Overview

Dataset statistics

Number of variables8
Number of observations1828
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory121.5 KiB
Average record size in memory68.1 B

Variable types

Numeric4
Text3
Categorical1

Dataset

Description2016년 9월기준 역코드로 주변 버스정류장 검색 할 수 있는 데이터입니다. 해당 데이터는 전철역코드,외부코드,전철역명,호선,정류장명,정류장ID 등을 포함한 정보를 제공합니다.
Author서울교통공사
URLhttps://www.data.go.kr/data/15044225/fileData.do

Alerts

전철역코드 is highly overall correlated with 호선High correlation
정류장ID is highly overall correlated with (Y)좌표High correlation
(Y)좌표 is highly overall correlated with 정류장IDHigh correlation
호선 is highly overall correlated with 전철역코드High correlation

Reproduction

Analysis started2023-12-12 12:29:16.427357
Analysis finished2023-12-12 12:29:19.119842
Duration2.69 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

전철역코드
Real number (ℝ)

HIGH CORRELATION 

Distinct354
Distinct (%)19.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1664.2697
Minimum150
Maximum4125
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.2 KiB
2023-12-12T21:29:19.214224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum150
5-th percentile202.35
Q1323
median1901
Q32644
95-th percentile4103
Maximum4125
Range3975
Interquartile range (IQR)2321

Descriptive statistics

Standard deviation1215.4712
Coefficient of variation (CV)0.7303331
Kurtosis-1.3042991
Mean1664.2697
Median Absolute Deviation (MAD)883
Skewness0.098722987
Sum3042285
Variance1477370.4
MonotonicityNot monotonic
2023-12-12T21:29:19.742736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2645 15
 
0.8%
150 15
 
0.8%
1018 13
 
0.7%
312 11
 
0.6%
417 11
 
0.6%
2530 11
 
0.6%
427 10
 
0.5%
426 10
 
0.5%
2750 10
 
0.5%
154 10
 
0.5%
Other values (344) 1712
93.7%
ValueCountFrequency (%)
150 15
0.8%
151 6
 
0.3%
152 10
0.5%
153 6
 
0.3%
154 10
0.5%
155 7
0.4%
156 5
 
0.3%
157 4
 
0.2%
158 7
0.4%
159 10
0.5%
ValueCountFrequency (%)
4125 8
0.4%
4124 1
 
0.1%
4123 4
0.2%
4122 5
0.3%
4121 2
 
0.1%
4120 4
0.2%
4119 9
0.5%
4117 3
 
0.2%
4116 2
 
0.1%
4115 3
 
0.2%

외부코드
Text

Distinct354
Distinct (%)19.4%
Missing0
Missing (%)0.0%
Memory size14.4 KiB
2023-12-12T21:29:20.188071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length3.1236324
Min length3

Characters and Unicode

Total characters5710
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)0.4%

Sample

1st row310
2nd row310
3rd row310
4th row150
5th row150
ValueCountFrequency (%)
133 15
 
0.8%
644 15
 
0.8%
120 13
 
0.7%
322 11
 
0.6%
417 11
 
0.6%
529 11
 
0.6%
533 10
 
0.5%
241 10
 
0.5%
614 10
 
0.5%
426 10
 
0.5%
Other values (344) 1712
93.7%
2023-12-12T21:29:20.804377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 1028
18.0%
1 964
16.9%
3 788
13.8%
4 705
12.3%
5 480
8.4%
7 413
7.2%
6 389
 
6.8%
0 292
 
5.1%
9 260
 
4.6%
8 217
 
3.8%
Other values (3) 174
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5536
97.0%
Uppercase Letter 122
 
2.1%
Dash Punctuation 52
 
0.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 1028
18.6%
1 964
17.4%
3 788
14.2%
4 705
12.7%
5 480
8.7%
7 413
7.5%
6 389
 
7.0%
0 292
 
5.3%
9 260
 
4.7%
8 217
 
3.9%
Uppercase Letter
ValueCountFrequency (%)
K 66
54.1%
P 56
45.9%
Dash Punctuation
ValueCountFrequency (%)
- 52
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5588
97.9%
Latin 122
 
2.1%

Most frequent character per script

Common
ValueCountFrequency (%)
2 1028
18.4%
1 964
17.3%
3 788
14.1%
4 705
12.6%
5 480
8.6%
7 413
7.4%
6 389
 
7.0%
0 292
 
5.2%
9 260
 
4.7%
8 217
 
3.9%
Latin
ValueCountFrequency (%)
K 66
54.1%
P 56
45.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5710
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 1028
18.0%
1 964
16.9%
3 788
13.8%
4 705
12.3%
5 480
8.4%
7 413
7.2%
6 389
 
6.8%
0 292
 
5.1%
9 260
 
4.6%
8 217
 
3.8%
Other values (3) 174
 
3.0%

전철역명
Text

Distinct294
Distinct (%)16.1%
Missing0
Missing (%)0.0%
Memory size14.4 KiB
2023-12-12T21:29:21.139405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length2
Mean length2.8752735
Min length2

Characters and Unicode

Total characters5256
Distinct characters219
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

Unique6 ?
Unique (%)0.3%

Sample

1st row대화
2nd row대화
3rd row대화
4th row송내
5th row송내
ValueCountFrequency (%)
석계 28
 
1.5%
서울 25
 
1.4%
공덕 21
 
1.1%
합정 20
 
1.1%
연신내 20
 
1.1%
불광 19
 
1.0%
왕십리 18
 
1.0%
동묘앞 18
 
1.0%
종로3가 18
 
1.0%
건대입구 17
 
0.9%
Other values (284) 1624
88.8%
2023-12-12T21:29:21.626233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
대 212
 
4.0%
구 177
 
3.4%
신 177
 
3.4%
산 142
 
2.7%
동 134
 
2.5%
정 106
 
2.0%
로 101
 
1.9%
입 100
 
1.9%
가 99
 
1.9%
지 89
 
1.7%
Other values (209) 3919
74.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5171
98.4%
Decimal Number 53
 
1.0%
Open Punctuation 16
 
0.3%
Close Punctuation 16
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
대 212
 
4.1%
구 177
 
3.4%
신 177
 
3.4%
산 142
 
2.7%
동 134
 
2.6%
정 106
 
2.0%
로 101
 
2.0%
입 100
 
1.9%
가 99
 
1.9%
지 89
 
1.7%
Other values (204) 3834
74.1%
Decimal Number
ValueCountFrequency (%)
3 28
52.8%
4 15
28.3%
5 10
 
18.9%
Open Punctuation
ValueCountFrequency (%)
( 16
100.0%
Close Punctuation
ValueCountFrequency (%)
) 16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5171
98.4%
Common 85
 
1.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
대 212
 
4.1%
구 177
 
3.4%
신 177
 
3.4%
산 142
 
2.7%
동 134
 
2.6%
정 106
 
2.0%
로 101
 
2.0%
입 100
 
1.9%
가 99
 
1.9%
지 89
 
1.7%
Other values (204) 3834
74.1%
Common
ValueCountFrequency (%)
3 28
32.9%
( 16
18.8%
) 16
18.8%
4 15
17.6%
5 10
 
11.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5171
98.4%
ASCII 85
 
1.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
대 212
 
4.1%
구 177
 
3.4%
신 177
 
3.4%
산 142
 
2.7%
동 134
 
2.6%
정 106
 
2.0%
로 101
 
2.0%
입 100
 
1.9%
가 99
 
1.9%
지 89
 
1.7%
Other values (204) 3834
74.1%
ASCII
ValueCountFrequency (%)
3 28
32.9%
( 16
18.8%
) 16
18.8%
4 15
17.6%
5 10
 
11.8%

호선
Categorical

HIGH CORRELATION 

Distinct11
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size14.4 KiB
2
287 
7
249 
5
238 
1
227 
6
223 
Other values (6)
604 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row1
5th row1

Common Values

ValueCountFrequency (%)
2 287
15.7%
7 249
13.6%
5 238
13.0%
1 227
12.4%
6 223
12.2%
3 196
10.7%
4 174
9.5%
9 95
 
5.2%
8 73
 
4.0%
B 46
 
2.5%

Length

2023-12-12T21:29:21.767888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2 287
15.7%
7 249
13.6%
5 238
13.0%
1 227
12.4%
6 223
12.2%
3 196
10.7%
4 174
9.5%
9 95
 
5.2%
8 73
 
4.0%
b 46
 
2.5%

정류장명
Text

Distinct859
Distinct (%)47.0%
Missing0
Missing (%)0.0%
Memory size14.4 KiB
2023-12-12T21:29:22.043194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length17
Mean length5.5300875
Min length2

Characters and Unicode

Total characters10109
Distinct characters363
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique375 ?
Unique (%)20.5%

Sample

1st row예비군훈련장
2nd row대화역
3rd row대화역
4th row송내역
5th row송내역
ValueCountFrequency (%)
서울역 17
 
0.9%
연신내역 16
 
0.9%
합정역 16
 
0.9%
종로3가 15
 
0.8%
성동구청 12
 
0.7%
당산역 12
 
0.7%
석계역 12
 
0.7%
동대문 11
 
0.6%
복정역 10
 
0.5%
대림역 10
 
0.5%
Other values (849) 1697
92.8%
2023-12-12T21:29:22.497192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
역 895
 
8.9%
동 358
 
3.5%
구 252
 
2.5%
대 233
 
2.3%
장 163
 
1.6%
리 160
 
1.6%
신 150
 
1.5%
지 140
 
1.4%
가 139
 
1.4%
앞 136
 
1.3%
Other values (353) 7483
74.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 9804
97.0%
Decimal Number 278
 
2.8%
Close Punctuation 10
 
0.1%
Open Punctuation 10
 
0.1%
Uppercase Letter 7
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
역 895
 
9.1%
동 358
 
3.7%
구 252
 
2.6%
대 233
 
2.4%
장 163
 
1.7%
리 160
 
1.6%
신 150
 
1.5%
지 140
 
1.4%
가 139
 
1.4%
앞 136
 
1.4%
Other values (336) 7178
73.2%
Decimal Number
ValueCountFrequency (%)
2 60
21.6%
1 59
21.2%
3 57
20.5%
6 25
9.0%
7 20
 
7.2%
5 20
 
7.2%
4 20
 
7.2%
8 10
 
3.6%
9 5
 
1.8%
0 2
 
0.7%
Uppercase Letter
ValueCountFrequency (%)
C 2
28.6%
B 2
28.6%
R 1
14.3%
S 1
14.3%
J 1
14.3%
Close Punctuation
ValueCountFrequency (%)
) 10
100.0%
Open Punctuation
ValueCountFrequency (%)
( 10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 9804
97.0%
Common 298
 
2.9%
Latin 7
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
역 895
 
9.1%
동 358
 
3.7%
구 252
 
2.6%
대 233
 
2.4%
장 163
 
1.7%
리 160
 
1.6%
신 150
 
1.5%
지 140
 
1.4%
가 139
 
1.4%
앞 136
 
1.4%
Other values (336) 7178
73.2%
Common
ValueCountFrequency (%)
2 60
20.1%
1 59
19.8%
3 57
19.1%
6 25
8.4%
7 20
 
6.7%
5 20
 
6.7%
4 20
 
6.7%
8 10
 
3.4%
) 10
 
3.4%
( 10
 
3.4%
Other values (2) 7
 
2.3%
Latin
ValueCountFrequency (%)
C 2
28.6%
B 2
28.6%
R 1
14.3%
S 1
14.3%
J 1
14.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 9804
97.0%
ASCII 305
 
3.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
역 895
 
9.1%
동 358
 
3.7%
구 252
 
2.6%
대 233
 
2.4%
장 163
 
1.7%
리 160
 
1.6%
신 150
 
1.5%
지 140
 
1.4%
가 139
 
1.4%
앞 136
 
1.4%
Other values (336) 7178
73.2%
ASCII
ValueCountFrequency (%)
2 60
19.7%
1 59
19.3%
3 57
18.7%
6 25
8.2%
7 20
 
6.6%
5 20
 
6.6%
4 20
 
6.6%
8 10
 
3.3%
) 10
 
3.3%
( 10
 
3.3%
Other values (7) 14
 
4.6%

정류장ID
Real number (ℝ)

HIGH CORRELATION 

Distinct1495
Distinct (%)81.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15408.499
Minimum1115
Maximum61061
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.2 KiB
2023-12-12T21:29:22.653354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1115
5-th percentile1768
Q16205.75
median14160.5
Q322158
95-th percentile38611.65
Maximum61061
Range59946
Interquartile range (IQR)15952.25

Descriptive statistics

Standard deviation11509.628
Coefficient of variation (CV)0.74696621
Kurtosis2.3656519
Mean15408.499
Median Absolute Deviation (MAD)7991
Skewness1.2995549
Sum28166736
Variance1.3247154 × 108
MonotonicityNot monotonic
2023-12-12T21:29:22.818837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22793 3
 
0.2%
3119 3
 
0.2%
4133 3
 
0.2%
4134 3
 
0.2%
22210 3
 
0.2%
22220 3
 
0.2%
22794 3
 
0.2%
4112 3
 
0.2%
4719 3
 
0.2%
2169 3
 
0.2%
Other values (1485) 1798
98.4%
ValueCountFrequency (%)
1115 1
0.1%
1116 1
0.1%
1118 1
0.1%
1119 1
0.1%
1120 1
0.1%
1125 1
0.1%
1126 1
0.1%
1127 1
0.1%
1129 1
0.1%
1131 1
0.1%
ValueCountFrequency (%)
61061 1
0.1%
61060 1
0.1%
61053 1
0.1%
61052 1
0.1%
61044 1
0.1%
61032 1
0.1%
61029 1
0.1%
61010 1
0.1%
61009 1
0.1%
59030 1
0.1%

(X)좌표
Real number (ℝ)

Distinct1433
Distinct (%)78.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean199210.56
Minimum177627
Maximum214960
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.2 KiB
2023-12-12T21:29:22.984253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum177627
5-th percentile185908.55
Q1193549.25
median199551
Q3205155
95-th percentile211219.75
Maximum214960
Range37333
Interquartile range (IQR)11605.75

Descriptive statistics

Standard deviation7563.3556
Coefficient of variation (CV)0.03796664
Kurtosis-0.51602196
Mean199210.56
Median Absolute Deviation (MAD)5746
Skewness-0.24257451
Sum3.6415691 × 108
Variance57204349
MonotonicityNot monotonic
2023-12-12T21:29:23.177569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
211231 5
 
0.3%
203209 4
 
0.2%
204457 4
 
0.2%
201256 4
 
0.2%
205706 4
 
0.2%
193130 4
 
0.2%
197960 4
 
0.2%
205878 4
 
0.2%
197933 4
 
0.2%
197031 3
 
0.2%
Other values (1423) 1788
97.8%
ValueCountFrequency (%)
177627 1
0.1%
177715 1
0.1%
177718 1
0.1%
178169 1
0.1%
178200 1
0.1%
178268 1
0.1%
178853 1
0.1%
178947 1
0.1%
179036 1
0.1%
179088 1
0.1%
ValueCountFrequency (%)
214960 1
0.1%
214945 1
0.1%
214597 1
0.1%
214582 1
0.1%
214552 1
0.1%
214540 1
0.1%
214162 1
0.1%
214112 1
0.1%
214092 1
0.1%
214050 1
0.1%

(Y)좌표
Real number (ℝ)

HIGH CORRELATION 

Distinct1435
Distinct (%)78.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean449609.84
Minimum424080
Maximum471105
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.2 KiB
2023-12-12T21:29:23.374669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum424080
5-th percentile441186
Q1445144.5
median450084
Q3452920
95-th percentile460490.65
Maximum471105
Range47025
Interquartile range (IQR)7775.5

Descriptive statistics

Standard deviation6491.094
Coefficient of variation (CV)0.014437171
Kurtosis1.1170164
Mean449609.84
Median Absolute Deviation (MAD)4105
Skewness-0.29140381
Sum8.2188678 × 108
Variance42134302
MonotonicityNot monotonic
2023-12-12T21:29:23.671878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
451525 6
 
0.3%
450084 6
 
0.3%
452133 5
 
0.3%
449442 4
 
0.2%
444815 4
 
0.2%
446975 4
 
0.2%
452221 4
 
0.2%
450628 4
 
0.2%
443723 4
 
0.2%
451843 4
 
0.2%
Other values (1425) 1783
97.5%
ValueCountFrequency (%)
424080 1
0.1%
424127 1
0.1%
426480 1
0.1%
426510 1
0.1%
427715 1
0.1%
427812 1
0.1%
427934 1
0.1%
427960 1
0.1%
428141 1
0.1%
428309 1
0.1%
ValueCountFrequency (%)
471105 1
0.1%
471050 1
0.1%
469263 1
0.1%
469247 1
0.1%
467570 1
0.1%
466995 1
0.1%
466952 1
0.1%
466753 1
0.1%
466744 1
0.1%
465506 2
0.1%

Interactions

2023-12-12T21:29:18.474207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:29:17.014904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:29:17.500270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:29:17.977654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:29:18.590580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:29:17.132293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:29:17.607460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:29:18.088725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:29:18.689736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:29:17.255383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:29:17.733463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:29:18.202211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:29:18.791192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:29:17.379361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:29:17.863698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:29:18.370193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T21:29:23.866164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
전철역코드호선정류장ID(X)좌표(Y)좌표
전철역코드1.0000.8930.6430.5680.599
호선0.8931.0000.6340.5920.520
정류장ID0.6430.6341.0000.7410.821
(X)좌표0.5680.5920.7411.0000.654
(Y)좌표0.5990.5200.8210.6541.000
2023-12-12T21:29:24.003561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
전철역코드정류장ID(X)좌표(Y)좌표호선
전철역코드1.0000.2600.047-0.0710.720
정류장ID0.2601.000-0.024-0.6230.351
(X)좌표0.047-0.0241.0000.0760.301
(Y)좌표-0.071-0.6230.0761.0000.253
호선0.7200.3510.3010.2531.000

Missing values

2023-12-12T21:29:18.937205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T21:29:19.068094image/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

전철역코드외부코드전철역명호선정류장명정류장ID(X)좌표(Y)좌표
01958310대화3예비군훈련장36262177627464279
11958310대화3대화역36281177715464099
21958310대화3대화역36601177718464073
31805150송내1송내역46039178169443269
41805150송내1송내역46731178200443181
51805150송내1송내역46086178268443268
61957311주엽3주엽역36700178853463443
71957311주엽3주엽역36699178947463403
81822149중동1부천여고46090179036443255
91822149중동1부천여고46087179088443229
전철역코드외부코드전철역명호선정류장명정류장ID(X)좌표(Y)좌표
18182823822남한산성입구8금광시장48046214050438972
18192823822남한산성입구8금광시장49036214092438942
18202823822남한산성입구8법원앞남한산성입구역48048214112439148
18212823822남한산성입구8법원앞남한산성입구역49026214162439138
18222554553상일동5고덕종합상가25133214540450801
18232554553상일동5고덕종합상가25134214552450776
18242554553상일동5신한은행25165214582450928
18252554553상일동5신한은행25166214597450950
18262554553상일동5상일동역25132214945450838
18272554553상일동5상일동역25131214960450866