Overview

Dataset statistics

Number of variables7
Number of observations140
Missing cells193
Missing cells (%)19.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.9 KiB
Average record size in memory57.9 B

Variable types

Numeric1
Categorical1
Text4
DateTime1

Dataset

Description경상남도 김해시 폐기물 수집운반업체 현황(구분,업소명,등록일자,전화번호,주소,영업구역,위도,경도)에 대한 데이터입니다.
Author경상남도 김해시
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15033432

Alerts

연번 is highly overall correlated with 구분High correlation
구분 is highly overall correlated with 연번High correlation
전화번호 has 56 (40.0%) missing valuesMissing
영업구역 has 136 (97.1%) missing valuesMissing
연번 has unique valuesUnique

Reproduction

Analysis started2024-03-13 00:13:26.942469
Analysis finished2024-03-13 00:13:27.557266
Duration0.61 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct140
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70.5
Minimum1
Maximum140
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-03-13T09:13:27.629539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile7.95
Q135.75
median70.5
Q3105.25
95-th percentile133.05
Maximum140
Range139
Interquartile range (IQR)69.5

Descriptive statistics

Standard deviation40.5586
Coefficient of variation (CV)0.57529928
Kurtosis-1.2
Mean70.5
Median Absolute Deviation (MAD)35
Skewness0
Sum9870
Variance1645
MonotonicityStrictly increasing
2024-03-13T09:13:27.770602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.7%
98 1
 
0.7%
92 1
 
0.7%
93 1
 
0.7%
94 1
 
0.7%
95 1
 
0.7%
96 1
 
0.7%
97 1
 
0.7%
99 1
 
0.7%
90 1
 
0.7%
Other values (130) 130
92.9%
ValueCountFrequency (%)
1 1
0.7%
2 1
0.7%
3 1
0.7%
4 1
0.7%
5 1
0.7%
6 1
0.7%
7 1
0.7%
8 1
0.7%
9 1
0.7%
10 1
0.7%
ValueCountFrequency (%)
140 1
0.7%
139 1
0.7%
138 1
0.7%
137 1
0.7%
136 1
0.7%
135 1
0.7%
134 1
0.7%
133 1
0.7%
132 1
0.7%
131 1
0.7%

구분
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
사업장일반폐기물
105 
건설폐기물
31 
생활폐기물
 
4

Length

Max length8
Median length8
Mean length7.25
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row생활폐기물
2nd row생활폐기물
3rd row생활폐기물
4th row생활폐기물
5th row건설폐기물

Common Values

ValueCountFrequency (%)
사업장일반폐기물 105
75.0%
건설폐기물 31
 
22.1%
생활폐기물 4
 
2.9%

Length

2024-03-13T09:13:27.894566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T09:13:27.974633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
사업장일반폐기물 105
75.0%
건설폐기물 31
 
22.1%
생활폐기물 4
 
2.9%

업소명
Text

Distinct127
Distinct (%)90.7%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
2024-03-13T09:13:28.187206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length17
Mean length5.8285714
Min length3

Characters and Unicode

Total characters816
Distinct characters177
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique117 ?
Unique (%)83.6%

Sample

1st row㈜김해환경
2nd row김해시공영(유)
3rd row(유)김해공영
4th row㈜정우환경
5th row금광개발㈜
ValueCountFrequency (%)
주식회사 4
 
2.7%
미래환경 3
 
2.0%
㈜태창크린텍 3
 
2.0%
㈜동건기업 3
 
2.0%
㈜대경오앤티 2
 
1.3%
㈜금화로지스 2
 
1.3%
대명산업개발 2
 
1.3%
세영개발 2
 
1.3%
세진환경산업㈜ 2
 
1.3%
㈜대한에코텍 2
 
1.3%
Other values (124) 125
83.3%
2024-03-13T09:13:28.510503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
㈜ 74
 
9.1%
경 48
 
5.9%
환 39
 
4.8%
업 34
 
4.2%
산 33
 
4.0%
이 19
 
2.3%
개 17
 
2.1%
원 17
 
2.1%
발 16
 
2.0%
대 16
 
2.0%
Other values (167) 503
61.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 701
85.9%
Other Symbol 74
 
9.1%
Uppercase Letter 12
 
1.5%
Space Separator 10
 
1.2%
Close Punctuation 6
 
0.7%
Open Punctuation 6
 
0.7%
Decimal Number 6
 
0.7%
Other Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
경 48
 
6.8%
환 39
 
5.6%
업 34
 
4.9%
산 33
 
4.7%
이 19
 
2.7%
개 17
 
2.4%
원 17
 
2.4%
발 16
 
2.3%
대 16
 
2.3%
주 14
 
2.0%
Other values (147) 448
63.9%
Uppercase Letter
ValueCountFrequency (%)
J 2
16.7%
H 2
16.7%
R 1
8.3%
B 1
8.3%
G 1
8.3%
C 1
8.3%
S 1
8.3%
E 1
8.3%
N 1
8.3%
O 1
8.3%
Decimal Number
ValueCountFrequency (%)
3 2
33.3%
9 1
16.7%
5 1
16.7%
6 1
16.7%
1 1
16.7%
Other Symbol
ValueCountFrequency (%)
㈜ 74
100.0%
Space Separator
ValueCountFrequency (%)
10
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6
100.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 775
95.0%
Common 29
 
3.6%
Latin 12
 
1.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
㈜ 74
 
9.5%
경 48
 
6.2%
환 39
 
5.0%
업 34
 
4.4%
산 33
 
4.3%
이 19
 
2.5%
개 17
 
2.2%
원 17
 
2.2%
발 16
 
2.1%
대 16
 
2.1%
Other values (148) 462
59.6%
Latin
ValueCountFrequency (%)
J 2
16.7%
H 2
16.7%
R 1
8.3%
B 1
8.3%
G 1
8.3%
C 1
8.3%
S 1
8.3%
E 1
8.3%
N 1
8.3%
O 1
8.3%
Common
ValueCountFrequency (%)
10
34.5%
) 6
20.7%
( 6
20.7%
3 2
 
6.9%
9 1
 
3.4%
5 1
 
3.4%
6 1
 
3.4%
. 1
 
3.4%
1 1
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 701
85.9%
None 74
 
9.1%
ASCII 41
 
5.0%

Most frequent character per block

None
ValueCountFrequency (%)
㈜ 74
100.0%
Hangul
ValueCountFrequency (%)
경 48
 
6.8%
환 39
 
5.6%
업 34
 
4.9%
산 33
 
4.7%
이 19
 
2.7%
개 17
 
2.4%
원 17
 
2.4%
발 16
 
2.3%
대 16
 
2.3%
주 14
 
2.0%
Other values (147) 448
63.9%
ASCII
ValueCountFrequency (%)
10
24.4%
) 6
14.6%
( 6
14.6%
J 2
 
4.9%
3 2
 
4.9%
H 2
 
4.9%
9 1
 
2.4%
R 1
 
2.4%
B 1
 
2.4%
5 1
 
2.4%
Other values (9) 9
22.0%

등록일자
Date

Distinct125
Distinct (%)89.9%
Missing1
Missing (%)0.7%
Memory size1.2 KiB
Minimum1987-09-05 00:00:00
Maximum2023-12-19 00:00:00
2024-03-13T09:13:28.620759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T09:13:28.725516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

전화번호
Text

MISSING 

Distinct77
Distinct (%)91.7%
Missing56
Missing (%)40.0%
Memory size1.2 KiB
2024-03-13T09:13:28.926460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters1008
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

Unique72 ?
Unique (%)85.7%

Sample

1st row055-333-4800
2nd row055-334-6676
3rd row055-312-9721
4th row055-337-9511
5th row055-339-8643
ValueCountFrequency (%)
055-322-0772 3
 
3.6%
055-346-4932 3
 
3.6%
055-327-9867 2
 
2.4%
055-345-5141 2
 
2.4%
055-322-3274 2
 
2.4%
055-337-2055 1
 
1.2%
055-323-9779 1
 
1.2%
055-346-1750 1
 
1.2%
055-343-1740 1
 
1.2%
055-342-3121 1
 
1.2%
Other values (67) 67
79.8%
2024-03-13T09:13:29.222833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 204
20.2%
- 168
16.7%
3 146
14.5%
0 125
12.4%
2 83
8.2%
4 60
 
6.0%
1 58
 
5.8%
7 54
 
5.4%
6 39
 
3.9%
8 38
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 840
83.3%
Dash Punctuation 168
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 204
24.3%
3 146
17.4%
0 125
14.9%
2 83
9.9%
4 60
 
7.1%
1 58
 
6.9%
7 54
 
6.4%
6 39
 
4.6%
8 38
 
4.5%
9 33
 
3.9%
Dash Punctuation
ValueCountFrequency (%)
- 168
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1008
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 204
20.2%
- 168
16.7%
3 146
14.5%
0 125
12.4%
2 83
8.2%
4 60
 
6.0%
1 58
 
5.8%
7 54
 
5.4%
6 39
 
3.9%
8 38
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1008
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 204
20.2%
- 168
16.7%
3 146
14.5%
0 125
12.4%
2 83
8.2%
4 60
 
6.0%
1 58
 
5.8%
7 54
 
5.4%
6 39
 
3.9%
8 38
 
3.8%

주소
Text

Distinct127
Distinct (%)90.7%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
2024-03-13T09:13:29.485787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length39
Median length32
Mean length24.75
Min length15

Characters and Unicode

Total characters3465
Distinct characters122
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique117 ?
Unique (%)83.6%

Sample

1st row경상남도 김해시 전하로 43
2nd row경상남도 김해시 김해대로2385번길 8
3rd row경상남도 김해시 부곡로 71
4th row경상남도 김해시 김해대로2596번길 23-35
5th row경상남도 김해시 주촌면 김해대로1538번길 142
ValueCountFrequency (%)
경상남도 140
19.9%
김해시 140
19.9%
한림면 31
 
4.4%
생림면 13
 
1.8%
김해대로 12
 
1.7%
진영읍 9
 
1.3%
주촌면 7
 
1.0%
2층 7
 
1.0%
상동면 6
 
0.9%
흥동로 5
 
0.7%
Other values (258) 333
47.4%
2024-03-13T09:13:29.887914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
567
 
16.4%
1 187
 
5.4%
김 174
 
5.0%
해 174
 
5.0%
상 153
 
4.4%
시 143
 
4.1%
남 141
 
4.1%
경 140
 
4.0%
도 140
 
4.0%
로 135
 
3.9%
Other values (112) 1511
43.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1954
56.4%
Decimal Number 786
22.7%
Space Separator 567
 
16.4%
Dash Punctuation 62
 
1.8%
Other Punctuation 46
 
1.3%
Close Punctuation 25
 
0.7%
Open Punctuation 25
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
김 174
 
8.9%
해 174
 
8.9%
상 153
 
7.8%
시 143
 
7.3%
남 141
 
7.2%
경 140
 
7.2%
도 140
 
7.2%
로 135
 
6.9%
면 62
 
3.2%
길 61
 
3.1%
Other values (97) 631
32.3%
Decimal Number
ValueCountFrequency (%)
1 187
23.8%
2 133
16.9%
5 87
11.1%
0 84
10.7%
3 73
 
9.3%
4 59
 
7.5%
6 49
 
6.2%
7 44
 
5.6%
8 35
 
4.5%
9 35
 
4.5%
Space Separator
ValueCountFrequency (%)
567
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 62
100.0%
Other Punctuation
ValueCountFrequency (%)
, 46
100.0%
Close Punctuation
ValueCountFrequency (%)
) 25
100.0%
Open Punctuation
ValueCountFrequency (%)
( 25
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1954
56.4%
Common 1511
43.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
김 174
 
8.9%
해 174
 
8.9%
상 153
 
7.8%
시 143
 
7.3%
남 141
 
7.2%
경 140
 
7.2%
도 140
 
7.2%
로 135
 
6.9%
면 62
 
3.2%
길 61
 
3.1%
Other values (97) 631
32.3%
Common
ValueCountFrequency (%)
567
37.5%
1 187
 
12.4%
2 133
 
8.8%
5 87
 
5.8%
0 84
 
5.6%
3 73
 
4.8%
- 62
 
4.1%
4 59
 
3.9%
6 49
 
3.2%
, 46
 
3.0%
Other values (5) 164
 
10.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1954
56.4%
ASCII 1511
43.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
567
37.5%
1 187
 
12.4%
2 133
 
8.8%
5 87
 
5.8%
0 84
 
5.6%
3 73
 
4.8%
- 62
 
4.1%
4 59
 
3.9%
6 49
 
3.2%
, 46
 
3.0%
Other values (5) 164
 
10.9%
Hangul
ValueCountFrequency (%)
김 174
 
8.9%
해 174
 
8.9%
상 153
 
7.8%
시 143
 
7.3%
남 141
 
7.2%
경 140
 
7.2%
도 140
 
7.2%
로 135
 
6.9%
면 62
 
3.2%
길 61
 
3.1%
Other values (97) 631
32.3%

영업구역
Text

MISSING 

Distinct4
Distinct (%)100.0%
Missing136
Missing (%)97.1%
Memory size1.2 KiB
2024-03-13T09:13:30.051821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length33
Median length21.5
Mean length21.5
Min length10

Characters and Unicode

Total characters86
Distinct characters36
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

Unique4 ?
Unique (%)100.0%

Sample

1st row주촌면, 진례면, 내외면, 칠산서부동
2nd row동상동, 회현동, 부원동, 활천동, 북부동
3rd row장유1, 2, 3동
4th row생림면, 상동면, 대동면, 삼안동, 불암동, 진영읍, 한림면
ValueCountFrequency (%)
주촌면 1
 
5.3%
2 1
 
5.3%
진영읍 1
 
5.3%
불암동 1
 
5.3%
삼안동 1
 
5.3%
대동면 1
 
5.3%
상동면 1
 
5.3%
생림면 1
 
5.3%
3동 1
 
5.3%
장유1 1
 
5.3%
Other values (9) 9
47.4%
2024-03-13T09:13:30.283283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 15
17.4%
15
17.4%
동 12
14.0%
면 7
 
8.1%
부 3
 
3.5%
진 2
 
2.3%
림 2
 
2.3%
상 2
 
2.3%
주 1
 
1.2%
2 1
 
1.2%
Other values (26) 26
30.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 53
61.6%
Other Punctuation 15
 
17.4%
Space Separator 15
 
17.4%
Decimal Number 3
 
3.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 12
22.6%
면 7
 
13.2%
부 3
 
5.7%
진 2
 
3.8%
림 2
 
3.8%
상 2
 
3.8%
주 1
 
1.9%
생 1
 
1.9%
대 1
 
1.9%
안 1
 
1.9%
Other values (21) 21
39.6%
Decimal Number
ValueCountFrequency (%)
2 1
33.3%
3 1
33.3%
1 1
33.3%
Other Punctuation
ValueCountFrequency (%)
, 15
100.0%
Space Separator
ValueCountFrequency (%)
15
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 53
61.6%
Common 33
38.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 12
22.6%
면 7
 
13.2%
부 3
 
5.7%
진 2
 
3.8%
림 2
 
3.8%
상 2
 
3.8%
주 1
 
1.9%
생 1
 
1.9%
대 1
 
1.9%
안 1
 
1.9%
Other values (21) 21
39.6%
Common
ValueCountFrequency (%)
, 15
45.5%
15
45.5%
2 1
 
3.0%
3 1
 
3.0%
1 1
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 53
61.6%
ASCII 33
38.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 15
45.5%
15
45.5%
2 1
 
3.0%
3 1
 
3.0%
1 1
 
3.0%
Hangul
ValueCountFrequency (%)
동 12
22.6%
면 7
 
13.2%
부 3
 
5.7%
진 2
 
3.8%
림 2
 
3.8%
상 2
 
3.8%
주 1
 
1.9%
생 1
 
1.9%
대 1
 
1.9%
안 1
 
1.9%
Other values (21) 21
39.6%

Interactions

2024-03-13T09:13:27.207552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-13T09:13:30.354622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번구분전화번호영업구역
연번1.0000.8070.930NaN
구분0.8071.0000.863NaN
전화번호0.9300.8631.0001.000
영업구역NaNNaN1.0001.000
2024-03-13T09:13:30.428740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번구분
연번1.0000.682
구분0.6821.000

Missing values

2024-03-13T09:13:27.321961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-13T09:13:27.427105image/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.
2024-03-13T09:13:27.511176image/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

연번구분업소명등록일자전화번호주소영업구역
01생활폐기물㈜김해환경1989-09-22055-333-4800경상남도 김해시 전하로 43주촌면, 진례면, 내외면, 칠산서부동
12생활폐기물김해시공영(유)1989-09-22055-334-6676경상남도 김해시 김해대로2385번길 8동상동, 회현동, 부원동, 활천동, 북부동
23생활폐기물(유)김해공영1987-09-05055-312-9721경상남도 김해시 부곡로 71장유1, 2, 3동
34생활폐기물㈜정우환경2012-07-13055-337-9511경상남도 김해시 김해대로2596번길 23-35생림면, 상동면, 대동면, 삼안동, 불암동, 진영읍, 한림면
45건설폐기물금광개발㈜1995-10-25055-339-8643경상남도 김해시 주촌면 김해대로1538번길 142<NA>
56건설폐기물남도산업㈜1997-10-24055-343-0059경상남도 김해시 한림면 안곡로333번길 82<NA>
67건설폐기물㈜중앙환경2004-10-27055-343-7755경상남도 김해시 한림면 안하로 178<NA>
78건설폐기물㈜성창산업2007-03-16055-339-0416경상남도 김해시 김해대로2453번길 3 (삼정동)<NA>
89건설폐기물㈜경부이엔티2007-11-08055-326-9123경상남도 김해시 생림면 나전로 76<NA>
910건설폐기물㈜태창크린텍2008-12-09055-322-3273경상남도 김해시 진영읍 본산로219번길 10<NA>
연번구분업소명등록일자전화번호주소영업구역
130131사업장일반폐기물이루리환경2023-09-08<NA>경상남도 김해시 삼안로52번길 15-11<NA>
131132사업장일반폐기물대명산업개발2023-09-22<NA>경상남도 김해시 장유로 53<NA>
132133사업장일반폐기물대운환경자원2023-10-06<NA>경상남도 김해시 김해대로2529번길 15, 602호<NA>
133134사업장일반폐기물㈜산과들애2023-11-13<NA>경상남도 김해시 삼계로 30, 206호<NA>
134135사업장일반폐기물지니자원2023-11-15<NA>경상남도 김해시 가야로 522, 2층<NA>
135136사업장일반폐기물이도39환경2023-11-15<NA>경상남도 김해시 덕정로 108, 105동 901호<NA>
136137사업장일반폐기물바른자원2023-12-08<NA>경상남도 김해시 장유로 360, 102동 1001호<NA>
137138사업장일반폐기물㈜삼림2023-12-12055-323-9160경상남도 김해시 생림면 생림대로669번길 17-1<NA>
138139사업장일반폐기물㈜경남타이어2023-12-15055-327-6900경상남도 김해시 생림면 생림대로 1021<NA>
139140사업장일반폐기물㈜남도환경2023-12-19<NA>경상남도 김해시 삼안로 226, 105동 1110호<NA>