Overview

Dataset statistics

Number of variables10
Number of observations5292
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory444.6 KiB
Average record size in memory86.0 B

Variable types

Categorical4
Text2
Numeric4

Dataset

Description농림축산식품부에서 토양 개량제 지원 사업 관련 정보를 제공하며 개방 항목은 다음과 같습니다.
Author농림축산식품부
URLhttps://www.data.go.kr/data/15083302/fileData.do

Alerts

사업년도 has constant value ""Constant
신청면적(제곱미터) is highly overall correlated with 양(kg) and 2 other fieldsHigh correlation
양(kg) is highly overall correlated with 신청면적(제곱미터) and 2 other fieldsHigh correlation
신청(포) is highly overall correlated with 신청면적(제곱미터) and 2 other fieldsHigh correlation
선정(포) is highly overall correlated with 신청면적(제곱미터) and 2 other fieldsHigh correlation

Reproduction

Analysis started2023-12-12 10:02:29.568472
Analysis finished2023-12-12 10:02:33.098111
Duration3.53 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

사업년도
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size41.5 KiB
2011
5292 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2011
2nd row2011
3rd row2011
4th row2011
5th row2011

Common Values

ValueCountFrequency (%)
2011 5292
100.0%

Length

2023-12-12T19:02:33.158259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T19:02:33.252810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2011 5292
100.0%

선정년도
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size41.5 KiB
2011
1928 
2012
1694 
2013
1670 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2012
2nd row2012
3rd row2011
4th row2012
5th row2011

Common Values

ValueCountFrequency (%)
2011 1928
36.4%
2012 1694
32.0%
2013 1670
31.6%

Length

2023-12-12T19:02:33.347948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T19:02:33.437678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2011 1928
36.4%
2012 1694
32.0%
2013 1670
31.6%

시도
Categorical

Distinct17
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size41.5 KiB
경상북도
850 
경상남도
710 
경기도
699 
전라남도
674 
전라북도
515 
Other values (12)
1844 

Length

Max length7
Median length4
Mean length3.9480348
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경상북도 850
16.1%
경상남도 710
13.4%
경기도 699
13.2%
전라남도 674
12.7%
전라북도 515
9.7%
충청남도 418
7.9%
강원도 411
7.8%
충청북도 332
 
6.3%
인천광역시 173
 
3.3%
대전광역시 91
 
1.7%
Other values (7) 419
7.9%

Length

2023-12-12T19:02:33.563201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경상북도 850
16.1%
경상남도 710
13.4%
경기도 699
13.2%
전라남도 674
12.7%
전라북도 515
9.7%
충청남도 418
7.9%
강원도 411
7.8%
충청북도 332
 
6.3%
인천광역시 173
 
3.3%
대전광역시 91
 
1.7%
Other values (7) 419
7.9%

시군
Text

Distinct183
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Memory size41.5 KiB
2023-12-12T19:02:33.918679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length2.9754346
Min length2

Characters and Unicode

Total characters15746
Distinct characters123
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)0.1%

Sample

1st row중랑구
2nd row중랑구
3rd row노원구
4th row은평구
5th row양천구
ValueCountFrequency (%)
강화군 76
 
1.4%
구미시 74
 
1.4%
의령군 73
 
1.4%
사천시 73
 
1.4%
상주시 64
 
1.2%
익산시 63
 
1.2%
서구 58
 
1.1%
창원시 56
 
1.1%
진주시 56
 
1.1%
강서구 56
 
1.1%
Other values (173) 4643
87.7%
2023-12-12T19:02:34.499803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 2636
 
16.7%
군 2338
 
14.8%
주 716
 
4.5%
천 647
 
4.1%
구 536
 
3.4%
산 497
 
3.2%
성 429
 
2.7%
양 384
 
2.4%
안 319
 
2.0%
영 265
 
1.7%
Other values (113) 6979
44.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 15746
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 2636
 
16.7%
군 2338
 
14.8%
주 716
 
4.5%
천 647
 
4.1%
구 536
 
3.4%
산 497
 
3.2%
성 429
 
2.7%
양 384
 
2.4%
안 319
 
2.0%
영 265
 
1.7%
Other values (113) 6979
44.3%

Most occurring scripts

ValueCountFrequency (%)
Hangul 15746
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 2636
 
16.7%
군 2338
 
14.8%
주 716
 
4.5%
천 647
 
4.1%
구 536
 
3.4%
산 497
 
3.2%
성 429
 
2.7%
양 384
 
2.4%
안 319
 
2.0%
영 265
 
1.7%
Other values (113) 6979
44.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 15746
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 2636
 
16.7%
군 2338
 
14.8%
주 716
 
4.5%
천 647
 
4.1%
구 536
 
3.4%
산 497
 
3.2%
성 429
 
2.7%
양 384
 
2.4%
안 319
 
2.0%
영 265
 
1.7%
Other values (113) 6979
44.3%

읍면동
Text

Distinct1893
Distinct (%)35.8%
Missing0
Missing (%)0.0%
Memory size41.5 KiB
2023-12-12T19:02:34.893684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length3
Mean length3.0757748
Min length2

Characters and Unicode

Total characters16277
Distinct characters310
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

Unique152 ?
Unique (%)2.9%

Sample

1st row신내1동
2nd row신내2동
3rd row중계본동
4th row진관동
5th row신월7동
ValueCountFrequency (%)
남면 38
 
0.7%
서면 30
 
0.6%
북면 23
 
0.4%
중앙동 17
 
0.3%
동면 14
 
0.3%
금성면 14
 
0.3%
마산면 12
 
0.2%
대산면 12
 
0.2%
군내면 11
 
0.2%
송정동 11
 
0.2%
Other values (1883) 5110
96.6%
2023-12-12T19:02:35.448911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
면 3097
 
19.0%
동 1866
 
11.5%
읍 621
 
3.8%
산 448
 
2.8%
천 282
 
1.7%
성 264
 
1.6%
남 259
 
1.6%
서 219
 
1.3%
양 204
 
1.3%
대 187
 
1.1%
Other values (300) 8830
54.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 15893
97.6%
Decimal Number 374
 
2.3%
Other Punctuation 10
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
면 3097
 
19.5%
동 1866
 
11.7%
읍 621
 
3.9%
산 448
 
2.8%
천 282
 
1.8%
성 264
 
1.7%
남 259
 
1.6%
서 219
 
1.4%
양 204
 
1.3%
대 187
 
1.2%
Other values (292) 8446
53.1%
Decimal Number
ValueCountFrequency (%)
1 170
45.5%
2 140
37.4%
3 40
 
10.7%
4 14
 
3.7%
5 5
 
1.3%
6 4
 
1.1%
7 1
 
0.3%
Other Punctuation
ValueCountFrequency (%)
. 10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 15893
97.6%
Common 384
 
2.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
면 3097
 
19.5%
동 1866
 
11.7%
읍 621
 
3.9%
산 448
 
2.8%
천 282
 
1.8%
성 264
 
1.7%
남 259
 
1.6%
서 219
 
1.4%
양 204
 
1.3%
대 187
 
1.2%
Other values (292) 8446
53.1%
Common
ValueCountFrequency (%)
1 170
44.3%
2 140
36.5%
3 40
 
10.4%
4 14
 
3.6%
. 10
 
2.6%
5 5
 
1.3%
6 4
 
1.0%
7 1
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 15893
97.6%
ASCII 384
 
2.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
면 3097
 
19.5%
동 1866
 
11.7%
읍 621
 
3.9%
산 448
 
2.8%
천 282
 
1.8%
성 264
 
1.7%
남 259
 
1.6%
서 219
 
1.4%
양 204
 
1.3%
대 187
 
1.2%
Other values (292) 8446
53.1%
ASCII
ValueCountFrequency (%)
1 170
44.3%
2 140
36.5%
3 40
 
10.4%
4 14
 
3.6%
. 10
 
2.6%
5 5
 
1.3%
6 4
 
1.0%
7 1
 
0.3%

비종
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size41.5 KiB
규산질
2430 
석회질
2293 
패화석
569 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
규산질 2430
45.9%
석회질 2293
43.3%
패화석 569
 
10.8%

Length

2023-12-12T19:02:35.592338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T19:02:35.683182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
규산질 2430
45.9%
석회질 2293
43.3%
패화석 569
 
10.8%

신청면적(제곱미터)
Real number (ℝ)

HIGH CORRELATION 

Distinct5277
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1904119.9
Minimum9
Maximum41125939
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.6 KiB
2023-12-12T19:02:35.798290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile6025.65
Q1119497.75
median775051.85
Q32354751.5
95-th percentile7862417.2
Maximum41125939
Range41125930
Interquartile range (IQR)2235253.7

Descriptive statistics

Standard deviation3049518.3
Coefficient of variation (CV)1.6015369
Kurtosis25.930309
Mean1904119.9
Median Absolute Deviation (MAD)741279.35
Skewness3.8632296
Sum1.0076602 × 1010
Variance9.2995618 × 1012
MonotonicityNot monotonic
2023-12-12T19:02:35.950133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
596100.0 2
 
< 0.1%
2331.0 2
 
< 0.1%
2182.0 2
 
< 0.1%
1038.0 2
 
< 0.1%
1240.0 2
 
< 0.1%
36451.0 2
 
< 0.1%
20231.0 2
 
< 0.1%
275103.0 2
 
< 0.1%
60795.0 2
 
< 0.1%
5269.0 2
 
< 0.1%
Other values (5267) 5272
99.6%
ValueCountFrequency (%)
9.0 1
< 0.1%
19.0 1
< 0.1%
31.0 1
< 0.1%
33.0 1
< 0.1%
53.0 1
< 0.1%
62.0 2
< 0.1%
73.0 1
< 0.1%
80.0 1
< 0.1%
106.0 1
< 0.1%
125.0 1
< 0.1%
ValueCountFrequency (%)
41125939.4 1
< 0.1%
39782288.4 1
< 0.1%
39338103.4 1
< 0.1%
36470824.85 1
< 0.1%
28351395.5 1
< 0.1%
25756554.3 1
< 0.1%
24417109.32 1
< 0.1%
22432407.0 1
< 0.1%
22044527.54 1
< 0.1%
21879265.46 1
< 0.1%

양(kg)
Real number (ℝ)

HIGH CORRELATION 

Distinct5231
Distinct (%)98.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean424473.37
Minimum0
Maximum13936154
Zeros3
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size46.6 KiB
2023-12-12T19:02:36.089219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1170.1
Q124684.75
median167101
Q3521427.75
95-th percentile1731234.4
Maximum13936154
Range13936154
Interquartile range (IQR)496743

Descriptive statistics

Standard deviation708136.37
Coefficient of variation (CV)1.6682704
Kurtosis50.517257
Mean424473.37
Median Absolute Deviation (MAD)160371.5
Skewness4.8285677
Sum2.2463131 × 109
Variance5.0145712 × 1011
MonotonicityNot monotonic
2023-12-12T19:02:36.270249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
460 3
 
0.1%
239 3
 
0.1%
174 3
 
0.1%
1205 3
 
0.1%
0 3
 
0.1%
218 2
 
< 0.1%
644 2
 
< 0.1%
20004 2
 
< 0.1%
366774 2
 
< 0.1%
4343 2
 
< 0.1%
Other values (5221) 5267
99.5%
ValueCountFrequency (%)
0 3
0.1%
2 1
 
< 0.1%
5 1
 
< 0.1%
6 2
< 0.1%
8 1
 
< 0.1%
11 1
 
< 0.1%
12 1
 
< 0.1%
14 1
 
< 0.1%
15 1
 
< 0.1%
16 1
 
< 0.1%
ValueCountFrequency (%)
13936154 1
< 0.1%
12398736 1
< 0.1%
6341445 1
< 0.1%
6294099 1
< 0.1%
5953853 1
< 0.1%
5827420 1
< 0.1%
5665325 1
< 0.1%
5627744 1
< 0.1%
5150525 1
< 0.1%
5017314 1
< 0.1%

신청(포)
Real number (ℝ)

HIGH CORRELATION 

Distinct4500
Distinct (%)85.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21223.693
Minimum0
Maximum696808
Zeros8
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size46.6 KiB
2023-12-12T19:02:36.449633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile58.55
Q11234
median8355
Q326071.25
95-th percentile86561.35
Maximum696808
Range696808
Interquartile range (IQR)24837.25

Descriptive statistics

Standard deviation35406.826
Coefficient of variation (CV)1.6682689
Kurtosis50.517275
Mean21223.693
Median Absolute Deviation (MAD)8018.5
Skewness4.8285682
Sum1.1231578 × 108
Variance1.2536433 × 109
MonotonicityNot monotonic
2023-12-12T19:02:36.602567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12 11
 
0.2%
17 9
 
0.2%
23 9
 
0.2%
43 9
 
0.2%
1 9
 
0.2%
0 8
 
0.2%
16 7
 
0.1%
45 7
 
0.1%
60 7
 
0.1%
4 7
 
0.1%
Other values (4490) 5209
98.4%
ValueCountFrequency (%)
0 8
0.2%
1 9
0.2%
2 2
 
< 0.1%
3 6
0.1%
4 7
0.1%
5 3
 
0.1%
6 4
0.1%
7 2
 
< 0.1%
8 5
0.1%
9 5
0.1%
ValueCountFrequency (%)
696808 1
< 0.1%
619937 1
< 0.1%
317072 1
< 0.1%
314705 1
< 0.1%
297693 1
< 0.1%
291371 1
< 0.1%
283266 1
< 0.1%
281387 1
< 0.1%
257526 1
< 0.1%
250866 1
< 0.1%

선정(포)
Real number (ℝ)

HIGH CORRELATION 

Distinct4501
Distinct (%)85.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21234.452
Minimum0
Maximum696867
Zeros13
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size46.6 KiB
2023-12-12T19:02:36.772951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile59
Q11234.75
median8362.5
Q326077
95-th percentile86633.05
Maximum696867
Range696867
Interquartile range (IQR)24842.25

Descriptive statistics

Standard deviation35419.264
Coefficient of variation (CV)1.6680093
Kurtosis50.461412
Mean21234.452
Median Absolute Deviation (MAD)8022
Skewness4.8259455
Sum1.1237272 × 108
Variance1.2545243 × 109
MonotonicityNot monotonic
2023-12-12T19:02:36.993565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 13
 
0.2%
12 11
 
0.2%
23 10
 
0.2%
45 9
 
0.2%
43 8
 
0.2%
16 8
 
0.2%
17 8
 
0.2%
28 7
 
0.1%
65 7
 
0.1%
19 6
 
0.1%
Other values (4491) 5205
98.4%
ValueCountFrequency (%)
0 13
0.2%
1 6
0.1%
2 2
 
< 0.1%
3 6
0.1%
4 5
 
0.1%
5 3
 
0.1%
6 4
 
0.1%
7 2
 
< 0.1%
8 5
 
0.1%
9 5
 
0.1%
ValueCountFrequency (%)
696867 1
< 0.1%
619954 1
< 0.1%
317105 1
< 0.1%
314742 1
< 0.1%
297719 1
< 0.1%
291387 1
< 0.1%
283302 1
< 0.1%
281421 1
< 0.1%
257560 1
< 0.1%
250889 1
< 0.1%

Interactions

2023-12-12T19:02:32.336891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:02:30.769061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:02:31.215780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:02:31.743610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:02:32.471956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:02:30.858799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:02:31.328341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:02:31.887199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:02:32.626422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:02:30.992404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:02:31.454855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:02:32.033253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:02:32.741559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:02:31.104887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:02:31.602526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:02:32.178425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T19:02:37.118995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
선정년도시도비종신청면적(제곱미터)양(kg)신청(포)선정(포)
선정년도1.0000.1680.0900.0000.0000.0000.000
시도0.1681.0000.5810.3020.2930.2930.294
비종0.0900.5811.0000.3920.2250.2250.226
신청면적(제곱미터)0.0000.3020.3921.0000.8710.8710.871
양(kg)0.0000.2930.2250.8711.0001.0001.000
신청(포)0.0000.2930.2250.8711.0001.0001.000
선정(포)0.0000.2940.2260.8711.0001.0001.000
2023-12-12T19:02:37.272819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
비종시도선정년도
비종1.0000.3830.027
시도0.3831.0000.090
선정년도0.0270.0901.000
2023-12-12T19:02:37.405757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
신청면적(제곱미터)양(kg)신청(포)선정(포)선정년도시도비종
신청면적(제곱미터)1.0000.9860.9860.9860.0000.1250.188
양(kg)0.9861.0001.0001.0000.0000.1360.154
신청(포)0.9861.0001.0001.0000.0000.1360.154
선정(포)0.9861.0001.0001.0000.0000.1370.155
선정년도0.0000.0000.0000.0001.0000.0900.027
시도0.1250.1360.1360.1370.0901.0000.383
비종0.1880.1540.1540.1550.0270.3831.000

Missing values

2023-12-12T19:02:32.884217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T19:02:33.038061image/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

사업년도선정년도시도시군읍면동비종신청면적(제곱미터)양(kg)신청(포)선정(포)
020112012서울특별시중랑구신내1동석회질19407.04368218219
120112012서울특별시중랑구신내2동석회질26538.05969298298
220112011서울특별시노원구중계본동석회질9000.02694135135
320112012서울특별시은평구진관동석회질28661.09745487488
420112011서울특별시양천구신월7동석회질4000.012006060
520112011서울특별시양천구신정3동석회질3699.011105655
620112011서울특별시강서구가양1동규산질5858.013026565
720112011서울특별시강서구가양1동석회질30332.03367168168
820112012서울특별시강서구가양1동석회질399026.05357526792678
920112013서울특별시강서구가양1동석회질22207.03230162161
사업년도선정년도시도시군읍면동비종신청면적(제곱미터)양(kg)신청(포)선정(포)
528220112012세종특별자치시세종시금남면규산질5049631.7611279345639756547
528320112012세종특별자치시세종시금남면석회질2471589.85783512891828953
528420112011세종특별자치시세종시연서면규산질5349404.013283076641566431
528520112011세종특별자치시세종시연서면석회질4448706.29702894851448533
528620112013세종특별자치시세종시전의면규산질3058106.07167803583935840
528720112013세종특별자치시세종시전의면석회질718183.016048080248030
528820112013세종특별자치시세종시전동면규산질4823526.012174986087560888
528920112013세종특별자치시세종시전동면석회질2832080.66186183093130948
529020112013세종특별자치시세종시소정면규산질1094978.02397921199011985
529120112013세종특별자치시세종시소정면석회질235647.04824924122415