Overview

Dataset statistics

Number of variables9
Number of observations39
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.9 KiB
Average record size in memory77.4 B

Variable types

Numeric2
Categorical2
Text4
DateTime1

Dataset

Description부산광역시 해운대구의 폐기물 처리업(수집운반) 현황을 공개하여, 민원요청에 대응하고 관련 업체의 변경사항을 업데이트 하기위함
URLhttps://www.data.go.kr/data/3075791/fileData.do

Alerts

업종명 has constant value ""Constant
연번 is highly overall correlated with 업종구분High correlation
업종구분 is highly overall correlated with 연번High correlation
연번 has unique valuesUnique

Reproduction

Analysis started2023-12-12 20:32:30.294649
Analysis finished2023-12-12 20:32:31.173598
Duration0.88 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct39
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20
Minimum1
Maximum39
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size483.0 B
2023-12-13T05:32:31.246840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.9
Q110.5
median20
Q329.5
95-th percentile37.1
Maximum39
Range38
Interquartile range (IQR)19

Descriptive statistics

Standard deviation11.401754
Coefficient of variation (CV)0.57008771
Kurtosis-1.2
Mean20
Median Absolute Deviation (MAD)10
Skewness0
Sum780
Variance130
MonotonicityStrictly increasing
2023-12-13T05:32:31.379092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
1 1
 
2.6%
2 1
 
2.6%
23 1
 
2.6%
24 1
 
2.6%
25 1
 
2.6%
26 1
 
2.6%
27 1
 
2.6%
28 1
 
2.6%
29 1
 
2.6%
30 1
 
2.6%
Other values (29) 29
74.4%
ValueCountFrequency (%)
1 1
2.6%
2 1
2.6%
3 1
2.6%
4 1
2.6%
5 1
2.6%
6 1
2.6%
7 1
2.6%
8 1
2.6%
9 1
2.6%
10 1
2.6%
ValueCountFrequency (%)
39 1
2.6%
38 1
2.6%
37 1
2.6%
36 1
2.6%
35 1
2.6%
34 1
2.6%
33 1
2.6%
32 1
2.6%
31 1
2.6%
30 1
2.6%

업종명
Categorical

CONSTANT 

Distinct1
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size444.0 B
수집운반업
39 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
수집운반업 39
100.0%

Length

2023-12-13T05:32:31.511213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:32:31.616690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
수집운반업 39
100.0%

업종구분
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)10.3%
Missing0
Missing (%)0.0%
Memory size444.0 B
건설
12 
대행업소
10 
사업장 비배출계
10 
사업장 배출계
7 

Length

Max length9
Median length8
Mean length5.3846154
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
건설 12
30.8%
대행업소 10
25.6%
사업장 비배출계 10
25.6%
사업장 배출계 7
17.9%

Length

2023-12-13T05:32:31.729485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:32:31.877913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
사업장 17
30.4%
건설 12
21.4%
대행업소 10
17.9%
비배출계 10
17.9%
배출계 7
12.5%

업체명
Text

Distinct32
Distinct (%)82.1%
Missing0
Missing (%)0.0%
Memory size444.0 B
2023-12-13T05:32:32.143982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length6.2564103
Min length2

Characters and Unicode

Total characters244
Distinct characters79
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

Unique26 ?
Unique (%)66.7%

Sample

1st row(주)청도
2nd row해동환경(주)
3rd row(주)신해환경
4th row(주)희망환경
5th row(주)민하산업
ValueCountFrequency (%)
태양환경 3
 
7.1%
주)희망환경 2
 
4.8%
해동환경(주 2
 
4.8%
㈜청심리사이클링 2
 
4.8%
주)청도 2
 
4.8%
주)신해환경 2
 
4.8%
sh 1
 
2.4%
주)명진아이텍 1
 
2.4%
동래덤프 1
 
2.4%
일성환경 1
 
2.4%
Other values (25) 25
59.5%
2023-12-13T05:32:32.555058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
환 22
 
9.0%
경 22
 
9.0%
( 16
 
6.6%
주 16
 
6.6%
) 16
 
6.6%
산 7
 
2.9%
이 7
 
2.9%
㈜ 7
 
2.9%
성 6
 
2.5%
리 6
 
2.5%
Other values (69) 119
48.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 192
78.7%
Open Punctuation 16
 
6.6%
Close Punctuation 16
 
6.6%
Uppercase Letter 9
 
3.7%
Other Symbol 7
 
2.9%
Space Separator 3
 
1.2%
Other Punctuation 1
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
환 22
 
11.5%
경 22
 
11.5%
주 16
 
8.3%
산 7
 
3.6%
이 7
 
3.6%
성 6
 
3.1%
리 6
 
3.1%
링 5
 
2.6%
클 5
 
2.6%
업 4
 
2.1%
Other values (58) 92
47.9%
Uppercase Letter
ValueCountFrequency (%)
S 2
22.2%
T 2
22.2%
H 2
22.2%
E 1
11.1%
C 1
11.1%
B 1
11.1%
Open Punctuation
ValueCountFrequency (%)
( 16
100.0%
Close Punctuation
ValueCountFrequency (%)
) 16
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 7
100.0%
Space Separator
ValueCountFrequency (%)
3
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 199
81.6%
Common 36
 
14.8%
Latin 9
 
3.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
환 22
 
11.1%
경 22
 
11.1%
주 16
 
8.0%
산 7
 
3.5%
이 7
 
3.5%
㈜ 7
 
3.5%
성 6
 
3.0%
리 6
 
3.0%
링 5
 
2.5%
클 5
 
2.5%
Other values (59) 96
48.2%
Latin
ValueCountFrequency (%)
S 2
22.2%
T 2
22.2%
H 2
22.2%
E 1
11.1%
C 1
11.1%
B 1
11.1%
Common
ValueCountFrequency (%)
( 16
44.4%
) 16
44.4%
3
 
8.3%
, 1
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 192
78.7%
ASCII 45
 
18.4%
None 7
 
2.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
환 22
 
11.5%
경 22
 
11.5%
주 16
 
8.3%
산 7
 
3.6%
이 7
 
3.6%
성 6
 
3.1%
리 6
 
3.1%
링 5
 
2.6%
클 5
 
2.6%
업 4
 
2.1%
Other values (58) 92
47.9%
ASCII
ValueCountFrequency (%)
( 16
35.6%
) 16
35.6%
3
 
6.7%
S 2
 
4.4%
T 2
 
4.4%
H 2
 
4.4%
E 1
 
2.2%
C 1
 
2.2%
B 1
 
2.2%
, 1
 
2.2%
None
ValueCountFrequency (%)
㈜ 7
100.0%

소재지 주소
Text

Distinct30
Distinct (%)76.9%
Missing0
Missing (%)0.0%
Memory size444.0 B
2023-12-13T05:32:32.822958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length23
Mean length20.384615
Min length16

Characters and Unicode

Total characters795
Distinct characters50
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

Unique22 ?
Unique (%)56.4%

Sample

1st row부산광역시 해운대구 해운대로 575
2nd row부산광역시 해운대구 해운대로1105번길 28
3rd row부산광역시 해운대구 해운대로 1199-7
4th row부산광역시 해운대구 송정동579-1
5th row부산광역시 해운대구 석대로31
ValueCountFrequency (%)
부산광역시 39
25.7%
해운대구 39
25.7%
해운대로 8
 
5.3%
송정2로 3
 
2.0%
5 3
 
2.0%
송정1로 3
 
2.0%
해운대로1105번길 2
 
1.3%
윗반송로 2
 
1.3%
9 2
 
1.3%
선수촌로 2
 
1.3%
Other values (41) 49
32.2%
2023-12-13T05:32:33.285315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
113
 
14.2%
대 56
 
7.0%
해 53
 
6.7%
운 53
 
6.7%
광 40
 
5.0%
부 39
 
4.9%
역 39
 
4.9%
시 39
 
4.9%
구 39
 
4.9%
산 39
 
4.9%
Other values (40) 285
35.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 523
65.8%
Decimal Number 148
 
18.6%
Space Separator 113
 
14.2%
Dash Punctuation 11
 
1.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
대 56
10.7%
해 53
10.1%
운 53
10.1%
광 40
7.6%
부 39
7.5%
역 39
7.5%
시 39
7.5%
구 39
7.5%
산 39
7.5%
로 35
 
6.7%
Other values (28) 91
17.4%
Decimal Number
ValueCountFrequency (%)
1 36
24.3%
2 22
14.9%
6 16
10.8%
9 16
10.8%
3 15
10.1%
5 14
 
9.5%
7 11
 
7.4%
8 8
 
5.4%
0 6
 
4.1%
4 4
 
2.7%
Space Separator
ValueCountFrequency (%)
113
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 523
65.8%
Common 272
34.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
대 56
10.7%
해 53
10.1%
운 53
10.1%
광 40
7.6%
부 39
7.5%
역 39
7.5%
시 39
7.5%
구 39
7.5%
산 39
7.5%
로 35
 
6.7%
Other values (28) 91
17.4%
Common
ValueCountFrequency (%)
113
41.5%
1 36
 
13.2%
2 22
 
8.1%
6 16
 
5.9%
9 16
 
5.9%
3 15
 
5.5%
5 14
 
5.1%
- 11
 
4.0%
7 11
 
4.0%
8 8
 
2.9%
Other values (2) 10
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 523
65.8%
ASCII 272
34.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
113
41.5%
1 36
 
13.2%
2 22
 
8.1%
6 16
 
5.9%
9 16
 
5.9%
3 15
 
5.5%
5 14
 
5.1%
- 11
 
4.0%
7 11
 
4.0%
8 8
 
2.9%
Other values (2) 10
 
3.7%
Hangul
ValueCountFrequency (%)
대 56
10.7%
해 53
10.1%
운 53
10.1%
광 40
7.6%
부 39
7.5%
역 39
7.5%
시 39
7.5%
구 39
7.5%
산 39
7.5%
로 35
 
6.7%
Other values (28) 91
17.4%

허가일
Date

Distinct34
Distinct (%)87.2%
Missing0
Missing (%)0.0%
Memory size444.0 B
Minimum1993-03-08 00:00:00
Maximum2023-03-08 00:00:00
2023-12-13T05:32:33.457803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:32:33.619071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)

차량현황(대)
Real number (ℝ)

Distinct8
Distinct (%)20.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.3333333
Minimum2
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size483.0 B
2023-12-13T05:32:33.722226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q13
median3
Q36
95-th percentile7.1
Maximum9
Range7
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.8257419
Coefficient of variation (CV)0.42132504
Kurtosis-0.49075391
Mean4.3333333
Median Absolute Deviation (MAD)1
Skewness0.65126783
Sum169
Variance3.3333333
MonotonicityNot monotonic
2023-12-13T05:32:33.840528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
3 16
41.0%
6 9
23.1%
5 4
 
10.3%
2 4
 
10.3%
4 2
 
5.1%
7 2
 
5.1%
8 1
 
2.6%
9 1
 
2.6%
ValueCountFrequency (%)
2 4
 
10.3%
3 16
41.0%
4 2
 
5.1%
5 4
 
10.3%
6 9
23.1%
7 2
 
5.1%
8 1
 
2.6%
9 1
 
2.6%
ValueCountFrequency (%)
9 1
 
2.6%
8 1
 
2.6%
7 2
 
5.1%
6 9
23.1%
5 4
 
10.3%
4 2
 
5.1%
3 16
41.0%
2 4
 
10.3%

전화번호
Text

Distinct31
Distinct (%)79.5%
Missing0
Missing (%)0.0%
Memory size444.0 B
2023-12-13T05:32:34.075439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters468
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

Unique24 ?
Unique (%)61.5%

Sample

1st row051-746-2720
2nd row051-702-1201
3rd row051-743-0562
4th row051-722-0557
5th row051-782-3511
ValueCountFrequency (%)
051-701-2443 3
 
7.7%
051-722-0557 2
 
5.1%
051-264-0358 2
 
5.1%
051-702-1201 2
 
5.1%
051-703-8878 2
 
5.1%
051-746-2720 2
 
5.1%
051-743-0562 2
 
5.1%
051-783-5055 1
 
2.6%
051-746-7904 1
 
2.6%
051-523-0404 1
 
2.6%
Other values (21) 21
53.8%
2023-12-13T05:32:34.469368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 81
17.3%
- 78
16.7%
1 66
14.1%
5 62
13.2%
7 44
9.4%
2 40
8.5%
4 26
 
5.6%
8 21
 
4.5%
6 20
 
4.3%
3 18
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 390
83.3%
Dash Punctuation 78
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 81
20.8%
1 66
16.9%
5 62
15.9%
7 44
11.3%
2 40
10.3%
4 26
 
6.7%
8 21
 
5.4%
6 20
 
5.1%
3 18
 
4.6%
9 12
 
3.1%
Dash Punctuation
ValueCountFrequency (%)
- 78
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 468
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 81
17.3%
- 78
16.7%
1 66
14.1%
5 62
13.2%
7 44
9.4%
2 40
8.5%
4 26
 
5.6%
8 21
 
4.5%
6 20
 
4.3%
3 18
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 468
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 81
17.3%
- 78
16.7%
1 66
14.1%
5 62
13.2%
7 44
9.4%
2 40
8.5%
4 26
 
5.6%
8 21
 
4.5%
6 20
 
4.3%
3 18
 
3.8%

팩스번호
Text

Distinct30
Distinct (%)76.9%
Missing0
Missing (%)0.0%
Memory size444.0 B
2023-12-13T05:32:34.714984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.051282
Min length12

Characters and Unicode

Total characters470
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

Unique22 ?
Unique (%)56.4%

Sample

1st row051-744-1878
2nd row051-703-0444
3rd row051-744-0561
4th row051-722-0556
5th row051-523-8517
ValueCountFrequency (%)
051-701-6443 3
 
7.7%
051-703-0444 2
 
5.1%
051-703-9555 2
 
5.1%
051-783-5054 2
 
5.1%
051-722-0556 2
 
5.1%
051-744-0561 2
 
5.1%
051-744-1878 2
 
5.1%
051-262-7749 2
 
5.1%
051-781-5557 1
 
2.6%
051-797-8557 1
 
2.6%
Other values (20) 20
51.3%
2023-12-13T05:32:35.121906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 78
16.6%
5 75
16.0%
0 73
15.5%
1 55
11.7%
7 51
10.9%
4 40
8.5%
2 25
 
5.3%
6 21
 
4.5%
3 19
 
4.0%
8 19
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 392
83.4%
Dash Punctuation 78
 
16.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 75
19.1%
0 73
18.6%
1 55
14.0%
7 51
13.0%
4 40
10.2%
2 25
 
6.4%
6 21
 
5.4%
3 19
 
4.8%
8 19
 
4.8%
9 14
 
3.6%
Dash Punctuation
ValueCountFrequency (%)
- 78
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 470
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 78
16.6%
5 75
16.0%
0 73
15.5%
1 55
11.7%
7 51
10.9%
4 40
8.5%
2 25
 
5.3%
6 21
 
4.5%
3 19
 
4.0%
8 19
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 470
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 78
16.6%
5 75
16.0%
0 73
15.5%
1 55
11.7%
7 51
10.9%
4 40
8.5%
2 25
 
5.3%
6 21
 
4.5%
3 19
 
4.0%
8 19
 
4.0%

Interactions

2023-12-13T05:32:30.797506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:32:30.599967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:32:30.880792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:32:30.680225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T05:32:35.253360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번업종구분업체명소재지 주소허가일차량현황(대)전화번호팩스번호
연번1.0000.9710.4420.2540.8640.2690.3500.254
업종구분0.9711.0000.9100.7891.0000.7610.8250.789
업체명0.4420.9101.0001.0000.9970.7171.0001.000
소재지 주소0.2540.7891.0001.0000.9970.7871.0001.000
허가일0.8641.0000.9970.9971.0000.5280.9970.997
차량현황(대)0.2690.7610.7170.7870.5281.0000.6830.787
전화번호0.3500.8251.0001.0000.9970.6831.0001.000
팩스번호0.2540.7891.0001.0000.9970.7871.0001.000
2023-12-13T05:32:35.387603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번차량현황(대)업종구분
연번1.000-0.3720.870
차량현황(대)-0.3721.0000.397
업종구분0.8700.3971.000

Missing values

2023-12-13T05:32:30.994083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T05:32:31.121746image/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

연번업종명업종구분업체명소재지 주소허가일차량현황(대)전화번호팩스번호
01수집운반업대행업소(주)청도부산광역시 해운대구 해운대로 5751993-03-086051-746-2720051-744-1878
12수집운반업대행업소해동환경(주)부산광역시 해운대구 해운대로1105번길 281996-03-083051-702-1201051-703-0444
23수집운반업대행업소(주)신해환경부산광역시 해운대구 해운대로 1199-71998-05-255051-743-0562051-744-0561
34수집운반업대행업소(주)희망환경부산광역시 해운대구 송정동579-12013-06-173051-722-0557051-722-0556
45수집운반업대행업소(주)민하산업부산광역시 해운대구 석대로311996-06-046051-782-3511051-523-8517
56수집운반업대행업소센텀환경부산광역시 해운대구 송정동 562-62015-03-278051-702-0111051-703-0789
67수집운반업대행업소(주)청도부산광역시 해운대구 해운대로 5751993-03-084051-746-2720051-744-1878
78수집운반업대행업소해동환경(주)부산광역시 해운대구 해운대로1105번길 281996-03-089051-702-1201051-703-0444
89수집운반업대행업소(주)신해환경부산광역시 해운대구 해운대로 1199-71998-05-256051-743-0562051-744-0561
910수집운반업대행업소(주)희망환경부산광역시 해운대구 송정동579-12013-06-173051-722-0557051-722-0556
연번업종명업종구분업체명소재지 주소허가일차량현황(대)전화번호팩스번호
2930수집운반업건설유성환경산업부산광역시 해운대구 해운대로 1199-212004-07-216051-704-2420051-703-2421
3031수집운반업건설대동환경부산광역시 해운대구 해운대로 1199-232015-11-253051-704-9919051-703-9919
3132수집운반업건설SH TECH부산광역시 해운대구 해운대로61번길 902012-07-033051-783-5055051-783-5054
3233수집운반업건설서봉리사이클링(주)부산광역시 해운대구 윗반송로 642002-11-253051-264-0358051-262-7749
3334수집운반업건설일성환경부산광역시 해운대구 반송순환로 1322014-12-194051-632-9860051-701-0243
3435수집운반업건설동래덤프부산광역시 해운대구 선수촌로 2182001-01-296051-523-0404051-556-7766
3536수집운반업건설(주)명진아이텍부산광역시 해운대구 해운대로61번가길62016-08-123051-781-5552051-781-5557
3637수집운반업건설태양환경부산광역시 해운대구 송정1로 52016-08-123051-701-2443051-701-6443
3738수집운반업건설천하BTS부산광역시 해운대구 송정광어골로82번길 92020-08-133051-746-7904051-797-8557
3839수집운반업건설수성스틸, 수성환경산업부산광역시 해운대구 반송로623번길 302021-02-023051-505-9906051-505-6604