Overview

Dataset statistics

Number of variables9
Number of observations10000
Missing cells6152
Missing cells (%)6.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory791.0 KiB
Average record size in memory81.0 B

Variable types

Text3
Categorical5
Numeric1

Dataset

Description빅데이터 분석을 통해 연안사고 다발구역을 예측한 모델의 결과값입니다. 연안을 격자단위로 나누어 각 격자별로 위험도를 예측하였습니다. 격자좌표정보(geometry)에서 사용된 좌표계는 EPSG:5179 입니다. 격자좌표정보(geometry)의 데이터 길이가 길어서 엑셀로 열기에 제한이 있을 수 있습니다. (엑셀은 한 칸에 들어갈 수 있는 최대 글자수가 제한되어 있음) 엑셀보다 메모장과 같은 문서뷰어 프로그램, 또는 공간정보프로그램을 통해 열어보아야 데이터가 명확하게 표시됩니다.
URLhttps://www.data.go.kr/data/15118590/fileData.do

Alerts

관할지방해양경찰청 is highly overall correlated with 관할해양경찰서High correlation
관할해양경찰서 is highly overall correlated with 관할지방해양경찰청High correlation
위험지수 is highly overall correlated with 위험등급(전역) and 1 other fieldsHigh correlation
위험등급(전역) is highly overall correlated with 위험지수 and 1 other fieldsHigh correlation
위험등급(지역) is highly overall correlated with 위험지수 and 1 other fieldsHigh correlation
위험등급(전역) is highly imbalanced (56.4%)Imbalance
위험등급(지역) is highly imbalanced (55.1%)Imbalance
관할파출소 has 6152 (61.5%) missing valuesMissing

Reproduction

Analysis started2023-12-12 05:39:31.120943
Analysis finished2023-12-12 05:39:32.668779
Duration1.55 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

격자번호
Text

Distinct4511
Distinct (%)45.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T14:39:33.001829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters110000
Distinct characters30
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1061 ?
Unique (%)10.6%

Sample

1st rowGR3_G1J43_Q
2nd rowGR3_F4G34_E
3rd rowGR3_F2N14_X
4th rowGR3_F2I21_I
5th rowGR3_F4G21_Q
ValueCountFrequency (%)
gr3_f4g42_m 4
 
< 0.1%
gr3_g3a34_o 4
 
< 0.1%
gr3_f4c34_s 4
 
< 0.1%
gr3_f4l31_s 4
 
< 0.1%
gr3_f4f22_n 4
 
< 0.1%
gr3_g1l13_h 4
 
< 0.1%
gr3_g3b22_p 4
 
< 0.1%
gr3_f4c43_w 4
 
< 0.1%
gr3_f4k14_q 4
 
< 0.1%
gr3_g1n23_o 4
 
< 0.1%
Other values (4501) 9960
99.6%
2023-12-12T14:39:33.866513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
_ 20000
18.2%
3 16245
14.8%
G 13898
12.6%
R 10398
9.5%
4 10360
9.4%
F 8496
7.7%
2 7072
 
6.4%
1 6323
 
5.7%
K 1956
 
1.8%
E 1348
 
1.2%
Other values (20) 13904
12.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 50000
45.5%
Decimal Number 40000
36.4%
Connector Punctuation 20000
 
18.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
G 13898
27.8%
R 10398
20.8%
F 8496
17.0%
K 1956
 
3.9%
E 1348
 
2.7%
N 1301
 
2.6%
H 1238
 
2.5%
J 1148
 
2.3%
C 1091
 
2.2%
B 1083
 
2.2%
Other values (15) 8043
16.1%
Decimal Number
ValueCountFrequency (%)
3 16245
40.6%
4 10360
25.9%
2 7072
17.7%
1 6323
 
15.8%
Connector Punctuation
ValueCountFrequency (%)
_ 20000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 60000
54.5%
Latin 50000
45.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
G 13898
27.8%
R 10398
20.8%
F 8496
17.0%
K 1956
 
3.9%
E 1348
 
2.7%
N 1301
 
2.6%
H 1238
 
2.5%
J 1148
 
2.3%
C 1091
 
2.2%
B 1083
 
2.2%
Other values (15) 8043
16.1%
Common
ValueCountFrequency (%)
_ 20000
33.3%
3 16245
27.1%
4 10360
17.3%
2 7072
 
11.8%
1 6323
 
10.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 110000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
_ 20000
18.2%
3 16245
14.8%
G 13898
12.6%
R 10398
9.5%
4 10360
9.4%
F 8496
7.7%
2 7072
 
6.4%
1 6323
 
5.7%
K 1956
 
1.8%
E 1348
 
1.2%
Other values (20) 13904
12.6%

관할지방해양경찰청
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
서해지방해양경찰청
3950 
중부지방해양경찰청
2090 
동해지방해양경찰청
1552 
제주지방해양경찰청
1214 
남해지방해양경찰청
1194 

Length

Max length9
Median length9
Mean length9
Min length9

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row동해지방해양경찰청
2nd row서해지방해양경찰청
3rd row중부지방해양경찰청
4th row중부지방해양경찰청
5th row서해지방해양경찰청

Common Values

ValueCountFrequency (%)
서해지방해양경찰청 3950
39.5%
중부지방해양경찰청 2090
20.9%
동해지방해양경찰청 1552
 
15.5%
제주지방해양경찰청 1214
 
12.1%
남해지방해양경찰청 1194
 
11.9%

Length

2023-12-12T14:39:34.053834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T14:39:34.162195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서해지방해양경찰청 3950
39.5%
중부지방해양경찰청 2090
20.9%
동해지방해양경찰청 1552
 
15.5%
제주지방해양경찰청 1214
 
12.1%
남해지방해양경찰청 1194
 
11.9%

관할해양경찰서
Categorical

HIGH CORRELATION 

Distinct21
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
목포해양경찰서
1894 
인천해양경찰서
927 
여수해양경찰서
881 
동해해양경찰서
762 
제주해양경찰서
674 
Other values (16)
4862 

Length

Max length8
Median length7
Mean length7.0505
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row울진해양경찰서
2nd row완도해양경찰서
3rd row태안해양경찰서
4th row인천해양경찰서
5th row목포해양경찰서

Common Values

ValueCountFrequency (%)
목포해양경찰서 1894
18.9%
인천해양경찰서 927
 
9.3%
여수해양경찰서 881
 
8.8%
동해해양경찰서 762
 
7.6%
제주해양경찰서 674
 
6.7%
완도해양경찰서 593
 
5.9%
태안해양경찰서 521
 
5.2%
서귀포해양경찰서 511
 
5.1%
통영해양경찰서 421
 
4.2%
평택해양경찰서 353
 
3.5%
Other values (11) 2463
24.6%

Length

2023-12-12T14:39:34.309294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
목포해양경찰서 1894
18.9%
인천해양경찰서 927
 
9.3%
여수해양경찰서 881
 
8.8%
동해해양경찰서 762
 
7.6%
제주해양경찰서 674
 
6.7%
완도해양경찰서 593
 
5.9%
태안해양경찰서 521
 
5.2%
서귀포해양경찰서 511
 
5.1%
통영해양경찰서 421
 
4.2%
평택해양경찰서 353
 
3.5%
Other values (11) 2463
24.6%

관할파출소
Text

MISSING 

Distinct96
Distinct (%)2.5%
Missing6152
Missing (%)61.5%
Memory size156.2 KiB
2023-12-12T14:39:34.658508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length5
Mean length5.2284304
Min length5

Characters and Unicode

Total characters20119
Distinct characters112
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

Unique0 ?
Unique (%)0.0%

Sample

1st row땅끝파출소
2nd row백령파출소
3rd row추자파출소
4th row추자파출소
5th row영광파출소
ValueCountFrequency (%)
완도파출소 175
 
4.5%
진도파출소 154
 
4.0%
영광파출소 124
 
3.2%
암태파출소 121
 
3.1%
흑산파출소 113
 
2.9%
땅끝파출소 107
 
2.8%
지도파출소 104
 
2.7%
영흥파출소 103
 
2.7%
추자파출소 99
 
2.6%
대천파출소 96
 
2.5%
Other values (86) 2652
68.9%
2023-12-12T14:39:35.170267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
파 3869
19.2%
출 3848
19.1%
소 3848
19.1%
도 562
 
2.8%
산 379
 
1.9%
영 346
 
1.7%
진 342
 
1.7%
포 337
 
1.7%
항 241
 
1.2%
대 217
 
1.1%
Other values (102) 6130
30.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 20119
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
파 3869
19.2%
출 3848
19.1%
소 3848
19.1%
도 562
 
2.8%
산 379
 
1.9%
영 346
 
1.7%
진 342
 
1.7%
포 337
 
1.7%
항 241
 
1.2%
대 217
 
1.1%
Other values (102) 6130
30.5%

Most occurring scripts

ValueCountFrequency (%)
Hangul 20119
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
파 3869
19.2%
출 3848
19.1%
소 3848
19.1%
도 562
 
2.8%
산 379
 
1.9%
영 346
 
1.7%
진 342
 
1.7%
포 337
 
1.7%
항 241
 
1.2%
대 217
 
1.1%
Other values (102) 6130
30.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 20119
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
파 3869
19.2%
출 3848
19.1%
소 3848
19.1%
도 562
 
2.8%
산 379
 
1.9%
영 346
 
1.7%
진 342
 
1.7%
포 337
 
1.7%
항 241
 
1.2%
대 217
 
1.1%
Other values (102) 6130
30.5%

위험지수
Real number (ℝ)

HIGH CORRELATION 

Distinct9628
Distinct (%)96.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.15483617
Minimum0
Maximum0.99999977
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T14:39:35.358491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.9835 × 10-9
Q10.00047836408
median0.025381548
Q30.19874355
95-th percentile0.79412199
Maximum0.99999977
Range0.99999977
Interquartile range (IQR)0.19826519

Descriptive statistics

Standard deviation0.25132535
Coefficient of variation (CV)1.6231695
Kurtosis2.5933388
Mean0.15483617
Median Absolute Deviation (MAD)0.025380123
Skewness1.8826088
Sum1548.3617
Variance0.063164433
MonotonicityNot monotonic
2023-12-12T14:39:35.561642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.49e-05 5
 
0.1%
1.36e-05 5
 
0.1%
2.14e-05 5
 
0.1%
4.18e-05 5
 
0.1%
3.28e-05 4
 
< 0.1%
1.6e-05 4
 
< 0.1%
1.41e-05 4
 
< 0.1%
1.61e-05 4
 
< 0.1%
2.63e-05 4
 
< 0.1%
4e-16 4
 
< 0.1%
Other values (9618) 9956
99.6%
ValueCountFrequency (%)
0.0 1
< 0.1%
8.199999999999999e-90 1
< 0.1%
8.690000000000001e-88 1
< 0.1%
8.57e-87 1
< 0.1%
2.35e-86 1
< 0.1%
4.2800000000000005e-86 1
< 0.1%
1.34e-85 1
< 0.1%
4.16e-85 1
< 0.1%
7.06e-85 1
< 0.1%
7.36e-85 1
< 0.1%
ValueCountFrequency (%)
0.9999997727 1
< 0.1%
0.9999983223 1
< 0.1%
0.99999658268 1
< 0.1%
0.99999625709 1
< 0.1%
0.99998828361 1
< 0.1%
0.99998179835 1
< 0.1%
0.99998102482 1
< 0.1%
0.99997980168 1
< 0.1%
0.99994088043 1
< 0.1%
0.9999318922 1
< 0.1%

위험등급(전역)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
안전
8111 
주의
 
789
보통
 
714
위험
 
319
고위험
 
67

Length

Max length3
Median length2
Mean length2.0067
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
안전 8111
81.1%
주의 789
 
7.9%
보통 714
 
7.1%
위험 319
 
3.2%
고위험 67
 
0.7%

Length

2023-12-12T14:39:35.698590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T14:39:35.840652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
안전 8111
81.1%
주의 789
 
7.9%
보통 714
 
7.1%
위험 319
 
3.2%
고위험 67
 
0.7%

위험등급(지역)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
안전
8111 
보통
 
714
주의
 
683
위험
 
295
고위험
 
197

Length

Max length3
Median length2
Mean length2.0197
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
안전 8111
81.1%
보통 714
 
7.1%
주의 683
 
6.8%
위험 295
 
2.9%
고위험 197
 
2.0%

Length

2023-12-12T14:39:36.001229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T14:39:36.177446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
안전 8111
81.1%
보통 714
 
7.1%
주의 683
 
6.8%
위험 295
 
2.9%
고위험 197
 
2.0%

월
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
09월
2516 
10월
2512 
11월
2504 
12월
2468 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row11월
2nd row11월
3rd row12월
4th row09월
5th row10월

Common Values

ValueCountFrequency (%)
09월 2516
25.2%
10월 2512
25.1%
11월 2504
25.0%
12월 2468
24.7%

Length

2023-12-12T14:39:36.374580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T14:39:36.503372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
09월 2516
25.2%
10월 2512
25.1%
11월 2504
25.0%
12월 2468
24.7%

공간정보(좌표)
Text

Distinct4511
Distinct (%)45.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T14:39:36.770208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length195
Median length194
Mean length193.6348
Min length163

Characters and Unicode

Total characters1936348
Distinct characters21
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

Unique1061 ?
Unique (%)10.6%

Sample

1st rowPOLYGON ((1182172.475359509 1902117.103173927, 1186616.246554279 1902214.222523016, 1186738.885055047 1896666.462814532, 1182292.190309518 1896569.391743599, 1182172.475359509 1902117.103173927))
2nd rowPOLYGON ((903317.7620893706 1584559.647492268, 907921.8552758088 1584513.286786381, 907867.4229095171 1578968.86969416, 903260.6071737213 1579015.198342383, 903317.7620893706 1584559.647492268))
3rd rowPOLYGON ((812042.5867110684 1841191.094377813, 816518.3300190638 1841094.52231298, 816400.1509726429 1835547.259383161, 811921.5215506461 1835643.779928303, 812042.5867110684 1841191.094377813))
4th rowPOLYGON ((749582.424288719 1998284.336725871, 753976.5819295258 1998150.989854847, 753809.7707471226 1992601.823076765, 749412.6276228931 1992735.111185321, 749582.424288719 1998284.336725871))
5th rowPOLYGON ((913151.6358722888 1651005.272631693, 917722.737620428 1650963.098990693, 917672.9937514564 1645418.209100489, 913099.1276978115 1645460.355342696, 913151.6358722888 1651005.272631693))
ValueCountFrequency (%)
polygon 10000
 
9.1%
1000000 265
 
0.2%
1561925.468181048 33
 
< 0.1%
1556396.162358109 32
 
< 0.1%
1951681.716330011 32
 
< 0.1%
1584116.424795719 31
 
< 0.1%
1584101.727265486 31
 
< 0.1%
1600703.807612741 30
 
< 0.1%
1606890.956214383 30
 
< 0.1%
1578589.229228149 30
 
< 0.1%
Other values (9465) 99486
90.4%
2023-12-12T14:39:37.316441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 216431
11.2%
9 165080
8.5%
8 164425
8.5%
6 156318
8.1%
7 155710
8.0%
5 154929
8.0%
3 145439
7.5%
2 144568
7.5%
4 144073
7.4%
0 139640
 
7.2%
Other values (11) 349735
18.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1586613
81.9%
Other Punctuation 139735
 
7.2%
Space Separator 100000
 
5.2%
Uppercase Letter 70000
 
3.6%
Close Punctuation 20000
 
1.0%
Open Punctuation 20000
 
1.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 216431
13.6%
9 165080
10.4%
8 164425
10.4%
6 156318
9.9%
7 155710
9.8%
5 154929
9.8%
3 145439
9.2%
2 144568
9.1%
4 144073
9.1%
0 139640
8.8%
Uppercase Letter
ValueCountFrequency (%)
O 20000
28.6%
N 10000
14.3%
G 10000
14.3%
Y 10000
14.3%
L 10000
14.3%
P 10000
14.3%
Other Punctuation
ValueCountFrequency (%)
. 99735
71.4%
, 40000
28.6%
Space Separator
ValueCountFrequency (%)
100000
100.0%
Close Punctuation
ValueCountFrequency (%)
) 20000
100.0%
Open Punctuation
ValueCountFrequency (%)
( 20000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1866348
96.4%
Latin 70000
 
3.6%

Most frequent character per script

Common
ValueCountFrequency (%)
1 216431
11.6%
9 165080
8.8%
8 164425
8.8%
6 156318
8.4%
7 155710
8.3%
5 154929
8.3%
3 145439
7.8%
2 144568
7.7%
4 144073
7.7%
0 139640
7.5%
Other values (5) 279735
15.0%
Latin
ValueCountFrequency (%)
O 20000
28.6%
N 10000
14.3%
G 10000
14.3%
Y 10000
14.3%
L 10000
14.3%
P 10000
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1936348
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 216431
11.2%
9 165080
8.5%
8 164425
8.5%
6 156318
8.1%
7 155710
8.0%
5 154929
8.0%
3 145439
7.5%
2 144568
7.5%
4 144073
7.4%
0 139640
 
7.2%
Other values (11) 349735
18.1%

Interactions

2023-12-12T14:39:32.184675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T14:39:37.432477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관할지방해양경찰청관할해양경찰서관할파출소위험지수위험등급(전역)위험등급(지역)월
관할지방해양경찰청1.0001.0000.9990.2770.2430.2450.000
관할해양경찰서1.0001.0001.0000.3670.3310.3340.000
관할파출소0.9991.0001.0000.5280.5340.5210.000
위험지수0.2770.3670.5281.0000.9880.9970.285
위험등급(전역)0.2430.3310.5340.9881.0000.9900.159
위험등급(지역)0.2450.3340.5210.9970.9901.0000.162
월0.0000.0000.0000.2850.1590.1621.000
2023-12-12T14:39:37.561773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관할지방해양경찰청위험등급(전역)관할해양경찰서월위험등급(지역)
관할지방해양경찰청1.0000.0930.9950.0000.093
위험등급(전역)0.0931.0000.1480.1300.852
관할해양경찰서0.9950.1481.0000.0000.150
월0.0000.1300.0001.0000.133
위험등급(지역)0.0930.8520.1500.1331.000
2023-12-12T14:39:37.721541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위험지수관할지방해양경찰청관할해양경찰서위험등급(전역)위험등급(지역)월
위험지수1.0000.1180.1240.8450.9210.174
관할지방해양경찰청0.1181.0000.9950.0930.0930.000
관할해양경찰서0.1240.9951.0000.1480.1500.000
위험등급(전역)0.8450.0930.1481.0000.8520.130
위험등급(지역)0.9210.0930.1500.8521.0000.133
월0.1740.0000.0000.1300.1331.000

Missing values

2023-12-12T14:39:32.385316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T14:39:32.579102image/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

격자번호관할지방해양경찰청관할해양경찰서관할파출소위험지수위험등급(전역)위험등급(지역)월공간정보(좌표)
10049GR3_G1J43_Q동해지방해양경찰청울진해양경찰서<NA>0.000083안전안전11월POLYGON ((1182172.475359509 1902117.103173927, 1186616.246554279 1902214.222523016, 1186738.885055047 1896666.462814532, 1182292.190309518 1896569.391743599, 1182172.475359509 1902117.103173927))
12914GR3_F4G34_E서해지방해양경찰청완도해양경찰서땅끝파출소0.000127안전안전11월POLYGON ((903317.7620893706 1584559.647492268, 907921.8552758088 1584513.286786381, 907867.4229095171 1578968.86969416, 903260.6071737213 1579015.198342383, 903317.7620893706 1584559.647492268))
18916GR3_F2N14_X중부지방해양경찰청태안해양경찰서<NA>0.000041안전안전12월POLYGON ((812042.5867110684 1841191.094377813, 816518.3300190638 1841094.52231298, 816400.1509726429 1835547.259383161, 811921.5215506461 1835643.779928303, 812042.5867110684 1841191.094377813))
3833GR3_F2I21_I중부지방해양경찰청인천해양경찰서백령파출소0.027397안전안전09월POLYGON ((749582.424288719 1998284.336725871, 753976.5819295258 1998150.989854847, 753809.7707471226 1992601.823076765, 749412.6276228931 1992735.111185321, 749582.424288719 1998284.336725871))
7412GR3_F4G21_Q서해지방해양경찰청목포해양경찰서<NA>0.08172안전안전10월POLYGON ((913151.6358722888 1651005.272631693, 917722.737620428 1650963.098990693, 917672.9937514564 1645418.209100489, 913099.1276978115 1645460.355342696, 913151.6358722888 1651005.272631693))
7629GR3_F4F13_M서해지방해양경찰청목포해양경찰서<NA>0.000024안전안전10월POLYGON ((780044.6723723325 1631035.617544426, 784628.0001926325 1630927.536182786, 784498.6744994429 1625381.402963978, 779912.5901464541 1625489.412738812, 780044.6723723325 1631035.617544426))
10329GR3_G3B23_U남해지방해양경찰청울산해양경찰서<NA>0.012773안전안전11월POLYGON ((1181285.754591277 1730063.777340198, 1185818.522437474 1730156.959607469, 1185933.881913 1724610.505130071, 1181398.296949014 1724517.378847171, 1181285.754591277 1730063.777340198))
6137GR3_F4K12_H제주지방해양경찰청제주해양경찰서추자파출소0.103489안전안전10월POLYGON ((893735.1034723977 1551392.699337512, 898355.5365975028 1551342.027318304, 898296.123508117 1545797.805704383, 893672.9886669947 1545848.441613664, 893735.1034723977 1551392.699337512))
12698GR3_F4H42_I서해지방해양경찰청여수해양경찰서<NA>0.027463안전안전11월POLYGON ((1036743.316355239 1606310.452078507, 1041336.263285386 1606329.72474805, 1041360.881075458 1600785.37683512, 1036765.198688435 1600766.117226122, 1036743.316355239 1606310.452078507))
15714GR3_F4K21_K제주지방해양경찰청제주해양경찰서추자파출소0.00122안전안전12월POLYGON ((907542.3060164964 1545703.287787403, 912165.3563649456 1545659.40555205, 912114.1145862634 1540115.326480195, 907488.366457082 1540159.177284967, 907542.3060164964 1545703.287787403))
격자번호관할지방해양경찰청관할해양경찰서관할파출소위험지수위험등급(전역)위험등급(지역)월공간정보(좌표)
3655GR3_F4H43_Y서해지방해양경찰청여수해양경찰서<NA>0.002287안전안전09월POLYGON ((1018458.752070641 1561902.919720309, 1023073.450002841 1561913.066505346, 1023086.985723605 1556369.123184866, 1018469.580608287 1556358.983561843, 1018458.752070641 1561902.919720309))
6640GR3_F4N33_K제주지방해양경찰청제주해양경찰서<NA>0.002482안전안전10월POLYGON ((764214.1920388679 1353985.648179825, 768931.1766504275 1353877.15208568, 768805.0253214437 1348332.701838058, 764085.4607986746 1348441.106509205, 764214.1920388679 1353985.648179825))
7015GR3_F4C42_H서해지방해양경찰청목포해양경찰서<NA>0.31132보통보통10월POLYGON ((941012.558473494 1717327.25841829, 945550.1110194223 1717298.549377273, 945516.4501250526 1711753.248810137, 940976.0921561985 1711781.940379933, 941012.558473494 1717327.25841829))
293GR3_G1K41_A동해지방해양경찰청동해해양경찰서<NA>0.0안전안전09월POLYGON ((1265201.869574411 1948755.133910937, 1269622.980932478 1948897.362988441, 1269802.875810698 1943348.196540076, 1265378.807740679 1943206.034257439, 1265201.869574411 1948755.133910937))
8669GR3_F2K23_A중부지방해양경찰청인천해양경찰서<NA>0.222047안전안전10월POLYGON ((911904.326146937 1972733.258534083, 916309.2146292176 1972687.363597387, 916252.9284108073 1967139.825919221, 911845.0766814118 1967185.699951642, 911904.326146937 1972733.258534083))
4091GR3_F2J41_I중부지방해양경찰청인천해양경찰서<NA>0.239674안전안전09월POLYGON ((836361.9112135689 1940585.940955567, 840784.9501746603 1940500.244690294, 840678.995846831 1934952.46090511, 836253.0108767073 1935038.116515358, 836361.9112135689 1940585.940955567))
5923GR3_G3E14_F남해지방해양경찰청통영해양경찰서통영파출소0.839707주의위험10월POLYGON ((1068688.281612733 1634215.943812923, 1073267.602694548 1634251.207888109, 1073311.650681732 1628706.502522644, 1068729.576099342 1628671.261726499, 1068688.281612733 1634215.943812923))
1877GR3_F4N33_P제주지방해양경찰청서귀포해양경찰서<NA>0.04373안전안전09월POLYGON ((764085.4607986746 1348441.106509205, 768805.0253214437 1348332.701838058, 768679.0501774971 1342788.28740642, 763956.9093389286 1342896.600324672, 764085.4607986746 1348441.106509205))
17816GR3_F4G33_N서해지방해양경찰청목포해양경찰서<NA>0.089702안전안전12월POLYGON ((875545.6945206877 1573796.018579211, 880155.4138040892 1573736.165227508, 880084.8267467408 1568191.618202429, 875472.3910280612 1568251.429755482, 875545.6945206877 1573796.018579211))
15902GR3_F4K31_B제주지방해양경찰청제주해양경찰서<NA>0.000068안전안전12월POLYGON ((865310.5430795744 1501844.994313564, 869955.3776965619 1501781.220489324, 869880.6424647125 1496237.138547223, 865233.1369380262 1496300.864888111, 865310.5430795744 1501844.994313564))