Overview

Dataset statistics

Number of variables7
Number of observations3051
Missing cells15
Missing cells (%)0.1%
Duplicate rows5
Duplicate rows (%)0.2%
Total size in memory175.9 KiB
Average record size in memory59.0 B

Variable types

Numeric3
Text3
Categorical1

Dataset

Description낙동강수계 토지매수정보시스템 사후관리 조성정보에 대한 데이터로 번호,접수번호,토지고유코드,생태복원유무,년도,면적,조성내용의 항목을 제공합니다.
URLhttps://www.data.go.kr/data/15069000/fileData.do

Alerts

Dataset has 5 (0.2%) duplicate rowsDuplicates
생태복원유무 is highly imbalanced (97.7%)Imbalance

Reproduction

Analysis started2023-12-12 05:13:32.908881
Analysis finished2023-12-12 05:13:34.999089
Duration2.09 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

번호
Real number (ℝ)

Distinct3045
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3163.1442
Minimum2
Maximum7179
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.9 KiB
2023-12-12T14:13:35.073627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile345.5
Q11722.5
median3175
Q34654
95-th percentile6007.5
Maximum7179
Range7177
Interquartile range (IQR)2931.5

Descriptive statistics

Standard deviation1752.0953
Coefficient of variation (CV)0.55390941
Kurtosis-1.0453798
Mean3163.1442
Median Absolute Deviation (MAD)1457
Skewness0.017410017
Sum9650753
Variance3069838.1
MonotonicityNot monotonic
2023-12-12T14:13:35.222444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4407 3
 
0.1%
4408 2
 
0.1%
4409 2
 
0.1%
4030 2
 
0.1%
5832 2
 
0.1%
255 1
 
< 0.1%
2117 1
 
< 0.1%
1241 1
 
< 0.1%
2713 1
 
< 0.1%
4832 1
 
< 0.1%
Other values (3035) 3035
99.5%
ValueCountFrequency (%)
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
6 1
< 0.1%
11 1
< 0.1%
12 1
< 0.1%
13 1
< 0.1%
14 1
< 0.1%
16 1
< 0.1%
20 1
< 0.1%
ValueCountFrequency (%)
7179 1
< 0.1%
6891 1
< 0.1%
6890 1
< 0.1%
6865 1
< 0.1%
6864 1
< 0.1%
6863 1
< 0.1%
6862 1
< 0.1%
6861 1
< 0.1%
6860 1
< 0.1%
6858 1
< 0.1%

접수번호
Text

Distinct3038
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Memory size24.0 KiB
2023-12-12T14:13:35.467340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length13
Mean length13.036382
Min length13

Characters and Unicode

Total characters39774
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

Unique3027 ?
Unique (%)99.2%

Sample

1st row2004-1-0113-1
2nd row2004-1-0113-2
3rd row2004-1-0113-3
4th row2004-1-0113-4
5th row2004-1-0124-1
ValueCountFrequency (%)
2010-1-0114-1 3
 
0.1%
2006-1-0050-1 3
 
0.1%
2005-1-1231-2 2
 
0.1%
2014-1-0034-2 2
 
0.1%
2010-1-0143-1 2
 
0.1%
2004-1-0596-4 2
 
0.1%
2013-1-0009-1 2
 
0.1%
2003-1-0015-6 2
 
0.1%
2004-1-0391-1 2
 
0.1%
2005-1-1231-1 2
 
0.1%
Other values (3028) 3029
99.3%
2023-12-12T14:13:35.851478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 10206
25.7%
- 9153
23.0%
1 6103
15.3%
2 5562
14.0%
4 2015
 
5.1%
5 1586
 
4.0%
3 1401
 
3.5%
6 1099
 
2.8%
8 928
 
2.3%
7 872
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30621
77.0%
Dash Punctuation 9153
 
23.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 10206
33.3%
1 6103
19.9%
2 5562
18.2%
4 2015
 
6.6%
5 1586
 
5.2%
3 1401
 
4.6%
6 1099
 
3.6%
8 928
 
3.0%
7 872
 
2.8%
9 849
 
2.8%
Dash Punctuation
ValueCountFrequency (%)
- 9153
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 39774
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 10206
25.7%
- 9153
23.0%
1 6103
15.3%
2 5562
14.0%
4 2015
 
5.1%
5 1586
 
4.0%
3 1401
 
3.5%
6 1099
 
2.8%
8 928
 
2.3%
7 872
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 39774
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 10206
25.7%
- 9153
23.0%
1 6103
15.3%
2 5562
14.0%
4 2015
 
5.1%
5 1586
 
4.0%
3 1401
 
3.5%
6 1099
 
2.8%
8 928
 
2.3%
7 872
 
2.2%

토지고유코드
Real number (ℝ)

Distinct3038
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.7803811 × 1018
Minimum4.711335 × 1018
Maximum4.889039 × 1018
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.9 KiB
2023-12-12T14:13:36.029485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.711335 × 1018
5-th percentile4.713034 × 1018
Q14.723036 × 1018
median4.775037 × 1018
Q34.782035 × 1018
95-th percentile4.886037 × 1018
Maximum4.889039 × 1018
Range1.7770401 × 1017
Interquartile range (IQR)5.8999012 × 1016

Descriptive statistics

Standard deviation5.5700052 × 1016
Coefficient of variation (CV)0.011651802
Kurtosis-0.31204104
Mean4.7803811 × 1018
Median Absolute Deviation (MAD)6.997998 × 1015
Skewness0.79180846
Sum-6.4318548 × 1018
Variance3.1024958 × 1033
MonotonicityNot monotonic
2023-12-12T14:13:36.215837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4776031042102390000 3
 
0.1%
4776031038101430001 3
 
0.1%
4776031042102420000 2
 
0.1%
4886025034101370001 2
 
0.1%
4776031042102410000 2
 
0.1%
4773033032103660003 2
 
0.1%
4775036041105160002 2
 
0.1%
4775025023103330000 2
 
0.1%
4723036021124360000 2
 
0.1%
4775037037100300002 2
 
0.1%
Other values (3028) 3029
99.3%
ValueCountFrequency (%)
4711335024104930000 1
< 0.1%
4711335024105410000 1
< 0.1%
4711335024105870000 1
< 0.1%
4711335024106070002 1
< 0.1%
4711335024106210000 1
< 0.1%
4711335025100450004 1
< 0.1%
4711335025100480001 1
< 0.1%
4711335025100480004 1
< 0.1%
4711335025100480005 1
< 0.1%
4711335025100490000 1
< 0.1%
ValueCountFrequency (%)
4889039032108330024 1
< 0.1%
4889039032101410004 1
< 0.1%
4889039032101410001 1
< 0.1%
4889038027110900004 1
< 0.1%
4889038027110900002 1
< 0.1%
4889038027110900001 1
< 0.1%
4889038027110900000 1
< 0.1%
4889038027110890000 1
< 0.1%
4887037025112630000 1
< 0.1%
4887037025112620001 1
< 0.1%

생태복원유무
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size24.0 KiB
유
3044 
N
 
7

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
유 3044
99.8%
N 7
 
0.2%

Length

2023-12-12T14:13:36.438828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T14:13:36.548412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
유 3044
99.8%
n 7
 
0.2%

년도
Real number (ℝ)

Distinct13
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2012.6601
Minimum2006
Maximum2018
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.9 KiB
2023-12-12T14:13:36.649655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2006
5-th percentile2008
Q12010
median2012
Q32015
95-th percentile2017
Maximum2018
Range12
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.7736781
Coefficient of variation (CV)0.0013781155
Kurtosis-0.99103659
Mean2012.6601
Median Absolute Deviation (MAD)2
Skewness0.0058852696
Sum6140626
Variance7.6932903
MonotonicityNot monotonic
2023-12-12T14:13:36.768565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
2010 452
14.8%
2011 439
14.4%
2012 361
11.8%
2017 352
11.5%
2015 306
10.0%
2014 295
9.7%
2016 275
9.0%
2013 236
7.7%
2009 157
 
5.1%
2008 86
 
2.8%
Other values (3) 92
 
3.0%
ValueCountFrequency (%)
2006 5
 
0.2%
2007 84
 
2.8%
2008 86
 
2.8%
2009 157
 
5.1%
2010 452
14.8%
2011 439
14.4%
2012 361
11.8%
2013 236
7.7%
2014 295
9.7%
2015 306
10.0%
ValueCountFrequency (%)
2018 3
 
0.1%
2017 352
11.5%
2016 275
9.0%
2015 306
10.0%
2014 295
9.7%
2013 236
7.7%
2012 361
11.8%
2011 439
14.4%
2010 452
14.8%
2009 157
 
5.1%

면적
Text

Distinct1908
Distinct (%)62.8%
Missing11
Missing (%)0.4%
Memory size24.0 KiB
2023-12-12T14:13:37.270545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length4
Mean length3.5914474
Min length1

Characters and Unicode

Total characters10918
Distinct characters12
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

Unique1243 ?
Unique (%)40.9%

Sample

1st row153
2nd row288
3rd row542
4th row185
5th row314
ValueCountFrequency (%)
648 8
 
0.3%
288 7
 
0.2%
1574 7
 
0.2%
357 7
 
0.2%
129 7
 
0.2%
992 7
 
0.2%
734 7
 
0.2%
466 6
 
0.2%
793 6
 
0.2%
198 6
 
0.2%
Other values (1898) 2972
97.8%
2023-12-12T14:13:37.950796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 1809
16.6%
2 1468
13.4%
3 1184
10.8%
4 1030
9.4%
5 948
8.7%
6 939
8.6%
9 912
8.4%
8 894
8.2%
0 868
8.0%
7 854
7.8%
Other values (2) 12
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10906
99.9%
Other Punctuation 12
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1809
16.6%
2 1468
13.5%
3 1184
10.9%
4 1030
9.4%
5 948
8.7%
6 939
8.6%
9 912
8.4%
8 894
8.2%
0 868
8.0%
7 854
7.8%
Other Punctuation
ValueCountFrequency (%)
. 9
75.0%
, 3
 
25.0%

Most occurring scripts

ValueCountFrequency (%)
Common 10918
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1809
16.6%
2 1468
13.4%
3 1184
10.8%
4 1030
9.4%
5 948
8.7%
6 939
8.6%
9 912
8.4%
8 894
8.2%
0 868
8.0%
7 854
7.8%
Other values (2) 12
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10918
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1809
16.6%
2 1468
13.4%
3 1184
10.8%
4 1030
9.4%
5 948
8.7%
6 939
8.6%
9 912
8.4%
8 894
8.2%
0 868
8.0%
7 854
7.8%
Other values (2) 12
 
0.1%

조성내용
Text

Distinct1469
Distinct (%)48.2%
Missing4
Missing (%)0.1%
Memory size24.0 KiB
2023-12-12T14:13:38.339719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length248
Median length126
Mean length79.612406
Min length6

Characters and Unicode

Total characters242579
Distinct characters301
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique984 ?
Unique (%)32.3%

Sample

1st row경북 영천시 자양면 신방리 688, 690, 752번지와 한 대상지임배롱나무 등 7종 49주 식재
2nd row경북 영천시 자양면 신방리 687, 690, 752번지와 한 대상지임배롱나무 등 7종 49주 식재
3rd row경북 영천시 자양면 신방리 687, 688, 752번지와 한 대상지임배롱나무 등 7종 49주 식재
4th row경북 영천시 자양면 신방리 687, 688, 690번지와 한 대상지임배롱나무 등 7종 49주 식재
5th row경북 영천시 자양면 신방리 816, 819-1, 836번지와 한 대상지임 배롱나무 등 6종 157주 식재
ValueCountFrequency (%)
식재 2668
 
5.5%
등 2318
 
4.8%
한 2112
 
4.4%
경북 1411
 
2.9%
청송군 622
 
1.3%
경상북도 517
 
1.1%
같은 511
 
1.1%
5종 455
 
0.9%
영양군 446
 
0.9%
3종 422
 
0.9%
Other values (3833) 36627
76.1%
2023-12-12T14:13:38.888166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
45101
18.6%
, 17083
 
7.0%
1 14662
 
6.0%
2 9868
 
4.1%
3 9547
 
3.9%
- 9316
 
3.8%
지 8358
 
3.4%
4 7732
 
3.2%
5 7230
 
3.0%
0 6245
 
2.6%
Other values (291) 107437
44.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 92318
38.1%
Decimal Number 77166
31.8%
Space Separator 45101
18.6%
Other Punctuation 17851
 
7.4%
Dash Punctuation 9316
 
3.8%
Lowercase Letter 453
 
0.2%
Uppercase Letter 174
 
0.1%
Close Punctuation 77
 
< 0.1%
Open Punctuation 77
 
< 0.1%
Math Symbol 46
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 8358
 
9.1%
번 5392
 
5.8%
상 3658
 
4.0%
주 3595
 
3.9%
식 3139
 
3.4%
리 3114
 
3.4%
대 3067
 
3.3%
나 3029
 
3.3%
무 3007
 
3.3%
재 2944
 
3.2%
Other values (262) 53015
57.4%
Decimal Number
ValueCountFrequency (%)
1 14662
19.0%
2 9868
12.8%
3 9547
12.4%
4 7732
10.0%
5 7230
9.4%
0 6245
8.1%
6 6085
7.9%
7 5563
 
7.2%
8 5333
 
6.9%
9 4901
 
6.4%
Uppercase Letter
ValueCountFrequency (%)
P 71
40.8%
E 65
37.4%
C 11
 
6.3%
I 11
 
6.3%
D 11
 
6.3%
A 5
 
2.9%
Other Punctuation
ValueCountFrequency (%)
, 17083
95.7%
. 753
 
4.2%
& 5
 
< 0.1%
; 5
 
< 0.1%
# 5
 
< 0.1%
Lowercase Letter
ValueCountFrequency (%)
m 449
99.1%
a 2
 
0.4%
e 2
 
0.4%
Space Separator
ValueCountFrequency (%)
45101
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 9316
100.0%
Close Punctuation
ValueCountFrequency (%)
) 77
100.0%
Open Punctuation
ValueCountFrequency (%)
( 77
100.0%
Math Symbol
ValueCountFrequency (%)
~ 46
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 149634
61.7%
Hangul 92318
38.1%
Latin 627
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 8358
 
9.1%
번 5392
 
5.8%
상 3658
 
4.0%
주 3595
 
3.9%
식 3139
 
3.4%
리 3114
 
3.4%
대 3067
 
3.3%
나 3029
 
3.3%
무 3007
 
3.3%
재 2944
 
3.2%
Other values (262) 53015
57.4%
Common
ValueCountFrequency (%)
45101
30.1%
, 17083
 
11.4%
1 14662
 
9.8%
2 9868
 
6.6%
3 9547
 
6.4%
- 9316
 
6.2%
4 7732
 
5.2%
5 7230
 
4.8%
0 6245
 
4.2%
6 6085
 
4.1%
Other values (10) 16765
 
11.2%
Latin
ValueCountFrequency (%)
m 449
71.6%
P 71
 
11.3%
E 65
 
10.4%
C 11
 
1.8%
I 11
 
1.8%
D 11
 
1.8%
A 5
 
0.8%
a 2
 
0.3%
e 2
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 150261
61.9%
Hangul 92318
38.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
45101
30.0%
, 17083
 
11.4%
1 14662
 
9.8%
2 9868
 
6.6%
3 9547
 
6.4%
- 9316
 
6.2%
4 7732
 
5.1%
5 7230
 
4.8%
0 6245
 
4.2%
6 6085
 
4.0%
Other values (19) 17392
 
11.6%
Hangul
ValueCountFrequency (%)
지 8358
 
9.1%
번 5392
 
5.8%
상 3658
 
4.0%
주 3595
 
3.9%
식 3139
 
3.4%
리 3114
 
3.4%
대 3067
 
3.3%
나 3029
 
3.3%
무 3007
 
3.3%
재 2944
 
3.2%
Other values (262) 53015
57.4%

Interactions

2023-12-12T14:13:34.389410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:13:33.681796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:13:34.058865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:13:34.492644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:13:33.782810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:13:34.173992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:13:34.593482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:13:33.940491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:13:34.290629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T14:13:39.034433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호토지고유코드생태복원유무년도
번호1.0000.3840.1270.758
토지고유코드0.3841.0000.0990.335
생태복원유무0.1270.0991.0000.051
년도0.7580.3350.0511.000
2023-12-12T14:13:39.173644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호토지고유코드년도생태복원유무
번호1.0000.0820.4450.097
토지고유코드0.0821.0000.0780.071
년도0.4450.0781.0000.038
생태복원유무0.0970.0710.0381.000

Missing values

2023-12-12T14:13:34.740467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T14:13:34.859142image/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.
2023-12-12T14:13:34.952359image/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

번호접수번호토지고유코드생태복원유무년도면적조성내용
036592004-1-0113-14723036026106870000유2010153경북 영천시 자양면 신방리 688, 690, 752번지와 한 대상지임배롱나무 등 7종 49주 식재
136582004-1-0113-24723036026106880000유2010288경북 영천시 자양면 신방리 687, 690, 752번지와 한 대상지임배롱나무 등 7종 49주 식재
236572004-1-0113-34723036026106900000유2010542경북 영천시 자양면 신방리 687, 688, 752번지와 한 대상지임배롱나무 등 7종 49주 식재
336562004-1-0113-44723036026107520000유2010185경북 영천시 자양면 신방리 687, 688, 690번지와 한 대상지임배롱나무 등 7종 49주 식재
436972004-1-0124-14723036026108150000유2010314경북 영천시 자양면 신방리 816, 819-1, 836번지와 한 대상지임 배롱나무 등 6종 157주 식재
536962004-1-0124-24723036026108160000유2010698경북 영천시 자양면 신방리 815, 819-1, 836번지와 한 대상지임 배롱나무 등 6종 157주 식재
635602004-1-0080-14723036026108190001유20101488경북 영천시 자양면 신방리 815, 816, 836번지와 한 대상지임 배롱나무 등 6종 157주 식재
731122004-1-0857-14723036026108360000유20101931경북 영천시 자양면 신방리 815, 816, 819-1번지와 한 대상지임 배롱나무 등 6종 157주 식재
836952004-1-0123-14723036021104030000유20101518경북 영천시 자양면 보현리 424번지와 한 대상지임상수리나무 등 6종 236주 식재
936942004-1-0123-24723036021104240000유20102830경북 영천시 자양면 보현리 403번지와 한 대상지임상수리나무 등 6종 236주 식재
번호접수번호토지고유코드생태복원유무년도면적조성내용
30418762005-1-0226-14775037038106710000유20171281경북 청송군 진보면 후평리 647-5, 667, 671는 한 대상지임느티나무 등 5종 173주 식재
304215952005-1-0437-54775037050107160000유20172165경북 청송군 진보면 괴정리 716 대상지임소나무 1종 104주 식재
304358032013-1-0119-14775037046102750000유20172692경북 청송군 진보면 월전리 275 대상지임소나무 1종 103주 식재
304464922015-1-0010-14711335034103330000유20171438경북 포항시 죽장면 방흥리 333 대상지임습지 1식
304550252010-1-0013-14711335026101940000유20171540경북 포항시 죽장면 지동리 194, 195, 197, 187-1, 193-1, 196는 한 대상지임상수리나무 등 2종 191주 식재
304650242010-1-0013-24711335026101950000유2017873경북 포항시 죽장면 지동리 194, 195, 197, 187-1, 193-1, 196는 한 대상지임상수리나무 등 2종 191주 식재
304749052010-1-0118-14711335026101970000유20171025경북 포항시 죽장면 지동리 194, 195, 197, 187-1, 193-1, 196는 한 대상지임상수리나무 등 2종 191주 식재
304849042005-1-1018-14711335026101870001유2017572경북 포항시 죽장면 지동리 194, 195, 197, 187-1, 193-1, 196는 한 대상지임상수리나무 등 2종 191주 식재
304950512010-1-0056-14711335026101930001유2017506경북 포항시 죽장면 지동리 194, 195, 197, 187-1, 193-1, 196는 한 대상지임상수리나무 등 2종 191주 식재
305044592012-1-0012-14711335026101960000유2017569경북 포항시 죽장면 지동리 194, 195, 197, 187-1, 193-1, 196는 한 대상지임상수리나무 등 2종 191주 식재

Duplicate rows

Most frequently occurring

번호접수번호토지고유코드생태복원유무년도면적조성내용# duplicates
040302004-1-0391-14723036023101510000유2017793경북 영천시 자양면 충효리 151, 170, 163, 166는 한 대상지임산수유 1종 14주 식재, 배수로 48.5m2
144072006-1-0050-14776031042102390000유20121894경북 영양군 입암면 산해리 235-6번지, 239번지, 241번지, 242번지, 251번지, 310-1번지, 310-2번지, 558-1번지, 559-2번지, 579-1번지, 579-2번지, 589번지는 한 대상지임벚나무 등 교목 9종 1,268주 식재2
244082005-1-1231-14776031042102410000유20121002경북 영양군 입암면 산해리 235-6번지, 239번지, 241번지, 242번지, 251번지, 310-1번지, 310-2번지, 558-1번지, 559-2번지, 579-1번지, 579-2번지, 589번지는 한 대상지임벚나무 등 교목 9종 1,268주 식재2
344092005-1-1231-24776031042102420000유20122337경북 영양군 입암면 산해리 235-6번지, 239번지, 241번지, 242번지, 251번지, 310-1번지, 310-2번지, 558-1번지, 559-2번지, 579-1번지, 579-2번지, 589번지는 한 대상지임벚나무 등 교목 9종 1,268주 식재2
458322013-1-0009-14773033032103660003유20161574경북 의성군 옥산면 정자리 366-3, -5, -6, -7, 367는 한 대상지임꽃사과(수분수) 등 4종 415주 식재, 배수로 319.2m2