터)High correlation
연번 has unique valuesUnique
공중화장실명 has unique valuesUnique

Reproduction

Analysis started2023-12-12 09:59:48.114960
Analysis finished2023-12-12 09:59:49.736661
Duration1.62 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

UNIQUE 

Distinct33
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17
Minimum1
Maximum33
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size429.0 B
2023-12-12T18:59:49.806899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.6
Q19
median17
Q325
95-th percentile31.4
Maximum33
Range32
Interquartile range (IQR)16

Descriptive statistics

Standard deviation9.6695398
Coefficient of variation (CV)0.56879646
Kurtosis-1.2
Mean17
Median Absolute Deviation (MAD)8
Skewness0
Sum561
Variance93.5
MonotonicityStrictly increasing
2023-12-12T18:59:49.937773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
1 1
 
3.0%
26 1
 
3.0%
20 1
 
3.0%
21 1
 
3.0%
22 1
 
3.0%
23 1
 
3.0%
24 1
 
3.0%
25 1
 
3.0%
27 1
 
3.0%
2 1
 
3.0%
Other values (23) 23
69.7%
ValueCountFrequency (%)
1 1
3.0%
2 1
3.0%
3 1
3.0%
4 1
3.0%
5 1
3.0%
6 1
3.0%
7 1
3.0%
8 1
3.0%
9 1
3.0%
10 1
3.0%
ValueCountFrequency (%)
33 1
3.0%
32 1
3.0%
31 1
3.0%
30 1
3.0%
29 1
3.0%
28 1
3.0%
27 1
3.0%
26 1
3.0%
25 1
3.0%
24 1
3.0%

공중화장실명
Text

UNIQUE 

Distinct33
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size396.0 B
2023-12-12T18:59:50.454736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length12
Mean length9.5757576
Min length3

Characters and Unicode

Total characters316
Distinct characters99
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

Unique33 ?
Unique (%)100.0%

Sample

1st row유엔기념공원 주차장
2nd row우룡산공원
3rd row신선대유원지(임도입구)
4th row신선대유원지(중턱)
5th row이기대공원(큰고개쉼터)
ValueCountFrequency (%)
체육공원 7
 
16.7%
유엔기념공원 1
 
2.4%
평화공원 1
 
2.4%
편백나무단지(황령산 1
 
2.4%
백운포체육공원(마사구장 1
 
2.4%
문현약수터옆(황령산 1
 
2.4%
문수사 1
 
2.4%
입구 1
 
2.4%
문현4동 1
 
2.4%
벚꽃약수터옆(황령산 1
 
2.4%
Other values (26) 26
61.9%
2023-12-12T18:59:50.818544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
원 25
 
7.9%
공 22
 
7.0%
) 15
 
4.7%
( 15
 
4.7%
체 12
 
3.8%
육 12
 
3.8%
산 10
 
3.2%
대 9
 
2.8%
9
 
2.8%
구 8
 
2.5%
Other values (89) 179
56.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 272
86.1%
Close Punctuation 15
 
4.7%
Open Punctuation 15
 
4.7%
Space Separator 9
 
2.8%
Decimal Number 5
 
1.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
원 25
 
9.2%
공 22
 
8.1%
체 12
 
4.4%
육 12
 
4.4%
산 10
 
3.7%
대 9
 
3.3%
구 8
 
2.9%
령 7
 
2.6%
동 7
 
2.6%
황 7
 
2.6%
Other values (82) 153
56.2%
Decimal Number
ValueCountFrequency (%)
1 2
40.0%
4 1
20.0%
5 1
20.0%
2 1
20.0%
Close Punctuation
ValueCountFrequency (%)
) 15
100.0%
Open Punctuation
ValueCountFrequency (%)
( 15
100.0%
Space Separator
ValueCountFrequency (%)
9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 272
86.1%
Common 44
 
13.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
원 25
 
9.2%
공 22
 
8.1%
체 12
 
4.4%
육 12
 
4.4%
산 10
 
3.7%
대 9
 
3.3%
구 8
 
2.9%
령 7
 
2.6%
동 7
 
2.6%
황 7
 
2.6%
Other values (82) 153
56.2%
Common
ValueCountFrequency (%)
) 15
34.1%
( 15
34.1%
9
20.5%
1 2
 
4.5%
4 1
 
2.3%
5 1
 
2.3%
2 1
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 272
86.1%
ASCII 44
 
13.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
원 25
 
9.2%
공 22
 
8.1%
체 12
 
4.4%
육 12
 
4.4%
산 10
 
3.7%
대 9
 
3.3%
구 8
 
2.9%
령 7
 
2.6%
동 7
 
2.6%
황 7
 
2.6%
Other values (82) 153
56.2%
ASCII
ValueCountFrequency (%)
) 15
34.1%
( 15
34.1%
9
20.5%
1 2
 
4.5%
4 1
 
2.3%
5 1
 
2.3%
2 1
 
2.3%

소재지
Text

Distinct30
Distinct (%)90.9%
Missing0
Missing (%)0.0%
Memory size396.0 B
2023-12-12T18:59:51.046695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length10
Mean length9.1515152
Min length7

Characters and Unicode

Total characters302
Distinct characters23
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

Unique28 ?
Unique (%)84.8%

Sample

1st row대연4동 779-3
2nd row대연1동 산165-9
3rd row용호4동 산241-7
4th row용당동 산170
5th row용호동산118-2
ValueCountFrequency (%)
용호4동 5
 
7.8%
대연3동 4
 
6.2%
용호동 3
 
4.7%
895-3 3
 
4.7%
문현1동 3
 
4.7%
용호3동 3
 
4.7%
용호2동 2
 
3.1%
문현동 2
 
3.1%
대연4동 2
 
3.1%
용당동 2
 
3.1%
Other values (34) 35
54.7%
2023-12-12T18:59:51.563987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 33
 
10.9%
31
 
10.3%
1 24
 
7.9%
- 23
 
7.6%
산 21
 
7.0%
용 18
 
6.0%
5 18
 
6.0%
3 16
 
5.3%
호 16
 
5.3%
2 15
 
5.0%
Other values (13) 87
28.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 128
42.4%
Other Letter 120
39.7%
Space Separator 31
 
10.3%
Dash Punctuation 23
 
7.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 33
27.5%
산 21
17.5%
용 18
15.0%
호 16
13.3%
연 8
 
6.7%
대 8
 
6.7%
문 6
 
5.0%
현 6
 
5.0%
당 2
 
1.7%
우 1
 
0.8%
Decimal Number
ValueCountFrequency (%)
1 24
18.8%
5 18
14.1%
3 16
12.5%
2 15
11.7%
7 15
11.7%
4 13
10.2%
9 13
10.2%
8 9
 
7.0%
0 3
 
2.3%
6 2
 
1.6%
Space Separator
ValueCountFrequency (%)
31
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 23
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 182
60.3%
Hangul 120
39.7%

Most frequent character per script

Common
ValueCountFrequency (%)
31
17.0%
1 24
13.2%
- 23
12.6%
5 18
9.9%
3 16
8.8%
2 15
8.2%
7 15
8.2%
4 13
7.1%
9 13
7.1%
8 9
 
4.9%
Other values (2) 5
 
2.7%
Hangul
ValueCountFrequency (%)
동 33
27.5%
산 21
17.5%
용 18
15.0%
호 16
13.3%
연 8
 
6.7%
대 8
 
6.7%
문 6
 
5.0%
현 6
 
5.0%
당 2
 
1.7%
우 1
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 182
60.3%
Hangul 120
39.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 33
27.5%
산 21
17.5%
용 18
15.0%
호 16
13.3%
연 8
 
6.7%
대 8
 
6.7%
문 6
 
5.0%
현 6
 
5.0%
당 2
 
1.7%
우 1
 
0.8%
ASCII
ValueCountFrequency (%)
31
17.0%
1 24
13.2%
- 23
12.6%
5 18
9.9%
3 16
8.8%
2 15
8.2%
7 15
8.2%
4 13
7.1%
9 13
7.1%
8 9
 
4.9%
Other values (2) 5
 
2.7%

변기수(남)
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)21.2%
Missing0
Missing (%)0.0%
Memory size396.0 B
1(1)
16 
1(2)
7 
2(3)
6 
3(3)
 
1
5(4)
 
1
Other values (2)
2 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique4 ?
Unique (%)12.1%

Sample

1st row3(3)
2nd row1(1)
3rd row1(1)
4th row1(1)
5th row1(2)

Common Values

ValueCountFrequency (%)
1(1) 16
48.5%
1(2) 7
21.2%
2(3) 6
 
18.2%
3(3) 1
 
3.0%
5(4) 1
 
3.0%
3(4) 1
 
3.0%
2(2) 1
 
3.0%

Length

2023-12-12T18:59:51.725640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:59:51.860498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1(1 16
48.5%
1(2 7
21.2%
2(3 6
 
18.2%
3(3 1
 
3.0%
5(4 1
 
3.0%
3(4 1
 
3.0%
2(2 1
 
3.0%

변기수(여)
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)21.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.969697
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size429.0 B
2023-12-12T18:59:51.988634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q34
95-th percentile7
Maximum8
Range7
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.7407766
Coefficient of variation (CV)0.58617986
Kurtosis1.893681
Mean2.969697
Median Absolute Deviation (MAD)1
Skewness1.5249613
Sum98
Variance3.030303
MonotonicityNot monotonic
2023-12-12T18:59:52.127252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2 16
48.5%
3 5
 
15.2%
4 4
 
12.1%
1 3
 
9.1%
7 2
 
6.1%
5 2
 
6.1%
8 1
 
3.0%
ValueCountFrequency (%)
1 3
 
9.1%
2 16
48.5%
3 5
 
15.2%
4 4
 
12.1%
5 2
 
6.1%
7 2
 
6.1%
8 1
 
3.0%
ValueCountFrequency (%)
8 1
 
3.0%
7 2
 
6.1%
5 2
 
6.1%
4 4
 
12.1%
3 5
 
15.2%
2 16
48.5%
1 3
 
9.1%

면적(제곱미터)
Real number (ℝ)

HIGH CORRELATION 

Distinct16
Distinct (%)48.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.803939
Minimum3.7
Maximum122.83
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size429.0 B
2023-12-12T18:59:52.305599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.7
5-th percentile4.98
Q111.5
median11.54
Q330
95-th percentile62.272
Maximum122.83
Range119.13
Interquartile range (IQR)18.5

Descriptive statistics

Standard deviation23.567622
Coefficient of variation (CV)1.0334891
Kurtosis9.9510732
Mean22.803939
Median Absolute Deviation (MAD)5.74
Skewness2.8518993
Sum752.53
Variance555.4328
MonotonicityNot monotonic
2023-12-12T18:59:52.486034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
11.5 5
15.2%
11.54 5
15.2%
19.5 3
9.1%
11.53 3
9.1%
5.8 3
9.1%
32.0 3
9.1%
28.0 2
 
6.1%
38.8 1
 
3.0%
55.04 1
 
3.0%
30.0 1
 
3.0%
Other values (6) 6
18.2%
ValueCountFrequency (%)
3.7 1
 
3.0%
3.75 1
 
3.0%
5.8 3
9.1%
11.5 5
15.2%
11.53 3
9.1%
11.54 5
15.2%
11.6 1
 
3.0%
19.5 3
9.1%
28.0 2
 
6.1%
30.0 1
 
3.0%
ValueCountFrequency (%)
122.83 1
 
3.0%
73.12 1
 
3.0%
55.04 1
 
3.0%
38.8 1
 
3.0%
36.0 1
 
3.0%
32.0 3
9.1%
30.0 1
 
3.0%
28.0 2
6.1%
19.5 3
9.1%
11.6 1
 
3.0%

처리방법
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Memory size396.0 B
포세식
18 
수세식
12 
자연발효식
3 

Length

Max length5
Median length3
Mean length3.1818182
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row수세식
2nd row포세식
3rd row포세식
4th row포세식
5th row포세식

Common Values

ValueCountFrequency (%)
포세식 18
54.5%
수세식 12
36.4%
자연발효식 3
 
9.1%

Length

2023-12-12T18:59:52.664267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:59:52.826517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
포세식 18
54.5%
수세식 12
36.4%
자연발효식 3
 
9.1%

설치연도(개선연도)
Text

Distinct29
Distinct (%)87.9%
Missing0
Missing (%)0.0%
Memory size396.0 B
2023-12-12T18:59:53.039552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length8.7272727
Min length4

Characters and Unicode

Total characters288
Distinct characters12
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

Unique26 ?
Unique (%)78.8%

Sample

1st row1986(2020)
2nd row2003(2014)
3rd row2002(2014)
4th row2003(2014)
5th row2005(2014)
ValueCountFrequency (%)
2005(2014 3
 
9.1%
2002(2013 2
 
6.1%
2003(2014 2
 
6.1%
2009(2018 1
 
3.0%
1986(2020 1
 
3.0%
2012(2019 1
 
3.0%
2021 1
 
3.0%
2014(2021 1
 
3.0%
2014 1
 
3.0%
2013 1
 
3.0%
Other values (19) 19
57.6%
2023-12-12T18:59:53.468012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 83
28.8%
2 72
25.0%
1 36
12.5%
( 26
 
9.0%
) 26
 
9.0%
4 9
 
3.1%
3 8
 
2.8%
8 8
 
2.8%
9 8
 
2.8%
5 6
 
2.1%
Other values (2) 6
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 236
81.9%
Open Punctuation 26
 
9.0%
Close Punctuation 26
 
9.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 83
35.2%
2 72
30.5%
1 36
15.3%
4 9
 
3.8%
3 8
 
3.4%
8 8
 
3.4%
9 8
 
3.4%
5 6
 
2.5%
7 3
 
1.3%
6 3
 
1.3%
Open Punctuation
ValueCountFrequency (%)
( 26
100.0%
Close Punctuation
ValueCountFrequency (%)
) 26
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 288
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 83
28.8%
2 72
25.0%
1 36
12.5%
( 26
 
9.0%
) 26
 
9.0%
4 9
 
3.1%
3 8
 
2.8%
8 8
 
2.8%
9 8
 
2.8%
5 6
 
2.1%
Other values (2) 6
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 288
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 83
28.8%
2 72
25.0%
1 36
12.5%
( 26
 
9.0%
) 26
 
9.0%
4 9
 
3.1%
3 8
 
2.8%
8 8
 
2.8%
9 8
 
2.8%
5 6
 
2.1%
Other values (2) 6
 
2.1%

Interactions

2023-12-12T18:59:49.148225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:59:48.491488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:59:48.803721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:59:49.285579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:59:48.595120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:59:48.899344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:59:49.409701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:59:48.699625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:59:49.002383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T18:59:53.617533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번공중화장실명소재지변기수(남)변기수(여)면적(제곱미터)처리방법설치연도(개선연도)
연번1.0001.0000.7570.5430.0000.3320.4920.923
공중화장실명1.0001.0001.0001.0001.0001.0001.0001.000
소재지0.7571.0001.0000.9871.0000.0001.0000.915
변기수(남)0.5431.0000.9871.0000.9450.8820.6320.964
변기수(여)0.0001.0001.0000.9451.0000.8580.6080.956
면적(제곱미터)0.3321.0000.0000.8820.8581.0000.8840.929
처리방법0.4921.0001.0000.6320.6080.8841.0000.961
설치연도(개선연도)0.9231.0000.9150.9640.9560.9290.9611.000
2023-12-12T18:59:53.764473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
변기수(남)처리방법
변기수(남)1.0000.489
처리방법0.4891.000
2023-12-12T18:59:53.880123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번변기수(여)면적(제곱미터)변기수(남)처리방법
연번1.0000.3290.1850.2660.290
변기수(여)0.3291.0000.9110.6410.463
면적(제곱미터)0.1850.9111.0000.7470.561
변기수(남)0.2660.6410.7471.0000.489
처리방법0.2900.4630.5610.4891.000

Missing values

2023-12-12T18:59:49.557443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T18:59:49.682668image/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

연번공중화장실명소재지변기수(남)변기수(여)면적(제곱미터)처리방법설치연도(개선연도)
01유엔기념공원 주차장대연4동 779-33(3)338.8수세식1986(2020)
12우룡산공원대연1동 산165-91(1)211.54포세식2003(2014)
23신선대유원지(임도입구)용호4동 산241-71(1)211.54포세식2002(2014)
34신선대유원지(중턱)용당동 산1701(1)211.54포세식2003(2014)
45이기대공원(큰고개쉼터)용호동산118-21(2)319.5포세식2005(2014)
56이기대공원(매점옆등산로)용호동산28-11(2)211.53포세식2001(2013)
67이기대공원(공룡발자국입구)용호동 산 271(2)211.53포세식2002(2013)
78이기대공원(관리초소입구)용호동 산28-11(2)319.5포세식2002(2020)
89대연5동 체육공원대연5동 산127-51(1)13.7포세식2007(2015)
910용호1동 체육공원용호1동 산591(1)25.8자연발효식1994(2018)
연번공중화장실명소재지변기수(남)변기수(여)면적(제곱미터)처리방법설치연도(개선연도)
2324문현4동 체육공원문현4동 산1381(1)211.5포세식2010(2018)
2425벚꽃약수터옆(황령산)문현1동 산18-51(1)211.5포세식2010(2017)
2526백운포체육공원(인조잔디구장)용호4동 895-32(3)455.04수세식2011
2627황령산유원지생태숲(입구)문현동 산212(2)432.0수세식2018
2728섶자리용호3동 9731(2)319.5포세식2012
2829황령산봉수대대연3동 산53-11(2)211.53포세식2013
2930이기대동생말용호3동 5-71(1)211.6수세식2014
3031해파랑길용호2동 950-12(3)730.0수세식2014(2021)
3132용호별빛공원용호동 5-52(3)528.0수세식2021
3233황령산유원지생태숲(상부)문현동 산19-21(2)328.0수세식2022