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

Description1989년부터 연도별로 발생한 국제 항공수송실적(여객, 여객킬로미터, 화물, 화물톤킬로미터, 운항, 운항킬로미터) 정보를 제공
URLhttps://www.data.go.kr/data/15052303/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(편) 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(편) has unique valuesUnique
운항킬로(Km) has unique valuesUnique

Reproduction

Analysis started2023-12-12 15:02:39.005400
Analysis finished2023-12-12 15:02:44.162130
Duration5.16 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
2023-12-13T00:02:44.249038image/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
2023-12-13T00:02:44.366503image/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%
Mean31884237
Minimum3208695
Maximum90385640
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-13T00:02:44.485218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3208695
5-th percentile9156277.2
Q114330629
median22088053
Q342001649
95-th percentile80095068
Maximum90385640
Range87176945
Interquartile range (IQR)27671020

Descriptive statistics

Standard deviation23504120
Coefficient of variation (CV)0.73717054
Kurtosis0.40534798
Mean31884237
Median Absolute Deviation (MAD)11128272
Skewness1.1330705
Sum1.0840641 × 109
Variance5.5244367 × 1014
MonotonicityNot monotonic
2023-12-13T00:02:44.591952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
8283164 1
 
2.9%
61434404 1
 
2.9%
33513556 1
 
2.9%
40060948 1
 
2.9%
42648549 1
 
2.9%
47702644 1
 
2.9%
50986891 1
 
2.9%
56778759 1
 
2.9%
73000810 1
 
2.9%
36867209 1
 
2.9%
Other values (24) 24
70.6%
ValueCountFrequency (%)
3208695 1
2.9%
8283164 1
2.9%
9626415 1
2.9%
10270666 1
2.9%
11257011 1
2.9%
11651345 1
2.9%
13075979 1
2.9%
14104367 1
2.9%
14239922 1
2.9%
14602751 1
2.9%
ValueCountFrequency (%)
90385640 1
2.9%
85925288 1
2.9%
76955719 1
2.9%
73000810 1
2.9%
61434404 1
2.9%
56778759 1
2.9%
50986891 1
2.9%
47702644 1
2.9%
42648549 1
2.9%
40060948 1
2.9%

여객킬로(Km)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct34
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0573131 × 1011
Minimum1.9425505 × 1010
Maximum2.6932216 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-13T00:02:44.700606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.9425505 × 1010
5-th percentile3.4091697 × 1010
Q15.7205075 × 1010
median8.6899112 × 1010
Q31.3693943 × 1011
95-th percentile2.3852023 × 1011
Maximum2.6932216 × 1011
Range2.4989665 × 1011
Interquartile range (IQR)7.9734352 × 1010

Descriptive statistics

Standard deviation6.5412158 × 1010
Coefficient of variation (CV)0.61866405
Kurtosis0.32836592
Mean1.0573131 × 1011
Median Absolute Deviation (MAD)3.3408554 × 1010
Skewness1.039679
Sum3.5948644 × 1012
Variance4.2787505 × 1021
MonotonicityNot monotonic
2023-12-13T00:02:44.829327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
29795423539 1
 
2.9%
187736721402 1
 
2.9%
109203059493 1
 
2.9%
127760197379 1
 
2.9%
139999170306 1
 
2.9%
154025032053 1
 
2.9%
163869564223 1
 
2.9%
173643425127 1
 
2.9%
212261253909 1
 
2.9%
114781804194 1
 
2.9%
Other values (24) 24
70.6%
ValueCountFrequency (%)
19425504763 1
2.9%
29795423539 1
2.9%
36405075593 1
2.9%
38521435036 1
2.9%
45220452753 1
2.9%
47627320253 1
2.9%
52825973790 1
2.9%
54155141982 1
2.9%
55735664549 1
2.9%
61613305631 1
2.9%
ValueCountFrequency (%)
269322159636 1
2.9%
251747144593 1
2.9%
231398037659 1
2.9%
212261253909 1
2.9%
187736721402 1
2.9%
173643425127 1
2.9%
163869564223 1
2.9%
154025032053 1
2.9%
139999170306 1
2.9%
127760197379 1
2.9%

화물(톤)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct34
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2459515.9
Minimum711295
Maximum4168783
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-13T00:02:45.021587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum711295
5-th percentile783637.95
Q11510831.8
median2735176
Q33301890.2
95-th percentile4021422.1
Maximum4168783
Range3457488
Interquartile range (IQR)1791058.5

Descriptive statistics

Standard deviation1075116.6
Coefficient of variation (CV)0.43712531
Kurtosis-1.2617469
Mean2459515.9
Median Absolute Deviation (MAD)784709.5
Skewness-0.22221171
Sum83623539
Variance1.1558758 × 1012
MonotonicityNot monotonic
2023-12-13T00:02:45.139971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
711295 1
 
2.9%
3518772 1
 
2.9%
2872469 1
 
2.9%
3326884 1
 
2.9%
3238105 1
 
2.9%
3208780 1
 
2.9%
3246253 1
 
2.9%
3410742 1
 
2.9%
3780908 1
 
2.9%
3138110 1
 
2.9%
Other values (24) 24
70.6%
ValueCountFrequency (%)
711295 1
2.9%
776759 1
2.9%
787342 1
2.9%
837215 1
2.9%
950574 1
2.9%
1112841 1
2.9%
1290749 1
2.9%
1430974 1
2.9%
1470751 1
2.9%
1631074 1
2.9%
ValueCountFrequency (%)
4168783 1
2.9%
4031516 1
2.9%
4015987 1
2.9%
3780908 1
2.9%
3518772 1
2.9%
3420163 1
2.9%
3410742 1
2.9%
3326884 1
2.9%
3320436 1
2.9%
3246253 1
2.9%

화물톤킬로(Km)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct34
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2783195 × 1010
Minimum4.2682809 × 109
Maximum1.9342426 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-13T00:02:45.258183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.2682809 × 109
5-th percentile4.5251948 × 109
Q19.5832698 × 109
median1.3810317 × 1010
Q31.6178732 × 1010
95-th percentile1.8472149 × 1010
Maximum1.9342426 × 1010
Range1.5074145 × 1010
Interquartile range (IQR)6.5954623 × 109

Descriptive statistics

Standard deviation4.5640672 × 109
Coefficient of variation (CV)0.35703651
Kurtosis-0.84742585
Mean1.2783195 × 1010
Median Absolute Deviation (MAD)2.9604808 × 109
Skewness-0.54070861
Sum4.3462861 × 1011
Variance2.0830709 × 1019
MonotonicityNot monotonic
2023-12-13T00:02:45.399463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
4268280900 1
 
2.9%
16668181349 1
 
2.9%
13974345353 1
 
2.9%
16269479765 1
 
2.9%
15906489014 1
 
2.9%
15570007045 1
 
2.9%
15552806369 1
 
2.9%
16362231739 1
 
2.9%
17364874916 1
 
2.9%
15751272660 1
 
2.9%
Other values (24) 24
70.6%
ValueCountFrequency (%)
4268280900 1
2.9%
4470003436 1
2.9%
4554913269 1
2.9%
4968222114 1
2.9%
6182815941 1
2.9%
6991924086 1
2.9%
8095571850 1
2.9%
9043163394 1
2.9%
9260279496 1
2.9%
10552240545 1
2.9%
ValueCountFrequency (%)
19342425708 1
2.9%
18763256304 1
2.9%
18315399325 1
2.9%
18206036913 1
2.9%
17796219327 1
2.9%
17364874916 1
2.9%
16668181349 1
2.9%
16362231739 1
2.9%
16269479765 1
2.9%
15906489014 1
2.9%

운항(편)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct34
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean206357.24
Minimum44219
Maximum528239
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-13T00:02:45.658026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum44219
5-th percentile57657.25
Q195908
median168714
Q3274598
95-th percentile471095.5
Maximum528239
Range484020
Interquartile range (IQR)178690

Descriptive statistics

Standard deviation137904.28
Coefficient of variation (CV)0.66827937
Kurtosis-0.21016879
Mean206357.24
Median Absolute Deviation (MAD)82655
Skewness0.89333828
Sum7016146
Variance1.9017591 × 1010
MonotonicityNot monotonic
2023-12-13T00:02:46.111135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
44219 1
 
2.9%
388008 1
 
2.9%
232482 1
 
2.9%
256688 1
 
2.9%
280568 1
 
2.9%
313726 1
 
2.9%
338988 1
 
2.9%
366485 1
 
2.9%
438856 1
 
2.9%
253026 1
 
2.9%
Other values (24) 24
70.6%
ValueCountFrequency (%)
44219 1
2.9%
53442 1
2.9%
59927 1
2.9%
66363 1
2.9%
68615 1
2.9%
77332 1
2.9%
89880 1
2.9%
90801 1
2.9%
94635 1
2.9%
99727 1
2.9%
ValueCountFrequency (%)
528239 1
2.9%
497089 1
2.9%
457099 1
2.9%
438856 1
2.9%
388008 1
2.9%
366485 1
2.9%
338988 1
2.9%
313726 1
2.9%
280568 1
2.9%
256688 1
2.9%

운항킬로(Km)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct34
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.1320791 × 108
Minimum2.1295928 × 108
Maximum1.4718968 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-13T00:02:46.264211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.1295928 × 108
5-th percentile2.6142575 × 108
Q14.5282967 × 108
median6.7845603 × 108
Q39.1450833 × 108
95-th percentile1.3249261 × 109
Maximum1.4718968 × 109
Range1.2589375 × 109
Interquartile range (IQR)4.6167866 × 108

Descriptive statistics

Standard deviation3.3927708 × 108
Coefficient of variation (CV)0.47570571
Kurtosis-0.47907769
Mean7.1320791 × 108
Median Absolute Deviation (MAD)2.3834429 × 108
Skewness0.5586614
Sum2.4249069 × 1010
Variance1.1510893 × 1017
MonotonicityNot monotonic
2023-12-13T00:02:46.398572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
212959283 1
 
2.9%
1129168964 1
 
2.9%
733250621 1
 
2.9%
837835526 1
 
2.9%
924896637 1
 
2.9%
994113658 1
 
2.9%
1052112177 1
 
2.9%
1088367933 1
 
2.9%
1220195279 1
 
2.9%
809520260 1
 
2.9%
Other values (24) 24
70.6%
ValueCountFrequency (%)
212959283 1
2.9%
249721301 1
2.9%
267728140 1
2.9%
307090481 1
2.9%
324066393 1
2.9%
368526347 1
2.9%
417823447 1
2.9%
434778500 1
2.9%
445444973 1
2.9%
474983759 1
2.9%
ValueCountFrequency (%)
1471896831 1
2.9%
1384385156 1
2.9%
1292909720 1
2.9%
1220195279 1
2.9%
1129168964 1
2.9%
1088367933 1
2.9%
1052112177 1
2.9%
994113658 1
2.9%
924896637 1
2.9%
883343409 1
2.9%

Interactions

2023-12-13T00:02:43.358619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:02:39.206459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:02:39.885078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:02:40.838779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:02:41.530269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:02:42.191252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:02:42.838453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:02:43.444580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:02:39.312557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:02:40.282507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:02:40.940830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:02:41.643611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:02:42.277924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:02:42.921954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:02:43.511162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:02:39.390109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:02:40.373568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:02:41.026343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:02:41.728636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:02:42.371983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:02:42.986969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:02:43.590886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:02:39.499433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:02:40.467114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:02:41.117425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:02:41.809198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:02:42.449842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:02:43.052064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:02:43.671489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:02:39.601671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:02:40.554101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:02:41.216172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:02:41.904048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:02:42.550244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:02:43.127316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:02:43.766411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:02:39.697998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:02:40.654857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:02:41.334739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:02:42.021034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:02:42.650667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:02:43.209259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:02:43.857976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:02:39.789351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:02:40.739289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:02:41.424656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:02:42.104428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:02:42.747106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:02:43.277939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T00:02:46.505191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분여객(명)여객킬로(Km)화물(톤)화물톤킬로(Km)운항(편)운항킬로(Km)
구분1.0000.8480.8970.9500.9390.9320.914
여객(명)0.8481.0000.9690.8090.7450.9050.921
여객킬로(Km)0.8970.9691.0000.8640.8670.9570.965
화물(톤)0.9500.8090.8641.0000.9550.9040.954
화물톤킬로(Km)0.9390.7450.8670.9551.0000.8110.833
운항(편)0.9320.9050.9570.9040.8111.0000.979
운항킬로(Km)0.9140.9210.9650.9540.8330.9791.000
2023-12-13T00:02:46.638878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분여객(명)여객킬로(Km)화물(톤)화물톤킬로(Km)운항(편)운항킬로(Km)
구분1.0000.6730.6780.9540.9660.8510.911
여객(명)0.6731.0000.9920.8150.7550.9530.901
여객킬로(Km)0.6780.9921.0000.8170.7610.9480.908
화물(톤)0.9540.8150.8171.0000.9890.9410.973
화물톤킬로(Km)0.9660.7550.7610.9891.0000.9000.947
운항(편)0.8510.9530.9480.9410.9001.0000.984
운항킬로(Km)0.9110.9010.9080.9730.9470.9841.000

Missing values

2023-12-13T00:02:43.986073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T00:02:44.111952image/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(편)운항킬로(Km)
01989828316429795423539711295426828090044219212959283
11990962641536405075593776759455491326953442249721301
219911027066638521435036787342447000343659927267728140
319921125701145220452753837215496822211466363307090481
419931165134547627320253950574618281594168615324066393
5199413075979541551419821112841699192408677332368526347
6199514602751616133056311290749809557185089880417823447
7199615992455704878239841430974904316339499727474983759
819971659827374374822877163107410552240545105267531408207
9199814104367557356645491470751926027949690801434778500
구분여객(명)여객킬로(Km)화물(톤)화물톤킬로(Km)운항(편)운항킬로(Km)
242013509868911638695642233246253155528063693389881052112177
252014567787591736434251273410742163622317393664851088367933
262015614344041877367214023518772166681813493880081129168964
272016730008102122612539093780908173648749164388561220195279
282017769557192313980376594031516187632563044570991292909720
292018859252882517471445934168783193424257084970891384385156
302019903856402693221596364015987182060369135282391471896831
3120201423992252825973790307099315739129769167211707024399
322021320869519425504763342016318315399325131442718003390
3320221949992093185602310332043617796219327182761883343409