Overview

Dataset statistics

Number of variables9
Number of observations36
Missing cells2
Missing cells (%)0.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.8 KiB
Average record size in memory78.7 B

Variable types

Numeric3
Text2
DateTime1
Categorical2
Boolean1

Dataset

Description안양시 관내 빗물이용시설 현황(위치, 시설명, 주소, 설치시기, 집수면적(제곱미터), 처리방법, 저류조용량(제곱미터), 활용용도, 법적시설여부) 입니다.
URLhttps://www.data.go.kr/data/15114088/fileData.do

Alerts

처리방법 is highly overall correlated with 법적시설여부High correlation
법적시설여부 is highly overall correlated with 처리방법High correlation
법적시설여부 is highly imbalanced (57.8%)Imbalance
저류조용량(제곱미터) has 1 (2.8%) missing valuesMissing
법적시설여부 has 1 (2.8%) missing valuesMissing
구분 has unique valuesUnique
시설명 has unique valuesUnique
집수면적(제곱미터) has unique valuesUnique

Reproduction

Analysis started2023-12-12 16:43:33.131606
Analysis finished2023-12-12 16:43:34.971730
Duration1.84 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Real number (ℝ)

UNIQUE 

Distinct36
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.5
Minimum1
Maximum36
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-13T01:43:35.048549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.75
Q19.75
median18.5
Q327.25
95-th percentile34.25
Maximum36
Range35
Interquartile range (IQR)17.5

Descriptive statistics

Standard deviation10.535654
Coefficient of variation (CV)0.5694948
Kurtosis-1.2
Mean18.5
Median Absolute Deviation (MAD)9
Skewness0
Sum666
Variance111
MonotonicityStrictly increasing
2023-12-13T01:43:35.204416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
1 1
 
2.8%
20 1
 
2.8%
22 1
 
2.8%
23 1
 
2.8%
24 1
 
2.8%
25 1
 
2.8%
26 1
 
2.8%
27 1
 
2.8%
28 1
 
2.8%
29 1
 
2.8%
Other values (26) 26
72.2%
ValueCountFrequency (%)
1 1
2.8%
2 1
2.8%
3 1
2.8%
4 1
2.8%
5 1
2.8%
6 1
2.8%
7 1
2.8%
8 1
2.8%
9 1
2.8%
10 1
2.8%
ValueCountFrequency (%)
36 1
2.8%
35 1
2.8%
34 1
2.8%
33 1
2.8%
32 1
2.8%
31 1
2.8%
30 1
2.8%
29 1
2.8%
28 1
2.8%
27 1
2.8%

시설명
Text

UNIQUE 

Distinct36
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size420.0 B
2023-12-13T01:43:35.463836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length13
Mean length7.9722222
Min length4

Characters and Unicode

Total characters287
Distinct characters131
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

Unique36 ?
Unique (%)100.0%

Sample

1st row박달복합청사
2nd row평촌어바인퍼스트
3rd row비산1동 행정복지센터
4th rowLS타워
5th row경인교대
ValueCountFrequency (%)
행정복지센터 4
 
9.3%
박달복합청사 1
 
2.3%
fine 1
 
2.3%
호암초교 1
 
2.3%
화창초교 1
 
2.3%
안양공공하수처리장 1
 
2.3%
관양1동 1
 
2.3%
평촌더샵아이파크아파트 1
 
2.3%
석수3동 1
 
2.3%
아이에스비즈타워 1
 
2.3%
Other values (30) 30
69.8%
2023-12-13T01:43:35.811150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
교 11
 
3.8%
트 9
 
3.1%
아 9
 
3.1%
안 7
 
2.4%
파 7
 
2.4%
7
 
2.4%
산 7
 
2.4%
센 7
 
2.4%
복 6
 
2.1%
양 6
 
2.1%
Other values (121) 211
73.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 264
92.0%
Space Separator 7
 
2.4%
Uppercase Letter 6
 
2.1%
Decimal Number 4
 
1.4%
Open Punctuation 2
 
0.7%
Close Punctuation 2
 
0.7%
Lowercase Letter 1
 
0.3%
Dash Punctuation 1
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
교 11
 
4.2%
트 9
 
3.4%
아 9
 
3.4%
안 7
 
2.7%
파 7
 
2.7%
산 7
 
2.7%
센 7
 
2.7%
복 6
 
2.3%
양 6
 
2.3%
지 6
 
2.3%
Other values (107) 189
71.6%
Uppercase Letter
ValueCountFrequency (%)
F 1
16.7%
I 1
16.7%
N 1
16.7%
E 1
16.7%
S 1
16.7%
L 1
16.7%
Decimal Number
ValueCountFrequency (%)
1 2
50.0%
3 1
25.0%
2 1
25.0%
Space Separator
ValueCountFrequency (%)
7
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 1
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 264
92.0%
Common 16
 
5.6%
Latin 7
 
2.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
교 11
 
4.2%
트 9
 
3.4%
아 9
 
3.4%
안 7
 
2.7%
파 7
 
2.7%
산 7
 
2.7%
센 7
 
2.7%
복 6
 
2.3%
양 6
 
2.3%
지 6
 
2.3%
Other values (107) 189
71.6%
Common
ValueCountFrequency (%)
7
43.8%
( 2
 
12.5%
) 2
 
12.5%
1 2
 
12.5%
3 1
 
6.2%
2 1
 
6.2%
- 1
 
6.2%
Latin
ValueCountFrequency (%)
F 1
14.3%
I 1
14.3%
N 1
14.3%
E 1
14.3%
e 1
14.3%
S 1
14.3%
L 1
14.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 264
92.0%
ASCII 23
 
8.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
교 11
 
4.2%
트 9
 
3.4%
아 9
 
3.4%
안 7
 
2.7%
파 7
 
2.7%
산 7
 
2.7%
센 7
 
2.7%
복 6
 
2.3%
양 6
 
2.3%
지 6
 
2.3%
Other values (107) 189
71.6%
ASCII
ValueCountFrequency (%)
7
30.4%
( 2
 
8.7%
) 2
 
8.7%
1 2
 
8.7%
F 1
 
4.3%
I 1
 
4.3%
3 1
 
4.3%
N 1
 
4.3%
E 1
 
4.3%
2 1
 
4.3%
Other values (4) 4
17.4%

주소
Text

Distinct35
Distinct (%)97.2%
Missing0
Missing (%)0.0%
Memory size420.0 B
2023-12-13T01:43:36.041173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length29
Median length26
Mean length21.833333
Min length19

Characters and Unicode

Total characters786
Distinct characters47
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

Unique34 ?
Unique (%)94.4%

Sample

1st row경기도 안양시 만안구 박달동 141-2
2nd row경기도 안양시 동안구 호계동 956
3rd row경기도 안양시 동안구 비산동 488-15 등 16필지
4th row경기도 안양시 동안구 호계1동 1026-6
5th row경기도 안양시 만안구 석수1동 산6-8
ValueCountFrequency (%)
경기도 36
19.4%
안양시 36
19.4%
동안구 22
 
11.8%
만안구 14
 
7.5%
호계동 6
 
3.2%
비산동 4
 
2.2%
석수1동 3
 
1.6%
안양동 3
 
1.6%
관양동 3
 
1.6%
일원 2
 
1.1%
Other values (53) 57
30.6%
2023-12-13T01:43:36.414769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
150
19.1%
안 77
 
9.8%
동 53
 
6.7%
양 45
 
5.7%
1 40
 
5.1%
경 37
 
4.7%
시 37
 
4.7%
도 36
 
4.6%
기 36
 
4.6%
구 36
 
4.6%
Other values (37) 239
30.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 452
57.5%
Decimal Number 158
 
20.1%
Space Separator 150
 
19.1%
Dash Punctuation 25
 
3.2%
Other Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
안 77
17.0%
동 53
11.7%
양 45
10.0%
경 37
8.2%
시 37
8.2%
도 36
8.0%
기 36
8.0%
구 36
8.0%
만 14
 
3.1%
산 10
 
2.2%
Other values (24) 71
15.7%
Decimal Number
ValueCountFrequency (%)
1 40
25.3%
2 21
13.3%
0 17
10.8%
6 16
 
10.1%
5 14
 
8.9%
8 13
 
8.2%
4 11
 
7.0%
3 9
 
5.7%
9 9
 
5.7%
7 8
 
5.1%
Space Separator
ValueCountFrequency (%)
150
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 25
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 452
57.5%
Common 334
42.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
안 77
17.0%
동 53
11.7%
양 45
10.0%
경 37
8.2%
시 37
8.2%
도 36
8.0%
기 36
8.0%
구 36
8.0%
만 14
 
3.1%
산 10
 
2.2%
Other values (24) 71
15.7%
Common
ValueCountFrequency (%)
150
44.9%
1 40
 
12.0%
- 25
 
7.5%
2 21
 
6.3%
0 17
 
5.1%
6 16
 
4.8%
5 14
 
4.2%
8 13
 
3.9%
4 11
 
3.3%
3 9
 
2.7%
Other values (3) 18
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 452
57.5%
ASCII 334
42.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
150
44.9%
1 40
 
12.0%
- 25
 
7.5%
2 21
 
6.3%
0 17
 
5.1%
6 16
 
4.8%
5 14
 
4.2%
8 13
 
3.9%
4 11
 
3.3%
3 9
 
2.7%
Other values (3) 18
 
5.4%
Hangul
ValueCountFrequency (%)
안 77
17.0%
동 53
11.7%
양 45
10.0%
경 37
8.2%
시 37
8.2%
도 36
8.0%
기 36
8.0%
구 36
8.0%
만 14
 
3.1%
산 10
 
2.2%
Other values (24) 71
15.7%

설치시기
Date

Distinct33
Distinct (%)91.7%
Missing0
Missing (%)0.0%
Memory size420.0 B
Minimum1992-03-01 00:00:00
Maximum2021-11-30 00:00:00
2023-12-13T01:43:36.569644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:43:36.707721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)

집수면적(제곱미터)
Real number (ℝ)

UNIQUE 

Distinct36
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4075.0417
Minimum90
Maximum23370.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-13T01:43:36.823925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum90
5-th percentile567.5
Q11283
median2287.5
Q34561.75
95-th percentile16740.25
Maximum23370.9
Range23280.9
Interquartile range (IQR)3278.75

Descriptive statistics

Standard deviation5129.2053
Coefficient of variation (CV)1.2586878
Kurtosis6.2789024
Mean4075.0417
Median Absolute Deviation (MAD)1152.5
Skewness2.4975171
Sum146701.5
Variance26308747
MonotonicityNot monotonic
2023-12-13T01:43:36.969118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
1798.2 1
 
2.8%
1460.0 1
 
2.8%
2875.0 1
 
2.8%
2500.0 1
 
2.8%
2484.0 1
 
2.8%
590.0 1
 
2.8%
6800.0 1
 
2.8%
1321.0 1
 
2.8%
788.0 1
 
2.8%
1769.0 1
 
2.8%
Other values (26) 26
72.2%
ValueCountFrequency (%)
90.0 1
2.8%
500.0 1
2.8%
590.0 1
2.8%
698.6 1
2.8%
788.0 1
2.8%
829.5 1
2.8%
1047.8 1
2.8%
1095.0 1
2.8%
1232.0 1
2.8%
1300.0 1
2.8%
ValueCountFrequency (%)
23370.9 1
2.8%
17107.0 1
2.8%
16618.0 1
2.8%
9781.9 1
2.8%
7122.0 1
2.8%
6800.0 1
2.8%
6630.0 1
2.8%
4900.0 1
2.8%
4735.0 1
2.8%
4504.0 1
2.8%

처리방법
Categorical

HIGH CORRELATION 

Distinct11
Distinct (%)30.6%
Missing0
Missing (%)0.0%
Memory size420.0 B
스크린
13 
필터
9 
스크린, 산화수처리장치
6 
초기우수배제, AOP처리방법
 
1
소독
 
1
Other values (6)
6 

Length

Max length17
Median length15
Mean length5.4444444
Min length2

Unique

Unique8 ?
Unique (%)22.2%

Sample

1st row스크린, 산화수처리장치
2nd row스크린, 산화수처리장치
3rd row초기우수배제, AOP처리방법
4th row필터
5th row스크린

Common Values

ValueCountFrequency (%)
스크린 13
36.1%
필터 9
25.0%
스크린, 산화수처리장치 6
16.7%
초기우수배제, AOP처리방법 1
 
2.8%
소독 1
 
2.8%
집수정,스크린 1
 
2.8%
초기우수배제, 여과 1
 
2.8%
미세여과방식 1
 
2.8%
초기우수배제 1
 
2.8%
<NA> 1
 
2.8%

Length

2023-12-13T01:43:37.115222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
스크린 19
42.2%
필터 9
20.0%
산화수처리장치 6
 
13.3%
초기우수배제 3
 
6.7%
aop처리방법 1
 
2.2%
소독 1
 
2.2%
집수정,스크린 1
 
2.2%
여과 1
 
2.2%
미세여과방식 1
 
2.2%
na 1
 
2.2%
Other values (2) 2
 
4.4%

저류조용량(제곱미터)
Real number (ℝ)

MISSING 

Distinct32
Distinct (%)91.4%
Missing1
Missing (%)2.8%
Infinite0
Infinite (%)0.0%
Mean210.77371
Minimum4
Maximum830
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-13T01:43:37.259850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile12
Q138
median78
Q3374.5
95-th percentile779
Maximum830
Range826
Interquartile range (IQR)336.5

Descriptive statistics

Standard deviation249.22644
Coefficient of variation (CV)1.1824361
Kurtosis0.63148596
Mean210.77371
Median Absolute Deviation (MAD)58
Skewness1.3446328
Sum7377.08
Variance62113.82
MonotonicityNot monotonic
2023-12-13T01:43:37.420189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
100.0 2
 
5.6%
20.0 2
 
5.6%
12.0 2
 
5.6%
36.0 1
 
2.8%
60.0 1
 
2.8%
30.0 1
 
2.8%
438.0 1
 
2.8%
78.0 1
 
2.8%
55.0 1
 
2.8%
135.0 1
 
2.8%
Other values (22) 22
61.1%
ValueCountFrequency (%)
4.0 1
2.8%
12.0 2
5.6%
15.0 1
2.8%
20.0 2
5.6%
28.0 1
2.8%
30.0 1
2.8%
36.0 1
2.8%
40.0 1
2.8%
50.0 1
2.8%
55.0 1
2.8%
ValueCountFrequency (%)
830.0 1
2.8%
800.0 1
2.8%
770.0 1
2.8%
603.0 1
2.8%
534.08 1
2.8%
500.0 1
2.8%
438.0 1
2.8%
420.0 1
2.8%
399.0 1
2.8%
350.0 1
2.8%

활용용도
Categorical

Distinct14
Distinct (%)38.9%
Missing0
Missing (%)0.0%
Memory size420.0 B
조경
18 
조경, 소화
2 
화장실
2 
화장실,조경
2 
조경,청소
2 
Other values (9)
10 

Length

Max length8
Median length2
Mean length3.7222222
Min length2

Unique

Unique8 ?
Unique (%)22.2%

Sample

1st row조경
2nd row조경
3rd row조경,화장실
4th row조경, 살수
5th row조경

Common Values

ValueCountFrequency (%)
조경 18
50.0%
조경, 소화 2
 
5.6%
화장실 2
 
5.6%
화장실,조경 2
 
5.6%
조경,청소 2
 
5.6%
조경, 청소 2
 
5.6%
조경,화장실 1
 
2.8%
조경, 살수 1
 
2.8%
분수 1
 
2.8%
조경, 수변 1
 
2.8%
Other values (4) 4
 
11.1%

Length

2023-12-13T01:43:37.567105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
조경 26
57.8%
화장실 4
 
8.9%
소화 2
 
4.4%
화장실,조경 2
 
4.4%
조경,청소 2
 
4.4%
청소 2
 
4.4%
조경,화장실 1
 
2.2%
살수 1
 
2.2%
분수 1
 
2.2%
수변 1
 
2.2%
Other values (3) 3
 
6.7%

법적시설여부
Boolean

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)5.7%
Missing1
Missing (%)2.8%
Memory size204.0 B
False
32 
True
 
3
(Missing)
 
1
ValueCountFrequency (%)
False 32
88.9%
True 3
 
8.3%
(Missing) 1
 
2.8%
2023-12-13T01:43:37.667081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Interactions

2023-12-13T01:43:34.256302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:43:33.623365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:43:33.966808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:43:34.378151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:43:33.754961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:43:34.054142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:43:34.482797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:43:33.868582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:43:34.150009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T01:43:37.738615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분시설명주소설치시기집수면적(제곱미터)처리방법저류조용량(제곱미터)활용용도법적시설여부
구분1.0001.0001.0000.7250.0000.3210.4240.0000.553
시설명1.0001.0001.0001.0001.0001.0001.0001.0001.000
주소1.0001.0001.0000.9760.7660.0001.0000.9681.000
설치시기0.7251.0000.9761.0000.8941.0000.0000.9361.000
집수면적(제곱미터)0.0001.0000.7660.8941.0000.0000.0000.0000.441
처리방법0.3211.0000.0001.0000.0001.0000.2320.0000.598
저류조용량(제곱미터)0.4241.0001.0000.0000.0000.2321.0000.0000.259
활용용도0.0001.0000.9680.9360.0000.0000.0001.0000.000
법적시설여부0.5531.0001.0001.0000.4410.5980.2590.0001.000
2023-12-13T01:43:37.859039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
처리방법법적시설여부활용용도
처리방법1.0000.5280.000
법적시설여부0.5281.0000.000
활용용도0.0000.0001.000
2023-12-13T01:43:37.985011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분집수면적(제곱미터)저류조용량(제곱미터)처리방법활용용도법적시설여부
구분1.000-0.0840.0120.1020.0580.363
집수면적(제곱미터)-0.0841.000-0.0830.0000.0000.431
저류조용량(제곱미터)0.012-0.0831.0000.0630.0000.159
처리방법0.1020.0000.0631.0000.0000.528
활용용도0.0580.0000.0000.0001.0000.000
법적시설여부0.3630.4310.1590.5280.0001.000

Missing values

2023-12-13T01:43:34.603748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T01:43:34.792759image/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.
2023-12-13T01:43:34.913887image/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

구분시설명주소설치시기집수면적(제곱미터)처리방법저류조용량(제곱미터)활용용도법적시설여부
01박달복합청사경기도 안양시 만안구 박달동 141-22020-08-311798.2스크린, 산화수처리장치770.0조경Y
12평촌어바인퍼스트경기도 안양시 동안구 호계동 9562020-11-2723370.9스크린, 산화수처리장치75.0조경Y
23비산1동 행정복지센터경기도 안양시 동안구 비산동 488-15 등 16필지2021-03-15698.6초기우수배제, AOP처리방법150.0조경,화장실N
34LS타워경기도 안양시 동안구 호계1동 1026-62008-04-012091.0필터830.0조경, 살수N
45경인교대경기도 안양시 만안구 석수1동 산6-82006-01-0116618.0스크린63.0조경N
56경인교대(기숙사)경기도 안양시 만안구 석수1동 산6-82016-12-011095.0소독40.0조경, 소화N
67나눔초교경기도 안양시 동안구 평촌동 108-12006-03-011612.0필터50.0분수N
78비산-e편한세상경기도 안양시 동안구 비산동 11652008-12-01500.0필터350.0조경, 수변N
89두산벤처다임경기도 안양시 동안구 평촌동 126-12006-03-016630.0필터800.0조경N
910래미안안양메가트리아아파트경기도 안양시 만안구 안양천서로 1772016-11-017122.0스크린4.0조경N
구분시설명주소설치시기집수면적(제곱미터)처리방법저류조용량(제곱미터)활용용도법적시설여부
2627호계대성유니드아파트경기도 안양시 동안구 호계동 891-62019-03-131321.0스크린36.0조경N
2728석수3동 행정복지센터경기도 안양시 만안구 충훈로 90번길 362019-08-24788.0스크린, 산화수처리장치55.0조경, 청소N
2829FINE 사옥경기도 안양시 만안구 안양동 200-72020-02-101769.0스크린100.0조경N
2930정보사 주거시설경기도 안양시 만안구 석수동 283-22020-01-141300.0스크린28.0화장실, 조경N
3031비산2동 행정복지센터경기도 안양시 동안구 비산동 414-52020-07-30829.5스크린, 산화수처리장치500.0조경N
3132안양씨엘포레자이아파트경기도 안양시 만안구 안양동 585-22020-12-169781.9스크린, 산화수처리장치164.0조경N
3233에이스하이테크시티 지식산업센터경기도 안양시 동안구 호계동 900, 900-22021-02-014735.0스크린56.0조경N
3334한국계측기기연구센터경기도 안양시 동안구 관양동 1664-22021-03-101047.8초기우수배제534.08조경Y
3435아이에스비즈타워경기도 안양시 만안구 안양동 1892021-11-3017107.0<NA>399.0조경N
3536평촌두산위브리버뷰아파트(구사거리지구)경기도 안양시 동안구 호계동 13092021-09-301622.6초기빗물 배제+스크린+여과+살균<NA><NA><NA>