Overview

Dataset statistics

Number of variables8
Number of observations138
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.0 KiB
Average record size in memory67.0 B

Variable types

Text4
Categorical1
Numeric2
DateTime1

Dataset

Description농림축산식품부 국립농산물품질관리원에서 관리하고 있는 CCTV 관리현황(관리기관명, 소재지도로명주소, 설치목적구분, 카메라대수, 관리기관전화번호, 위도, 경도, 데이터기준일자)
Author국립농산물품질관리원
URLhttps://data.mafra.go.kr/opendata/data/indexOpenDataDetail.do?data_id=20191011000000001210

Alerts

설치목적구분 has constant value ""Constant
데이터기준일자 has constant value ""Constant
관리기관명 has unique valuesUnique

Reproduction

Analysis started2024-04-06 08:00:15.570285
Analysis finished2024-04-06 08:00:17.282509
Duration1.71 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

관리기관명
Text

UNIQUE 

Distinct138
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
2024-04-06T17:00:17.729035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length21
Mean length21.528986
Min length13

Characters and Unicode

Total characters2971
Distinct characters130
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique138 ?
Unique (%)100.0%

Sample

1st row국립농산물품질관리원 본원
2nd row국립농산물품질관리원 시험연구소
3rd row국립농산물품질관리원 경기지원 유통관리과
4th row국립농산물품질관리원 경기지원 서울사무소
5th row국립농산물품질관리원 경기지원 서울사무소(분소)
ValueCountFrequency (%)
국립농산물품질관리원 138
33.5%
경북지원 24
 
5.8%
전남지원 20
 
4.9%
경기지원 20
 
4.9%
경남지원 18
 
4.4%
강원지원 15
 
3.6%
충남지원 15
 
3.6%
전북지원 13
 
3.2%
충북지원 9
 
2.2%
유통관리과 8
 
1.9%
Other values (129) 132
32.0%
2024-04-06T17:00:18.476846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
원 294
 
9.9%
274
 
9.2%
산 152
 
5.1%
리 147
 
4.9%
관 146
 
4.9%
립 138
 
4.6%
국 138
 
4.6%
질 138
 
4.6%
품 138
 
4.6%
물 138
 
4.6%
Other values (120) 1268
42.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2666
89.7%
Space Separator 274
 
9.2%
Other Punctuation 21
 
0.7%
Close Punctuation 5
 
0.2%
Open Punctuation 5
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
원 294
 
11.0%
산 152
 
5.7%
리 147
 
5.5%
관 146
 
5.5%
립 138
 
5.2%
국 138
 
5.2%
질 138
 
5.2%
품 138
 
5.2%
물 138
 
5.2%
농 138
 
5.2%
Other values (116) 1099
41.2%
Space Separator
ValueCountFrequency (%)
274
100.0%
Other Punctuation
ValueCountFrequency (%)
· 21
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2666
89.7%
Common 305
 
10.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
원 294
 
11.0%
산 152
 
5.7%
리 147
 
5.5%
관 146
 
5.5%
립 138
 
5.2%
국 138
 
5.2%
질 138
 
5.2%
품 138
 
5.2%
물 138
 
5.2%
농 138
 
5.2%
Other values (116) 1099
41.2%
Common
ValueCountFrequency (%)
274
89.8%
· 21
 
6.9%
) 5
 
1.6%
( 5
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2666
89.7%
ASCII 284
 
9.6%
None 21
 
0.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
원 294
 
11.0%
산 152
 
5.7%
리 147
 
5.5%
관 146
 
5.5%
립 138
 
5.2%
국 138
 
5.2%
질 138
 
5.2%
품 138
 
5.2%
물 138
 
5.2%
농 138
 
5.2%
Other values (116) 1099
41.2%
ASCII
ValueCountFrequency (%)
274
96.5%
) 5
 
1.8%
( 5
 
1.8%
None
ValueCountFrequency (%)
· 21
100.0%

소재지도로명주소
Text

Distinct137
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
2024-04-06T17:00:19.044228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length42
Median length36.5
Mean length31.137681
Min length19

Characters and Unicode

Total characters4297
Distinct characters246
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

Unique136 ?
Unique (%)98.6%

Sample

1st row경상북도 김천시 용전로 141 (율곡동 970)
2nd row경상북도 김천시 용전로 141 (율곡동 970)
3rd row경기도 안양시 만안구 안양로 114 (안양6동 532-9)
4th row서울특별시 송파구 충민로2길 12 (장지동 840-1)
5th row서울특별시 동대문구 장한로 11 (장안동 432-2)
ValueCountFrequency (%)
경상북도 24
 
2.6%
전라남도 19
 
2.1%
경기도 17
 
1.8%
경상남도 16
 
1.7%
강원도 15
 
1.6%
충청남도 13
 
1.4%
전라북도 13
 
1.4%
충청북도 9
 
1.0%
16 6
 
0.7%
읍내리 5
 
0.5%
Other values (744) 783
85.1%
2024-04-06T17:00:19.856130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
786
 
18.3%
1 207
 
4.8%
) 136
 
3.2%
( 136
 
3.2%
도 136
 
3.2%
- 132
 
3.1%
2 131
 
3.0%
로 107
 
2.5%
6 99
 
2.3%
3 94
 
2.2%
Other values (236) 2333
54.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2140
49.8%
Decimal Number 966
22.5%
Space Separator 786
 
18.3%
Close Punctuation 136
 
3.2%
Open Punctuation 136
 
3.2%
Dash Punctuation 132
 
3.1%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 136
 
6.4%
로 107
 
5.0%
동 87
 
4.1%
리 82
 
3.8%
군 80
 
3.7%
읍 79
 
3.7%
시 68
 
3.2%
남 66
 
3.1%
경 64
 
3.0%
북 54
 
2.5%
Other values (221) 1317
61.5%
Decimal Number
ValueCountFrequency (%)
1 207
21.4%
2 131
13.6%
6 99
10.2%
3 94
9.7%
4 89
9.2%
5 87
9.0%
7 68
 
7.0%
0 66
 
6.8%
8 64
 
6.6%
9 61
 
6.3%
Space Separator
ValueCountFrequency (%)
786
100.0%
Close Punctuation
ValueCountFrequency (%)
) 136
100.0%
Open Punctuation
ValueCountFrequency (%)
( 136
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 132
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2157
50.2%
Hangul 2140
49.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 136
 
6.4%
로 107
 
5.0%
동 87
 
4.1%
리 82
 
3.8%
군 80
 
3.7%
읍 79
 
3.7%
시 68
 
3.2%
남 66
 
3.1%
경 64
 
3.0%
북 54
 
2.5%
Other values (221) 1317
61.5%
Common
ValueCountFrequency (%)
786
36.4%
1 207
 
9.6%
) 136
 
6.3%
( 136
 
6.3%
- 132
 
6.1%
2 131
 
6.1%
6 99
 
4.6%
3 94
 
4.4%
4 89
 
4.1%
5 87
 
4.0%
Other values (5) 260
 
12.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2157
50.2%
Hangul 2140
49.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
786
36.4%
1 207
 
9.6%
) 136
 
6.3%
( 136
 
6.3%
- 132
 
6.1%
2 131
 
6.1%
6 99
 
4.6%
3 94
 
4.4%
4 89
 
4.1%
5 87
 
4.0%
Other values (5) 260
 
12.1%
Hangul
ValueCountFrequency (%)
도 136
 
6.4%
로 107
 
5.0%
동 87
 
4.1%
리 82
 
3.8%
군 80
 
3.7%
읍 79
 
3.7%
시 68
 
3.2%
남 66
 
3.1%
경 64
 
3.0%
북 54
 
2.5%
Other values (221) 1317
61.5%

설치목적구분
Categorical

CONSTANT 

Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
시설물관리
138 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
시설물관리 138
100.0%

Length

2024-04-06T17:00:20.095090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T17:00:20.255208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
시설물관리 138
100.0%

카메라대수
Real number (ℝ)

Distinct11
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.2246377
Minimum1
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-04-06T17:00:20.389838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median4
Q35
95-th percentile8.15
Maximum21
Range20
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.6138824
Coefficient of variation (CV)0.61872345
Kurtosis16.621966
Mean4.2246377
Median Absolute Deviation (MAD)1
Skewness3.1820401
Sum583
Variance6.8323813
MonotonicityNot monotonic
2024-04-06T17:00:20.564227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
3 34
24.6%
4 34
24.6%
5 27
19.6%
2 12
 
8.7%
1 11
 
8.0%
9 5
 
3.6%
7 5
 
3.6%
8 4
 
2.9%
6 4
 
2.9%
21 1
 
0.7%
ValueCountFrequency (%)
1 11
 
8.0%
2 12
 
8.7%
3 34
24.6%
4 34
24.6%
5 27
19.6%
6 4
 
2.9%
7 5
 
3.6%
8 4
 
2.9%
9 5
 
3.6%
18 1
 
0.7%
ValueCountFrequency (%)
21 1
 
0.7%
18 1
 
0.7%
9 5
 
3.6%
8 4
 
2.9%
7 5
 
3.6%
6 4
 
2.9%
5 27
19.6%
4 34
24.6%
3 34
24.6%
2 12
 
8.7%

관리기관전화번호
Text

Distinct132
Distinct (%)95.7%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
2024-04-06T17:00:20.885512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.014493
Min length12

Characters and Unicode

Total characters1658
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

Unique126 ?
Unique (%)91.3%

Sample

1st row054-429-4011
2nd row054-429-4011
3rd row031-470-2810
4th row02-3484-3310
5th row02-3484-3310
ValueCountFrequency (%)
054-429-4011 2
 
1.4%
054-830-0214 2
 
1.4%
054-440-0801 2
 
1.4%
02-3484-3310 2
 
1.4%
054-870-1413 2
 
1.4%
043-646-6060 2
 
1.4%
054-750-1301 1
 
0.7%
061-543-9556 1
 
0.7%
061-373-6161 1
 
0.7%
053-320-5201 1
 
0.7%
Other values (122) 122
88.4%
2024-04-06T17:00:21.418335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 387
23.3%
- 276
16.6%
6 183
11.0%
3 173
10.4%
1 152
 
9.2%
5 141
 
8.5%
4 132
 
8.0%
2 68
 
4.1%
8 59
 
3.6%
7 50
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1382
83.4%
Dash Punctuation 276
 
16.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 387
28.0%
6 183
13.2%
3 173
12.5%
1 152
 
11.0%
5 141
 
10.2%
4 132
 
9.6%
2 68
 
4.9%
8 59
 
4.3%
7 50
 
3.6%
9 37
 
2.7%
Dash Punctuation
ValueCountFrequency (%)
- 276
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1658
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 387
23.3%
- 276
16.6%
6 183
11.0%
3 173
10.4%
1 152
 
9.2%
5 141
 
8.5%
4 132
 
8.0%
2 68
 
4.1%
8 59
 
3.6%
7 50
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1658
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 387
23.3%
- 276
16.6%
6 183
11.0%
3 173
10.4%
1 152
 
9.2%
5 141
 
8.5%
4 132
 
8.0%
2 68
 
4.1%
8 59
 
3.6%
7 50
 
3.0%

위도
Real number (ℝ)

Distinct136
Distinct (%)98.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.228095
Minimum33.265555
Maximum38.386826
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-04-06T17:00:21.658909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33.265555
5-th percentile34.673418
Q135.417155
median36.150585
Q337.04619
95-th percentile37.893177
Maximum38.386826
Range5.1212715
Interquartile range (IQR)1.6290355

Descriptive statistics

Standard deviation1.0569032
Coefficient of variation (CV)0.02917358
Kurtosis-0.52657537
Mean36.228095
Median Absolute Deviation (MAD)0.8328875
Skewness-0.066102616
Sum4999.4771
Variance1.1170444
MonotonicityNot monotonic
2024-04-06T17:00:21.879440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36.123176 2
 
1.4%
35.94436 2
 
1.4%
36.0594517 1
 
0.7%
34.679734 1
 
0.7%
35.065723 1
 
0.7%
34.568395 1
 
0.7%
34.484512 1
 
0.7%
35.065153 1
 
0.7%
35.886231 1
 
0.7%
35.58157 1
 
0.7%
Other values (126) 126
91.3%
ValueCountFrequency (%)
33.265555 1
0.7%
33.490454 1
0.7%
34.317745 1
0.7%
34.484512 1
0.7%
34.568395 1
0.7%
34.609778 1
0.7%
34.637626 1
0.7%
34.679734 1
0.7%
34.756388 1
0.7%
34.763289 1
0.7%
ValueCountFrequency (%)
38.3868265 1
0.7%
38.1892406 1
0.7%
38.1455199 1
0.7%
38.1070208 1
0.7%
38.1053402 1
0.7%
38.067772 1
0.7%
37.904239 1
0.7%
37.891225 1
0.7%
37.855162 1
0.7%
37.833438 1
0.7%

경도
Text

Distinct136
Distinct (%)98.6%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
2024-04-06T17:00:22.305547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length10
Mean length10.231884
Min length8

Characters and Unicode

Total characters1412
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

Unique134 ?
Unique (%)97.1%

Sample

1st row128.196886
2nd row128.196886
3rd row126.933089
4th row127.126756
5th row127.064913
ValueCountFrequency (%)
128.196886 2
 
1.4%
128.5592 2
 
1.4%
126.933089 1
 
0.7%
128.6109981 1
 
0.7%
128.39955 1
 
0.7%
128.347785 1
 
0.7%
128.714091 1
 
0.7%
128.182013 1
 
0.7%
129.178123 1
 
0.7%
129.3766243 1
 
0.7%
Other values (126) 126
91.3%
2024-04-06T17:00:22.896589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 239
16.9%
2 211
14.9%
7 152
10.8%
. 140
9.9%
6 134
9.5%
8 130
9.2%
9 101
7.2%
5 82
 
5.8%
4 82
 
5.8%
3 71
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1272
90.1%
Other Punctuation 140
 
9.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 239
18.8%
2 211
16.6%
7 152
11.9%
6 134
10.5%
8 130
10.2%
9 101
7.9%
5 82
 
6.4%
4 82
 
6.4%
3 71
 
5.6%
0 70
 
5.5%
Other Punctuation
ValueCountFrequency (%)
. 140
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1412
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 239
16.9%
2 211
14.9%
7 152
10.8%
. 140
9.9%
6 134
9.5%
8 130
9.2%
9 101
7.2%
5 82
 
5.8%
4 82
 
5.8%
3 71
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1412
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 239
16.9%
2 211
14.9%
7 152
10.8%
. 140
9.9%
6 134
9.5%
8 130
9.2%
9 101
7.2%
5 82
 
5.8%
4 82
 
5.8%
3 71
 
5.0%

데이터기준일자
Date

CONSTANT 

Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
Minimum2022-04-05 00:00:00
Maximum2022-04-05 00:00:00
2024-04-06T17:00:23.069817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:00:23.203145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2024-04-06T17:00:16.661228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:00:16.040284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:00:16.812193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:00:16.518975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-06T17:00:23.320719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
카메라대수위도
카메라대수1.0000.000
위도0.0001.000
2024-04-06T17:00:23.428766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
카메라대수위도
카메라대수1.000-0.122
위도-0.1221.000

Missing values

2024-04-06T17:00:16.979854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-06T17:00:17.196702image/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

관리기관명소재지도로명주소설치목적구분카메라대수관리기관전화번호위도경도데이터기준일자
0국립농산물품질관리원 본원경상북도 김천시 용전로 141 (율곡동 970)시설물관리21054-429-401136.123176128.1968862022-04-05
1국립농산물품질관리원 시험연구소경상북도 김천시 용전로 141 (율곡동 970)시설물관리8054-429-401136.123176128.1968862022-04-05
2국립농산물품질관리원 경기지원 유통관리과경기도 안양시 만안구 안양로 114 (안양6동 532-9)시설물관리9031-470-281037.385509126.9330892022-04-05
3국립농산물품질관리원 경기지원 서울사무소서울특별시 송파구 충민로2길 12 (장지동 840-1)시설물관리202-3484-331037.478529127.1267562022-04-05
4국립농산물품질관리원 경기지원 서울사무소(분소)서울특별시 동대문구 장한로 11 (장안동 432-2)시설물관리102-3484-331037.562646127.0649132022-04-05
5국립농산물품질관리원 경기지원 인천사무소인천광역시 연수구 넘말로 47번길 30 (선학동 390-6)시설물관리3032-310-361037.429019126.69558432022-04-05
6국립농산물품질관리원 경기지원 수원사무소경기도 수원시 권선구 서호동로14번길 84 (서둔동 27-59)시설물관리3031-259-510037.264524126.99063192022-04-05
7국립농산물품질관리원 경기지원 가평사무소경기도 가평군 가평읍 석봉로 200-21 (읍내리 616-7)시설물관리4031-589-391037.833438127.50997632022-04-05
8국립농산물품질관리원 경기지원 광주사무소경기도 광주시 경충대로 1422번길 11-11 (쌍령동 337-5)시설물관리5031-538-691037.398549127.27586892022-04-05
9국립농산물품질관리원 경기지원 화성·오산사무소경기도 화성시 경기대로 1014 (병점동 379-10 병점프라자)시설물관리1031-538-131037.206449127.03662132022-04-05
관리기관명소재지도로명주소설치목적구분카메라대수관리기관전화번호위도경도데이터기준일자
128국립농산물품질관리원 경남지원 의령사무소경상남도 의령군 의령읍 백산로 16 (동동리 463-131)시설물관리4055-570-620135.321065128.26310492022-04-05
129국립농산물품질관리원 경남지원 창녕사무소경상남도 창녕군 창녕읍 여초길 104 (여초리 213)시설물관리4055-530-380135.512578128.50874962022-04-05
130국립농산물품질관리원 경남지원 하동사무소경상남도 하동군 하동읍 경서대로 75 (광평리 427)시설물관리6055-880-561335.064478127.7447362022-04-05
131국립농산물품질관리원 경남지원 남해사무소경상남도 남해군 고현면 탑동로 37 (대사리 684)시설물관리8055-864-606034.894764127.8741282022-04-05
132국립농산물품질관리원 경남지원 함양사무소경상남도 함양군 함양읍 영림서길 32 (백연리 67-1)시설물관리4055-962-606035.515205127.7248732022-04-05
133국립농산물품질관리원 경남지원 산청사무소경상남도 산청군 산청읍 중앙로 26 (산청리 239)시설물관리5055-970-160135.415005127.8771682022-04-05
134국립농산물품질관리원 경남지원 합천사무소경상남도 합천군 합천읍 핫들1로 50 (합천리 1636)시설물관리5055-931-606035.572625128.1655542022-04-05
135국립농산물품질관리원 경남지원 거창사무소경상남도 거창군 거창읍 수남로 2119 (정장리 967-53)시설물관리5055-940-460135.668035127.9110562022-04-05
136국립농산물품질관리원 제주지원 경영직불팀제주특별자치도 청사로 59 (도남동 662)시설물관리1064-728-525333.490454126.52530792022-04-05
137국립농산물품질관리원 제주지원 서귀포사무소제주특별자치도 서귀포시 신효중앙로 17 (신효동 633-2)시설물관리5064-735-490333.265555126.613572022-04-05