Overview

Dataset statistics

Number of variables10
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.6 KiB
Average record size in memory88.3 B

Variable types

Numeric5
Categorical5

Alerts

측정일 has constant value ""Constant
시군구명 is highly overall correlated with 기본키 and 5 other fieldsHigh correlation
지점 is highly overall correlated with 기본키 and 5 other fieldsHigh correlation
시도명 is highly overall correlated with 기본키 and 4 other fieldsHigh correlation
시가지 CO 량(g/km) is highly overall correlated with 기본키 and 4 other fieldsHigh correlation
기본키 is highly overall correlated with 지점 and 3 other fieldsHigh correlation
도로인접 CO 량(g/km) is highly overall correlated with 도로인접 PM 량(g/km)High correlation
도로인접 PM 량(g/km) is highly overall correlated with 도로인접 CO 량(g/km)High correlation
시가지 PM 2.5 량(g/km) is highly overall correlated with 지점 and 3 other fieldsHigh correlation
시가지 PM 10 량(g/km) is highly overall correlated with 지점 and 3 other fieldsHigh correlation
기본키 has unique valuesUnique
도로인접 CO 량(g/km) has 7 (7.0%) zerosZeros
도로인접 PM 량(g/km) has 8 (8.0%) zerosZeros
시가지 PM 2.5 량(g/km) has 10 (10.0%) zerosZeros

Reproduction

Analysis started2023-12-10 11:36:56.166564
Analysis finished2023-12-10 11:37:00.584979
Duration4.42 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기본키
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.5
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:37:00.694350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5.95
Q125.75
median50.5
Q375.25
95-th percentile95.05
Maximum100
Range99
Interquartile range (IQR)49.5

Descriptive statistics

Standard deviation29.011492
Coefficient of variation (CV)0.57448499
Kurtosis-1.2
Mean50.5
Median Absolute Deviation (MAD)25
Skewness0
Sum5050
Variance841.66667
MonotonicityStrictly increasing
2023-12-10T20:37:00.922832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.0%
65 1
 
1.0%
75 1
 
1.0%
74 1
 
1.0%
73 1
 
1.0%
72 1
 
1.0%
71 1
 
1.0%
70 1
 
1.0%
69 1
 
1.0%
68 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
1 1
1.0%
2 1
1.0%
3 1
1.0%
4 1
1.0%
5 1
1.0%
6 1
1.0%
7 1
1.0%
8 1
1.0%
9 1
1.0%
10 1
1.0%
ValueCountFrequency (%)
100 1
1.0%
99 1
1.0%
98 1
1.0%
97 1
1.0%
96 1
1.0%
95 1
1.0%
94 1
1.0%
93 1
1.0%
92 1
1.0%
91 1
1.0%

측정일
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
20200601
100 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20200601
2nd row20200601
3rd row20200601
4th row20200601
5th row20200601

Common Values

ValueCountFrequency (%)
20200601 100
100.0%

Length

2023-12-10T20:37:01.138216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:37:01.292084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20200601 100
100.0%

지점
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)14.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
A-0010-3801E-10
10 
A-0010-3880E-10
10 
A-1000-1038S-9
9 
A-1100-0249S-9
9 
A-1000-0893S-8
8 
Other values (9)
54 

Length

Max length15
Median length14
Mean length14.2
Min length14

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA-1000-0893S-8
2nd rowA-1000-0893S-8
3rd rowA-1000-0893S-8
4th rowA-1000-0893S-8
5th rowA-1000-0893S-8

Common Values

ValueCountFrequency (%)
A-0010-3801E-10 10
10.0%
A-0010-3880E-10 10
10.0%
A-1000-1038S-9 9
9.0%
A-1100-0249S-9 9
9.0%
A-1000-0893S-8 8
8.0%
A-1200-0149E-8 8
8.0%
A-1200-0178S-8 8
8.0%
A-1000-1002S-8 8
8.0%
A-1100-0054E-6 6
 
6.0%
A-1100-0129S-6 6
 
6.0%
Other values (4) 18
18.0%

Length

2023-12-10T20:37:01.421299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
a-0010-3801e-10 10
10.0%
a-0010-3880e-10 10
10.0%
a-1000-1038s-9 9
9.0%
a-1100-0249s-9 9
9.0%
a-1000-0893s-8 8
8.0%
a-1200-0149e-8 8
8.0%
a-1200-0178s-8 8
8.0%
a-1000-1002s-8 8
8.0%
a-1100-0054e-6 6
 
6.0%
a-1100-0129s-6 6
 
6.0%
Other values (4) 18
18.0%

시도명
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
경기
78 
인천
22 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row인천
2nd row인천
3rd row인천
4th row인천
5th row인천

Common Values

ValueCountFrequency (%)
경기 78
78.0%
인천 22
 
22.0%

Length

2023-12-10T20:37:01.598076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:37:01.752752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경기 78
78.0%
인천 22
 
22.0%

시군구명
Categorical

HIGH CORRELATION 

Distinct9
Distinct (%)9.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
시흥시
29 
부평구
16 
부천시
10 
화성시
10 
용인시
10 
Other values (4)
25 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row부평구
2nd row부평구
3rd row부평구
4th row부평구
5th row부평구

Common Values

ValueCountFrequency (%)
시흥시 29
29.0%
부평구 16
16.0%
부천시 10
 
10.0%
화성시 10
 
10.0%
용인시 10
 
10.0%
안양시 9
 
9.0%
연수구 6
 
6.0%
광명시 6
 
6.0%
광주시 4
 
4.0%

Length

2023-12-10T20:37:01.908261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:37:02.089043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
시흥시 29
29.0%
부평구 16
16.0%
부천시 10
 
10.0%
화성시 10
 
10.0%
용인시 10
 
10.0%
안양시 9
 
9.0%
연수구 6
 
6.0%
광명시 6
 
6.0%
광주시 4
 
4.0%

도로인접 CO 량(g/km)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct94
Distinct (%)94.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10100.844
Minimum0
Maximum29481.61
Zeros7
Zeros (%)7.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:37:02.272595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16534.425
median10317.41
Q313111.565
95-th percentile19794.374
Maximum29481.61
Range29481.61
Interquartile range (IQR)6577.14

Descriptive statistics

Standard deviation6396.5838
Coefficient of variation (CV)0.63327222
Kurtosis0.784346
Mean10100.844
Median Absolute Deviation (MAD)3637.6
Skewness0.52346792
Sum1010084.4
Variance40916285
MonotonicityNot monotonic
2023-12-10T20:37:02.488621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 7
 
7.0%
27932.17 1
 
1.0%
6901.18 1
 
1.0%
10951.29 1
 
1.0%
309.13 1
 
1.0%
19390.93 1
 
1.0%
222.08 1
 
1.0%
18800.79 1
 
1.0%
27.25 1
 
1.0%
3.85 1
 
1.0%
Other values (84) 84
84.0%
ValueCountFrequency (%)
0.0 7
7.0%
0.29 1
 
1.0%
3.85 1
 
1.0%
27.25 1
 
1.0%
71.16 1
 
1.0%
222.08 1
 
1.0%
309.13 1
 
1.0%
704.87 1
 
1.0%
2862.43 1
 
1.0%
3781.35 1
 
1.0%
ValueCountFrequency (%)
29481.61 1
1.0%
28440.36 1
1.0%
27932.17 1
1.0%
23531.94 1
1.0%
20490.42 1
1.0%
19757.74 1
1.0%
19390.93 1
1.0%
19299.13 1
1.0%
19274.98 1
1.0%
18800.79 1
1.0%

도로인접 PM 량(g/km)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct93
Distinct (%)93.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean691.456
Minimum0
Maximum2735.2
Zeros8
Zeros (%)8.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:37:02.727266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1101.165
median568.195
Q31068.535
95-th percentile1850.903
Maximum2735.2
Range2735.2
Interquartile range (IQR)967.37

Descriptive statistics

Standard deviation661.22037
Coefficient of variation (CV)0.95627251
Kurtosis0.68821738
Mean691.456
Median Absolute Deviation (MAD)471.62
Skewness1.0266947
Sum69145.6
Variance437212.37
MonotonicityNot monotonic
2023-12-10T20:37:02.945641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 8
 
8.0%
1604.96 1
 
1.0%
606.8 1
 
1.0%
1241.56 1
 
1.0%
1176.81 1
 
1.0%
33.57 1
 
1.0%
1775.84 1
 
1.0%
29.31 1
 
1.0%
188.8 1
 
1.0%
0.65 1
 
1.0%
Other values (83) 83
83.0%
ValueCountFrequency (%)
0.0 8
8.0%
0.26 1
 
1.0%
0.65 1
 
1.0%
2.84 1
 
1.0%
15.1 1
 
1.0%
22.1 1
 
1.0%
29.31 1
 
1.0%
33.57 1
 
1.0%
37.39 1
 
1.0%
53.44 1
 
1.0%
ValueCountFrequency (%)
2735.2 1
1.0%
2622.25 1
1.0%
2605.36 1
1.0%
2239.96 1
1.0%
1903.78 1
1.0%
1848.12 1
1.0%
1775.84 1
1.0%
1702.78 1
1.0%
1604.96 1
1.0%
1586.55 1
1.0%

시가지 CO 량(g/km)
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
0.4
74 
0.3
16 
0.2
10 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.4
2nd row0.4
3rd row0.4
4th row0.4
5th row0.4

Common Values

ValueCountFrequency (%)
0.4 74
74.0%
0.3 16
 
16.0%
0.2 10
 
10.0%

Length

2023-12-10T20:37:03.128536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:37:03.275398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.4 74
74.0%
0.3 16
 
16.0%
0.2 10
 
10.0%

시가지 PM 2.5 량(g/km)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)9.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.93
Minimum0
Maximum28
Zeros10
Zeros (%)10.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:37:03.393497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q112
median20
Q321
95-th percentile28
Maximum28
Range28
Interquartile range (IQR)9

Descriptive statistics

Standard deviation7.6465546
Coefficient of variation (CV)0.4516571
Kurtosis0.19663742
Mean16.93
Median Absolute Deviation (MAD)4
Skewness-0.88921443
Sum1693
Variance58.469798
MonotonicityNot monotonic
2023-12-10T20:37:03.536170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
21 29
29.0%
12 16
16.0%
17 10
 
10.0%
20 10
 
10.0%
0 10
 
10.0%
26 9
 
9.0%
16 6
 
6.0%
28 6
 
6.0%
6 4
 
4.0%
ValueCountFrequency (%)
0 10
 
10.0%
6 4
 
4.0%
12 16
16.0%
16 6
 
6.0%
17 10
 
10.0%
20 10
 
10.0%
21 29
29.0%
26 9
 
9.0%
28 6
 
6.0%
ValueCountFrequency (%)
28 6
 
6.0%
26 9
 
9.0%
21 29
29.0%
20 10
 
10.0%
17 10
 
10.0%
16 6
 
6.0%
12 16
16.0%
6 4
 
4.0%
0 10
 
10.0%

시가지 PM 10 량(g/km)
Real number (ℝ)

HIGH CORRELATION 

Distinct8
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.2
Minimum5
Maximum27
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:37:03.692250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile5
Q112
median14
Q322
95-th percentile27
Maximum27
Range22
Interquartile range (IQR)10

Descriptive statistics

Standard deviation7.3745399
Coefficient of variation (CV)0.45521851
Kurtosis-1.2639402
Mean16.2
Median Absolute Deviation (MAD)8
Skewness-0.14328291
Sum1620
Variance54.383838
MonotonicityNot monotonic
2023-12-10T20:37:03.862972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
22 29
29.0%
13 20
20.0%
5 16
16.0%
27 10
 
10.0%
14 9
 
9.0%
12 6
 
6.0%
25 6
 
6.0%
6 4
 
4.0%
ValueCountFrequency (%)
5 16
16.0%
6 4
 
4.0%
12 6
 
6.0%
13 20
20.0%
14 9
 
9.0%
22 29
29.0%
25 6
 
6.0%
27 10
 
10.0%
ValueCountFrequency (%)
27 10
 
10.0%
25 6
 
6.0%
22 29
29.0%
14 9
 
9.0%
13 20
20.0%
12 6
 
6.0%
6 4
 
4.0%
5 16
16.0%

Interactions

2023-12-10T20:36:59.516561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:36:56.765196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:36:57.731673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:36:58.362427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:36:58.966375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:36:59.634197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:36:56.888947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:36:57.855025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:36:58.469780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:36:59.069046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:36:59.843535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:36:57.023496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:36:57.996336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:36:58.596076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:36:59.189097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:36:59.963593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:36:57.490670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:36:58.118026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:36:58.724800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:36:59.296164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:37:00.089474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:36:57.625620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:36:58.242755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:36:58.851236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:36:59.414885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T20:37:04.007412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기본키지점시도명시군구명도로인접 CO 량(g/km)도로인접 PM 량(g/km)시가지 CO 량(g/km)시가지 PM 2.5 량(g/km)시가지 PM 10 량(g/km)
기본키1.0000.9690.8970.8860.5340.3170.7580.8310.952
지점0.9691.0001.0001.0000.6320.5701.0001.0001.000
시도명0.8971.0001.0001.0000.4300.4880.1071.0000.601
시군구명0.8861.0001.0001.0000.6900.7081.0001.0001.000
도로인접 CO 량(g/km)0.5340.6320.4300.6901.0000.6850.7220.5730.379
도로인접 PM 량(g/km)0.3170.5700.4880.7080.6851.0000.6110.5140.374
시가지 CO 량(g/km)0.7581.0000.1071.0000.7220.6111.0001.0000.736
시가지 PM 2.5 량(g/km)0.8311.0001.0001.0000.5730.5141.0001.0000.891
시가지 PM 10 량(g/km)0.9521.0000.6011.0000.3790.3740.7360.8911.000
2023-12-10T20:37:04.186890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구명지점시도명시가지 CO 량(g/km)
시군구명1.0000.9720.9640.969
지점0.9721.0000.9370.942
시도명0.9640.9371.0000.175
시가지 CO 량(g/km)0.9690.9420.1751.000
2023-12-10T20:37:04.326744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기본키도로인접 CO 량(g/km)도로인접 PM 량(g/km)시가지 PM 2.5 량(g/km)시가지 PM 10 량(g/km)지점시도명시군구명시가지 CO 량(g/km)
기본키1.000-0.272-0.2130.3050.4280.8440.7030.6680.608
도로인접 CO 량(g/km)-0.2721.0000.731-0.061-0.3460.3170.4140.2830.414
도로인접 PM 량(g/km)-0.2130.7311.0000.037-0.2960.2720.4700.2960.322
시가지 PM 2.5 량(g/km)0.305-0.0610.0371.0000.3670.9620.9740.9890.979
시가지 PM 10 량(g/km)0.428-0.346-0.2960.3671.0000.9510.7140.9790.723
지점0.8440.3170.2720.9620.9511.0000.9370.9720.942
시도명0.7030.4140.4700.9740.7140.9371.0000.9640.175
시군구명0.6680.2830.2960.9890.9790.9720.9641.0000.969
시가지 CO 량(g/km)0.6080.4140.3220.9790.7230.9420.1750.9691.000

Missing values

2023-12-10T20:37:00.273222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T20:37:00.499035image/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

기본키측정일지점시도명시군구명도로인접 CO 량(g/km)도로인접 PM 량(g/km)시가지 CO 량(g/km)시가지 PM 2.5 량(g/km)시가지 PM 10 량(g/km)
0120200601A-1000-0893S-8인천부평구27932.171604.960.4125
1220200601A-1000-0893S-8인천부평구20490.42159.540.4125
2320200601A-1000-0893S-8인천부평구14498.661456.420.4125
3420200601A-1000-0893S-8인천부평구29481.612735.20.4125
4520200601A-1000-0893S-8인천부평구0.00.00.4125
5620200601A-1000-0893S-8인천부평구16292.78350.090.4125
6720200601A-1000-0893S-8인천부평구23531.941467.410.4125
7820200601A-1000-0893S-8인천부평구3781.3522.10.4125
8920200601A-1100-0054E-6인천연수구10863.35110.10.31612
91020200601A-1100-0054E-6인천연수구19757.742622.250.31612
기본키측정일지점시도명시군구명도로인접 CO 량(g/km)도로인접 PM 량(g/km)시가지 CO 량(g/km)시가지 PM 2.5 량(g/km)시가지 PM 10 량(g/km)
909120200601A-1100-0249S-9경기안양시10861.62864.530.42614
919220200601A-1100-0249S-9경기안양시10239.49560.460.42614
929320200601A-1100-0249S-9경기안양시16637.88634.320.42614
939420200601A-1100-0249S-9경기안양시9304.75154.940.42614
949520200601A-1100-0249S-9경기안양시0.00.00.42614
959620200601A-1100-0249S-9경기안양시13923.09641.420.42614
969720200601A-1100-0249S-9경기안양시15834.12799.440.42614
979820200601A-1100-0249S-9경기안양시3840.22112.620.42614
989920200601A-1200-0224S-8경기부천시10324.72101.720.31713
9910020200601A-1200-0224S-8경기부천시12075.38470.020.31713