Overview

Dataset statistics

Number of variables10
Number of observations39
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.5 KiB
Average record size in memory91.4 B

Variable types

Categorical4
Numeric6

Dataset

Description한국전기안전공사에서 2020년~2022년 긴급출동고충처리를 진행한 내역으로, 대상(국민기초생활보호자, 차상위계층, 장애인, 국가유공자, 독립유공자, 518민주유공자, 사회복지시설,취약시설) 및 지역(서울, 부산울산, 대구경북, 인천, 광주전남, 대전충남, 경기, 경기북부, 강원, 충북, 전북, 경남, 제주, 전기안전보안관)을 공개한 데이터입니다.
URLhttps://www.data.go.kr/data/15086434/fileData.do

Alerts

기초생활수급권자 is highly overall correlated with 차상위계층 and 2 other fieldsHigh correlation
차상위계층 is highly overall correlated with 기초생활수급권자High correlation
장애인 is highly overall correlated with 기초생활수급권자 and 3 other fieldsHigh correlation
참전유공자 is highly overall correlated with 기초생활수급권자 and 1 other fieldsHigh correlation
독립유공자 is highly overall correlated with 장애인High correlation
사회복지시설 is highly overall correlated with 장애인 and 1 other fieldsHigh correlation
연도 is highly overall correlated with 사회복지시설High correlation
독립유공자 has 11 (28.2%) zerosZeros

Reproduction

Analysis started2023-12-12 05:52:23.625704
Analysis finished2023-12-12 05:52:28.066938
Duration4.44 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

사업소
Categorical

Distinct14
Distinct (%)35.9%
Missing0
Missing (%)0.0%
Memory size444.0 B
서울
3 
부산울산
3 
대구경북
3 
인천
3 
광주전남
3 
Other values (9)
24 

Length

Max length4
Median length2
Mean length2.7692308
Min length2

Unique

Unique1 ?
Unique (%)2.6%

Sample

1st row서울
2nd row부산울산
3rd row대구경북
4th row인천
5th row광주전남

Common Values

ValueCountFrequency (%)
서울 3
 
7.7%
부산울산 3
 
7.7%
대구경북 3
 
7.7%
인천 3
 
7.7%
광주전남 3
 
7.7%
경기 3
 
7.7%
경기북부 3
 
7.7%
강원 3
 
7.7%
충북 3
 
7.7%
전북 3
 
7.7%
Other values (4) 9
23.1%

Length

2023-12-12T14:52:28.199267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
서울 3
 
7.7%
부산울산 3
 
7.7%
대구경북 3
 
7.7%
인천 3
 
7.7%
광주전남 3
 
7.7%
경기 3
 
7.7%
경기북부 3
 
7.7%
강원 3
 
7.7%
충북 3
 
7.7%
전북 3
 
7.7%
Other values (4) 9
23.1%

연도
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Memory size444.0 B
2020
13 
2021
13 
2022
13 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020
2nd row2020
3rd row2020
4th row2020
5th row2020

Common Values

ValueCountFrequency (%)
2020 13
33.3%
2021 13
33.3%
2022 13
33.3%

Length

2023-12-12T14:52:28.359735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T14:52:28.466502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020 13
33.3%
2021 13
33.3%
2022 13
33.3%

기초생활수급권자
Real number (ℝ)

HIGH CORRELATION 

Distinct38
Distinct (%)97.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean267.92308
Minimum38
Maximum585
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size483.0 B
2023-12-12T14:52:28.587641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum38
5-th percentile71.3
Q1162.5
median230
Q3378.5
95-th percentile512.2
Maximum585
Range547
Interquartile range (IQR)216

Descriptive statistics

Standard deviation150.00937
Coefficient of variation (CV)0.55989714
Kurtosis-0.84563177
Mean267.92308
Median Absolute Deviation (MAD)106
Skewness0.47106052
Sum10449
Variance22502.81
MonotonicityNot monotonic
2023-12-12T14:52:28.774307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
261 2
 
5.1%
508 1
 
2.6%
168 1
 
2.6%
357 1
 
2.6%
466 1
 
2.6%
178 1
 
2.6%
450 1
 
2.6%
328 1
 
2.6%
194 1
 
2.6%
230 1
 
2.6%
Other values (28) 28
71.8%
ValueCountFrequency (%)
38 1
2.6%
47 1
2.6%
74 1
2.6%
95 1
2.6%
98 1
2.6%
116 1
2.6%
118 1
2.6%
128 1
2.6%
141 1
2.6%
159 1
2.6%
ValueCountFrequency (%)
585 1
2.6%
550 1
2.6%
508 1
2.6%
493 1
2.6%
486 1
2.6%
466 1
2.6%
450 1
2.6%
437 1
2.6%
413 1
2.6%
396 1
2.6%

차상위계층
Real number (ℝ)

HIGH CORRELATION 

Distinct38
Distinct (%)97.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean579.46154
Minimum20
Maximum1958
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size483.0 B
2023-12-12T14:52:28.944929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile79.6
Q1295.5
median426
Q3745.5
95-th percentile1553
Maximum1958
Range1938
Interquartile range (IQR)450

Descriptive statistics

Standard deviation490.6413
Coefficient of variation (CV)0.84671936
Kurtosis1.7233508
Mean579.46154
Median Absolute Deviation (MAD)241
Skewness1.4741776
Sum22599
Variance240728.89
MonotonicityNot monotonic
2023-12-12T14:52:29.145948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
116 2
 
5.1%
176 1
 
2.6%
303 1
 
2.6%
361 1
 
2.6%
838 1
 
2.6%
909 1
 
2.6%
84 1
 
2.6%
463 1
 
2.6%
270 1
 
2.6%
322 1
 
2.6%
Other values (28) 28
71.8%
ValueCountFrequency (%)
20 1
2.6%
40 1
2.6%
84 1
2.6%
116 2
5.1%
127 1
2.6%
155 1
2.6%
176 1
2.6%
270 1
2.6%
291 1
2.6%
300 1
2.6%
ValueCountFrequency (%)
1958 1
2.6%
1949 1
2.6%
1509 1
2.6%
1437 1
2.6%
1268 1
2.6%
1259 1
2.6%
909 1
2.6%
838 1
2.6%
795 1
2.6%
791 1
2.6%

장애인
Real number (ℝ)

HIGH CORRELATION 

Distinct38
Distinct (%)97.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean138.15385
Minimum32
Maximum340
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size483.0 B
2023-12-12T14:52:29.326465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum32
5-th percentile47.2
Q183.5
median119
Q3184.5
95-th percentile268.7
Maximum340
Range308
Interquartile range (IQR)101

Descriptive statistics

Standard deviation75.410993
Coefficient of variation (CV)0.54584794
Kurtosis0.016905765
Mean138.15385
Median Absolute Deviation (MAD)43
Skewness0.84914996
Sum5388
Variance5686.8178
MonotonicityNot monotonic
2023-12-12T14:52:29.488292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
119 2
 
5.1%
340 1
 
2.6%
108 1
 
2.6%
48 1
 
2.6%
103 1
 
2.6%
147 1
 
2.6%
52 1
 
2.6%
217 1
 
2.6%
105 1
 
2.6%
92 1
 
2.6%
Other values (28) 28
71.8%
ValueCountFrequency (%)
32 1
2.6%
40 1
2.6%
48 1
2.6%
52 1
2.6%
62 1
2.6%
64 1
2.6%
65 1
2.6%
74 1
2.6%
80 1
2.6%
81 1
2.6%
ValueCountFrequency (%)
340 1
2.6%
284 1
2.6%
267 1
2.6%
256 1
2.6%
238 1
2.6%
235 1
2.6%
231 1
2.6%
217 1
2.6%
214 1
2.6%
205 1
2.6%

참전유공자
Real number (ℝ)

HIGH CORRELATION 

Distinct34
Distinct (%)87.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean106.66667
Minimum25
Maximum230
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size483.0 B
2023-12-12T14:52:29.633260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum25
5-th percentile39.8
Q167
median100
Q3152
95-th percentile187.4
Maximum230
Range205
Interquartile range (IQR)85

Descriptive statistics

Standard deviation52.943532
Coefficient of variation (CV)0.49634561
Kurtosis-0.795877
Mean106.66667
Median Absolute Deviation (MAD)40
Skewness0.4608686
Sum4160
Variance2803.0175
MonotonicityNot monotonic
2023-12-12T14:52:29.799700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
79 2
 
5.1%
56 2
 
5.1%
103 2
 
5.1%
67 2
 
5.1%
69 2
 
5.1%
68 1
 
2.6%
72 1
 
2.6%
109 1
 
2.6%
153 1
 
2.6%
60 1
 
2.6%
Other values (24) 24
61.5%
ValueCountFrequency (%)
25 1
2.6%
29 1
2.6%
41 1
2.6%
45 1
2.6%
53 1
2.6%
55 1
2.6%
56 2
5.1%
60 1
2.6%
67 2
5.1%
68 1
2.6%
ValueCountFrequency (%)
230 1
2.6%
200 1
2.6%
186 1
2.6%
185 1
2.6%
176 1
2.6%
172 1
2.6%
167 1
2.6%
160 1
2.6%
158 1
2.6%
153 1
2.6%

독립유공자
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)17.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7692308
Minimum0
Maximum17
Zeros11
Zeros (%)28.2%
Negative0
Negative (%)0.0%
Memory size483.0 B
2023-12-12T14:52:29.934047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile4.1
Maximum17
Range17
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.8047097
Coefficient of variation (CV)1.5852707
Kurtosis23.679481
Mean1.7692308
Median Absolute Deviation (MAD)1
Skewness4.4285076
Sum69
Variance7.8663968
MonotonicityNot monotonic
2023-12-12T14:52:30.084205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1 13
33.3%
0 11
28.2%
3 6
15.4%
2 6
15.4%
17 1
 
2.6%
5 1
 
2.6%
4 1
 
2.6%
ValueCountFrequency (%)
0 11
28.2%
1 13
33.3%
2 6
15.4%
3 6
15.4%
4 1
 
2.6%
5 1
 
2.6%
17 1
 
2.6%
ValueCountFrequency (%)
17 1
 
2.6%
5 1
 
2.6%
4 1
 
2.6%
3 6
15.4%
2 6
15.4%
1 13
33.3%
0 11
28.2%

518민주유공자
Categorical

Distinct3
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Memory size444.0 B
0
32 
1
5 
2
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row2

Common Values

ValueCountFrequency (%)
0 32
82.1%
1 5
 
12.8%
2 2
 
5.1%

Length

2023-12-12T14:52:30.237934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T14:52:30.371706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 32
82.1%
1 5
 
12.8%
2 2
 
5.1%

사회복지시설
Real number (ℝ)

HIGH CORRELATION 

Distinct37
Distinct (%)94.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean388.17949
Minimum15
Maximum1798
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size483.0 B
2023-12-12T14:52:30.508220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile18.6
Q155.5
median267
Q3536
95-th percentile1148.2
Maximum1798
Range1783
Interquartile range (IQR)480.5

Descriptive statistics

Standard deviation429.91375
Coefficient of variation (CV)1.1075128
Kurtosis1.828826
Mean388.17949
Median Absolute Deviation (MAD)213
Skewness1.4436449
Sum15139
Variance184825.84
MonotonicityNot monotonic
2023-12-12T14:52:30.670509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
15 2
 
5.1%
30 2
 
5.1%
1294 1
 
2.6%
19 1
 
2.6%
57 1
 
2.6%
28 1
 
2.6%
290 1
 
2.6%
325 1
 
2.6%
593 1
 
2.6%
146 1
 
2.6%
Other values (27) 27
69.2%
ValueCountFrequency (%)
15 2
5.1%
19 1
2.6%
28 1
2.6%
30 2
5.1%
31 1
2.6%
47 1
2.6%
50 1
2.6%
54 1
2.6%
57 1
2.6%
64 1
2.6%
ValueCountFrequency (%)
1798 1
2.6%
1294 1
2.6%
1132 1
2.6%
1059 1
2.6%
951 1
2.6%
904 1
2.6%
837 1
2.6%
802 1
2.6%
793 1
2.6%
593 1
2.6%

취약시설
Categorical

Distinct14
Distinct (%)35.9%
Missing0
Missing (%)0.0%
Memory size444.0 B
0
26 
3,063
 
1
550
 
1
1,892
 
1
604
 
1
Other values (9)
9 

Length

Max length5
Median length1
Mean length1.7948718
Min length1

Unique

Unique13 ?
Unique (%)33.3%

Sample

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

Common Values

ValueCountFrequency (%)
0 26
66.7%
3,063 1
 
2.6%
550 1
 
2.6%
1,892 1
 
2.6%
604 1
 
2.6%
664 1
 
2.6%
990 1
 
2.6%
1,410 1
 
2.6%
170 1
 
2.6%
622 1
 
2.6%
Other values (4) 4
 
10.3%

Length

2023-12-12T14:52:30.845433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0 26
66.7%
3,063 1
 
2.6%
550 1
 
2.6%
1,892 1
 
2.6%
604 1
 
2.6%
664 1
 
2.6%
990 1
 
2.6%
1,410 1
 
2.6%
170 1
 
2.6%
622 1
 
2.6%
Other values (4) 4
 
10.3%

Interactions

2023-12-12T14:52:27.149782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:52:24.032267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:52:24.560717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:52:25.086586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:52:25.620434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:52:26.155667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:52:27.259341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:52:24.134605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:52:24.649836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:52:25.181715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:52:25.698337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:52:26.263057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:52:27.354747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:52:24.232167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:52:24.736744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:52:25.278876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:52:25.799452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:52:26.366742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:52:27.497072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:52:24.327035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:52:24.818824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:52:25.363874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:52:25.889435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:52:26.459879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:52:27.595896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:52:24.400541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:52:24.894173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:52:25.431866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:52:25.966948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:52:26.615217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:52:27.698639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:52:24.483086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:52:24.987839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:52:25.516573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:52:26.054820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:52:27.037816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T14:52:30.955905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
사업소연도기초생활수급권자차상위계층장애인참전유공자독립유공자518민주유공자사회복지시설취약시설
사업소1.0000.0000.4000.6350.0000.8270.0000.7240.0000.617
연도0.0001.0000.4120.4030.5470.0000.1390.0880.9120.682
기초생활수급권자0.4000.4121.0000.3840.7490.6370.0900.0000.3650.000
차상위계층0.6350.4030.3841.0000.0440.4470.1750.0000.0000.000
장애인0.0000.5470.7490.0441.0000.7900.8340.3230.8000.000
참전유공자0.8270.0000.6370.4470.7901.0000.7300.4600.6230.474
독립유공자0.0000.1390.0900.1750.8340.7301.0000.1840.7220.000
518민주유공자0.7240.0880.0000.0000.3230.4600.1841.0000.6240.000
사회복지시설0.0000.9120.3650.0000.8000.6230.7220.6241.0000.000
취약시설0.6170.6820.0000.0000.0000.4740.0000.0000.0001.000
2023-12-12T14:52:31.101924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
518민주유공자연도사업소취약시설
518민주유공자1.0000.0000.4500.000
연도0.0001.0000.0000.408
사업소0.4500.0001.0000.169
취약시설0.0000.4080.1691.000
2023-12-12T14:52:31.225147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기초생활수급권자차상위계층장애인참전유공자독립유공자사회복지시설사업소연도518민주유공자취약시설
기초생활수급권자1.0000.5550.5730.6480.113-0.0940.1220.2320.0000.000
차상위계층0.5551.0000.4200.4160.137-0.2130.2930.2520.0000.000
장애인0.5730.4201.0000.6000.5030.5430.0000.3430.1660.000
참전유공자0.6480.4160.6001.0000.2170.2560.4770.0000.2700.167
독립유공자0.1130.1370.5030.2171.0000.3110.0000.1220.1660.000
사회복지시설-0.094-0.2130.5430.2560.3111.0000.0000.5970.3060.000
사업소0.1220.2930.0000.4770.0000.0001.0000.0000.4500.169
연도0.2320.2520.3430.0000.1220.5970.0001.0000.0000.408
518민주유공자0.0000.0000.1660.2700.1660.3060.4500.0001.0000.000
취약시설0.0000.0000.0000.1670.0000.0000.1690.4080.0001.000

Missing values

2023-12-12T14:52:27.822973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T14:52:27.993649image/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

사업소연도기초생활수급권자차상위계층장애인참전유공자독립유공자518민주유공자사회복지시설취약시설
0서울202050817634020017112940
1부산울산2020413700235103109040
2대구경북20203964012841581011320
3인천202019915516445308020
4광주전남20202081437231103228370
5대전충남20202374662141303010590
6경기20202613912051673117980
7경기북부202018811611156109510
8강원202011651815567503360
9충북20201416679053104430
사업소연도기초생활수급권자차상위계층장애인참전유공자독립유공자518민주유공자사회복지시설취약시설
29인천20222303031086710146604
30광주전남20221683009213502398664
31대전세종202216679111612900433990
32경기2022486547119153103001,410
33경기북부20229512764680094170
34강원202298329656910231622
35충북202238116405610188351
36전북2022159419867200267565
37경남202212829110910900394449
38제주202247203225107966