Overview

Dataset statistics

Number of variables9
Number of observations38
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.9 KiB
Average record size in memory78.5 B

Variable types

Numeric3
Text3
DateTime1
Categorical2

Dataset

Description경상남도 시군별 상수원 보호구역 및 지정일자 등 현황입니다. 보호구역, 면적, 지정일자, 취수장 등에 관한 정보를 제공합니다.
URLhttps://www.data.go.kr/data/3034246/fileData.do

Alerts

연번 is highly overall correlated with 시군High correlation
면적(킬로미터) is highly overall correlated with 시군High correlation
거주인구(명) is highly overall correlated with 시군High correlation
시군 is highly overall correlated with 연번 and 3 other fieldsHigh correlation
수도사업자 is highly overall correlated with 시군High correlation
연번 has unique valuesUnique
면적(킬로미터) has unique valuesUnique
거주인구(명) has 26 (68.4%) zerosZeros

Reproduction

Analysis started2023-12-12 07:05:09.105264
Analysis finished2023-12-12 07:05:10.824727
Duration1.72 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct38
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.789474
Minimum1
Maximum40
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size474.0 B
2023-12-12T16:05:10.925581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.85
Q110.25
median19.5
Q328.75
95-th percentile37.3
Maximum40
Range39
Interquartile range (IQR)18.5

Descriptive statistics

Standard deviation11.524728
Coefficient of variation (CV)0.58236655
Kurtosis-1.146422
Mean19.789474
Median Absolute Deviation (MAD)9.5
Skewness0.079621825
Sum752
Variance132.81935
MonotonicityStrictly increasing
2023-12-12T16:05:11.085603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
1 1
 
2.6%
31 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%
32 1
 
2.6%
Other values (28) 28
73.7%
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 (%)
40 1
2.6%
39 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%
29 1
2.6%

보호구역
Text

Distinct36
Distinct (%)94.7%
Missing0
Missing (%)0.0%
Memory size436.0 B
2023-12-12T16:05:11.368735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length2
Mean length2.2894737
Min length2

Characters and Unicode

Total characters87
Distinct characters55
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique34 ?
Unique (%)89.5%

Sample

1st row성주 수원지
2nd row진양호
3rd row욕지
4th row우동
5th row곤명
ValueCountFrequency (%)
진양호 2
 
5.1%
밀양댐 2
 
5.1%
거창 1
 
2.6%
옥천 1
 
2.6%
하동 1
 
2.6%
옥종 1
 
2.6%
항도 1
 
2.6%
남면 1
 
2.6%
대곡 1
 
2.6%
선원 1
 
2.6%
Other values (27) 27
69.2%
2023-12-12T16:05:11.819889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
양 6
 
6.9%
진 4
 
4.6%
동 4
 
4.6%
호 3
 
3.4%
댐 3
 
3.4%
지 3
 
3.4%
주 3
 
3.4%
성 3
 
3.4%
안 2
 
2.3%
원 2
 
2.3%
Other values (45) 54
62.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 85
97.7%
Space Separator 1
 
1.1%
Decimal Number 1
 
1.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
양 6
 
7.1%
진 4
 
4.7%
동 4
 
4.7%
호 3
 
3.5%
댐 3
 
3.5%
지 3
 
3.5%
주 3
 
3.5%
성 3
 
3.5%
안 2
 
2.4%
원 2
 
2.4%
Other values (43) 52
61.2%
Space Separator
ValueCountFrequency (%)
1
100.0%
Decimal Number
ValueCountFrequency (%)
2 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 85
97.7%
Common 2
 
2.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
양 6
 
7.1%
진 4
 
4.7%
동 4
 
4.7%
호 3
 
3.5%
댐 3
 
3.5%
지 3
 
3.5%
주 3
 
3.5%
성 3
 
3.5%
안 2
 
2.4%
원 2
 
2.4%
Other values (43) 52
61.2%
Common
ValueCountFrequency (%)
1
50.0%
2 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 85
97.7%
ASCII 2
 
2.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
양 6
 
7.1%
진 4
 
4.7%
동 4
 
4.7%
호 3
 
3.5%
댐 3
 
3.5%
지 3
 
3.5%
주 3
 
3.5%
성 3
 
3.5%
안 2
 
2.4%
원 2
 
2.4%
Other values (43) 52
61.2%
ASCII
ValueCountFrequency (%)
1
50.0%
2 1
50.0%

면적(킬로미터)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct38
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.1893684
Minimum0.012
Maximum39.821
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size474.0 B
2023-12-12T16:05:11.986695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.012
5-th percentile0.23795
Q10.62775
median1.7415
Q33.178
95-th percentile6.46465
Maximum39.821
Range39.809
Interquartile range (IQR)2.55025

Descriptive statistics

Standard deviation6.4823297
Coefficient of variation (CV)2.0324807
Kurtosis29.254143
Mean3.1893684
Median Absolute Deviation (MAD)1.2285
Skewness5.173659
Sum121.196
Variance42.020599
MonotonicityNot monotonic
2023-12-12T16:05:12.166583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
5.359 1
 
2.6%
0.264 1
 
2.6%
1.317 1
 
2.6%
1.17 1
 
2.6%
0.957 1
 
2.6%
0.822 1
 
2.6%
0.012 1
 
2.6%
3.573 1
 
2.6%
3.061 1
 
2.6%
0.4 1
 
2.6%
Other values (28) 28
73.7%
ValueCountFrequency (%)
0.012 1
2.6%
0.13 1
2.6%
0.257 1
2.6%
0.264 1
2.6%
0.281 1
2.6%
0.4 1
2.6%
0.496 1
2.6%
0.518 1
2.6%
0.537 1
2.6%
0.614 1
2.6%
ValueCountFrequency (%)
39.821 1
2.6%
11.336 1
2.6%
5.605 1
2.6%
5.359 1
2.6%
4.872 1
2.6%
4.54 1
2.6%
4.371 1
2.6%
3.75 1
2.6%
3.573 1
2.6%
3.217 1
2.6%

지정일자
Date

Distinct30
Distinct (%)78.9%
Missing0
Missing (%)0.0%
Memory size436.0 B
Minimum1964-08-03 00:00:00
Maximum2017-04-26 00:00:00
2023-12-12T16:05:12.327668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:05:12.465795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)

취수장
Text

Distinct36
Distinct (%)94.7%
Missing0
Missing (%)0.0%
Memory size436.0 B
2023-12-12T16:05:12.702009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length2
Mean length2.2894737
Min length2

Characters and Unicode

Total characters87
Distinct characters56
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique34 ?
Unique (%)89.5%

Sample

1st row성주 수원지
2nd row진양호
3rd row욕지
4th row우동
5th row곤명
ValueCountFrequency (%)
진양호 2
 
5.1%
밀양댐 2
 
5.1%
거창 1
 
2.6%
옥천 1
 
2.6%
하동 1
 
2.6%
청룡 1
 
2.6%
항도 1
 
2.6%
남면 1
 
2.6%
대곡 1
 
2.6%
선원 1
 
2.6%
Other values (27) 27
69.2%
2023-12-12T16:05:13.086015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
양 6
 
6.9%
동 4
 
4.6%
진 3
 
3.4%
호 3
 
3.4%
댐 3
 
3.4%
지 3
 
3.4%
주 3
 
3.4%
성 3
 
3.4%
안 2
 
2.3%
원 2
 
2.3%
Other values (46) 55
63.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 85
97.7%
Space Separator 1
 
1.1%
Decimal Number 1
 
1.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
양 6
 
7.1%
동 4
 
4.7%
진 3
 
3.5%
호 3
 
3.5%
댐 3
 
3.5%
지 3
 
3.5%
주 3
 
3.5%
성 3
 
3.5%
안 2
 
2.4%
원 2
 
2.4%
Other values (44) 53
62.4%
Space Separator
ValueCountFrequency (%)
1
100.0%
Decimal Number
ValueCountFrequency (%)
2 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 85
97.7%
Common 2
 
2.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
양 6
 
7.1%
동 4
 
4.7%
진 3
 
3.5%
호 3
 
3.5%
댐 3
 
3.5%
지 3
 
3.5%
주 3
 
3.5%
성 3
 
3.5%
안 2
 
2.4%
원 2
 
2.4%
Other values (44) 53
62.4%
Common
ValueCountFrequency (%)
1
50.0%
2 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 85
97.7%
ASCII 2
 
2.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
양 6
 
7.1%
동 4
 
4.7%
진 3
 
3.5%
호 3
 
3.5%
댐 3
 
3.5%
지 3
 
3.5%
주 3
 
3.5%
성 3
 
3.5%
안 2
 
2.4%
원 2
 
2.4%
Other values (44) 53
62.4%
ASCII
ValueCountFrequency (%)
1
50.0%
2 1
50.0%

시군
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)42.1%
Missing0
Missing (%)0.0%
Memory size436.0 B
남해군
12 
하동군
3 
산청군
3 
통영시
2 
사천시
2 
Other values (11)
16 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique6 ?
Unique (%)15.8%

Sample

1st row창원시
2nd row진주시
3rd row통영시
4th row통영시
5th row사천시

Common Values

ValueCountFrequency (%)
남해군 12
31.6%
하동군 3
 
7.9%
산청군 3
 
7.9%
통영시 2
 
5.3%
사천시 2
 
5.3%
밀양시 2
 
5.3%
함안군 2
 
5.3%
함양군 2
 
5.3%
거창군 2
 
5.3%
합천군 2
 
5.3%
Other values (6) 6
15.8%

Length

2023-12-12T16:05:13.247497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
남해군 12
31.6%
하동군 3
 
7.9%
산청군 3
 
7.9%
통영시 2
 
5.3%
사천시 2
 
5.3%
밀양시 2
 
5.3%
함안군 2
 
5.3%
함양군 2
 
5.3%
거창군 2
 
5.3%
합천군 2
 
5.3%
Other values (6) 6
15.8%

읍면동
Text

Distinct33
Distinct (%)86.8%
Missing0
Missing (%)0.0%
Memory size436.0 B
2023-12-12T16:05:13.458772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length3
Mean length4.2105263
Min length2

Characters and Unicode

Total characters160
Distinct characters67
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique29 ?
Unique (%)76.3%

Sample

1st row천선동
2nd row판문동, 내동면, 명석면, 대평면,수곡면
3rd row욕지면
4th row광도면
5th row곤명면
ValueCountFrequency (%)
남해읍 3
 
6.4%
고현면 2
 
4.3%
미조면 2
 
4.3%
곤명면 2
 
4.3%
단성면 2
 
4.3%
옥종면 1
 
2.1%
천선동 1
 
2.1%
창선면 1
 
2.1%
화개면 1
 
2.1%
쌍책면 1
 
2.1%
Other values (31) 31
66.0%
2023-12-12T16:05:13.840769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
면 39
24.4%
9
 
5.6%
동 8
 
5.0%
, 7
 
4.4%
읍 5
 
3.1%
남 4
 
2.5%
해 3
 
1.9%
단 3
 
1.9%
조 3
 
1.9%
고 3
 
1.9%
Other values (57) 76
47.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 142
88.8%
Space Separator 9
 
5.6%
Other Punctuation 9
 
5.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
면 39
27.5%
동 8
 
5.6%
읍 5
 
3.5%
남 4
 
2.8%
해 3
 
2.1%
단 3
 
2.1%
조 3
 
2.1%
고 3
 
2.1%
명 3
 
2.1%
안 3
 
2.1%
Other values (54) 68
47.9%
Other Punctuation
ValueCountFrequency (%)
, 7
77.8%
. 2
 
22.2%
Space Separator
ValueCountFrequency (%)
9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 142
88.8%
Common 18
 
11.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
면 39
27.5%
동 8
 
5.6%
읍 5
 
3.5%
남 4
 
2.8%
해 3
 
2.1%
단 3
 
2.1%
조 3
 
2.1%
고 3
 
2.1%
명 3
 
2.1%
안 3
 
2.1%
Other values (54) 68
47.9%
Common
ValueCountFrequency (%)
9
50.0%
, 7
38.9%
. 2
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 142
88.8%
ASCII 18
 
11.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
면 39
27.5%
동 8
 
5.6%
읍 5
 
3.5%
남 4
 
2.8%
해 3
 
2.1%
단 3
 
2.1%
조 3
 
2.1%
고 3
 
2.1%
명 3
 
2.1%
안 3
 
2.1%
Other values (54) 68
47.9%
ASCII
ValueCountFrequency (%)
9
50.0%
, 7
38.9%
. 2
 
11.1%

수도사업자
Categorical

HIGH CORRELATION 

Distinct15
Distinct (%)39.5%
Missing0
Missing (%)0.0%
Memory size436.0 B
남해군
12 
수공
5 
하동군
3 
통영시
2 
함안군
2 
Other values (10)
14 

Length

Max length6
Median length3
Mean length2.9473684
Min length2

Unique

Unique6 ?
Unique (%)15.8%

Sample

1st row창원시
2nd row진주시 수공
3rd row통영시
4th row통영시
5th row사천시

Common Values

ValueCountFrequency (%)
남해군 12
31.6%
수공 5
13.2%
하동군 3
 
7.9%
통영시 2
 
5.3%
함안군 2
 
5.3%
산청군 2
 
5.3%
함양군 2
 
5.3%
거창군 2
 
5.3%
합천군 2
 
5.3%
창원시 1
 
2.6%
Other values (5) 5
13.2%

Length

2023-12-12T16:05:13.991563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
남해군 12
30.8%
수공 6
15.4%
하동군 3
 
7.7%
통영시 2
 
5.1%
함안군 2
 
5.1%
산청군 2
 
5.1%
함양군 2
 
5.1%
거창군 2
 
5.1%
합천군 2
 
5.1%
창원시 1
 
2.6%
Other values (5) 5
12.8%

거주인구(명)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct12
Distinct (%)31.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean55.815789
Minimum0
Maximum696
Zeros26
Zeros (%)68.4%
Negative0
Negative (%)0.0%
Memory size474.0 B
2023-12-12T16:05:14.100926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33
95-th percentile416.7
Maximum696
Range696
Interquartile range (IQR)3

Descriptive statistics

Standard deviation161.32942
Coefficient of variation (CV)2.8903903
Kurtosis10.463689
Mean55.815789
Median Absolute Deviation (MAD)0
Skewness3.308795
Sum2121
Variance26027.181
MonotonicityNot monotonic
2023-12-12T16:05:14.202852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 26
68.4%
3 2
 
5.3%
10 1
 
2.6%
138 1
 
2.6%
2 1
 
2.6%
378 1
 
2.6%
636 1
 
2.6%
6 1
 
2.6%
136 1
 
2.6%
4 1
 
2.6%
Other values (2) 2
 
5.3%
ValueCountFrequency (%)
0 26
68.4%
2 1
 
2.6%
3 2
 
5.3%
4 1
 
2.6%
6 1
 
2.6%
10 1
 
2.6%
109 1
 
2.6%
136 1
 
2.6%
138 1
 
2.6%
378 1
 
2.6%
ValueCountFrequency (%)
696 1
2.6%
636 1
2.6%
378 1
2.6%
138 1
2.6%
136 1
2.6%
109 1
2.6%
10 1
2.6%
6 1
2.6%
4 1
2.6%
3 2
5.3%

Interactions

2023-12-12T16:05:10.253893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:05:09.560190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:05:09.897567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:05:10.373982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:05:09.669254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:05:10.010339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:05:10.477535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:05:09.785506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:05:10.128098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T16:05:14.289307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번보호구역면적(킬로미터)지정일자취수장시군읍면동수도사업자거주인구(명)
연번1.0000.8750.1500.9720.8750.8780.9180.8730.000
보호구역0.8751.0000.0000.8651.0000.0000.9260.9530.000
면적(킬로미터)0.1500.0001.0000.9810.0000.9940.9680.8090.957
지정일자0.9720.8650.9811.0000.8651.0000.9661.0000.107
취수장0.8751.0000.0000.8651.0000.0000.9260.9530.000
시군0.8780.0000.9941.0000.0001.0001.0000.9860.964
읍면동0.9180.9260.9680.9660.9261.0001.0000.9850.937
수도사업자0.8730.9530.8091.0000.9530.9860.9851.0000.000
거주인구(명)0.0000.0000.9570.1070.0000.9640.9370.0001.000
2023-12-12T16:05:14.395706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
수도사업자시군
수도사업자1.0000.875
시군0.8751.000
2023-12-12T16:05:14.481205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번면적(킬로미터)거주인구(명)시군수도사업자
연번1.000-0.421-0.1340.5230.499
면적(킬로미터)-0.4211.0000.4850.7220.498
거주인구(명)-0.1340.4851.0000.6120.000
시군0.5230.7220.6121.0000.875
수도사업자0.4990.4980.0000.8751.000

Missing values

2023-12-12T16:05:10.607186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T16:05:10.769273image/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

연번보호구역면적(킬로미터)지정일자취수장시군읍면동수도사업자거주인구(명)
01성주 수원지5.3591964-08-03성주 수원지창원시천선동창원시10
12진양호39.8212004-06-11진양호진주시판문동, 내동면, 명석면, 대평면,수곡면진주시 수공138
23욕지0.5371983-06-23욕지통영시욕지면통영시2
34우동2.4651982-01-15우동통영시광도면통영시0
45곤명0.5181982-02-12곤명사천시곤명면사천시0
56진양호24.542004-06-28진양호2사천시곤명면수공0
67교동2.3911996-02-23교동밀양시교동, 산외면, 상동면밀양시0
78밀양댐4.3712000-10-30밀양댐밀양시단장면수공0
89연초댐11.3361982-07-14연초댐거제시연초면수공378
910밀양댐5.6052000-11-10밀양댐양산시원동면수공636
연번보호구역면적(킬로미터)지정일자취수장시군읍면동수도사업자거주인구(명)
2829옥종3.0611998-09-08청룡하동군옥종면하동군0
2931생초0.2642004-01-10생초산청군생초면산청군3
3032단성0.42004-01-10단성산청군단성면, 신안면산청군0
3133진양호2.9752004-07-09진양호산청군단성면수공0
3234함양0.2811982-01-21함양함양군함양읍. 병곡면함양군0
3335안의0.132005-03-03안의함양군안의면함양군0
3436거창4.8722015-05-11거창거창군거창읍거창군696
3537가조2.1752015-05-11가조거창군가조면거창군109
3639적중0.6691990-09-21적중합천군쌍책면. 적중면합천군0
3740용주0.882017-04-26용주합천군용주면합천군0