Overview

Dataset statistics

Number of variables16
Number of observations27
Missing cells24
Missing cells (%)5.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.7 KiB
Average record size in memory141.9 B

Variable types

Numeric7
Categorical3
Text5
Boolean1

Dataset

Description국립공원내 28개 대피소에 대해 이동식 GPS측량 및 시설물 조사를 통해 구축된 데이터로서 주소, 수용인원, 위치 등을 표기하고 있습니다.
Author국립공원공단
URLhttps://www.data.go.kr/data/2535749/fileData.do

Alerts

분류코드 has constant value ""Constant
이용구분(0:no,1:yes) has constant value ""Constant
심볼코드 has constant value ""Constant
공원사무소코드 is highly overall correlated with 경도 and 1 other fieldsHigh correlation
수용인원 is highly overall correlated with 고도(m)High correlation
고도(m) is highly overall correlated with 수용인원High correlation
경도 is highly overall correlated with 공원사무소코드 and 2 other fieldsHigh correlation
위도 is highly overall correlated with 경도 and 1 other fieldsHigh correlation
예약방법(1:인터넷,2:현장) is highly overall correlated with 공원사무소코드 and 2 other fieldsHigh correlation
주소(새주소) has 5 (18.5%) missing valuesMissing
전화번호 has 19 (70.4%) missing valuesMissing
순번 has unique valuesUnique
명칭(한글) has unique valuesUnique
명칭(영문) has unique valuesUnique
경도 has unique valuesUnique
위도 has unique valuesUnique
수용인원 has 1 (3.7%) zerosZeros

Reproduction

Analysis started2023-12-12 03:05:46.607876
Analysis finished2023-12-12 03:05:53.934593
Duration7.33 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순번
Real number (ℝ)

UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14
Minimum1
Maximum27
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-12T12:05:54.026912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.3
Q17.5
median14
Q320.5
95-th percentile25.7
Maximum27
Range26
Interquartile range (IQR)13

Descriptive statistics

Standard deviation7.9372539
Coefficient of variation (CV)0.56694671
Kurtosis-1.2
Mean14
Median Absolute Deviation (MAD)7
Skewness0
Sum378
Variance63
MonotonicityStrictly increasing
2023-12-12T12:05:54.203441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
1 1
 
3.7%
2 1
 
3.7%
27 1
 
3.7%
26 1
 
3.7%
25 1
 
3.7%
24 1
 
3.7%
23 1
 
3.7%
22 1
 
3.7%
21 1
 
3.7%
20 1
 
3.7%
Other values (17) 17
63.0%
ValueCountFrequency (%)
1 1
3.7%
2 1
3.7%
3 1
3.7%
4 1
3.7%
5 1
3.7%
6 1
3.7%
7 1
3.7%
8 1
3.7%
9 1
3.7%
10 1
3.7%
ValueCountFrequency (%)
27 1
3.7%
26 1
3.7%
25 1
3.7%
24 1
3.7%
23 1
3.7%
22 1
3.7%
21 1
3.7%
20 1
3.7%
19 1
3.7%
18 1
3.7%

공원사무소코드
Real number (ℝ)

HIGH CORRELATION 

Distinct9
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean938.25926
Minimum101
Maximum2001
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-12T12:05:54.313918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum101
5-th percentile101
Q1103
median801
Q31751.5
95-th percentile2001
Maximum2001
Range1900
Interquartile range (IQR)1648.5

Descriptive statistics

Standard deviation790.70872
Coefficient of variation (CV)0.84274012
Kurtosis-1.685548
Mean938.25926
Median Absolute Deviation (MAD)700
Skewness0.30670553
Sum25333
Variance625220.28
MonotonicityNot monotonic
2023-12-12T12:05:54.453926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
2001 7
25.9%
401 5
18.5%
101 5
18.5%
1501 3
11.1%
103 2
 
7.4%
801 2
 
7.4%
102 1
 
3.7%
901 1
 
3.7%
1502 1
 
3.7%
ValueCountFrequency (%)
101 5
18.5%
102 1
 
3.7%
103 2
 
7.4%
401 5
18.5%
801 2
 
7.4%
901 1
 
3.7%
1501 3
11.1%
1502 1
 
3.7%
2001 7
25.9%
ValueCountFrequency (%)
2001 7
25.9%
1502 1
 
3.7%
1501 3
11.1%
901 1
 
3.7%
801 2
 
7.4%
401 5
18.5%
103 2
 
7.4%
102 1
 
3.7%
101 5
18.5%

분류코드
Categorical

CONSTANT 

Distinct1
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Memory size348.0 B
105
27 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row105
2nd row105
3rd row105
4th row105
5th row105

Common Values

ValueCountFrequency (%)
105 27
100.0%

Length

2023-12-12T12:05:54.635141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T12:05:54.773785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
105 27
100.0%

일련번호
Real number (ℝ)

Distinct7
Distinct (%)25.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8888889
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-12T12:05:54.899649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q34
95-th percentile6
Maximum7
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.8257419
Coefficient of variation (CV)0.63198757
Kurtosis-0.57655385
Mean2.8888889
Median Absolute Deviation (MAD)1
Skewness0.70934284
Sum78
Variance3.3333333
MonotonicityNot monotonic
2023-12-12T12:05:55.044107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1 8
29.6%
2 6
22.2%
3 4
14.8%
4 3
 
11.1%
5 3
 
11.1%
6 2
 
7.4%
7 1
 
3.7%
ValueCountFrequency (%)
1 8
29.6%
2 6
22.2%
3 4
14.8%
4 3
 
11.1%
5 3
 
11.1%
6 2
 
7.4%
7 1
 
3.7%
ValueCountFrequency (%)
7 1
 
3.7%
6 2
 
7.4%
5 3
 
11.1%
4 3
 
11.1%
3 4
14.8%
2 6
22.2%
1 8
29.6%

명칭(한글)
Text

UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size348.0 B
2023-12-12T12:05:55.328391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7
Mean length5.9259259
Min length5

Characters and Unicode

Total characters160
Distinct characters59
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

Unique27 ?
Unique (%)100.0%

Sample

1st row중청대피소
2nd row수렴동대피소
3rd row소청대피소
4th row희운각대피소
5th row양폭대피소
ValueCountFrequency (%)
중청대피소 1
 
3.7%
장터목대피소 1
 
3.7%
향적봉대피소 1
 
3.7%
북한산인수대피소 1
 
3.7%
백운대피소 1
 
3.7%
북한대피소 1
 
3.7%
도봉대피소 1
 
3.7%
노인봉대피소 1
 
3.7%
로타리대피소 1
 
3.7%
벽소령대피소 1
 
3.7%
Other values (17) 17
63.0%
2023-12-12T12:05:55.787822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
소 29
18.1%
피 28
17.5%
대 27
16.9%
봉 4
 
2.5%
세 3
 
1.9%
밭 3
 
1.9%
한 2
 
1.2%
골 2
 
1.2%
노 2
 
1.2%
청 2
 
1.2%
Other values (49) 58
36.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 154
96.2%
Close Punctuation 2
 
1.2%
Open Punctuation 2
 
1.2%
Decimal Number 2
 
1.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
소 29
18.8%
피 28
18.2%
대 27
17.5%
봉 4
 
2.6%
세 3
 
1.9%
밭 3
 
1.9%
한 2
 
1.3%
골 2
 
1.3%
노 2
 
1.3%
청 2
 
1.3%
Other values (45) 52
33.8%
Decimal Number
ValueCountFrequency (%)
2 1
50.0%
1 1
50.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 154
96.2%
Common 6
 
3.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
소 29
18.8%
피 28
18.2%
대 27
17.5%
봉 4
 
2.6%
세 3
 
1.9%
밭 3
 
1.9%
한 2
 
1.3%
골 2
 
1.3%
노 2
 
1.3%
청 2
 
1.3%
Other values (45) 52
33.8%
Common
ValueCountFrequency (%)
) 2
33.3%
( 2
33.3%
2 1
16.7%
1 1
16.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 154
96.2%
ASCII 6
 
3.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
소 29
18.8%
피 28
18.2%
대 27
17.5%
봉 4
 
2.6%
세 3
 
1.9%
밭 3
 
1.9%
한 2
 
1.3%
골 2
 
1.3%
노 2
 
1.3%
청 2
 
1.3%
Other values (45) 52
33.8%
ASCII
ValueCountFrequency (%)
) 2
33.3%
( 2
33.3%
2 1
16.7%
1 1
16.7%

명칭(영문)
Text

UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size348.0 B
2023-12-12T12:05:56.020579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length21
Mean length16.740741
Min length14

Characters and Unicode

Total characters452
Distinct characters39
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique27 ?
Unique (%)100.0%

Sample

1st rowJungcheong Shelter
2nd rowSuryeomdong Shelter
3rd rowSocheong Shelter
4th rowHuiungak Shelter
5th rowYangpok Shelter
ValueCountFrequency (%)
shelter 25
43.9%
shelters 2
 
3.5%
witse 2
 
3.5%
jungcheong 1
 
1.8%
noinbong 1
 
1.8%
seseok 1
 
1.8%
piagol 1
 
1.8%
byeoksoryeong 1
 
1.8%
rotari 1
 
1.8%
dobong 1
 
1.8%
Other values (21) 21
36.8%
2023-12-12T12:05:56.502172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 72
15.9%
t 35
 
7.7%
h 34
 
7.5%
r 32
 
7.1%
30
 
6.6%
l 29
 
6.4%
o 28
 
6.2%
S 26
 
5.8%
n 25
 
5.5%
a 24
 
5.3%
Other values (29) 117
25.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 368
81.4%
Uppercase Letter 48
 
10.6%
Space Separator 30
 
6.6%
Close Punctuation 2
 
0.4%
Open Punctuation 2
 
0.4%
Decimal Number 2
 
0.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 72
19.6%
t 35
9.5%
h 34
9.2%
r 32
8.7%
l 29
7.9%
o 28
 
7.6%
n 25
 
6.8%
a 24
 
6.5%
g 19
 
5.2%
s 15
 
4.1%
Other values (11) 55
14.9%
Uppercase Letter
ValueCountFrequency (%)
S 26
54.2%
B 4
 
8.3%
J 3
 
6.2%
Y 2
 
4.2%
N 2
 
4.2%
P 2
 
4.2%
H 2
 
4.2%
W 2
 
4.2%
C 1
 
2.1%
R 1
 
2.1%
Other values (3) 3
 
6.2%
Decimal Number
ValueCountFrequency (%)
1 1
50.0%
2 1
50.0%
Space Separator
ValueCountFrequency (%)
30
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 416
92.0%
Common 36
 
8.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 72
17.3%
t 35
 
8.4%
h 34
 
8.2%
r 32
 
7.7%
l 29
 
7.0%
o 28
 
6.7%
S 26
 
6.2%
n 25
 
6.0%
a 24
 
5.8%
g 19
 
4.6%
Other values (24) 92
22.1%
Common
ValueCountFrequency (%)
30
83.3%
) 2
 
5.6%
( 2
 
5.6%
1 1
 
2.8%
2 1
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 452
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 72
15.9%
t 35
 
7.7%
h 34
 
7.5%
r 32
 
7.1%
30
 
6.6%
l 29
 
6.4%
o 28
 
6.2%
S 26
 
5.8%
n 25
 
5.5%
a 24
 
5.3%
Other values (29) 117
25.9%

주소(지번)
Text

Distinct24
Distinct (%)88.9%
Missing0
Missing (%)0.0%
Memory size348.0 B
2023-12-12T12:05:56.836081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length21
Mean length19.740741
Min length15

Characters and Unicode

Total characters533
Distinct characters89
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

Unique22 ?
Unique (%)81.5%

Sample

1st row강원도 양양군 서면 오색리 산1-24
2nd row강원도 인제군 북면 용대리 산12-1
3rd row강원도 인제군 북면 용대리 산12-1
4th row강원도 인제군 북면 용대리 산12-1
5th row강원도 속초시 설악동 산41
ValueCountFrequency (%)
산 8
 
5.9%
제주 7
 
5.1%
경상남도 6
 
4.4%
강원도 5
 
3.7%
제주시 5
 
3.7%
고양시 3
 
2.2%
경기도 3
 
2.2%
인제군 3
 
2.2%
산청군 3
 
2.2%
산12-1 3
 
2.2%
Other values (71) 90
66.2%
2023-12-12T12:05:57.313013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
109
20.5%
산 32
 
6.0%
1 30
 
5.6%
도 20
 
3.8%
리 19
 
3.6%
시 16
 
3.0%
제 15
 
2.8%
면 15
 
2.8%
- 14
 
2.6%
군 13
 
2.4%
Other values (79) 250
46.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 333
62.5%
Space Separator 109
 
20.5%
Decimal Number 77
 
14.4%
Dash Punctuation 14
 
2.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 32
 
9.6%
도 20
 
6.0%
리 19
 
5.7%
시 16
 
4.8%
제 15
 
4.5%
면 15
 
4.5%
군 13
 
3.9%
주 13
 
3.9%
남 10
 
3.0%
동 10
 
3.0%
Other values (67) 170
51.1%
Decimal Number
ValueCountFrequency (%)
1 30
39.0%
2 13
16.9%
0 9
 
11.7%
3 7
 
9.1%
8 5
 
6.5%
6 4
 
5.2%
5 3
 
3.9%
7 3
 
3.9%
4 2
 
2.6%
9 1
 
1.3%
Space Separator
ValueCountFrequency (%)
109
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 14
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 333
62.5%
Common 200
37.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 32
 
9.6%
도 20
 
6.0%
리 19
 
5.7%
시 16
 
4.8%
제 15
 
4.5%
면 15
 
4.5%
군 13
 
3.9%
주 13
 
3.9%
남 10
 
3.0%
동 10
 
3.0%
Other values (67) 170
51.1%
Common
ValueCountFrequency (%)
109
54.5%
1 30
 
15.0%
- 14
 
7.0%
2 13
 
6.5%
0 9
 
4.5%
3 7
 
3.5%
8 5
 
2.5%
6 4
 
2.0%
5 3
 
1.5%
7 3
 
1.5%
Other values (2) 3
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 333
62.5%
ASCII 200
37.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
109
54.5%
1 30
 
15.0%
- 14
 
7.0%
2 13
 
6.5%
0 9
 
4.5%
3 7
 
3.5%
8 5
 
2.5%
6 4
 
2.0%
5 3
 
1.5%
7 3
 
1.5%
Other values (2) 3
 
1.5%
Hangul
ValueCountFrequency (%)
산 32
 
9.6%
도 20
 
6.0%
리 19
 
5.7%
시 16
 
4.8%
제 15
 
4.5%
면 15
 
4.5%
군 13
 
3.9%
주 13
 
3.9%
남 10
 
3.0%
동 10
 
3.0%
Other values (67) 170
51.1%

주소(새주소)
Text

MISSING 

Distinct16
Distinct (%)72.7%
Missing5
Missing (%)18.5%
Memory size348.0 B
2023-12-12T12:05:57.604726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length29
Median length26.5
Mean length20.272727
Min length9

Characters and Unicode

Total characters446
Distinct characters89
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14 ?
Unique (%)63.6%

Sample

1st row강원도 양양군 서면 대청봉길 1
2nd row강원도 인제군 북면 백담로 1220 수렴동대피소
3rd row강원도 인제군 북면 백담로 1755 소청대피소
4th row강원도 인제군 북면 백담로 1925 희운각산장
5th row강원도 속초시 설악산로 1119-180 (설악동)
ValueCountFrequency (%)
미변환 6
 
5.8%
지번 6
 
5.8%
강원도 6
 
5.8%
주소 6
 
5.8%
경상남도 4
 
3.9%
인제군 3
 
2.9%
산청군 3
 
2.9%
백담로 3
 
2.9%
북면 3
 
2.9%
제주시 2
 
1.9%
Other values (53) 61
59.2%
2023-12-12T12:05:58.113233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
81
 
18.2%
1 20
 
4.5%
도 16
 
3.6%
0 16
 
3.6%
로 11
 
2.5%
면 10
 
2.2%
주 10
 
2.2%
소 10
 
2.2%
산 9
 
2.0%
군 9
 
2.0%
Other values (79) 254
57.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 266
59.6%
Decimal Number 83
 
18.6%
Space Separator 81
 
18.2%
Dash Punctuation 8
 
1.8%
Close Punctuation 4
 
0.9%
Open Punctuation 4
 
0.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 16
 
6.0%
로 11
 
4.1%
면 10
 
3.8%
주 10
 
3.8%
소 10
 
3.8%
산 9
 
3.4%
군 9
 
3.4%
시 9
 
3.4%
강 8
 
3.0%
제 7
 
2.6%
Other values (65) 167
62.8%
Decimal Number
ValueCountFrequency (%)
1 20
24.1%
0 16
19.3%
2 9
10.8%
5 8
 
9.6%
3 7
 
8.4%
7 7
 
8.4%
6 5
 
6.0%
8 4
 
4.8%
4 4
 
4.8%
9 3
 
3.6%
Space Separator
ValueCountFrequency (%)
81
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 8
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 266
59.6%
Common 180
40.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 16
 
6.0%
로 11
 
4.1%
면 10
 
3.8%
주 10
 
3.8%
소 10
 
3.8%
산 9
 
3.4%
군 9
 
3.4%
시 9
 
3.4%
강 8
 
3.0%
제 7
 
2.6%
Other values (65) 167
62.8%
Common
ValueCountFrequency (%)
81
45.0%
1 20
 
11.1%
0 16
 
8.9%
2 9
 
5.0%
- 8
 
4.4%
5 8
 
4.4%
3 7
 
3.9%
7 7
 
3.9%
6 5
 
2.8%
) 4
 
2.2%
Other values (4) 15
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 266
59.6%
ASCII 180
40.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
81
45.0%
1 20
 
11.1%
0 16
 
8.9%
2 9
 
5.0%
- 8
 
4.4%
5 8
 
4.4%
3 7
 
3.9%
7 7
 
3.9%
6 5
 
2.8%
) 4
 
2.2%
Other values (4) 15
 
8.3%
Hangul
ValueCountFrequency (%)
도 16
 
6.0%
로 11
 
4.1%
면 10
 
3.8%
주 10
 
3.8%
소 10
 
3.8%
산 9
 
3.4%
군 9
 
3.4%
시 9
 
3.4%
강 8
 
3.0%
제 7
 
2.6%
Other values (65) 167
62.8%

전화번호
Text

MISSING 

Distinct8
Distinct (%)100.0%
Missing19
Missing (%)70.4%
Memory size348.0 B
2023-12-12T12:05:58.301580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length11.5
Mean length11.5
Min length11

Characters and Unicode

Total characters92
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

Unique8 ?
Unique (%)100.0%

Sample

1st row061-783-1507
2nd row055-972-7772
3rd row061-783-1928
4th row02-954-5209
5th row02-954-9241
ValueCountFrequency (%)
061-783-1507 1
12.5%
055-972-7772 1
12.5%
061-783-1928 1
12.5%
02-954-5209 1
12.5%
02-954-9241 1
12.5%
02-905-0909 1
12.5%
02-996-5306 1
12.5%
063-322-1614 1
12.5%
2023-12-12T12:05:58.752268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 16
17.4%
0 14
15.2%
9 11
12.0%
2 11
12.0%
5 8
8.7%
1 7
7.6%
7 7
7.6%
6 6
 
6.5%
3 5
 
5.4%
4 4
 
4.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 76
82.6%
Dash Punctuation 16
 
17.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14
18.4%
9 11
14.5%
2 11
14.5%
5 8
10.5%
1 7
9.2%
7 7
9.2%
6 6
7.9%
3 5
 
6.6%
4 4
 
5.3%
8 3
 
3.9%
Dash Punctuation
ValueCountFrequency (%)
- 16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 92
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 16
17.4%
0 14
15.2%
9 11
12.0%
2 11
12.0%
5 8
8.7%
1 7
7.6%
7 7
7.6%
6 6
 
6.5%
3 5
 
5.4%
4 4
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 92
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 16
17.4%
0 14
15.2%
9 11
12.0%
2 11
12.0%
5 8
8.7%
1 7
7.6%
7 7
7.6%
6 6
 
6.5%
3 5
 
5.4%
4 4
 
4.3%

수용인원
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct17
Distinct (%)63.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64
Minimum0
Maximum190
Zeros1
Zeros (%)3.7%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-12T12:05:58.904402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10.3
Q135
median50
Q390
95-th percentile130.5
Maximum190
Range190
Interquartile range (IQR)55

Descriptive statistics

Standard deviation43.487399
Coefficient of variation (CV)0.6794906
Kurtosis1.2734501
Mean64
Median Absolute Deviation (MAD)20
Skewness1.0698775
Sum1728
Variance1891.1538
MonotonicityNot monotonic
2023-12-12T12:05:59.058596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
50 3
11.1%
60 3
11.1%
120 2
 
7.4%
100 2
 
7.4%
40 2
 
7.4%
30 2
 
7.4%
35 2
 
7.4%
70 2
 
7.4%
25 1
 
3.7%
80 1
 
3.7%
Other values (7) 7
25.9%
ValueCountFrequency (%)
0 1
 
3.7%
7 1
 
3.7%
18 1
 
3.7%
25 1
 
3.7%
30 2
7.4%
35 2
7.4%
40 2
7.4%
45 1
 
3.7%
50 3
11.1%
60 3
11.1%
ValueCountFrequency (%)
190 1
 
3.7%
135 1
 
3.7%
120 2
7.4%
108 1
 
3.7%
100 2
7.4%
80 1
 
3.7%
70 2
7.4%
60 3
11.1%
50 3
11.1%
45 1
 
3.7%

예약방법(1:인터넷,2:현장)
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)7.4%
Missing0
Missing (%)0.0%
Memory size348.0 B
1
15 
2
12 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row2
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 15
55.6%
2 12
44.4%

Length

2023-12-12T12:05:59.202926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T12:05:59.321279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 15
55.6%
2 12
44.4%

이용구분(0:no,1:yes)
Boolean

CONSTANT 

Distinct1
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Memory size159.0 B
True
27 
ValueCountFrequency (%)
True 27
100.0%
2023-12-12T12:05:59.428345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

고도(m)
Real number (ℝ)

HIGH CORRELATION 

Distinct26
Distinct (%)96.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1211.24
Minimum258
Maximum1784
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-12T12:05:59.541295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum258
5-th percentile503.3
Q1857.75
median1339
Q31544.56
95-th percentile1694.294
Maximum1784
Range1526
Interquartile range (IQR)686.81

Descriptive statistics

Standard deviation439.19752
Coefficient of variation (CV)0.36260157
Kurtosis-0.756305
Mean1211.24
Median Absolute Deviation (MAD)257
Skewness-0.70322374
Sum32703.48
Variance192894.46
MonotonicityNot monotonic
2023-12-12T12:05:59.724721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
574.0 2
 
7.4%
1596.0 1
 
3.7%
1656.0 1
 
3.7%
1222.0 1
 
3.7%
1581.0 1
 
3.7%
473.0 1
 
3.7%
645.0 1
 
3.7%
258.0 1
 
3.7%
1324.82 1
 
3.7%
1339.0 1
 
3.7%
Other values (16) 16
59.3%
ValueCountFrequency (%)
258.0 1
3.7%
473.0 1
3.7%
574.0 2
7.4%
645.0 1
3.7%
687.0 1
3.7%
799.0 1
3.7%
916.5 1
3.7%
1065.0 1
3.7%
1116.53 1
3.7%
1222.0 1
3.7%
ValueCountFrequency (%)
1784.0 1
3.7%
1694.36 1
3.7%
1694.14 1
3.7%
1656.0 1
3.7%
1596.0 1
3.7%
1581.0 1
3.7%
1563.0 1
3.7%
1526.12 1
3.7%
1518.53 1
3.7%
1498.0 1
3.7%

경도
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.45752
Minimum126.51757
Maximum128.64012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-12T12:05:59.892091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.51757
5-th percentile126.52156
Q1126.78116
median127.61312
Q3127.74868
95-th percentile128.47122
Maximum128.64012
Range2.1225507
Interquartile range (IQR)0.96751655

Descriptive statistics

Standard deviation0.72352449
Coefficient of variation (CV)0.0056765933
Kurtosis-1.2473482
Mean127.45752
Median Absolute Deviation (MAD)0.6320008
Skewness0.10161929
Sum3441.353
Variance0.52348769
MonotonicityNot monotonic
2023-12-12T12:06:00.403934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
128.4603949 1
 
3.7%
128.4141587 1
 
3.7%
127.7049397 1
 
3.7%
127.7468363 1
 
3.7%
126.9851452 1
 
3.7%
126.9811215 1
 
3.7%
126.9823051 1
 
3.7%
127.0225538 1
 
3.7%
128.6401229 1
 
3.7%
127.7373653 1
 
3.7%
Other values (17) 17
63.0%
ValueCountFrequency (%)
126.5175722 1
3.7%
126.5176471 1
3.7%
126.5307022 1
3.7%
126.5397697 1
3.7%
126.545123 1
3.7%
126.5555104 1
3.7%
126.581201 1
3.7%
126.9811215 1
3.7%
126.9823051 1
3.7%
126.9851452 1
3.7%
ValueCountFrequency (%)
128.6401229 1
3.7%
128.4739011 1
3.7%
128.4649575 1
3.7%
128.4603949 1
3.7%
128.4531738 1
3.7%
128.4141587 1
3.7%
127.7505193 1
3.7%
127.7468363 1
3.7%
127.7373653 1
3.7%
127.7162686 1
3.7%

위도
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.812056
Minimum33.348728
Maximum38.146411
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-12T12:06:00.568994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33.348728
5-th percentile33.361918
Q134.34309
median35.332402
Q337.677487
95-th percentile38.137826
Maximum38.146411
Range4.7976835
Interquartile range (IQR)3.3343971

Descriptive statistics

Standard deviation1.841841
Coefficient of variation (CV)0.051430753
Kurtosis-1.4581005
Mean35.812056
Median Absolute Deviation (MAD)1.962564
Skewness-0.047079831
Sum966.92552
Variance3.3923783
MonotonicityNot monotonic
2023-12-12T12:06:00.745868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
38.121099 1
 
3.7%
38.146411 1
 
3.7%
35.7911157 1
 
3.7%
35.8587751 1
 
3.7%
37.6612482 1
 
3.7%
37.6584998 1
 
3.7%
37.6493139 1
 
3.7%
37.6937261 1
 
3.7%
37.7808591 1
 
3.7%
35.3272409 1
 
3.7%
Other values (17) 17
63.0%
ValueCountFrequency (%)
33.3487275 1
3.7%
33.3618801 1
3.7%
33.3620078 1
3.7%
33.3698376 1
3.7%
33.3767072 1
3.7%
33.3801692 1
3.7%
33.4001493 1
3.7%
35.2860309 1
3.7%
35.295971 1
3.7%
35.3181129 1
3.7%
ValueCountFrequency (%)
38.146411 1
3.7%
38.1400263 1
3.7%
38.1326918 1
3.7%
38.126612 1
3.7%
38.121099 1
3.7%
37.7808591 1
3.7%
37.6937261 1
3.7%
37.6612482 1
3.7%
37.6584998 1
3.7%
37.6493139 1
3.7%

심볼코드
Categorical

CONSTANT 

Distinct1
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Memory size348.0 B
E
27 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
E 27
100.0%

Length

2023-12-12T12:06:00.975940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T12:06:01.107797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
e 27
100.0%

Interactions

2023-12-12T12:05:52.628639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:05:47.297510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:05:47.969804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:05:48.786198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:05:49.759115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:05:51.021961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:05:51.889863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:05:52.733104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:05:47.412834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:05:48.062232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:05:48.933589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:05:49.890919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:05:51.143003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:05:51.985634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:05:52.823389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:05:47.527903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:05:48.164919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:05:49.126512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:05:50.020118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:05:51.307881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:05:52.082464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:05:52.915163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:05:47.623363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:05:48.263182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:05:49.260844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:05:50.523829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:05:51.433067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:05:52.197859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:05:53.023455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:05:47.715976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:05:48.379280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:05:49.396482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:05:50.653324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:05:51.550104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:05:52.308937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:05:53.146885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:05:47.805217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:05:48.546141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:05:49.519136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:05:50.802306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:05:51.672166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:05:52.436872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:05:53.246732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:05:47.881392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:05:48.671883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:05:49.635041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:05:50.914480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:05:51.782534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:05:52.527742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T12:06:01.204316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번공원사무소코드일련번호명칭(한글)명칭(영문)주소(지번)주소(새주소)전화번호수용인원예약방법(1:인터넷,2:현장)고도(m)경도위도
순번1.0000.9000.0001.0001.0000.8900.7341.0000.0000.2060.3450.8010.955
공원사무소코드0.9001.0000.0001.0001.0001.0000.8211.0000.0000.8830.4120.9670.954
일련번호0.0000.0001.0001.0001.0000.7650.8491.0000.0000.1010.0000.0000.000
명칭(한글)1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
명칭(영문)1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
주소(지번)0.8901.0000.7651.0001.0001.0000.8801.0000.8880.7040.9030.0001.000
주소(새주소)0.7340.8210.8491.0001.0000.8801.0001.0000.8000.4430.7560.8970.568
전화번호1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
수용인원0.0000.0000.0001.0001.0000.8880.8001.0001.0000.4810.0000.0000.000
예약방법(1:인터넷,2:현장)0.2060.8830.1011.0001.0000.7040.4431.0000.4811.0000.1980.8360.519
고도(m)0.3450.4120.0001.0001.0000.9030.7561.0000.0000.1981.0000.5840.359
경도0.8010.9670.0001.0001.0000.0000.8971.0000.0000.8360.5841.0000.860
위도0.9550.9540.0001.0001.0001.0000.5681.0000.0000.5190.3590.8601.000
2023-12-12T12:06:01.393603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번공원사무소코드일련번호수용인원고도(m)경도위도예약방법(1:인터넷,2:현장)
순번1.000-0.122-0.475-0.173-0.262-0.0580.0140.063
공원사무소코드-0.1221.0000.123-0.239-0.122-0.674-0.3580.635
일련번호-0.4750.1231.0000.0000.0830.021-0.0880.022
수용인원-0.173-0.2390.0001.0000.722-0.084-0.4040.398
고도(m)-0.262-0.1220.0830.7221.000-0.056-0.4500.098
경도-0.058-0.6740.021-0.084-0.0561.0000.7900.584
위도0.014-0.358-0.088-0.404-0.4500.7901.0000.587
예약방법(1:인터넷,2:현장)0.0630.6350.0220.3980.0980.5840.5871.000

Missing values

2023-12-12T12:05:53.413260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T12:05:53.713857image/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.
2023-12-12T12:05:53.866342image/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

순번공원사무소코드분류코드일련번호명칭(한글)명칭(영문)주소(지번)주소(새주소)전화번호수용인원예약방법(1:인터넷,2:현장)이용구분(0:no,1:yes)고도(m)경도위도심볼코드
014011053중청대피소Jungcheong Shelter강원도 양양군 서면 오색리 산1-24강원도 양양군 서면 대청봉길 1<NA>1201Y1596.0128.46039538.121099E
124011052수렴동대피소Suryeomdong Shelter강원도 인제군 북면 용대리 산12-1강원도 인제군 북면 백담로 1220 수렴동대피소<NA>181Y574.0128.41415938.146411E
234011054소청대피소Socheong Shelter강원도 인제군 북면 용대리 산12-1강원도 인제군 북면 백담로 1755 소청대피소<NA>702Y1450.0128.45317438.126612E
344011055희운각대피소Huiungak Shelter강원도 인제군 북면 용대리 산12-1강원도 인제군 북면 백담로 1925 희운각산장<NA>351Y1065.0128.46495738.132692E
454011056양폭대피소Yangpok Shelter강원도 속초시 설악동 산41강원도 속초시 설악산로 1119-180 (설악동)<NA>301Y687.0128.47390138.140026E
5620011052윗세대피소(2)Witse shelter (2)제주 제주시 애월읍 광령리 산 183-6제주 제주시 애월읍 1100로 2070-510<NA>1002Y1694.36126.51757233.36188E
6720011051윗세대피소(1)Witse shelter (1)제주 제주시 애월읍 광령리 산 183-6제주 제주시 애월읍 1100로 2070-510<NA>602Y1694.14126.51764733.362008E
7820011057속밭대피소Sokbat shelter제주 제주시 조천읍 교래리 산 137-2<NA><NA>252Y1116.53126.58120133.380169E
8920011055진달래밭대피소Jindalraebat shelter제주 서귀포시 남원읍 하예리 산 1<NA><NA>1002Y1518.53126.5555133.369838E
91020011053삼각봉대피소Samgakbong shelter제주 제주시 오라동 산 107-20<NA><NA>802Y1526.12126.53070233.376707E
순번공원사무소코드분류코드일련번호명칭(한글)명칭(영문)주소(지번)주소(새주소)전화번호수용인원예약방법(1:인터넷,2:현장)이용구분(0:no,1:yes)고도(m)경도위도심볼코드
17181031052피아골대피소Piagol Shelter전라남도 구례군 토지면 내동리 368미변환 지번 주소061-783-1928502Y799.0127.55598735.286031E
18191011051벽소령대피소Byeoksoryeong Shelter경상남도 함양군 마천면 삼정리 산161경상남도 함양군 마천면 마천삼정로 1256<NA>1201Y1323.0127.6428135.32604E
19201011052로타리대피소Rotari Shelter경상남도 산청군 시천면 중산리 산208경상남도 산청군 시천면 지리산대로 320-103<NA>351Y1339.0127.73736535.327241E
20219011051노인봉대피소Noinbong Shelters강원 강릉시 연곡면 삼산리 산 1-12강원도 강릉시 연곡면 소금강길 487<NA>502Y1324.82128.64012337.780859E
212215021051도봉대피소Dobong Shelter서울특별시 도봉구 도봉동 산31서울특별시 도봉구 도봉산길 92-6 (도봉동)02-954-520971Y258.0127.02255437.693726E
222315011052북한대피소Bukhan Shelter경기도 고양시 북한동 산1-1경기도 고양시 덕양구 대서문길 375 (북한동)02-954-9241301Y574.0126.98230537.649314E
232415011053백운대피소Baekun Shelter경기도 고양시 덕양구 효자동 산1-1미변환 지번 주소02-905-0909501Y645.0126.98112237.6585E
242515011051북한산인수대피소Bukhansan Insu Shelter경기도 고양시 덕양구 효자동경기도 고양시 덕양구 북한산로 364-13 (효자동)02-996-530601Y473.0126.98514537.661248E
25268011051향적봉대피소Hyangjeokbong Shelter전라북도 무주군 설천면 삼공리 산109미변환 지번 주소063-322-1614602Y1581.0127.74683635.858775E
26278011052삿갓골재대피소Satgatjae Shelter경상남도 거창군 북상면 월성리 산282-3미변환 지번 주소<NA>451Y1222.0127.7049435.791116E