Overview

Dataset statistics

Number of variables18
Number of observations9380
Missing cells150012
Missing cells (%)88.8%
Duplicate rows1
Duplicate rows (%)< 0.1%
Total size in memory3.5 MiB
Average record size in memory387.9 B

Variable types

Text15
DateTime1
Categorical2

Dataset

Description공공데이터 제공 표준데이터 속성정보(허용값, 표현형식/단위 등)는 [공공데이터 제공 표준] 전문을 참고하시기 바랍니다.(공공데이터포털>정보공유>자료실) 경찰청과 도로교통공단에서 관리하는 교통사고다발지역(교통사고가 밀집되어 발생한 곳(사고다발지역)의 폴리곤 정보)
Author도로교통공단
URLhttps://www.data.go.kr/data/15029185/standard.do

Alerts

데이터기준일자 has constant value ""Constant
Dataset has 1 (< 0.1%) duplicate rowsDuplicates
제공기관명 is highly overall correlated with 제공기관코드High correlation
제공기관코드 is highly overall correlated with 제공기관명High correlation
제공기관코드 is highly imbalanced (99.5%)Imbalance
제공기관명 is highly imbalanced (99.5%)Imbalance
사고지역관리번호 has 9375 (99.9%) missing valuesMissing
사고연도 has 9375 (99.9%) missing valuesMissing
사고유형구분 has 9375 (99.9%) missing valuesMissing
위치코드 has 9375 (99.9%) missing valuesMissing
사고다발지역시도시군구 has 9375 (99.9%) missing valuesMissing
사고지역위치명 has 9376 (> 99.9%) missing valuesMissing
사고건수 has 9376 (> 99.9%) missing valuesMissing
사상자수 has 9376 (> 99.9%) missing valuesMissing
사망자수 has 9376 (> 99.9%) missing valuesMissing
중상자수 has 9376 (> 99.9%) missing valuesMissing
경상자수 has 9376 (> 99.9%) missing valuesMissing
부상신고자수 has 9376 (> 99.9%) missing valuesMissing
위도 has 9376 (> 99.9%) missing valuesMissing
경도 has 9376 (> 99.9%) missing valuesMissing
사고다발지역폴리곤정보 has 9377 (> 99.9%) missing valuesMissing
데이터기준일자 has 9376 (> 99.9%) missing valuesMissing

Reproduction

Analysis started2023-12-12 15:40:41.748175
Analysis finished2023-12-12 15:40:46.488112
Duration4.74 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

사고지역관리번호
Text

MISSING 

Distinct5
Distinct (%)100.0%
Missing9375
Missing (%)99.9%
Memory size2.3 MiB
2023-12-13T00:40:46.582689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters60
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)100.0%

Sample

1st row[127.0358359
2nd row[126.9034876
3rd row[127.0514658
4th row[126.9723934
5th row[126.8364647
ValueCountFrequency (%)
127.0358359 1
20.0%
126.9034876 1
20.0%
127.0514658 1
20.0%
126.9723934 1
20.0%
126.8364647 1
20.0%
2023-12-13T00:40:46.895252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6 7
11.7%
1 6
10.0%
2 6
10.0%
3 6
10.0%
[ 5
8.3%
7 5
8.3%
. 5
8.3%
4 5
8.3%
5 4
6.7%
8 4
6.7%
Other values (2) 7
11.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 50
83.3%
Open Punctuation 5
 
8.3%
Other Punctuation 5
 
8.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
6 7
14.0%
1 6
12.0%
2 6
12.0%
3 6
12.0%
7 5
10.0%
4 5
10.0%
5 4
8.0%
8 4
8.0%
9 4
8.0%
0 3
6.0%
Open Punctuation
ValueCountFrequency (%)
[ 5
100.0%
Other Punctuation
ValueCountFrequency (%)
. 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 60
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
6 7
11.7%
1 6
10.0%
2 6
10.0%
3 6
10.0%
[ 5
8.3%
7 5
8.3%
. 5
8.3%
4 5
8.3%
5 4
6.7%
8 4
6.7%
Other values (2) 7
11.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 60
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6 7
11.7%
1 6
10.0%
2 6
10.0%
3 6
10.0%
[ 5
8.3%
7 5
8.3%
. 5
8.3%
4 5
8.3%
5 4
6.7%
8 4
6.7%
Other values (2) 7
11.7%

사고연도
Text

MISSING 

Distinct5
Distinct (%)100.0%
Missing9375
Missing (%)99.9%
Memory size2.3 MiB
2023-12-13T00:40:47.072487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length11
Mean length11.8
Min length11

Characters and Unicode

Total characters59
Distinct characters14
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)100.0%

Sample

1st row37.4861885]
2nd row35.1524581]
3rd row37.7232528]
4th row35.9359808]
5th row37.5219279]]]}"
ValueCountFrequency (%)
37.4861885 1
20.0%
35.1524581 1
20.0%
37.7232528 1
20.0%
35.9359808 1
20.0%
37.5219279 1
20.0%
2023-12-13T00:40:47.511608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 8
13.6%
3 7
11.9%
8 7
11.9%
] 7
11.9%
2 6
10.2%
7 5
8.5%
. 5
8.5%
1 4
6.8%
9 4
6.8%
4 2
 
3.4%
Other values (4) 4
6.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 45
76.3%
Close Punctuation 8
 
13.6%
Other Punctuation 6
 
10.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 8
17.8%
3 7
15.6%
8 7
15.6%
2 6
13.3%
7 5
11.1%
1 4
8.9%
9 4
8.9%
4 2
 
4.4%
6 1
 
2.2%
0 1
 
2.2%
Close Punctuation
ValueCountFrequency (%)
] 7
87.5%
} 1
 
12.5%
Other Punctuation
ValueCountFrequency (%)
. 5
83.3%
" 1
 
16.7%

Most occurring scripts

ValueCountFrequency (%)
Common 59
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 8
13.6%
3 7
11.9%
8 7
11.9%
] 7
11.9%
2 6
10.2%
7 5
8.5%
. 5
8.5%
1 4
6.8%
9 4
6.8%
4 2
 
3.4%
Other values (4) 4
6.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 59
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 8
13.6%
3 7
11.9%
8 7
11.9%
] 7
11.9%
2 6
10.2%
7 5
8.5%
. 5
8.5%
1 4
6.8%
9 4
6.8%
4 2
 
3.4%
Other values (4) 4
6.8%

사고유형구분
Text

MISSING 

Distinct5
Distinct (%)100.0%
Missing9375
Missing (%)99.9%
Memory size2.3 MiB
2023-12-13T00:40:47.705601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length11.6
Min length10

Characters and Unicode

Total characters58
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)100.0%

Sample

1st row[127.0360396
2nd row[126.9036854
3rd row[127.0516701
4th row[126.9725931
5th row2023-01-25
ValueCountFrequency (%)
127.0360396 1
20.0%
126.9036854 1
20.0%
127.0516701 1
20.0%
126.9725931 1
20.0%
2023-01-25 1
20.0%
2023-12-13T00:40:48.088128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 8
13.8%
2 8
13.8%
0 7
12.1%
6 6
10.3%
3 5
8.6%
[ 4
6.9%
7 4
6.9%
. 4
6.9%
9 4
6.9%
5 4
6.9%
Other values (3) 4
6.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 48
82.8%
Open Punctuation 4
 
6.9%
Other Punctuation 4
 
6.9%
Dash Punctuation 2
 
3.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 8
16.7%
2 8
16.7%
0 7
14.6%
6 6
12.5%
3 5
10.4%
7 4
8.3%
9 4
8.3%
5 4
8.3%
8 1
 
2.1%
4 1
 
2.1%
Open Punctuation
ValueCountFrequency (%)
[ 4
100.0%
Other Punctuation
ValueCountFrequency (%)
. 4
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 58
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 8
13.8%
2 8
13.8%
0 7
12.1%
6 6
10.3%
3 5
8.6%
[ 4
6.9%
7 4
6.9%
. 4
6.9%
9 4
6.9%
5 4
6.9%
Other values (3) 4
6.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 58
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 8
13.8%
2 8
13.8%
0 7
12.1%
6 6
10.3%
3 5
8.6%
[ 4
6.9%
7 4
6.9%
. 4
6.9%
9 4
6.9%
5 4
6.9%
Other values (3) 4
6.9%

위치코드
Text

MISSING 

Distinct5
Distinct (%)100.0%
Missing9375
Missing (%)99.9%
Memory size2.3 MiB
2023-12-13T00:40:48.248937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length11
Mean length10.2
Min length7

Characters and Unicode

Total characters51
Distinct characters13
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)100.0%

Sample

1st row37.4865804]
2nd row35.1528501]
3rd row37.7236446]
4th row35.9363728]
5th rowB552061
ValueCountFrequency (%)
37.4865804 1
20.0%
35.1528501 1
20.0%
37.7236446 1
20.0%
35.9363728 1
20.0%
b552061 1
20.0%
2023-12-13T00:40:48.563772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 7
13.7%
5 7
13.7%
6 5
9.8%
7 4
7.8%
. 4
7.8%
4 4
7.8%
8 4
7.8%
] 4
7.8%
2 4
7.8%
0 3
5.9%
Other values (3) 5
9.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 42
82.4%
Other Punctuation 4
 
7.8%
Close Punctuation 4
 
7.8%
Uppercase Letter 1
 
2.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 7
16.7%
5 7
16.7%
6 5
11.9%
7 4
9.5%
4 4
9.5%
8 4
9.5%
2 4
9.5%
0 3
7.1%
1 3
7.1%
9 1
 
2.4%
Other Punctuation
ValueCountFrequency (%)
. 4
100.0%
Close Punctuation
ValueCountFrequency (%)
] 4
100.0%
Uppercase Letter
ValueCountFrequency (%)
B 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 50
98.0%
Latin 1
 
2.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 7
14.0%
5 7
14.0%
6 5
10.0%
7 4
8.0%
. 4
8.0%
4 4
8.0%
8 4
8.0%
] 4
8.0%
2 4
8.0%
0 3
6.0%
Other values (2) 4
8.0%
Latin
ValueCountFrequency (%)
B 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 51
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 7
13.7%
5 7
13.7%
6 5
9.8%
7 4
7.8%
. 4
7.8%
4 4
7.8%
8 4
7.8%
] 4
7.8%
2 4
7.8%
0 3
5.9%
Other values (3) 5
9.8%

사고다발지역시도시군구
Text

MISSING 

Distinct5
Distinct (%)100.0%
Missing9375
Missing (%)99.9%
Memory size2.3 MiB
2023-12-13T00:40:48.732894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length10.6
Min length6

Characters and Unicode

Total characters53
Distinct characters18
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

Unique5 ?
Unique (%)100.0%

Sample

1st row[127.0361638
2nd row[126.903806
3rd row[127.0517948
4th row[126.9727149
5th row도로교통공단
ValueCountFrequency (%)
127.0361638 1
20.0%
126.903806 1
20.0%
127.0517948 1
20.0%
126.9727149 1
20.0%
도로교통공단 1
20.0%
2023-12-13T00:40:49.047368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 7
13.2%
6 5
9.4%
2 5
9.4%
7 5
9.4%
[ 4
7.5%
9 4
7.5%
0 4
7.5%
. 4
7.5%
3 3
 
5.7%
8 3
 
5.7%
Other values (8) 9
17.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 39
73.6%
Other Letter 6
 
11.3%
Open Punctuation 4
 
7.5%
Other Punctuation 4
 
7.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 7
17.9%
6 5
12.8%
2 5
12.8%
7 5
12.8%
9 4
10.3%
0 4
10.3%
3 3
7.7%
8 3
7.7%
4 2
 
5.1%
5 1
 
2.6%
Other Letter
ValueCountFrequency (%)
도 1
16.7%
로 1
16.7%
교 1
16.7%
통 1
16.7%
공 1
16.7%
단 1
16.7%
Open Punctuation
ValueCountFrequency (%)
[ 4
100.0%
Other Punctuation
ValueCountFrequency (%)
. 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 47
88.7%
Hangul 6
 
11.3%

Most frequent character per script

Common
ValueCountFrequency (%)
1 7
14.9%
6 5
10.6%
2 5
10.6%
7 5
10.6%
[ 4
8.5%
9 4
8.5%
0 4
8.5%
. 4
8.5%
3 3
6.4%
8 3
6.4%
Other values (2) 3
6.4%
Hangul
ValueCountFrequency (%)
도 1
16.7%
로 1
16.7%
교 1
16.7%
통 1
16.7%
공 1
16.7%
단 1
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 47
88.7%
Hangul 6
 
11.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 7
14.9%
6 5
10.6%
2 5
10.6%
7 5
10.6%
[ 4
8.5%
9 4
8.5%
0 4
8.5%
. 4
8.5%
3 3
6.4%
8 3
6.4%
Other values (2) 3
6.4%
Hangul
ValueCountFrequency (%)
도 1
16.7%
로 1
16.7%
교 1
16.7%
통 1
16.7%
공 1
16.7%
단 1
16.7%

사고지역위치명
Text

MISSING 

Distinct4
Distinct (%)100.0%
Missing9376
Missing (%)> 99.9%
Memory size2.3 MiB
2023-12-13T00:40:49.235285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length11
Mean length10.75
Min length10

Characters and Unicode

Total characters43
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)100.0%

Sample

1st row37.4869928]
2nd row35.1532627]
3rd row37.724057]
4th row35.9367853]
ValueCountFrequency (%)
37.4869928 1
25.0%
35.1532627 1
25.0%
37.724057 1
25.0%
35.9367853 1
25.0%
2023-12-13T00:40:49.802009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 7
16.3%
7 6
14.0%
5 5
11.6%
. 4
9.3%
2 4
9.3%
] 4
9.3%
8 3
7.0%
6 3
7.0%
9 3
7.0%
4 2
 
4.7%
Other values (2) 2
 
4.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 35
81.4%
Other Punctuation 4
 
9.3%
Close Punctuation 4
 
9.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 7
20.0%
7 6
17.1%
5 5
14.3%
2 4
11.4%
8 3
8.6%
6 3
8.6%
9 3
8.6%
4 2
 
5.7%
1 1
 
2.9%
0 1
 
2.9%
Other Punctuation
ValueCountFrequency (%)
. 4
100.0%
Close Punctuation
ValueCountFrequency (%)
] 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 43
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 7
16.3%
7 6
14.0%
5 5
11.6%
. 4
9.3%
2 4
9.3%
] 4
9.3%
8 3
7.0%
6 3
7.0%
9 3
7.0%
4 2
 
4.7%
Other values (2) 2
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 43
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 7
16.3%
7 6
14.0%
5 5
11.6%
. 4
9.3%
2 4
9.3%
] 4
9.3%
8 3
7.0%
6 3
7.0%
9 3
7.0%
4 2
 
4.7%
Other values (2) 2
 
4.7%

사고건수
Text

MISSING 

Distinct4
Distinct (%)100.0%
Missing9376
Missing (%)> 99.9%
Memory size2.3 MiB
2023-12-13T00:40:49.970682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters48
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)100.0%

Sample

1st row[127.0362056
2nd row[126.9038465
3rd row[127.0518367
4th row[126.9727558
ValueCountFrequency (%)
127.0362056 1
25.0%
126.9038465 1
25.0%
127.0518367 1
25.0%
126.9727558 1
25.0%
2023-12-13T00:40:50.268506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 6
12.5%
6 6
12.5%
1 5
10.4%
7 5
10.4%
5 5
10.4%
[ 4
8.3%
. 4
8.3%
0 4
8.3%
3 3
6.2%
8 3
6.2%
Other values (2) 3
6.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 40
83.3%
Open Punctuation 4
 
8.3%
Other Punctuation 4
 
8.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 6
15.0%
6 6
15.0%
1 5
12.5%
7 5
12.5%
5 5
12.5%
0 4
10.0%
3 3
7.5%
8 3
7.5%
9 2
 
5.0%
4 1
 
2.5%
Open Punctuation
ValueCountFrequency (%)
[ 4
100.0%
Other Punctuation
ValueCountFrequency (%)
. 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 48
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 6
12.5%
6 6
12.5%
1 5
10.4%
7 5
10.4%
5 5
10.4%
[ 4
8.3%
. 4
8.3%
0 4
8.3%
3 3
6.2%
8 3
6.2%
Other values (2) 3
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 48
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 6
12.5%
6 6
12.5%
1 5
10.4%
7 5
10.4%
5 5
10.4%
[ 4
8.3%
. 4
8.3%
0 4
8.3%
3 3
6.2%
8 3
6.2%
Other values (2) 3
6.2%

사상자수
Text

MISSING 

Distinct4
Distinct (%)100.0%
Missing9376
Missing (%)> 99.9%
Memory size2.3 MiB
2023-12-13T00:40:50.454093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters44
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)100.0%

Sample

1st row37.4874157]
2nd row35.1536857]
3rd row37.7244798]
4th row35.9372082]
ValueCountFrequency (%)
37.4874157 1
25.0%
35.1536857 1
25.0%
37.7244798 1
25.0%
35.9372082 1
25.0%
2023-12-13T00:40:50.802292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
7 8
18.2%
3 6
13.6%
5 5
11.4%
. 4
9.1%
4 4
9.1%
8 4
9.1%
] 4
9.1%
2 3
 
6.8%
1 2
 
4.5%
9 2
 
4.5%
Other values (2) 2
 
4.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 36
81.8%
Other Punctuation 4
 
9.1%
Close Punctuation 4
 
9.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
7 8
22.2%
3 6
16.7%
5 5
13.9%
4 4
11.1%
8 4
11.1%
2 3
 
8.3%
1 2
 
5.6%
9 2
 
5.6%
6 1
 
2.8%
0 1
 
2.8%
Other Punctuation
ValueCountFrequency (%)
. 4
100.0%
Close Punctuation
ValueCountFrequency (%)
] 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 44
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
7 8
18.2%
3 6
13.6%
5 5
11.4%
. 4
9.1%
4 4
9.1%
8 4
9.1%
] 4
9.1%
2 3
 
6.8%
1 2
 
4.5%
9 2
 
4.5%
Other values (2) 2
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 44
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
7 8
18.2%
3 6
13.6%
5 5
11.4%
. 4
9.1%
4 4
9.1%
8 4
9.1%
] 4
9.1%
2 3
 
6.8%
1 2
 
4.5%
9 2
 
4.5%
Other values (2) 2
 
4.5%

사망자수
Text

MISSING 

Distinct4
Distinct (%)100.0%
Missing9376
Missing (%)> 99.9%
Memory size2.3 MiB
2023-12-13T00:40:51.012816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length11.75
Min length11

Characters and Unicode

Total characters47
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)100.0%

Sample

1st row[127.0361639
2nd row[126.903806
3rd row[127.0517948
4th row[126.9727149
ValueCountFrequency (%)
127.0361639 1
25.0%
126.903806 1
25.0%
127.0517948 1
25.0%
126.9727149 1
25.0%
2023-12-13T00:40:51.378627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 7
14.9%
2 5
10.6%
7 5
10.6%
6 5
10.6%
9 5
10.6%
[ 4
8.5%
. 4
8.5%
0 4
8.5%
3 3
6.4%
8 2
 
4.3%
Other values (2) 3
6.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 39
83.0%
Open Punctuation 4
 
8.5%
Other Punctuation 4
 
8.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 7
17.9%
2 5
12.8%
7 5
12.8%
6 5
12.8%
9 5
12.8%
0 4
10.3%
3 3
7.7%
8 2
 
5.1%
4 2
 
5.1%
5 1
 
2.6%
Open Punctuation
ValueCountFrequency (%)
[ 4
100.0%
Other Punctuation
ValueCountFrequency (%)
. 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 47
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 7
14.9%
2 5
10.6%
7 5
10.6%
6 5
10.6%
9 5
10.6%
[ 4
8.5%
. 4
8.5%
0 4
8.5%
3 3
6.4%
8 2
 
4.3%
Other values (2) 3
6.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 47
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 7
14.9%
2 5
10.6%
7 5
10.6%
6 5
10.6%
9 5
10.6%
[ 4
8.5%
. 4
8.5%
0 4
8.5%
3 3
6.4%
8 2
 
4.3%
Other values (2) 3
6.4%

중상자수
Text

MISSING 

Distinct4
Distinct (%)100.0%
Missing9376
Missing (%)> 99.9%
Memory size2.3 MiB
2023-12-13T00:40:51.560757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters44
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)100.0%

Sample

1st row37.4878385]
2nd row35.1541087]
3rd row37.7249027]
4th row35.9376312]
ValueCountFrequency (%)
37.4878385 1
25.0%
35.1541087 1
25.0%
37.7249027 1
25.0%
35.9376312 1
25.0%
2023-12-13T00:40:51.950850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 7
15.9%
7 7
15.9%
. 4
9.1%
8 4
9.1%
5 4
9.1%
] 4
9.1%
4 3
6.8%
1 3
6.8%
2 3
6.8%
0 2
 
4.5%
Other values (2) 3
6.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 36
81.8%
Other Punctuation 4
 
9.1%
Close Punctuation 4
 
9.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 7
19.4%
7 7
19.4%
8 4
11.1%
5 4
11.1%
4 3
8.3%
1 3
8.3%
2 3
8.3%
0 2
 
5.6%
9 2
 
5.6%
6 1
 
2.8%
Other Punctuation
ValueCountFrequency (%)
. 4
100.0%
Close Punctuation
ValueCountFrequency (%)
] 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 44
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 7
15.9%
7 7
15.9%
. 4
9.1%
8 4
9.1%
5 4
9.1%
] 4
9.1%
4 3
6.8%
1 3
6.8%
2 3
6.8%
0 2
 
4.5%
Other values (2) 3
6.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 44
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 7
15.9%
7 7
15.9%
. 4
9.1%
8 4
9.1%
5 4
9.1%
] 4
9.1%
4 3
6.8%
1 3
6.8%
2 3
6.8%
0 2
 
4.5%
Other values (2) 3
6.8%

경상자수
Text

MISSING 

Distinct4
Distinct (%)100.0%
Missing9376
Missing (%)> 99.9%
Memory size2.3 MiB
2023-12-13T00:40:52.176448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters48
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)100.0%

Sample

1st row[127.0360396
2nd row[126.9036854
3rd row[127.0516702
4th row[126.9725931
ValueCountFrequency (%)
127.0360396 1
25.0%
126.9036854 1
25.0%
127.0516702 1
25.0%
126.9725931 1
25.0%
2023-12-13T00:40:52.612546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 6
12.5%
2 6
12.5%
6 6
12.5%
0 5
10.4%
[ 4
8.3%
7 4
8.3%
. 4
8.3%
3 4
8.3%
9 4
8.3%
5 3
6.2%
Other values (2) 2
 
4.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 40
83.3%
Open Punctuation 4
 
8.3%
Other Punctuation 4
 
8.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 6
15.0%
2 6
15.0%
6 6
15.0%
0 5
12.5%
7 4
10.0%
3 4
10.0%
9 4
10.0%
5 3
7.5%
8 1
 
2.5%
4 1
 
2.5%
Open Punctuation
ValueCountFrequency (%)
[ 4
100.0%
Other Punctuation
ValueCountFrequency (%)
. 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 48
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 6
12.5%
2 6
12.5%
6 6
12.5%
0 5
10.4%
[ 4
8.3%
7 4
8.3%
. 4
8.3%
3 4
8.3%
9 4
8.3%
5 3
6.2%
Other values (2) 2
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 48
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 6
12.5%
2 6
12.5%
6 6
12.5%
0 5
10.4%
[ 4
8.3%
7 4
8.3%
. 4
8.3%
3 4
8.3%
9 4
8.3%
5 3
6.2%
Other values (2) 2
 
4.2%

부상신고자수
Text

MISSING 

Distinct4
Distinct (%)100.0%
Missing9376
Missing (%)> 99.9%
Memory size2.3 MiB
2023-12-13T00:40:52.844853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length11
Mean length10.75
Min length10

Characters and Unicode

Total characters43
Distinct characters11
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)100.0%

Sample

1st row37.488251]
2nd row35.1545213]
3rd row37.7253151]
4th row35.9380437]
ValueCountFrequency (%)
37.488251 1
25.0%
35.1545213 1
25.0%
37.7253151 1
25.0%
35.9380437 1
25.0%
2023-12-13T00:40:53.224308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 8
18.6%
5 7
16.3%
1 5
11.6%
7 4
9.3%
. 4
9.3%
] 4
9.3%
4 3
 
7.0%
8 3
 
7.0%
2 3
 
7.0%
9 1
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 35
81.4%
Other Punctuation 4
 
9.3%
Close Punctuation 4
 
9.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 8
22.9%
5 7
20.0%
1 5
14.3%
7 4
11.4%
4 3
 
8.6%
8 3
 
8.6%
2 3
 
8.6%
9 1
 
2.9%
0 1
 
2.9%
Other Punctuation
ValueCountFrequency (%)
. 4
100.0%
Close Punctuation
ValueCountFrequency (%)
] 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 43
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 8
18.6%
5 7
16.3%
1 5
11.6%
7 4
9.3%
. 4
9.3%
] 4
9.3%
4 3
 
7.0%
8 3
 
7.0%
2 3
 
7.0%
9 1
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 43
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 8
18.6%
5 7
16.3%
1 5
11.6%
7 4
9.3%
. 4
9.3%
] 4
9.3%
4 3
 
7.0%
8 3
 
7.0%
2 3
 
7.0%
9 1
 
2.3%

위도
Text

MISSING 

Distinct4
Distinct (%)100.0%
Missing9376
Missing (%)> 99.9%
Memory size2.3 MiB
2023-12-13T00:40:53.441797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length11.75
Min length11

Characters and Unicode

Total characters47
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)100.0%

Sample

1st row[127.035836
2nd row[126.9034877
3rd row[127.0514659
4th row[126.9723935
ValueCountFrequency (%)
127.035836 1
25.0%
126.9034877 1
25.0%
127.0514659 1
25.0%
126.9723935 1
25.0%
2023-12-13T00:40:53.849080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 5
10.6%
2 5
10.6%
7 5
10.6%
3 5
10.6%
[ 4
8.5%
. 4
8.5%
5 4
8.5%
6 4
8.5%
9 4
8.5%
0 3
6.4%
Other values (2) 4
8.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 39
83.0%
Open Punctuation 4
 
8.5%
Other Punctuation 4
 
8.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 5
12.8%
2 5
12.8%
7 5
12.8%
3 5
12.8%
5 4
10.3%
6 4
10.3%
9 4
10.3%
0 3
7.7%
8 2
 
5.1%
4 2
 
5.1%
Open Punctuation
ValueCountFrequency (%)
[ 4
100.0%
Other Punctuation
ValueCountFrequency (%)
. 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 47
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 5
10.6%
2 5
10.6%
7 5
10.6%
3 5
10.6%
[ 4
8.5%
. 4
8.5%
5 4
8.5%
6 4
8.5%
9 4
8.5%
0 3
6.4%
Other values (2) 4
8.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 47
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 5
10.6%
2 5
10.6%
7 5
10.6%
3 5
10.6%
[ 4
8.5%
. 4
8.5%
5 4
8.5%
6 4
8.5%
9 4
8.5%
0 3
6.4%
Other values (2) 4
8.5%

경도
Text

MISSING 

Distinct4
Distinct (%)100.0%
Missing9376
Missing (%)> 99.9%
Memory size2.3 MiB
2023-12-13T00:40:54.068193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length11
Mean length10.75
Min length10

Characters and Unicode

Total characters43
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)100.0%

Sample

1st row37.4886428]
2nd row35.1549134]
3rd row37.725707]
4th row35.9384357]
ValueCountFrequency (%)
37.4886428 1
25.0%
35.1549134 1
25.0%
37.725707 1
25.0%
35.9384357 1
25.0%
2023-12-13T00:40:54.433208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 7
16.3%
7 6
14.0%
4 5
11.6%
5 5
11.6%
. 4
9.3%
8 4
9.3%
] 4
9.3%
2 2
 
4.7%
1 2
 
4.7%
9 2
 
4.7%
Other values (2) 2
 
4.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 35
81.4%
Other Punctuation 4
 
9.3%
Close Punctuation 4
 
9.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 7
20.0%
7 6
17.1%
4 5
14.3%
5 5
14.3%
8 4
11.4%
2 2
 
5.7%
1 2
 
5.7%
9 2
 
5.7%
6 1
 
2.9%
0 1
 
2.9%
Other Punctuation
ValueCountFrequency (%)
. 4
100.0%
Close Punctuation
ValueCountFrequency (%)
] 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 43
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 7
16.3%
7 6
14.0%
4 5
11.6%
5 5
11.6%
. 4
9.3%
8 4
9.3%
] 4
9.3%
2 2
 
4.7%
1 2
 
4.7%
9 2
 
4.7%
Other values (2) 2
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 43
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 7
16.3%
7 6
14.0%
4 5
11.6%
5 5
11.6%
. 4
9.3%
8 4
9.3%
] 4
9.3%
2 2
 
4.7%
1 2
 
4.7%
9 2
 
4.7%
Other values (2) 2
 
4.7%

사고다발지역폴리곤정보
Text

MISSING 

Distinct3
Distinct (%)100.0%
Missing9377
Missing (%)> 99.9%
Memory size2.3 MiB
2023-12-13T00:40:54.618899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length5
Mean length5.6666667
Min length5

Characters and Unicode

Total characters17
Distinct characters8
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)100.0%

Sample

1st row[127.03
2nd row[126.
3rd row[127.
ValueCountFrequency (%)
127.03 1
33.3%
126 1
33.3%
127 1
33.3%
2023-12-13T00:40:55.017201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
[ 3
17.6%
1 3
17.6%
2 3
17.6%
. 3
17.6%
7 2
11.8%
0 1
 
5.9%
3 1
 
5.9%
6 1
 
5.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 11
64.7%
Open Punctuation 3
 
17.6%
Other Punctuation 3
 
17.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 3
27.3%
2 3
27.3%
7 2
18.2%
0 1
 
9.1%
3 1
 
9.1%
6 1
 
9.1%
Open Punctuation
ValueCountFrequency (%)
[ 3
100.0%
Other Punctuation
ValueCountFrequency (%)
. 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 17
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
[ 3
17.6%
1 3
17.6%
2 3
17.6%
. 3
17.6%
7 2
11.8%
0 1
 
5.9%
3 1
 
5.9%
6 1
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 17
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
[ 3
17.6%
1 3
17.6%
2 3
17.6%
. 3
17.6%
7 2
11.8%
0 1
 
5.9%
3 1
 
5.9%
6 1
 
5.9%

데이터기준일자
Date

CONSTANT  MISSING 

Distinct1
Distinct (%)25.0%
Missing9376
Missing (%)> 99.9%
Memory size2.3 MiB
Minimum2018-01-11 00:00:00
Maximum2018-01-11 00:00:00
2023-12-13T00:40:55.176317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:40:55.300559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

제공기관코드
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
<NA>
9376 
AAAAAAA
 
4

Length

Max length7
Median length4
Mean length4.0012793
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAAAAAAA
2nd rowAAAAAAA
3rd rowAAAAAAA
4th rowAAAAAAA
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 9376
> 99.9%
AAAAAAA 4
 
< 0.1%

Length

2023-12-13T00:40:55.440316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:40:55.557565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9376
> 99.9%
aaaaaaa 4
 
< 0.1%

제공기관명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
<NA>
9376 
공공데이터활용지원센터
 
4

Length

Max length11
Median length4
Mean length4.0029851
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row공공데이터활용지원센터
2nd row공공데이터활용지원센터
3rd row공공데이터활용지원센터
4th row공공데이터활용지원센터
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 9376
> 99.9%
공공데이터활용지원센터 4
 
< 0.1%

Length

2023-12-13T00:40:55.745831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:40:55.893538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9376
> 99.9%
공공데이터활용지원센터 4
 
< 0.1%

Correlations

2023-12-13T00:40:55.976767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
사고지역관리번호사고연도사고유형구분위치코드사고다발지역시도시군구사고지역위치명사고건수사상자수사망자수중상자수경상자수부상신고자수위도경도사고다발지역폴리곤정보
사고지역관리번호1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
사고연도1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
사고유형구분1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
위치코드1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
사고다발지역시도시군구1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
사고지역위치명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
사고건수1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
사상자수1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
사망자수1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
중상자수1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
경상자수1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
부상신고자수1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
위도1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
경도1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
사고다발지역폴리곤정보1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
2023-12-13T00:40:56.142812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
제공기관명제공기관코드
제공기관명1.0001.000
제공기관코드1.0001.000
2023-12-13T00:40:56.257295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
제공기관코드제공기관명
제공기관코드1.0001.000
제공기관명1.0001.000

Missing values

2023-12-13T00:40:45.695685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T00:40:45.952897image/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.
2023-12-13T00:40:46.216593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

사고지역관리번호사고연도사고유형구분위치코드사고다발지역시도시군구사고지역위치명사고건수사상자수사망자수중상자수경상자수부상신고자수위도경도사고다발지역폴리곤정보데이터기준일자제공기관코드제공기관명
1699212015보행노인사고11110400서울특별시 종로구서울특별시 중구 오장동(오장동사거리 부근)40031037.564700127.003907{type:Polygon,coordinates:[[[127.0063079,37.5666114],[127.0059027,37.5668869],[127.0054484,37.5671085],[127.0049561,37.5672708],[127.0044379,37.5673698],[127.0039067,37.5674031],[127.0033755,37.5673698],[127.0028574,37.5672708],[127.0023651,37.5671085],[127.0019108,37.5668869],[127.0015056,37.5666114],[127.0011596,37.5662889],[127.0008812,37.5659272],[127.0006773,37.5655353],[127.0005529,37.5651229],[127.0005111,37.5647001],[127.0005529,37.5642772],[127.0006773,37.5638648],[127.0008813,37.5634729],[127.0011597,37.5631113],[127.0015057,37.5627888],[127.0019109,37.5625133],[127.0023652,37.5622917],[127.0028575,37.5621294],[127.0033756,37.5620304],[127.0039067,37.5619971],[127.0044379,37.5620304],[127.004956,37.5621294],[127.0054483,37.5622917],[127.0059026,37.5625133],[127.0063077,37.5627888],[127.0066538,37.5631113],[127.0069322,37.5634729],[127.0071361,37.5638648],[127.0072605,37.5642772],[127.0073024,37.5647001],[127.0072606,37.5651229],[127.0071362,37.5655353],[127.0069323,37.565927,2018-01-11,AAAAAAA,공공데이터활용지원센터\n220162,2015,보행노인사고,11113400,서울특별시 종로구,서울특별시 강남구 도곡동(부영빌딩 부근),5,5,0,5,0,0,37.48741572,127.0328135,{type:Polygoncoordinates:[[[127.035212237.489327][127.034807437.4896025][127.034353637.4898241][127.033861837.4899864][127.033344237.4900855][127.032813537.4901187][127.032282837.4900855][127.031765337.4899864][127.031273537.4898241][127.030819637.4896025][127.030414937.489327][127.030069237.4890045][127.029791137.4886428][127.029587437.488251][127.029463137.4878385][127.029421437.4874157][127.029463237.4869928][127.029587437.4865804][127.029791237.4861885][127.030069337.4858269][127.03041537.4855044][127.030819737.4852289][127.031273637.4850073][127.031765337.484845][127.032282937.484746][127.032813537.4847127][127.033344137.484746][127.033861737.484845][127.034353537.4850073][127.034807337.4852289][127.03521237.4855044][127.035557737.4858269][127.035835937.4861885][127.036039637.4865804][127.036163837.4869928][127.036205637.4874157][127.036163937.4878385][127.036039637.488251][127.03583637.4886428][127.032018-01-11AAAAAAA공공데이터활용지원센터
2201642015보행노인사고27220500서울특별시 종로구대구광역시 수성구 수성동4가(신천시장네거리 부근)56132035.862361128.617914{type:Polygon,coordinates:[[[128.6202629,35.8642732],[128.6198665,35.8645487],[128.6194221,35.8647704],[128.6189406,35.8649328],[128.6184338,35.8650318],[128.6179141,35.8650651],[128.6173945,35.8650318],[128.6168877,35.8649328],[128.6164062,35.8647704],[128.6159618,35.8645487],[128.6155654,35.8642732],[128.6152269,35.8639506],[128.6149546,35.8635888],[128.6147552,35.8631968],[128.6146335,35.8627843],[128.6145926,35.8623613],[128.6146335,35.8619384],[128.6147552,35.8615258],[128.6149547,35.8611339],[128.615227,35.8607721],[128.6155655,35.8604495],[128.6159619,35.860174],[128.6164063,35.8599523],[128.6168878,35.8597899],[128.6173946,35.8596909],[128.6179141,35.8596576],[128.6184337,35.8596909],[128.6189405,35.8597899],[128.619422,35.8599523],[128.6198664,35.860174],[128.6202628,35.8604495],[128.6206013,35.8607721],[128.6208736,35.8611339],[128.6210731,35.8615258],[128.6211948,35.8619384],[128.6212357,35.8623613],[128.6211948,35.8627843],[128.6210731,35.8631968],[128.6208737,35.8635888],,2018-01-11,AAAAAAA,공공데이터활용지원센터\n220166,2015,보행노인사고,29240300,서울특별시 종로구,광주광역시 서구 양동(양유교종점 부근),5,5,2,2,1,0,35.15368578,126.9005541,{type:Polygoncoordinates:[[[126.902882335.1555978][126.902489435.1558734][126.902048935.1560951][126.901571535.1562575][126.901069235.1563566][126.900554135.1563899][126.90003935.1563566][126.899536635.1562575][126.899059335.1560951][126.898618835.1558734][126.898225935.1555978][126.897890435.1552752][126.897620435.1549134][126.897422735.1545213][126.897302135.1541087][126.897261635.1536857][126.897302235.1532627][126.897422835.1528501][126.897620535.1524581][126.897890535.1520963][126.89822635.1517737][126.898618935.1514981][126.899059435.1512764][126.899536735.151114][126.90003935.151015][126.900554135.1509817][126.901069135.151015][126.901571535.151114][126.902048835.1512764][126.902489335.1514981][126.902882235.1517737][126.903217735.1520963][126.903487635.1524581][126.903685435.1528501][126.90380635.1532627][126.903846535.1536857][126.90380635.1541087][126.903685435.1545213][126.903487735.1549134][126.2018-01-11AAAAAAA공공데이터활용지원센터
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2201722015보행노인사고28230200서울특별시 종로구인천광역시 동구 송림동(박문로터리 부근)78062037.470134126.648815{type:Polygon,coordinates:[[[126.651213,37.4720449],[126.6508083,37.4723203],[126.6503545,37.472542],[126.6498629,37.4727043],[126.6493454,37.4728033],[126.6488149,37.4728366],[126.6482843,37.4728033],[126.6477668,37.4727043],[126.6472752,37.472542],[126.6468214,37.4723203],[126.6464168,37.4720449],[126.6460712,37.4717223],[126.6457931,37.4713607],[126.6455895,37.4709688],[126.6454653,37.4705564],[126.6454235,37.4701335],[126.6454653,37.4697107],[126.6455895,37.4692982],[126.6457932,37.4689064],[126.6460713,37.4685447],[126.6464169,37.4682222],[126.6468215,37.4679467],[126.6472753,37.4677251],[126.6477669,37.4675628],[126.6482844,37.4674638],[126.6488149,37.4674305],[126.6493454,37.4674638],[126.6498628,37.4675628],[126.6503544,37.4677251],[126.6508082,37.4679467],[126.6512128,37.4682222],[126.6515585,37.4685447],[126.6518365,37.4689064],[126.6520402,37.4692982],[126.6521644,37.4697107],[126.6522062,37.4701335],[126.6521645,37.4705564],[126.6520403,37.4709688],[126.6518366,37.4713607],2018-01-11,AAAAAAA,공공데이터활용지원센터\n220174,2015,보행노인사고,45170500,서울특별시 종로구,전라북도 익산시 마동((우림그린맨션) 부근),3,3,2,1,0,0,35.93720829,126.9694312,{type:Polygoncoordinates:[[[126.971782135.9391201][126.971385435.9393956][126.970940635.9396173][126.970458635.9397797][126.969951335.9398787][126.969431235.939912][126.968911135.9398787][126.968403835.9397797][126.967921835.9396173][126.967476935.9393956][126.967080235.9391201][126.966741435.9387975][126.966468935.9384357][126.966269235.9380437][126.966147435.9376312][126.966106535.9372082][126.966147535.9367853][126.966269335.9363728][126.966468935.9359808][126.966741535.935619][126.967080435.9352964][126.967477135.9350209][126.967921935.9347992][126.968403835.9346369][126.968911135.9345378][126.969431235.9345046][126.969951335.9345378][126.970458535.9346369][126.970940535.9347992][126.971385335.9350209][126.97178235.9352964][126.972120835.935619][126.972393435.9359808][126.972593135.9363728][126.972714935.9367853][126.972755835.9372082][126.972714935.9376312][126.972593135.9380437][126.972393535.9384357]<NA>2018-01-11AAAAAAA공공데이터활용지원센터
2201762015보행노인사고28231000서울특별시 종로구인천광역시 부평구 부평동(굴다리오거리 부근)910073037.490690126.729381{type:Polygon,coordinates:[[[126.7317798,37.4926011],[126.731375,37.4928766],[126.7309211,37.4930982],[126.7304293,37.4932606],[126.7299117,37.4933596],[126.729381,37.4933929],[126.7288504,37.4933596],[126.7283327,37.4932606],[126.7278409,37.4930982],[126.7273871,37.4928766],[126.7269823,37.4926011],[126.7266366,37.4922786],[126.7263585,37.4919169],[126.7261548,37.4915251],[126.7260305,37.4911126],[126.7259888,37.4906898],[126.7260306,37.4902669],[126.7261548,37.4898545],[126.7263586,37.4894627],[126.7266367,37.489101],[126.7269824,37.4887785],[126.7273872,37.488503],[126.727841,37.4882814],[126.7283328,37.4881191],[126.7288504,37.4880201],[126.729381,37.4879868],[126.7299117,37.4880201],[126.7304293,37.4881191],[126.730921,37.4882814],[126.7313749,37.488503],[126.7317797,37.4887785],[126.7321254,37.489101],[126.7324035,37.4894627],[126.7326072,37.4898545],[126.7327315,37.4902669],[126.7327733,37.4906898],[126.7327316,37.4911126],[126.7326073,37.4915251],[126.7324036,37.4919169],[126.,2018-01-11,AAAAAAA,공공데이터활용지원센터\n2013098,2012,보행노인,11470001,서울특별시 양천구1,서울특별시 양천구 신월동(대흥빌딩 부근),3,3,0,1,2,0,37.52065366,126.83486486,{type:Polygoncoordinates:[[[126.836464737.5219279][126.836121837.522152][126.835730737.5223185][126.835306237.522421][126.834864937.5224557][126.834423537.522421][126.833999137.5223185][126.833607937.522152][126.833265137.5219279][126.832983737.5216548][126.832774637.5213432][126.832645937.5210052][126.832602437.5206536][126.832645937.5203021][126.832774737.519964][126.832983837.5196525][126.833265137.5193794][126.83360837.5191553][126.833999137.5189888][126.834423537.5188863][126.834864937.5188517][126.835306237.5188863][126.835730637.5189888][126.836121837.5191553][126.836464637.5193794][126.83674637.5196525][126.83695537.519964][126.837083837.5203021][126.837127337.5206536][126.837083837.5210052][126.836955137.5213432][126.83674637.5216548][126.836464737.5219279]]]}"2023-01-25B552061도로교통공단<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
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11500001서울특별시 강서구1서울특별시 강서구 화곡동(홍익병원앞_등촌로_진입_1 부근)33012037.530665126.863875{type:Polygon,coordinates:[[[126.8654752,37.5319393],[126.8651323,37.5321634],[126.8647411,37.5323299],[126.8643166,37.5324324],[126.8638752,37.5324671],[126.8634338,37.5324324],[126.8630093,37.5323299],[126.8626181,37.5321634],[126.8622752,37.5319393],[126.8619938,37.5316662],[126.8617847,37.5313546],[126.861656,37.5310166],[126.8616125,37.530665],[126.861656,37.5303135],[126.8617847,37.5299754],[126.8619938,37.5296639],[126.8622752,37.5293908],[126.8626181,37.5291667],[126.8630093,37.5290002],[126.8634338,37.5288977],[126.8638752,37.5288631],[126.8643166,37.5288977],[126.8647411,37.5290002],[126.8651323,37.5291667],[126.8654752,37.5293908],[126.8657566,37.5296639],[126.8659657,37.5299754],[126.8660944,37.5303135],[126.8661379,37.530665],[126.8660945,37.5310166],[126.8659657,37.5313546],[126.8657566,37.5316662],[126.8654752,37.5319393]]]}2023-01-25B552061도로교통공단NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
11500002서울특별시 강서구2서울특별시 강서구 화곡동(강서구청입구 부근)33021037.556113126.852410{type:Polygon,coordinates:[[[126.8540101,37.5573867],[126.8536671,37.5576108],[126.8532757,37.5577774],[126.8528511,37.5578799],[126.8524095,37.5579145],[126.8519679,37.5578799],[126.8515433,37.5577774],[126.851152,37.5576108],[126.850809,37.5573867],[126.8505275,37.5571137],[126.8503183,37.5568021],[126.8501895,37.5564641],[126.850146,37.5561125],[126.8501895,37.555761],[126.8503184,37.5554229],[126.8505275,37.5551114],[126.850809,37.5548383],[126.851152,37.5546142],[126.8515433,37.5544477],[126.851968,37.5543452],[126.8524095,37.5543105],[126.8528511,37.5543452],[126.8532757,37.5544477],[126.853667,37.5546142],[126.85401,37.5548383],[126.8542915,37.5551114],[126.8545007,37.5554229],[126.8546295,37.555761],[126.854673,37.5561125],[126.8546295,37.5564641],[126.8545007,37.5568021],[126.8542916,37.5571137],[126.8540101,37.5573867]]]}2023-01-25B552061도로교통공단NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
11500003서울특별시 강서구3서울특별시 강서구 화곡동(화곡사거리 부근)44022037.530715126.847285{type:Polygon,coordinates:[[[126.848885,37.5319893],[126.8485421,37.5322134],[126.8481509,37.5323799],[126.8477264,37.5324824],[126.8472849,37.5325171],[126.8468435,37.5324824],[126.846419,37.5323799],[126.8460278,37.5322134],[126.8456849,37.5319893],[126.8454035,37.5317162],[126.8451944,37.5314046],[126.8450657,37.5310666],[126.8450222,37.530715],[126.8450657,37.5303635],[126.8451945,37.5300255],[126.8454036,37.5297139],[126.845685,37.5294408],[126.8460279,37.5292168],[126.8464191,37.5290502],[126.8468435,37.5289477],[126.8472849,37.5289131],[126.8477264,37.5289477],[126.8481508,37.5290502],[126.848542,37.5292168],[126.8488849,37.5294408],[126.8491663,37.5297139],[126.8493754,37.5300255],[126.8495042,37.5303635],[126.8495477,37.530715],[126.8495042,37.5310666],[126.8493754,37.5314046],[126.8491664,37.5317162],[126.848885,37.5319893]]]}2023-01-25B552061도로교통공단NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
11500004서울특별시 강서구4서울특별시 강서구 염창동(태진빌딩 부근)44022037.546968126.876047{type:Polygon,coordinates:[[[126.877647,37.5482423],[126.8773041,37.5484664],[126.8769128,37.5486329],[126.8764882,37.5487354],[126.8760467,37.5487701],[126.8756051,37.5487354],[126.8751806,37.5486329],[126.8747893,37.5484664],[126.8744463,37.5482423],[126.8741649,37.5479692],[126.8739557,37.5476576],[126.8738269,37.5473196],[126.8737835,37.546968],[126.873827,37.5466165],[126.8739558,37.5462784],[126.8741649,37.5459669],[126.8744464,37.5456938],[126.8747893,37.5454697],[126.8751806,37.5453032],[126.8756052,37.5452007],[126.8760467,37.5451661],[126.8764882,37.5452007],[126.8769128,37.5453032],[126.877304,37.5454697],[126.877647,37.5456938],[126.8779284,37.5459669],[126.8781376,37.5462784],[126.8782664,37.5466165],[126.8783099,37.546968],[126.8782664,37.5473196],[126.8781376,37.5476576],[126.8779285,37.5479692],[126.877647,37.5482423]]]}2023-01-25B552061도로교통공단NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
사고지역관리번호사고연도사고유형구분위치코드사고다발지역시도시군구사고지역위치명사고건수사상자수사망자수중상자수경상자수부상신고자수위도경도사고다발지역폴리곤정보데이터기준일자제공기관코드제공기관명
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47111001경상북도 포항시 남구1경상북도 포항시 남구 대도동(상대초교 부근)33021036.022011129.361155{type:Polygon,coordinates:[[[129.36295138,36.02201139],[129.36291686,36.02172791],[129.36281462,36.02145531],[129.36264859,36.02120409],[129.36242516,36.02098389],[129.3621529,36.02080318],[129.36184229,36.02066889],[129.36150525,36.0205862],[129.36115475,36.02055828],[129.36080424,36.0205862],[129.36046721,36.02066889],[129.36015659,36.02080318],[129.35988434,36.02098389],[129.35966091,36.02120409],[129.35949488,36.02145531],[129.35939264,36.02172791],[129.35935812,36.02201139],[129.35939264,36.02229488],[129.35949488,36.02256747],[129.35966091,36.02281869],[129.35988434,36.02303888],[129.36015659,36.02321959],[129.36046721,36.02335387],[129.36080424,36.02343656],[129.36115475,36.02346448],[129.36150525,36.02343656],[129.36184229,36.02335387],[129.3621529,36.02321959],[129.36242516,36.02303888],[129.36264859,36.02281869],[129.36281462,36.02256747],[129.36291686,36.02229488],[129.36295138,36.02201139]]]}2023-01-25B552061도로교통공단NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
47190001경상북도 구미시1경상북도 구미시 구평동(부영홈마트 부근)33021036.091768128.435538{type:Polygon,coordinates:[[[128.43733418,36.09176752],[128.43729966,36.09148428],[128.43719742,36.09121193],[128.43703139,36.09096093],[128.43680796,36.09074093],[128.4365357,36.09056037],[128.43622509,36.09042621],[128.43588805,36.09034359],[128.43553755,36.09031569],[128.43518704,36.09034359],[128.43485001,36.09042621],[128.43453939,36.09056037],[128.43426714,36.09074093],[128.4340437,36.09096093],[128.43387768,36.09121193],[128.43377544,36.09148428],[128.43374092,36.09176752],[128.43377544,36.09205075],[128.43387768,36.0923231],[128.4340437,36.0925741],[128.43426714,36.0927941],[128.43453939,36.09297465],[128.43485001,36.09310881],[128.43518704,36.09319142],[128.43553755,36.09321932],[128.43588805,36.09319142],[128.43622509,36.09310881],[128.4365357,36.09297465],[128.43680796,36.0927941],[128.43703139,36.0925741],[128.43719742,36.0923231],[128.43729966,36.09205075],[128.43733418,36.09176752]]]}2023-01-25B552061도로교통공단NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
47190002경상북도 구미시2경상북도 구미시 인의동(우리홀푸드마트 부근)33012036.106250128.424260{type:Polygon,coordinates:[[[128.42605638,36.10625024],[128.42602186,36.10596706],[128.42591962,36.10569476],[128.42575359,36.10544381],[128.42553016,36.10522384],[128.4252579,36.10504332],[128.42494729,36.10490918],[128.42461025,36.10482658],[128.42425975,36.10479869],[128.42390924,36.10482658],[128.42357221,36.10490918],[128.42326159,36.10504332],[128.42298934,36.10522384],[128.4227659,36.10544381],[128.42259988,36.10569476],[128.42249764,36.10596706],[128.42246312,36.10625024],[128.42249764,36.10653343],[128.42259988,36.10680572],[128.4227659,36.10705667],[128.42298934,36.10727663],[128.42326159,36.10745715],[128.42357221,36.10759128],[128.42390924,36.10767388],[128.42425975,36.10770177],[128.42461025,36.10767388],[128.42494729,36.10759128],[128.4252579,36.10745715],[128.42553016,36.10727663],[128.42575359,36.10705667],[128.42591962,36.10680572],[128.42602186,36.10653343],[128.42605638,36.10625024]]]}2023-01-25B552061도로교통공단NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
50110001제주특별자치도 제주시1제주특별자치도 제주시 이도이동(이도초교 부근)44013033.488629126.533997{type:Polygon,coordinates:[[[126.53579316,33.48862939],[126.53575864,33.48833707],[126.5356564,33.48805598],[126.53549038,33.48779693],[126.53526694,33.48756987],[126.53499469,33.48738352],[126.53468407,33.48724505],[126.53434704,33.48715979],[126.53399653,33.48713099],[126.53364603,33.48715979],[126.53330899,33.48724505],[126.53299838,33.48738352],[126.53272612,33.48756987],[126.53250269,33.48779693],[126.53233666,33.48805598],[126.53223442,33.48833707],[126.5321999,33.48862939],[126.53223442,33.48892171],[126.53233666,33.48920279],[126.53250269,33.48946184],[126.53272612,33.4896889],[126.53299838,33.48987524],[126.53330899,33.4900137],[126.53364603,33.49009897],[126.53399653,33.49012776],[126.53434704,33.49009897],[126.53468407,33.4900137],[126.53499469,33.48987524],[126.53526694,33.4896889],[126.53549038,33.48946184],[126.5356564,33.48920279],[126.53575864,33.48892171],[126.53579316,33.48862939]]]}2023-01-25B552061도로교통공단NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
50110002제주특별자치도 제주시2제주특별자치도 제주시 연동(한라초교 부근)33012033.478200126.487356{type:Polygon,coordinates:[[[126.48915295,33.47819995],[126.48911842,33.47790759],[126.48901619,33.47762647],[126.48885016,33.47736739],[126.48862673,33.4771403],[126.48835447,33.47695393],[126.48804386,33.47681545],[126.48770682,33.47673017],[126.48735632,33.47670137],[126.48700581,33.47673017],[126.48666877,33.47681545],[126.48635816,33.47695393],[126.48608591,33.4771403],[126.48586247,33.47736739],[126.48569645,33.47762647],[126.48559421,33.47790759],[126.48555968,33.47819995],[126.48559421,33.4784923],[126.48569645,33.47877342],[126.48586247,33.4790325],[126.48608591,33.47925959],[126.48635816,33.47944595],[126.48666877,33.47958443],[126.48700581,33.4796697],[126.48735632,33.4796985],[126.48770682,33.4796697],[126.48804386,33.47958443],[126.48835447,33.47944595],[126.48862673,33.47925959],[126.48885016,33.4790325],[126.48901619,33.47877342],[126.48911842,33.4784923],[126.48915295,33.47819995]]]}2023-01-25B552061도로교통공단NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
50110003제주특별자치도 제주시3제주특별자치도 제주시 화북일동(삼화초교 인근)33012033.514935126.576084{type:Polygon,coordinates:[[[126.57788071,33.51493462],[126.57784619,33.51464239],[126.57774395,33.51436139],[126.57757792,33.51410241],[126.57735449,33.51387542],[126.57708223,33.51368913],[126.57677162,33.5135507],[126.57643459,33.51346546],[126.57608408,33.51343668],[126.57573358,33.51346546],[126.57539654,33.5135507],[126.57508593,33.51368913],[126.57481367,33.51387542],[126.57459024,33.51410241],[126.57442421,33.51436139],[126.57432197,33.51464239],[126.57428745,33.51493462],[126.57432197,33.51522685],[126.57442421,33.51550785],[126.57459024,33.51576682],[126.57481367,33.51599381],[126.57508593,33.51618009],[126.57539654,33.51631851],[126.57573358,33.51640375],[126.57608408,33.51643253],[126.57643459,33.51640375],[126.57677162,33.51631851],[126.57708223,33.51618009],[126.57735449,33.51599381],[126.57757792,33.51576682],[126.57774395,33.51550785],[126.57784619,33.51522685],[126.57788071,33.51493462]]]}2023-01-25B552061도로교통공단NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
50110004제주특별자치도 제주시4제주특별자치도 제주시 노형동(월랑초교 부근)33003033.491519126.478216{type:Polygon,coordinates:[[[126.48001292,33.49151906],[126.4799784,33.49122675],[126.47987616,33.49094567],[126.47971014,33.49068663],[126.4794867,33.49045957],[126.47921445,33.49027323],[126.47890383,33.49013477],[126.4785668,33.49004951],[126.47821629,33.49002071],[126.47786579,33.49004951],[126.47752875,33.49013477],[126.47721814,33.49027323],[126.47694588,33.49045957],[126.47672245,33.49068663],[126.47655642,33.49094567],[126.47645419,33.49122675],[126.47641966,33.49151906],[126.47645419,33.49181137],[126.47655642,33.49209244],[126.47672245,33.49235148],[126.47694588,33.49257853],[126.47721814,33.49276487],[126.47752875,33.49290333],[126.47786579,33.49298859],[126.47821629,33.49301738],[126.4785668,33.49298859],[126.47890383,33.49290333],[126.47921445,33.49276487],[126.4794867,33.49257853],[126.47971014,33.49235148],[126.47987616,33.49209244],[126.4799784,33.49181137],[126.48001292,33.49151906]]]}2023-01-25B552061도로교통공단NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
20190362018보행노인11110001서울특별시 종로구1서울특별시 종로구 낙원동(낙원지하상가 부근)99071137.572581126.987729{type:Polygon,coordinates:[[[126.98952522,37.57258091],[126.9894907,37.5723031],[126.98938846,37.57203597],[126.98922244,37.57178978],[126.988999,37.571574],[126.98872675,37.57139691],[126.98841613,37.57126531],[126.9880791,37.57118428],[126.98772859,37.57115692],[126.98737809,37.57118428],[126.98704105,37.57126531],[126.98673044,37.57139691],[126.98645818,37.571574],[126.98623475,37.57178978],[126.98606872,37.57203597],[126.98596648,37.5723031],[126.98593196,37.57258091],[126.98596648,37.57285871],[126.98606872,37.57312584],[126.98623475,37.57337202],[126.98645818,37.5735878],[126.98673044,37.57376489],[126.98704105,37.57389648],[126.98737809,37.57397751],[126.98772859,37.57400487],[126.9880791,37.57397751],[126.98841613,37.57389648],[126.98872675,37.57376489],[126.988999,37.5735878],[126.98922244,37.57337202],[126.98938846,37.57312584],[126.9894907,37.57285871],[126.98952522,37.57258091]]]}2023-01-25B552061도로교통공단NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
11110002서울특별시 종로구2서울특별시 종로구 종로3가(IBK기업은행 종로지점 부근)99054037.570236126.990385{type:Polygon,coordinates:[[[126.9921819,37.57023557],[126.99214737,37.56995776],[126.99204514,37.56969062],[126.99187911,37.56944443],[126.99165567,37.56922863],[126.99138342,37.56905153],[126.99107281,37.56891994],[126.99073577,37.5688389],[126.99038527,37.56881154],[126.99003476,37.5688389],[126.98969772,37.56891994],[126.98938711,37.56905153],[126.98911486,37.56922863],[126.98889142,37.56944443],[126.98872539,37.56969062],[126.98862316,37.56995776],[126.98858863,37.57023557],[126.98862316,37.57051339],[126.98872539,37.57078052],[126.98889142,37.57102671],[126.98911486,37.5712425],[126.98938711,37.57141959],[126.98969772,37.57155119],[126.99003476,37.57163222],[126.99038527,37.57165958],[126.99073577,37.57163222],[126.99107281,37.57155119],[126.99138342,37.57141959],[126.99165567,37.5712425],[126.99187911,37.57102671],[126.99204514,37.57078052],[126.99214737,37.57051339],[126.9921819,37.57023557]]]}2023-01-25B552061도로교통공단NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>

Duplicate rows

Most frequently occurring

사고지역관리번호사고연도사고유형구분위치코드사고다발지역시도시군구사고지역위치명사고건수사상자수사망자수중상자수경상자수부상신고자수위도경도사고다발지역폴리곤정보데이터기준일자제공기관코드제공기관명# duplicates
0<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>9375