Overview

Dataset statistics

Number of variables11
Number of observations3559
Missing cells566
Missing cells (%)1.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory316.4 KiB
Average record size in memory91.0 B

Variable types

Numeric3
Categorical6
Text2

Dataset

Description창원시 횡단보도 현황 자료(지장물부호, 횡단보도 설치 행정동, 관리기관, 설치일자, 존재유무, 보도턱낮춤 유무, 장애시설 유무, 횡단보도 폭, 횡단보도 길이, 횡단보도방식)
Author경상남도 창원시
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15106905

Alerts

지형지물부호 has constant value ""Constant
FID is highly overall correlated with 관리기관High correlation
관리기관 is highly overall correlated with FIDHigh correlation
보도턱낮춤유무 is highly overall correlated with 장애시설유무High correlation
장애시설유무 is highly overall correlated with 보도턱낮춤유무High correlation
보도턱낮춤유무 is highly imbalanced (65.1%)Imbalance
행정읍면동 has 352 (9.9%) missing valuesMissing
설치일자 has 214 (6.0%) missing valuesMissing
FID has unique valuesUnique

Reproduction

Analysis started2023-12-11 00:53:25.075430
Analysis finished2023-12-11 00:53:27.313306
Duration2.24 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

FID
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct3559
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3105.8432
Minimum1
Maximum6626
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.4 KiB
2023-12-11T09:53:27.383010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile178.9
Q1890.5
median3220
Q35208.5
95-th percentile6176.1
Maximum6626
Range6625
Interquartile range (IQR)4318

Descriptive statistics

Standard deviation2107.336
Coefficient of variation (CV)0.67850687
Kurtosis-1.4311617
Mean3105.8432
Median Absolute Deviation (MAD)2189
Skewness0.019789007
Sum11053696
Variance4440864.9
MonotonicityStrictly decreasing
2023-12-11T09:53:27.548125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6626 1
 
< 0.1%
1398 1
 
< 0.1%
1416 1
 
< 0.1%
1412 1
 
< 0.1%
1411 1
 
< 0.1%
1410 1
 
< 0.1%
1409 1
 
< 0.1%
1406 1
 
< 0.1%
1403 1
 
< 0.1%
1402 1
 
< 0.1%
Other values (3549) 3549
99.7%
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 (%)
6626 1
< 0.1%
6625 1
< 0.1%
6624 1
< 0.1%
6623 1
< 0.1%
6622 1
< 0.1%
6621 1
< 0.1%
6620 1
< 0.1%
6619 1
< 0.1%
6618 1
< 0.1%
6617 1
< 0.1%

지형지물부호
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size27.9 KiB
횡단보도
3559 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
횡단보도 3559
100.0%

Length

2023-12-11T09:53:27.756791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:53:27.846190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
횡단보도 3559
100.0%

행정읍면동
Text

MISSING 

Distinct59
Distinct (%)1.8%
Missing352
Missing (%)9.9%
Memory size27.9 KiB
2023-12-11T09:53:28.069216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length7.7224821
Min length3

Characters and Unicode

Total characters24766
Distinct characters71
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
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 row문화동
2nd row문화동
3rd row문화동
4th row문화동
5th row문화동
ValueCountFrequency (%)
진해구 1081
16.9%
마산회원구 646
 
10.1%
의창구 632
 
9.9%
마산합포구 557
 
8.7%
북면 281
 
4.4%
성산구 270
 
4.2%
웅동2동 255
 
4.0%
내서읍 240
 
3.8%
웅천동 219
 
3.4%
현동 216
 
3.4%
Other values (50) 1988
31.1%
2023-12-11T09:53:28.406618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
구 3264
 
13.2%
3178
 
12.8%
동 2845
 
11.5%
산 1678
 
6.8%
마 1203
 
4.9%
진 1115
 
4.5%
해 1081
 
4.4%
회 766
 
3.1%
원 715
 
2.9%
포 673
 
2.7%
Other values (61) 8248
33.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 20966
84.7%
Space Separator 3178
 
12.8%
Decimal Number 622
 
2.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 3264
15.6%
동 2845
13.6%
산 1678
 
8.0%
마 1203
 
5.7%
진 1115
 
5.3%
해 1081
 
5.2%
회 766
 
3.7%
원 715
 
3.4%
포 673
 
3.2%
의 661
 
3.2%
Other values (58) 6965
33.2%
Decimal Number
ValueCountFrequency (%)
2 445
71.5%
1 177
 
28.5%
Space Separator
ValueCountFrequency (%)
3178
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 20966
84.7%
Common 3800
 
15.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 3264
15.6%
동 2845
13.6%
산 1678
 
8.0%
마 1203
 
5.7%
진 1115
 
5.3%
해 1081
 
5.2%
회 766
 
3.7%
원 715
 
3.4%
포 673
 
3.2%
의 661
 
3.2%
Other values (58) 6965
33.2%
Common
ValueCountFrequency (%)
3178
83.6%
2 445
 
11.7%
1 177
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 20966
84.7%
ASCII 3800
 
15.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
구 3264
15.6%
동 2845
13.6%
산 1678
 
8.0%
마 1203
 
5.7%
진 1115
 
5.3%
해 1081
 
5.2%
회 766
 
3.7%
원 715
 
3.4%
포 673
 
3.2%
의 661
 
3.2%
Other values (58) 6965
33.2%
ASCII
ValueCountFrequency (%)
3178
83.6%
2 445
 
11.7%
1 177
 
4.7%

관리기관
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size27.9 KiB
진해구 경제교통과
1081 
마산회원구 경제교통과
646 
의창구 경제교통과
632 
마산합포구 경제교통과
578 
<NA>
352 

Length

Max length11
Median length9
Mean length9.1933127
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row마산합포구 경제교통과
2nd row마산합포구 경제교통과
3rd row마산합포구 경제교통과
4th row마산합포구 경제교통과
5th row마산합포구 경제교통과

Common Values

ValueCountFrequency (%)
진해구 경제교통과 1081
30.4%
마산회원구 경제교통과 646
18.2%
의창구 경제교통과 632
17.8%
마산합포구 경제교통과 578
16.2%
<NA> 352
 
9.9%
성산구 경제교통과 270
 
7.6%

Length

2023-12-11T09:53:28.531815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:53:28.638341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경제교통과 3207
47.4%
진해구 1081
 
16.0%
마산회원구 646
 
9.5%
의창구 632
 
9.3%
마산합포구 578
 
8.5%
na 352
 
5.2%
성산구 270
 
4.0%

설치일자
Text

MISSING 

Distinct83
Distinct (%)2.5%
Missing214
Missing (%)6.0%
Memory size27.9 KiB
2023-12-11T09:53:28.846241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters33450
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)0.3%

Sample

1st row2021-12-01
2nd row2021-12-01
3rd row2021-12-01
4th row2021-12-01
5th row2021-12-01
ValueCountFrequency (%)
1974-01-01 1248
37.3%
2014-01-01 200
 
6.0%
2013-09-30 192
 
5.7%
2019-05-31 128
 
3.8%
2019-01-01 125
 
3.7%
2013-12-31 107
 
3.2%
2017-01-01 94
 
2.8%
2021-01-01 91
 
2.7%
2016-06-30 84
 
2.5%
2013-05-11 84
 
2.5%
Other values (73) 992
29.7%
2023-12-11T09:53:29.188390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 8497
25.4%
0 8329
24.9%
- 6690
20.0%
2 2899
 
8.7%
9 1844
 
5.5%
4 1508
 
4.5%
7 1457
 
4.4%
3 1229
 
3.7%
5 447
 
1.3%
6 361
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 26760
80.0%
Dash Punctuation 6690
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 8497
31.8%
0 8329
31.1%
2 2899
 
10.8%
9 1844
 
6.9%
4 1508
 
5.6%
7 1457
 
5.4%
3 1229
 
4.6%
5 447
 
1.7%
6 361
 
1.3%
8 189
 
0.7%
Dash Punctuation
ValueCountFrequency (%)
- 6690
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 33450
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 8497
25.4%
0 8329
24.9%
- 6690
20.0%
2 2899
 
8.7%
9 1844
 
5.5%
4 1508
 
4.5%
7 1457
 
4.4%
3 1229
 
3.7%
5 447
 
1.3%
6 361
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 33450
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 8497
25.4%
0 8329
24.9%
- 6690
20.0%
2 2899
 
8.7%
9 1844
 
5.5%
4 1508
 
4.5%
7 1457
 
4.4%
3 1229
 
3.7%
5 447
 
1.3%
6 361
 
1.1%

존재유무
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size27.9 KiB
미분류
1334 
무
1229 
유
996 

Length

Max length3
Median length1
Mean length1.7496488
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row유
2nd row무
3rd row무
4th row유
5th row유

Common Values

ValueCountFrequency (%)
미분류 1334
37.5%
무 1229
34.5%
유 996
28.0%

Length

2023-12-11T09:53:29.318063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:53:29.431476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
미분류 1334
37.5%
무 1229
34.5%
유 996
28.0%

보도턱낮춤유무
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size27.9 KiB
있음
3105 
없음
453 
<NA>
 
1

Length

Max length4
Median length2
Mean length2.000562
Min length2

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
있음 3105
87.2%
없음 453
 
12.7%
<NA> 1
 
< 0.1%

Length

2023-12-11T09:53:29.558812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:53:29.661971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
있음 3105
87.2%
없음 453
 
12.7%
na 1
 
< 0.1%

장애시설유무
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size27.9 KiB
있음
2781 
없음
703 
<NA>
 
75

Length

Max length4
Median length2
Mean length2.0421467
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
있음 2781
78.1%
없음 703
 
19.8%
<NA> 75
 
2.1%

Length

2023-12-11T09:53:29.777508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:53:29.877820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
있음 2781
78.1%
없음 703
 
19.8%
na 75
 
2.1%

횡단보도폭
Real number (ℝ)

Distinct637
Distinct (%)17.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.1537988
Minimum0.8
Maximum41.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.4 KiB
2023-12-11T09:53:29.993718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.8
5-th percentile2
Q13.94
median4.04
Q36
95-th percentile8.613
Maximum41.4
Range40.6
Interquartile range (IQR)2.06

Descriptive statistics

Standard deviation2.9025501
Coefficient of variation (CV)0.56318654
Kurtosis22.8229
Mean5.1537988
Median Absolute Deviation (MAD)1.15
Skewness3.6712192
Sum18342.37
Variance8.4247971
MonotonicityNot monotonic
2023-12-11T09:53:30.120096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.0 391
 
11.0%
6.0 284
 
8.0%
3.99 107
 
3.0%
3.98 90
 
2.5%
3.9 71
 
2.0%
3.97 69
 
1.9%
4.01 64
 
1.8%
3.96 53
 
1.5%
5.9 52
 
1.5%
5.99 48
 
1.3%
Other values (627) 2330
65.5%
ValueCountFrequency (%)
0.8 1
 
< 0.1%
0.9 1
 
< 0.1%
1.0 2
 
0.1%
1.07 1
 
< 0.1%
1.09 1
 
< 0.1%
1.1 2
 
0.1%
1.2 5
0.1%
1.21 1
 
< 0.1%
1.25 1
 
< 0.1%
1.3 4
0.1%
ValueCountFrequency (%)
41.4 1
 
< 0.1%
33.98 1
 
< 0.1%
29.45 1
 
< 0.1%
26.9 1
 
< 0.1%
26.6 1
 
< 0.1%
25.4 1
 
< 0.1%
24.33 1
 
< 0.1%
22.9 4
0.1%
22.76 1
 
< 0.1%
22.56 1
 
< 0.1%

횡단보도길이
Real number (ℝ)

Distinct1331
Distinct (%)37.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.831739
Minimum0
Maximum90.23
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size31.4 KiB
2023-12-11T09:53:30.243114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.639
Q15.89
median8.58
Q313.96
95-th percentile24.622
Maximum90.23
Range90.23
Interquartile range (IQR)8.07

Descriptive statistics

Standard deviation6.9975561
Coefficient of variation (CV)0.64602332
Kurtosis8.4158048
Mean10.831739
Median Absolute Deviation (MAD)3.58
Skewness1.9583079
Sum38550.16
Variance48.965791
MonotonicityNot monotonic
2023-12-11T09:53:30.378539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.0 105
 
3.0%
5.9 36
 
1.0%
5.8 27
 
0.8%
5.87 26
 
0.7%
5.85 26
 
0.7%
5.86 24
 
0.7%
7.6 22
 
0.6%
9.45 22
 
0.6%
5.0 22
 
0.6%
3.9 21
 
0.6%
Other values (1321) 3228
90.7%
ValueCountFrequency (%)
0.0 1
< 0.1%
0.6 1
< 0.1%
1.0 1
< 0.1%
1.41 1
< 0.1%
1.6 1
< 0.1%
1.8 1
< 0.1%
1.87 1
< 0.1%
1.89 1
< 0.1%
1.9 2
0.1%
1.95 1
< 0.1%
ValueCountFrequency (%)
90.23 1
< 0.1%
65.79 1
< 0.1%
63.91 1
< 0.1%
55.1 1
< 0.1%
42.5 1
< 0.1%
42.3 1
< 0.1%
40.51 1
< 0.1%
39.3 1
< 0.1%
38.3 2
0.1%
38.29 1
< 0.1%

횡단보도방식
Categorical

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size27.9 KiB
일자형
1839 
지그재그형
1703 
미분류
 
12
기타
 
5

Length

Max length5
Median length3
Mean length3.9556055
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row지그재그형
2nd row지그재그형
3rd row일자형
4th row지그재그형
5th row지그재그형

Common Values

ValueCountFrequency (%)
일자형 1839
51.7%
지그재그형 1703
47.9%
미분류 12
 
0.3%
기타 5
 
0.1%

Length

2023-12-11T09:53:30.519576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:53:30.614719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일자형 1839
51.7%
지그재그형 1703
47.9%
미분류 12
 
0.3%
기타 5
 
0.1%

Interactions

2023-12-11T09:53:26.682762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:53:26.038585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:53:26.372731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:53:26.775832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:53:26.165289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:53:26.482135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:53:26.864827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:53:26.283823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:53:26.590470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T09:53:30.993847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
FID행정읍면동관리기관설치일자존재유무보도턱낮춤유무장애시설유무횡단보도폭횡단보도길이횡단보도방식
FID1.0000.9780.9590.9310.5960.2690.2900.2730.1680.281
행정읍면동0.9781.0001.0000.9800.8140.4900.4790.5010.4610.537
관리기관0.9591.0001.0000.9390.5340.1610.1150.1930.2310.115
설치일자0.9310.9800.9391.0000.9330.4120.4850.1490.1400.481
존재유무0.5960.8140.5340.9331.0000.1820.1910.3590.3580.236
보도턱낮춤유무0.2690.4900.1610.4120.1821.0000.8430.1200.1150.175
장애시설유무0.2900.4790.1150.4850.1910.8431.0000.1800.1760.226
횡단보도폭0.2730.5010.1930.1490.3590.1200.1801.0000.3100.467
횡단보도길이0.1680.4610.2310.1400.3580.1150.1760.3101.0000.367
횡단보도방식0.2810.5370.1150.4810.2360.1750.2260.4670.3671.000
2023-12-11T09:53:31.132460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
보도턱낮춤유무관리기관존재유무장애시설유무횡단보도방식
보도턱낮춤유무1.0000.1960.2990.6380.116
관리기관0.1961.0000.4740.1400.094
존재유무0.2990.4741.0000.3140.225
장애시설유무0.6380.1400.3141.0000.150
횡단보도방식0.1160.0940.2250.1501.000
2023-12-11T09:53:31.298647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
FID횡단보도폭횡단보도길이관리기관존재유무보도턱낮춤유무장애시설유무횡단보도방식
FID1.000-0.118-0.1020.7210.4390.2060.2220.171
횡단보도폭-0.1181.0000.1990.1110.2300.0980.1430.299
횡단보도길이-0.1020.1991.0000.1500.2440.0860.1320.171
관리기관0.7210.1110.1501.0000.4740.1960.1400.094
존재유무0.4390.2300.2440.4741.0000.2990.3140.225
보도턱낮춤유무0.2060.0980.0860.1960.2991.0000.6380.116
장애시설유무0.2220.1430.1320.1400.3140.6381.0000.150
횡단보도방식0.1710.2990.1710.0940.2250.1160.1501.000

Missing values

2023-12-11T09:53:26.976310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T09:53:27.129948image/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.
2023-12-11T09:53:27.239749image/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

FID지형지물부호행정읍면동관리기관설치일자존재유무보도턱낮춤유무장애시설유무횡단보도폭횡단보도길이횡단보도방식
06626횡단보도문화동마산합포구 경제교통과2021-12-01유있음있음5.9211.69지그재그형
16625횡단보도문화동마산합포구 경제교통과2021-12-01무있음있음5.9311.72지그재그형
26624횡단보도문화동마산합포구 경제교통과2021-12-01무있음있음4.015.79일자형
36623횡단보도문화동마산합포구 경제교통과2021-12-01유있음있음5.869.44지그재그형
46622횡단보도문화동마산합포구 경제교통과2021-12-01유있음있음5.8914.46지그재그형
56621횡단보도문화동마산합포구 경제교통과2021-12-01무있음있음5.946.29지그재그형
66620횡단보도문화동마산합포구 경제교통과2021-12-01유있음있음5.8326.14지그재그형
76619횡단보도문화동마산합포구 경제교통과2021-12-01무있음있음5.916.32지그재그형
86618횡단보도문화동마산합포구 경제교통과2021-12-01유있음있음5.9212.6지그재그형
96617횡단보도문화동마산합포구 경제교통과2021-12-01무있음있음5.934.53지그재그형
FID지형지물부호행정읍면동관리기관설치일자존재유무보도턱낮춤유무장애시설유무횡단보도폭횡단보도길이횡단보도방식
354910횡단보도진해구 웅동2동진해구 경제교통과2013-09-30유있음있음7.9713.94지그재그형
35509횡단보도진해구 웅동2동진해구 경제교통과2013-09-30유있음있음8.022.45지그재그형
35518횡단보도진해구 웅동2동진해구 경제교통과2013-09-30유있음있음7.9922.03지그재그형
35527횡단보도진해구 웅동2동진해구 경제교통과2013-09-30무있음있음7.9911.27지그재그형
35536횡단보도진해구 웅동2동진해구 경제교통과2013-09-30무있음있음7.9413.99지그재그형
35545횡단보도진해구 웅동2동진해구 경제교통과2013-09-30유있음있음7.9820.22지그재그형
35554횡단보도진해구 웅동2동진해구 경제교통과2013-09-30유있음있음7.9619.34지그재그형
35563횡단보도진해구 웅동2동진해구 경제교통과2013-09-30무있음있음7.9310.76지그재그형
35572횡단보도진해구 웅동2동진해구 경제교통과2013-09-30유있음있음7.9915.76지그재그형
35581횡단보도진해구 웅동2동진해구 경제교통과2013-09-30유있음있음8.0119.36지그재그형