Overview

Dataset statistics

Number of variables7
Number of observations34
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.2 KiB
Average record size in memory66.9 B

Variable types

Numeric7

Dataset

Description1989년 부터 연도별 국내선과 국제선의 통합된 항공수송실적 정보를 통계적으로 활용할 수 있도록 제공 합니다(연도, 여객,화물,운항실적)
URLhttps://www.data.go.kr/data/15061960/fileData.do

Alerts

구분 is highly overall correlated with 여객(명) and 5 other fieldsHigh correlation
여객(명) is highly overall correlated with 구분 and 5 other fieldsHigh correlation
여객킬로(Km) is highly overall correlated with 구분 and 5 other fieldsHigh correlation
화물(톤) is highly overall correlated with 구분 and 5 other fieldsHigh correlation
화물톤킬로(Km) is highly overall correlated with 구분 and 5 other fieldsHigh correlation
운항(회) is highly overall correlated with 구분 and 5 other fieldsHigh correlation
운항킬로(Km) is highly overall correlated with 구분 and 5 other fieldsHigh correlation
구분 has unique valuesUnique
여객(명) has unique valuesUnique
여객킬로(Km) has unique valuesUnique
화물(톤) has unique valuesUnique
화물톤킬로(Km) has unique valuesUnique
운항(회) has unique valuesUnique
운항킬로(Km) has unique valuesUnique

Reproduction

Analysis started2023-12-12 20:50:08.051579
Analysis finished2023-12-12 20:50:13.481524
Duration5.43 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct34
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2005.5
Minimum1989
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-13T05:50:13.564049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1989
5-th percentile1990.65
Q11997.25
median2005.5
Q32013.75
95-th percentile2020.35
Maximum2022
Range33
Interquartile range (IQR)16.5

Descriptive statistics

Standard deviation9.9582462
Coefficient of variation (CV)0.004965468
Kurtosis-1.2
Mean2005.5
Median Absolute Deviation (MAD)8.5
Skewness0
Sum68187
Variance99.166667
MonotonicityStrictly increasing
2023-12-13T05:50:13.713846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
1989 1
 
2.9%
2015 1
 
2.9%
2009 1
 
2.9%
2010 1
 
2.9%
2011 1
 
2.9%
2012 1
 
2.9%
2013 1
 
2.9%
2014 1
 
2.9%
2016 1
 
2.9%
2007 1
 
2.9%
Other values (24) 24
70.6%
ValueCountFrequency (%)
1989 1
2.9%
1990 1
2.9%
1991 1
2.9%
1992 1
2.9%
1993 1
2.9%
1994 1
2.9%
1995 1
2.9%
1996 1
2.9%
1997 1
2.9%
1998 1
2.9%
ValueCountFrequency (%)
2022 1
2.9%
2021 1
2.9%
2020 1
2.9%
2019 1
2.9%
2018 1
2.9%
2017 1
2.9%
2016 1
2.9%
2015 1
2.9%
2014 1
2.9%
2013 1
2.9%

여객(명)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct34
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53766204
Minimum17234880
Maximum1.2336661 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-13T05:50:13.864995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum17234880
5-th percentile21882011
Q136740668
median44894366
Q362791340
95-th percentile1.1221935 × 108
Maximum1.2336661 × 108
Range1.0613173 × 108
Interquartile range (IQR)26050672

Descriptive statistics

Standard deviation27614010
Coefficient of variation (CV)0.51359418
Kurtosis0.71849699
Mean53766204
Median Absolute Deviation (MAD)11109788
Skewness1.1758322
Sum1.828051 × 109
Variance7.6253353 × 1014
MonotonicityNot monotonic
2023-12-13T05:50:14.012661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
17234880 1
 
2.9%
89414538 1
 
2.9%
51574629 1
 
2.9%
60277303 1
 
2.9%
63629352 1
 
2.9%
69304162 1
 
2.9%
73340261 1
 
2.9%
81426297 1
 
2.9%
103913732 1
 
2.9%
53715079 1
 
2.9%
Other values (24) 24
70.6%
ValueCountFrequency (%)
17234880 1
2.9%
20690235 1
2.9%
22523737 1
2.9%
25811748 1
2.9%
27201333 1
2.9%
31481845 1
2.9%
33608780 1
2.9%
35611282 1
2.9%
36356010 1
2.9%
37894642 1
2.9%
ValueCountFrequency (%)
123366608 1
2.9%
117525898 1
2.9%
109361974 1
2.9%
103913732 1
2.9%
89414538 1
2.9%
81426297 1
2.9%
73340261 1
2.9%
69304162 1
2.9%
63629352 1
2.9%
60277303 1
2.9%

여객킬로(Km)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct34
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.139256 × 1011
Minimum3.1777993 × 1010
Maximum2.8175539 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-13T05:50:14.155661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.1777993 × 1010
5-th percentile3.7811157 × 1010
Q16.421411 × 1010
median9.4379113 × 1010
Q31.4523818 × 1011
95-th percentile2.5063708 × 1011
Maximum2.8175539 × 1011
Range2.499774 × 1011
Interquartile range (IQR)8.1024067 × 1010

Descriptive statistics

Standard deviation6.7053534 × 1010
Coefficient of variation (CV)0.588573
Kurtosis0.35109562
Mean1.139256 × 1011
Median Absolute Deviation (MAD)3.2903679 × 1010
Skewness1.0499582
Sum3.8734705 × 1012
Variance4.4961764 × 1021
MonotonicityNot monotonic
2023-12-13T05:50:14.287783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
32973999372 1
 
2.9%
198443391184 1
 
2.9%
116308231361 1
 
2.9%
135770783780 1
 
2.9%
148393975328 1
 
2.9%
162734832587 1
 
2.9%
172962561667 1
 
2.9%
183113042574 1
 
2.9%
224081038844 1
 
2.9%
121307694103 1
 
2.9%
Other values (24) 24
70.6%
ValueCountFrequency (%)
31777992723 1
2.9%
32973999372 1
2.9%
40415780780 1
2.9%
42968310434 1
2.9%
50453658708 1
2.9%
53138303731 1
2.9%
60636514958 1
2.9%
62314352681 1
2.9%
62612530888 1
2.9%
69018847961 1
2.9%
ValueCountFrequency (%)
281755393409 1
2.9%
263629301103 1
2.9%
243641271520 1
2.9%
224081038844 1
2.9%
198443391184 1
2.9%
183113042574 1
2.9%
172962561667 1
2.9%
162734832587 1
2.9%
148393975328 1
2.9%
135770783780 1
2.9%

화물(톤)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct34
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2760547.7
Minimum865713
Maximum4441975
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-13T05:50:14.415661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum865713
5-th percentile977331.1
Q11880321.8
median3065174.5
Q33542152.5
95-th percentile4291140.4
Maximum4441975
Range3576262
Interquartile range (IQR)1661830.8

Descriptive statistics

Standard deviation1070992.7
Coefficient of variation (CV)0.38796384
Kurtosis-1.0582784
Mean2760547.7
Median Absolute Deviation (MAD)711486
Skewness-0.34643359
Sum93858622
Variance1.1470253 × 1012
MonotonicityNot monotonic
2023-12-13T05:50:14.547896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
865713 1
 
2.9%
3806553 1
 
2.9%
3141146 1
 
2.9%
3588741 1
 
2.9%
3519237 1
 
2.9%
3474057 1
 
2.9%
3498939 1
 
2.9%
3693861 1
 
2.9%
4073795 1
 
2.9%
3454508 1
 
2.9%
Other values (24) 24
70.6%
ValueCountFrequency (%)
865713 1
2.9%
959590 1
2.9%
986884 1
2.9%
1078832 1
2.9%
1223888 1
2.9%
1418923 1
2.9%
1613469 1
2.9%
1782337 1
2.9%
1834298 1
2.9%
2018393 1
2.9%
ValueCountFrequency (%)
4441975 1
2.9%
4321641 1
2.9%
4274717 1
2.9%
4073795 1
2.9%
3806553 1
2.9%
3693861 1
2.9%
3624832 1
2.9%
3588741 1
2.9%
3549791 1
2.9%
3519237 1
2.9%

화물톤킬로(Km)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct34
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2901695 × 1010
Minimum4.3314316 × 109
Maximum1.9447759 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-13T05:50:14.689498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.3314316 × 109
5-th percentile4.5991985 × 109
Q19.7252897 × 109
median1.39475 × 1010
Q31.6287409 × 1010
95-th percentile1.856395 × 1010
Maximum1.9447759 × 1010
Range1.5116327 × 1010
Interquartile range (IQR)6.5621191 × 109

Descriptive statistics

Standard deviation4.5655041 × 109
Coefficient of variation (CV)0.35386854
Kurtosis-0.82510296
Mean1.2901695 × 1010
Median Absolute Deviation (MAD)2.9410877 × 109
Skewness-0.55563399
Sum4.3865764 × 1011
Variance2.0843827 × 1019
MonotonicityNot monotonic
2023-12-13T05:50:14.848094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
4331431570 1
 
2.9%
16780475856 1
 
2.9%
14085183952 1
 
2.9%
16376088401 1
 
2.9%
16021369980 1
 
2.9%
15678617167 1
 
2.9%
15657045872 1
 
2.9%
16472894383 1
 
2.9%
17477169423 1
 
2.9%
15880213082 1
 
2.9%
Other values (24) 24
70.6%
ValueCountFrequency (%)
4331431570 1
2.9%
4548580506 1
2.9%
4626454321 1
2.9%
5062484859 1
2.9%
6287703403 1
2.9%
7107986755 1
2.9%
8218439669 1
2.9%
9176947009 1
2.9%
9400102457 1
2.9%
10700851468 1
2.9%
ValueCountFrequency (%)
19447758637 1
2.9%
18875336169 1
2.9%
18396280846 1
2.9%
18306326147 1
2.9%
17886623141 1
2.9%
17477169423 1
2.9%
16780475856 1
2.9%
16472894383 1
2.9%
16376088401 1
2.9%
16021369980 1
2.9%

운항(회)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct34
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean360471.65
Minimum122857
Maximum723592
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-13T05:50:15.024632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum122857
5-th percentile146721.9
Q1249782.25
median326861
Q3424884
95-th percentile666907.45
Maximum723592
Range600735
Interquartile range (IQR)175101.75

Descriptive statistics

Standard deviation158668.39
Coefficient of variation (CV)0.44016886
Kurtosis-0.074442419
Mean360471.65
Median Absolute Deviation (MAD)79312
Skewness0.75139095
Sum12256036
Variance2.5175659 × 1010
MonotonicityNot monotonic
2023-12-13T05:50:15.156963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
122857 1
 
2.9%
570591 1
 
2.9%
379865 1
 
2.9%
403296 1
 
2.9%
432080 1
 
2.9%
469335 1
 
2.9%
500738 1
 
2.9%
536586 1
 
2.9%
629853 1
 
2.9%
386314 1
 
2.9%
Other values (24) 24
70.6%
ValueCountFrequency (%)
122857 1
2.9%
139529 1
2.9%
150595 1
2.9%
177627 1
2.9%
201182 1
2.9%
214424 1
2.9%
234027 1
2.9%
246910 1
2.9%
248188 1
2.9%
254565 1
2.9%
ValueCountFrequency (%)
723592 1
2.9%
691521 1
2.9%
653654 1
2.9%
629853 1
2.9%
570591 1
2.9%
536586 1
2.9%
500738 1
2.9%
469335 1
2.9%
432080 1
2.9%
403296 1
2.9%

운항킬로(Km)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct34
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.6995083 × 108
Minimum2.4072893 × 108
Maximum1.543833 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-13T05:50:15.298891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.4072893 × 108
5-th percentile2.9354123 × 108
Q15.0606189 × 108
median7.3163864 × 108
Q39.7952206 × 108
95-th percentile1.39719 × 109
Maximum1.543833 × 109
Range1.3031041 × 109
Interquartile range (IQR)4.7346017 × 108

Descriptive statistics

Standard deviation3.4897233 × 108
Coefficient of variation (CV)0.45323976
Kurtosis-0.48478161
Mean7.6995083 × 108
Median Absolute Deviation (MAD)2.3780138 × 108
Skewness0.52561918
Sum2.6178328 × 1010
Variance1.2178168 × 1017
MonotonicityNot monotonic
2023-12-13T05:50:15.433604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
240728926 1
 
2.9%
1197486056 1
 
2.9%
790370533 1
 
2.9%
894995910 1
 
2.9%
984715383 1
 
2.9%
1055832994 1
 
2.9%
1116671455 1
 
2.9%
1151922788 1
 
2.9%
1291889819 1
 
2.9%
860482950 1
 
2.9%
Other values (24) 24
70.6%
ValueCountFrequency (%)
240728926 1
2.9%
280656064 1
2.9%
300479394 1
2.9%
347095622 1
2.9%
371002537 1
2.9%
416475276 1
2.9%
468101617 1
2.9%
489105234 1
2.9%
498569299 1
2.9%
528539658 1
2.9%
ValueCountFrequency (%)
1543833004 1
2.9%
1455708646 1
2.9%
1365679888 1
2.9%
1291889819 1
2.9%
1197486056 1
2.9%
1151922788 1
2.9%
1116671455 1
2.9%
1055832994 1
2.9%
984715383 1
2.9%
963942086 1
2.9%

Interactions

2023-12-13T05:50:12.593566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:08.240645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:09.028718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:09.757274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:10.510491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:11.168932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:11.770137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:12.673505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:08.350566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:09.155741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:09.861842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:10.615267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:11.251429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:11.876736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:12.752767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:08.489183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:09.259126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:09.971495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:10.702025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:11.334342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:11.962874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:12.837318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:08.596138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:09.351219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:10.057105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:10.780174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:11.411196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:12.038375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:12.962076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:08.699110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:09.460425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:10.155863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:10.881467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:11.503553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:12.365386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:13.067779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:08.814154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:09.563495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:10.274808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:10.986780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:11.594010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:12.443999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:13.158089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:08.918613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:09.653960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:10.374662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:11.084807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:11.676997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:12.517056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T05:50:15.523233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분여객(명)여객킬로(Km)화물(톤)화물톤킬로(Km)운항(회)운항킬로(Km)
구분1.0000.8600.9150.9380.9390.9710.917
여객(명)0.8601.0000.9710.7660.7090.9280.941
여객킬로(Km)0.9150.9711.0000.9200.8830.9550.982
화물(톤)0.9380.7660.9201.0000.9820.9370.946
화물톤킬로(Km)0.9390.7090.8830.9821.0000.9100.841
운항(회)0.9710.9280.9550.9370.9101.0000.975
운항킬로(Km)0.9170.9410.9820.9460.8410.9751.000
2023-12-13T05:50:15.634235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분여객(명)여객킬로(Km)화물(톤)화물톤킬로(Km)운항(회)운항킬로(Km)
구분1.0000.7990.6910.9500.9660.9090.919
여객(명)0.7991.0000.9800.9100.8620.9670.961
여객킬로(Km)0.6910.9801.0000.8320.7710.9140.903
화물(톤)0.9500.9100.8321.0000.9890.9760.979
화물톤킬로(Km)0.9660.8620.7710.9891.0000.9450.952
운항(회)0.9090.9670.9140.9760.9451.0000.998
운항킬로(Km)0.9190.9610.9030.9790.9520.9981.000

Missing values

2023-12-13T05:50:13.301500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T05:50:13.430032image/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.

Sample

구분여객(명)여객킬로(Km)화물(톤)화물톤킬로(Km)운항(회)운항킬로(Km)
0198917234880329739993728657134331431570122857240728926
1199020690235404157807809595904626454321139529280656064
2199122523737429683104349868844548580506150595300479394
31992258117485045365870810788325062484859177627347095622
41993272013335313830373112238886287703403201182371002537
51994314818456063651495814189237107986755214424416475276
61995356112826901884796116134698218439669234027468101617
71996395590437877550127317823379176947009254565528539658
819974217692683426879836201839310700851468277048591150615
91998336087806261253088818342989400102457248188489105234
구분여객(명)여객킬로(Km)화물(톤)화물톤킬로(Km)운항(회)운항킬로(Km)
242013733402611729625616673498939156570458725007381116671455
252014814262971831130425743693861164728943835365861151922788
262015894145381984433911843806553167804758565705911197486056
2720161039137322240810388444073795174771694236298531291889819
2820171093619742436412715204321641188753361696536541365679888
2920181175258982636293011034441975194477586376915211455708646
3020191233666082817553934094274717183063261477235921543833004
3120203940396062314352681325277815811549626339594770665128
3220213635601031777992723362483218396280846344140796210636
33202255828355106873081476354979117886623141399207963942086