Overview

Dataset statistics

Number of variables7
Number of observations923
Missing cells548
Missing cells (%)8.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory51.5 KiB
Average record size in memory57.1 B

Variable types

Text4
Categorical2
Numeric1

Dataset

Description한국문화관광연구원 관광지식정보시스템의 관광지식채널에서 매월 관광산업에 대한 동향을 확인할 수 있는 보고서 자료 등 발행 중인 정기간행물 데이터를 제공합니다.
Author충청남도
URLhttps://alldam.chungnam.go.kr/bigdata/collect/view.chungnam?menuCd=DOM_000000201001001000&apiIdx=3001

Alerts

SUB_DESCRIPTION is highly overall correlated with PUBLISHERHigh correlation
PUBLISHER is highly overall correlated with VIEW_COUNT and 1 other fieldsHigh correlation
VIEW_COUNT is highly overall correlated with PUBLISHERHigh correlation
SUB_DESCRIPTION is highly imbalanced (76.4%)Imbalance
VIEW_COUNT has 59 (6.4%) missing valuesMissing
설명 has 489 (53.0%) missing valuesMissing

Reproduction

Analysis started2024-01-09 21:48:51.084281
Analysis finished2024-01-09 21:48:52.130609
Duration1.05 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

제목
Text

Distinct863
Distinct (%)93.5%
Missing0
Missing (%)0.0%
Memory size7.3 KiB
2024-01-10T06:48:52.315360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length75
Median length56
Mean length31.271939
Min length9

Characters and Unicode

Total characters28864
Distinct characters561
Distinct categories15 ?
Distinct scripts4 ?
Distinct blocks7 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique804 ?
Unique (%)87.1%

Sample

1st row제46호 한국관광정책
2nd row한국관광동향 2005 4분기
3rd row성공적 국제기구 활동을 위한 제언-OECD관광위원회 중심(한국관광정책4호. 2000)
4th row수익성과 공익성의 적절한 조화 독일뮌헨 올림픽공원(한국관광정책4호. 2000)
5th row우리나라의 컨벤션 정책동향과 과제(한국관광정책4호. 2000)
ValueCountFrequency (%)
관광동향분석 108
 
2.3%
2000 104
 
2.2%
2003 99
 
2.1%
2002 83
 
1.8%
및 80
 
1.7%
2001 60
 
1.3%
관광산업 48
 
1.0%
kcti-info 39
 
0.8%
위한 37
 
0.8%
kcti 34
 
0.7%
Other values (1976) 3962
85.1%
2024-01-10T06:48:52.699123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3730
 
12.9%
0 1615
 
5.6%
관 1281
 
4.4%
광 1231
 
4.3%
2 1192
 
4.1%
1 839
 
2.9%
국 698
 
2.4%
한 676
 
2.3%
( 602
 
2.1%
) 602
 
2.1%
Other values (551) 16398
56.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 16046
55.6%
Decimal Number 5016
 
17.4%
Space Separator 3731
 
12.9%
Other Punctuation 934
 
3.2%
Uppercase Letter 919
 
3.2%
Lowercase Letter 717
 
2.5%
Open Punctuation 662
 
2.3%
Close Punctuation 662
 
2.3%
Dash Punctuation 145
 
0.5%
Initial Punctuation 8
 
< 0.1%
Other values (5) 24
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
관 1281
 
8.0%
광 1231
 
7.7%
국 698
 
4.3%
한 676
 
4.2%
정 594
 
3.7%
호 587
 
3.7%
책 546
 
3.4%
향 287
 
1.8%
년 279
 
1.7%
기 270
 
1.7%
Other values (467) 9597
59.8%
Lowercase Letter
ValueCountFrequency (%)
u 99
13.8%
i 76
10.6%
r 74
10.3%
s 72
10.0%
t 71
9.9%
o 49
6.8%
e 43
 
6.0%
n 41
 
5.7%
m 39
 
5.4%
h 37
 
5.2%
Other values (12) 116
16.2%
Uppercase Letter
ValueCountFrequency (%)
I 178
19.4%
T 160
17.4%
C 133
14.5%
K 77
8.4%
O 71
 
7.7%
S 56
 
6.1%
N 51
 
5.5%
F 46
 
5.0%
A 44
 
4.8%
E 24
 
2.6%
Other values (10) 79
8.6%
Other Punctuation
ValueCountFrequency (%)
. 576
61.7%
& 105
 
11.2%
; 73
 
7.8%
# 71
 
7.6%
? 35
 
3.7%
/ 22
 
2.4%
: 20
 
2.1%
! 15
 
1.6%
· 13
 
1.4%
' 2
 
0.2%
Decimal Number
ValueCountFrequency (%)
0 1615
32.2%
2 1192
23.8%
1 839
16.7%
4 322
 
6.4%
3 268
 
5.3%
5 228
 
4.5%
6 164
 
3.3%
7 145
 
2.9%
9 136
 
2.7%
8 107
 
2.1%
Open Punctuation
ValueCountFrequency (%)
( 602
90.9%
[ 59
 
8.9%
「 1
 
0.2%
Close Punctuation
ValueCountFrequency (%)
) 602
90.9%
] 59
 
8.9%
」 1
 
0.2%
Letter Number
ValueCountFrequency (%)
Ⅰ 4
50.0%
Ⅱ 3
37.5%
Ⅲ 1
 
12.5%
Space Separator
ValueCountFrequency (%)
3730
> 99.9%
　 1
 
< 0.1%
Dash Punctuation
ValueCountFrequency (%)
- 144
99.3%
― 1
 
0.7%
Initial Punctuation
ValueCountFrequency (%)
‘ 6
75.0%
“ 2
 
25.0%
Final Punctuation
ValueCountFrequency (%)
’ 6
75.0%
” 2
 
25.0%
Math Symbol
ValueCountFrequency (%)
~ 2
50.0%
+ 2
50.0%
Connector Punctuation
ValueCountFrequency (%)
_ 3
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 16039
55.6%
Common 11174
38.7%
Latin 1644
 
5.7%
Han 7
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
관 1281
 
8.0%
광 1231
 
7.7%
국 698
 
4.4%
한 676
 
4.2%
정 594
 
3.7%
호 587
 
3.7%
책 546
 
3.4%
향 287
 
1.8%
년 279
 
1.7%
기 270
 
1.7%
Other values (461) 9590
59.8%
Latin
ValueCountFrequency (%)
I 178
 
10.8%
T 160
 
9.7%
C 133
 
8.1%
u 99
 
6.0%
K 77
 
4.7%
i 76
 
4.6%
r 74
 
4.5%
s 72
 
4.4%
O 71
 
4.3%
t 71
 
4.3%
Other values (35) 633
38.5%
Common
ValueCountFrequency (%)
3730
33.4%
0 1615
14.5%
2 1192
 
10.7%
1 839
 
7.5%
( 602
 
5.4%
) 602
 
5.4%
. 576
 
5.2%
4 322
 
2.9%
3 268
 
2.4%
5 228
 
2.0%
Other values (29) 1200
 
10.7%
Han
ValueCountFrequency (%)
新 2
28.6%
馬 1
14.3%
乘 1
14.3%
法 1
14.3%
游 1
14.3%
旅 1
14.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 16039
55.6%
ASCII 12777
44.3%
Punctuation 17
 
0.1%
None 16
 
0.1%
Number Forms 8
 
< 0.1%
CJK 6
 
< 0.1%
CJK Compat Ideographs 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3730
29.2%
0 1615
12.6%
2 1192
 
9.3%
1 839
 
6.6%
( 602
 
4.7%
) 602
 
4.7%
. 576
 
4.5%
4 322
 
2.5%
3 268
 
2.1%
5 228
 
1.8%
Other values (62) 2803
21.9%
Hangul
ValueCountFrequency (%)
관 1281
 
8.0%
광 1231
 
7.7%
국 698
 
4.4%
한 676
 
4.2%
정 594
 
3.7%
호 587
 
3.7%
책 546
 
3.4%
향 287
 
1.8%
년 279
 
1.7%
기 270
 
1.7%
Other values (461) 9590
59.8%
None
ValueCountFrequency (%)
· 13
81.2%
　 1
 
6.2%
」 1
 
6.2%
「 1
 
6.2%
Punctuation
ValueCountFrequency (%)
‘ 6
35.3%
’ 6
35.3%
” 2
 
11.8%
“ 2
 
11.8%
― 1
 
5.9%
Number Forms
ValueCountFrequency (%)
Ⅰ 4
50.0%
Ⅱ 3
37.5%
Ⅲ 1
 
12.5%
CJK
ValueCountFrequency (%)
新 2
33.3%
馬 1
16.7%
乘 1
16.7%
法 1
16.7%
游 1
16.7%
CJK Compat Ideographs
ValueCountFrequency (%)
旅 1
100.0%

UCI
Text

Distinct864
Distinct (%)93.6%
Missing0
Missing (%)0.0%
Memory size7.3 KiB
2024-01-10T06:48:52.898234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length35
Median length35
Mean length35
Min length35

Characters and Unicode

Total characters32305
Distinct characters26
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

Unique806 ?
Unique (%)87.3%

Sample

1st rowG706+KTCC06-C.periodical.0000100668
2nd rowG706+KTCC06-C.periodical.0000004296
3rd rowG706+KTCC06-C.periodical.0000004327
4th rowG706+KTCC06-C.periodical.0000004328
5th rowG706+KTCC06-C.periodical.0000004329
ValueCountFrequency (%)
g706+ktcc06-c.periodical.0000004296 3
 
0.3%
g706+ktcc06-c.periodical.0000004525 2
 
0.2%
g706+ktcc06-c.periodical.0000004619 2
 
0.2%
g706+ktcc06-c.periodical.0000100668 2
 
0.2%
g706+ktcc06-c.periodical.0000004618 2
 
0.2%
g706+ktcc06-c.periodical.0000004617 2
 
0.2%
g706+ktcc06-c.periodical.0000004524 2
 
0.2%
g706+ktcc06-c.periodical.0000004523 2
 
0.2%
g706+ktcc06-c.periodical.0000004522 2
 
0.2%
g706+ktcc06-c.periodical.0000004307 2
 
0.2%
Other values (854) 902
97.7%
2024-01-10T06:48:53.177826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 7235
22.4%
C 2769
 
8.6%
6 2200
 
6.8%
i 1846
 
5.7%
. 1846
 
5.7%
7 1169
 
3.6%
r 923
 
2.9%
l 923
 
2.9%
a 923
 
2.9%
c 923
 
2.9%
Other values (16) 11548
35.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 13845
42.9%
Lowercase Letter 9230
28.6%
Uppercase Letter 5538
 
17.1%
Other Punctuation 1846
 
5.7%
Dash Punctuation 923
 
2.9%
Math Symbol 923
 
2.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 7235
52.3%
6 2200
 
15.9%
7 1169
 
8.4%
4 802
 
5.8%
1 711
 
5.1%
2 559
 
4.0%
3 344
 
2.5%
5 328
 
2.4%
8 260
 
1.9%
9 237
 
1.7%
Lowercase Letter
ValueCountFrequency (%)
i 1846
20.0%
r 923
10.0%
l 923
10.0%
a 923
10.0%
c 923
10.0%
d 923
10.0%
o 923
10.0%
e 923
10.0%
p 923
10.0%
Uppercase Letter
ValueCountFrequency (%)
C 2769
50.0%
G 923
 
16.7%
T 923
 
16.7%
K 923
 
16.7%
Other Punctuation
ValueCountFrequency (%)
. 1846
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 923
100.0%
Math Symbol
ValueCountFrequency (%)
+ 923
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 17537
54.3%
Latin 14768
45.7%

Most frequent character per script

Common
ValueCountFrequency (%)
0 7235
41.3%
6 2200
 
12.5%
. 1846
 
10.5%
7 1169
 
6.7%
- 923
 
5.3%
+ 923
 
5.3%
4 802
 
4.6%
1 711
 
4.1%
2 559
 
3.2%
3 344
 
2.0%
Other values (3) 825
 
4.7%
Latin
ValueCountFrequency (%)
C 2769
18.8%
i 1846
12.5%
r 923
 
6.2%
l 923
 
6.2%
a 923
 
6.2%
c 923
 
6.2%
d 923
 
6.2%
o 923
 
6.2%
G 923
 
6.2%
e 923
 
6.2%
Other values (3) 2769
18.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 32305
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 7235
22.4%
C 2769
 
8.6%
6 2200
 
6.8%
i 1846
 
5.7%
. 1846
 
5.7%
7 1169
 
3.6%
r 923
 
2.9%
l 923
 
2.9%
a 923
 
2.9%
c 923
 
2.9%
Other values (16) 11548
35.7%

URL
Text

Distinct866
Distinct (%)93.8%
Missing0
Missing (%)0.0%
Memory size7.3 KiB
2024-01-10T06:48:53.417518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length104
Median length102
Mean length102.50271
Min length98

Characters and Unicode

Total characters94610
Distinct characters36
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique809 ?
Unique (%)87.6%

Sample

1st rowhttp://know.tour.go.kr/ptourknow/knowplus/kChannel/kChannelPeriod/kChannelPeriodDetail.do?seq=100668
2nd rowhttp://know.tour.go.kr/ptourknow/knowplus/kChannel/kChannelPeriod/kChannelPeriodDetail.do?seq=4296
3rd rowhttp://know.tour.go.kr/ptourknow/knowplus/kChannel/kChannelPeriod/kChannelPeriodDetail.do?seq=4327
4th rowhttp://know.tour.go.kr/ptourknow/knowplus/kChannel/kChannelPeriod/kChannelPeriodDetail.do?seq=4328
5th rowhttp://know.tour.go.kr/ptourknow/knowplus/kChannel/kChannelPeriod/kChannelPeriodDetail.do?seq=4329
ValueCountFrequency (%)
http://know.tour.go.kr/ptourknow/knowplus/kchannel/kchannelperiod/kchannelperioddetail19re.do?seq=4296 2
 
0.2%
http://know.tour.go.kr/ptourknow/knowplus/kchannel/kchannelperiod/kchannelperioddetail19re.do?seq=4306 2
 
0.2%
http://know.tour.go.kr/ptourknow/knowplus/kchannel/kchannelperiod/kchannelperioddetail19re.do?seq=4305 2
 
0.2%
http://know.tour.go.kr/ptourknow/knowplus/kchannel/kchannelperiod/kchannelperioddetail19re.do?seq=4622 2
 
0.2%
http://know.tour.go.kr/ptourknow/knowplus/kchannel/kchannelperiod/kchannelperioddetail19re.do?seq=4421 2
 
0.2%
http://know.tour.go.kr/ptourknow/knowplus/kchannel/kchannelperiod/kchannelperioddetail19re.do?seq=4621 2
 
0.2%
http://know.tour.go.kr/ptourknow/knowplus/kchannel/kchannelperiod/kchannelperioddetail19re.do?seq=4618 2
 
0.2%
http://know.tour.go.kr/ptourknow/knowplus/kchannel/kchannelperiod/kchannelperioddetail19re.do?seq=4617 2
 
0.2%
http://know.tour.go.kr/ptourknow/knowplus/kchannel/kchannelperiod/kchannelperioddetail19re.do?seq=4525 2
 
0.2%
http://know.tour.go.kr/ptourknow/knowplus/kchannel/kchannelperiod/kchannelperioddetail19re.do?seq=4523 2
 
0.2%
Other values (856) 903
97.8%
2024-01-10T06:48:53.772901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 8307
 
8.8%
o 8307
 
8.8%
e 7288
 
7.7%
/ 6461
 
6.8%
k 6461
 
6.8%
r 4615
 
4.9%
t 4615
 
4.9%
l 4615
 
4.9%
h 3692
 
3.9%
a 3692
 
3.9%
Other values (26) 36557
38.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 69129
73.1%
Other Punctuation 11999
 
12.7%
Uppercase Letter 6365
 
6.7%
Decimal Number 6194
 
6.5%
Math Symbol 923
 
1.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 8307
12.0%
o 8307
12.0%
e 7288
10.5%
k 6461
9.3%
r 4615
 
6.7%
t 4615
 
6.7%
l 4615
 
6.7%
h 3692
 
5.3%
a 3692
 
5.3%
u 2769
 
4.0%
Other values (7) 14768
21.4%
Decimal Number
ValueCountFrequency (%)
1 1538
24.8%
9 1064
17.2%
4 802
12.9%
0 699
11.3%
2 559
 
9.0%
6 354
 
5.7%
3 344
 
5.6%
5 328
 
5.3%
8 260
 
4.2%
7 246
 
4.0%
Other Punctuation
ValueCountFrequency (%)
/ 6461
53.8%
. 3692
30.8%
? 923
 
7.7%
: 923
 
7.7%
Uppercase Letter
ValueCountFrequency (%)
C 2769
43.5%
P 1846
29.0%
D 923
 
14.5%
R 827
 
13.0%
Math Symbol
ValueCountFrequency (%)
= 923
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 75494
79.8%
Common 19116
 
20.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 8307
 
11.0%
o 8307
 
11.0%
e 7288
 
9.7%
k 6461
 
8.6%
r 4615
 
6.1%
t 4615
 
6.1%
l 4615
 
6.1%
h 3692
 
4.9%
a 3692
 
4.9%
u 2769
 
3.7%
Other values (11) 21133
28.0%
Common
ValueCountFrequency (%)
/ 6461
33.8%
. 3692
19.3%
1 1538
 
8.0%
9 1064
 
5.6%
? 923
 
4.8%
= 923
 
4.8%
: 923
 
4.8%
4 802
 
4.2%
0 699
 
3.7%
2 559
 
2.9%
Other values (5) 1532
 
8.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 94610
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 8307
 
8.8%
o 8307
 
8.8%
e 7288
 
7.7%
/ 6461
 
6.8%
k 6461
 
6.8%
r 4615
 
4.9%
t 4615
 
4.9%
l 4615
 
4.9%
h 3692
 
3.9%
a 3692
 
3.9%
Other values (26) 36557
38.6%

SUB_DESCRIPTION
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size7.3 KiB
접근권한:전체
861 
<NA>
 
59
접근권한:연구원내부공개용
 
3

Length

Max length13
Median length7
Mean length6.8277356
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row접근권한:전체
4th row접근권한:전체
5th row접근권한:전체

Common Values

ValueCountFrequency (%)
접근권한:전체 861
93.3%
<NA> 59
 
6.4%
접근권한:연구원내부공개용 3
 
0.3%

Length

2024-01-10T06:48:53.869361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T06:48:53.936227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
접근권한:전체 861
93.3%
na 59
 
6.4%
접근권한:연구원내부공개용 3
 
0.3%

VIEW_COUNT
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct507
Distinct (%)58.7%
Missing59
Missing (%)6.4%
Infinite0
Infinite (%)0.0%
Mean734.09722
Minimum3
Maximum19974
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.2 KiB
2024-01-10T06:48:54.024307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile94
Q1124
median197
Q31015.25
95-th percentile2478.65
Maximum19974
Range19971
Interquartile range (IQR)891.25

Descriptive statistics

Standard deviation1262.809
Coefficient of variation (CV)1.7202204
Kurtosis72.408474
Mean734.09722
Median Absolute Deviation (MAD)99
Skewness6.4189241
Sum634260
Variance1594686.6
MonotonicityNot monotonic
2024-01-10T06:48:54.133088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
121 11
 
1.2%
111 9
 
1.0%
94 8
 
0.9%
104 8
 
0.9%
126 8
 
0.9%
120 8
 
0.9%
117 7
 
0.8%
103 7
 
0.8%
102 7
 
0.8%
144 7
 
0.8%
Other values (497) 784
84.9%
(Missing) 59
 
6.4%
ValueCountFrequency (%)
3 1
0.1%
4 1
0.1%
6 1
0.1%
7 1
0.1%
16 1
0.1%
20 1
0.1%
38 1
0.1%
61 1
0.1%
70 1
0.1%
72 1
0.1%
ValueCountFrequency (%)
19974 1
0.1%
10074 1
0.1%
9446 1
0.1%
8123 1
0.1%
7952 1
0.1%
7739 1
0.1%
6581 1
0.1%
6372 1
0.1%
5533 1
0.1%
5275 1
0.1%

PUBLISHER
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.3 KiB
KTCC06
770 
<NA>
153 

Length

Max length6
Median length6
Mean length5.6684724
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
KTCC06 770
83.4%
<NA> 153
 
16.6%

Length

2024-01-10T06:48:54.240770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T06:48:54.316537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
ktcc06 770
83.4%
na 153
 
16.6%

설명
Text

MISSING 

Distinct420
Distinct (%)96.8%
Missing489
Missing (%)53.0%
Memory size7.3 KiB
2024-01-10T06:48:54.427574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length1024
Median length981
Mean length696.62212
Min length13

Characters and Unicode

Total characters302334
Distinct characters769
Distinct categories17 ?
Distinct scripts4 ?
Distinct blocks13 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique414 ?
Unique (%)95.4%

Sample

1st row<p><br />[ 목&nbsp; 차 ]</p><p>관광시론 :&nbsp; 관광산업의 정책환경과 2012년 관광진흥 정책방향 (노일식|문화체육관광부 관광정책과장)</p><p>기획특집 : 중국관광객 300만 유치 과제와 전망<br />&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; ① 중국 시장 현황과 전망 (한화준|한국관광공사 중국팀장)<br />&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; ② 중국관광객 수용태세 및 숙박시설 확충방안 (구본상|서울특별시 관광과장)<br />&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; ③ 제주 중국관광객 유치성과와 발전방안 (서용건|제주대학교 관광경영학과 교수)<br />&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; ④ 중국인 개별관광객 유치 활성화를 위한 정책 대응 방향 (최경은|한국문화관광연구원 책임연구원)</p><p>관광과 현장 : 관광형 커뮤니티 비즈니스 활성화 방안<br />&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; - 진&nbsp; 행 : 강신겸|
2nd row<p><img alt="image" src="/FileSystem/NaverEdit/2019_10_08_210343.jpg"></p>
3rd row<p style="text-align: center; " align="center"><img src="/FileSystem/NaverEdit/2020_07_14_112116.png" alt="image">&nbsp;</p><p style="text-align: center; " align="center">&nbsp;</p><p style="text-align: center; " align="center">&nbsp;</p><p style="text-align: left;" align="left"><b><span style="font-size: 14pt;">「여행의 맛! 2019년 국민여행조사&#44; 알려드립니다!」</span></b></p><p style="text-align: left;" align="left"><b><span style="font-size: 12pt;">- 2019년 국민여행조사 주요 결과 -</span></b></p><p style="text-align: left;" align="left">&nbsp;</p><p style="text-align: left;" align="left">여행의 맛! 2019년 국민여행조사 주요 결과를 알려드립니다!</p><p style="text-align: left;" align="left">우리 국민의 국내여행 경험률은 92.4%이며&#44; 만 15세 이상 전국민 1인 평균 7.6회 여행을 다녀온 것으로 분석됩니다.</p><p style="text-align: left;" align="left">또한&#44; 1인 평균 연 12.9일 국내여행을 했으며&#44; 평균 97만 6천원을 소비하는 것으로 조사되었습니다.</p><p style="text-align: left;" align="left">&nbsp;</p><p style="text-align: left;" align="left">우리 국민의 해외여행 경험률은 23.2%로&#44; 1회 평균 여행일수는 4.8일로 나타났습니다.</p><p style="text-align: left;" align
4th row<p><img alt="image" src="/FileSystem/NaverEdit/2019_10_08_213655.gif"></p><p>&nbsp;</p><p><span style="color: rgb(29&#44; 33&#44; 41); font-family: Helvetica&#44; Arial&#44; sans-serif; font-size: 14px; background-color: rgb(255&#44; 255&#44; 255);">[평창 동계 올림픽 돌아보기! 강원도로 얼마나 많은 관광객이 다녀갔을까요?]</span></p><p><span style="color: rgb(29&#44; 33&#44; 41); font-family: Helvetica&#44; Arial&#44; sans-serif; font-size: 14px; background-color: rgb(255&#44; 255&#44; 255);">평창 동계 올림픽 기간 동안 관광객이 가장 많이 방문한 곳은 &lt;강릉시&gt; 가장 오래 머무른 곳은 &lt;평창군&gt;으로 나타났습니다. 강릉시와 평창군 정성군의 방문객은 평창 동계올림픽 기간을 기준으로 전년대비 77.7% 증가했습니다. </span><font color="#1d2129" face="Helvetica&#44; Arial&#44; sans-serif"><span style="font-size: 14px;">&nbsp;관광객이 가장 많이 다녀간 곳은 1순위 강릉시(163만명)였고&#44; 2순위 평창군(134만 2천명)&#44; 3순위 정선군(59만 1천명)이었습니다.&nbsp;</span></font><span style="color: rgb(29&#44; 33&#44; 41); font-family: Helvetica&#44; Arial&#44; sans-serif; font-size: 14px; background-color: rgb(255&#44; 255&#44; 255);">가장 많이 머무른 곳은 1순위 평창군(2.02일)&#44; 2순위 강릉시(1.94일
5th row<p><img src="/FileSystem/NaverEdit/2019_12_04_154815.png" alt="image">&nbsp;</p>
ValueCountFrequency (%)
761
 
2.7%
대비 384
 
1.4%
0px 373
 
1.3%
style="font-size 256
 
0.9%
style="position 244
 
0.9%
및 210
 
0.7%
font-size 179
 
0.6%
style="text-align 178
 
0.6%
수 147
 
0.5%
br 147
 
0.5%
Other values (8767) 25376
89.8%
2024-01-10T06:48:54.727202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
28919
 
9.6%
< 10196
 
3.4%
> 10097
 
3.3%
p 9465
 
3.1%
t 8453
 
2.8%
s 8115
 
2.7%
e 7979
 
2.6%
/ 7845
 
2.6%
n 7547
 
2.5%
4 7026
 
2.3%
Other values (759) 196692
65.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 94332
31.2%
Other Letter 66044
21.8%
Other Punctuation 37870
12.5%
Space Separator 28919
 
9.6%
Decimal Number 25824
 
8.5%
Math Symbol 24657
 
8.2%
Uppercase Letter 16689
 
5.5%
Dash Punctuation 3904
 
1.3%
Close Punctuation 1365
 
0.5%
Open Punctuation 1361
 
0.5%
Other values (7) 1369
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
관 2952
 
4.5%
광 2811
 
4.3%
국 1489
 
2.3%
한 1189
 
1.8%
년 1001
 
1.5%
가 964
 
1.5%
로 947
 
1.4%
으 913
 
1.4%
기 907
 
1.4%
객 898
 
1.4%
Other values (642) 51973
78.7%
Lowercase Letter
ValueCountFrequency (%)
p 9465
 
10.0%
t 8453
 
9.0%
s 8115
 
8.6%
e 7979
 
8.5%
n 7547
 
8.0%
o 5791
 
6.1%
l 5673
 
6.0%
r 5542
 
5.9%
a 4834
 
5.1%
i 4781
 
5.1%
Other values (16) 26152
27.7%
Uppercase Letter
ValueCountFrequency (%)
T 1677
 
10.0%
I 1669
 
10.0%
O 1564
 
9.4%
N 1328
 
8.0%
R 1139
 
6.8%
S 1092
 
6.5%
P 1065
 
6.4%
E 996
 
6.0%
D 880
 
5.3%
F 701
 
4.2%
Other values (14) 4578
27.4%
Other Punctuation
ValueCountFrequency (%)
/ 7845
20.7%
" 7010
18.5%
; 6382
16.9%
& 4971
13.1%
. 3826
10.1%
: 3357
8.9%
# 3093
 
8.2%
% 602
 
1.6%
? 257
 
0.7%
' 219
 
0.6%
Other values (6) 308
 
0.8%
Decimal Number
ValueCountFrequency (%)
4 7026
27.2%
1 4290
16.6%
0 3886
15.0%
2 3317
12.8%
5 1772
 
6.9%
3 1506
 
5.8%
6 1051
 
4.1%
7 1009
 
3.9%
8 1008
 
3.9%
9 959
 
3.7%
Math Symbol
ValueCountFrequency (%)
< 10196
41.4%
> 10097
40.9%
= 4104
16.6%
| 205
 
0.8%
~ 29
 
0.1%
→ 23
 
0.1%
∼ 2
 
< 0.1%
+ 1
 
< 0.1%
Other Number
ValueCountFrequency (%)
① 34
30.6%
② 32
28.8%
③ 21
18.9%
④ 15
13.5%
⑤ 8
 
7.2%
⑥ 1
 
0.9%
Other Symbol
ValueCountFrequency (%)
△ 29
56.9%
☞ 8
 
15.7%
■ 7
 
13.7%
○ 4
 
7.8%
◈ 2
 
3.9%
□ 1
 
2.0%
Letter Number
ValueCountFrequency (%)
Ⅰ 13
39.4%
Ⅱ 10
30.3%
Ⅲ 6
18.2%
Ⅳ 2
 
6.1%
Ⅴ 2
 
6.1%
Close Punctuation
ValueCountFrequency (%)
) 1222
89.5%
] 87
 
6.4%
」 48
 
3.5%
〉 8
 
0.6%
Open Punctuation
ValueCountFrequency (%)
( 1217
89.4%
[ 87
 
6.4%
「 49
 
3.6%
〈 8
 
0.6%
Initial Punctuation
ValueCountFrequency (%)
‘ 111
87.4%
“ 16
 
12.6%
Final Punctuation
ValueCountFrequency (%)
’ 98
88.3%
” 13
 
11.7%
Space Separator
ValueCountFrequency (%)
28919
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3904
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 932
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 125236
41.4%
Latin 111054
36.7%
Hangul 66016
21.8%
Han 28
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
관 2952
 
4.5%
광 2811
 
4.3%
국 1489
 
2.3%
한 1189
 
1.8%
년 1001
 
1.5%
가 964
 
1.5%
로 947
 
1.4%
으 913
 
1.4%
기 907
 
1.4%
객 898
 
1.4%
Other values (628) 51945
78.7%
Common
ValueCountFrequency (%)
28919
23.1%
< 10196
 
8.1%
> 10097
 
8.1%
/ 7845
 
6.3%
4 7026
 
5.6%
" 7010
 
5.6%
; 6382
 
5.1%
& 4971
 
4.0%
1 4290
 
3.4%
= 4104
 
3.3%
Other values (52) 34396
27.5%
Latin
ValueCountFrequency (%)
p 9465
 
8.5%
t 8453
 
7.6%
s 8115
 
7.3%
e 7979
 
7.2%
n 7547
 
6.8%
o 5791
 
5.2%
l 5673
 
5.1%
r 5542
 
5.0%
a 4834
 
4.4%
i 4781
 
4.3%
Other values (45) 42874
38.6%
Han
ValueCountFrequency (%)
前 5
17.9%
馬 3
10.7%
乘 3
10.7%
遊 2
 
7.1%
人 2
 
7.1%
法 2
 
7.1%
游 2
 
7.1%
旅 2
 
7.1%
株 2
 
7.1%
小 1
 
3.6%
Other values (4) 4
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 235528
77.9%
Hangul 65989
 
21.8%
Punctuation 313
 
0.1%
None 229
 
0.1%
Enclosed Alphanum 111
 
< 0.1%
Geometric Shapes 43
 
< 0.1%
Number Forms 33
 
< 0.1%
Compat Jamo 27
 
< 0.1%
CJK 26
 
< 0.1%
Arrows 23
 
< 0.1%
Other values (3) 12
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
28919
 
12.3%
< 10196
 
4.3%
> 10097
 
4.3%
p 9465
 
4.0%
t 8453
 
3.6%
s 8115
 
3.4%
e 7979
 
3.4%
/ 7845
 
3.3%
n 7547
 
3.2%
4 7026
 
3.0%
Other values (76) 129886
55.1%
Hangul
ValueCountFrequency (%)
관 2952
 
4.5%
광 2811
 
4.3%
국 1489
 
2.3%
한 1189
 
1.8%
년 1001
 
1.5%
가 964
 
1.5%
로 947
 
1.4%
으 913
 
1.4%
기 907
 
1.4%
객 898
 
1.4%
Other values (626) 51918
78.7%
None
ValueCountFrequency (%)
· 114
49.8%
「 49
21.4%
」 48
21.0%
〉 8
 
3.5%
〈 8
 
3.5%
？ 2
 
0.9%
Punctuation
ValueCountFrequency (%)
‘ 111
35.5%
’ 98
31.3%
※ 73
23.3%
“ 16
 
5.1%
” 13
 
4.2%
… 2
 
0.6%
Enclosed Alphanum
ValueCountFrequency (%)
① 34
30.6%
② 32
28.8%
③ 21
18.9%
④ 15
13.5%
⑤ 8
 
7.2%
⑥ 1
 
0.9%
Geometric Shapes
ValueCountFrequency (%)
△ 29
67.4%
■ 7
 
16.3%
○ 4
 
9.3%
◈ 2
 
4.7%
□ 1
 
2.3%
Compat Jamo
ValueCountFrequency (%)
ㅣ 24
88.9%
ㆍ 3
 
11.1%
Arrows
ValueCountFrequency (%)
→ 23
100.0%
Number Forms
ValueCountFrequency (%)
Ⅰ 13
39.4%
Ⅱ 10
30.3%
Ⅲ 6
18.2%
Ⅳ 2
 
6.1%
Ⅴ 2
 
6.1%
Misc Symbols
ValueCountFrequency (%)
☞ 8
100.0%
CJK
ValueCountFrequency (%)
前 5
19.2%
馬 3
11.5%
乘 3
11.5%
遊 2
 
7.7%
人 2
 
7.7%
法 2
 
7.7%
游 2
 
7.7%
株 2
 
7.7%
小 1
 
3.8%
高 1
 
3.8%
Other values (3) 3
11.5%
CJK Compat Ideographs
ValueCountFrequency (%)
旅 2
100.0%
Math Operators
ValueCountFrequency (%)
∼ 2
100.0%

Interactions

2024-01-10T06:48:51.818539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-10T06:48:54.799561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
SUB_DESCRIPTIONVIEW_COUNT
SUB_DESCRIPTION1.0000.000
VIEW_COUNT0.0001.000
2024-01-10T06:48:54.870640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
SUB_DESCRIPTIONPUBLISHER
SUB_DESCRIPTION1.0001.000
PUBLISHER1.0001.000
2024-01-10T06:48:54.935302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
VIEW_COUNTSUB_DESCRIPTIONPUBLISHER
VIEW_COUNT1.0000.0001.000
SUB_DESCRIPTION0.0001.0001.000
PUBLISHER1.0001.0001.000

Missing values

2024-01-10T06:48:51.918646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-10T06:48:52.004764image/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.
2024-01-10T06:48:52.082384image/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

제목UCIURLSUB_DESCRIPTIONVIEW_COUNTPUBLISHER설명
0제46호 한국관광정책G706+KTCC06-C.periodical.0000100668http://know.tour.go.kr/ptourknow/knowplus/kChannel/kChannelPeriod/kChannelPeriodDetail.do?seq=100668<NA><NA><NA><p><br />[ 목&nbsp; 차 ]</p><p>관광시론 :&nbsp; 관광산업의 정책환경과 2012년 관광진흥 정책방향 (노일식|문화체육관광부 관광정책과장)</p><p>기획특집 : 중국관광객 300만 유치 과제와 전망<br />&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; ① 중국 시장 현황과 전망 (한화준|한국관광공사 중국팀장)<br />&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; ② 중국관광객 수용태세 및 숙박시설 확충방안 (구본상|서울특별시 관광과장)<br />&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; ③ 제주 중국관광객 유치성과와 발전방안 (서용건|제주대학교 관광경영학과 교수)<br />&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; ④ 중국인 개별관광객 유치 활성화를 위한 정책 대응 방향 (최경은|한국문화관광연구원 책임연구원)</p><p>관광과 현장 : 관광형 커뮤니티 비즈니스 활성화 방안<br />&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; - 진&nbsp; 행 : 강신겸|
1한국관광동향 2005 4분기G706+KTCC06-C.periodical.0000004296http://know.tour.go.kr/ptourknow/knowplus/kChannel/kChannelPeriod/kChannelPeriodDetail.do?seq=4296<NA><NA><NA><NA>
2성공적 국제기구 활동을 위한 제언-OECD관광위원회 중심(한국관광정책4호. 2000)G706+KTCC06-C.periodical.0000004327http://know.tour.go.kr/ptourknow/knowplus/kChannel/kChannelPeriod/kChannelPeriodDetail.do?seq=4327접근권한:전체150<NA><NA>
3수익성과 공익성의 적절한 조화 독일뮌헨 올림픽공원(한국관광정책4호. 2000)G706+KTCC06-C.periodical.0000004328http://know.tour.go.kr/ptourknow/knowplus/kChannel/kChannelPeriod/kChannelPeriodDetail.do?seq=4328접근권한:전체341<NA><NA>
4우리나라의 컨벤션 정책동향과 과제(한국관광정책4호. 2000)G706+KTCC06-C.periodical.0000004329http://know.tour.go.kr/ptourknow/knowplus/kChannel/kChannelPeriod/kChannelPeriodDetail.do?seq=4329접근권한:전체188<NA><NA>
5인력개발의 필요성과 전략(한국관광정책4호. 2000)G706+KTCC06-C.periodical.0000004330http://know.tour.go.kr/ptourknow/knowplus/kChannel/kChannelPeriod/kChannelPeriodDetail.do?seq=4330접근권한:전체164<NA><NA>
6일본의 관광정책(한국관광정책4호. 2000)G706+KTCC06-C.periodical.0000004331http://know.tour.go.kr/ptourknow/knowplus/kChannel/kChannelPeriod/kChannelPeriodDetail.do?seq=4331접근권한:전체231<NA><NA>
7잉글랜드의 관광정책(한국관광정책4호. 2000)G706+KTCC06-C.periodical.0000004332http://know.tour.go.kr/ptourknow/knowplus/kChannel/kChannelPeriod/kChannelPeriodDetail.do?seq=4332접근권한:전체144<NA><NA>
8주요국들의 새 밀레니엄 기념 이니셔티브(한국관광정책4호. 2000)G706+KTCC06-C.periodical.0000004333http://know.tour.go.kr/ptourknow/knowplus/kChannel/kChannelPeriod/kChannelPeriodDetail.do?seq=4333접근권한:전체158<NA><NA>
9한국 관광산업의 국제화 전략(한국관광정책4호. 2000)G706+KTCC06-C.periodical.0000004334http://know.tour.go.kr/ptourknow/knowplus/kChannel/kChannelPeriod/kChannelPeriodDetail.do?seq=4334접근권한:전체153<NA><NA>
제목UCIURLSUB_DESCRIPTIONVIEW_COUNTPUBLISHER설명
9132017년 국민여행실태조사 요약(국내여행 편)G706+KTCC06-C.periodical.0000102814http://know.tour.go.kr/ptourknow/knowplus/kChannel/kChannelPeriod/kChannelPeriodDetail19Re.do?seq=102814접근권한:전체804KTCC06<p>"2017년&nbsp;국민여행실태조사 요약(국내여행 편)"은 첨부된 파일을 다운로드 받으시면&#44; 최적화된 상태에서 읽으실 수 있습니다.</p><p>&nbsp;</p><p><strong>2017년&nbsp;국민여행실태조사 요약(국내여행 편)</strong></p><p>&nbsp;</p><p>&lt;목차보기&gt;</p><p>1.&nbsp; 2017년 국민 국내여행 총량</p><p>&nbsp; - 2017년 국민 국내여행 총량</p><p>&nbsp;</p><p>2.&nbsp; 2017년 국내여행실태조사 주요결과</p><p>&nbsp; - 국내여행 평균 횟수&#44; 일수</p><p>&nbsp; - 국내여행&nbsp;시기</p><p>&nbsp; - 여행목적</p><p>&nbsp; - 이용 여행 상품</p><p>&nbsp;&nbsp;- 여행 지출액</p><p>&nbsp; - 여행 방문지</p><p>&nbsp;&nbsp;- 여행 방문지 선택이유</p><p>&nbsp; - 여행하지 않은 이유</p><p>&nbsp;</p><p>붙임: 투어고인사이트 제13호 1부. 끝.&nbsp;&nbsp;</p><p>&nbsp;</p>
914관광관련 부처예산 활용 방안-해양수산부(한국관광정책18호. 2003)G706+KTCC06-C.periodical.0000004590http://know.tour.go.kr/ptourknow/knowplus/kChannel/kChannelPeriod/kChannelPeriodDetail19Re.do?seq=4590접근권한:전체117KTCC06<NA>
915지방정부차원의 국제관광협력 활성화 방안(한국관광정책5호. 2000)G706+KTCC06-C.periodical.0000004348http://know.tour.go.kr/ptourknow/knowplus/kChannel/kChannelPeriod/kChannelPeriodDetail19Re.do?seq=4348접근권한:전체152KTCC06<NA>
916지방정부차원의 국제관광협력 활성화 방안-강원도사례 중심(한국관광정책5호. 2000)G706+KTCC06-C.periodical.0000004349http://know.tour.go.kr/ptourknow/knowplus/kChannel/kChannelPeriod/kChannelPeriodDetail19Re.do?seq=4349접근권한:전체138KTCC06<NA>
917태국의 21세기 관광정책(한국관광정책5호. 2000)G706+KTCC06-C.periodical.0000004350http://know.tour.go.kr/ptourknow/knowplus/kChannel/kChannelPeriod/kChannelPeriodDetail19Re.do?seq=4350접근권한:전체613KTCC06<NA>
918관광산업 TS-30 주가동향 2020년 7월 4주(7.17일 기준)G706+KTCC06-C.periodical.0000102964http://know.tour.go.kr/ptourknow/knowplus/kChannel/kChannelPeriod/kChannelPeriodDetail19Re.do?seq=102964접근권한:전체147KTCC06<p><span style="color: rgb(102&#44; 102&#44; 102); font-family: Roboto&#44; ng; font-size: 15px;"><b>o (TS-30 지수 = 87.93&#44; 7월 17일기준) 코로나19 재확산 우려에 관광 및 연관산업 투자심리 위축</b></span></p><p style="box-sizing: border-box; color: rgb(102&#44; 102&#44; 102); font-family: Roboto&#44; ng; font-size: 15px; line-height: 1.8;">: (TS-30 시가총액) 7.3일 55.8조원(73&#44;230/주) → 7.17일 55.6조원(71&#44;976/주)(시가총액 0.3% 감소&#44; 평균주가 1.7% 감소)</p><p style="box-sizing: border-box; color: rgb(102&#44; 102&#44; 102); font-family: Roboto&#44; ng; font-size: 15px; line-height: 1.8;">: 코로나19 발생(1.17일&#44; 시가총액 69조원)대비 7월 2주차 55.8조원으로 19.5% 감소</p><p style="box-sizing: border-box; color: rgb(102&#44; 102&#44; 102); font-family: Roboto&#44; ng; font-size: 15px;">&nbsp;</p><p style="box-sizing: border-box; color: rgb(102&#44; 102&#44; 102); font-family: Roboto&#44; ng; font-size: 15px; line-height: 1.8;"><b>o (세부지표) 전기 대비 TS-30 주가 1&#44;254원 감소&#44; 주가등락률 1.7% 감소</b></p><p style="box-sizing: border-box; color: rgb(102&#44; 102&#44; 102); font-famil
919관광산업 TS-30 주가동향 2020년 8월 1주(7.31일 기준)G706+KTCC06-C.periodical.0000102965http://know.tour.go.kr/ptourknow/knowplus/kChannel/kChannelPeriod/kChannelPeriodDetail19Re.do?seq=102965접근권한:전체147KTCC06<p><span style="color: rgb(102&#44; 102&#44; 102); font-family: Roboto&#44; ng; font-size: 15px;"><b>o (TS-30 지수 = 90.14) 코로나19에도 불구하고 국내/하계휴가철 관광시장 회복 기대. 여행업&#44; 숙박업 투자심리 다소 회복</b></span></p><p style="box-sizing: border-box; color: rgb(102&#44; 102&#44; 102); font-family: Roboto&#44; ng; font-size: 15px; line-height: 1.8;">&nbsp; : (TS-30 시가총액) 7.17일 55.6조원(71&#44;976/주) → 7.31일 57.0조원(72&#44;585/주)(시가총액 2.5% 증가&#44; 평균주가 0.8% 증가)</p><p style="box-sizing: border-box; color: rgb(102&#44; 102&#44; 102); font-family: Roboto&#44; ng; font-size: 15px; line-height: 1.8;">&nbsp; : 코로나19 발생(1.17일&#44; 시가총액 69조원)대비 7월 4주차 57.0조원으로 17.5% 감소</p><p style="box-sizing: border-box; color: rgb(102&#44; 102&#44; 102); font-family: Roboto&#44; ng; font-size: 15px;">&nbsp;</p><p style="box-sizing: border-box; color: rgb(102&#44; 102&#44; 102); font-family: Roboto&#44; ng; font-size: 15px; line-height: 1.8;"><b>o (세부지표) 전기 대비 TS-30 주가 609원 증가&#44; 주가등락률 0.8% 증가</b></p><p style="box-sizing: border-box; color: rgb(102&#44; 102&
920도시관광 Ⅱ - 도시관광 영향력 순위G706+KTCC06-C.periodical.0000102848http://know.tour.go.kr/ptourknow/knowplus/kChannel/kChannelPeriod/kChannelPeriodDetail19Re.do?seq=102848접근권한:전체1184KTCC06도시관광 Ⅱ - 도시관광 영향력 순위 의 첨부된 파일을 다운로드 받으시면&#44; 최적화된 상태에서 읽으실 수 있습니다. 도시관광 Ⅱ - 도시관광 영향력 순위 <목차보기> 1. 도시관광 영향력 순위 - 도시관광 영향력 개요 - 주요 관광도시 유형별 분류 - 주요 관광도시 유형별 특성 2. 도시관광 기여도 분석 - 관광산업의 기여도가 큰 도시 - 관광산업의 기여도가 낮은 도시 - 관광 GDP 상위권 도시 - 외래관광객 지출액 상위권 도시 - 도시별지출액 분석 붙임: 투어고인사이트 제18호 1부. 끝.
9212019년 2월기준 관광동향분석(일부데이터 수정)G706+KTCC06-C.periodical.0000102849http://know.tour.go.kr/ptourknow/knowplus/kChannel/kChannelPeriod/kChannelPeriodDetail19Re.do?seq=102849접근권한:전체823KTCC06<p>※ 2019.06.05.</p><p>&nbsp; 한국은행 국제수지 과거 시계열(1980.1월~2018.11월) 변경*에 따라 한국관광수지 데이터수정 완료</p><p>&nbsp; * ① 해외 역직구(온라인 해외판매) 등 새로운 경제활동의 출현&#44; ② 면세점 상품수입 등 기초자료 신규 입수 등을 반영</p><p>&nbsp;</p><p>2019년 2월 방한외래관광객은 1&#44;201&#44;802명으로 전년동기(1&#44;045&#44;415명) 대비 15.0% 증가하였으며&#44; 국민해외관광객은 2&#44;617&#44;946명으로 전년동기(2&#44;311&#44;009명) 대비 13.3% 증가하였습니다. 주요 국가별 외래관광객을 살펴보면 일본관광객은 213&#44;200명으로 전년동기(168&#44;241명) 대비 26.7% 증가하였으며&#44; 중국관광객은 453&#44;379명으로 전년동기(345&#44;341명) 대비 31.3% 증가한 것으로 나타났습니다.</p><p>&nbsp;</p><p>※ '19년 2월 관광동향분석은 기존 관광동향분석에 활용되는 자료 중&nbsp;</p><p>&nbsp; &nbsp;원자료의 출처가 다음과 같이&nbsp;변경되어 발간시기가 늦어졌습니다.</p><p>&nbsp;</p><p>&nbsp; &nbsp; ○ 최신참고자료의 매월 항공시장동향 활용 원자료 변경</p><p>&nbsp; &nbsp; &nbsp; &nbsp;- 국토교통부&#44; 「항공운송동향 및 분석」&#44; 익월 말 발간</p><p>&nbsp; &nbsp; &nbsp; &nbsp; &nbsp;☞ 한국항공협회&#44; 「항공시장동향」&#44; 익익월 중순 발간</p><p>&nbsp; &nbsp; ○ 활용 원자료 변경 사유</p><p>&nbsp; &nbsp; &nbsp; &nbsp;- 기존 활용하던 원자료의 발간 주기 변경(매월 → 분기별)</p><p>&nbsp;</p><p>&nbsp; &nbsp;이에 양해부탁드리며&#44; 다음 월 관광동향분석부터는 월 2회(항공시장동향 포함여부)에 걸쳐 업로드
922제14호 트렌드검색포스트 관광지·관광단지·관광특구G706+KTCC06-C.periodical.0000102850http://know.tour.go.kr/ptourknow/knowplus/kChannel/kChannelPeriod/kChannelPeriodDetail19Re.do?seq=102850접근권한:전체781KTCC06<p><b>「제14호 트렌드검색포스트 : 관광지·관광단지·관광특구」</b></p><p>&nbsp;</p><p>&nbsp;</p><p>&lt;목 차&gt;</p><p>&nbsp;1. 배경</p><p>&nbsp;2. 관광지</p><p>&nbsp;3. 관광단지</p><p>&nbsp;4. 관광특구</p><p>&nbsp;5. 요약</p><p>&nbsp;</p><p>&nbsp;</p><p><span id="husky_bookmark_end_1556176375491"></span>* 제14호 트렌드검색포스트는 2019년 2월 관광지식정보시스템 8위 검색어인 관광단지와 함께 관광지와 관광특구를 포함하여 작성되었습니다.</p><p>&nbsp; 첨부파일을 다운로드 받으시면 자세한 내용을 보실 수 있습니다.</p>