Overview

Dataset statistics

Number of variables7
Number of observations47
Missing cells3
Missing cells (%)0.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.8 KiB
Average record size in memory61.8 B

Variable types

Categorical1
Text3
Numeric3

Dataset

Description코로나19 예방접종센터 현황
Author경기도
URLhttps://data.gg.go.kr/portal/data/service/selectServicePage.do?&infId=Z6DL370HR48WFLD2JOL131303967&infSeq=1

Alerts

센터유형 has constant value ""Constant
우편번호 is highly overall correlated with 정제WGS84위도High correlation
정제WGS84위도 is highly overall correlated with 우편번호High correlation
우편번호 has 1 (2.1%) missing valuesMissing
정제WGS84위도 has 1 (2.1%) missing valuesMissing
정제WGS84경도 has 1 (2.1%) missing valuesMissing
시설명 has unique valuesUnique
주소 has unique valuesUnique

Reproduction

Analysis started2023-12-10 21:56:43.574859
Analysis finished2023-12-10 21:56:44.757662
Duration1.18 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

센터유형
Categorical

CONSTANT 

Distinct1
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size508.0 B
지역
47 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
지역 47
100.0%

Length

2023-12-11T06:56:44.806672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T06:56:44.873632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
지역 47
100.0%

센터명
Text

Distinct43
Distinct (%)91.5%
Missing0
Missing (%)0.0%
Memory size508.0 B
2023-12-11T06:56:45.020635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length20
Mean length21.553191
Min length20

Characters and Unicode

Total characters1013
Distinct characters72
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique39 ?
Unique (%)83.0%

Sample

1st row코로나19 경기도 안산시 단원구 예방접종센터
2nd row코로나19 경기도 화성시 예방접종센터
3rd row코로나19 경기도 광명시 예방접종센터
4th row코로나19 경기도 평택시 송탄 예방접종센터
5th row코로나19 경기도 여주시 예방접종센터
ValueCountFrequency (%)
코로나19 47
22.9%
예방접종센터 47
22.9%
경기도 47
22.9%
수원시 4
 
2.0%
용인시 3
 
1.5%
성남시 3
 
1.5%
고양시 3
 
1.5%
남양주시 2
 
1.0%
평택시 2
 
1.0%
시흥시 2
 
1.0%
Other values (41) 45
22.0%
2023-12-11T06:56:45.305759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
158
 
15.6%
기 48
 
4.7%
코 47
 
4.6%
예 47
 
4.6%
터 47
 
4.6%
센 47
 
4.6%
접 47
 
4.6%
방 47
 
4.6%
종 47
 
4.6%
로 47
 
4.6%
Other values (62) 431
42.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 761
75.1%
Space Separator 158
 
15.6%
Decimal Number 94
 
9.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
기 48
 
6.3%
코 47
 
6.2%
예 47
 
6.2%
터 47
 
6.2%
센 47
 
6.2%
접 47
 
6.2%
방 47
 
6.2%
종 47
 
6.2%
로 47
 
6.2%
도 47
 
6.2%
Other values (59) 290
38.1%
Decimal Number
ValueCountFrequency (%)
1 47
50.0%
9 47
50.0%
Space Separator
ValueCountFrequency (%)
158
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 761
75.1%
Common 252
 
24.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
기 48
 
6.3%
코 47
 
6.2%
예 47
 
6.2%
터 47
 
6.2%
센 47
 
6.2%
접 47
 
6.2%
방 47
 
6.2%
종 47
 
6.2%
로 47
 
6.2%
도 47
 
6.2%
Other values (59) 290
38.1%
Common
ValueCountFrequency (%)
158
62.7%
1 47
 
18.7%
9 47
 
18.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 761
75.1%
ASCII 252
 
24.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
158
62.7%
1 47
 
18.7%
9 47
 
18.7%
Hangul
ValueCountFrequency (%)
기 48
 
6.3%
코 47
 
6.2%
예 47
 
6.2%
터 47
 
6.2%
센 47
 
6.2%
접 47
 
6.2%
방 47
 
6.2%
종 47
 
6.2%
로 47
 
6.2%
도 47
 
6.2%
Other values (59) 290
38.1%

시설명
Text

UNIQUE 

Distinct47
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size508.0 B
2023-12-11T06:56:45.513836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length12
Mean length9.7659574
Min length5

Characters and Unicode

Total characters459
Distinct characters133
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique47 ?
Unique (%)100.0%

Sample

1st row올림픽기념관 체육관
2nd row동탄나래울 종합사회복지관
3rd row광명시민체육관
4th row이충문화체육센터
5th row여주시 실내체육관
ValueCountFrequency (%)
실내체육관 6
 
7.8%
체육관 3
 
3.9%
다목적체육관 2
 
2.6%
시민회관 2
 
2.6%
대회의실 2
 
2.6%
화성종합경기타운 1
 
1.3%
부천체육관 1
 
1.3%
하남종합운동장 1
 
1.3%
제2체육관 1
 
1.3%
동두천시 1
 
1.3%
Other values (57) 57
74.0%
2023-12-11T06:56:45.821015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
관 32
 
7.0%
30
 
6.5%
체 27
 
5.9%
육 27
 
5.9%
시 16
 
3.5%
센 12
 
2.6%
터 12
 
2.6%
종 10
 
2.2%
합 10
 
2.2%
장 10
 
2.2%
Other values (123) 273
59.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 426
92.8%
Space Separator 30
 
6.5%
Close Punctuation 1
 
0.2%
Open Punctuation 1
 
0.2%
Decimal Number 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
관 32
 
7.5%
체 27
 
6.3%
육 27
 
6.3%
시 16
 
3.8%
센 12
 
2.8%
터 12
 
2.8%
종 10
 
2.3%
합 10
 
2.3%
장 10
 
2.3%
민 10
 
2.3%
Other values (119) 260
61.0%
Space Separator
ValueCountFrequency (%)
30
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Decimal Number
ValueCountFrequency (%)
2 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 425
92.6%
Common 33
 
7.2%
Han 1
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
관 32
 
7.5%
체 27
 
6.4%
육 27
 
6.4%
시 16
 
3.8%
센 12
 
2.8%
터 12
 
2.8%
종 10
 
2.4%
합 10
 
2.4%
장 10
 
2.4%
민 10
 
2.4%
Other values (118) 259
60.9%
Common
ValueCountFrequency (%)
30
90.9%
) 1
 
3.0%
( 1
 
3.0%
2 1
 
3.0%
Han
ValueCountFrequency (%)
舊 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 425
92.6%
ASCII 33
 
7.2%
CJK 1
 
0.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
관 32
 
7.5%
체 27
 
6.4%
육 27
 
6.4%
시 16
 
3.8%
센 12
 
2.8%
터 12
 
2.8%
종 10
 
2.4%
합 10
 
2.4%
장 10
 
2.4%
민 10
 
2.4%
Other values (118) 259
60.9%
ASCII
ValueCountFrequency (%)
30
90.9%
) 1
 
3.0%
( 1
 
3.0%
2 1
 
3.0%
CJK
ValueCountFrequency (%)
舊 1
100.0%

우편번호
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct46
Distinct (%)100.0%
Missing1
Missing (%)2.1%
Infinite0
Infinite (%)0.0%
Mean14188.848
Minimum10068
Maximum18588
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size555.0 B
2023-12-11T06:56:45.962117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10068
5-th percentile10267.25
Q112067.75
median14005.5
Q316495.75
95-th percentile18074
Maximum18588
Range8520
Interquartile range (IQR)4428

Descriptive statistics

Standard deviation2604.8238
Coefficient of variation (CV)0.18358247
Kurtosis-1.2588166
Mean14188.848
Median Absolute Deviation (MAD)2391.5
Skewness0.028655461
Sum652687
Variance6785106.9
MonotonicityNot monotonic
2023-12-11T06:56:46.069494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
15335 1
 
2.1%
18588 1
 
2.1%
12912 1
 
2.1%
10110 1
 
2.1%
14093 1
 
2.1%
11154 1
 
2.1%
10471 1
 
2.1%
16648 1
 
2.1%
11340 1
 
2.1%
11497 1
 
2.1%
Other values (36) 36
76.6%
ValueCountFrequency (%)
10068 1
2.1%
10110 1
2.1%
10223 1
2.1%
10400 1
2.1%
10471 1
2.1%
10932 1
2.1%
11027 1
2.1%
11154 1
2.1%
11340 1
2.1%
11497 1
2.1%
ValueCountFrequency (%)
18588 1
2.1%
18427 1
2.1%
18131 1
2.1%
17903 1
2.1%
17731 1
2.1%
17568 1
2.1%
17321 1
2.1%
16997 1
2.1%
16912 1
2.1%
16835 1
2.1%

주소
Text

UNIQUE 

Distinct47
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size508.0 B
2023-12-11T06:56:46.317679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length21
Mean length18.148936
Min length13

Characters and Unicode

Total characters853
Distinct characters130
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique47 ?
Unique (%)100.0%

Sample

1st row경기도 안산시 단원구 적금로202
2nd row경기도 화성시 여울로2길 33
3rd row경기도 광명시 오리로 703(하안동)
4th row경기도 평택시 장안웃길 149
5th row경기도 여주시 영릉로 123
ValueCountFrequency (%)
경기도 47
 
22.6%
33 5
 
2.4%
수원시 4
 
1.9%
용인시 3
 
1.4%
성남시 3
 
1.4%
고양시 3
 
1.4%
남양주시 2
 
1.0%
화성시 2
 
1.0%
김포시 2
 
1.0%
안양시 2
 
1.0%
Other values (131) 135
64.9%
2023-12-11T06:56:46.682246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
161
18.9%
경 49
 
5.7%
기 49
 
5.7%
도 47
 
5.5%
시 47
 
5.5%
로 44
 
5.2%
1 25
 
2.9%
3 23
 
2.7%
구 18
 
2.1%
2 17
 
2.0%
Other values (120) 373
43.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 546
64.0%
Space Separator 161
 
18.9%
Decimal Number 139
 
16.3%
Dash Punctuation 3
 
0.4%
Open Punctuation 2
 
0.2%
Close Punctuation 2
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
경 49
 
9.0%
기 49
 
9.0%
도 47
 
8.6%
시 47
 
8.6%
로 44
 
8.1%
구 18
 
3.3%
남 11
 
2.0%
안 11
 
2.0%
양 11
 
2.0%
수 10
 
1.8%
Other values (106) 249
45.6%
Decimal Number
ValueCountFrequency (%)
1 25
18.0%
3 23
16.5%
2 17
12.2%
6 15
10.8%
9 15
10.8%
5 12
8.6%
0 12
8.6%
8 7
 
5.0%
4 7
 
5.0%
7 6
 
4.3%
Space Separator
ValueCountFrequency (%)
161
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 546
64.0%
Common 307
36.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
경 49
 
9.0%
기 49
 
9.0%
도 47
 
8.6%
시 47
 
8.6%
로 44
 
8.1%
구 18
 
3.3%
남 11
 
2.0%
안 11
 
2.0%
양 11
 
2.0%
수 10
 
1.8%
Other values (106) 249
45.6%
Common
ValueCountFrequency (%)
161
52.4%
1 25
 
8.1%
3 23
 
7.5%
2 17
 
5.5%
6 15
 
4.9%
9 15
 
4.9%
5 12
 
3.9%
0 12
 
3.9%
8 7
 
2.3%
4 7
 
2.3%
Other values (4) 13
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 546
64.0%
ASCII 307
36.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
161
52.4%
1 25
 
8.1%
3 23
 
7.5%
2 17
 
5.5%
6 15
 
4.9%
9 15
 
4.9%
5 12
 
3.9%
0 12
 
3.9%
8 7
 
2.3%
4 7
 
2.3%
Other values (4) 13
 
4.2%
Hangul
ValueCountFrequency (%)
경 49
 
9.0%
기 49
 
9.0%
도 47
 
8.6%
시 47
 
8.6%
로 44
 
8.1%
구 18
 
3.3%
남 11
 
2.0%
안 11
 
2.0%
양 11
 
2.0%
수 10
 
1.8%
Other values (106) 249
45.6%

정제WGS84위도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct46
Distinct (%)100.0%
Missing1
Missing (%)2.1%
Infinite0
Infinite (%)0.0%
Mean37.453063
Minimum36.987563
Maximum38.023551
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size555.0 B
2023-12-11T06:56:46.799393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.987563
5-th percentile37.083907
Q137.293119
median37.40763
Q337.634577
95-th percentile37.87209
Maximum38.023551
Range1.0359884
Interquartile range (IQR)0.34145804

Descriptive statistics

Standard deviation0.24208174
Coefficient of variation (CV)0.0064636031
Kurtosis-0.38047274
Mean37.453063
Median Absolute Deviation (MAD)0.15881807
Skewness0.33022896
Sum1722.8409
Variance0.058603568
MonotonicityNot monotonic
2023-12-11T06:56:46.909010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
37.3261656358 1
 
2.1%
37.1374720666 1
 
2.1%
37.567259578 1
 
2.1%
37.6171786703 1
 
2.1%
37.3846057882 1
 
2.1%
37.8882212477 1
 
2.1%
37.6490659292 1
 
2.1%
37.2475366726 1
 
2.1%
37.9053331012 1
 
2.1%
37.7991133016 1
 
2.1%
Other values (36) 36
76.6%
ValueCountFrequency (%)
36.9875627893 1
2.1%
37.0173057984 1
2.1%
37.0660526248 1
2.1%
37.1374720666 1
2.1%
37.1587092333 1
2.1%
37.2051692503 1
2.1%
37.2475366726 1
2.1%
37.2496234432 1
2.1%
37.2736295476 1
2.1%
37.2829746624 1
2.1%
ValueCountFrequency (%)
38.0235512237 1
2.1%
37.9053331012 1
2.1%
37.8882212477 1
2.1%
37.823695763 1
2.1%
37.7991133016 1
2.1%
37.7592016315 1
2.1%
37.7369045993 1
2.1%
37.72711634 1
2.1%
37.6776854126 1
2.1%
37.653754696 1
2.1%

정제WGS84경도
Real number (ℝ)

MISSING 

Distinct46
Distinct (%)100.0%
Missing1
Missing (%)2.1%
Infinite0
Infinite (%)0.0%
Mean127.03958
Minimum126.64489
Maximum127.6229
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size555.0 B
2023-12-11T06:56:47.015704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.64489
5-th percentile126.74072
Q1126.88438
median127.04623
Q3127.14385
95-th percentile127.49393
Maximum127.6229
Range0.97800675
Interquartile range (IQR)0.25947704

Descriptive statistics

Standard deviation0.21805845
Coefficient of variation (CV)0.0017164607
Kurtosis0.5084013
Mean127.03958
Median Absolute Deviation (MAD)0.12057482
Skewness0.60288702
Sum5843.8208
Variance0.047549488
MonotonicityNot monotonic
2023-12-11T06:56:47.124362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
126.8274798975 1
 
2.1%
126.9240117072 1
 
2.1%
127.1916460424 1
 
2.1%
126.7180615692 1
 
2.1%
126.9313328724 1
 
2.1%
127.2015939831 1
 
2.1%
126.8354045961 1
 
2.1%
126.9896215269 1
 
2.1%
127.0430797486 1
 
2.1%
127.0451660532 1
 
2.1%
Other values (36) 36
76.6%
ValueCountFrequency (%)
126.6448926808 1
2.1%
126.7180615692 1
2.1%
126.7404072295 1
2.1%
126.7416721875 1
2.1%
126.7639150864 1
2.1%
126.7686925095 1
2.1%
126.7807224813 1
2.1%
126.7848344957 1
2.1%
126.8274798975 1
2.1%
126.8354045961 1
2.1%
ValueCountFrequency (%)
127.6228994282 1
2.1%
127.5070335546 1
2.1%
127.4966607536 1
2.1%
127.4857456592 1
2.1%
127.260297175 1
2.1%
127.2586794827 1
2.1%
127.2015939831 1
2.1%
127.1916460424 1
2.1%
127.1897248398 1
2.1%
127.1758197051 1
2.1%

Interactions

2023-12-11T06:56:44.318569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:56:43.908772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:56:44.120456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:56:44.382269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:56:43.978064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:56:44.181982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:56:44.445860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:56:44.045755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:56:44.249654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T06:56:47.199298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
센터명시설명우편번호주소정제WGS84위도정제WGS84경도
센터명1.0001.0001.0001.0000.8780.960
시설명1.0001.0001.0001.0001.0001.000
우편번호1.0001.0001.0001.0000.8940.710
주소1.0001.0001.0001.0001.0001.000
정제WGS84위도0.8781.0000.8941.0001.0000.252
정제WGS84경도0.9601.0000.7101.0000.2521.000
2023-12-11T06:56:47.276484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
우편번호정제WGS84위도정제WGS84경도
우편번호1.000-0.9130.177
정제WGS84위도-0.9131.000-0.137
정제WGS84경도0.177-0.1371.000

Missing values

2023-12-11T06:56:44.541697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T06:56:44.640773image/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.
2023-12-11T06:56:44.717252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

센터유형센터명시설명우편번호주소정제WGS84위도정제WGS84경도
0지역코로나19 경기도 안산시 단원구 예방접종센터올림픽기념관 체육관15335경기도 안산시 단원구 적금로20237.326166126.82748
1지역코로나19 경기도 화성시 예방접종센터동탄나래울 종합사회복지관18427경기도 화성시 여울로2길 3337.205169127.051572
2지역코로나19 경기도 광명시 예방접종센터광명시민체육관14259경기도 광명시 오리로 703(하안동)37.462522126.871163
3지역코로나19 경기도 평택시 송탄 예방접종센터이충문화체육센터17731경기도 평택시 장안웃길 14937.066053127.070716
4지역코로나19 경기도 여주시 예방접종센터여주시 실내체육관12625경기도 여주시 영릉로 12337.293039127.622899
5지역코로나19 경기도 양평군 예방접종센터물맑은실내체육관12546경기도 양평군 양평읍 마유산로 1137.496724127.485746
6지역코로나19 경기도 의왕시 예방접종센터의왕시청소년수련관 다목적체육관16077경기도 의왕시 문화공원로 3337.341871126.971959
7지역코로나19 경기도 광주시 예방접종센터시민체육관12794경기도 광주시 오포읍 청석로 8537.395669127.258679
8지역코로나19 경기도 군포시 예방접종센터시민체육광장15862경기도 군포시 산본로 26737.353332126.935814
9지역코로나19 경기도 구리시 예방접종센터인창도서관대강당11922경기도 구리시 건원대로34번길 9037.605285127.145854
센터유형센터명시설명우편번호주소정제WGS84위도정제WGS84경도
37지역코로나19 경기도 성남시 중원구 예방접종센터성남종합스포츠센터 다목적체육관13363경기도 성남시 중원구 제일로 6037.43174127.137846
38지역코로나19 경기도 안성시 예방접종센터경기도의료원 안성병원17568경기도 안성시 남파로 9537.017306127.260297
39지역코로나19 경기도 용인시 수지구 예방접종센터수지구청 대회의실16835경기도 용인시 수지구 포은대로 43537.322245127.097401
40지역코로나19 경기도 용인시 기흥구 예방접종센터(舊)경찰대 실내체육관16912경기도 용인시 기흥구 언남로 7437.298353127.13764
41지역코로나19 경기도 고양시 일산서구 예방접종센터고양체육관10223경기도 고양시 일산서구 중앙로 160137.677685126.741672
42지역코로나19 경기도 시흥시 예방접종센터시흥시체육관14902경기도 시흥시 서해안로 158937.447735126.784834
43지역코로나19 경기도 김포시 예방접종센터김포생활체육관10068경기도 김포시 김포한강8로 198-337.640376126.644893
44지역코로나19 경기도 평택시 예방접종센터평택시청소년문화센터 실내체육관17903경기도 평택시 평남로 61636.987563127.10651
45지역코로나19 경기도 과천시 예방접종센터청소년수련관 실내체육관13828경기도 과천시 참마을로937.426428127.000887
46지역코로나19 경기도 가평군 예방접종센터가평체육관12416경기도 가평군 가평읍 문화로 13137.823696127.507034