Overview

Dataset statistics

Number of variables8
Number of observations65
Missing cells1
Missing cells (%)0.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.5 KiB
Average record size in memory71.0 B

Variable types

Numeric5
Categorical2
Text1

Dataset

Description사업별 잔여지의 보상현황을 아래와 같이 제공 합니다. 제공현황 - 사업종류,사업명,사업시행자,토지_면적(㎡),토지_금액(원),토지이외_건수,토지이외_금액(원)
URLhttps://www.data.go.kr/data/15049039/fileData.do

Alerts

순번 is highly overall correlated with 토지이외_건수 and 1 other fieldsHigh correlation
토지_면적(제곱미터) is highly overall correlated with 토지_금액(원) and 2 other fieldsHigh correlation
토지_금액(원) is highly overall correlated with 토지_면적(제곱미터) and 2 other fieldsHigh correlation
토지이외_건수 is highly overall correlated with 순번 and 3 other fieldsHigh correlation
토지이외_금액(원) is highly overall correlated with 순번 and 3 other fieldsHigh correlation
사업시행자 is highly imbalanced (56.9%)Imbalance
토지이외_금액(원) has 1 (1.5%) missing valuesMissing
순번 has unique valuesUnique
사업명 has unique valuesUnique
토지_금액(원) has unique valuesUnique
토지_금액(원) has 1 (1.5%) zerosZeros
토지이외_건수 has 20 (30.8%) zerosZeros
토지이외_금액(원) has 20 (30.8%) zerosZeros

Reproduction

Analysis started2023-12-12 13:17:18.680115
Analysis finished2023-12-12 13:17:21.567708
Duration2.89 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct65
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33
Minimum1
Maximum65
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size717.0 B
2023-12-12T22:17:21.635770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4.2
Q117
median33
Q349
95-th percentile61.8
Maximum65
Range64
Interquartile range (IQR)32

Descriptive statistics

Standard deviation18.90767
Coefficient of variation (CV)0.57295971
Kurtosis-1.2
Mean33
Median Absolute Deviation (MAD)16
Skewness0
Sum2145
Variance357.5
MonotonicityStrictly increasing
2023-12-12T22:17:21.748782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.5%
50 1
 
1.5%
36 1
 
1.5%
37 1
 
1.5%
38 1
 
1.5%
39 1
 
1.5%
40 1
 
1.5%
41 1
 
1.5%
42 1
 
1.5%
43 1
 
1.5%
Other values (55) 55
84.6%
ValueCountFrequency (%)
1 1
1.5%
2 1
1.5%
3 1
1.5%
4 1
1.5%
5 1
1.5%
6 1
1.5%
7 1
1.5%
8 1
1.5%
9 1
1.5%
10 1
1.5%
ValueCountFrequency (%)
65 1
1.5%
64 1
1.5%
63 1
1.5%
62 1
1.5%
61 1
1.5%
60 1
1.5%
59 1
1.5%
58 1
1.5%
57 1
1.5%
56 1
1.5%

사업종류
Categorical

Distinct3
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Memory size652.0 B
수도
31 
댐
25 
단지
9 

Length

Max length2
Median length2
Mean length1.6153846
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row댐
2nd row수도
3rd row댐
4th row수도
5th row수도

Common Values

ValueCountFrequency (%)
수도 31
47.7%
댐 25
38.5%
단지 9
 
13.8%

Length

2023-12-12T22:17:21.857698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:17:21.940073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
수도 31
47.7%
댐 25
38.5%
단지 9
 
13.8%

사업명
Text

UNIQUE 

Distinct65
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size652.0 B
2023-12-12T22:17:22.126541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length32
Median length23
Mean length16.861538
Min length8

Characters and Unicode

Total characters1096
Distinct characters165
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique65 ?
Unique (%)100.0%

Sample

1st row부항다목적댐건설사업
2nd row금강북부급수체계조정(청양계통)
3rd row주암(조)댐 하류하천 정비사업
4th row포천복합화력 용수공급사업
5th row고덕산업단지 용수공급시설 설치사업
ValueCountFrequency (%)
건설사업 6
 
3.9%
시화2단계(송산그린시티 4
 
2.6%
운문댐 3
 
1.9%
직하류 3
 
1.9%
개량사업 3
 
1.9%
용수공급시설 3
 
1.9%
설치사업 3
 
1.9%
급수체계조정사업 3
 
1.9%
시화mtv 2
 
1.3%
노후관 2
 
1.3%
Other values (114) 122
79.2%
2023-12-12T22:17:22.468264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
89
 
8.1%
업 71
 
6.5%
사 63
 
5.7%
수 41
 
3.7%
) 30
 
2.7%
( 30
 
2.7%
설 26
 
2.4%
시 25
 
2.3%
댐 25
 
2.3%
도 24
 
2.2%
Other values (155) 672
61.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 906
82.7%
Space Separator 89
 
8.1%
Close Punctuation 30
 
2.7%
Open Punctuation 30
 
2.7%
Decimal Number 19
 
1.7%
Uppercase Letter 16
 
1.5%
Letter Number 4
 
0.4%
Connector Punctuation 2
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
업 71
 
7.8%
사 63
 
7.0%
수 41
 
4.5%
설 26
 
2.9%
시 25
 
2.8%
댐 25
 
2.8%
도 24
 
2.6%
계 24
 
2.6%
공 23
 
2.5%
하 21
 
2.3%
Other values (139) 563
62.1%
Uppercase Letter
ValueCountFrequency (%)
V 4
25.0%
T 4
25.0%
M 4
25.0%
I 2
12.5%
S 1
 
6.2%
K 1
 
6.2%
Decimal Number
ValueCountFrequency (%)
2 9
47.4%
1 5
26.3%
3 3
 
15.8%
7 2
 
10.5%
Letter Number
ValueCountFrequency (%)
Ⅲ 2
50.0%
Ⅱ 2
50.0%
Space Separator
ValueCountFrequency (%)
89
100.0%
Close Punctuation
ValueCountFrequency (%)
) 30
100.0%
Open Punctuation
ValueCountFrequency (%)
( 30
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 906
82.7%
Common 170
 
15.5%
Latin 20
 
1.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
업 71
 
7.8%
사 63
 
7.0%
수 41
 
4.5%
설 26
 
2.9%
시 25
 
2.8%
댐 25
 
2.8%
도 24
 
2.6%
계 24
 
2.6%
공 23
 
2.5%
하 21
 
2.3%
Other values (139) 563
62.1%
Common
ValueCountFrequency (%)
89
52.4%
) 30
 
17.6%
( 30
 
17.6%
2 9
 
5.3%
1 5
 
2.9%
3 3
 
1.8%
7 2
 
1.2%
_ 2
 
1.2%
Latin
ValueCountFrequency (%)
V 4
20.0%
T 4
20.0%
M 4
20.0%
Ⅲ 2
10.0%
Ⅱ 2
10.0%
I 2
10.0%
S 1
 
5.0%
K 1
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 906
82.7%
ASCII 186
 
17.0%
Number Forms 4
 
0.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
89
47.8%
) 30
 
16.1%
( 30
 
16.1%
2 9
 
4.8%
1 5
 
2.7%
V 4
 
2.2%
T 4
 
2.2%
M 4
 
2.2%
3 3
 
1.6%
7 2
 
1.1%
Other values (4) 6
 
3.2%
Hangul
ValueCountFrequency (%)
업 71
 
7.8%
사 63
 
7.0%
수 41
 
4.5%
설 26
 
2.9%
시 25
 
2.8%
댐 25
 
2.8%
도 24
 
2.6%
계 24
 
2.6%
공 23
 
2.5%
하 21
 
2.3%
Other values (139) 563
62.1%
Number Forms
ValueCountFrequency (%)
Ⅲ 2
50.0%
Ⅱ 2
50.0%

사업시행자
Categorical

IMBALANCE 

Distinct7
Distinct (%)10.8%
Missing0
Missing (%)0.0%
Memory size652.0 B
국토교통부
51 
환경부
6 
한국수자원공사
 
4
세종특별자치시
 
1
영산강유역환경청
 
1
Other values (2)
 
2

Length

Max length8
Median length5
Mean length5.0153846
Min length3

Unique

Unique4 ?
Unique (%)6.2%

Sample

1st row국토교통부
2nd row한국수자원공사
3rd row국토교통부
4th row국토교통부
5th row국토교통부

Common Values

ValueCountFrequency (%)
국토교통부 51
78.5%
환경부 6
 
9.2%
한국수자원공사 4
 
6.2%
세종특별자치시 1
 
1.5%
영산강유역환경청 1
 
1.5%
금강유역환경청 1
 
1.5%
강원도 1
 
1.5%

Length

2023-12-12T22:17:22.584407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:17:22.675978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
국토교통부 51
78.5%
환경부 6
 
9.2%
한국수자원공사 4
 
6.2%
세종특별자치시 1
 
1.5%
영산강유역환경청 1
 
1.5%
금강유역환경청 1
 
1.5%
강원도 1
 
1.5%

토지_면적(제곱미터)
Real number (ℝ)

HIGH CORRELATION 

Distinct64
Distinct (%)98.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3567.2769
Minimum33
Maximum28266
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size717.0 B
2023-12-12T22:17:22.778981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33
5-th percentile106.2
Q1388
median712
Q33833
95-th percentile18393.6
Maximum28266
Range28233
Interquartile range (IQR)3445

Descriptive statistics

Standard deviation5826.3068
Coefficient of variation (CV)1.6332645
Kurtosis6.0052104
Mean3567.2769
Median Absolute Deviation (MAD)507.7
Skewness2.4233914
Sum231873
Variance33945851
MonotonicityNot monotonic
2023-12-12T22:17:22.890476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
594.0 2
 
3.1%
19745.0 1
 
1.5%
260.0 1
 
1.5%
1644.0 1
 
1.5%
321.0 1
 
1.5%
712.0 1
 
1.5%
1503.0 1
 
1.5%
9205.0 1
 
1.5%
1168.0 1
 
1.5%
1998.0 1
 
1.5%
Other values (54) 54
83.1%
ValueCountFrequency (%)
33.0 1
1.5%
64.0 1
1.5%
73.0 1
1.5%
86.0 1
1.5%
187.0 1
1.5%
206.0 1
1.5%
215.0 1
1.5%
243.0 1
1.5%
244.0 1
1.5%
250.0 1
1.5%
ValueCountFrequency (%)
28266.0 1
1.5%
20233.5 1
1.5%
20036.5 1
1.5%
19745.0 1
1.5%
12988.0 1
1.5%
12270.0 1
1.5%
10000.0 1
1.5%
9283.8 1
1.5%
9205.0 1
1.5%
8858.8 1
1.5%

토지_금액(원)
Real number (ℝ)

HIGH CORRELATION  UNIQUE  ZEROS 

Distinct65
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.269498 × 108
Minimum0
Maximum4.0640062 × 109
Zeros1
Zeros (%)1.5%
Negative0
Negative (%)0.0%
Memory size717.0 B
2023-12-12T22:17:23.007570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6311120
Q117199000
median49770740
Q32.17128 × 108
95-th percentile1.5189455 × 109
Maximum4.0640062 × 109
Range4.0640062 × 109
Interquartile range (IQR)1.99929 × 108

Descriptive statistics

Standard deviation7.1535416 × 108
Coefficient of variation (CV)2.1879633
Kurtosis14.192629
Mean3.269498 × 108
Median Absolute Deviation (MAD)38698240
Skewness3.5674835
Sum2.1251737 × 1010
Variance5.1173158 × 1017
MonotonicityNot monotonic
2023-12-12T22:17:23.131629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
292984380 1
 
1.5%
11072500 1
 
1.5%
59173990 1
 
1.5%
31834000 1
 
1.5%
15227950 1
 
1.5%
59713660 1
 
1.5%
980898160 1
 
1.5%
216934890 1
 
1.5%
25488850 1
 
1.5%
55357300 1
 
1.5%
Other values (55) 55
84.6%
ValueCountFrequency (%)
0 1
1.5%
4318800 1
1.5%
6157660 1
1.5%
6192900 1
1.5%
6784000 1
1.5%
7349500 1
1.5%
9632000 1
1.5%
10374000 1
1.5%
10642500 1
1.5%
11072500 1
1.5%
ValueCountFrequency (%)
4064006228 1
1.5%
3000000000 1
1.5%
2274814140 1
1.5%
1653457280 1
1.5%
980898160 1
1.5%
921286580 1
1.5%
915405880 1
1.5%
873344400 1
1.5%
782471100 1
1.5%
766911270 1
1.5%

토지이외_건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct23
Distinct (%)35.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.338462
Minimum0
Maximum112
Zeros20
Zeros (%)30.8%
Negative0
Negative (%)0.0%
Memory size717.0 B
2023-12-12T22:17:23.249405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q39
95-th percentile57.6
Maximum112
Range112
Interquartile range (IQR)9

Descriptive statistics

Standard deviation24.013197
Coefficient of variation (CV)1.9462068
Kurtosis7.7695211
Mean12.338462
Median Absolute Deviation (MAD)2
Skewness2.7658163
Sum802
Variance576.63365
MonotonicityNot monotonic
2023-12-12T22:17:23.416641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0 20
30.8%
1 7
 
10.8%
2 6
 
9.2%
3 3
 
4.6%
5 3
 
4.6%
6 3
 
4.6%
4 3
 
4.6%
43 2
 
3.1%
7 2
 
3.1%
10 2
 
3.1%
Other values (13) 14
21.5%
ValueCountFrequency (%)
0 20
30.8%
1 7
 
10.8%
2 6
 
9.2%
3 3
 
4.6%
4 3
 
4.6%
5 3
 
4.6%
6 3
 
4.6%
7 2
 
3.1%
8 1
 
1.5%
9 2
 
3.1%
ValueCountFrequency (%)
112 1
1.5%
106 1
1.5%
83 1
1.5%
60 1
1.5%
48 1
1.5%
45 1
1.5%
43 2
3.1%
37 1
1.5%
32 1
1.5%
27 1
1.5%

토지이외_금액(원)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct45
Distinct (%)70.3%
Missing1
Missing (%)1.5%
Infinite0
Infinite (%)0.0%
Mean13046510
Minimum0
Maximum3.170354 × 108
Zeros20
Zeros (%)30.8%
Negative0
Negative (%)0.0%
Memory size717.0 B
2023-12-12T22:17:23.543105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1114155
Q35517763
95-th percentile51129659
Maximum3.170354 × 108
Range3.170354 × 108
Interquartile range (IQR)5517763

Descriptive statistics

Standard deviation41808088
Coefficient of variation (CV)3.2045419
Kurtosis45.792041
Mean13046510
Median Absolute Deviation (MAD)1114155
Skewness6.387205
Sum8.3497664 × 108
Variance1.7479162 × 1015
MonotonicityNot monotonic
2023-12-12T22:17:23.682973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
0 20
30.8%
48753860 1
 
1.5%
106610 1
 
1.5%
3078930 1
 
1.5%
56589383 1
 
1.5%
317035400 1
 
1.5%
3403000 1
 
1.5%
2175780 1
 
1.5%
893740 1
 
1.5%
25270 1
 
1.5%
Other values (35) 35
53.8%
ValueCountFrequency (%)
0 20
30.8%
25270 1
 
1.5%
106610 1
 
1.5%
195390 1
 
1.5%
289080 1
 
1.5%
328580 1
 
1.5%
373160 1
 
1.5%
800000 1
 
1.5%
893740 1
 
1.5%
912420 1
 
1.5%
ValueCountFrequency (%)
317035400 1
1.5%
74194690 1
1.5%
56589383 1
1.5%
51482560 1
1.5%
49129890 1
1.5%
48753860 1
1.5%
35101030 1
1.5%
32916840 1
1.5%
24817810 1
1.5%
15686460 1
1.5%

Interactions

2023-12-12T22:17:21.005435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:17:19.013066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:17:19.426792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:17:19.888833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:17:20.576575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:17:21.078679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:17:19.087233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:17:19.508227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:17:19.989739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:17:20.654721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:17:21.151811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:17:19.171734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:17:19.597937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:17:20.092030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:17:20.769303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:17:21.237642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:17:19.262719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:17:19.706966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:17:20.405187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:17:20.858218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:17:21.312276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:17:19.341724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:17:19.787027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:17:20.493552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:17:20.929379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T22:17:23.776778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번사업종류사업명사업시행자토지_면적(제곱미터)토지_금액(원)토지이외_건수토지이외_금액(원)
순번1.0000.0001.0000.0000.0000.3230.0000.506
사업종류0.0001.0001.0000.0000.2830.4270.5510.129
사업명1.0001.0001.0001.0001.0001.0001.0001.000
사업시행자0.0000.0001.0001.0000.0000.7680.0000.000
토지_면적(제곱미터)0.0000.2831.0000.0001.0000.8690.9310.879
토지_금액(원)0.3230.4271.0000.7680.8691.0000.7860.795
토지이외_건수0.0000.5511.0000.0000.9310.7861.0000.977
토지이외_금액(원)0.5060.1291.0000.0000.8790.7950.9771.000
2023-12-12T22:17:23.915079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
사업종류사업시행자
사업종류1.0000.000
사업시행자0.0001.000
2023-12-12T22:17:24.012647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번토지_면적(제곱미터)토지_금액(원)토지이외_건수토지이외_금액(원)사업종류사업시행자
순번1.000-0.290-0.334-0.616-0.6520.0000.000
토지_면적(제곱미터)-0.2901.0000.8440.6360.6600.1740.000
토지_금액(원)-0.3340.8441.0000.6470.6750.3050.363
토지이외_건수-0.6160.6360.6471.0000.9590.3980.000
토지이외_금액(원)-0.6520.6600.6750.9591.0000.1180.000
사업종류0.0000.1740.3050.3980.1181.0000.000
사업시행자0.0000.0000.3630.0000.0000.0001.000

Missing values

2023-12-12T22:17:21.411786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T22:17:21.523809image/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

순번사업종류사업명사업시행자토지_면적(제곱미터)토지_금액(원)토지이외_건수토지이외_금액(원)
01댐부항다목적댐건설사업국토교통부19745.02929843804348753860
12수도금강북부급수체계조정(청양계통)한국수자원공사250.061576601373160
23댐주암(조)댐 하류하천 정비사업국토교통부388.05519300073423450
34수도포천복합화력 용수공급사업국토교통부4795.02171280002328580
45수도고덕산업단지 용수공급시설 설치사업국토교통부20036.522748141404532916840
56수도낙동강중부권급수체계구축사업국토교통부672.02354650031123730
67수도영산강권(2차) 급수체계조정사업국토교통부73.0124586182195390
78수도금강북부권(2차) 급수체계조정사업국토교통부565.04113025051723490
89수도금산무주권광역상수도사업국토교통부628.03185280022467620
910단지구미 하이테크밸리 개발사업국토교통부8551.08733444004314634510
순번사업종류사업명사업시행자토지_면적(제곱미터)토지_금액(원)토지이외_건수토지이외_금액(원)
5556댐충주댐 치수능력증대사업국토교통부412.02139639000
5657수도창원공업용수도 관로시설 개량사업국토교통부86.0963200000
5758댐남강댐 속사제 잔여구간 하천공사국토교통부206.01407658000
5859수도시화MTV공업용수도국토교통부580.01719900000
5960수도송산그린시티 용수공급시설 설치사업국토교통부594.04630549000
6061댐임하댐치수능력증대사업국토교통부499.0734950000
6162수도금강남부권급수체계구축사업(김제계통)국토교통부442.0619290000
6263댐연초댐 안정성강화사업환경부334.01340174000
6364댐이유미_실습용사업강원도10000.0300000000000
6465수도한강하류권(3차) 급수체계조정사업국토교통부2825.07493539000