Overview

Dataset statistics

Number of variables6
Number of observations8081
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory394.7 KiB
Average record size in memory50.0 B

Variable types

Numeric2
Text3
Categorical1

Dataset

Description공공데이터 중장기 개방계획에 따라 공개하는 경상남도 하천관리 시스템의 데이터 입니다. 하천관리시스템의 지적도도곽 정보를 포함하고있습니다.
Author경상남도
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15093557

Alerts

구분코드 has constant value ""Constant
공간아이디 has unique valuesUnique

Reproduction

Analysis started2023-12-10 23:44:29.626497
Analysis finished2023-12-10 23:44:30.626683
Duration1 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

공간아이디
Real number (ℝ)

UNIQUE 

Distinct8081
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4041
Minimum1
Maximum8081
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size71.2 KiB
2023-12-11T08:44:30.686126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile405
Q12021
median4041
Q36061
95-th percentile7677
Maximum8081
Range8080
Interquartile range (IQR)4040

Descriptive statistics

Standard deviation2332.9281
Coefficient of variation (CV)0.57731455
Kurtosis-1.2
Mean4041
Median Absolute Deviation (MAD)2020
Skewness0
Sum32655321
Variance5442553.5
MonotonicityNot monotonic
2023-12-11T08:44:30.811532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
5384 1
 
< 0.1%
5397 1
 
< 0.1%
5396 1
 
< 0.1%
5395 1
 
< 0.1%
5394 1
 
< 0.1%
5393 1
 
< 0.1%
5392 1
 
< 0.1%
5391 1
 
< 0.1%
5390 1
 
< 0.1%
Other values (8071) 8071
99.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
8081 1
< 0.1%
8080 1
< 0.1%
8079 1
< 0.1%
8078 1
< 0.1%
8077 1
< 0.1%
8076 1
< 0.1%
8075 1
< 0.1%
8074 1
< 0.1%
8073 1
< 0.1%
8072 1
< 0.1%

하천관리코드
Text

Distinct689
Distinct (%)8.5%
Missing0
Missing (%)0.0%
Memory size63.3 KiB
2023-12-11T08:44:30.998409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters153539
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
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 row20274002020F02Q0101
2nd row20274002020F02Q0101
3rd row20274002020F02Q0101
4th row20274002020F02Q0101
5th row20274002020F02Q0101
ValueCountFrequency (%)
20231501993f01q0101 83
 
1.0%
20268002012f02q0101 81
 
1.0%
20265801995f02q0101 80
 
1.0%
20231502019f02q0101 79
 
1.0%
20237601988f01q0101 68
 
0.8%
27201701996f01q0101 67
 
0.8%
20272501986f01q0101 67
 
0.8%
20228802004f01q0101 58
 
0.7%
20231502004f02q0101 55
 
0.7%
20272002012f02q0101 54
 
0.7%
Other values (679) 7389
91.4%
2023-12-11T08:44:31.304904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 51368
33.5%
1 28742
18.7%
2 27884
18.2%
F 8081
 
5.3%
Q 8081
 
5.3%
9 7143
 
4.7%
7 4453
 
2.9%
6 4089
 
2.7%
4 3851
 
2.5%
5 3527
 
2.3%
Other values (2) 6320
 
4.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 137377
89.5%
Uppercase Letter 16162
 
10.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 51368
37.4%
1 28742
20.9%
2 27884
20.3%
9 7143
 
5.2%
7 4453
 
3.2%
6 4089
 
3.0%
4 3851
 
2.8%
5 3527
 
2.6%
3 3444
 
2.5%
8 2876
 
2.1%
Uppercase Letter
ValueCountFrequency (%)
F 8081
50.0%
Q 8081
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 137377
89.5%
Latin 16162
 
10.5%

Most frequent character per script

Common
ValueCountFrequency (%)
0 51368
37.4%
1 28742
20.9%
2 27884
20.3%
9 7143
 
5.2%
7 4453
 
3.2%
6 4089
 
3.0%
4 3851
 
2.8%
5 3527
 
2.6%
3 3444
 
2.5%
8 2876
 
2.1%
Latin
ValueCountFrequency (%)
F 8081
50.0%
Q 8081
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 153539
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 51368
33.5%
1 28742
18.7%
2 27884
18.2%
F 8081
 
5.3%
Q 8081
 
5.3%
9 7143
 
4.7%
7 4453
 
2.9%
6 4089
 
2.7%
4 3851
 
2.5%
5 3527
 
2.3%
Other values (2) 6320
 
4.1%

구분코드
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size63.3 KiB
I03
8081 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowI03
2nd rowI03
3rd rowI03
4th rowI03
5th rowI03

Common Values

ValueCountFrequency (%)
I03 8081
100.0%

Length

2023-12-11T08:44:31.426529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:44:31.768864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
i03 8081
100.0%

일련번호
Real number (ℝ)

Distinct104
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.533969
Minimum1
Maximum104
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size71.2 KiB
2023-12-11T08:44:31.885289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median8
Q317
95-th percentile46
Maximum104
Range103
Interquartile range (IQR)13

Descriptive statistics

Standard deviation15.305717
Coefficient of variation (CV)1.1309112
Kurtosis6.3011002
Mean13.533969
Median Absolute Deviation (MAD)5
Skewness2.3058291
Sum109368
Variance234.26497
MonotonicityNot monotonic
2023-12-11T08:44:32.040704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 631
 
7.8%
2 624
 
7.7%
3 615
 
7.6%
4 582
 
7.2%
5 537
 
6.6%
6 467
 
5.8%
7 413
 
5.1%
8 362
 
4.5%
9 301
 
3.7%
10 273
 
3.4%
Other values (94) 3276
40.5%
ValueCountFrequency (%)
1 631
7.8%
2 624
7.7%
3 615
7.6%
4 582
7.2%
5 537
6.6%
6 467
5.8%
7 413
5.1%
8 362
4.5%
9 301
3.7%
10 273
3.4%
ValueCountFrequency (%)
104 2
< 0.1%
103 1
< 0.1%
102 2
< 0.1%
101 2
< 0.1%
100 2
< 0.1%
99 2
< 0.1%
98 2
< 0.1%
97 2
< 0.1%
96 2
< 0.1%
95 2
< 0.1%

도엽번호
Text

Distinct1866
Distinct (%)23.1%
Missing0
Missing (%)0.0%
Memory size63.3 KiB
2023-12-11T08:44:32.390475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length2.0909541
Min length1

Characters and Unicode

Total characters16897
Distinct characters98
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1581 ?
Unique (%)19.6%

Sample

1st row우곡11
2nd row우곡12
3rd row우곡10
4th row우곡09
5th row우곡08
ValueCountFrequency (%)
1 411
 
5.1%
2 410
 
5.0%
3 406
 
5.0%
4 397
 
4.9%
5 371
 
4.6%
6 336
 
4.1%
7 308
 
3.8%
8 273
 
3.4%
9 230
 
2.8%
10 210
 
2.6%
Other values (1838) 4784
58.8%
2023-12-11T08:44:32.980218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 3027
17.9%
2 1892
11.2%
3 1430
 
8.5%
0 1274
 
7.5%
4 1199
 
7.1%
5 1027
 
6.1%
6 898
 
5.3%
7 783
 
4.6%
8 672
 
4.0%
9 584
 
3.5%
Other values (88) 4111
24.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 12786
75.7%
Uppercase Letter 2649
 
15.7%
Other Letter 1136
 
6.7%
Dash Punctuation 149
 
0.9%
Other Punctuation 86
 
0.5%
Space Separator 55
 
0.3%
Close Punctuation 18
 
0.1%
Open Punctuation 18
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
천 174
 
15.3%
신 71
 
6.2%
리 69
 
6.1%
곡 69
 
6.1%
해 40
 
3.5%
음 35
 
3.1%
의 31
 
2.7%
송 29
 
2.6%
문 29
 
2.6%
대 29
 
2.6%
Other values (52) 560
49.3%
Uppercase Letter
ValueCountFrequency (%)
G 460
17.4%
S 377
14.2%
C 342
12.9%
J 243
9.2%
H 233
8.8%
Y 197
7.4%
D 155
 
5.9%
B 125
 
4.7%
W 78
 
2.9%
A 75
 
2.8%
Other values (11) 364
13.7%
Decimal Number
ValueCountFrequency (%)
1 3027
23.7%
2 1892
14.8%
3 1430
11.2%
0 1274
10.0%
4 1199
 
9.4%
5 1027
 
8.0%
6 898
 
7.0%
7 783
 
6.1%
8 672
 
5.3%
9 584
 
4.6%
Dash Punctuation
ValueCountFrequency (%)
- 149
100.0%
Other Punctuation
ValueCountFrequency (%)
, 86
100.0%
Space Separator
ValueCountFrequency (%)
55
100.0%
Close Punctuation
ValueCountFrequency (%)
) 18
100.0%
Open Punctuation
ValueCountFrequency (%)
( 18
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 13112
77.6%
Latin 2649
 
15.7%
Hangul 1136
 
6.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
천 174
 
15.3%
신 71
 
6.2%
리 69
 
6.1%
곡 69
 
6.1%
해 40
 
3.5%
음 35
 
3.1%
의 31
 
2.7%
송 29
 
2.6%
문 29
 
2.6%
대 29
 
2.6%
Other values (52) 560
49.3%
Latin
ValueCountFrequency (%)
G 460
17.4%
S 377
14.2%
C 342
12.9%
J 243
9.2%
H 233
8.8%
Y 197
7.4%
D 155
 
5.9%
B 125
 
4.7%
W 78
 
2.9%
A 75
 
2.8%
Other values (11) 364
13.7%
Common
ValueCountFrequency (%)
1 3027
23.1%
2 1892
14.4%
3 1430
10.9%
0 1274
9.7%
4 1199
 
9.1%
5 1027
 
7.8%
6 898
 
6.8%
7 783
 
6.0%
8 672
 
5.1%
9 584
 
4.5%
Other values (5) 326
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15761
93.3%
Hangul 1136
 
6.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 3027
19.2%
2 1892
12.0%
3 1430
9.1%
0 1274
8.1%
4 1199
 
7.6%
5 1027
 
6.5%
6 898
 
5.7%
7 783
 
5.0%
8 672
 
4.3%
9 584
 
3.7%
Other values (26) 2975
18.9%
Hangul
ValueCountFrequency (%)
천 174
 
15.3%
신 71
 
6.2%
리 69
 
6.1%
곡 69
 
6.1%
해 40
 
3.5%
음 35
 
3.1%
의 31
 
2.7%
송 29
 
2.6%
문 29
 
2.6%
대 29
 
2.6%
Other values (52) 560
49.3%

공간정보
Text

Distinct8041
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Memory size63.3 KiB
2023-12-11T08:44:33.243623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length405
Median length364
Mean length215.66564
Min length200

Characters and Unicode

Total characters1742794
Distinct characters25
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8001 ?
Unique (%)99.0%

Sample

1st rowMULTIPOLYGON (((1122454.834852885 1716539.304033838, 1122460.9054557788 1716139.4371162583, 1121961.072218306 1716131.8492101333, 1121955.001634811 1716531.71545699, 1122454.834852885 1716539.304033838)))
2nd rowMULTIPOLYGON (((1122460.9054557788 1716139.4371162583, 1122454.834852885 1716539.304033838, 1122954.6689093434 1716546.8926349576, 1122960.7395316744 1716147.025046655, 1122460.9054557788 1716139.4371162583)))
3rd rowMULTIPOLYGON (((1121961.072218306 1716131.8492101333, 1122460.9054557788 1716139.4371162583, 1122466.975522081 1715739.570183144, 1122367.0088036298 1715738.0527341298, 1121967.1422652118 1715731.9829477724, 1121961.072218306 1716131.8492101333)))
4th rowMULTIPOLYGON (((1121967.1422652118 1715731.9829477724, 1122367.0088036298 1715738.0527341298, 1122373.0783294355 1715338.1859196394, 1121873.2452208681 1715330.5993599007, 1121867.1757144516 1715730.4655036072, 1121967.1422652118 1715731.9829477724)))
5th rowMULTIPOLYGON (((1122373.0783294355 1715338.1859196394, 1122379.1473186002 1714938.3190896222, 1121979.2807491533 1714932.2503765444, 1121879.314190645 1714930.733200698, 1121873.2452208681 1715330.5993599007, 1122373.0783294355 1715338.1859196394)))
ValueCountFrequency (%)
multipolygon 8081
 
8.8%
1104671.5464664744 9
 
< 0.1%
1656456.6155625929 9
 
< 0.1%
1719376.6036946478 9
 
< 0.1%
1082626.3984872699 9
 
< 0.1%
1719369.0033615727 8
 
< 0.1%
1104171.745998052 8
 
< 0.1%
1745718.2507352948 8
 
< 0.1%
1712473.0703456404 8
 
< 0.1%
1758537.300106001 8
 
< 0.1%
Other values (39482) 83254
91.1%
2023-12-11T08:44:33.644269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 211825
12.2%
7 149328
8.6%
0 147998
8.5%
6 138832
8.0%
2 128107
 
7.4%
5 125723
 
7.2%
4 125388
 
7.2%
3 124920
 
7.2%
8 124405
 
7.1%
9 120566
 
6.9%
Other values (15) 345702
19.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1397092
80.2%
Other Punctuation 116914
 
6.7%
Uppercase Letter 96972
 
5.6%
Space Separator 83330
 
4.8%
Close Punctuation 24243
 
1.4%
Open Punctuation 24243
 
1.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 211825
15.2%
7 149328
10.7%
0 147998
10.6%
6 138832
9.9%
2 128107
9.2%
5 125723
9.0%
4 125388
9.0%
3 124920
8.9%
8 124405
8.9%
9 120566
8.6%
Uppercase Letter
ValueCountFrequency (%)
O 16162
16.7%
L 16162
16.7%
U 8081
8.3%
N 8081
8.3%
G 8081
8.3%
Y 8081
8.3%
P 8081
8.3%
I 8081
8.3%
T 8081
8.3%
M 8081
8.3%
Other Punctuation
ValueCountFrequency (%)
. 83330
71.3%
, 33584
28.7%
Space Separator
ValueCountFrequency (%)
83330
100.0%
Close Punctuation
ValueCountFrequency (%)
) 24243
100.0%
Open Punctuation
ValueCountFrequency (%)
( 24243
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1645822
94.4%
Latin 96972
 
5.6%

Most frequent character per script

Common
ValueCountFrequency (%)
1 211825
12.9%
7 149328
9.1%
0 147998
9.0%
6 138832
8.4%
2 128107
7.8%
5 125723
7.6%
4 125388
7.6%
3 124920
7.6%
8 124405
7.6%
9 120566
7.3%
Other values (5) 248730
15.1%
Latin
ValueCountFrequency (%)
O 16162
16.7%
L 16162
16.7%
U 8081
8.3%
N 8081
8.3%
G 8081
8.3%
Y 8081
8.3%
P 8081
8.3%
I 8081
8.3%
T 8081
8.3%
M 8081
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1742794
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 211825
12.2%
7 149328
8.6%
0 147998
8.5%
6 138832
8.0%
2 128107
 
7.4%
5 125723
 
7.2%
4 125388
 
7.2%
3 124920
 
7.2%
8 124405
 
7.1%
9 120566
 
6.9%
Other values (15) 345702
19.8%

Interactions

2023-12-11T08:44:30.310377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:44:30.146323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:44:30.400761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:44:30.229290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T08:44:33.743124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
공간아이디일련번호
공간아이디1.0000.297
일련번호0.2971.000
2023-12-11T08:44:33.823227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
공간아이디일련번호
공간아이디1.000-0.132
일련번호-0.1321.000

Missing values

2023-12-11T08:44:30.491781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T08:44:30.581911image/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

공간아이디하천관리코드구분코드일련번호도엽번호공간정보
0120274002020F02Q0101I0311우곡11MULTIPOLYGON (((1122454.834852885 1716539.304033838, 1122460.9054557788 1716139.4371162583, 1121961.072218306 1716131.8492101333, 1121955.001634811 1716531.71545699, 1122454.834852885 1716539.304033838)))
1220274002020F02Q0101I0312우곡12MULTIPOLYGON (((1122460.9054557788 1716139.4371162583, 1122454.834852885 1716539.304033838, 1122954.6689093434 1716546.8926349576, 1122960.7395316744 1716147.025046655, 1122460.9054557788 1716139.4371162583)))
2320274002020F02Q0101I0310우곡10MULTIPOLYGON (((1121961.072218306 1716131.8492101333, 1122460.9054557788 1716139.4371162583, 1122466.975522081 1715739.570183144, 1122367.0088036298 1715738.0527341298, 1121967.1422652118 1715731.9829477724, 1121961.072218306 1716131.8492101333)))
3420274002020F02Q0101I039우곡09MULTIPOLYGON (((1121967.1422652118 1715731.9829477724, 1122367.0088036298 1715738.0527341298, 1122373.0783294355 1715338.1859196394, 1121873.2452208681 1715330.5993599007, 1121867.1757144516 1715730.4655036072, 1121967.1422652118 1715731.9829477724)))
4520274002020F02Q0101I038우곡08MULTIPOLYGON (((1122373.0783294355 1715338.1859196394, 1122379.1473186002 1714938.3190896222, 1121979.2807491533 1714932.2503765444, 1121879.314190645 1714930.733200698, 1121873.2452208681 1715330.5993599007, 1122373.0783294355 1715338.1859196394)))
5620274002020F02Q0101I037우곡07MULTIPOLYGON (((1121979.2807491533 1714932.2503765444, 1122379.1473186002 1714938.3190896222, 1122479.1140448176 1714939.8362703172, 1122485.1825012 1714539.969290605, 1122185.2824115353 1714535.4181539393, 1121985.3491861392 1714532.3840676772, 1121979.2807491533 1714932.2503765444)))
6720274002020F02Q0101I036우곡06MULTIPOLYGON (((1122485.1825012 1714539.969290605, 1122685.1160620232 1714543.0033865685, 1122691.183989489 1714143.1361229743, 1122591.2171884186 1714141.6192086816, 1122191.3503195893 1714135.5515612205, 1122185.2824115353 1714535.4181539393, 1122485.1825012 1714539.969290605)))
7820274002020F02Q0101I035우곡05MULTIPOLYGON (((1122191.3503195893 1714135.5515612205, 1122591.2171884186 1714141.6192086816, 1122597.2845752845 1713741.7520637265, 1122097.451053706 1713734.1681777304, 1122091.3836862415 1714134.0346517803, 1122191.3503195893 1714135.5515612205)))
8920274002020F02Q0101I034우곡04MULTIPOLYGON (((1122597.2845752845 1713741.7520637265, 1122603.3514254116 1713341.884903234, 1122303.4512001816 1713337.3349712892, 1122103.5178844328 1713334.301688175, 1122097.451053706 1713734.1681777304, 1122597.2845752845 1713741.7520637265)))
91020274002020F02Q0101I033우곡03MULTIPOLYGON (((1122303.4512001816 1713337.3349712892, 1122603.3514254116 1713341.884903234, 1122803.2850766373 1713344.9181960523, 1122804.801707243 1713244.9513364066, 1122809.3513977758 1712945.0507516367, 1122309.517501902 1712937.4681978417, 1122303.4512001816 1713337.3349712892)))
공간아이디하천관리코드구분코드일련번호도엽번호공간정보
8071807220257502020F02Q0101I0315대곡천15MULTIPOLYGON (((1060113.1125275956 1697606.0306266074, 1060712.788253933 1697615.097510895, 1060718.8325834542 1697215.3132842756, 1060119.1568395363 1697206.2472063885, 1060113.1125275956 1697606.0306266074)))
8072807320257502020F02Q0101I0314대곡천14MULTIPOLYGON (((1060319.0486197737 1697209.2692294205, 1060918.7247669012 1697218.3353161039, 1060924.7685646822 1696818.5508089326, 1060325.092399957 1696809.485528688, 1060319.0486197737 1697209.2692294205)))
8073807420257502020F02Q0101I0313대곡천13MULTIPOLYGON (((1060425.0383434054 1696810.996406896, 1061024.7147097448 1696820.061691541, 1061030.7579728256 1696420.2770382077, 1060431.0815888797 1696411.2125600378, 1060425.0383434054 1696810.996406896)))
8074807526210002020F01Q0101I031YR1MULTIPOLYGON (((1145923.633178808 1702504.4448697688, 1146523.4786654145 1702513.5323833474, 1146529.5367512263 1702113.6350001602, 1145929.691237036 1702104.5482943654, 1145923.633178808 1702504.4448697688)))
8075807626210002020F01Q0101I032YR2MULTIPOLYGON (((1145320.7596865438 1702695.3052744577, 1145920.6039477494 1702704.3931505834, 1145926.6622752373 1702304.4965843637, 1145326.8179865065 1702295.4095160093, 1145320.7596865438 1702695.3052744577)))
8076807726210002020F01Q0101I033YR3MULTIPOLYGON (((1144717.887298766 1702886.1649080717, 1145317.7303346286 1702895.2531468074, 1145323.7889038424 1702495.3573975253, 1144723.9458405084 1702486.2699665327, 1144717.887298766 1702886.1649080717)))
8077807826210002020F01Q0101I034YR4MULTIPOLYGON (((1144119.560153553 1702777.1031787656, 1144719.4019846867 1702786.1911744028, 1144725.460391807 1702386.2962282877, 1144125.6185332558 1702377.209040407, 1144119.560153553 1702777.1031787656)))
8078807920238002020F01Q0101I031토곡천1MULTIPOLYGON (((1068809.8236347137 1717432.0477815801, 1068210.1318787392 1717422.9408492483, 1068204.0603315055 1717822.7349448195, 1068803.7520691087 1717831.8426817292, 1068809.8236347137 1717432.0477815801)))
8079808020238002020F01Q0101I032토곡천2MULTIPOLYGON (((1068213.1674512106 1717223.0437968774, 1067613.4768929707 1717213.9372943626, 1067607.4056322454 1717613.7305914531, 1068207.0961721698 1717622.837898563, 1068213.1674512106 1717223.0437968774)))
8080808120238002020F01Q0101I033토곡천3MULTIPOLYGON (((1067616.2234615579 1717033.063791616, 1067016.5341019752 1717023.9576805579, 1067010.463102191 1717423.7501785532, 1067610.1524435089 1717432.8570942236, 1067616.2234615579 1717033.063791616)))