Overview

Dataset statistics

Number of variables10
Number of observations5917
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory497.1 KiB
Average record size in memory86.0 B

Variable types

Categorical4
Text2
Numeric4

Dataset

Description토양개량제 지역별(읍면동) 신청,배정현황
Author농림축산식품부
URLhttps://data.mafra.go.kr/opendata/data/indexOpenDataDetail.do?data_id=20220216000000001966

Alerts

사업년도 has constant value ""Constant
신청면적(㎡) is highly overall correlated with 양(kg) and 2 other fieldsHigh correlation
양(kg) is highly overall correlated with 신청면적(㎡) and 2 other fieldsHigh correlation
신청(포) is highly overall correlated with 신청면적(㎡) and 2 other fieldsHigh correlation
선정(포) is highly overall correlated with 신청면적(㎡) and 2 other fieldsHigh correlation

Reproduction

Analysis started2023-12-11 03:29:26.306680
Analysis finished2023-12-11 03:29:28.965765
Duration2.66 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

사업년도
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size46.4 KiB
2014
5917 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2014
2nd row2014
3rd row2014
4th row2014
5th row2014

Common Values

ValueCountFrequency (%)
2014 5917
100.0%

Length

2023-12-11T12:29:29.047409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T12:29:29.150645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2014 5917
100.0%

선정년도
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size46.4 KiB
2014
2036 
2015
1961 
2016
1920 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2014
2nd row2014
3rd row2014
4th row2016
5th row2014

Common Values

ValueCountFrequency (%)
2014 2036
34.4%
2015 1961
33.1%
2016 1920
32.4%

Length

2023-12-11T12:29:29.264747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T12:29:29.372386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2014 2036
34.4%
2015 1961
33.1%
2016 1920
32.4%

시도
Categorical

Distinct17
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size46.4 KiB
경상북도
1041 
경기도
780 
전라남도
757 
경상남도
737 
전라북도
591 
Other values (12)
2011 

Length

Max length7
Median length4
Mean length3.9668751
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경상북도 1041
17.6%
경기도 780
13.2%
전라남도 757
12.8%
경상남도 737
12.5%
전라북도 591
10.0%
충청남도 441
7.5%
강원도 410
 
6.9%
충청북도 394
 
6.7%
인천광역시 195
 
3.3%
광주광역시 95
 
1.6%
Other values (7) 476
8.0%

Length

2023-12-11T12:29:29.489540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경상북도 1041
17.6%
경기도 780
13.2%
전라남도 757
12.8%
경상남도 737
12.5%
전라북도 591
10.0%
충청남도 441
7.5%
강원도 410
 
6.9%
충청북도 394
 
6.7%
인천광역시 195
 
3.3%
광주광역시 95
 
1.6%
Other values (7) 476
8.0%

시군
Text

Distinct182
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size46.4 KiB
2023-12-11T12:29:29.860509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length2.9832685
Min length2

Characters and Unicode

Total characters17652
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

Unique2 ?
Unique (%)< 0.1%

Sample

1st row중랑구
2nd row중랑구
3rd row중랑구
4th row노원구
5th row노원구
ValueCountFrequency (%)
상주시 91
 
1.5%
강화군 79
 
1.3%
구미시 78
 
1.3%
포항시 78
 
1.3%
의령군 76
 
1.3%
나주시 76
 
1.3%
정읍시 73
 
1.2%
사천시 73
 
1.2%
익산시 69
 
1.2%
진주시 65
 
1.1%
Other values (172) 5159
87.2%
2023-12-11T12:29:30.382130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 3047
 
17.3%
군 2545
 
14.4%
주 885
 
5.0%
천 706
 
4.0%
구 557
 
3.2%
산 527
 
3.0%
성 456
 
2.6%
양 451
 
2.6%
안 403
 
2.3%
영 313
 
1.8%
Other values (113) 7762
44.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 17652
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 3047
 
17.3%
군 2545
 
14.4%
주 885
 
5.0%
천 706
 
4.0%
구 557
 
3.2%
산 527
 
3.0%
성 456
 
2.6%
양 451
 
2.6%
안 403
 
2.3%
영 313
 
1.8%
Other values (113) 7762
44.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 17652
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 3047
 
17.3%
군 2545
 
14.4%
주 885
 
5.0%
천 706
 
4.0%
구 557
 
3.2%
산 527
 
3.0%
성 456
 
2.6%
양 451
 
2.6%
안 403
 
2.3%
영 313
 
1.8%
Other values (113) 7762
44.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 17652
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 3047
 
17.3%
군 2545
 
14.4%
주 885
 
5.0%
천 706
 
4.0%
구 557
 
3.2%
산 527
 
3.0%
성 456
 
2.6%
양 451
 
2.6%
안 403
 
2.3%
영 313
 
1.8%
Other values (113) 7762
44.0%

읍면동
Text

Distinct1885
Distinct (%)31.9%
Missing0
Missing (%)0.0%
Memory size46.4 KiB
2023-12-11T12:29:30.760167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length3
Mean length3.0850093
Min length2

Characters and Unicode

Total characters18254
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

Unique177 ?
Unique (%)3.0%

Sample

1st row망우본동
2nd row신내1동
3rd row신내2동
4th row하계1동
5th row상계9동
ValueCountFrequency (%)
남면 40
 
0.7%
서면 27
 
0.5%
북면 23
 
0.4%
금성면 18
 
0.3%
중앙동 17
 
0.3%
동면 15
 
0.3%
산동면 14
 
0.2%
군북면 13
 
0.2%
대산면 13
 
0.2%
덕산면 13
 
0.2%
Other values (1875) 5724
96.7%
2023-12-11T12:29:31.302711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
면 3554
 
19.5%
동 1985
 
10.9%
읍 692
 
3.8%
산 536
 
2.9%
천 293
 
1.6%
남 288
 
1.6%
성 288
 
1.6%
서 260
 
1.4%
양 226
 
1.2%
곡 206
 
1.1%
Other values (300) 9926
54.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 17838
97.7%
Decimal Number 405
 
2.2%
Other Punctuation 11
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
면 3554
 
19.9%
동 1985
 
11.1%
읍 692
 
3.9%
산 536
 
3.0%
천 293
 
1.6%
남 288
 
1.6%
성 288
 
1.6%
서 260
 
1.5%
양 226
 
1.3%
곡 206
 
1.2%
Other values (291) 9510
53.3%
Decimal Number
ValueCountFrequency (%)
1 189
46.7%
2 137
33.8%
3 49
 
12.1%
4 14
 
3.5%
6 6
 
1.5%
5 5
 
1.2%
7 3
 
0.7%
9 2
 
0.5%
Other Punctuation
ValueCountFrequency (%)
. 11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 17838
97.7%
Common 416
 
2.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
면 3554
 
19.9%
동 1985
 
11.1%
읍 692
 
3.9%
산 536
 
3.0%
천 293
 
1.6%
남 288
 
1.6%
성 288
 
1.6%
서 260
 
1.5%
양 226
 
1.3%
곡 206
 
1.2%
Other values (291) 9510
53.3%
Common
ValueCountFrequency (%)
1 189
45.4%
2 137
32.9%
3 49
 
11.8%
4 14
 
3.4%
. 11
 
2.6%
6 6
 
1.4%
5 5
 
1.2%
7 3
 
0.7%
9 2
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 17838
97.7%
ASCII 416
 
2.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
면 3554
 
19.9%
동 1985
 
11.1%
읍 692
 
3.9%
산 536
 
3.0%
천 293
 
1.6%
남 288
 
1.6%
성 288
 
1.6%
서 260
 
1.5%
양 226
 
1.3%
곡 206
 
1.2%
Other values (291) 9510
53.3%
ASCII
ValueCountFrequency (%)
1 189
45.4%
2 137
32.9%
3 49
 
11.8%
4 14
 
3.4%
. 11
 
2.6%
6 6
 
1.4%
5 5
 
1.2%
7 3
 
0.7%
9 2
 
0.5%

비종
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size46.4 KiB
규산질
2368 
석회질
2258 
패화석
1291 

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 2368
40.0%
석회질 2258
38.2%
패화석 1291
21.8%

Length

2023-12-11T12:29:31.450285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T12:29:31.559371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
규산질 2368
40.0%
석회질 2258
38.2%
패화석 1291
21.8%

신청면적(㎡)
Real number (ℝ)

HIGH CORRELATION 

Distinct5884
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1546378.8
Minimum106
Maximum43937633
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size52.1 KiB
2023-12-11T12:29:31.675447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum106
5-th percentile3340
Q163311
median477996
Q31857011.8
95-th percentile6684275.2
Maximum43937633
Range43937527
Interquartile range (IQR)1793700.8

Descriptive statistics

Standard deviation2728987.6
Coefficient of variation (CV)1.7647601
Kurtosis37.784939
Mean1546378.8
Median Absolute Deviation (MAD)467171
Skewness4.5354807
Sum9.1499233 × 109
Variance7.4473734 × 1012
MonotonicityNot monotonic
2023-12-11T12:29:32.145402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3340.0 3
 
0.1%
5517.0 2
 
< 0.1%
5584.0 2
 
< 0.1%
2000.0 2
 
< 0.1%
35125.0 2
 
< 0.1%
18538.0 2
 
< 0.1%
18524.0 2
 
< 0.1%
11643.0 2
 
< 0.1%
43742.0 2
 
< 0.1%
1018.0 2
 
< 0.1%
Other values (5874) 5896
99.6%
ValueCountFrequency (%)
106.0 1
< 0.1%
125.0 1
< 0.1%
135.0 1
< 0.1%
179.0 1
< 0.1%
198.0 1
< 0.1%
230.0 1
< 0.1%
236.0 1
< 0.1%
248.0 1
< 0.1%
280.0 1
< 0.1%
291.0 1
< 0.1%
ValueCountFrequency (%)
43937632.57 1
< 0.1%
38779098.57 1
< 0.1%
37875580.0 1
< 0.1%
37410466.8 1
< 0.1%
30477610.5 1
< 0.1%
29143439.0 1
< 0.1%
24537111.1 1
< 0.1%
24398912.3 1
< 0.1%
22305109.1 1
< 0.1%
20410249.8 1
< 0.1%

양(kg)
Real number (ℝ)

HIGH CORRELATION 

Distinct5768
Distinct (%)97.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean337923.78
Minimum0
Maximum9469365
Zeros11
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size52.1 KiB
2023-12-11T12:29:32.295146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile703.8
Q113074
median99485
Q3407124
95-th percentile1417921.8
Maximum9469365
Range9469365
Interquartile range (IQR)394050

Descriptive statistics

Standard deviation596199.79
Coefficient of variation (CV)1.7643026
Kurtosis30.819649
Mean337923.78
Median Absolute Deviation (MAD)97386
Skewness4.1876777
Sum1.999495 × 109
Variance3.5545419 × 1011
MonotonicityNot monotonic
2023-12-11T12:29:32.454022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 11
 
0.2%
919 4
 
0.1%
560 3
 
0.1%
677 3
 
0.1%
233 3
 
0.1%
1187 3
 
0.1%
762 3
 
0.1%
305 3
 
0.1%
274 3
 
0.1%
744 3
 
0.1%
Other values (5758) 5878
99.3%
ValueCountFrequency (%)
0 11
0.2%
10 1
 
< 0.1%
19 1
 
< 0.1%
22 1
 
< 0.1%
27 1
 
< 0.1%
35 2
 
< 0.1%
42 1
 
< 0.1%
43 1
 
< 0.1%
47 1
 
< 0.1%
50 1
 
< 0.1%
ValueCountFrequency (%)
9469365 1
< 0.1%
7924191 1
< 0.1%
7694934 1
< 0.1%
6211493 1
< 0.1%
6206269 1
< 0.1%
6120171 1
< 0.1%
5811228 1
< 0.1%
4887315 1
< 0.1%
4604584 1
< 0.1%
4553917 1
< 0.1%

신청(포)
Real number (ℝ)

HIGH CORRELATION 

Distinct4578
Distinct (%)77.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16896.213
Minimum0
Maximum473468
Zeros11
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size52.1 KiB
2023-12-11T12:29:32.587633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile35
Q1654
median4974
Q320356
95-th percentile70896.4
Maximum473468
Range473468
Interquartile range (IQR)19702

Descriptive statistics

Standard deviation29809.989
Coefficient of variation (CV)1.7643
Kurtosis30.819666
Mean16896.213
Median Absolute Deviation (MAD)4869
Skewness4.187679
Sum99974892
Variance8.8863546 × 108
MonotonicityNot monotonic
2023-12-11T12:29:32.723508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24 13
 
0.2%
18 12
 
0.2%
33 12
 
0.2%
9 12
 
0.2%
28 11
 
0.2%
12 11
 
0.2%
8 11
 
0.2%
37 11
 
0.2%
6 11
 
0.2%
0 11
 
0.2%
Other values (4568) 5802
98.1%
ValueCountFrequency (%)
0 11
0.2%
1 4
 
0.1%
2 5
0.1%
3 5
0.1%
4 10
0.2%
5 10
0.2%
6 11
0.2%
7 8
0.1%
8 11
0.2%
9 12
0.2%
ValueCountFrequency (%)
473468 1
< 0.1%
396210 1
< 0.1%
384747 1
< 0.1%
310575 1
< 0.1%
310313 1
< 0.1%
306009 1
< 0.1%
290561 1
< 0.1%
244366 1
< 0.1%
230229 1
< 0.1%
227696 1
< 0.1%

선정(포)
Real number (ℝ)

HIGH CORRELATION 

Distinct4574
Distinct (%)77.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16901.595
Minimum0
Maximum473494
Zeros10
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size52.1 KiB
2023-12-11T12:29:32.914382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile35
Q1654
median4975
Q320375
95-th percentile70904
Maximum473494
Range473494
Interquartile range (IQR)19721

Descriptive statistics

Standard deviation29812.942
Coefficient of variation (CV)1.7639129
Kurtosis30.810776
Mean16901.595
Median Absolute Deviation (MAD)4870
Skewness4.1866749
Sum1.0000674 × 108
Variance8.888115 × 108
MonotonicityNot monotonic
2023-12-11T12:29:33.062772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33 13
 
0.2%
9 12
 
0.2%
22 12
 
0.2%
28 12
 
0.2%
37 12
 
0.2%
8 11
 
0.2%
6 11
 
0.2%
24 11
 
0.2%
18 11
 
0.2%
39 10
 
0.2%
Other values (4564) 5802
98.1%
ValueCountFrequency (%)
0 10
0.2%
1 4
 
0.1%
2 4
 
0.1%
3 6
0.1%
4 10
0.2%
5 10
0.2%
6 11
0.2%
7 8
0.1%
8 11
0.2%
9 12
0.2%
ValueCountFrequency (%)
473494 1
< 0.1%
396214 1
< 0.1%
384789 1
< 0.1%
310607 1
< 0.1%
310349 1
< 0.1%
306031 1
< 0.1%
290562 1
< 0.1%
244392 1
< 0.1%
230246 1
< 0.1%
227722 1
< 0.1%

Interactions

2023-12-11T12:29:28.153791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:29:27.094506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:29:27.428482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:29:27.781700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:29:28.240067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:29:27.176707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:29:27.526635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:29:27.869574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:29:28.342755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:29:27.258753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:29:27.616387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:29:27.962444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:29:28.466775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:29:27.346945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:29:27.698898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:29:28.067214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T12:29:33.155839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
선정년도시도비종신청면적(㎡)양(kg)신청(포)선정(포)
선정년도1.0000.1680.0000.0230.0000.0000.000
시도0.1681.0000.4490.2480.2550.2550.255
비종0.0000.4491.0000.4030.3920.3920.392
신청면적(㎡)0.0230.2480.4031.0000.9480.9480.948
양(kg)0.0000.2550.3920.9481.0001.0001.000
신청(포)0.0000.2550.3920.9481.0001.0001.000
선정(포)0.0000.2550.3920.9481.0001.0001.000
2023-12-11T12:29:33.260685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시도비종선정년도
시도1.0000.2730.090
비종0.2731.0000.000
선정년도0.0900.0001.000
2023-12-11T12:29:33.352367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
신청면적(㎡)양(kg)신청(포)선정(포)선정년도시도비종
신청면적(㎡)1.0000.9890.9890.9890.0100.1010.195
양(kg)0.9891.0001.0001.0000.0000.1040.188
신청(포)0.9891.0001.0001.0000.0000.1040.188
선정(포)0.9891.0001.0001.0000.0000.1040.188
선정년도0.0100.0000.0000.0001.0000.0900.000
시도0.1010.1040.1040.1040.0901.0000.273
비종0.1950.1880.1880.1880.0000.2731.000

Missing values

2023-12-11T12:29:28.624081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T12:29:28.877046image/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(㎡)양(kg)신청(포)선정(포)
020142014서울특별시중랑구망우본동석회질10982.32471124124
120142014서울특별시중랑구신내1동석회질17882.04024201201
220142014서울특별시중랑구신내2동석회질37839.08513426426
320142016서울특별시노원구하계1동석회질3300.09414747
420142014서울특별시노원구상계9동석회질3300.09884949
520142016서울특별시노원구상계9동석회질4859.014547373
620142015서울특별시은평구진관동석회질34127.011603580581
720142014서울특별시양천구신월7동석회질2847.08554343
820142014서울특별시양천구신정3동석회질3226.09684848
920142016서울특별시강서구가양1동석회질170649.019632982981
사업년도선정년도시도시군읍면동비종신청면적(㎡)양(kg)신청(포)선정(포)
590720142015세종특별자치시세종시장군면규산질5643849.6512168766084460852
590820142015세종특별자치시세종시장군면석회질2429543.526316943158531597
590920142014세종특별자치시세종시연서면규산질4966795.711613375806758088
591020142014세종특별자치시세종시연서면석회질4023718.810327075163551649
591120142016세종특별자치시세종시전의면규산질3618733.68349204174641741
591220142016세종특별자치시세종시전의면석회질1257683.03216051608016091
591320142016세종특별자치시세종시전동면규산질4178901.010274295137151382
591420142016세종특별자치시세종시전동면석회질2287986.05389972695026963
591520142016세종특별자치시세종시소정면규산질1110589.432599631299812995
591620142016세종특별자치시세종시소정면석회질347080.09024345124514