Overview

Dataset statistics

Number of variables10
Number of observations10000
Missing cells7405
Missing cells (%)7.4%
Duplicate rows1428
Duplicate rows (%)14.3%
Total size in memory878.9 KiB
Average record size in memory90.0 B

Variable types

Categorical7
Numeric1
Text2

Dataset

Description경기도의 구급활동 현황입니다. 출동소방서명, 신고시각, 접수경로, 현장거리, 환자연령 등의 정보를 제공합니다. ※ Sheet탭에서는 최신 1개년 데이터를 확인하실 수 있으며, 전체 데이터는 File탭에서 내려받을 수 있는 파일의 형태로 제공됩니다.
Author경기도
URLhttps://data.gg.go.kr/portal/data/service/selectServicePage.do?&infId=SE00GA6F273B8PIJ9N8412495661&infSeq=1

Alerts

집계년도 has constant value ""Constant
Dataset has 1428 (14.3%) duplicate rowsDuplicates
시군명 is highly overall correlated with 출동소방서 and 1 other fieldsHigh correlation
출동안전센터 is highly overall correlated with 시군명 and 1 other fieldsHigh correlation
출동소방서 is highly overall correlated with 시군명 and 1 other fieldsHigh correlation
국적 is highly imbalanced (88.9%)Imbalance
환자연령대 has 2303 (23.0%) missing valuesMissing
환자증상1 has 5102 (51.0%) missing valuesMissing
환자연령대 has 382 (3.8%) zerosZeros

Reproduction

Analysis started2024-03-12 23:27:09.468577
Analysis finished2024-03-12 23:27:10.511208
Duration1.04 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

집계년도
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2019
10000 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2019
2nd row2019
3rd row2019
4th row2019
5th row2019

Common Values

ValueCountFrequency (%)
2019 10000
100.0%

Length

2024-03-13T08:27:10.555909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T08:27:10.619782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2019 10000
100.0%

시군명
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
고양시
4893 
광주시
1735 
광명시
1481 
가평군
833 
구리시
717 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row광주시
2nd row고양시
3rd row가평군
4th row구리시
5th row광주시

Common Values

ValueCountFrequency (%)
고양시 4893
48.9%
광주시 1735
 
17.3%
광명시 1481
 
14.8%
가평군 833
 
8.3%
구리시 717
 
7.2%
과천시 341
 
3.4%

Length

2024-03-13T08:27:10.690862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T08:27:10.766607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
고양시 4893
48.9%
광주시 1735
 
17.3%
광명시 1481
 
14.8%
가평군 833
 
8.3%
구리시 717
 
7.2%
과천시 341
 
3.4%

출동소방서
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
일산소방서
2739 
고양소방서
2154 
광주소방서
1735 
광명소방서
1481 
가평소방서
833 
Other values (2)
1058 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row광주소방서
2nd row일산소방서
3rd row가평소방서
4th row구리소방서
5th row광주소방서

Common Values

ValueCountFrequency (%)
일산소방서 2739
27.4%
고양소방서 2154
21.5%
광주소방서 1735
17.3%
광명소방서 1481
14.8%
가평소방서 833
 
8.3%
구리소방서 717
 
7.2%
과천소방서 341
 
3.4%

Length

2024-03-13T08:27:10.858563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T08:27:10.942680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일산소방서 2739
27.4%
고양소방서 2154
21.5%
광주소방서 1735
17.3%
광명소방서 1481
14.8%
가평소방서 833
 
8.3%
구리소방서 717
 
7.2%
과천소방서 341
 
3.4%

출동안전센터
Categorical

HIGH CORRELATION 

Distinct34
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
119구조대
837 
원당119안전센터
 
689
송정119안전센터
 
638
중산119안전센터
 
456
소하119안전센터
 
439
Other values (29)
6941 

Length

Max length10
Median length9
Mean length8.5484
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row송정119안전센터
2nd row풍동119안전센터
3rd row조종119안전센터
4th row119구조대
5th row능평119안전센터

Common Values

ValueCountFrequency (%)
119구조대 837
 
8.4%
원당119안전센터 689
 
6.9%
송정119안전센터 638
 
6.4%
중산119안전센터 456
 
4.6%
소하119안전센터 439
 
4.4%
하안119안전센터 381
 
3.8%
행신119안전센터 378
 
3.8%
장항119안전센터 375
 
3.8%
백석119안전센터 363
 
3.6%
능곡119안전센터 359
 
3.6%
Other values (24) 5085
50.8%

Length

2024-03-13T08:27:11.039240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
119구조대 837
 
8.4%
원당119안전센터 689
 
6.9%
송정119안전센터 638
 
6.4%
중산119안전센터 456
 
4.6%
소하119안전센터 439
 
4.4%
하안119안전센터 381
 
3.8%
행신119안전센터 378
 
3.8%
장항119안전센터 375
 
3.8%
백석119안전센터 363
 
3.6%
능곡119안전센터 359
 
3.6%
Other values (24) 5085
50.8%

환자연령대
Real number (ℝ)

MISSING  ZEROS 

Distinct11
Distinct (%)0.1%
Missing2303
Missing (%)23.0%
Infinite0
Infinite (%)0.0%
Mean49.760946
Minimum0
Maximum100
Zeros382
Zeros (%)3.8%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-13T08:27:11.128898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10
Q130
median50
Q370
95-th percentile80
Maximum100
Range100
Interquartile range (IQR)40

Descriptive statistics

Standard deviation23.50952
Coefficient of variation (CV)0.47244922
Kurtosis-0.69075909
Mean49.760946
Median Absolute Deviation (MAD)20
Skewness-0.40248198
Sum383010
Variance552.69752
MonotonicityNot monotonic
2024-03-13T08:27:11.212956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
50 1291
12.9%
70 1150
11.5%
60 1114
11.1%
80 1018
10.2%
40 890
 
8.9%
30 682
 
6.8%
20 606
 
6.1%
0 382
 
3.8%
10 366
 
3.7%
90 196
 
2.0%
(Missing) 2303
23.0%
ValueCountFrequency (%)
0 382
 
3.8%
10 366
 
3.7%
20 606
6.1%
30 682
6.8%
40 890
8.9%
50 1291
12.9%
60 1114
11.1%
70 1150
11.5%
80 1018
10.2%
90 196
 
2.0%
ValueCountFrequency (%)
100 2
 
< 0.1%
90 196
 
2.0%
80 1018
10.2%
70 1150
11.5%
60 1114
11.1%
50 1291
12.9%
40 890
8.9%
30 682
6.8%
20 606
6.1%
10 366
 
3.7%

환자성별
Categorical

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
남
4181 
여
3579 
<NA>
2228 
미상
 
10
20
 
1

Length

Max length4
Median length1
Mean length1.6696
Min length1

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row여
2nd row여
3rd row남
4th row남
5th row<NA>

Common Values

ValueCountFrequency (%)
남 4181
41.8%
여 3579
35.8%
<NA> 2228
22.3%
미상 10
 
0.1%
20 1
 
< 0.1%
80 1
 
< 0.1%

Length

2024-03-13T08:27:11.303720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T08:27:11.400963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
남 4181
41.8%
여 3579
35.8%
na 2228
22.3%
미상 10
 
0.1%
20 1
 
< 0.1%
80 1
 
< 0.1%

외국인유무
Text

Distinct200
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-03-13T08:27:11.571719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length1
Mean length1.1847
Min length1

Characters and Unicode

Total characters11847
Distinct characters158
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique73 ?
Unique (%)0.7%

Sample

1st rowN
2nd rowN
3rd rowN
4th rowN
5th row능원리
ValueCountFrequency (%)
n 9010
90.1%
y 77
 
0.8%
광명동 47
 
0.5%
행신동 33
 
0.3%
철산동 28
 
0.3%
화정동 27
 
0.3%
장항동 23
 
0.2%
소하동 23
 
0.2%
대화동 22
 
0.2%
송정동 21
 
0.2%
Other values (190) 689
 
6.9%
2024-03-13T08:27:11.840881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 9010
76.1%
동 708
 
6.0%
리 234
 
2.0%
산 105
 
0.9%
Y 77
 
0.6%
신 65
 
0.5%
화 55
 
0.5%
정 54
 
0.5%
대 52
 
0.4%
하 50
 
0.4%
Other values (148) 1437
 
12.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 9087
76.7%
Other Letter 2760
 
23.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 708
25.7%
리 234
 
8.5%
산 105
 
3.8%
신 65
 
2.4%
화 55
 
2.0%
정 54
 
2.0%
대 52
 
1.9%
하 50
 
1.8%
명 49
 
1.8%
광 47
 
1.7%
Other values (146) 1341
48.6%
Uppercase Letter
ValueCountFrequency (%)
N 9010
99.2%
Y 77
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 9087
76.7%
Hangul 2760
 
23.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 708
25.7%
리 234
 
8.5%
산 105
 
3.8%
신 65
 
2.4%
화 55
 
2.0%
정 54
 
2.0%
대 52
 
1.9%
하 50
 
1.8%
명 49
 
1.8%
광 47
 
1.7%
Other values (146) 1341
48.6%
Latin
ValueCountFrequency (%)
N 9010
99.2%
Y 77
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9087
76.7%
Hangul 2760
 
23.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 9010
99.2%
Y 77
 
0.8%
Hangul
ValueCountFrequency (%)
동 708
25.7%
리 234
 
8.5%
산 105
 
3.8%
신 65
 
2.4%
화 55
 
2.0%
정 54
 
2.0%
대 52
 
1.9%
하 50
 
1.8%
명 49
 
1.8%
광 47
 
1.7%
Other values (146) 1341
48.6%

국적
Categorical

IMBALANCE 

Distinct26
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9020 
N
913 
중국
 
24
몽골
 
6
미상
 
4
Other values (21)
 
33

Length

Max length7
Median length4
Mean length3.716
Min length1

Unique

Unique13 ?
Unique (%)0.1%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th rowN

Common Values

ValueCountFrequency (%)
<NA> 9020
90.2%
N 913
 
9.1%
중국 24
 
0.2%
몽골 6
 
0.1%
미상 4
 
< 0.1%
필리핀 4
 
< 0.1%
일본 3
 
< 0.1%
미국 3
 
< 0.1%
태국 2
 
< 0.1%
스리랑카 2
 
< 0.1%
Other values (16) 19
 
0.2%

Length

2024-03-13T08:27:11.948548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 9020
90.2%
n 913
 
9.1%
중국 24
 
0.2%
몽골 6
 
0.1%
미상 4
 
< 0.1%
필리핀 4
 
< 0.1%
일본 3
 
< 0.1%
미국 3
 
< 0.1%
베트남 2
 
< 0.1%
중국인 2
 
< 0.1%
Other values (16) 19
 
0.2%

구급처종명
Categorical

Distinct14
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
집
4628 
<NA>
1627 
도로
1121 
도로외교통지역
637 
상업시설
518 
Other values (9)
1469 

Length

Max length10
Median length9
Mean length2.781
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row집
2nd row상업시설
3rd row집
4th row상업시설
5th row<NA>

Common Values

ValueCountFrequency (%)
집 4628
46.3%
<NA> 1627
 
16.3%
도로 1121
 
11.2%
도로외교통지역 637
 
6.4%
상업시설 518
 
5.2%
기타 450
 
4.5%
집단거주시설 207
 
2.1%
오락/문화시설 199
 
2.0%
의료관련시설 190
 
1.9%
바다/강/산/논밭 126
 
1.3%
Other values (4) 297
 
3.0%

Length

2024-03-13T08:27:12.055897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
집 4628
46.3%
na 1627
 
16.3%
도로 1121
 
11.2%
도로외교통지역 637
 
6.4%
상업시설 518
 
5.2%
기타 450
 
4.5%
집단거주시설 207
 
2.1%
오락/문화시설 199
 
2.0%
의료관련시설 190
 
1.9%
바다/강/산/논밭 126
 
1.3%
Other values (4) 297
 
3.0%

환자증상1
Text

MISSING 

Distinct157
Distinct (%)3.2%
Missing5102
Missing (%)51.0%
Memory size156.2 KiB
2024-03-13T08:27:12.233476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length14
Mean length3.0963659
Min length1

Characters and Unicode

Total characters15166
Distinct characters217
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

Unique97 ?
Unique (%)2.0%

Sample

1st row호흡곤란
2nd row기타통증
3rd row도로
4th row흉통
5th row기타통증
ValueCountFrequency (%)
기타통증 930
18.5%
기타 507
 
10.1%
복통 469
 
9.3%
두통 262
 
5.2%
오심/구토 250
 
5.0%
요통 245
 
4.9%
집 217
 
4.3%
전신쇠약 212
 
4.2%
열상 169
 
3.4%
의식장애 142
 
2.8%
Other values (167) 1615
32.2%
2024-03-13T08:27:12.507169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
통 2052
 
13.5%
타 1468
 
9.7%
기 1456
 
9.6%
증 931
 
6.1%
복 472
 
3.1%
심 387
 
2.6%
/ 360
 
2.4%
상 348
 
2.3%
열 308
 
2.0%
신 289
 
1.9%
Other values (207) 7095
46.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 14681
96.8%
Other Punctuation 360
 
2.4%
Space Separator 120
 
0.8%
Decimal Number 5
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
통 2052
 
14.0%
타 1468
 
10.0%
기 1456
 
9.9%
증 931
 
6.3%
복 472
 
3.2%
심 387
 
2.6%
상 348
 
2.4%
열 308
 
2.1%
신 289
 
2.0%
토 272
 
1.9%
Other values (201) 6698
45.6%
Decimal Number
ValueCountFrequency (%)
3 2
40.0%
1 1
20.0%
4 1
20.0%
2 1
20.0%
Other Punctuation
ValueCountFrequency (%)
/ 360
100.0%
Space Separator
ValueCountFrequency (%)
120
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 14681
96.8%
Common 485
 
3.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
통 2052
 
14.0%
타 1468
 
10.0%
기 1456
 
9.9%
증 931
 
6.3%
복 472
 
3.2%
심 387
 
2.6%
상 348
 
2.4%
열 308
 
2.1%
신 289
 
2.0%
토 272
 
1.9%
Other values (201) 6698
45.6%
Common
ValueCountFrequency (%)
/ 360
74.2%
120
 
24.7%
3 2
 
0.4%
1 1
 
0.2%
4 1
 
0.2%
2 1
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 14681
96.8%
ASCII 485
 
3.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
통 2052
 
14.0%
타 1468
 
10.0%
기 1456
 
9.9%
증 931
 
6.3%
복 472
 
3.2%
심 387
 
2.6%
상 348
 
2.4%
열 308
 
2.1%
신 289
 
2.0%
토 272
 
1.9%
Other values (201) 6698
45.6%
ASCII
ValueCountFrequency (%)
/ 360
74.2%
120
 
24.7%
3 2
 
0.4%
1 1
 
0.2%
4 1
 
0.2%
2 1
 
0.2%

Interactions

2024-03-13T08:27:10.152466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-13T08:27:12.597059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명출동소방서출동안전센터환자연령대환자성별국적구급처종명
시군명1.0001.0000.9860.0840.0300.3270.237
출동소방서1.0001.0000.9900.0880.0350.3220.239
출동안전센터0.9860.9901.0000.1600.2320.4380.357
환자연령대0.0840.0880.1601.0000.1560.5800.342
환자성별0.0300.0350.2320.1561.0000.6830.298
국적0.3270.3220.4380.5800.6831.0000.570
구급처종명0.2370.2390.3570.3420.2980.5701.000
2024-03-13T08:27:12.695450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명출동안전센터국적환자성별출동소방서구급처종명
시군명1.0000.9170.1510.0201.0000.119
출동안전센터0.9171.0000.1150.1100.9280.119
국적0.1510.1151.0000.3520.1410.155
환자성별0.0200.1100.3521.0000.0220.176
출동소방서1.0000.9280.1410.0221.0000.113
구급처종명0.1190.1190.1550.1760.1131.000
2024-03-13T08:27:12.772995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
환자연령대시군명출동소방서출동안전센터환자성별국적구급처종명
환자연령대1.0000.0410.0400.0520.0930.0810.135
시군명0.0411.0001.0000.9170.0200.1510.119
출동소방서0.0401.0001.0000.9280.0220.1410.113
출동안전센터0.0520.9170.9281.0000.1100.1150.119
환자성별0.0930.0200.0220.1101.0000.3520.176
국적0.0810.1510.1410.1150.3521.0000.155
구급처종명0.1350.1190.1130.1190.1760.1551.000

Missing values

2024-03-13T08:27:10.243909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-13T08:27:10.354317image/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.
2024-03-13T08:27:10.452005image/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

집계년도시군명출동소방서출동안전센터환자연령대환자성별외국인유무국적구급처종명환자증상1
777532019광주시광주소방서송정119안전센터80여N<NA>집호흡곤란
532462019고양시일산소방서풍동119안전센터50여N<NA>상업시설<NA>
63582019가평군가평소방서조종119안전센터70남N<NA>집<NA>
955382019구리시구리소방서119구조대50남N<NA>상업시설기타통증
883562019광주시광주소방서능평119안전센터<NA><NA>능원리N<NA>도로
747322019광명시광명소방서소하119안전센터0여N<NA>집<NA>
540492019고양시일산소방서풍동119안전센터60여N<NA>집<NA>
24502019가평군가평소방서청평119지역대40남N<NA><NA><NA>
35212019가평군가평소방서북면119지역대30여N<NA>오락/문화시설흉통
328002019고양시일산소방서중산119안전센터80남N<NA>도로기타통증
집계년도시군명출동소방서출동안전센터환자연령대환자성별외국인유무국적구급처종명환자증상1
436032019고양시일산소방서주엽119안전센터70남N<NA><NA>찰과상
714292019광명시광명소방서광명119안전센터<NA><NA>광명동N<NA>도로외교통지역
366372019고양시일산소방서고봉119안전센터<NA><NA>N<NA><NA><NA>
657722019광명시광명소방서광명119안전센터80남N<NA><NA><NA>
22722019가평군가평소방서가평119안전센터40남N<NA>바다/강/산/논밭기타
947792019구리시구리소방서119구조대40남N<NA>기타기타
579172019과천시과천소방서과천119안전센터40남N<NA>도로기타통증
275242019고양시고양소방서원당119안전센터60남N<NA>집<NA>
609502019광명시광명소방서광남119안전센터50남N<NA>집복통
878132019광주시광주소방서초월119안전센터30남N<NA>도로<NA>

Duplicate rows

Most frequently occurring

집계년도시군명출동소방서출동안전센터환자연령대환자성별외국인유무국적구급처종명환자증상1# duplicates
12402019광주시광주소방서송정119안전센터<NA><NA>N<NA><NA><NA>45
3232019고양시고양소방서원당119안전센터<NA><NA>N<NA>집<NA>39
10252019광명시광명소방서소하119안전센터<NA><NA>N<NA><NA><NA>35
6532019고양시일산소방서장항119안전센터<NA><NA>N<NA><NA><NA>32
14272019구리시구리소방서인창119안전센터<NA><NA>N<NA><NA><NA>30
9102019광명시광명소방서광남119안전센터<NA><NA>N<NA><NA><NA>29
13332019광주시광주소방서초월119안전센터<NA><NA>N<NA><NA><NA>29
14072019구리시구리소방서교문119안전센터<NA><NA>N<NA><NA><NA>27
12972019광주시광주소방서오포119안전센터<NA><NA>N<NA><NA><NA>24
1662019고양시고양소방서능곡119안전센터<NA><NA>N<NA>집<NA>22