Overview

Dataset statistics

Number of variables8
Number of observations88
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.2 KiB
Average record size in memory72.5 B

Variable types

Numeric7
Categorical1

Dataset

Description회계연도별 지목별 국유지 현황정보를 제공하는 자료로 필지 수, 면적, 금액 각각에 대한 정보를 제공합니다. 지목정보는 전, 답, 과수원, 임야, 대, 도로, 하천, 구거, 잡종지에 대한 정보를 제공합니다.
Author기획재정부
URLhttps://www.data.go.kr/data/15087536/fileData.do

Alerts

필지수 is highly overall correlated with 필지수비율(퍼센트) and 3 other fieldsHigh correlation
필지수비율(퍼센트) is highly overall correlated with 필지수 and 3 other fieldsHigh correlation
면적(제곱킬로미터) is highly overall correlated with 필지수 and 5 other fieldsHigh correlation
면적비율(퍼센트) is highly overall correlated with 필지수 and 5 other fieldsHigh correlation
금액(억원) is highly overall correlated with 면적(제곱킬로미터) and 2 other fieldsHigh correlation
금액비율(퍼센트) is highly overall correlated with 면적(제곱킬로미터) and 3 other fieldsHigh correlation
구분 is highly overall correlated with 필지수 and 4 other fieldsHigh correlation
필지수 has unique valuesUnique
금액(억원) has unique valuesUnique
필지수비율(퍼센트) has 8 (9.1%) zerosZeros
면적비율(퍼센트) has 16 (18.2%) zerosZeros
금액비율(퍼센트) has 8 (9.1%) zerosZeros

Reproduction

Analysis started2023-12-12 06:28:41.674797
Analysis finished2023-12-12 06:28:48.436897
Duration6.76 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

회계연도
Real number (ℝ)

Distinct8
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2017.5
Minimum2014
Maximum2021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size924.0 B
2023-12-12T15:28:48.497230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2014
5-th percentile2014
Q12015.75
median2017.5
Q32019.25
95-th percentile2021
Maximum2021
Range7
Interquartile range (IQR)3.5

Descriptive statistics

Standard deviation2.3044185
Coefficient of variation (CV)0.0011422149
Kurtosis-1.2400235
Mean2017.5
Median Absolute Deviation (MAD)2
Skewness0
Sum177540
Variance5.3103448
MonotonicityDecreasing
2023-12-12T15:28:48.659772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
2021 11
12.5%
2020 11
12.5%
2019 11
12.5%
2018 11
12.5%
2017 11
12.5%
2016 11
12.5%
2015 11
12.5%
2014 11
12.5%
ValueCountFrequency (%)
2014 11
12.5%
2015 11
12.5%
2016 11
12.5%
2017 11
12.5%
2018 11
12.5%
2019 11
12.5%
2020 11
12.5%
2021 11
12.5%
ValueCountFrequency (%)
2021 11
12.5%
2020 11
12.5%
2019 11
12.5%
2018 11
12.5%
2017 11
12.5%
2016 11
12.5%
2015 11
12.5%
2014 11
12.5%

구분
Categorical

HIGH CORRELATION 

Distinct11
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Memory size836.0 B
전
8 
답
8 
과수원
8 
임야
8 
대
8 
Other values (6)
48 

Length

Max length3
Median length2
Mean length1.9090909
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row전
2nd row답
3rd row과수원
4th row임야
5th row대

Common Values

ValueCountFrequency (%)
전 8
9.1%
답 8
9.1%
과수원 8
9.1%
임야 8
9.1%
대 8
9.1%
도로 8
9.1%
하천 8
9.1%
구거 8
9.1%
잡종지 8
9.1%
기타 8
9.1%

Length

2023-12-12T15:28:48.862161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
전 8
9.1%
답 8
9.1%
과수원 8
9.1%
임야 8
9.1%
대 8
9.1%
도로 8
9.1%
하천 8
9.1%
구거 8
9.1%
잡종지 8
9.1%
기타 8
9.1%

필지수
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct88
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1032465.6
Minimum8284
Maximum5883270
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size924.0 B
2023-12-12T15:28:49.017581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8284
5-th percentile10174.5
Q1222484.25
median410027
Q3931005.75
95-th percentile5683852
Maximum5883270
Range5874986
Interquartile range (IQR)708521.5

Descriptive statistics

Standard deviation1585741.8
Coefficient of variation (CV)1.5358786
Kurtosis4.269635
Mean1032465.6
Median Absolute Deviation (MAD)189701.5
Skewness2.3302113
Sum90856976
Variance2.5145772 × 1012
MonotonicityNot monotonic
2023-12-12T15:28:49.183468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
410848 1
 
1.1%
460213 1
 
1.1%
5686281 1
 
1.1%
568130 1
 
1.1%
129421 1
 
1.1%
934192 1
 
1.1%
393132 1
 
1.1%
2170936 1
 
1.1%
226309 1
 
1.1%
343565 1
 
1.1%
Other values (78) 78
88.6%
ValueCountFrequency (%)
8284 1
1.1%
9105 1
1.1%
9849 1
1.1%
10109 1
1.1%
10157 1
1.1%
10207 1
1.1%
10255 1
1.1%
10612 1
1.1%
124520 1
1.1%
127706 1
1.1%
ValueCountFrequency (%)
5883270 1
1.1%
5824894 1
1.1%
5724535 1
1.1%
5695320 1
1.1%
5686281 1
1.1%
5679341 1
1.1%
5516900 1
1.1%
5417947 1
1.1%
2336863 1
1.1%
2306538 1
1.1%

필지수비율(퍼센트)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct15
Distinct (%)17.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.159091
Minimum0
Maximum100
Zeros8
Zeros (%)9.1%
Negative0
Negative (%)0.0%
Memory size924.0 B
2023-12-12T15:28:49.334928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median7
Q316
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)12

Descriptive statistics

Standard deviation27.933783
Coefficient of variation (CV)1.5382809
Kurtosis4.2295298
Mean18.159091
Median Absolute Deviation (MAD)3
Skewness2.3230961
Sum1598
Variance780.29624
MonotonicityNot monotonic
2023-12-12T15:28:49.479266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
7 15
17.0%
8 8
9.1%
0 8
9.1%
6 8
9.1%
4 8
9.1%
2 8
9.1%
100 8
9.1%
16 6
 
6.8%
10 6
 
6.8%
39 4
 
4.5%
Other values (5) 9
10.2%
ValueCountFrequency (%)
0 8
9.1%
2 8
9.1%
4 8
9.1%
6 8
9.1%
7 15
17.0%
8 8
9.1%
9 1
 
1.1%
10 6
 
6.8%
11 2
 
2.3%
16 6
 
6.8%
ValueCountFrequency (%)
100 8
9.1%
40 2
 
2.3%
39 4
4.5%
38 2
 
2.3%
17 2
 
2.3%
16 6
6.8%
11 2
 
2.3%
10 6
6.8%
9 1
 
1.1%
8 8
9.1%

면적(제곱킬로미터)
Real number (ℝ)

HIGH CORRELATION 

Distinct75
Distinct (%)85.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4545.1705
Minimum11
Maximum25355
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size924.0 B
2023-12-12T15:28:49.653790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile14
Q1329
median1280
Q32581.25
95-th percentile24976.4
Maximum25355
Range25344
Interquartile range (IQR)2252.25

Descriptive statistics

Standard deviation7925.2203
Coefficient of variation (CV)1.7436574
Kurtosis1.8199171
Mean4545.1705
Median Absolute Deviation (MAD)954
Skewness1.8455598
Sum399975
Variance62809116
MonotonicityNot monotonic
2023-12-12T15:28:49.864826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14 4
 
4.5%
13 3
 
3.4%
101 3
 
3.4%
332 2
 
2.3%
315 2
 
2.3%
102 2
 
2.3%
1574 2
 
2.3%
1546 2
 
2.3%
329 2
 
2.3%
16151 1
 
1.1%
Other values (65) 65
73.9%
ValueCountFrequency (%)
11 1
 
1.1%
13 3
3.4%
14 4
4.5%
99 1
 
1.1%
101 3
3.4%
102 2
2.3%
103 1
 
1.1%
104 1
 
1.1%
315 2
2.3%
317 1
 
1.1%
ValueCountFrequency (%)
25355 1
1.1%
25239 1
1.1%
25158 1
1.1%
25062 1
1.1%
24996 1
1.1%
24940 1
1.1%
24718 1
1.1%
24521 1
1.1%
16654 1
1.1%
16607 1
1.1%

면적비율(퍼센트)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.045455
Minimum0
Maximum100
Zeros16
Zeros (%)18.2%
Negative0
Negative (%)0.0%
Memory size924.0 B
2023-12-12T15:28:50.047632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median5
Q310
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)9

Descriptive statistics

Standard deviation31.802519
Coefficient of variation (CV)1.7623562
Kurtosis1.7974306
Mean18.045455
Median Absolute Deviation (MAD)4
Skewness1.8406898
Sum1588
Variance1011.4002
MonotonicityNot monotonic
2023-12-12T15:28:50.201694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
1 16
18.2%
0 16
18.2%
66 8
9.1%
6 8
9.1%
2 8
9.1%
5 8
9.1%
100 8
9.1%
10 6
 
6.8%
7 6
 
6.8%
8 2
 
2.3%
ValueCountFrequency (%)
0 16
18.2%
1 16
18.2%
2 8
9.1%
5 8
9.1%
6 8
9.1%
7 6
 
6.8%
8 2
 
2.3%
10 6
 
6.8%
11 2
 
2.3%
66 8
9.1%
ValueCountFrequency (%)
100 8
9.1%
66 8
9.1%
11 2
 
2.3%
10 6
 
6.8%
8 2
 
2.3%
7 6
 
6.8%
6 8
9.1%
5 8
9.1%
2 8
9.1%
1 16
18.2%

금액(억원)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct88
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean890535.94
Minimum3024
Maximum6301025
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size924.0 B
2023-12-12T15:28:50.401825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3024
5-th percentile4846.25
Q1138448.25
median514095
Q3947977.5
95-th percentile4656086.3
Maximum6301025
Range6298001
Interquartile range (IQR)809529.25

Descriptive statistics

Standard deviation1331518.2
Coefficient of variation (CV)1.4951874
Kurtosis6.2104208
Mean890535.94
Median Absolute Deviation (MAD)388770.5
Skewness2.6579248
Sum78367163
Variance1.7729406 × 1012
MonotonicityNot monotonic
2023-12-12T15:28:50.589328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
141942 1
 
1.1%
118102 1
 
1.1%
4670080 1
 
1.1%
547270 1
 
1.1%
528608 1
 
1.1%
302077 1
 
1.1%
938365 1
 
1.1%
1022702 1
 
1.1%
737779 1
 
1.1%
335196 1
 
1.1%
Other values (78) 78
88.6%
ValueCountFrequency (%)
3024 1
1.1%
4633 1
1.1%
4674 1
1.1%
4719 1
1.1%
4820 1
1.1%
4895 1
1.1%
4983 1
1.1%
5943 1
1.1%
104672 1
1.1%
111511 1
1.1%
ValueCountFrequency (%)
6301025 1
1.1%
5196070 1
1.1%
4848771 1
1.1%
4677016 1
1.1%
4670080 1
1.1%
4630098 1
1.1%
4485830 1
1.1%
4374692 1
1.1%
1292367 1
1.1%
1272611 1
1.1%

금액비율(퍼센트)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct18
Distinct (%)20.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.204545
Minimum0
Maximum100
Zeros8
Zeros (%)9.1%
Negative0
Negative (%)0.0%
Memory size924.0 B
2023-12-12T15:28:50.771380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median11
Q320
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)17

Descriptive statistics

Standard deviation26.909879
Coefficient of variation (CV)1.4781956
Kurtosis5.3533272
Mean18.204545
Median Absolute Deviation (MAD)8
Skewness2.5603478
Sum1602
Variance724.14159
MonotonicityNot monotonic
2023-12-12T15:28:50.925408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
3 11
12.5%
12 10
11.4%
7 10
11.4%
0 8
9.1%
100 8
9.1%
20 6
 
6.8%
2 5
 
5.7%
21 5
 
5.7%
6 4
 
4.5%
11 4
 
4.5%
Other values (8) 17
19.3%
ValueCountFrequency (%)
0 8
9.1%
2 5
5.7%
3 11
12.5%
6 4
 
4.5%
7 10
11.4%
8 1
 
1.1%
9 1
 
1.1%
10 2
 
2.3%
11 4
 
4.5%
12 10
11.4%
ValueCountFrequency (%)
100 8
9.1%
23 2
 
2.3%
22 3
 
3.4%
21 5
5.7%
20 6
6.8%
17 3
 
3.4%
16 3
 
3.4%
14 2
 
2.3%
12 10
11.4%
11 4
 
4.5%

Interactions

2023-12-12T15:28:47.303241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:28:42.003794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:28:42.885215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:28:43.723797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:28:44.570389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:28:45.314109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:28:46.483482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:28:47.437614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:28:42.127345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:28:43.026453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:28:43.873020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:28:44.675010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:28:45.467577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:28:46.598300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:28:47.548629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:28:42.257116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:28:43.142893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:28:44.016550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:28:44.791269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:28:45.582810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:28:46.723402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:28:47.674960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:28:42.365094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:28:43.243141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:28:44.139269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:28:44.902195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:28:45.999671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:28:46.834442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:28:47.796634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:28:42.500278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:28:43.340486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:28:44.261738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:28:44.998791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:28:46.141842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:28:46.946349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:28:47.902745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:28:42.635048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:28:43.455902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:28:44.394289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:28:45.107016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:28:46.251935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:28:47.064110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:28:48.026419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:28:42.772621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:28:43.602181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:28:44.493243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:28:45.220093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:28:46.365848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:28:47.185063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T15:28:51.024788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
회계연도구분필지수필지수비율(퍼센트)면적(제곱킬로미터)면적비율(퍼센트)금액(억원)금액비율(퍼센트)
회계연도1.0000.0000.0000.0000.0000.0000.0000.000
구분0.0001.0000.9751.0001.0001.0000.7301.000
필지수0.0000.9751.0000.9980.8970.8970.7400.945
필지수비율(퍼센트)0.0001.0000.9981.0000.9010.9010.7450.947
면적(제곱킬로미터)0.0001.0000.8970.9011.0001.0000.7430.951
면적비율(퍼센트)0.0001.0000.8970.9011.0001.0000.7430.951
금액(억원)0.0000.7300.7400.7450.7430.7431.0000.822
금액비율(퍼센트)0.0001.0000.9450.9470.9510.9510.8221.000
2023-12-12T15:28:51.195456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
회계연도필지수필지수비율(퍼센트)면적(제곱킬로미터)면적비율(퍼센트)금액(억원)금액비율(퍼센트)구분
회계연도1.0000.067-0.0040.0390.0030.125-0.0140.000
필지수0.0671.0000.9940.5810.5740.4580.4530.920
필지수비율(퍼센트)-0.0040.9941.0000.5950.5940.4710.4760.925
면적(제곱킬로미터)0.0390.5810.5951.0000.9920.6310.6340.957
면적비율(퍼센트)0.0030.5740.5940.9921.0000.5940.6000.806
금액(억원)0.1250.4580.4710.6310.5941.0000.9830.463
금액비율(퍼센트)-0.0140.4530.4760.6340.6000.9831.0000.850
구분0.0000.9200.9250.9570.8060.4630.8501.000

Missing values

2023-12-12T15:28:48.177315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T15:28:48.354723image/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

회계연도구분필지수필지수비율(퍼센트)면적(제곱킬로미터)면적비율(퍼센트)금액(억원)금액비율(퍼센트)
02021전410848733211419422
12021답451186831511432402
22021과수원10612014059430
32021임야340869616654664629737
42021대22281741040104529417
52021도로23368634018988127261120
62021하천4074907264510129236721
72021구거96162016155164572787
82021잡종지1422482524272537912
92021기타598717101318575399812
회계연도구분필지수필지수비율(퍼센트)면적(제곱킬로미터)면적비율(퍼센트)금액(억원)금액비율(퍼센트)
782014답427953832511046722
792014과수원8284011030240
802014임야313669616151663019857
812014대217791499060593314
822014도로2094090391762795331622
832014하천3702087255710100252223
842014구거93228617157463788739
852014잡종지1245202488243541210
862014기타535546101221547744411
872014합계5417947100245211004374692100