Overview

Dataset statistics

Number of variables9
Number of observations86
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.5 KiB
Average record size in memory77.5 B

Variable types

Categorical3
Text2
Numeric4

Dataset

Description광주광역시 관내 저수지 현황 정보에 대한 데이터로 용도, 명칭, 준공연도, 저수용량, 소재지지번주소 등의 내용을 제공합니다
Author광주광역시
URLhttps://www.data.go.kr/data/15002182/fileData.do

Alerts

데이터기준일자 has constant value ""Constant
준공년도 is highly overall correlated with 용도 and 1 other fieldsHigh correlation
폭 is highly overall correlated with 용도 and 1 other fieldsHigh correlation
용도 is highly overall correlated with 준공년도 and 2 other fieldsHigh correlation
구분 is highly overall correlated with 준공년도 and 2 other fieldsHigh correlation
용도 is highly imbalanced (78.2%)Imbalance
구분 is highly imbalanced (78.2%)Imbalance
명칭 has unique valuesUnique

Reproduction

Analysis started2023-12-12 06:37:34.515291
Analysis finished2023-12-12 06:37:36.495371
Duration1.98 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

용도
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size820.0 B
저수지
83 
댐
 
3

Length

Max length3
Median length3
Mean length2.9302326
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
저수지 83
96.5%
댐 3
 
3.5%

Length

2023-12-12T15:37:36.567924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:37:36.676409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
저수지 83
96.5%
댐 3
 
3.5%

구분
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size820.0 B
농업용
83 
상수전용
 
3

Length

Max length4
Median length3
Mean length3.0348837
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
농업용 83
96.5%
상수전용 3
 
3.5%

Length

2023-12-12T15:37:36.761836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:37:36.849496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
농업용 83
96.5%
상수전용 3
 
3.5%

명칭
Text

UNIQUE 

Distinct86
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size820.0 B
2023-12-12T15:37:37.119813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length2
Mean length2.5581395
Min length2

Characters and Unicode

Total characters220
Distinct characters86
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique86 ?
Unique (%)100.0%

Sample

1st row선교제
2nd row내지제
3rd row주남제
4th row화산제
5th row소태제
ValueCountFrequency (%)
선교제 1
 
1.2%
내등 1
 
1.2%
북산 1
 
1.2%
본촌 1
 
1.2%
복만 1
 
1.2%
방혜 1
 
1.2%
박호 1
 
1.2%
두정 1
 
1.2%
내산2 1
 
1.2%
송계 1
 
1.2%
Other values (76) 76
88.4%
2023-12-12T15:37:37.525218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
제 27
 
12.3%
산 12
 
5.5%
2 7
 
3.2%
수 7
 
3.2%
1 7
 
3.2%
정 6
 
2.7%
남 6
 
2.7%
동 6
 
2.7%
촌 6
 
2.7%
장 5
 
2.3%
Other values (76) 131
59.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 204
92.7%
Decimal Number 16
 
7.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
제 27
 
13.2%
산 12
 
5.9%
수 7
 
3.4%
정 6
 
2.9%
남 6
 
2.9%
동 6
 
2.9%
촌 6
 
2.9%
장 5
 
2.5%
석 5
 
2.5%
내 4
 
2.0%
Other values (72) 120
58.8%
Decimal Number
ValueCountFrequency (%)
2 7
43.8%
1 7
43.8%
3 1
 
6.2%
4 1
 
6.2%

Most occurring scripts

ValueCountFrequency (%)
Hangul 204
92.7%
Common 16
 
7.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
제 27
 
13.2%
산 12
 
5.9%
수 7
 
3.4%
정 6
 
2.9%
남 6
 
2.9%
동 6
 
2.9%
촌 6
 
2.9%
장 5
 
2.5%
석 5
 
2.5%
내 4
 
2.0%
Other values (72) 120
58.8%
Common
ValueCountFrequency (%)
2 7
43.8%
1 7
43.8%
3 1
 
6.2%
4 1
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 204
92.7%
ASCII 16
 
7.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
제 27
 
13.2%
산 12
 
5.9%
수 7
 
3.4%
정 6
 
2.9%
남 6
 
2.9%
동 6
 
2.9%
촌 6
 
2.9%
장 5
 
2.5%
석 5
 
2.5%
내 4
 
2.0%
Other values (72) 120
58.8%
ASCII
ValueCountFrequency (%)
2 7
43.8%
1 7
43.8%
3 1
 
6.2%
4 1
 
6.2%

준공년도
Real number (ℝ)

HIGH CORRELATION 

Distinct19
Distinct (%)22.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1953.593
Minimum1931
Maximum1978
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size906.0 B
2023-12-12T15:37:37.650003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1931
5-th percentile1943.25
Q11945
median1945
Q31968
95-th percentile1971.75
Maximum1978
Range47
Interquartile range (IQR)23

Descriptive statistics

Standard deviation11.909836
Coefficient of variation (CV)0.006096375
Kurtosis-1.3895072
Mean1953.593
Median Absolute Deviation (MAD)1
Skewness0.54494657
Sum168009
Variance141.84419
MonotonicityNot monotonic
2023-12-12T15:37:37.754962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
1945 42
48.8%
1968 16
 
18.6%
1974 3
 
3.5%
1961 3
 
3.5%
1943 3
 
3.5%
1970 3
 
3.5%
1944 2
 
2.3%
1946 2
 
2.3%
1967 2
 
2.3%
1939 1
 
1.2%
Other values (9) 9
 
10.5%
ValueCountFrequency (%)
1931 1
 
1.2%
1939 1
 
1.2%
1943 3
 
3.5%
1944 2
 
2.3%
1945 42
48.8%
1946 2
 
2.3%
1948 1
 
1.2%
1949 1
 
1.2%
1950 1
 
1.2%
1961 3
 
3.5%
ValueCountFrequency (%)
1978 1
 
1.2%
1974 3
 
3.5%
1972 1
 
1.2%
1971 1
 
1.2%
1970 3
 
3.5%
1969 1
 
1.2%
1968 16
18.6%
1967 2
 
2.3%
1966 1
 
1.2%
1961 3
 
3.5%

폭
Real number (ℝ)

HIGH CORRELATION 

Distinct40
Distinct (%)46.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.9348837
Minimum2.5
Maximum44.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size906.0 B
2023-12-12T15:37:37.866858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.5
5-th percentile3.35
Q14.5
median5.2
Q37.15
95-th percentile11.9
Maximum44.7
Range42.2
Interquartile range (IQR)2.65

Descriptive statistics

Standard deviation5.8120922
Coefficient of variation (CV)0.83809512
Kurtosis22.868383
Mean6.9348837
Median Absolute Deviation (MAD)1.2
Skewness4.3361262
Sum596.4
Variance33.780416
MonotonicityNot monotonic
2023-12-12T15:37:37.988962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
5.0 10
 
11.6%
4.0 6
 
7.0%
4.5 5
 
5.8%
5.2 4
 
4.7%
5.8 4
 
4.7%
3.5 4
 
4.7%
11.0 3
 
3.5%
4.8 3
 
3.5%
3.0 3
 
3.5%
11.6 2
 
2.3%
Other values (30) 42
48.8%
ValueCountFrequency (%)
2.5 1
 
1.2%
3.0 3
3.5%
3.3 1
 
1.2%
3.5 4
4.7%
3.6 1
 
1.2%
4.0 6
7.0%
4.3 2
 
2.3%
4.4 2
 
2.3%
4.5 5
5.8%
4.7 2
 
2.3%
ValueCountFrequency (%)
44.7 1
 
1.2%
25.0 2
2.3%
24.5 1
 
1.2%
12.0 1
 
1.2%
11.6 2
2.3%
11.0 3
3.5%
10.0 1
 
1.2%
9.3 1
 
1.2%
9.0 1
 
1.2%
8.1 1
 
1.2%

길이
Real number (ℝ)

Distinct69
Distinct (%)80.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean91.780233
Minimum3
Maximum227
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size906.0 B
2023-12-12T15:37:38.100383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile6
Q153.25
median86
Q3131.5
95-th percentile180.75
Maximum227
Range224
Interquartile range (IQR)78.25

Descriptive statistics

Standard deviation54.118249
Coefficient of variation (CV)0.58965038
Kurtosis-0.5341323
Mean91.780233
Median Absolute Deviation (MAD)38
Skewness0.22899783
Sum7893.1
Variance2928.7849
MonotonicityNot monotonic
2023-12-12T15:37:38.220350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
140.0 3
 
3.5%
77.0 3
 
3.5%
65.0 2
 
2.3%
150.0 2
 
2.3%
145.0 2
 
2.3%
69.0 2
 
2.3%
6.0 2
 
2.3%
86.0 2
 
2.3%
50.0 2
 
2.3%
80.0 2
 
2.3%
Other values (59) 64
74.4%
ValueCountFrequency (%)
3.0 1
1.2%
4.3 1
1.2%
4.5 1
1.2%
5.0 1
1.2%
6.0 2
2.3%
7.2 1
1.2%
7.7 1
1.2%
7.8 1
1.2%
11.6 1
1.2%
25.0 1
1.2%
ValueCountFrequency (%)
227.0 1
1.2%
217.0 1
1.2%
192.0 1
1.2%
188.0 1
1.2%
182.0 1
1.2%
177.0 1
1.2%
171.0 1
1.2%
170.0 1
1.2%
158.0 1
1.2%
157.0 1
1.2%

저수용량(톤)
Real number (ℝ)

Distinct76
Distinct (%)88.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33779.884
Minimum116
Maximum216490
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size906.0 B
2023-12-12T15:37:38.390826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum116
5-th percentile1150
Q19000
median19000
Q339782.5
95-th percentile115545
Maximum216490
Range216374
Interquartile range (IQR)30782.5

Descriptive statistics

Standard deviation42081.676
Coefficient of variation (CV)1.2457614
Kurtosis6.5189327
Mean33779.884
Median Absolute Deviation (MAD)13565
Skewness2.4647298
Sum2905070
Variance1.7708675 × 109
MonotonicityNot monotonic
2023-12-12T15:37:38.535989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30000 4
 
4.7%
20000 3
 
3.5%
1000 2
 
2.3%
40000 2
 
2.3%
9000 2
 
2.3%
50000 2
 
2.3%
16300 2
 
2.3%
39130 1
 
1.2%
160000 1
 
1.2%
4000 1
 
1.2%
Other values (66) 66
76.7%
ValueCountFrequency (%)
116 1
1.2%
525 1
1.2%
600 1
1.2%
1000 2
2.3%
1600 1
1.2%
1909 1
1.2%
2400 1
1.2%
3000 1
1.2%
3550 1
1.2%
4000 1
1.2%
ValueCountFrequency (%)
216490 1
1.2%
180000 1
1.2%
176000 1
1.2%
160000 1
1.2%
116460 1
1.2%
112800 1
1.2%
99530 1
1.2%
97200 1
1.2%
90000 1
1.2%
72400 1
1.2%

소지재지번주소
Text

Distinct84
Distinct (%)97.7%
Missing0
Missing (%)0.0%
Memory size820.0 B
2023-12-12T15:37:38.840985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length20
Mean length18.639535
Min length15

Characters and Unicode

Total characters1603
Distinct characters81
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique82 ?
Unique (%)95.3%

Sample

1st row광주광역시 동구 선교동 184번지
2nd row광주광역시 동구 내남동 766번지
3rd row광주광역시 동구 월남동 44-2번지
4th row광주광역시 동구 용산동 611-1번지
5th row광주광역시 동구 소태동 33-1번지
ValueCountFrequency (%)
광주광역시 86
25.1%
광산구 36
 
10.5%
남구 21
 
6.1%
북구 12
 
3.5%
서구 9
 
2.6%
동구 6
 
1.8%
등임 4
 
1.2%
광산 4
 
1.2%
일원 3
 
0.9%
양과동 3
 
0.9%
Other values (131) 158
46.2%
2023-12-12T15:37:39.320520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
256
16.0%
광 212
13.2%
역 86
 
5.4%
시 86
 
5.4%
주 86
 
5.4%
구 85
 
5.3%
지 84
 
5.2%
번 83
 
5.2%
동 57
 
3.6%
1 56
 
3.5%
Other values (71) 512
31.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1034
64.5%
Decimal Number 272
 
17.0%
Space Separator 256
 
16.0%
Dash Punctuation 41
 
2.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
광 212
20.5%
역 86
8.3%
시 86
8.3%
주 86
8.3%
구 85
8.2%
지 84
 
8.1%
번 83
 
8.0%
동 57
 
5.5%
산 52
 
5.0%
남 26
 
2.5%
Other values (59) 177
17.1%
Decimal Number
ValueCountFrequency (%)
1 56
20.6%
3 35
12.9%
2 34
12.5%
5 30
11.0%
6 27
9.9%
4 22
 
8.1%
0 21
 
7.7%
9 17
 
6.2%
8 16
 
5.9%
7 14
 
5.1%
Space Separator
ValueCountFrequency (%)
256
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 41
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1034
64.5%
Common 569
35.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
광 212
20.5%
역 86
8.3%
시 86
8.3%
주 86
8.3%
구 85
8.2%
지 84
 
8.1%
번 83
 
8.0%
동 57
 
5.5%
산 52
 
5.0%
남 26
 
2.5%
Other values (59) 177
17.1%
Common
ValueCountFrequency (%)
256
45.0%
1 56
 
9.8%
- 41
 
7.2%
3 35
 
6.2%
2 34
 
6.0%
5 30
 
5.3%
6 27
 
4.7%
4 22
 
3.9%
0 21
 
3.7%
9 17
 
3.0%
Other values (2) 30
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1034
64.5%
ASCII 569
35.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
256
45.0%
1 56
 
9.8%
- 41
 
7.2%
3 35
 
6.2%
2 34
 
6.0%
5 30
 
5.3%
6 27
 
4.7%
4 22
 
3.9%
0 21
 
3.7%
9 17
 
3.0%
Other values (2) 30
 
5.3%
Hangul
ValueCountFrequency (%)
광 212
20.5%
역 86
8.3%
시 86
8.3%
주 86
8.3%
구 85
8.2%
지 84
 
8.1%
번 83
 
8.0%
동 57
 
5.5%
산 52
 
5.0%
남 26
 
2.5%
Other values (59) 177
17.1%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size820.0 B
2022-11-23
86 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022-11-23
2nd row2022-11-23
3rd row2022-11-23
4th row2022-11-23
5th row2022-11-23

Common Values

ValueCountFrequency (%)
2022-11-23 86
100.0%

Length

2023-12-12T15:37:39.567780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:37:39.661349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022-11-23 86
100.0%

Interactions

2023-12-12T15:37:35.958804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:34.949191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:35.280152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:35.582563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:36.077802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:35.052672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:35.360192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:35.691736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:36.150655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:35.129777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:35.433099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:35.788011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:36.233716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:35.205186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:35.509208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:35.864053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T15:37:39.729325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
용도구분명칭준공년도폭길이저수용량(톤)소지재지번주소
용도1.0000.9621.0000.7470.7360.6540.1251.000
구분0.9621.0001.0000.7470.7360.6540.1251.000
명칭1.0001.0001.0001.0001.0001.0001.0001.000
준공년도0.7470.7471.0001.0000.5760.4140.4971.000
폭0.7360.7361.0000.5761.0000.7060.5831.000
길이0.6540.6541.0000.4140.7061.0000.6280.956
저수용량(톤)0.1250.1251.0000.4970.5830.6281.0000.000
소지재지번주소1.0001.0001.0001.0001.0000.9560.0001.000
2023-12-12T15:37:39.884552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분용도
구분1.0000.825
용도0.8251.000
2023-12-12T15:37:39.974393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
준공년도폭길이저수용량(톤)용도구분
준공년도1.0000.259-0.483-0.1270.5430.543
폭0.2591.000-0.1520.2710.8550.855
길이-0.483-0.1521.0000.4500.4820.482
저수용량(톤)-0.1270.2710.4501.0000.1150.115
용도0.5430.8550.4820.1151.0000.825
구분0.5430.8550.4820.1150.8251.000

Missing values

2023-12-12T15:37:36.332032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T15:37:36.445580image/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

용도구분명칭준공년도폭길이저수용량(톤)소지재지번주소데이터기준일자
0저수지농업용선교제19689.065.072400광주광역시 동구 선교동 184번지2022-11-23
1저수지농업용내지제19667.250.020100광주광역시 동구 내남동 766번지2022-11-23
2저수지농업용주남제19695.243.09700광주광역시 동구 월남동 44-2번지2022-11-23
3저수지농업용화산제19315.8110.035300광주광역시 동구 용산동 611-1번지2022-11-23
4저수지농업용소태제196111.060.016300광주광역시 동구 소태동 33-1번지2022-11-23
5저수지농업용만호제19434.0108.015600광주광역시 서구 금호동 676번지2022-11-23
6저수지농업용전평제19433.5147.059400광주광역시 서구 매월동 519-1번지2022-11-23
7저수지농업용봉학제19435.0125.033700광주광역시 서구 용두동 145-1번지2022-11-23
8저수지농업용매월제19445.868.013600광주광역시 서구 매월동 684-2번지2022-11-23
9저수지농업용마현제19444.0150.015500광주광역시 서구 용두동 503-1번지2022-11-23
용도구분명칭준공년도폭길이저수용량(톤)소지재지번주소데이터기준일자
76저수지농업용운수19677.580.030500광주광역시 광산구 운수 264-1번지2022-11-23
77저수지농업용장수19454.5158.014000광주광역시 광산구 장수 543-1번지2022-11-23
78저수지농업용종산19685.049.05500광주광역시 광산구 광산 271번지2022-11-23
79저수지농업용칠성19456.6122.046710광주광역시 광산구 삼거 342-1번지2022-11-23
80저수지농업용하림19685.330.01000광주광역시 광산구 광산 503-1번지2022-11-23
81저수지농업용황산194511.672.037000광주광역시 광산구 북산 13-2번지2022-11-23
82저수지농업용흑석19453.0118.05470광주광역시 광산구 선 162-1번지2022-11-23
83댐상수전용동복댐197144.7188.099530광주광역시 화순군 동복면 일원2022-11-23
84댐상수전용제2수원지193925.0143.0525광주광역시 동구 용연동 일원2022-11-23
85댐상수전용제4수원지196724.5227.01909광주광역시 북구 석곡동 일원2022-11-23