Overview

Dataset statistics

Number of variables6
Number of observations48
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.5 KiB
Average record size in memory53.8 B

Variable types

Numeric3
Text3

Dataset

Description전북특별자치도 군산시 버스정보 안내기 현황정보 정류장번호, 정류장이름, 지번주소, 도로명주소, 위도, 경도 항목 제공
Author전라북도
URLhttps://www.bigdatahub.go.kr/index.jeonbuk?startPage=2&menuCd=DOM_000000103007001000&pListTypeStr=&pId=3059622

Alerts

정류장번호 is highly overall correlated with 경도High correlation
경도 is highly overall correlated with 정류장번호High correlation
정류장번호 has unique valuesUnique
정류장이름 has unique valuesUnique
도로명 주소 has unique valuesUnique
위도 has unique valuesUnique
경도 has unique valuesUnique

Reproduction

Analysis started2024-03-14 03:09:01.758448
Analysis finished2024-03-14 03:09:02.854160
Duration1.1 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

정류장번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct48
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2609450.6
Minimum2601810
Maximum2614590
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size564.0 B
2024-03-14T12:09:02.943695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2601810
5-th percentile2602003.5
Q12608205
median2611100
Q32612640
95-th percentile2613419
Maximum2614590
Range12780
Interquartile range (IQR)4435

Descriptive statistics

Standard deviation4038.3209
Coefficient of variation (CV)0.0015475751
Kurtosis-0.64774043
Mean2609450.6
Median Absolute Deviation (MAD)1845
Skewness-0.91898152
Sum1.2525363 × 108
Variance16308036
MonotonicityNot monotonic
2024-03-14T12:09:03.102560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
2611010 1
 
2.1%
2602010 1
 
2.1%
2608550 1
 
2.1%
2601840 1
 
2.1%
2611990 1
 
2.1%
2611400 1
 
2.1%
2608130 1
 
2.1%
2612670 1
 
2.1%
2601810 1
 
2.1%
2602000 1
 
2.1%
Other values (38) 38
79.2%
ValueCountFrequency (%)
2601810 1
2.1%
2601840 1
2.1%
2602000 1
2.1%
2602010 1
2.1%
2602220 1
2.1%
2602440 1
2.1%
2603000 1
2.1%
2603130 1
2.1%
2603250 1
2.1%
2603280 1
2.1%
ValueCountFrequency (%)
2614590 1
2.1%
2613470 1
2.1%
2613440 1
2.1%
2613380 1
2.1%
2613090 1
2.1%
2612980 1
2.1%
2612970 1
2.1%
2612950 1
2.1%
2612940 1
2.1%
2612850 1
2.1%

정류장이름
Text

UNIQUE 

Distinct48
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size516.0 B
2024-03-14T12:09:03.269048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length14
Mean length9.2708333
Min length4

Characters and Unicode

Total characters445
Distinct characters141
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique48 ?
Unique (%)100.0%

Sample

1st row해동가축병원
2nd row롯데마트 앞
3rd row시립도서관 앞
4th row동아아파트 앞
5th row팔마광장 앞
ValueCountFrequency (%)
앞 5
 
8.6%
해동가축병원 1
 
1.7%
요양병원(나운동 1
 
1.7%
공설시장(구역전 1
 
1.7%
시청정문 1
 
1.7%
금강부동산(소룡동 1
 
1.7%
수송동주민센터(건너편 1
 
1.7%
롯데마트건너편 1
 
1.7%
나운사거리(산북동방향 1
 
1.7%
타이어뱅크옆 1
 
1.7%
Other values (44) 44
75.9%
2024-03-14T12:09:03.571655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
( 22
 
4.9%
) 22
 
4.9%
앞 16
 
3.6%
동 14
 
3.1%
트 13
 
2.9%
사 12
 
2.7%
시 11
 
2.5%
리 11
 
2.5%
아 11
 
2.5%
향 11
 
2.5%
Other values (131) 302
67.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 385
86.5%
Open Punctuation 22
 
4.9%
Close Punctuation 22
 
4.9%
Space Separator 10
 
2.2%
Uppercase Letter 3
 
0.7%
Decimal Number 2
 
0.4%
Dash Punctuation 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
앞 16
 
4.2%
동 14
 
3.6%
트 13
 
3.4%
사 12
 
3.1%
시 11
 
2.9%
리 11
 
2.9%
아 11
 
2.9%
향 11
 
2.9%
방 10
 
2.6%
거 10
 
2.6%
Other values (122) 266
69.1%
Uppercase Letter
ValueCountFrequency (%)
T 1
33.3%
E 1
33.3%
K 1
33.3%
Decimal Number
ValueCountFrequency (%)
3 1
50.0%
2 1
50.0%
Open Punctuation
ValueCountFrequency (%)
( 22
100.0%
Close Punctuation
ValueCountFrequency (%)
) 22
100.0%
Space Separator
ValueCountFrequency (%)
10
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 385
86.5%
Common 57
 
12.8%
Latin 3
 
0.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
앞 16
 
4.2%
동 14
 
3.6%
트 13
 
3.4%
사 12
 
3.1%
시 11
 
2.9%
리 11
 
2.9%
아 11
 
2.9%
향 11
 
2.9%
방 10
 
2.6%
거 10
 
2.6%
Other values (122) 266
69.1%
Common
ValueCountFrequency (%)
( 22
38.6%
) 22
38.6%
10
17.5%
3 1
 
1.8%
2 1
 
1.8%
- 1
 
1.8%
Latin
ValueCountFrequency (%)
T 1
33.3%
E 1
33.3%
K 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 385
86.5%
ASCII 60
 
13.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
( 22
36.7%
) 22
36.7%
10
16.7%
3 1
 
1.7%
T 1
 
1.7%
E 1
 
1.7%
K 1
 
1.7%
2 1
 
1.7%
- 1
 
1.7%
Hangul
ValueCountFrequency (%)
앞 16
 
4.2%
동 14
 
3.6%
트 13
 
3.4%
사 12
 
3.1%
시 11
 
2.9%
리 11
 
2.9%
아 11
 
2.9%
향 11
 
2.9%
방 10
 
2.6%
거 10
 
2.6%
Other values (122) 266
69.1%

지번주소
Text

Distinct46
Distinct (%)95.8%
Missing0
Missing (%)0.0%
Memory size516.0 B
2024-03-14T12:09:03.783632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length25
Mean length21.145833
Min length19

Characters and Unicode

Total characters1015
Distinct characters50
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

Unique45 ?
Unique (%)93.8%

Sample

1st row전북특별자치도 군산시 장재동 200-5
2nd row전북특별자치도 군산시 수송동 831-7
3rd row전북특별자치도 군산시 나운동 1536-6
4th row전북특별자치도 군산시 소룡동 1513-2
5th row전북특별자치도 군산시 대명동 388-11
ValueCountFrequency (%)
전북특별자치도 48
24.5%
군산시 48
24.5%
나운동 12
 
6.1%
조촌동 5
 
2.6%
수송동 5
 
2.6%
경장동 4
 
2.0%
874 3
 
1.5%
소룡동 3
 
1.5%
대명동 3
 
1.5%
미장동 2
 
1.0%
Other values (59) 63
32.1%
2024-03-14T12:09:04.129668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
149
 
14.7%
산 49
 
4.8%
전 48
 
4.7%
도 48
 
4.7%
북 48
 
4.7%
시 48
 
4.7%
군 48
 
4.7%
치 48
 
4.7%
자 48
 
4.7%
별 48
 
4.7%
Other values (40) 433
42.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 635
62.6%
Decimal Number 198
 
19.5%
Space Separator 149
 
14.7%
Dash Punctuation 33
 
3.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 49
 
7.7%
전 48
 
7.6%
도 48
 
7.6%
북 48
 
7.6%
시 48
 
7.6%
군 48
 
7.6%
치 48
 
7.6%
자 48
 
7.6%
별 48
 
7.6%
특 48
 
7.6%
Other values (28) 154
24.3%
Decimal Number
ValueCountFrequency (%)
1 32
16.2%
5 27
13.6%
2 26
13.1%
8 23
11.6%
3 18
9.1%
6 17
8.6%
4 16
8.1%
7 16
8.1%
0 15
7.6%
9 8
 
4.0%
Space Separator
ValueCountFrequency (%)
149
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 33
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 635
62.6%
Common 380
37.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 49
 
7.7%
전 48
 
7.6%
도 48
 
7.6%
북 48
 
7.6%
시 48
 
7.6%
군 48
 
7.6%
치 48
 
7.6%
자 48
 
7.6%
별 48
 
7.6%
특 48
 
7.6%
Other values (28) 154
24.3%
Common
ValueCountFrequency (%)
149
39.2%
- 33
 
8.7%
1 32
 
8.4%
5 27
 
7.1%
2 26
 
6.8%
8 23
 
6.1%
3 18
 
4.7%
6 17
 
4.5%
4 16
 
4.2%
7 16
 
4.2%
Other values (2) 23
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 635
62.6%
ASCII 380
37.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
149
39.2%
- 33
 
8.7%
1 32
 
8.4%
5 27
 
7.1%
2 26
 
6.8%
8 23
 
6.1%
3 18
 
4.7%
6 17
 
4.5%
4 16
 
4.2%
7 16
 
4.2%
Other values (2) 23
 
6.1%
Hangul
ValueCountFrequency (%)
산 49
 
7.7%
전 48
 
7.6%
도 48
 
7.6%
북 48
 
7.6%
시 48
 
7.6%
군 48
 
7.6%
치 48
 
7.6%
자 48
 
7.6%
별 48
 
7.6%
특 48
 
7.6%
Other values (28) 154
24.3%

도로명 주소
Text

UNIQUE 

Distinct48
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size516.0 B
2024-03-14T12:09:04.342897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length22
Mean length19.479167
Min length17

Characters and Unicode

Total characters935
Distinct characters65
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique48 ?
Unique (%)100.0%

Sample

1st row전북특별자치도 군산시 중앙로 61
2nd row전북특별자치도 군산시 월명로 248
3rd row전북특별자치도 군산시 서수송안1길 12
4th row전북특별자치도 군산시 동아로 160
5th row전북특별자치도 군산시 중앙로 14
ValueCountFrequency (%)
전북특별자치도 48
25.0%
군산시 48
25.0%
대학로 9
 
4.7%
수송로 4
 
2.1%
번영로 4
 
2.1%
중앙로 3
 
1.6%
월명로 3
 
1.6%
공단대로 2
 
1.0%
백릉로 2
 
1.0%
구암3.1로 2
 
1.0%
Other values (61) 67
34.9%
2024-03-14T12:09:04.642712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
166
17.8%
시 49
 
5.2%
전 48
 
5.1%
도 48
 
5.1%
북 48
 
5.1%
산 48
 
5.1%
군 48
 
5.1%
치 48
 
5.1%
자 48
 
5.1%
별 48
 
5.1%
Other values (55) 336
35.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 632
67.6%
Space Separator 166
 
17.8%
Decimal Number 131
 
14.0%
Dash Punctuation 4
 
0.4%
Other Punctuation 2
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 49
 
7.8%
전 48
 
7.6%
도 48
 
7.6%
북 48
 
7.6%
산 48
 
7.6%
군 48
 
7.6%
치 48
 
7.6%
자 48
 
7.6%
별 48
 
7.6%
특 48
 
7.6%
Other values (42) 151
23.9%
Decimal Number
ValueCountFrequency (%)
1 30
22.9%
2 22
16.8%
3 13
9.9%
4 12
 
9.2%
5 11
 
8.4%
8 10
 
7.6%
9 9
 
6.9%
7 9
 
6.9%
6 8
 
6.1%
0 7
 
5.3%
Space Separator
ValueCountFrequency (%)
166
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%
Other Punctuation
ValueCountFrequency (%)
. 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 632
67.6%
Common 303
32.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 49
 
7.8%
전 48
 
7.6%
도 48
 
7.6%
북 48
 
7.6%
산 48
 
7.6%
군 48
 
7.6%
치 48
 
7.6%
자 48
 
7.6%
별 48
 
7.6%
특 48
 
7.6%
Other values (42) 151
23.9%
Common
ValueCountFrequency (%)
166
54.8%
1 30
 
9.9%
2 22
 
7.3%
3 13
 
4.3%
4 12
 
4.0%
5 11
 
3.6%
8 10
 
3.3%
9 9
 
3.0%
7 9
 
3.0%
6 8
 
2.6%
Other values (3) 13
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 632
67.6%
ASCII 303
32.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
166
54.8%
1 30
 
9.9%
2 22
 
7.3%
3 13
 
4.3%
4 12
 
4.0%
5 11
 
3.6%
8 10
 
3.3%
9 9
 
3.0%
7 9
 
3.0%
6 8
 
2.6%
Other values (3) 13
 
4.3%
Hangul
ValueCountFrequency (%)
시 49
 
7.8%
전 48
 
7.6%
도 48
 
7.6%
북 48
 
7.6%
산 48
 
7.6%
군 48
 
7.6%
치 48
 
7.6%
자 48
 
7.6%
별 48
 
7.6%
특 48
 
7.6%
Other values (42) 151
23.9%

위도
Real number (ℝ)

UNIQUE 

Distinct48
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.969769
Minimum35.945584
Maximum35.987098
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size564.0 B
2024-03-14T12:09:04.764811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.945584
5-th percentile35.951889
Q135.964081
median35.968815
Q335.976485
95-th percentile35.985438
Maximum35.987098
Range0.041514
Interquartile range (IQR)0.01240425

Descriptive statistics

Standard deviation0.0099049679
Coefficient of variation (CV)0.00027536924
Kurtosis-0.0015086615
Mean35.969769
Median Absolute Deviation (MAD)0.006933
Skewness-0.3166984
Sum1726.5489
Variance9.810839 × 10-5
MonotonicityNot monotonic
2024-03-14T12:09:04.897258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
35.979494 1
 
2.1%
35.975783 1
 
2.1%
35.980291 1
 
2.1%
35.966892 1
 
2.1%
35.973863 1
 
2.1%
35.96384 1
 
2.1%
35.966247 1
 
2.1%
35.963199 1
 
2.1%
35.965235 1
 
2.1%
35.963554 1
 
2.1%
Other values (38) 38
79.2%
ValueCountFrequency (%)
35.945584 1
2.1%
35.947081 1
2.1%
35.94865 1
2.1%
35.957904 1
2.1%
35.95899 1
2.1%
35.960511 1
2.1%
35.96057 1
2.1%
35.961274 1
2.1%
35.963199 1
2.1%
35.963406 1
2.1%
ValueCountFrequency (%)
35.987098 1
2.1%
35.986756 1
2.1%
35.986015 1
2.1%
35.984366 1
2.1%
35.983072 1
2.1%
35.981566 1
2.1%
35.980291 1
2.1%
35.980086 1
2.1%
35.97995 1
2.1%
35.979868 1
2.1%

경도
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct48
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.71782
Minimum126.67853
Maximum126.81158
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size564.0 B
2024-03-14T12:09:04.998920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.67853
5-th percentile126.68507
Q1126.69973
median126.71579
Q3126.72743
95-th percentile126.74871
Maximum126.81158
Range0.133046
Interquartile range (IQR)0.0277005

Descriptive statistics

Standard deviation0.026251633
Coefficient of variation (CV)0.00020716607
Kurtosis4.9315141
Mean126.71782
Median Absolute Deviation (MAD)0.016178
Skewness1.715337
Sum6082.4554
Variance0.00068914822
MonotonicityNot monotonic
2024-03-14T12:09:05.143335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
126.719953 1
 
2.1%
126.739253 1
 
2.1%
126.720395 1
 
2.1%
126.736324 1
 
2.1%
126.678529 1
 
2.1%
126.718314 1
 
2.1%
126.715272 1
 
2.1%
126.692239 1
 
2.1%
126.735917 1
 
2.1%
126.738835 1
 
2.1%
Other values (38) 38
79.2%
ValueCountFrequency (%)
126.678529 1
2.1%
126.679377 1
2.1%
126.684896 1
2.1%
126.685391 1
2.1%
126.690071 1
2.1%
126.691773 1
2.1%
126.692239 1
2.1%
126.693499 1
2.1%
126.697249 1
2.1%
126.698783 1
2.1%
ValueCountFrequency (%)
126.811575 1
2.1%
126.809417 1
2.1%
126.750843 1
2.1%
126.74474 1
2.1%
126.739253 1
2.1%
126.738835 1
2.1%
126.736324 1
2.1%
126.735917 1
2.1%
126.734854 1
2.1%
126.734778 1
2.1%

Interactions

2024-03-14T12:09:02.404595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:09:01.996084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:09:02.210786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:09:02.479081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:09:02.073551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:09:02.280289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:09:02.560868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:09:02.138400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:09:02.336118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T12:09:05.253001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
정류장번호정류장이름지번주소도로명 주소위도경도
정류장번호1.0001.0001.0001.0000.0000.671
정류장이름1.0001.0001.0001.0001.0001.000
지번주소1.0001.0001.0001.0000.0000.924
도로명 주소1.0001.0001.0001.0001.0001.000
위도0.0001.0000.0001.0001.0000.635
경도0.6711.0000.9241.0000.6351.000
2024-03-14T12:09:05.341999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
정류장번호위도경도
정류장번호1.000-0.392-0.505
위도-0.3921.0000.136
경도-0.5050.1361.000

Missing values

2024-03-14T12:09:02.685966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T12:09:02.809551image/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

정류장번호정류장이름지번주소도로명 주소위도경도
02611010해동가축병원전북특별자치도 군산시 장재동 200-5전북특별자치도 군산시 중앙로 6135.979494126.719953
12608270롯데마트 앞전북특별자치도 군산시 수송동 831-7전북특별자치도 군산시 월명로 24835.966039126.715878
22603130시립도서관 앞전북특별자치도 군산시 나운동 1536-6전북특별자치도 군산시 서수송안1길 1235.965389126.710593
32612220동아아파트 앞전북특별자치도 군산시 소룡동 1513-2전북특별자치도 군산시 동아로 16035.969421126.684896
42611350팔마광장 앞전북특별자치도 군산시 대명동 388-11전북특별자치도 군산시 중앙로 1435.976304126.723379
52603250중앙사거리 앞전북특별자치도 군산시 중앙로1가 12-4전북특별자치도 군산시 대학로 3435.987098126.711642
62612230군산대 후문전북특별자치도 군산시 미룡동 455-5전북특별자치도 군산시 대학로 52535.94865126.685391
72612970한울아파트전북특별자치도 군산시 나운동 158전북특별자치도 군산시 하나운로 1835.96057126.699384
82612800나운지구대전북특별자치도 군산시 나운동 874전북특별자치도 군산시 대학로 34235.963406126.693499
92611190명산사거리(아름다운가게)전북특별자치도 군산시 명산동 23-7전북특별자치도 군산시 대학로 6535.984366126.709657
정류장번호정류장이름지번주소도로명 주소위도경도
382604340대야우체국앞(시내방향)전북특별자치도 군산시 대야면 지경리 2002전북특별자치도 군산시 번영로 88135.947081126.809417
392609840삼성쉐르빌아파트전북특별자치도 군산시 미장동 597전북특별자치도 군산시 수송로 29135.964161126.723488
402614590풍경채앞(세무서쪽)전북특별자치도 군산시 미장동 525전북특별자치도 군산시 수송로 35635.964186126.733389
412612940정다운병원앞(나운동사거리방향)전북특별자치도 군산시 나운동 743-7전북특별자치도 군산시 상신5길 235.971427126.698873
422612580나운3동주민센터(시내방향)전북특별자치도 군산시 나운동 874전북특별자치도 군산시 대학로 41035.957904126.690071
432610510시청사거리(롯데마트방향)전북특별자치도 군산시 경장동 520-13전북특별자치도 군산시 미장길 97-235.965834126.734854
442612020소룡농협전북특별자치도 군산시 소룡동 1576전북특별자치도 군산시 공항로 8535.97577126.679377
452611410수송동주민센터(롯데마트방향)전북특별자치도 군산시 수송동 891전북특별자치도 군산시 수송로 20735.964202126.718348
462612630요양병원(나운동)전북특별자치도 군산시 나운동 1191-52전북특별자치도 군산시 대학로 37535.960511126.691773
472613090나운삼성아파트(유앤미사우나앞)전북특별자치도 군산시 나운동 100-14전북특별자치도 군산시 하나운로 7235.964848126.702316