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
Categorical2
Numeric2

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
소재지도로명주소 has unique valuesUnique

Reproduction

Analysis started2024-04-06 08:00:33.018675
Analysis finished2024-04-06 08:00:34.830449
Duration1.81 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
2024-04-06T17:00:35.259528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length30
Median length21
Mean length21.73913
Min length19

Characters and Unicode

Total characters3000
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·시험연구소
2nd row국립농산물품질관리원 경기지원 유통관리과
3rd row국립농산물품질관리원 경기지원 서울사무소
4th row국립농산물품질관리원 경기지원 서울사무소(분소)
5th row국립농산물품질관리원 경기지원 인천사무소
ValueCountFrequency (%)
국립농산물품질관리원 138
33.4%
경북지원 24
 
5.8%
전남지원 20
 
4.8%
경기지원 20
 
4.8%
경남지원 18
 
4.4%
충남지원 16
 
3.9%
강원지원 15
 
3.6%
전북지원 13
 
3.1%
충북지원 9
 
2.2%
유통관리과 8
 
1.9%
Other values (129) 132
32.0%
2024-04-06T17:00:36.035763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
원 295
 
9.8%
275
 
9.2%
산 153
 
5.1%
리 147
 
4.9%
관 146
 
4.9%
국 138
 
4.6%
농 138
 
4.6%
물 138
 
4.6%
품 138
 
4.6%
질 138
 
4.6%
Other values (120) 1294
43.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2687
89.6%
Space Separator 275
 
9.2%
Other Punctuation 26
 
0.9%
Close Punctuation 6
 
0.2%
Open Punctuation 6
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
원 295
 
11.0%
산 153
 
5.7%
리 147
 
5.5%
관 146
 
5.4%
국 138
 
5.1%
농 138
 
5.1%
물 138
 
5.1%
품 138
 
5.1%
질 138
 
5.1%
립 138
 
5.1%
Other values (116) 1118
41.6%
Space Separator
ValueCountFrequency (%)
275
100.0%
Other Punctuation
ValueCountFrequency (%)
· 26
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2687
89.6%
Common 313
 
10.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
원 295
 
11.0%
산 153
 
5.7%
리 147
 
5.5%
관 146
 
5.4%
국 138
 
5.1%
농 138
 
5.1%
물 138
 
5.1%
품 138
 
5.1%
질 138
 
5.1%
립 138
 
5.1%
Other values (116) 1118
41.6%
Common
ValueCountFrequency (%)
275
87.9%
· 26
 
8.3%
) 6
 
1.9%
( 6
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2687
89.6%
ASCII 287
 
9.6%
None 26
 
0.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
원 295
 
11.0%
산 153
 
5.7%
리 147
 
5.5%
관 146
 
5.4%
국 138
 
5.1%
농 138
 
5.1%
물 138
 
5.1%
품 138
 
5.1%
질 138
 
5.1%
립 138
 
5.1%
Other values (116) 1118
41.6%
ASCII
ValueCountFrequency (%)
275
95.8%
) 6
 
2.1%
( 6
 
2.1%
None
ValueCountFrequency (%)
· 26
100.0%

소재지도로명주소
Text

UNIQUE 

Distinct138
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
2024-04-06T17:00:36.684343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length42
Median length38
Mean length31.637681
Min length22

Characters and Unicode

Total characters4366
Distinct characters254
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

Unique138 ?
Unique (%)100.0%

Sample

1st row경상북도 김천시 용전로 141 (율곡동 970)
2nd row경기도 안양시 만안구 안양로 114 (안양6동 532-9)
3rd row서울특별시 송파구 충민로2길 12 (장지동 840-1)
4th row서울특별시 마포구 토정로 194(구수동 68-29)
5th row인천광역시 연수구 넘말로 47번길 30 (선학동 390-6)
ValueCountFrequency (%)
경상북도 23
 
2.5%
전라남도 19
 
2.0%
경기도 17
 
1.8%
경상남도 16
 
1.7%
강원도 15
 
1.6%
충청남도 14
 
1.5%
전라북도 13
 
1.4%
충청북도 9
 
1.0%
중앙로 5
 
0.5%
읍내리 5
 
0.5%
Other values (757) 792
85.3%
2024-04-06T17:00:37.583387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
794
 
18.2%
1 202
 
4.6%
2 138
 
3.2%
) 138
 
3.2%
( 138
 
3.2%
도 136
 
3.1%
- 133
 
3.0%
로 105
 
2.4%
6 99
 
2.3%
5 91
 
2.1%
Other values (244) 2392
54.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2183
50.0%
Decimal Number 977
22.4%
Space Separator 794
 
18.2%
Close Punctuation 138
 
3.2%
Open Punctuation 138
 
3.2%
Dash Punctuation 133
 
3.0%
Other Punctuation 3
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 136
 
6.2%
로 105
 
4.8%
리 86
 
3.9%
동 82
 
3.8%
읍 81
 
3.7%
군 80
 
3.7%
시 67
 
3.1%
남 66
 
3.0%
경 63
 
2.9%
북 53
 
2.4%
Other values (229) 1364
62.5%
Decimal Number
ValueCountFrequency (%)
1 202
20.7%
2 138
14.1%
6 99
10.1%
5 91
9.3%
3 91
9.3%
4 88
9.0%
8 68
 
7.0%
0 68
 
7.0%
9 66
 
6.8%
7 66
 
6.8%
Space Separator
ValueCountFrequency (%)
794
100.0%
Close Punctuation
ValueCountFrequency (%)
) 138
100.0%
Open Punctuation
ValueCountFrequency (%)
( 138
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 133
100.0%
Other Punctuation
ValueCountFrequency (%)
, 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2183
50.0%
Hangul 2183
50.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 136
 
6.2%
로 105
 
4.8%
리 86
 
3.9%
동 82
 
3.8%
읍 81
 
3.7%
군 80
 
3.7%
시 67
 
3.1%
남 66
 
3.0%
경 63
 
2.9%
북 53
 
2.4%
Other values (229) 1364
62.5%
Common
ValueCountFrequency (%)
794
36.4%
1 202
 
9.3%
2 138
 
6.3%
) 138
 
6.3%
( 138
 
6.3%
- 133
 
6.1%
6 99
 
4.5%
5 91
 
4.2%
3 91
 
4.2%
4 88
 
4.0%
Other values (5) 271
 
12.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2183
50.0%
Hangul 2183
50.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
794
36.4%
1 202
 
9.3%
2 138
 
6.3%
) 138
 
6.3%
( 138
 
6.3%
- 133
 
6.1%
6 99
 
4.5%
5 91
 
4.2%
3 91
 
4.2%
4 88
 
4.0%
Other values (5) 271
 
12.4%
Hangul
ValueCountFrequency (%)
도 136
 
6.2%
로 105
 
4.8%
리 86
 
3.9%
동 82
 
3.8%
읍 81
 
3.7%
군 80
 
3.7%
시 67
 
3.1%
남 66
 
3.0%
경 63
 
2.9%
북 53
 
2.4%
Other values (229) 1364
62.5%

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

2024-04-06T17:00:37.824534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T17:00:37.986633image/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%
Mean4.5869565
Minimum1
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-04-06T17:00:38.126645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median4
Q35
95-th percentile8
Maximum30
Range29
Interquartile range (IQR)2

Descriptive statistics

Standard deviation3.0416291
Coefficient of variation (CV)0.66310397
Kurtosis37.338218
Mean4.5869565
Median Absolute Deviation (MAD)1
Skewness4.9476116
Sum633
Variance9.2515075
MonotonicityNot monotonic
2024-04-06T17:00:38.314423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
4 34
24.6%
3 31
22.5%
5 25
18.1%
6 14
10.1%
1 8
 
5.8%
7 8
 
5.8%
2 8
 
5.8%
8 5
 
3.6%
9 3
 
2.2%
30 1
 
0.7%
ValueCountFrequency (%)
1 8
 
5.8%
2 8
 
5.8%
3 31
22.5%
4 34
24.6%
5 25
18.1%
6 14
10.1%
7 8
 
5.8%
8 5
 
3.6%
9 3
 
2.2%
18 1
 
0.7%
ValueCountFrequency (%)
30 1
 
0.7%
18 1
 
0.7%
9 3
 
2.2%
8 5
 
3.6%
7 8
 
5.8%
6 14
10.1%
5 25
18.1%
4 34
24.6%
3 31
22.5%
2 8
 
5.8%

관리기관전화번호
Text

Distinct134
Distinct (%)97.1%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
2024-04-06T17:00:38.654014image/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

Unique130 ?
Unique (%)94.2%

Sample

1st row054-429-4011
2nd row031-470-2810
3rd row02-3484-3310
4th row02-2218-4010
5th row032-310-3610
ValueCountFrequency (%)
033-815-8490 2
 
1.4%
054-830-0201 2
 
1.4%
054-870-1401 2
 
1.4%
054-440-0801 2
 
1.4%
061-830-2401 1
 
0.7%
061-530-0660 1
 
0.7%
061-540-9580 1
 
0.7%
061-470-3050 1
 
0.7%
061-351-2016 1
 
0.7%
061-320-4020 1
 
0.7%
Other values (124) 124
89.9%
2024-04-06T17:00:39.290727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 396
23.9%
- 276
16.6%
1 164
9.9%
3 163
9.8%
6 152
 
9.2%
5 144
 
8.7%
4 125
 
7.5%
2 70
 
4.2%
8 69
 
4.2%
7 56
 
3.4%

Most occurring categories

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

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 396
28.7%
1 164
11.9%
3 163
11.8%
6 152
 
11.0%
5 144
 
10.4%
4 125
 
9.0%
2 70
 
5.1%
8 69
 
5.0%
7 56
 
4.1%
9 43
 
3.1%
Dash Punctuation
ValueCountFrequency (%)
- 276
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1658
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 396
23.9%
- 276
16.6%
1 164
9.9%
3 163
9.8%
6 152
 
9.2%
5 144
 
8.7%
4 125
 
7.5%
2 70
 
4.2%
8 69
 
4.2%
7 56
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1658
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 396
23.9%
- 276
16.6%
1 164
9.9%
3 163
9.8%
6 152
 
9.2%
5 144
 
8.7%
4 125
 
7.5%
2 70
 
4.2%
8 69
 
4.2%
7 56
 
3.4%

위도
Real number (ℝ)

Distinct137
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.236606
Minimum33.265555
Maximum38.386826
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-04-06T17:00:39.563071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33.265555
5-th percentile34.673418
Q135.417155
median36.181729
Q337.04619
95-th percentile37.893177
Maximum38.386826
Range5.1212715
Interquartile range (IQR)1.6290355

Descriptive statistics

Standard deviation1.0587265
Coefficient of variation (CV)0.029217043
Kurtosis-0.54397158
Mean36.236606
Median Absolute Deviation (MAD)0.81583985
Skewness-0.086716116
Sum5000.6516
Variance1.1209018
MonotonicityNot monotonic
2024-04-06T17:00:39.798002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.94436 2
 
1.4%
36.123176 1
 
0.7%
34.337712 1
 
0.7%
34.568395 1
 
0.7%
34.480706 1
 
0.7%
34.805211 1
 
0.7%
35.271753 1
 
0.7%
35.065723 1
 
0.7%
36.0627905 1
 
0.7%
35.991346 1
 
0.7%
Other values (127) 127
92.0%
ValueCountFrequency (%)
33.265555 1
0.7%
33.490454 1
0.7%
34.337712 1
0.7%
34.480706 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%
38.1068775 1
0.7%
38.1061629 1
0.7%
38.066435 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

Distinct137
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
2024-04-06T17:00:40.169986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length10
Mean length10.23913
Min length8

Characters and Unicode

Total characters1413
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

Unique136 ?
Unique (%)98.6%

Sample

1st row128.196886
2nd row126.933089
3rd row127.126756
4th row126.9323909
5th row126.6955843
ValueCountFrequency (%)
128.5592 2
 
1.4%
126.724856 1
 
0.7%
128.7113808 1
 
0.7%
128.8117803 1
 
0.7%
128.916077 1
 
0.7%
128.714091 1
 
0.7%
128.182013 1
 
0.7%
129.178123 1
 
0.7%
129.316541 1
 
0.7%
128.196886 1
 
0.7%
Other values (127) 127
92.0%
2024-04-06T17:00:40.853916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 235
16.6%
2 217
15.4%
7 155
11.0%
. 139
9.8%
6 131
9.3%
8 129
9.1%
9 96
6.8%
5 88
 
6.2%
4 85
 
6.0%
3 70
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1274
90.2%
Other Punctuation 139
 
9.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 235
18.4%
2 217
17.0%
7 155
12.2%
6 131
10.3%
8 129
10.1%
9 96
7.5%
5 88
 
6.9%
4 85
 
6.7%
3 70
 
5.5%
0 68
 
5.3%
Other Punctuation
ValueCountFrequency (%)
. 139
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1413
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 235
16.6%
2 217
15.4%
7 155
11.0%
. 139
9.8%
6 131
9.3%
8 129
9.1%
9 96
6.8%
5 88
 
6.2%
4 85
 
6.0%
3 70
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1413
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 235
16.6%
2 217
15.4%
7 155
11.0%
. 139
9.8%
6 131
9.3%
8 129
9.1%
9 96
6.8%
5 88
 
6.2%
4 85
 
6.0%
3 70
 
5.0%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
2024-03-29
138 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2024-03-29
2nd row2024-03-29
3rd row2024-03-29
4th row2024-03-29
5th row2024-03-29

Common Values

ValueCountFrequency (%)
2024-03-29 138
100.0%

Length

2024-04-06T17:00:41.111897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T17:00:41.300791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2024-03-29 138
100.0%

Interactions

2024-04-06T17:00:33.969143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:00:33.424929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:00:34.169613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:00:33.576551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-06T17:00:41.408300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
카메라대수위도
카메라대수1.0000.000
위도0.0001.000
2024-04-06T17:00:41.563741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
카메라대수위도
카메라대수1.000-0.159
위도-0.1591.000

Missing values

2024-04-06T17:00:34.435157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-06T17:00:34.718307image/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 본원·시험연구소경상북도 김천시 용전로 141 (율곡동 970)시설물관리30054-429-401136.123176128.1968862024-03-29
1국립농산물품질관리원 경기지원 유통관리과경기도 안양시 만안구 안양로 114 (안양6동 532-9)시설물관리9031-470-281037.385509126.9330892024-03-29
2국립농산물품질관리원 경기지원 서울사무소서울특별시 송파구 충민로2길 12 (장지동 840-1)시설물관리502-3484-331037.478529127.1267562024-03-29
3국립농산물품질관리원 경기지원 서울사무소(분소)서울특별시 마포구 토정로 194(구수동 68-29)시설물관리102-2218-401037.545215126.93239092024-03-29
4국립농산물품질관리원 경기지원 인천사무소인천광역시 연수구 넘말로 47번길 30 (선학동 390-6)시설물관리3032-310-361037.429019126.69558432024-03-29
5국립농산물품질관리원 경기지원 수원사무소경기도 수원시 권선구 서호동로14번길 84 (서둔동 27-59)시설물관리3031-259-511037.264524126.99063192024-03-29
6국립농산물품질관리원 경기지원 화성·오산사무소경기도 화성시 경기대로 1014 병점프라자 503호(병점동 379-10)시설물관리1031-538-131037.206449127.03662132024-03-29
7국립농산물품질관리원 경기지원 의정부사무소경기도 의정부시 금오로 56 (금오동 140-1)시설물관리7031-839-181037.754924127.0715542024-03-29
8국립농산물품질관리원 경기지원 평택사무소경기도 평택시 고덕면 고덕순환대로 568 (여염리 4234-2)시설물관리6031-550-441037.058376127.04785612024-03-29
9국립농산물품질관리원 경기지원 안성사무소경기도 안성시 석정1길 14 (석정동 265-2)시설물관리3031-8046-371037.009633127.26248742024-03-29
관리기관명소재지도로명주소설치목적구분카메라대수관리기관전화번호위도경도데이터기준일자
128국립농산물품질관리원 경남지원 의령사무소경상남도 의령군 의령읍 의합대로 44-6(무전리 173)시설물관리6055-570-620135.321072128.28260922024-03-29
129국립농산물품질관리원 경남지원 창녕사무소경상남도 창녕군 창녕읍 여초길 104 (여초리 213)시설물관리4055-530-380135.512578128.50874962024-03-29
130국립농산물품질관리원 경남지원 하동사무소경상남도 하동군 하동읍 경서대로 75 (광평리 427)시설물관리5055-884-606035.064478127.7447362024-03-29
131국립농산물품질관리원 경남지원 남해사무소경상남도 남해군 고현면 탑동로 37 (대사리 684)시설물관리8055-864-606034.894764127.8741282024-03-29
132국립농산물품질관리원 경남지원 함양사무소경상남도 함양군 함양읍 영림서길 32 (백연리 67-1)시설물관리4055-962-606035.515205127.7248732024-03-29
133국립농산물품질관리원 경남지원 산청사무소경상남도 산청군 산청읍 중앙로 26 (산청리 239)시설물관리6055-970-160135.415005127.8771682024-03-29
134국립농산물품질관리원 경남지원 합천사무소경상남도 합천군 합천읍 핫들1로 50 (합천리 1636)시설물관리5055-930-180135.572625128.1655542024-03-29
135국립농산물품질관리원 경남지원 거창사무소경상남도 거창군 거창읍 수남로 2119 (정장리 967-53)시설물관리5055-940-460135.668035127.9110562024-03-29
136국립농산물품질관리원 제주지원 경영직불팀제주특별자치도 청사로 59 (도남동 662)시설물관리2064-728-525033.490454126.52530792024-03-29
137국립농산물품질관리원 제주지원 서귀포사무소제주특별자치도 서귀포시 신효중앙로 17 (신효동 633-2)시설물관리4064-735-490333.265555126.613572024-03-29