Overview

Dataset statistics

Number of variables10
Number of observations31
Missing cells29
Missing cells (%)9.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.7 KiB
Average record size in memory88.3 B

Variable types

Numeric4
DateTime3
Categorical1
Text2

Dataset

Description영천시 공원 및 녹지점용 허가 현황으로 연번, 허가번호, 유형명, 지번주소, 토지면적, 허가시작일자, 허가종료일자, 공시지가 등의 데이터를 제공합니다.
Author경상북도 영천시
URLhttps://www.data.go.kr/data/15110616/fileData.do

Alerts

데이터기준일자 has constant value ""Constant
토지면적 is highly overall correlated with 점용료 and 1 other fieldsHigh correlation
점용료 is highly overall correlated with 토지면적High correlation
유형명 is highly overall correlated with 토지면적High correlation
유형명 is highly imbalanced (54.1%)Imbalance
공시지가 has 7 (22.6%) missing valuesMissing
점용료 has 22 (71.0%) missing valuesMissing
연번 has unique valuesUnique

Reproduction

Analysis started2023-12-16 15:56:35.231859
Analysis finished2023-12-16 15:56:45.920467
Duration10.69 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

UNIQUE 

Distinct31
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-16T15:56:46.332853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.5
Q18.5
median16
Q323.5
95-th percentile29.5
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation9.0921211
Coefficient of variation (CV)0.56825757
Kurtosis-1.2
Mean16
Median Absolute Deviation (MAD)8
Skewness0
Sum496
Variance82.666667
MonotonicityStrictly increasing
2023-12-16T15:56:47.152117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
1 1
 
3.2%
2 1
 
3.2%
31 1
 
3.2%
30 1
 
3.2%
29 1
 
3.2%
28 1
 
3.2%
27 1
 
3.2%
26 1
 
3.2%
25 1
 
3.2%
24 1
 
3.2%
Other values (21) 21
67.7%
ValueCountFrequency (%)
1 1
3.2%
2 1
3.2%
3 1
3.2%
4 1
3.2%
5 1
3.2%
6 1
3.2%
7 1
3.2%
8 1
3.2%
9 1
3.2%
10 1
3.2%
ValueCountFrequency (%)
31 1
3.2%
30 1
3.2%
29 1
3.2%
28 1
3.2%
27 1
3.2%
26 1
3.2%
25 1
3.2%
24 1
3.2%
23 1
3.2%
22 1
3.2%

허가번호
Date

Distinct19
Distinct (%)61.3%
Missing0
Missing (%)0.0%
Memory size380.0 B
Minimum2013-04-01 00:00:00
Maximum2016-09-01 00:00:00
2023-12-16T15:56:48.006367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:56:48.729732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)

유형명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Memory size380.0 B
녹지
28 
공원
3 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
녹지 28
90.3%
공원 3
 
9.7%

Length

2023-12-16T15:56:49.495837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-16T15:56:49.979089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
녹지 28
90.3%
공원 3
 
9.7%

지번주소
Text

Distinct28
Distinct (%)90.3%
Missing0
Missing (%)0.0%
Memory size380.0 B
2023-12-16T15:56:50.910717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length23
Mean length18.225806
Min length15

Characters and Unicode

Total characters565
Distinct characters47
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

Unique26 ?
Unique (%)83.9%

Sample

1st row경상북도 영천시 금호읍 냉천리 409-32
2nd row경상북도 영천시 성내동 391
3rd row경상북도 영천시 성내동 393
4th row경상북도 영천시 성내동 394
5th row경상북도 영천시 성내동 390
ValueCountFrequency (%)
경상북도 31
23.5%
영천시 31
23.5%
봉동 7
 
5.3%
금호읍 7
 
5.3%
성내동 4
 
3.0%
화룡동 3
 
2.3%
589-1 3
 
2.3%
도남동 3
 
2.3%
180 2
 
1.5%
냉천리 2
 
1.5%
Other values (34) 39
29.5%
2023-12-16T15:56:52.784327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
101
17.9%
도 34
 
6.0%
천 33
 
5.8%
경 31
 
5.5%
북 31
 
5.5%
영 31
 
5.5%
시 31
 
5.5%
상 31
 
5.5%
동 24
 
4.2%
- 21
 
3.7%
Other values (37) 197
34.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 327
57.9%
Decimal Number 116
 
20.5%
Space Separator 101
 
17.9%
Dash Punctuation 21
 
3.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 34
10.4%
천 33
10.1%
경 31
9.5%
북 31
9.5%
영 31
9.5%
시 31
9.5%
상 31
9.5%
동 24
 
7.3%
리 7
 
2.1%
읍 7
 
2.1%
Other values (25) 67
20.5%
Decimal Number
ValueCountFrequency (%)
0 16
13.8%
3 16
13.8%
1 15
12.9%
9 13
11.2%
4 10
8.6%
2 10
8.6%
8 10
8.6%
5 10
8.6%
7 9
7.8%
6 7
6.0%
Space Separator
ValueCountFrequency (%)
101
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 21
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 327
57.9%
Common 238
42.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 34
10.4%
천 33
10.1%
경 31
9.5%
북 31
9.5%
영 31
9.5%
시 31
9.5%
상 31
9.5%
동 24
 
7.3%
리 7
 
2.1%
읍 7
 
2.1%
Other values (25) 67
20.5%
Common
ValueCountFrequency (%)
101
42.4%
- 21
 
8.8%
0 16
 
6.7%
3 16
 
6.7%
1 15
 
6.3%
9 13
 
5.5%
4 10
 
4.2%
2 10
 
4.2%
8 10
 
4.2%
5 10
 
4.2%
Other values (2) 16
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 327
57.9%
ASCII 238
42.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
101
42.4%
- 21
 
8.8%
0 16
 
6.7%
3 16
 
6.7%
1 15
 
6.3%
9 13
 
5.5%
4 10
 
4.2%
2 10
 
4.2%
8 10
 
4.2%
5 10
 
4.2%
Other values (2) 16
 
6.7%
Hangul
ValueCountFrequency (%)
도 34
10.4%
천 33
10.1%
경 31
9.5%
북 31
9.5%
영 31
9.5%
시 31
9.5%
상 31
9.5%
동 24
 
7.3%
리 7
 
2.1%
읍 7
 
2.1%
Other values (25) 67
20.5%

토지면적
Real number (ℝ)

HIGH CORRELATION 

Distinct17
Distinct (%)54.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean204.53097
Minimum2
Maximum874.12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-16T15:56:53.881804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2.11
Q128.7
median42
Q3211.12
95-th percentile874.12
Maximum874.12
Range872.12
Interquartile range (IQR)182.42

Descriptive statistics

Standard deviation301.31068
Coefficient of variation (CV)1.4731788
Kurtosis0.97641874
Mean204.53097
Median Absolute Deviation (MAD)39.89
Skewness1.5845467
Sum6340.46
Variance90788.127
MonotonicityNot monotonic
2023-12-16T15:56:54.917083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
42.0 5
16.1%
874.12 4
12.9%
2.11 3
9.7%
18.72 2
 
6.5%
63.24 2
 
6.5%
597.0 2
 
6.5%
136.4 2
 
6.5%
285.84 2
 
6.5%
120.0 1
 
3.2%
88.0 1
 
3.2%
Other values (7) 7
22.6%
ValueCountFrequency (%)
2.0 1
 
3.2%
2.11 3
9.7%
3.0 1
 
3.2%
18.72 2
 
6.5%
26.4 1
 
3.2%
31.0 1
 
3.2%
32.0 1
 
3.2%
41.85 1
 
3.2%
42.0 5
16.1%
63.24 2
 
6.5%
ValueCountFrequency (%)
874.12 4
12.9%
597.0 2
 
6.5%
285.84 2
 
6.5%
136.4 2
 
6.5%
120.0 1
 
3.2%
88.0 1
 
3.2%
81.0 1
 
3.2%
63.24 2
 
6.5%
42.0 5
16.1%
41.85 1
 
3.2%

허가시작일자
Date

Distinct19
Distinct (%)61.3%
Missing0
Missing (%)0.0%
Memory size380.0 B
Minimum2013-12-01 00:00:00
Maximum2016-12-30 00:00:00
2023-12-16T15:56:56.189802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:56:57.118136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)

허가종료일자
Text

Distinct19
Distinct (%)61.3%
Missing0
Missing (%)0.0%
Memory size380.0 B
2023-12-16T15:56:58.154135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters310
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

Unique12 ?
Unique (%)38.7%

Sample

1st row2016-11-30
2nd row2999-12-31
3rd row2999-12-31
4th row2999-12-31
5th row2999-12-31
ValueCountFrequency (%)
2999-12-31 4
12.9%
2019-10-20 4
12.9%
2018-03-31 3
 
9.7%
2018-05-31 2
 
6.5%
2017-04-30 2
 
6.5%
2019-04-25 2
 
6.5%
2019-12-30 2
 
6.5%
2018-03-30 1
 
3.2%
2018-07-31 1
 
3.2%
2018-09-30 1
 
3.2%
Other values (9) 9
29.0%
2023-12-16T15:56:59.849430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 66
21.3%
- 62
20.0%
1 53
17.1%
2 46
14.8%
9 26
 
8.4%
3 26
 
8.4%
8 14
 
4.5%
4 6
 
1.9%
5 4
 
1.3%
7 4
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 248
80.0%
Dash Punctuation 62
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 66
26.6%
1 53
21.4%
2 46
18.5%
9 26
 
10.5%
3 26
 
10.5%
8 14
 
5.6%
4 6
 
2.4%
5 4
 
1.6%
7 4
 
1.6%
6 3
 
1.2%
Dash Punctuation
ValueCountFrequency (%)
- 62
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 310
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 66
21.3%
- 62
20.0%
1 53
17.1%
2 46
14.8%
9 26
 
8.4%
3 26
 
8.4%
8 14
 
4.5%
4 6
 
1.9%
5 4
 
1.3%
7 4
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 310
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 66
21.3%
- 62
20.0%
1 53
17.1%
2 46
14.8%
9 26
 
8.4%
3 26
 
8.4%
8 14
 
4.5%
4 6
 
1.9%
5 4
 
1.3%
7 4
 
1.3%

공시지가
Real number (ℝ)

MISSING 

Distinct20
Distinct (%)83.3%
Missing7
Missing (%)22.6%
Infinite0
Infinite (%)0.0%
Mean118527.5
Minimum7060
Maximum434900
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-16T15:57:00.551487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7060
5-th percentile43050
Q165900
median79750
Q3138150
95-th percentile400955
Maximum434900
Range427840
Interquartile range (IQR)72250

Descriptive statistics

Standard deviation106974.02
Coefficient of variation (CV)0.90252487
Kurtosis5.3578159
Mean118527.5
Median Absolute Deviation (MAD)25350
Skewness2.349103
Sum2844660
Variance1.144344 × 1010
MonotonicityNot monotonic
2023-12-16T15:57:01.166507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
71200 3
 
9.7%
434900 2
 
6.5%
52400 2
 
6.5%
41400 1
 
3.2%
137200 1
 
3.2%
90500 1
 
3.2%
86700 1
 
3.2%
56400 1
 
3.2%
123600 1
 
3.2%
141000 1
 
3.2%
Other values (10) 10
32.3%
(Missing) 7
22.6%
ValueCountFrequency (%)
7060 1
 
3.2%
41400 1
 
3.2%
52400 2
6.5%
56400 1
 
3.2%
64400 1
 
3.2%
66400 1
 
3.2%
71200 3
9.7%
75400 1
 
3.2%
79400 1
 
3.2%
80100 1
 
3.2%
ValueCountFrequency (%)
434900 2
6.5%
208600 1
3.2%
161500 1
3.2%
151300 1
3.2%
141000 1
3.2%
137200 1
3.2%
123600 1
3.2%
90500 1
3.2%
86700 1
3.2%
85500 1
3.2%

점용료
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct6
Distinct (%)66.7%
Missing22
Missing (%)71.0%
Infinite0
Infinite (%)0.0%
Mean74954.444
Minimum8270
Maximum260370
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-16T15:57:02.288579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8270
5-th percentile8270
Q18270
median11750
Q353950
95-th percentile260370
Maximum260370
Range252100
Interquartile range (IQR)45680

Descriptive statistics

Standard deviation106813.67
Coefficient of variation (CV)1.4250479
Kurtosis0.48647559
Mean74954.444
Median Absolute Deviation (MAD)3480
Skewness1.4910745
Sum674590
Variance1.140916 × 1010
MonotonicityNot monotonic
2023-12-16T15:57:02.967200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
8270 3
 
9.7%
260370 2
 
6.5%
53950 1
 
3.2%
11750 1
 
3.2%
9400 1
 
3.2%
53940 1
 
3.2%
(Missing) 22
71.0%
ValueCountFrequency (%)
8270 3
9.7%
9400 1
 
3.2%
11750 1
 
3.2%
53940 1
 
3.2%
53950 1
 
3.2%
260370 2
6.5%
ValueCountFrequency (%)
260370 2
6.5%
53950 1
 
3.2%
53940 1
 
3.2%
11750 1
 
3.2%
9400 1
 
3.2%
8270 3
9.7%

데이터기준일자
Date

CONSTANT 

Distinct1
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size380.0 B
Minimum2023-12-15 00:00:00
Maximum2023-12-15 00:00:00
2023-12-16T15:57:03.655217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:57:04.613440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-16T15:56:42.186301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:56:36.533915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:56:38.469549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:56:40.200360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:56:42.582613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:56:37.039839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:56:38.953349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:56:40.709636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:56:42.971992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:56:37.563593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:56:39.310609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:56:41.269573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:56:43.482836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:56:37.966109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:56:39.672294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:56:41.706838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-16T15:57:05.166784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번허가번호유형명지번주소토지면적허가시작일자허가종료일자공시지가점용료
연번1.0000.9570.7090.7400.8830.9570.9570.1730.556
허가번호0.9571.0001.0000.6101.0001.0001.0001.0001.000
유형명0.7091.0001.0000.0000.6441.0001.0000.0000.193
지번주소0.7400.6100.0001.0001.0000.6100.6101.0001.000
토지면적0.8831.0000.6441.0001.0001.0001.0000.000NaN
허가시작일자0.9571.0001.0000.6101.0001.0001.0001.0001.000
허가종료일자0.9571.0001.0000.6101.0001.0001.0001.0001.000
공시지가0.1731.0000.0001.0000.0001.0001.0001.0001.000
점용료0.5561.0000.1931.000NaN1.0001.0001.0001.000
2023-12-16T15:57:05.914566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번토지면적공시지가점용료유형명
연번1.000-0.1460.4540.2640.000
토지면적-0.1461.0000.0480.8910.731
공시지가0.4540.0481.0000.4850.060
점용료0.2640.8910.4851.0000.000
유형명0.0000.7310.0600.0001.000

Missing values

2023-12-16T15:56:44.374423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-16T15:56:45.352520image/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.
2023-12-16T15:56:45.767588image/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

연번허가번호유형명지번주소토지면적허가시작일자허가종료일자공시지가점용료데이터기준일자
012013-04녹지경상북도 영천시 금호읍 냉천리 409-3218.722013-12-012016-11-3052400539502023-12-15
122013-05녹지경상북도 영천시 성내동 391874.122013-12-112999-12-31<NA><NA>2023-12-15
232013-05녹지경상북도 영천시 성내동 393874.122013-12-112999-12-3175400<NA>2023-12-15
342013-05녹지경상북도 영천시 성내동 394874.122013-12-112999-12-3156400<NA>2023-12-15
452013-05녹지경상북도 영천시 성내동 390874.122013-12-112999-12-3179400<NA>2023-12-15
562014-02공원경상북도 영천시 도남동 1803.02014-04-302017-04-29<NA>117502023-12-15
672015-01녹지경상북도 영천시 문외동 230-141.852015-04-102018-03-2441400<NA>2023-12-15
782015-02녹지경상북도 영천시 완산동 709-5331.02015-04-222018-03-30208600<NA>2023-12-15
892015-03녹지경상북도 영천시 봉동 589-12.02015-07-022018-06-1071200<NA>2023-12-15
9102015-07녹지경상북도 영천시 화룡동 427-788.02015-08-192018-08-3080100<NA>2023-12-15
연번허가번호유형명지번주소토지면적허가시작일자허가종료일자공시지가점용료데이터기준일자
21222016-04녹지경상북도 영천시 금호읍 신월리 48-3597.02016-03-062017-04-30161500<NA>2023-12-15
22232016-05녹지경상북도 영천시 오수동 301-281.02016-09-012019-06-30141000<NA>2023-12-15
23242016-06녹지경상북도 영천시 금호읍 원제리 522-132.02016-09-202019-08-30123600<NA>2023-12-15
24252016-07녹지경상북도 영천시 봉동 59842.02016-10-212019-10-20<NA><NA>2023-12-15
25262016-07녹지경상북도 영천시 봉동 657-142.02016-10-212019-10-20<NA><NA>2023-12-15
26272016-07녹지경상북도 영천시 봉동 656-342.02016-10-212019-10-204349002603702023-12-15
27282016-07녹지경상북도 영천시 봉동 657-342.02016-10-212019-10-204349002603702023-12-15
28292016-08녹지경상북도 영천시 금호읍 냉천리 409-32번지18.722016-12-012019-11-3052400539402023-12-15
29302016-09녹지경상북도 영천시 금호읍 교대리 400-3663.242016-12-302019-12-3086700<NA>2023-12-15
30312016-09녹지경상북도 영천시 금호읍 교대리 400-4063.242016-12-302019-12-3090500<NA>2023-12-15