Overview

Dataset statistics

Number of variables8
Number of observations88
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.9 KiB
Average record size in memory68.5 B

Variable types

Numeric3
Categorical3
Text2

Dataset

Description대전광역시 중구에 위치한 민방위 대피시설 현황에 대한 정보입니다.This is information on the status of civil defense evacuation facilities located in Jung-gu, Daejeon.
Author대전광역시 중구
URLhttps://www.data.go.kr/data/15126559/fileData.do

Alerts

연번 is highly overall correlated with 동High correlation
규모 is highly overall correlated with 대피가능인원High correlation
대피가능인원 is highly overall correlated with 규모High correlation
동 is highly overall correlated with 연번High correlation
활용 is highly overall correlated with 개방시간High correlation
개방시간 is highly overall correlated with 활용High correlation
활용 is highly imbalanced (56.8%)Imbalance
연번 has unique valuesUnique

Reproduction

Analysis started2024-03-14 10:41:35.065259
Analysis finished2024-03-14 10:41:38.119560
Duration3.05 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct88
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.5
Minimum1
Maximum88
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size920.0 B
2024-03-14T19:41:38.332249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5.35
Q122.75
median44.5
Q366.25
95-th percentile83.65
Maximum88
Range87
Interquartile range (IQR)43.5

Descriptive statistics

Standard deviation25.547342
Coefficient of variation (CV)0.57409757
Kurtosis-1.2
Mean44.5
Median Absolute Deviation (MAD)22
Skewness0
Sum3916
Variance652.66667
MonotonicityStrictly increasing
2024-03-14T19:41:38.678471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.1%
46 1
 
1.1%
66 1
 
1.1%
65 1
 
1.1%
64 1
 
1.1%
63 1
 
1.1%
62 1
 
1.1%
61 1
 
1.1%
60 1
 
1.1%
59 1
 
1.1%
Other values (78) 78
88.6%
ValueCountFrequency (%)
1 1
1.1%
2 1
1.1%
3 1
1.1%
4 1
1.1%
5 1
1.1%
6 1
1.1%
7 1
1.1%
8 1
1.1%
9 1
1.1%
10 1
1.1%
ValueCountFrequency (%)
88 1
1.1%
87 1
1.1%
86 1
1.1%
85 1
1.1%
84 1
1.1%
83 1
1.1%
82 1
1.1%
81 1
1.1%
80 1
1.1%
79 1
1.1%

동
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)19.3%
Missing0
Missing (%)0.0%
Memory size832.0 B
태평1동
18 
목동
9 
중촌동
6 
문화1동
6 
용두동
5 
Other values (12)
44 

Length

Max length5
Median length4
Mean length3.3977273
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row은행선화동
2nd row은행선화동
3rd row은행선화동
4th row목동
5th row목동

Common Values

ValueCountFrequency (%)
태평1동 18
20.5%
목동 9
10.2%
중촌동 6
 
6.8%
문화1동 6
 
6.8%
용두동 5
 
5.7%
유천2동 5
 
5.7%
문창동 5
 
5.7%
오류동 4
 
4.5%
태평2동 4
 
4.5%
석교동 4
 
4.5%
Other values (7) 22
25.0%

Length

2024-03-14T19:41:38.918746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
태평1동 18
20.5%
목동 9
10.2%
중촌동 6
 
6.8%
문화1동 6
 
6.8%
용두동 5
 
5.7%
유천2동 5
 
5.7%
문창동 5
 
5.7%
부사동 4
 
4.5%
대흥동 4
 
4.5%
태평2동 4
 
4.5%
Other values (7) 22
25.0%

시설명
Text

Distinct86
Distinct (%)97.7%
Missing0
Missing (%)0.0%
Memory size832.0 B
2024-03-14T19:41:39.721604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length12
Mean length8.8636364
Min length3

Characters and Unicode

Total characters780
Distinct characters164
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

Unique84 ?
Unique (%)95.5%

Sample

1st row중앙로 지하상가
2nd row중앙로역
3rd row센트럴뷰아파트
4th row선병원
5th row을지의과대학
ValueCountFrequency (%)
현대아파트 5
 
4.7%
센트럴파크 2
 
1.9%
1차 2
 
1.9%
2단지 2
 
1.9%
주공아파트 2
 
1.9%
삼부아파트 2
 
1.9%
2차 2
 
1.9%
민방위대피소 1
 
0.9%
1단지아파트(143동 1
 
0.9%
중앙로 1
 
0.9%
Other values (86) 86
81.1%
2024-03-14T19:41:40.753575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
트 61
 
7.8%
파 60
 
7.7%
아 58
 
7.4%
1 41
 
5.3%
( 35
 
4.5%
) 35
 
4.5%
동 30
 
3.8%
0 20
 
2.6%
을 19
 
2.4%
18
 
2.3%
Other values (154) 403
51.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 598
76.7%
Decimal Number 90
 
11.5%
Open Punctuation 35
 
4.5%
Close Punctuation 35
 
4.5%
Space Separator 18
 
2.3%
Other Punctuation 2
 
0.3%
Uppercase Letter 2
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
트 61
 
10.2%
파 60
 
10.0%
아 58
 
9.7%
동 30
 
5.0%
을 19
 
3.2%
마 17
 
2.8%
대 16
 
2.7%
유 10
 
1.7%
지 9
 
1.5%
등 8
 
1.3%
Other values (138) 310
51.8%
Decimal Number
ValueCountFrequency (%)
1 41
45.6%
0 20
22.2%
4 7
 
7.8%
2 7
 
7.8%
6 4
 
4.4%
3 4
 
4.4%
5 2
 
2.2%
7 2
 
2.2%
9 2
 
2.2%
8 1
 
1.1%
Uppercase Letter
ValueCountFrequency (%)
K 1
50.0%
S 1
50.0%
Open Punctuation
ValueCountFrequency (%)
( 35
100.0%
Close Punctuation
ValueCountFrequency (%)
) 35
100.0%
Space Separator
ValueCountFrequency (%)
18
100.0%
Other Punctuation
ValueCountFrequency (%)
@ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 598
76.7%
Common 180
 
23.1%
Latin 2
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
트 61
 
10.2%
파 60
 
10.0%
아 58
 
9.7%
동 30
 
5.0%
을 19
 
3.2%
마 17
 
2.8%
대 16
 
2.7%
유 10
 
1.7%
지 9
 
1.5%
등 8
 
1.3%
Other values (138) 310
51.8%
Common
ValueCountFrequency (%)
1 41
22.8%
( 35
19.4%
) 35
19.4%
0 20
11.1%
18
10.0%
4 7
 
3.9%
2 7
 
3.9%
6 4
 
2.2%
3 4
 
2.2%
5 2
 
1.1%
Other values (4) 7
 
3.9%
Latin
ValueCountFrequency (%)
K 1
50.0%
S 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 598
76.7%
ASCII 182
 
23.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
트 61
 
10.2%
파 60
 
10.0%
아 58
 
9.7%
동 30
 
5.0%
을 19
 
3.2%
마 17
 
2.8%
대 16
 
2.7%
유 10
 
1.7%
지 9
 
1.5%
등 8
 
1.3%
Other values (138) 310
51.8%
ASCII
ValueCountFrequency (%)
1 41
22.5%
( 35
19.2%
) 35
19.2%
0 20
11.0%
18
9.9%
4 7
 
3.8%
2 7
 
3.8%
6 4
 
2.2%
3 4
 
2.2%
5 2
 
1.1%
Other values (6) 9
 
4.9%

위치
Text

Distinct66
Distinct (%)75.0%
Missing0
Missing (%)0.0%
Memory size832.0 B
2024-03-14T19:41:41.640914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length23
Mean length19.318182
Min length10

Characters and Unicode

Total characters1700
Distinct characters147
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique59 ?
Unique (%)67.0%

Sample

1st row중앙로 지하 145
2nd row중앙로 지하 145
3rd row중앙로 45(선화동, 센트럴뷰아파트)
4th row목중로 29(목동, 선병원)
5th row계룡로771번길 90(목동,을지의과대학(을지관)
ValueCountFrequency (%)
수침로 11
 
4.4%
대종로 8
 
3.2%
유등마을아파트 7
 
2.8%
평촌로 7
 
2.8%
138(태평동 7
 
2.8%
계룡로 6
 
2.4%
111(태평동 6
 
2.4%
태평아파트 6
 
2.4%
목동로 5
 
2.0%
목양마을아파트 5
 
2.0%
Other values (139) 182
72.8%
2024-03-14T19:41:43.051320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
162
 
9.5%
동 98
 
5.8%
로 88
 
5.2%
( 86
 
5.1%
) 86
 
5.1%
1 70
 
4.1%
, 68
 
4.0%
트 52
 
3.1%
파 51
 
3.0%
아 50
 
2.9%
Other values (137) 889
52.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1004
59.1%
Decimal Number 292
 
17.2%
Space Separator 162
 
9.5%
Open Punctuation 86
 
5.1%
Close Punctuation 86
 
5.1%
Other Punctuation 69
 
4.1%
Dash Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 98
 
9.8%
로 88
 
8.8%
트 52
 
5.2%
파 51
 
5.1%
아 50
 
5.0%
평 40
 
4.0%
대 35
 
3.5%
태 32
 
3.2%
목 25
 
2.5%
문 22
 
2.2%
Other values (121) 511
50.9%
Decimal Number
ValueCountFrequency (%)
1 70
24.0%
3 39
13.4%
2 38
13.0%
5 30
10.3%
6 26
 
8.9%
8 25
 
8.6%
7 20
 
6.8%
4 18
 
6.2%
9 16
 
5.5%
0 10
 
3.4%
Other Punctuation
ValueCountFrequency (%)
, 68
98.6%
@ 1
 
1.4%
Space Separator
ValueCountFrequency (%)
162
100.0%
Open Punctuation
ValueCountFrequency (%)
( 86
100.0%
Close Punctuation
ValueCountFrequency (%)
) 86
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1004
59.1%
Common 696
40.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 98
 
9.8%
로 88
 
8.8%
트 52
 
5.2%
파 51
 
5.1%
아 50
 
5.0%
평 40
 
4.0%
대 35
 
3.5%
태 32
 
3.2%
목 25
 
2.5%
문 22
 
2.2%
Other values (121) 511
50.9%
Common
ValueCountFrequency (%)
162
23.3%
( 86
12.4%
) 86
12.4%
1 70
10.1%
, 68
9.8%
3 39
 
5.6%
2 38
 
5.5%
5 30
 
4.3%
6 26
 
3.7%
8 25
 
3.6%
Other values (6) 66
9.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1004
59.1%
ASCII 696
40.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
162
23.3%
( 86
12.4%
) 86
12.4%
1 70
10.1%
, 68
9.8%
3 39
 
5.6%
2 38
 
5.5%
5 30
 
4.3%
6 26
 
3.7%
8 25
 
3.6%
Other values (6) 66
9.5%
Hangul
ValueCountFrequency (%)
동 98
 
9.8%
로 88
 
8.8%
트 52
 
5.2%
파 51
 
5.1%
아 50
 
5.0%
평 40
 
4.0%
대 35
 
3.5%
태 32
 
3.2%
목 25
 
2.5%
문 22
 
2.2%
Other values (121) 511
50.9%

규모
Real number (ℝ)

HIGH CORRELATION 

Distinct84
Distinct (%)95.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5456.346
Minimum172.2
Maximum25832
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size920.0 B
2024-03-14T19:41:43.292989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum172.2
5-th percentile312.431
Q11872.725
median3818.7
Q36911.17
95-th percentile16718.95
Maximum25832
Range25659.8
Interquartile range (IQR)5038.445

Descriptive statistics

Standard deviation5580.5037
Coefficient of variation (CV)1.0227547
Kurtosis3.7507565
Mean5456.346
Median Absolute Deviation (MAD)2494.8
Skewness1.9172997
Sum480158.45
Variance31142021
MonotonicityNot monotonic
2024-03-14T19:41:43.551181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7589.0 2
 
2.3%
2866.95 2
 
2.3%
4229.4 2
 
2.3%
2114.7 2
 
2.3%
1252.8 1
 
1.1%
7790.1 1
 
1.1%
5026.51 1
 
1.1%
6328.0 1
 
1.1%
2408.5 1
 
1.1%
6881.0 1
 
1.1%
Other values (74) 74
84.1%
ValueCountFrequency (%)
172.2 1
1.1%
244.9 1
1.1%
253.0 1
1.1%
274.38 1
1.1%
301.0 1
1.1%
333.66 1
1.1%
338.2 1
1.1%
453.0 1
1.1%
495.87 1
1.1%
535.54 1
1.1%
ValueCountFrequency (%)
25832.0 1
1.1%
24980.0 1
1.1%
22141.57 1
1.1%
22000.0 1
1.1%
16846.0 1
1.1%
16483.0 1
1.1%
15375.0 1
1.1%
13642.0 1
1.1%
13173.0 1
1.1%
13042.8 1
1.1%

활용
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct6
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Memory size832.0 B
지하주차장
68 
지하실
13 
지하역
 
4
지하상가
 
1
자하주차장
 
1

Length

Max length6
Median length5
Mean length4.6136364
Min length3

Unique

Unique3 ?
Unique (%)3.4%

Sample

1st row지하상가
2nd row지하역
3rd row지하주차장
4th row지하주차장
5th row지하주차장

Common Values

ValueCountFrequency (%)
지하주차장 68
77.3%
지하실 13
 
14.8%
지하역 4
 
4.5%
지하상가 1
 
1.1%
자하주차장 1
 
1.1%
지하주차장 1
 
1.1%

Length

2024-03-14T19:41:43.799242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T19:41:44.032177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
지하주차장 69
78.4%
지하실 13
 
14.8%
지하역 4
 
4.5%
지하상가 1
 
1.1%
자하주차장 1
 
1.1%

대피가능인원
Real number (ℝ)

HIGH CORRELATION 

Distinct84
Distinct (%)95.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6555.8977
Minimum208
Maximum31311
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size920.0 B
2024-03-14T19:41:44.416400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum208
5-th percentile344.6
Q12269.25
median4704.5
Q38544.5
95-th percentile20266.3
Maximum31311
Range31103
Interquartile range (IQR)6275.25

Descriptive statistics

Standard deviation6690.3091
Coefficient of variation (CV)1.0205024
Kurtosis3.9333419
Mean6555.8977
Median Absolute Deviation (MAD)3033
Skewness1.9386598
Sum576919
Variance44760236
MonotonicityNot monotonic
2024-03-14T19:41:44.852787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3475 2
 
2.3%
5126 2
 
2.3%
4419 2
 
2.3%
2563 2
 
2.3%
14935 1
 
1.1%
4642 1
 
1.1%
6092 1
 
1.1%
7670 1
 
1.1%
2919 1
 
1.1%
8340 1
 
1.1%
Other values (74) 74
84.1%
ValueCountFrequency (%)
208 1
1.1%
296 1
1.1%
306 1
1.1%
310 1
1.1%
332 1
1.1%
368 1
1.1%
404 1
1.1%
409 1
1.1%
549 1
1.1%
601 1
1.1%
ValueCountFrequency (%)
31311 1
1.1%
29831 1
1.1%
26838 1
1.1%
26666 1
1.1%
20421 1
1.1%
19979 1
1.1%
18636 1
1.1%
16224 1
1.1%
15809 1
1.1%
14935 1
1.1%

개방시간
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size832.0 B
상시
78 
비상발생시
10 

Length

Max length5
Median length2
Mean length2.3409091
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
상시 78
88.6%
비상발생시 10
 
11.4%

Length

2024-03-14T19:41:45.283828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T19:41:45.613059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
상시 78
88.6%
비상발생시 10
 
11.4%

Interactions

2024-03-14T19:41:36.929125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:41:36.084572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:41:36.513055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:41:37.068652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:41:36.234931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:41:36.645863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:41:37.264901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:41:36.368581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:41:36.783616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T19:41:45.822432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번동시설명위치규모활용대피가능인원개방시간
연번1.0000.9560.8780.9640.0000.1390.3080.400
동0.9561.0000.9201.0000.2380.4850.4840.544
시설명0.8780.9201.0000.9880.8191.0000.9351.000
위치0.9641.0000.9881.0000.8840.0000.8590.972
규모0.0000.2380.8190.8841.0000.4160.9970.000
활용0.1390.4851.0000.0000.4161.0000.5560.972
대피가능인원0.3080.4840.9350.8590.9970.5561.0000.222
개방시간0.4000.5441.0000.9720.0000.9720.2221.000
2024-03-14T19:41:46.109053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
활용개방시간동
활용1.0000.8310.231
개방시간0.8311.0000.445
동0.2310.4451.000
2024-03-14T19:41:46.362492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번규모대피가능인원동활용개방시간
연번1.0000.1010.0820.7690.0620.290
규모0.1011.0000.9950.1030.2200.000
대피가능인원0.0820.9951.0000.2090.3110.227
동0.7690.1030.2091.0000.2310.445
활용0.0620.2200.3110.2311.0000.831
개방시간0.2900.0000.2270.4450.8311.000

Missing values

2024-03-14T19:41:37.501734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T19:41:37.964612image/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

연번동시설명위치규모활용대피가능인원개방시간
01은행선화동중앙로 지하상가중앙로 지하 14512322.0지하상가14935상시
12은행선화동중앙로역중앙로 지하 1457589.0지하역9198상시
23은행선화동센트럴뷰아파트중앙로 45(선화동, 센트럴뷰아파트)22141.57지하주차장26838상시
34목동선병원목중로 29(목동, 선병원)6611.6지하주차장8014상시
45목동을지의과대학계룡로771번길 90(목동,을지의과대학(을지관)1652.9지하주차장2003상시
56목동목양마을아파트(102동)목동로 37(목동, 목양마을아파트)3933.0지하주차장4767상시
67목동금호한사랑아파트동서대로 1388(목동, 금호한사랑아파트)3049.0지하주차장5156상시
78목동더샵아파트목동로22번길 16(목동, 목동더샵)24980.0지하주차장29831상시
89목동목양마을아파트(104동)목동로 37(목동, 목양마을아파트)16483.0지하주차장19979상시
910목동목양마을아파트(106동)목동로 37(목동, 목양마을아파트)15375.0지하주차장18636상시
연번동시설명위치규모활용대피가능인원개방시간
7879문화1동문화마을아파트계백로1716번길 40(문화동, 문화마을아파트)6299.0지하주차장7635상시
7980문화1동센트럴파크@ 1단지서문로 95(문화동, 센트럴파크1단지아파트)5699.54지하주차장6909상시
8081문화1동센트럴파크@ 2단지서문로 96(문화동, 센트럴파크2단지아파트)8726.0지하주차장10576상시
8182문화1동한밭우성아파트서문로 32(문화동, 한밭우성아파트)13642.0지하실12380비상발생시
8283문화1동서대전네거리역계룡로 지하 889(용두동, 서대전네거리역)10970.0지하역13296상시
8384문화2동서문교회산성로138번길 8(문화동)301.0지하실368비상발생시
8485문화2동대전극동아파트송리로 6(문화동, 극동아파트)3478.85지하주차장4216상시
8586산성동중구 민방위대피소문화로46번길 50(산성동, 민방위교육장)1345.46지하실1630비상발생시
8687산성동우남스타원 아파트안영로 68(사정동, 성산마을우남스타원)25832.0지하주차장31311상시
8788산성동경성공원아파트보문산로141번길 11(산성동)5024.0지하주차장6089상시