Overview

Dataset statistics

Number of variables6
Number of observations30
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.5 KiB
Average record size in memory52.4 B

Variable types

Text6

Dataset

Description수도권 인천1호선의 도시광역철도역들에 대한 한글,영문,로마자,일본어,중국어(간체,번체) 등의 정보입니다.
Author국가철도공단
URLhttps://www.data.go.kr/data/15064661/fileData.do

Alerts

역명 has unique valuesUnique
역명(영문) has unique valuesUnique
역명(로마자) has unique valuesUnique
역명(일본어) has unique valuesUnique
역명(중국어 간체) has unique valuesUnique

Reproduction

Analysis started2023-12-12 15:17:14.796325
Analysis finished2023-12-12 15:17:15.313809
Duration0.52 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

역명
Text

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-13T00:17:15.452974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length6
Mean length3.7333333
Min length2

Characters and Unicode

Total characters112
Distinct characters75
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique30 ?
Unique (%)100.0%

Sample

1st row계양
2nd row귤현
3rd row박촌
4th row임학
5th row계산
ValueCountFrequency (%)
계양 1
 
3.3%
귤현 1
 
3.3%
국제업무지구 1
 
3.3%
센트럴파크 1
 
3.3%
인천대입구 1
 
3.3%
지식정보단지 1
 
3.3%
테크노파크 1
 
3.3%
캠퍼스타운 1
 
3.3%
동막 1
 
3.3%
동춘 1
 
3.3%
Other values (20) 20
66.7%
2023-12-13T00:17:15.800624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
인 5
 
4.5%
평 4
 
3.6%
구 4
 
3.6%
부 4
 
3.6%
천 3
 
2.7%
학 3
 
2.7%
동 3
 
2.7%
지 3
 
2.7%
크 3
 
2.7%
파 2
 
1.8%
Other values (65) 78
69.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 112
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
인 5
 
4.5%
평 4
 
3.6%
구 4
 
3.6%
부 4
 
3.6%
천 3
 
2.7%
학 3
 
2.7%
동 3
 
2.7%
지 3
 
2.7%
크 3
 
2.7%
파 2
 
1.8%
Other values (65) 78
69.6%

Most occurring scripts

ValueCountFrequency (%)
Hangul 112
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
인 5
 
4.5%
평 4
 
3.6%
구 4
 
3.6%
부 4
 
3.6%
천 3
 
2.7%
학 3
 
2.7%
동 3
 
2.7%
지 3
 
2.7%
크 3
 
2.7%
파 2
 
1.8%
Other values (65) 78
69.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 112
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
인 5
 
4.5%
평 4
 
3.6%
구 4
 
3.6%
부 4
 
3.6%
천 3
 
2.7%
학 3
 
2.7%
동 3
 
2.7%
지 3
 
2.7%
크 3
 
2.7%
파 2
 
1.8%
Other values (65) 78
69.6%

역명(영문)
Text

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-13T00:17:16.018253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length33
Median length21
Mean length13.166667
Min length5

Characters and Unicode

Total characters395
Distinct characters47
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

Unique30 ?
Unique (%)100.0%

Sample

1st rowGyeyang
2nd rowGyulhyeon
3rd rowBakchon
4th rowImhak
5th rowGyesan
ValueCountFrequency (%)
incheon 3
 
5.5%
bupyeong 2
 
3.6%
park 2
 
3.6%
gyeyang 1
 
1.8%
sports 1
 
1.8%
complex 1
 
1.8%
seonhak 1
 
1.8%
sinyeonsu 1
 
1.8%
woninjae 1
 
1.8%
dongchun 1
 
1.8%
Other values (41) 41
74.5%
2023-12-13T00:17:16.391753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 43
 
10.9%
o 33
 
8.4%
e 31
 
7.8%
a 26
 
6.6%
25
 
6.3%
i 18
 
4.6%
s 16
 
4.1%
t 16
 
4.1%
u 16
 
4.1%
g 15
 
3.8%
Other values (37) 156
39.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 310
78.5%
Uppercase Letter 55
 
13.9%
Space Separator 25
 
6.3%
Other Punctuation 4
 
1.0%
Dash Punctuation 1
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 43
13.9%
o 33
10.6%
e 31
 
10.0%
a 26
 
8.4%
i 18
 
5.8%
s 16
 
5.2%
t 16
 
5.2%
u 16
 
5.2%
g 15
 
4.8%
r 14
 
4.5%
Other values (13) 82
26.5%
Uppercase Letter
ValueCountFrequency (%)
B 8
14.5%
C 6
10.9%
G 6
10.9%
I 6
10.9%
S 4
 
7.3%
T 4
 
7.3%
D 4
 
7.3%
M 3
 
5.5%
U 2
 
3.6%
P 2
 
3.6%
Other values (9) 10
18.2%
Other Punctuation
ValueCountFrequency (%)
' 2
50.0%
. 1
25.0%
& 1
25.0%
Space Separator
ValueCountFrequency (%)
25
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 365
92.4%
Common 30
 
7.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 43
 
11.8%
o 33
 
9.0%
e 31
 
8.5%
a 26
 
7.1%
i 18
 
4.9%
s 16
 
4.4%
t 16
 
4.4%
u 16
 
4.4%
g 15
 
4.1%
r 14
 
3.8%
Other values (32) 137
37.5%
Common
ValueCountFrequency (%)
25
83.3%
' 2
 
6.7%
. 1
 
3.3%
& 1
 
3.3%
- 1
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 395
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 43
 
10.9%
o 33
 
8.4%
e 31
 
7.8%
a 26
 
6.6%
25
 
6.3%
i 18
 
4.6%
s 16
 
4.1%
t 16
 
4.1%
u 16
 
4.1%
g 15
 
3.8%
Other values (37) 156
39.5%

역명(로마자)
Text

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-13T00:17:16.593545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length33
Median length15
Mean length11.5
Min length1

Characters and Unicode

Total characters345
Distinct characters43
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

Unique30 ?
Unique (%)100.0%

Sample

1st rowGyeyang
2nd rowGyulhyeon
3rd rowBakchon
4th rowImhak
5th rowGyesan
ValueCountFrequency (%)
incheon 3
 
7.0%
bupyeong 2
 
4.7%
univ 2
 
4.7%
of 2
 
4.7%
woninjae 1
 
2.3%
center 1
 
2.3%
terminal 1
 
2.3%
munhak 1
 
2.3%
stadium 1
 
2.3%
seonhak 1
 
2.3%
Other values (28) 28
65.1%
2023-12-13T00:17:16.986438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 39
 
11.3%
e 36
 
10.4%
o 30
 
8.7%
a 24
 
7.0%
u 21
 
6.1%
i 16
 
4.6%
g 15
 
4.3%
13
 
3.8%
y 12
 
3.5%
k 12
 
3.5%
Other values (33) 127
36.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 284
82.3%
Uppercase Letter 40
 
11.6%
Space Separator 13
 
3.8%
Dash Punctuation 5
 
1.4%
Other Punctuation 3
 
0.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 39
13.7%
e 36
12.7%
o 30
10.6%
a 24
 
8.5%
u 21
 
7.4%
i 16
 
5.6%
g 15
 
5.3%
y 12
 
4.2%
k 12
 
4.2%
s 9
 
3.2%
Other values (12) 70
24.6%
Uppercase Letter
ValueCountFrequency (%)
G 7
17.5%
B 5
12.5%
I 4
10.0%
S 4
10.0%
D 3
7.5%
T 2
 
5.0%
C 2
 
5.0%
M 2
 
5.0%
U 2
 
5.0%
J 2
 
5.0%
Other values (7) 7
17.5%
Other Punctuation
ValueCountFrequency (%)
. 2
66.7%
' 1
33.3%
Space Separator
ValueCountFrequency (%)
13
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 324
93.9%
Common 21
 
6.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 39
 
12.0%
e 36
 
11.1%
o 30
 
9.3%
a 24
 
7.4%
u 21
 
6.5%
i 16
 
4.9%
g 15
 
4.6%
y 12
 
3.7%
k 12
 
3.7%
s 9
 
2.8%
Other values (29) 110
34.0%
Common
ValueCountFrequency (%)
13
61.9%
- 5
 
23.8%
. 2
 
9.5%
' 1
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 345
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 39
 
11.3%
e 36
 
10.4%
o 30
 
8.7%
a 24
 
7.0%
u 21
 
6.1%
i 16
 
4.6%
g 15
 
4.3%
13
 
3.8%
y 12
 
3.5%
k 12
 
3.5%
Other values (33) 127
36.8%

역명(일본어)
Text

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-13T00:17:17.279454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length10.5
Mean length7.0666667
Min length3

Characters and Unicode

Total characters212
Distinct characters50
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique30 ?
Unique (%)100.0%

Sample

1st rowケヤン
2nd rowキュルヒョン
3rd rowパクチョン
4th rowイムハク
5th rowケサン
ValueCountFrequency (%)
ケヤン 1
 
3.3%
キュルヒョン 1
 
3.3%
ククチェオンムジグ 1
 
3.3%
セントラルパ-ク 1
 
3.3%
インチョンデイック 1
 
3.3%
チシクチョンボダンジ 1
 
3.3%
テクノパ-ク 1
 
3.3%
キャンパスタウン 1
 
3.3%
トンマク 1
 
3.3%
トンチュン 1
 
3.3%
Other values (20) 20
66.7%
2023-12-13T00:17:17.599987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
ン 42
19.8%
ョ 17
 
8.0%
ク 16
 
7.5%
チ 14
 
6.6%
イ 8
 
3.8%
ル 6
 
2.8%
ェ 5
 
2.4%
ジ 5
 
2.4%
ピ 5
 
2.4%
ャ 4
 
1.9%
Other values (40) 90
42.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 207
97.6%
Dash Punctuation 3
 
1.4%
Other Punctuation 2
 
0.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
ン 42
20.3%
ョ 17
 
8.2%
ク 16
 
7.7%
チ 14
 
6.8%
イ 8
 
3.9%
ル 6
 
2.9%
ェ 5
 
2.4%
ジ 5
 
2.4%
ピ 5
 
2.4%
ャ 4
 
1.9%
Other values (38) 85
41.1%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%
Other Punctuation
ValueCountFrequency (%)
․ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Katakana 207
97.6%
Common 5
 
2.4%

Most frequent character per script

Katakana
ValueCountFrequency (%)
ン 42
20.3%
ョ 17
 
8.2%
ク 16
 
7.7%
チ 14
 
6.8%
イ 8
 
3.9%
ル 6
 
2.9%
ェ 5
 
2.4%
ジ 5
 
2.4%
ピ 5
 
2.4%
ャ 4
 
1.9%
Other values (38) 85
41.1%
Common
ValueCountFrequency (%)
- 3
60.0%
․ 2
40.0%

Most occurring blocks

ValueCountFrequency (%)
Katakana 207
97.6%
ASCII 3
 
1.4%
Punctuation 2
 
0.9%

Most frequent character per block

Katakana
ValueCountFrequency (%)
ン 42
20.3%
ョ 17
 
8.2%
ク 16
 
7.7%
チ 14
 
6.8%
イ 8
 
3.9%
ル 6
 
2.9%
ェ 5
 
2.4%
ジ 5
 
2.4%
ピ 5
 
2.4%
ャ 4
 
1.9%
Other values (38) 85
41.1%
ASCII
ValueCountFrequency (%)
- 3
100.0%
Punctuation
ValueCountFrequency (%)
․ 2
100.0%

역명(중국어 간체)
Text

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-13T00:17:17.821630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length6
Mean length3.7
Min length1

Characters and Unicode

Total characters111
Distinct characters74
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

Unique30 ?
Unique (%)100.0%

Sample

1st row桂阳
2nd row橘岘
3rd row朴村
4th row林鹤
5th row桂山
ValueCountFrequency (%)
桂阳 1
 
3.3%
橘岘 1
 
3.3%
国际业务园区 1
 
3.3%
中央公园 1
 
3.3%
仁川大入口 1
 
3.3%
知识信息园区(知识情报团地 1
 
3.3%
科技公园 1
 
3.3%
大学城 1
 
3.3%
东幕 1
 
3.3%
东春 1
 
3.3%
Other values (20) 20
66.7%
2023-12-13T00:17:18.202047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
仁 5
 
4.5%
平 4
 
3.6%
园 4
 
3.6%
口 4
 
3.6%
富 4
 
3.6%
大 3
 
2.7%
区 3
 
2.7%
川 3
 
2.7%
鹤 3
 
2.7%
东 3
 
2.7%
Other values (64) 75
67.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 108
97.3%
Open Punctuation 1
 
0.9%
Close Punctuation 1
 
0.9%
Dash Punctuation 1
 
0.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
仁 5
 
4.6%
平 4
 
3.7%
园 4
 
3.7%
口 4
 
3.7%
富 4
 
3.7%
大 3
 
2.8%
区 3
 
2.8%
川 3
 
2.8%
鹤 3
 
2.8%
东 3
 
2.8%
Other values (61) 72
66.7%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Han 108
97.3%
Common 3
 
2.7%

Most frequent character per script

Han
ValueCountFrequency (%)
仁 5
 
4.6%
平 4
 
3.7%
园 4
 
3.7%
口 4
 
3.7%
富 4
 
3.7%
大 3
 
2.8%
区 3
 
2.8%
川 3
 
2.8%
鹤 3
 
2.8%
东 3
 
2.8%
Other values (61) 72
66.7%
Common
ValueCountFrequency (%)
( 1
33.3%
) 1
33.3%
- 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
CJK 108
97.3%
ASCII 3
 
2.7%

Most frequent character per block

CJK
ValueCountFrequency (%)
仁 5
 
4.6%
平 4
 
3.7%
园 4
 
3.7%
口 4
 
3.7%
富 4
 
3.7%
大 3
 
2.8%
区 3
 
2.8%
川 3
 
2.8%
鹤 3
 
2.8%
东 3
 
2.8%
Other values (61) 72
66.7%
ASCII
ValueCountFrequency (%)
( 1
33.3%
) 1
33.3%
- 1
33.3%

역명(중국어 번체)
Text

Distinct28
Distinct (%)93.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-13T00:17:18.413066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length6
Mean length3.3333333
Min length1

Characters and Unicode

Total characters100
Distinct characters70
Distinct categories2 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique27 ?
Unique (%)90.0%

Sample

1st row桂陽
2nd row橘峴
3rd row朴村
4th row林鶴
5th row桂山
ValueCountFrequency (%)
3
 
10.0%
桂陽 1
 
3.3%
橘峴 1
 
3.3%
國際業務地區 1
 
3.3%
仁川大入口 1
 
3.3%
知識情報團地 1
 
3.3%
東幕 1
 
3.3%
東春 1
 
3.3%
源仁齋 1
 
3.3%
新延壽 1
 
3.3%
Other values (18) 18
60.0%
2023-12-13T00:17:18.786520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
仁 5
 
5.0%
富 4
 
4.0%
平 4
 
4.0%
- 3
 
3.0%
川 3
 
3.0%
鶴 3
 
3.0%
東 3
 
3.0%
桂 2
 
2.0%
地 2
 
2.0%
리 2
 
2.0%
Other values (60) 69
69.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 97
97.0%
Dash Punctuation 3
 
3.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
仁 5
 
5.2%
富 4
 
4.1%
平 4
 
4.1%
川 3
 
3.1%
鶴 3
 
3.1%
東 3
 
3.1%
桂 2
 
2.1%
地 2
 
2.1%
리 2
 
2.1%
거 2
 
2.1%
Other values (59) 67
69.1%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Han 88
88.0%
Hangul 9
 
9.0%
Common 3
 
3.0%

Most frequent character per script

Han
ValueCountFrequency (%)
仁 5
 
5.7%
富 4
 
4.5%
平 4
 
4.5%
川 3
 
3.4%
鶴 3
 
3.4%
東 3
 
3.4%
桂 2
 
2.3%
地 2
 
2.3%
場 2
 
2.3%
廳 2
 
2.3%
Other values (52) 58
65.9%
Hangul
ValueCountFrequency (%)
리 2
22.2%
거 2
22.2%
빛 1
11.1%
달 1
11.1%
터 1
11.1%
미 1
11.1%
널 1
11.1%
Common
ValueCountFrequency (%)
- 3
100.0%

Most occurring blocks

ValueCountFrequency (%)
CJK 87
87.0%
Hangul 9
 
9.0%
ASCII 3
 
3.0%
CJK Compat Ideographs 1
 
1.0%

Most frequent character per block

CJK
ValueCountFrequency (%)
仁 5
 
5.7%
富 4
 
4.6%
平 4
 
4.6%
川 3
 
3.4%
鶴 3
 
3.4%
東 3
 
3.4%
桂 2
 
2.3%
地 2
 
2.3%
場 2
 
2.3%
廳 2
 
2.3%
Other values (51) 57
65.5%
ASCII
ValueCountFrequency (%)
- 3
100.0%
Hangul
ValueCountFrequency (%)
리 2
22.2%
거 2
22.2%
빛 1
11.1%
달 1
11.1%
터 1
11.1%
미 1
11.1%
널 1
11.1%
CJK Compat Ideographs
ValueCountFrequency (%)
林 1
100.0%

Correlations

2023-12-13T00:17:18.894439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
역명역명(영문)역명(로마자)역명(일본어)역명(중국어 간체)역명(중국어 번체)
역명1.0001.0001.0001.0001.0001.000
역명(영문)1.0001.0001.0001.0001.0001.000
역명(로마자)1.0001.0001.0001.0001.0001.000
역명(일본어)1.0001.0001.0001.0001.0001.000
역명(중국어 간체)1.0001.0001.0001.0001.0001.000
역명(중국어 번체)1.0001.0001.0001.0001.0001.000

Missing values

2023-12-13T00:17:15.177944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T00:17:15.275499image/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

역명역명(영문)역명(로마자)역명(일본어)역명(중국어 간체)역명(중국어 번체)
0계양GyeyangGyeyangケヤン桂阳桂陽
1귤현GyulhyeonGyulhyeonキュルヒョン橘岘橘峴
2박촌BakchonBakchonパクチョン朴村朴村
3임학ImhakImhakイムハク林鹤林鶴
4계산GyesanGyesanケサン桂山桂山
5경인교대입구Gyeongin Nat'l Univ. of EducationGyeongin Nat'l Univ. of Educationキョンインギョデイック京仁敎大入口京仁敎大入口
6작전JakjeonJakjeonチャクチョン鹊田鵲田
7갈산GalsanGalsanカルサン葛山葛山
8부평구청Bupyeong-gu OfficeBupyeong-gu Officeプピョングチョン富平区厅富平區廳
9부평시장Bupyeong MarketBupyeong Marketプピョンシジャン富平市场富平市場
역명역명(영문)역명(로마자)역명(일본어)역명(중국어 간체)역명(중국어 번체)
20원인재WoninjaeWoninjaeウォニンジェ源仁斋源仁齋
21동춘DongchunDongchunトンチュン东春東春
22동막DongmakDongmakトンマク东幕東幕
23캠퍼스타운Campus TownKaempeoseutaunキャンパスタウン大学城-
24테크노파크TechnoparkTekeunopakeuテクノパ-ク科技公园-
25지식정보단지BIT ZoneJisikjeongbodanjiチシクチョンボダンジ知识信息园区(知识情报团地)知識情報團地
26인천대입구Incheon National UniversityUniv. of Incheonインチョンデイック仁川大入口仁川大入口
27센트럴파크Central ParkSenteureolpakeuセントラルパ-ク中央公园-
28국제업무지구Int'l Business DistrictGukjeeommuji-guククチェオンムジグ国际业务园区國際業務地區
29송도달빛축제공원Songdo Moonlight Festival Park-ソンドダルピッチュクチェゴンウォン-松島달빛祝祭公園