Overview

Dataset statistics

Number of variables11
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.5 KiB
Average record size in memory97.3 B

Variable types

Numeric5
Text1
Categorical5

Alerts

SD_CD has constant value ""Constant
SD_NM has constant value ""Constant
논 has constant value ""Constant
SGG_CD is highly overall correlated with SGG_KOR_NMHigh correlation
SGG_KOR_NM is highly overall correlated with SGG_CDHigh correlation
id is highly overall correlated with 과수High correlation
과수 is highly overall correlated with id and 1 other fieldsHigh correlation
밭 is highly overall correlated with 농업High correlation
비닐하우스 is highly overall correlated with 과수 and 1 other fieldsHigh correlation
농업 is highly overall correlated with 밭 and 1 other fieldsHigh correlation
id has unique valuesUnique
gid has unique valuesUnique
밭 has unique valuesUnique
농업 has unique valuesUnique

Reproduction

Analysis started2023-12-10 13:36:33.569736
Analysis finished2023-12-10 13:36:36.850521
Duration3.28 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

id
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.5
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:36:36.937023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5.95
Q125.75
median50.5
Q375.25
95-th percentile95.05
Maximum100
Range99
Interquartile range (IQR)49.5

Descriptive statistics

Standard deviation29.011492
Coefficient of variation (CV)0.57448499
Kurtosis-1.2
Mean50.5
Median Absolute Deviation (MAD)25
Skewness0
Sum5050
Variance841.66667
MonotonicityStrictly increasing
2023-12-10T22:36:37.127216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.0%
65 1
 
1.0%
75 1
 
1.0%
74 1
 
1.0%
73 1
 
1.0%
72 1
 
1.0%
71 1
 
1.0%
70 1
 
1.0%
69 1
 
1.0%
68 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
1 1
1.0%
2 1
1.0%
3 1
1.0%
4 1
1.0%
5 1
1.0%
6 1
1.0%
7 1
1.0%
8 1
1.0%
9 1
1.0%
10 1
1.0%
ValueCountFrequency (%)
100 1
1.0%
99 1
1.0%
98 1
1.0%
97 1
1.0%
96 1
1.0%
95 1
1.0%
94 1
1.0%
93 1
1.0%
92 1
1.0%
91 1
1.0%

gid
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T22:36:37.501876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters600
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique100 ?
Unique (%)100.0%

Sample

1st row나나7577
2nd row나나7578
3rd row나나7579
4th row나나7580
5th row나나7581
ValueCountFrequency (%)
나나7577 1
 
1.0%
나나8075 1
 
1.0%
나나8086 1
 
1.0%
나나8085 1
 
1.0%
나나8084 1
 
1.0%
나나8083 1
 
1.0%
나나8082 1
 
1.0%
나나8081 1
 
1.0%
나나8080 1
 
1.0%
나나8079 1
 
1.0%
Other values (90) 90
90.0%
2023-12-10T22:36:38.039681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
나 200
33.3%
7 131
21.8%
8 114
19.0%
1 28
 
4.7%
9 24
 
4.0%
0 24
 
4.0%
6 21
 
3.5%
5 19
 
3.2%
2 14
 
2.3%
4 13
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 400
66.7%
Other Letter 200
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
7 131
32.8%
8 114
28.5%
1 28
 
7.0%
9 24
 
6.0%
0 24
 
6.0%
6 21
 
5.2%
5 19
 
4.8%
2 14
 
3.5%
4 13
 
3.2%
3 12
 
3.0%
Other Letter
ValueCountFrequency (%)
나 200
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 400
66.7%
Hangul 200
33.3%

Most frequent character per script

Common
ValueCountFrequency (%)
7 131
32.8%
8 114
28.5%
1 28
 
7.0%
9 24
 
6.0%
0 24
 
6.0%
6 21
 
5.2%
5 19
 
4.8%
2 14
 
3.5%
4 13
 
3.2%
3 12
 
3.0%
Hangul
ValueCountFrequency (%)
나 200
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 400
66.7%
Hangul 200
33.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
나 200
100.0%
ASCII
ValueCountFrequency (%)
7 131
32.8%
8 114
28.5%
1 28
 
7.0%
9 24
 
6.0%
0 24
 
6.0%
6 21
 
5.2%
5 19
 
4.8%
2 14
 
3.5%
4 13
 
3.2%
3 12
 
3.0%

SD_CD
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
50
100 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row50
2nd row50
3rd row50
4th row50
5th row50

Common Values

ValueCountFrequency (%)
50 100
100.0%

Length

2023-12-10T22:36:38.238236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:36:38.377095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
50 100
100.0%

SD_NM
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
제주
100 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
제주 100
100.0%

Length

2023-12-10T22:36:38.530374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:36:38.658090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
제주 100
100.0%

SGG_CD
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
50110
69 
50130
31 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row50110
2nd row50110
3rd row50110
4th row50110
5th row50110

Common Values

ValueCountFrequency (%)
50110 69
69.0%
50130 31
31.0%

Length

2023-12-10T22:36:38.801090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:36:38.919847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
50110 69
69.0%
50130 31
31.0%

SGG_KOR_NM
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
제주시
69 
서귀포시
31 

Length

Max length4
Median length3
Mean length3.31
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
제주시 69
69.0%
서귀포시 31
31.0%

Length

2023-12-10T22:36:39.085810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:36:39.240810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
제주시 69
69.0%
서귀포시 31
31.0%

과수
Real number (ℝ)

HIGH CORRELATION 

Distinct68
Distinct (%)68.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36777.496
Minimum-99
Maximum257338.31
Zeros0
Zeros (%)0.0%
Negative33
Negative (%)33.0%
Memory size1.0 KiB
2023-12-10T22:36:39.402433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-99
5-th percentile-99
Q1-99
median12313.531
Q342220.665
95-th percentile169655.89
Maximum257338.31
Range257437.31
Interquartile range (IQR)42319.665

Descriptive statistics

Standard deviation57154.904
Coefficient of variation (CV)1.5540727
Kurtosis3.9795562
Mean36777.496
Median Absolute Deviation (MAD)12412.531
Skewness2.0580174
Sum3677749.6
Variance3.2666831 × 109
MonotonicityNot monotonic
2023-12-10T22:36:39.599904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-99.0 33
33.0%
73473.69457 1
 
1.0%
31289.64161 1
 
1.0%
81493.78828 1
 
1.0%
79326.58423 1
 
1.0%
2664.835157 1
 
1.0%
28761.02074 1
 
1.0%
56494.95877 1
 
1.0%
101877.3657 1
 
1.0%
31599.6762 1
 
1.0%
Other values (58) 58
58.0%
ValueCountFrequency (%)
-99.0 33
33.0%
564.9351609 1
 
1.0%
899.4837915 1
 
1.0%
1417.98747 1
 
1.0%
1526.632903 1
 
1.0%
1682.882289 1
 
1.0%
2164.807524 1
 
1.0%
2664.835157 1
 
1.0%
2759.632627 1
 
1.0%
3931.133959 1
 
1.0%
ValueCountFrequency (%)
257338.3078 1
1.0%
242891.654 1
1.0%
212014.1824 1
1.0%
193509.4602 1
1.0%
185211.1773 1
1.0%
168837.1881 1
1.0%
136414.4831 1
1.0%
135111.4132 1
1.0%
128508.4548 1
1.0%
128011.2456 1
1.0%

논
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
-99
100 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-99
2nd row-99
3rd row-99
4th row-99
5th row-99

Common Values

ValueCountFrequency (%)
-99 100
100.0%

Length

2023-12-10T22:36:40.063359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:36:40.169715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
99 100
100.0%

밭
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean395603.35
Minimum136.04962
Maximum853283.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:36:40.331739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum136.04962
5-th percentile15559.742
Q1247789.67
median419080.09
Q3545702.71
95-th percentile747452.9
Maximum853283.5
Range853147.45
Interquartile range (IQR)297913.03

Descriptive statistics

Standard deviation225898.88
Coefficient of variation (CV)0.57102368
Kurtosis-0.82027373
Mean395603.35
Median Absolute Deviation (MAD)157652.05
Skewness-0.074534182
Sum39560335
Variance5.1030303 × 1010
MonotonicityNot monotonic
2023-12-10T22:36:40.561356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29251.1951 1
 
1.0%
627477.0831 1
 
1.0%
495543.3051 1
 
1.0%
571623.183 1
 
1.0%
331625.4752 1
 
1.0%
216011.779 1
 
1.0%
305819.0351 1
 
1.0%
403388.0192 1
 
1.0%
427715.0569 1
 
1.0%
368278.5415 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
136.0496209 1
1.0%
4146.5904 1
1.0%
9197.250925 1
1.0%
10353.42263 1
1.0%
13439.85643 1
1.0%
15671.31468 1
1.0%
29251.1951 1
1.0%
39921.0973 1
1.0%
48083.28607 1
1.0%
54468.80661 1
1.0%
ValueCountFrequency (%)
853283.4955 1
1.0%
809634.1614 1
1.0%
807881.8253 1
1.0%
755357.7296 1
1.0%
754236.5248 1
1.0%
747095.8701 1
1.0%
745656.0987 1
1.0%
725529.2746 1
1.0%
722260.046 1
1.0%
718941.0806 1
1.0%

비닐하우스
Real number (ℝ)

HIGH CORRELATION 

Distinct92
Distinct (%)92.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37550.628
Minimum-99
Maximum251923.16
Zeros0
Zeros (%)0.0%
Negative9
Negative (%)9.0%
Memory size1.0 KiB
2023-12-10T22:36:40.761003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-99
5-th percentile-99
Q18185.4126
median28114.982
Q352506.326
95-th percentile122998.51
Maximum251923.16
Range252022.16
Interquartile range (IQR)44320.914

Descriptive statistics

Standard deviation42457.568
Coefficient of variation (CV)1.1306753
Kurtosis7.057882
Mean37550.628
Median Absolute Deviation (MAD)22093.36
Skewness2.2656344
Sum3755062.8
Variance1.8026451 × 109
MonotonicityNot monotonic
2023-12-10T22:36:40.932395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-99.0 9
 
9.0%
49755.52957 1
 
1.0%
12012.77686 1
 
1.0%
45528.01917 1
 
1.0%
20964.00993 1
 
1.0%
36286.28011 1
 
1.0%
30409.57046 1
 
1.0%
35218.9708 1
 
1.0%
28700.82087 1
 
1.0%
39758.64949 1
 
1.0%
Other values (82) 82
82.0%
ValueCountFrequency (%)
-99.0 9
9.0%
56.60354052 1
 
1.0%
115.1812928 1
 
1.0%
633.9267796 1
 
1.0%
898.9923592 1
 
1.0%
1007.525367 1
 
1.0%
1090.932577 1
 
1.0%
1790.856695 1
 
1.0%
2069.605769 1
 
1.0%
2774.841322 1
 
1.0%
ValueCountFrequency (%)
251923.1553 1
1.0%
175910.7012 1
1.0%
160570.7528 1
1.0%
140447.6868 1
1.0%
126611.5835 1
1.0%
122808.3475 1
1.0%
112873.3992 1
1.0%
112372.7452 1
1.0%
87967.80573 1
1.0%
87663.89165 1
1.0%

농업
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.43316288
Minimum0.00013605
Maximum0.85893557
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:36:41.118013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.00013605
5-th percentile0.02882605
Q10.28056325
median0.45542122
Q30.59366167
95-th percentile0.78192556
Maximum0.85893557
Range0.85879951
Interquartile range (IQR)0.31309842

Descriptive statistics

Standard deviation0.23670806
Coefficient of variation (CV)0.54646432
Kurtosis-0.81922911
Mean0.43316288
Median Absolute Deviation (MAD)0.14489888
Skewness-0.25690512
Sum43.316288
Variance0.056030706
MonotonicityNot monotonic
2023-12-10T22:36:41.345693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.029251195 1
 
1.0%
0.677232613 1
 
1.0%
0.496634238 1
 
1.0%
0.58363596 1
 
1.0%
0.377153494 1
 
1.0%
0.236975789 1
 
1.0%
0.342105315 1
 
1.0%
0.43379759 1
 
1.0%
0.462934028 1
 
1.0%
0.396979362 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
0.00013605 1
1.0%
0.00414659 1
1.0%
0.010353423 1
1.0%
0.015671315 1
1.0%
0.020748286 1
1.0%
0.029251195 1
1.0%
0.031942979 1
1.0%
0.039921097 1
1.0%
0.048717213 1
1.0%
0.070285841 1
1.0%
ValueCountFrequency (%)
0.858935565 1
1.0%
0.85337397 1
1.0%
0.834215824 1
1.0%
0.785538628 1
1.0%
0.784533053 1
1.0%
0.781788323 1
1.0%
0.772257629 1
1.0%
0.765536806 1
1.0%
0.757845218 1
1.0%
0.753460258 1
1.0%

Interactions

2023-12-10T22:36:35.968668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:36:33.894471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:36:34.339259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:36:34.855479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:36:35.385321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:36:36.081313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:36:33.996834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:36:34.420258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:36:34.956536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:36:35.507009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:36:36.186030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:36:34.088248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:36:34.509098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:36:35.076579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:36:35.619638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:36:36.273703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:36:34.172000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:36:34.606453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:36:35.183759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:36:35.736722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:36:36.365558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:36:34.260663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:36:34.738916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:36:35.286211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:36:35.842724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T22:36:41.481049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
idgidSGG_CDSGG_KOR_NM과수밭비닐하우스농업
id1.0001.0000.3450.3450.6190.4300.4920.585
gid1.0001.0001.0001.0001.0001.0001.0001.000
SGG_CD0.3451.0001.0000.9990.0000.4430.0230.409
SGG_KOR_NM0.3451.0000.9991.0000.0000.4430.0230.409
과수0.6191.0000.0000.0001.0000.3910.7600.371
밭0.4301.0000.4430.4430.3911.0000.3800.962
비닐하우스0.4921.0000.0230.0230.7600.3801.0000.336
농업0.5851.0000.4090.4090.3710.9620.3361.000
2023-12-10T22:36:41.634394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
SGG_CDSGG_KOR_NM
SGG_CD1.0000.976
SGG_KOR_NM0.9761.000
2023-12-10T22:36:41.754564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
id과수밭비닐하우스농업SGG_CDSGG_KOR_NM
id1.0000.586-0.0900.423-0.0270.2520.252
과수0.5861.0000.1540.6700.2160.0000.000
밭-0.0900.1541.0000.3790.9790.3250.325
비닐하우스0.4230.6700.3791.0000.5060.0000.000
농업-0.0270.2160.9790.5061.0000.3000.300
SGG_CD0.2520.0000.3250.0000.3001.0000.976
SGG_KOR_NM0.2520.0000.3250.0000.3000.9761.000

Missing values

2023-12-10T22:36:36.522270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T22:36:36.772811image/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

idgidSD_CDSD_NMSGG_CDSGG_KOR_NM과수논밭비닐하우스농업
01나나757750제주50110제주시-99.0-9929251.1951-99.00.029251
12나나757850제주50110제주시-99.0-99331193.65813665.8639310.33486
23나나757950제주50110제주시-99.0-9977342.35425-99.00.077342
34나나758050제주50110제주시-99.0-9997993.5105556.6035410.09805
45나나758150제주50110제주시-99.0-9910353.42263-99.00.010353
56나나758250제주50110제주시-99.0-99143962.24259860.2300760.153822
67나나758350제주50110제주시-99.0-9970170.65933115.1812930.070286
78나나758450제주50110제주시-99.0-994146.5904-99.00.004147
89나나767550제주50130서귀포시-99.0-99136.049621-99.00.000136
910나나767650제주50130서귀포시-99.0-99358653.51784963.2549390.363617
idgidSD_CDSD_NMSGG_CDSGG_KOR_NM과수논밭비닐하우스농업
9091나나818650제주50110제주시100446.5516-99395109.550953529.383780.448639
9192나나818750제주50110제주시12227.17416-99104865.875110673.777690.11554
9293나나818850제주50110제주시11071.82418-99106413.55376391.1752180.112805
9394나나818950제주50110제주시-99.0-9948083.28607633.926780.048717
9495나나819150제주50110제주시-99.0-9985584.79442-99.00.085585
9596나나827050제주50130서귀포시-99.0-9915671.31468-99.00.015671
9697나나827150제주50130서귀포시4856.608972-99172787.09919079.6723630.181867
9798나나827250제주50130서귀포시9883.846908-99538520.053154863.811770.593384
9899나나827350제주50130서귀포시18828.49365-99457326.678252079.626650.509406
99100나나827450제주50130서귀포시98342.99287-99373948.939979524.604870.453474