Overview

Dataset statistics

Number of variables8
Number of observations89
Missing cells7
Missing cells (%)1.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.8 KiB
Average record size in memory66.5 B

Variable types

Categorical4
Text3
Numeric1

Dataset

Description인천광역시 미추홀구의 산업폐기물 배출자 신고자현황에 대한 데이터로 사업장명, 소재지,전화번호, 주요품목, 처리방법 등의 항목을 포함하여 제공합니다.
URLhttps://www.data.go.kr/data/15060158/fileData.do

Alerts

유형 is highly overall correlated with 소재지 and 2 other fieldsHigh correlation
소재지 is highly overall correlated with 유형High correlation
주요품목 is highly overall correlated with 유형 and 1 other fieldsHigh correlation
처리방법 is highly overall correlated with 유형 and 1 other fieldsHigh correlation
전화번호 has 7 (7.9%) missing valuesMissing

Reproduction

Analysis started2023-12-12 08:51:52.823150
Analysis finished2023-12-12 08:51:54.122162
Duration1.3 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

유형
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size844.0 B
사업장폐기물
54 
지정폐기물
35 

Length

Max length6
Median length6
Mean length5.6067416
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
사업장폐기물 54
60.7%
지정폐기물 35
39.3%

Length

2023-12-12T17:51:54.215580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T17:51:54.366263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
사업장폐기물 54
60.7%
지정폐기물 35
39.3%

상호(사업장명)
Text

Distinct85
Distinct (%)95.5%
Missing0
Missing (%)0.0%
Memory size844.0 B
2023-12-12T17:51:54.690254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length14
Mean length7.5842697
Min length3

Characters and Unicode

Total characters675
Distinct characters192
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

Unique81 ?
Unique (%)91.0%

Sample

1st row한국세차장
2nd row(사)인천자동차검사 정비사업조합
3rd row인천IT타워관리위원회
4th row인하대학교
5th row태원엔진
ValueCountFrequency (%)
인하공업전문대학 2
 
1.9%
인천대학교 2
 
1.9%
인천광역시 2
 
1.9%
상수도사업본부 2
 
1.9%
수도시설관리소 2
 
1.9%
인하대학교 2
 
1.9%
㈜에스엔씨 2
 
1.9%
제물포캠퍼스 1
 
0.9%
㈜코아링크 1
 
0.9%
백송전자㈜ 1
 
0.9%
Other values (90) 90
84.1%
2023-12-12T17:51:55.236741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
㈜ 46
 
6.8%
인 32
 
4.7%
19
 
2.8%
천 18
 
2.7%
업 16
 
2.4%
스 15
 
2.2%
이 14
 
2.1%
대 14
 
2.1%
공 12
 
1.8%
학 11
 
1.6%
Other values (182) 478
70.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 599
88.7%
Other Symbol 46
 
6.8%
Space Separator 19
 
2.8%
Open Punctuation 4
 
0.6%
Close Punctuation 4
 
0.6%
Uppercase Letter 2
 
0.3%
Decimal Number 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
인 32
 
5.3%
천 18
 
3.0%
업 16
 
2.7%
스 15
 
2.5%
이 14
 
2.3%
대 14
 
2.3%
공 12
 
2.0%
학 11
 
1.8%
에 10
 
1.7%
지 10
 
1.7%
Other values (175) 447
74.6%
Uppercase Letter
ValueCountFrequency (%)
T 1
50.0%
I 1
50.0%
Other Symbol
ValueCountFrequency (%)
㈜ 46
100.0%
Space Separator
ValueCountFrequency (%)
19
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%
Decimal Number
ValueCountFrequency (%)
1 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 645
95.6%
Common 28
 
4.1%
Latin 2
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
㈜ 46
 
7.1%
인 32
 
5.0%
천 18
 
2.8%
업 16
 
2.5%
스 15
 
2.3%
이 14
 
2.2%
대 14
 
2.2%
공 12
 
1.9%
학 11
 
1.7%
에 10
 
1.6%
Other values (176) 457
70.9%
Common
ValueCountFrequency (%)
19
67.9%
( 4
 
14.3%
) 4
 
14.3%
1 1
 
3.6%
Latin
ValueCountFrequency (%)
T 1
50.0%
I 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 599
88.7%
None 46
 
6.8%
ASCII 30
 
4.4%

Most frequent character per block

None
ValueCountFrequency (%)
㈜ 46
100.0%
Hangul
ValueCountFrequency (%)
인 32
 
5.3%
천 18
 
3.0%
업 16
 
2.7%
스 15
 
2.5%
이 14
 
2.3%
대 14
 
2.3%
공 12
 
2.0%
학 11
 
1.8%
에 10
 
1.7%
지 10
 
1.7%
Other values (175) 447
74.6%
ASCII
ValueCountFrequency (%)
19
63.3%
( 4
 
13.3%
) 4
 
13.3%
T 1
 
3.3%
I 1
 
3.3%
1 1
 
3.3%

사업자등록번호
Text

Distinct84
Distinct (%)94.4%
Missing0
Missing (%)0.0%
Memory size844.0 B
2023-12-12T17:51:55.544728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters1068
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique79 ?
Unique (%)88.8%

Sample

1st row131-02-21676
2nd row131-82-02346
3rd row121-82-65801
4th row131-82-00372
5th row121-16-86913
ValueCountFrequency (%)
131-82-00445 2
 
2.2%
121-81-41573 2
 
2.2%
829-82-00256 2
 
2.2%
131-82-00372 2
 
2.2%
220-81-60348 2
 
2.2%
121-81-38874 1
 
1.1%
130-81-68654 1
 
1.1%
137-81-30805 1
 
1.1%
137-81-74439 1
 
1.1%
131-82-15711 1
 
1.1%
Other values (74) 74
83.1%
2023-12-12T17:51:56.047181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 201
18.8%
- 178
16.7%
0 123
11.5%
8 120
11.2%
3 115
10.8%
2 90
8.4%
5 63
 
5.9%
6 51
 
4.8%
7 50
 
4.7%
4 47
 
4.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 890
83.3%
Dash Punctuation 178
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 201
22.6%
0 123
13.8%
8 120
13.5%
3 115
12.9%
2 90
10.1%
5 63
 
7.1%
6 51
 
5.7%
7 50
 
5.6%
4 47
 
5.3%
9 30
 
3.4%
Dash Punctuation
ValueCountFrequency (%)
- 178
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1068
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 201
18.8%
- 178
16.7%
0 123
11.5%
8 120
11.2%
3 115
10.8%
2 90
8.4%
5 63
 
5.9%
6 51
 
4.8%
7 50
 
4.7%
4 47
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1068
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 201
18.8%
- 178
16.7%
0 123
11.5%
8 120
11.2%
3 115
10.8%
2 90
8.4%
5 63
 
5.9%
6 51
 
4.8%
7 50
 
4.7%
4 47
 
4.4%

소재지
Categorical

HIGH CORRELATION 

Distinct11
Distinct (%)12.4%
Missing0
Missing (%)0.0%
Memory size844.0 B
인천광역시 미추홀구 도화동
36 
인천광역시 미추홀구 주안동
16 
인천광역시 미추홀구 주안동
11 
인천광역시 미추홀구 용현동
9 
인천광역시 미추홀구 학익동
8 
Other values (6)
9 

Length

Max length15
Median length14
Mean length14.179775
Min length14

Unique

Unique3 ?
Unique (%)3.4%

Sample

1st row인천광역시 미추홀구 학익동
2nd row인천광역시 미추홀구 주안동
3rd row인천광역시 미추홀구 도화동
4th row인천광역시 미추홀구 용현동
5th row인천광역시 미추홀구 학익동

Common Values

ValueCountFrequency (%)
인천광역시 미추홀구 도화동 36
40.4%
인천광역시 미추홀구 주안동 16
18.0%
인천광역시 미추홀구 주안동 11
 
12.4%
인천광역시 미추홀구 용현동 9
 
10.1%
인천광역시 미추홀구 학익동 8
 
9.0%
인천광역시 미추홀구 학익동 2
 
2.2%
인천광역시 미추홀구 관교동 2
 
2.2%
인천광역시 미추홀구 숭의동 2
 
2.2%
인천광역시 미추홀구 문학동 1
 
1.1%
인천광역시 미추홀구 문학동 1
 
1.1%

Length

2023-12-12T17:51:56.237695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
인천광역시 89
33.3%
미추홀구 89
33.3%
도화동 36
13.5%
주안동 27
 
10.1%
학익동 10
 
3.7%
용현동 9
 
3.4%
관교동 3
 
1.1%
숭의동 2
 
0.7%
문학동 2
 
0.7%

전화번호
Text

MISSING 

Distinct76
Distinct (%)92.7%
Missing7
Missing (%)7.9%
Memory size844.0 B
2023-12-12T17:51:56.553691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length11.97561
Min length9

Characters and Unicode

Total characters982
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique70 ?
Unique (%)85.4%

Sample

1st row032-832-8304
2nd row032-420-1171
3rd row032-255-6995
4th row032-860-7107
5th row032-833-4507
ValueCountFrequency (%)
032-363-9785 2
 
2.4%
032-870-2062 2
 
2.4%
032-865-8441 2
 
2.4%
032-860-7107 2
 
2.4%
032-864-7013 2
 
2.4%
032-760-0307 2
 
2.4%
032-876-2731 1
 
1.2%
032-579-4100 1
 
1.2%
032-719-2018 1
 
1.2%
032-868-9928 1
 
1.2%
Other values (66) 66
80.5%
2023-12-12T17:51:57.105521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 163
16.6%
0 155
15.8%
3 145
14.8%
2 130
13.2%
8 82
8.4%
7 70
7.1%
5 60
 
6.1%
6 53
 
5.4%
1 53
 
5.4%
4 45
 
4.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 819
83.4%
Dash Punctuation 163
 
16.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 155
18.9%
3 145
17.7%
2 130
15.9%
8 82
10.0%
7 70
8.5%
5 60
 
7.3%
6 53
 
6.5%
1 53
 
6.5%
4 45
 
5.5%
9 26
 
3.2%
Dash Punctuation
ValueCountFrequency (%)
- 163
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 982
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 163
16.6%
0 155
15.8%
3 145
14.8%
2 130
13.2%
8 82
8.4%
7 70
7.1%
5 60
 
6.1%
6 53
 
5.4%
1 53
 
5.4%
4 45
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 982
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 163
16.6%
0 155
15.8%
3 145
14.8%
2 130
13.2%
8 82
8.4%
7 70
7.1%
5 60
 
6.1%
6 53
 
5.4%
1 53
 
5.4%
4 45
 
4.6%

신고연도
Real number (ℝ)

Distinct23
Distinct (%)25.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2012.4719
Minimum1996
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size933.0 B
2023-12-12T17:51:57.271680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1996
5-th percentile2001.8
Q12007
median2013
Q32019
95-th percentile2022
Maximum2023
Range27
Interquartile range (IQR)12

Descriptive statistics

Standard deviation7.0824525
Coefficient of variation (CV)0.0035192802
Kurtosis-0.92336373
Mean2012.4719
Median Absolute Deviation (MAD)6
Skewness-0.3685338
Sum179110
Variance50.161134
MonotonicityNot monotonic
2023-12-12T17:51:57.413375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
2021 8
 
9.0%
2004 7
 
7.9%
2012 7
 
7.9%
2003 6
 
6.7%
2018 6
 
6.7%
2011 5
 
5.6%
2019 5
 
5.6%
2022 4
 
4.5%
2007 4
 
4.5%
2020 4
 
4.5%
Other values (13) 33
37.1%
ValueCountFrequency (%)
1996 1
 
1.1%
1998 3
3.4%
2001 1
 
1.1%
2003 6
6.7%
2004 7
7.9%
2005 3
3.4%
2007 4
4.5%
2008 3
3.4%
2009 2
 
2.2%
2010 2
 
2.2%
ValueCountFrequency (%)
2023 2
 
2.2%
2022 4
4.5%
2021 8
9.0%
2020 4
4.5%
2019 5
5.6%
2018 6
6.7%
2017 4
4.5%
2016 4
4.5%
2015 3
 
3.4%
2014 2
 
2.2%

주요품목
Categorical

HIGH CORRELATION 

Distinct44
Distinct (%)49.4%
Missing0
Missing (%)0.0%
Memory size844.0 B
폐합성수지
19 
폐수처리오니
11 
폐황산이포함된2차폐축전지
6 
그밖의폐광물유
 
4
그 밖의 폐산
 
3
Other values (39)
46 

Length

Max length53
Median length39
Mean length9.3483146
Min length2

Unique

Unique33 ?
Unique (%)37.1%

Sample

1st row폐윤활유+폐유기용제+그 밖의 폐광물류+폐오일필터
2nd row폐황산이 포함된 2차 폐축전지+그 밖의 폐유기용제+폐페인트+폐윤활유+그 밖의 폐광물유+폐오일필터
3rd row그 밖의 폐산
4th row그 밖의 폐유기용제+그 밖의 폐산+그 밖의 폐알칼리+ 연구+검사용 폐시약
5th row폐윤활유+그 밖의 폐광물유+폐오일필터

Common Values

ValueCountFrequency (%)
폐합성수지 19
21.3%
폐수처리오니 11
 
12.4%
폐황산이포함된2차폐축전지 6
 
6.7%
그밖의폐광물유 4
 
4.5%
그 밖의 폐산 3
 
3.4%
그밖의폐산 3
 
3.4%
폐기계유+폐작동유 2
 
2.2%
폐연마유+비수용성폐절삭유+폐열처리유 2
 
2.2%
폐흡착제 2
 
2.2%
그밖의폐섬유 2
 
2.2%
Other values (34) 35
39.3%

Length

2023-12-12T17:51:57.552703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
밖의 21
 
15.1%
폐합성수지 19
 
13.7%
폐수처리오니 11
 
7.9%
그 9
 
6.5%
폐황산이포함된2차폐축전지 6
 
4.3%
그밖의폐광물유 4
 
2.9%
폐산 4
 
2.9%
폐유기용제+그 3
 
2.2%
그밖의폐산 3
 
2.2%
폐광물유+폐오일필터 2
 
1.4%
Other values (48) 57
41.0%

처리방법
Categorical

HIGH CORRELATION 

Distinct29
Distinct (%)32.6%
Missing0
Missing (%)0.0%
Memory size844.0 B
재활용(중간가공폐기물 제조)[2010]
17 
중간처분(일반소각)[2101]
13 
재활용(원료제조)
10 
매립(민간관리형 매립시설)[2201]
10 
재활용(연료+고형연료제품제조)+중간처리(일반소각)
 
3
Other values (24)
36 

Length

Max length48
Median length30
Mean length17.932584
Min length8

Unique

Unique15 ?
Unique (%)16.9%

Sample

1st row재활용(연료+고형연료제품제조)+중간처리(일반소각)
2nd row재활용(원료제조)+중간처리(일반소각)+재활용(연료+고형연료제품제조)+중간처리(고온소각)
3rd row재활용(원료 제조)
4th row중간처리(고온소각)+중간처리(증발농축)+중간처리(중화)
5th row재활용(제품제조)+중간처리(일반소각)

Common Values

ValueCountFrequency (%)
재활용(중간가공폐기물 제조)[2010] 17
19.1%
중간처분(일반소각)[2101] 13
14.6%
재활용(원료제조) 10
11.2%
매립(민간관리형 매립시설)[2201] 10
11.2%
재활용(연료+고형연료제품제조)+중간처리(일반소각) 3
 
3.4%
재활용(연료+고형연료제품제조) 3
 
3.4%
재활용(직접 제품제조)[2004] 3
 
3.4%
재활용(연료+고형연료제품제조) 3
 
3.4%
재활용(수리+수선재사용) 2
 
2.2%
중간처리(고온소각)+중간처리(증발농축) 2
 
2.2%
Other values (19) 23
25.8%

Length

2023-12-12T17:51:57.745221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
재활용(중간가공폐기물 19
15.2%
제조)[2010 17
13.6%
중간처분(일반소각)[2101 13
10.4%
재활용(원료제조 11
 
8.8%
매립(민간관리형 10
 
8.0%
매립시설)[2201 10
 
8.0%
재활용(연료+고형연료제품제조 6
 
4.8%
재활용(연료+고형연료제품제조)+중간처리(일반소각 3
 
2.4%
재활용(직접 3
 
2.4%
제품제조)[2004 3
 
2.4%
Other values (23) 30
24.0%

Interactions

2023-12-12T17:51:53.691181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T17:51:57.878723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
유형상호(사업장명)사업자등록번호소재지전화번호신고연도주요품목처리방법
유형1.0000.0000.0000.6090.3130.3531.0000.986
상호(사업장명)0.0001.0000.9991.0001.0000.8620.8420.000
사업자등록번호0.0000.9991.0000.9630.9990.0000.9870.907
소재지0.6091.0000.9631.0001.0000.0000.0000.000
전화번호0.3131.0000.9991.0001.0000.7530.8560.733
신고연도0.3530.8620.0000.0000.7531.0000.0000.000
주요품목1.0000.8420.9870.0000.8560.0001.0000.970
처리방법0.9860.0000.9070.0000.7330.0000.9701.000
2023-12-12T17:51:58.031897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
주요품목처리방법소재지유형
주요품목1.0000.5310.0000.719
처리방법0.5311.0000.0000.811
소재지0.0000.0001.0000.557
유형0.7190.8110.5571.000
2023-12-12T17:51:58.153260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
신고연도유형소재지주요품목처리방법
신고연도1.0000.2700.0000.0000.000
유형0.2701.0000.5570.7190.811
소재지0.0000.5571.0000.0000.000
주요품목0.0000.7190.0001.0000.531
처리방법0.0000.8110.0000.5311.000

Missing values

2023-12-12T17:51:53.834028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T17:51:54.059165image/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

유형상호(사업장명)사업자등록번호소재지전화번호신고연도주요품목처리방법
0지정폐기물한국세차장131-02-21676인천광역시 미추홀구 학익동032-832-83042003폐윤활유+폐유기용제+그 밖의 폐광물류+폐오일필터재활용(연료+고형연료제품제조)+중간처리(일반소각)
1지정폐기물(사)인천자동차검사 정비사업조합131-82-02346인천광역시 미추홀구 주안동032-420-11712003폐황산이 포함된 2차 폐축전지+그 밖의 폐유기용제+폐페인트+폐윤활유+그 밖의 폐광물유+폐오일필터재활용(원료제조)+중간처리(일반소각)+재활용(연료+고형연료제품제조)+중간처리(고온소각)
2지정폐기물인천IT타워관리위원회121-82-65801인천광역시 미추홀구 도화동032-255-69952007그 밖의 폐산재활용(원료 제조)
3지정폐기물인하대학교131-82-00372인천광역시 미추홀구 용현동032-860-71072007그 밖의 폐유기용제+그 밖의 폐산+그 밖의 폐알칼리+ 연구+검사용 폐시약중간처리(고온소각)+중간처리(증발농축)+중간처리(중화)
4지정폐기물태원엔진121-16-86913인천광역시 미추홀구 학익동032-833-45072008폐윤활유+그 밖의 폐광물유+폐오일필터재활용(제품제조)+중간처리(일반소각)
5지정폐기물경인지방식품의약품안전청121-83-04271인천광역시 미추홀구 주안동032-442-46032004할로겐족 폐유기용제+그 밖의 폐산+그 밖의 폐알칼리+그 밖의 폐유기용제중간처리(고온소각)+중간처리(증발농축)
6지정폐기물금창부식광고121-60-05359인천광역시 미추홀구 용현동032-773-54332009폐산재활용(원료제조)
7지정폐기물관교동아아파트131-80-03507인천광역시 미추홀구 관교동032-435-75572009그밖의폐산재활용(원료제조)
8지정폐기물인천대학교131-83-02358인천광역시 미추홀구 도화동032-835-95182011그 밖의 폐유기용제+그 밖의 폐산+폐유+폐알칼리중간처리(고온소각)+중간처리(증발농축)
9지정폐기물관교풍림아파트입주자대표회의133-82-60885인천광역시 미추홀구 관교동032-437-92512011그 밖의 폐산재활용(원료제조)
유형상호(사업장명)사업자등록번호소재지전화번호신고연도주요품목처리방법
79사업장폐기물로뎀요양병원683-98-00030인천광역시 미추홀구 주안동1544-25082019그밖의폐섬유중간처분(일반소각)[2101]
80사업장폐기물인일요양병원341-94-00045인천광역시 미추홀구 주안동032-863-00512020그밖의폐섬유중간처분(일반소각)[2101]
81사업장폐기물일영산업121-18-23950인천광역시 미추홀구 도화동032-773-09532020목재가공공장 부산물재활용(연료+고형연료제품제조)
82사업장폐기물한국보훈복지의료공단 인천보훈병원829-82-00256인천광역시 미추홀구 용현동032-363-97852021폐합성수지재활용(중간가공폐기물 제조)[2010]
83사업장폐기물㈜에코프라임829-82-00256인천광역시 미추홀구 용현동032-363-97852020폐수처리오니재활용(중간가공폐기물 제조)[2010]
84사업장폐기물㈜에스엔씨121-81-41573인천광역시 미추홀구 도화동032-864-70132022그밖의분진재활용(중간가공폐기물 제조)[2010]
85사업장폐기물에이치디씨현대산업개발334-86-00815인천광역시 미추홀구 용현동032-833-25762022그밖의폐유리매립(민간관리형 매립시설)[2201]
86사업장폐기물㈜소디프 비앤에프512-81-01914인천광역시 미추홀구 주안동032-863-33542022그밖의폐수처리오니매립(민간관리형 매립시설)[2201]
87사업장폐기물㈜나눌652-81-02225인천광역시 미추홀구 도화동<NA>2023폐수처리오니재활용(직접제품제조)[2004]
88사업장폐기물㈜드림씨앤씨528-81-01369인천광역시 미추홀구 도화동070-7543-92712023폐활성탄재활용(직접제품제조)[2004]