Overview

Dataset statistics

Number of variables9
Number of observations40
Missing cells73
Missing cells (%)20.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.2 KiB
Average record size in memory81.3 B

Variable types

Categorical3
Text1
Numeric5

Dataset

Description광주광역시 용도지역(주거지역, 녹지지역 등) 현황에 대한 데이터로 2006,2009년 자치구별 용도지역 현황 정보를 제공합니다.
Author광주광역시
URLhttps://www.data.go.kr/data/15124029/fileData.do

Alerts

구분2 is highly overall correlated with 구분1High correlation
구분1 is highly overall correlated with 동 and 3 other fieldsHigh correlation
동 is highly overall correlated with 서 and 3 other fieldsHigh correlation
서 is highly overall correlated with 동 and 4 other fieldsHigh correlation
남 is highly overall correlated with 동 and 3 other fieldsHigh correlation
북 is highly overall correlated with 동 and 4 other fieldsHigh correlation
광산구 is highly overall correlated with 서 and 2 other fieldsHigh correlation
동 has 20 (50.0%) missing valuesMissing
서 has 16 (40.0%) missing valuesMissing
남 has 14 (35.0%) missing valuesMissing
북 has 15 (37.5%) missing valuesMissing
광산구 has 8 (20.0%) missing valuesMissing

Reproduction

Analysis started2023-12-12 07:38:32.352469
Analysis finished2023-12-12 07:38:36.145764
Duration3.79 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연도
Categorical

Distinct2
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size452.0 B
2006
20 
2009
20 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2006
2nd row2006
3rd row2006
4th row2006
5th row2006

Common Values

ValueCountFrequency (%)
2006 20
50.0%
2009 20
50.0%

Length

2023-12-12T16:38:36.209732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T16:38:36.310549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2006 20
50.0%
2009 20
50.0%

구분1
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Memory size452.0 B
도시지역
32 
관리지역
6 
농
 
2

Length

Max length4
Median length4
Mean length3.85
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
도시지역 32
80.0%
관리지역 6
 
15.0%
농 2
 
5.0%

Length

2023-12-12T16:38:36.420913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T16:38:36.530032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
도시지역 32
80.0%
관리지역 6
 
15.0%
농 2
 
5.0%

구분2
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Memory size452.0 B
주거지역
12 
상업지역
8 
공업지역
6 
녹지지역
6 
관리지역
6 

Length

Max length4
Median length4
Mean length3.85
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
주거지역 12
30.0%
상업지역 8
20.0%
공업지역 6
15.0%
녹지지역 6
15.0%
관리지역 6
15.0%
농 2
 
5.0%

Length

2023-12-12T16:38:36.642197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T16:38:36.752512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
주거지역 12
30.0%
상업지역 8
20.0%
공업지역 6
15.0%
녹지지역 6
15.0%
관리지역 6
15.0%
농 2
 
5.0%

구분3
Text

Distinct20
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size452.0 B
2023-12-12T16:38:36.951445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length6
Mean length6.4
Min length1

Characters and Unicode

Total characters256
Distinct characters34
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

Unique0 ?
Unique (%)0.0%

Sample

1st row제1종전용주거지역
2nd row제2종전용주거지역
3rd row제1종일반주거지역
4th row제2종일반주거지역
5th row제3종일반주거지역
ValueCountFrequency (%)
제1종전용주거지역 2
 
5.0%
제2종전용주거지역 2
 
5.0%
계획관리지역 2
 
5.0%
생산관리지역 2
 
5.0%
보전관리지역 2
 
5.0%
자연녹지지역 2
 
5.0%
생산녹지지역 2
 
5.0%
보전녹지지역 2
 
5.0%
준공업지역 2
 
5.0%
일반공업지역 2
 
5.0%
Other values (10) 20
50.0%
2023-12-12T16:38:37.340044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
지 44
17.2%
역 38
14.8%
업 14
 
5.5%
주 12
 
4.7%
거 12
 
4.7%
반 10
 
3.9%
일 10
 
3.9%
제 10
 
3.9%
전 10
 
3.9%
종 10
 
3.9%
Other values (24) 86
33.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 246
96.1%
Decimal Number 10
 
3.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 44
17.9%
역 38
15.4%
업 14
 
5.7%
주 12
 
4.9%
거 12
 
4.9%
반 10
 
4.1%
일 10
 
4.1%
제 10
 
4.1%
전 10
 
4.1%
종 10
 
4.1%
Other values (21) 76
30.9%
Decimal Number
ValueCountFrequency (%)
2 4
40.0%
1 4
40.0%
3 2
20.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 246
96.1%
Common 10
 
3.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 44
17.9%
역 38
15.4%
업 14
 
5.7%
주 12
 
4.9%
거 12
 
4.9%
반 10
 
4.1%
일 10
 
4.1%
제 10
 
4.1%
전 10
 
4.1%
종 10
 
4.1%
Other values (21) 76
30.9%
Common
ValueCountFrequency (%)
2 4
40.0%
1 4
40.0%
3 2
20.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 246
96.1%
ASCII 10
 
3.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
지 44
17.9%
역 38
15.4%
업 14
 
5.7%
주 12
 
4.9%
거 12
 
4.9%
반 10
 
4.1%
일 10
 
4.1%
제 10
 
4.1%
전 10
 
4.1%
종 10
 
4.1%
Other values (21) 76
30.9%
ASCII
ValueCountFrequency (%)
2 4
40.0%
1 4
40.0%
3 2
20.0%

동
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct15
Distinct (%)75.0%
Missing20
Missing (%)50.0%
Infinite0
Infinite (%)0.0%
Mean4886333.1
Minimum107837
Maximum38247607
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size492.0 B
2023-12-12T16:38:37.485919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum107837
5-th percentile107837
Q1388150.25
median874549.5
Q31764309.2
95-th percentile38115000
Maximum38247607
Range38139770
Interquartile range (IQR)1376159

Descriptive statistics

Standard deviation11462606
Coefficient of variation (CV)2.3458504
Kurtosis6.7628931
Mean4886333.1
Median Absolute Deviation (MAD)606257.5
Skewness2.8190379
Sum97726662
Variance1.3139134 × 1014
MonotonicityNot monotonic
2023-12-12T16:38:37.646785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
718896 2
 
5.0%
1050000 2
 
5.0%
676997 2
 
5.0%
107837 2
 
5.0%
153800 2
 
5.0%
1761945 1
 
2.5%
4735402 1
 
2.5%
382784 1
 
2.5%
1030203 1
 
2.5%
38247607 1
 
2.5%
Other values (5) 5
 
12.5%
(Missing) 20
50.0%
ValueCountFrequency (%)
107837 2
5.0%
153800 2
5.0%
382784 1
2.5%
389939 1
2.5%
676997 2
5.0%
718896 2
5.0%
1030203 1
2.5%
1032268 1
2.5%
1050000 2
5.0%
1761945 1
2.5%
ValueCountFrequency (%)
38247607 1
2.5%
38108021 1
2.5%
4852031 1
2.5%
4735402 1
2.5%
1771402 1
2.5%
1761945 1
2.5%
1050000 2
5.0%
1032268 1
2.5%
1030203 1
2.5%
718896 2
5.0%

서
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct19
Distinct (%)79.2%
Missing16
Missing (%)40.0%
Infinite0
Infinite (%)0.0%
Mean3893540.2
Minimum30153
Maximum23763907
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size492.0 B
2023-12-12T16:38:37.822498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum30153
5-th percentile147903.9
Q1884484
median1353453
Q33678536.5
95-th percentile21065708
Maximum23763907
Range23733754
Interquartile range (IQR)2794052.5

Descriptive statistics

Standard deviation6341312.3
Coefficient of variation (CV)1.6286751
Kurtosis7.4124536
Mean3893540.2
Median Absolute Deviation (MAD)930797
Skewness2.8318604
Sum93444966
Variance4.0212241 × 1013
MonotonicityNot monotonic
2023-12-12T16:38:37.992827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
889312 2
 
5.0%
30153 2
 
5.0%
870000 2
 
5.0%
1556906 2
 
5.0%
1150000 2
 
5.0%
6219050 1
 
2.5%
23763907 1
 
2.5%
3135750 1
 
2.5%
1060066 1
 
2.5%
852865 1
 
2.5%
Other values (9) 9
22.5%
(Missing) 16
40.0%
ValueCountFrequency (%)
30153 2
5.0%
815159 1
2.5%
852865 1
2.5%
870000 2
5.0%
889312 2
5.0%
1060066 1
2.5%
1060067 1
2.5%
1150000 2
5.0%
1556906 2
5.0%
3135750 1
2.5%
ValueCountFrequency (%)
23763907 1
2.5%
23685706 1
2.5%
6219050 1
2.5%
5977805 1
2.5%
3947994 1
2.5%
3938041 1
2.5%
3592035 1
2.5%
3235898 1
2.5%
3167881 1
2.5%
3135750 1
2.5%

남
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct20
Distinct (%)76.9%
Missing14
Missing (%)35.0%
Infinite0
Infinite (%)0.0%
Mean4696323.1
Minimum15352
Maximum45795861
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size492.0 B
2023-12-12T16:38:38.165768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum15352
5-th percentile45685.25
Q1157466.25
median860000
Q33108095
95-th percentile35016742
Maximum45795861
Range45780509
Interquartile range (IQR)2950628.8

Descriptive statistics

Standard deviation12101771
Coefficient of variation (CV)2.5768609
Kurtosis9.8057219
Mean4696323.1
Median Absolute Deviation (MAD)810000
Skewness3.2953276
Sum1.221044 × 108
Variance1.4645287 × 1014
MonotonicityNot monotonic
2023-12-12T16:38:38.313507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
1164323 2
 
5.0%
50000 2
 
5.0%
860000 2
 
5.0%
260000 2
 
5.0%
3108095 2
 
5.0%
150000 2
 
5.0%
3446995 1
 
2.5%
45259662 1
 
2.5%
76556 1
 
2.5%
250421 1
 
2.5%
Other values (10) 10
25.0%
(Missing) 14
35.0%
ValueCountFrequency (%)
15352 1
2.5%
44247 1
2.5%
50000 2
5.0%
76556 1
2.5%
150000 2
5.0%
179865 1
2.5%
211827 1
2.5%
250421 1
2.5%
260000 2
5.0%
860000 2
5.0%
ValueCountFrequency (%)
45795861 1
2.5%
45259662 1
2.5%
4287983 1
2.5%
3823988 1
2.5%
3489249 1
2.5%
3446995 1
2.5%
3108095 2
5.0%
2064608 1
2.5%
1972951 1
2.5%
1164323 2
5.0%

북
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct19
Distinct (%)76.0%
Missing15
Missing (%)37.5%
Infinite0
Infinite (%)0.0%
Mean9739551.9
Minimum27623
Maximum82694149
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size492.0 B
2023-12-12T16:38:38.437431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum27623
5-th percentile340000
Q1535000
median3730000
Q35296204
95-th percentile68146948
Maximum82694149
Range82666526
Interquartile range (IQR)4761204

Descriptive statistics

Standard deviation22134917
Coefficient of variation (CV)2.2726833
Kurtosis9.2257314
Mean9739551.9
Median Absolute Deviation (MAD)2682900
Skewness3.2034156
Sum2.434888 × 108
Variance4.8995453 × 1014
MonotonicityNot monotonic
2023-12-12T16:38:38.601765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
340000 2
 
5.0%
1918410 2
 
5.0%
365926 2
 
5.0%
3730000 2
 
5.0%
535000 2
 
5.0%
6412900 2
 
5.0%
5198060 1
 
2.5%
82694149 1
 
2.5%
4398900 1
 
2.5%
1738079 1
 
2.5%
Other values (9) 9
22.5%
(Missing) 15
37.5%
ValueCountFrequency (%)
27623 1
2.5%
340000 2
5.0%
365926 2
5.0%
535000 2
5.0%
1595636 1
2.5%
1738079 1
2.5%
1918410 2
5.0%
3730000 2
5.0%
4249966 1
2.5%
4320016 1
2.5%
ValueCountFrequency (%)
82694149 1
2.5%
82668971 1
2.5%
10058854 1
2.5%
9998917 1
2.5%
6412900 2
5.0%
5296204 1
2.5%
5198060 1
2.5%
4638951 1
2.5%
4398900 1
2.5%
4320016 1
2.5%

광산구
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct27
Distinct (%)84.4%
Missing8
Missing (%)20.0%
Infinite0
Infinite (%)0.0%
Mean13931237
Minimum50000
Maximum1.5525864 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size492.0 B
2023-12-12T16:38:38.746010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum50000
5-th percentile86725.15
Q1868473
median4013012.5
Q37953885.2
95-th percentile79687216
Maximum1.5525864 × 108
Range1.5520864 × 108
Interquartile range (IQR)7085412.2

Descriptive statistics

Standard deviation37132036
Coefficient of variation (CV)2.6653796
Kurtosis12.72956
Mean13931237
Median Absolute Deviation (MAD)3472193.5
Skewness3.6982149
Sum4.457996 × 108
Variance1.3787881 × 1015
MonotonicityNot monotonic
2023-12-12T16:38:38.910056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
540819 2
 
5.0%
116773 2
 
5.0%
50000 2
 
5.0%
1549476 2
 
5.0%
132344 2
 
5.0%
977691 1
 
2.5%
4476025 1
 
2.5%
3546922 1
 
2.5%
5661615 1
 
2.5%
5377443 1
 
2.5%
Other values (17) 17
42.5%
(Missing) 8
20.0%
ValueCountFrequency (%)
50000 2
5.0%
116773 2
5.0%
132344 2
5.0%
540819 2
5.0%
977691 1
2.5%
1112782 1
2.5%
1549476 2
5.0%
1634864 1
2.5%
1829738 1
2.5%
3546922 1
2.5%
ValueCountFrequency (%)
155258636 1
2.5%
153506036 1
2.5%
19290000 1
2.5%
13934536 1
2.5%
10739921 1
2.5%
10512408 1
2.5%
10105596 1
2.5%
8393668 1
2.5%
7807291 1
2.5%
6397547 1
2.5%

Interactions

2023-12-12T16:38:35.185637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:38:32.725384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:38:33.376990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:38:34.177616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:38:34.639718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:38:35.311105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:38:32.864320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:38:33.794353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:38:34.272079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:38:34.745964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:38:35.401109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:38:32.992371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:38:33.879093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:38:34.349190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:38:34.842540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:38:35.489971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:38:33.133731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:38:33.967141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:38:34.444709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:38:34.950613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:38:35.586021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:38:33.261353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:38:34.068431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:38:34.520680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:38:35.061104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T16:38:39.039936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도구분1구분2구분3동서남북광산구
연도1.0000.0000.0000.0000.0000.0000.0000.0000.000
구분10.0001.0001.0001.000NaNNaN0.000NaN0.551
구분20.0001.0001.0001.0000.6340.7470.3040.3420.780
구분30.0001.0001.0001.0001.0001.0001.0001.0000.840
동0.000NaN0.6341.0001.0001.0001.0001.0001.000
서0.000NaN0.7471.0001.0001.0001.0001.0001.000
남0.0000.0000.3041.0001.0001.0001.0001.0001.000
북0.000NaN0.3421.0001.0001.0001.0001.0001.000
광산구0.0000.5510.7800.8401.0001.0001.0001.0001.000
2023-12-12T16:38:39.200049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도구분2구분1
연도1.0000.0000.000
구분20.0001.0000.959
구분10.0000.9591.000
2023-12-12T16:38:39.310111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
동서남북광산구연도구분1구분2
동1.0000.6850.9700.6130.3570.0001.0000.331
서0.6851.0000.6640.7590.6130.0001.0000.408
남0.9700.6641.0000.8080.5310.0000.0000.350
북0.6130.7590.8081.0000.8570.0001.0000.303
광산구0.3570.6130.5310.8571.0000.0000.2410.443
연도0.0000.0000.0000.0000.0001.0000.0000.000
구분11.0001.0000.0001.0000.2410.0001.0000.959
구분20.3310.4080.3500.3030.4430.0000.9591.000

Missing values

2023-12-12T16:38:35.762835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T16:38:35.946445image/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.
2023-12-12T16:38:36.074192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

연도구분1구분2구분3동서남북광산구
02006도시지역주거지역제1종전용주거지역<NA><NA>15352<NA>132344
12006도시지역주거지역제2종전용주거지역<NA><NA><NA><NA>116773
22006도시지역주거지역제1종일반주거지역17619453592035348924952962046246493
32006도시지역주거지역제2종일반주거지역47354025977805382398899989177807291
42006도시지역주거지역제3종일반주거지역7188963938041197295142499664476025
52006도시지역주거지역준주거지역38278481515911643231595636977691
62006도시지역상업지역중심상업지역1050000889312<NA>340000540819
72006도시지역상업지역일반상업지역676997106006721182719184101549476
82006도시지역상업지역근린상업지역<NA>301535000036592650000
92006도시지역상업지역유통상업지역107837<NA><NA><NA><NA>
연도구분1구분2구분3동서남북광산구
302009도시지역공업지역전용공업지역<NA><NA><NA><NA><NA>
312009도시지역공업지역일반공업지역<NA>870000860000373000013934536
322009도시지역공업지역준공업지역<NA>15569062600005350001829738
332009도시지역녹지지역보전녹지지역1032268115000031080956412900<NA>
342009도시지역녹지지역생산녹지지역153800313575076556439890010105596
352009도시지역녹지지역자연녹지지역38108021237639074525966282694149153506036
362009관리지역관리지역보전관리지역<NA><NA><NA><NA>6397547
372009관리지역관리지역생산관리지역<NA><NA>150000<NA>5377443
382009관리지역관리지역계획관리지역<NA><NA><NA><NA>5661615
392009농농농<NA><NA><NA><NA>3546922