Overview

Dataset statistics

Number of variables8
Number of observations433
Missing cells3
Missing cells (%)0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory27.6 KiB
Average record size in memory65.3 B

Variable types

Numeric1
Categorical4
Text3

Dataset

Description화재 발생시 초기진압으로 피해를 최소화를 하기 위한 소방용수시설 현황 데이터로, 관서명, 센터명, 지역대, 관리번호, 수리위치 , 주변 대상물, 수리형식의 데이터를 포함하고 있습니다.
Author경상남도
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15065273

Alerts

관서명 has constant value ""Constant
센터명 is highly overall correlated with 순번 and 1 other fieldsHigh correlation
지역대 is highly overall correlated with 센터명High correlation
순번 is highly overall correlated with 센터명High correlation
수리형식 is highly imbalanced (61.8%)Imbalance
순번 has unique valuesUnique
관리번호 has unique valuesUnique

Reproduction

Analysis started2023-12-10 23:44:37.734566
Analysis finished2023-12-10 23:44:38.686010
Duration0.95 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct433
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean217
Minimum1
Maximum433
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2023-12-11T08:44:38.771533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile22.6
Q1109
median217
Q3325
95-th percentile411.4
Maximum433
Range432
Interquartile range (IQR)216

Descriptive statistics

Standard deviation125.14059
Coefficient of variation (CV)0.57668474
Kurtosis-1.2
Mean217
Median Absolute Deviation (MAD)108
Skewness0
Sum93961
Variance15660.167
MonotonicityStrictly increasing
2023-12-11T08:44:39.223490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.2%
286 1
 
0.2%
297 1
 
0.2%
296 1
 
0.2%
295 1
 
0.2%
294 1
 
0.2%
293 1
 
0.2%
292 1
 
0.2%
291 1
 
0.2%
290 1
 
0.2%
Other values (423) 423
97.7%
ValueCountFrequency (%)
1 1
0.2%
2 1
0.2%
3 1
0.2%
4 1
0.2%
5 1
0.2%
6 1
0.2%
7 1
0.2%
8 1
0.2%
9 1
0.2%
10 1
0.2%
ValueCountFrequency (%)
433 1
0.2%
432 1
0.2%
431 1
0.2%
430 1
0.2%
429 1
0.2%
428 1
0.2%
427 1
0.2%
426 1
0.2%
425 1
0.2%
424 1
0.2%

관서명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
창녕
433 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
창녕 433
100.0%

Length

2023-12-11T08:44:39.367001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:44:39.500075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
창녕 433
100.0%

센터명
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
창녕
212 
남지
88 
영산
69 
부곡
64 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
창녕 212
49.0%
남지 88
20.3%
영산 69
 
15.9%
부곡 64
 
14.8%

Length

2023-12-11T08:44:39.646121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:44:39.748391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
창녕 212
49.0%
남지 88
20.3%
영산 69
 
15.9%
부곡 64
 
14.8%

지역대
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
<NA>
308 
대합
96 
이방
 
29

Length

Max length4
Median length4
Mean length3.4226328
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 308
71.1%
대합 96
 
22.2%
이방 29
 
6.7%

Length

2023-12-11T08:44:39.909621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:44:40.053771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 308
71.1%
대합 96
 
22.2%
이방 29
 
6.7%

관리번호
Text

UNIQUE 

Distinct433
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
2023-12-11T08:44:40.498960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length6.1177829
Min length5

Characters and Unicode

Total characters2649
Distinct characters16
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique433 ?
Unique (%)100.0%

Sample

1st row1-A-01
2nd row1-A-02
3rd row1-A-03
4th row1-A-04
5th row1-A-05
ValueCountFrequency (%)
1-a-01 1
 
0.2%
2-a-129 1
 
0.2%
2-d-6 1
 
0.2%
2-d-5 1
 
0.2%
2-d-4 1
 
0.2%
2-d-3 1
 
0.2%
2-d-1 1
 
0.2%
2-c-1 1
 
0.2%
2-b-11 1
 
0.2%
2-b-7 1
 
0.2%
Other values (423) 423
97.7%
2023-12-11T08:44:41.145516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 866
32.7%
1 447
16.9%
A 375
14.2%
2 191
 
7.2%
3 153
 
5.8%
4 151
 
5.7%
5 88
 
3.3%
6 78
 
2.9%
0 68
 
2.6%
7 64
 
2.4%
Other values (6) 168
 
6.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1349
50.9%
Dash Punctuation 866
32.7%
Uppercase Letter 433
 
16.3%
Space Separator 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 447
33.1%
2 191
14.2%
3 153
 
11.3%
4 151
 
11.2%
5 88
 
6.5%
6 78
 
5.8%
0 68
 
5.0%
7 64
 
4.7%
8 57
 
4.2%
9 52
 
3.9%
Uppercase Letter
ValueCountFrequency (%)
A 375
86.6%
D 29
 
6.7%
B 19
 
4.4%
C 10
 
2.3%
Dash Punctuation
ValueCountFrequency (%)
- 866
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2216
83.7%
Latin 433
 
16.3%

Most frequent character per script

Common
ValueCountFrequency (%)
- 866
39.1%
1 447
20.2%
2 191
 
8.6%
3 153
 
6.9%
4 151
 
6.8%
5 88
 
4.0%
6 78
 
3.5%
0 68
 
3.1%
7 64
 
2.9%
8 57
 
2.6%
Other values (2) 53
 
2.4%
Latin
ValueCountFrequency (%)
A 375
86.6%
D 29
 
6.7%
B 19
 
4.4%
C 10
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2649
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 866
32.7%
1 447
16.9%
A 375
14.2%
2 191
 
7.2%
3 153
 
5.8%
4 151
 
5.7%
5 88
 
3.3%
6 78
 
2.9%
0 68
 
2.6%
7 64
 
2.4%
Other values (6) 168
 
6.3%

수리위치
Text

Distinct405
Distinct (%)93.8%
Missing1
Missing (%)0.2%
Memory size3.5 KiB
2023-12-11T08:44:41.415186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length22
Mean length15.993056
Min length9

Characters and Unicode

Total characters6909
Distinct characters192
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique384 ?
Unique (%)88.9%

Sample

1st row창녕군 대합면 십이리길 29
2nd row창녕군 대합면 둔지길 31
3rd row창녕군 창녕읍 남창녕로 52
4th row창녕군 창녕읍 명덕로 121
5th row창녕군 창녕읍 종로 20
ValueCountFrequency (%)
창녕군 404
23.6%
대합면 80
 
4.7%
남지읍 76
 
4.4%
창녕읍 68
 
4.0%
부곡면 47
 
2.7%
영산면 45
 
2.6%
대합산업단지로 44
 
2.6%
이방면 22
 
1.3%
도천면 17
 
1.0%
성산면 16
 
0.9%
Other values (510) 892
52.1%
2023-12-11T08:44:41.835706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1307
18.9%
창 496
 
7.2%
녕 488
 
7.1%
군 406
 
5.9%
면 289
 
4.2%
길 272
 
3.9%
1 265
 
3.8%
로 165
 
2.4%
2 164
 
2.4%
대 164
 
2.4%
Other values (182) 2893
41.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4272
61.8%
Space Separator 1307
 
18.9%
Decimal Number 1199
 
17.4%
Dash Punctuation 118
 
1.7%
Open Punctuation 5
 
0.1%
Close Punctuation 5
 
0.1%
Other Punctuation 2
 
< 0.1%
Uppercase Letter 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
창 496
 
11.6%
녕 488
 
11.4%
군 406
 
9.5%
면 289
 
6.8%
길 272
 
6.4%
로 165
 
3.9%
대 164
 
3.8%
지 151
 
3.5%
산 148
 
3.5%
읍 144
 
3.4%
Other values (166) 1549
36.3%
Decimal Number
ValueCountFrequency (%)
1 265
22.1%
2 164
13.7%
3 135
11.3%
5 119
9.9%
6 109
9.1%
4 107
8.9%
9 82
 
6.8%
8 73
 
6.1%
7 73
 
6.1%
0 72
 
6.0%
Space Separator
ValueCountFrequency (%)
1307
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 118
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5
100.0%
Other Punctuation
ValueCountFrequency (%)
, 2
100.0%
Uppercase Letter
ValueCountFrequency (%)
M 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4272
61.8%
Common 2636
38.2%
Latin 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
창 496
 
11.6%
녕 488
 
11.4%
군 406
 
9.5%
면 289
 
6.8%
길 272
 
6.4%
로 165
 
3.9%
대 164
 
3.8%
지 151
 
3.5%
산 148
 
3.5%
읍 144
 
3.4%
Other values (166) 1549
36.3%
Common
ValueCountFrequency (%)
1307
49.6%
1 265
 
10.1%
2 164
 
6.2%
3 135
 
5.1%
5 119
 
4.5%
- 118
 
4.5%
6 109
 
4.1%
4 107
 
4.1%
9 82
 
3.1%
8 73
 
2.8%
Other values (5) 157
 
6.0%
Latin
ValueCountFrequency (%)
M 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4272
61.8%
ASCII 2637
38.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1307
49.6%
1 265
 
10.0%
2 164
 
6.2%
3 135
 
5.1%
5 119
 
4.5%
- 118
 
4.5%
6 109
 
4.1%
4 107
 
4.1%
9 82
 
3.1%
8 73
 
2.8%
Other values (6) 158
 
6.0%
Hangul
ValueCountFrequency (%)
창 496
 
11.6%
녕 488
 
11.4%
군 406
 
9.5%
면 289
 
6.8%
길 272
 
6.4%
로 165
 
3.9%
대 164
 
3.8%
지 151
 
3.5%
산 148
 
3.5%
읍 144
 
3.4%
Other values (166) 1549
36.3%

주변 대상물
Text

Distinct399
Distinct (%)92.6%
Missing2
Missing (%)0.5%
Memory size3.5 KiB
2023-12-11T08:44:42.057037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length20
Mean length8.6937355
Min length2

Characters and Unicode

Total characters3747
Distinct characters373
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique370 ?
Unique (%)85.8%

Sample

1st row십이리길 29 건물 앞 도로변
2nd row둔지교 입구
3rd row창녕도서관 앞
4th row송현사거리 고암방향 신호점멸기 앞
5th row미미분식 앞 전봇대
ValueCountFrequency (%)
앞 144
 
15.3%
주택 26
 
2.8%
옆 25
 
2.6%
맞은편 19
 
2.0%
입구 18
 
1.9%
마을회관 14
 
1.5%
건물 11
 
1.2%
전봇대 10
 
1.1%
삼거리 10
 
1.1%
건너편 7
 
0.7%
Other values (521) 660
69.9%
2023-12-11T08:44:42.420300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
541
 
14.4%
앞 156
 
4.2%
마 109
 
2.9%
을 100
 
2.7%
관 91
 
2.4%
회 90
 
2.4%
대 53
 
1.4%
리 48
 
1.3%
구 45
 
1.2%
지 44
 
1.2%
Other values (363) 2470
65.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2944
78.6%
Space Separator 541
 
14.4%
Decimal Number 161
 
4.3%
Uppercase Letter 56
 
1.5%
Dash Punctuation 10
 
0.3%
Lowercase Letter 10
 
0.3%
Other Symbol 9
 
0.2%
Open Punctuation 6
 
0.2%
Close Punctuation 6
 
0.2%
Other Punctuation 4
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
앞 156
 
5.3%
마 109
 
3.7%
을 100
 
3.4%
관 91
 
3.1%
회 90
 
3.1%
대 53
 
1.8%
리 48
 
1.6%
구 45
 
1.5%
지 44
 
1.5%
동 43
 
1.5%
Other values (325) 2165
73.5%
Uppercase Letter
ValueCountFrequency (%)
M 12
21.4%
C 9
16.1%
S 7
12.5%
K 6
10.7%
D 4
 
7.1%
T 4
 
7.1%
U 2
 
3.6%
P 2
 
3.6%
E 2
 
3.6%
W 2
 
3.6%
Other values (6) 6
10.7%
Decimal Number
ValueCountFrequency (%)
1 40
24.8%
2 27
16.8%
3 18
11.2%
0 17
10.6%
5 16
 
9.9%
9 13
 
8.1%
4 10
 
6.2%
6 8
 
5.0%
7 8
 
5.0%
8 4
 
2.5%
Lowercase Letter
ValueCountFrequency (%)
m 7
70.0%
c 1
 
10.0%
i 1
 
10.0%
b 1
 
10.0%
Other Symbol
ValueCountFrequency (%)
㈜ 8
88.9%
○ 1
 
11.1%
Other Punctuation
ValueCountFrequency (%)
& 3
75.0%
. 1
 
25.0%
Space Separator
ValueCountFrequency (%)
541
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 10
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2952
78.8%
Common 729
 
19.5%
Latin 66
 
1.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
앞 156
 
5.3%
마 109
 
3.7%
을 100
 
3.4%
관 91
 
3.1%
회 90
 
3.0%
대 53
 
1.8%
리 48
 
1.6%
구 45
 
1.5%
지 44
 
1.5%
동 43
 
1.5%
Other values (326) 2173
73.6%
Latin
ValueCountFrequency (%)
M 12
18.2%
C 9
13.6%
S 7
10.6%
m 7
10.6%
K 6
9.1%
D 4
 
6.1%
T 4
 
6.1%
U 2
 
3.0%
P 2
 
3.0%
E 2
 
3.0%
Other values (10) 11
16.7%
Common
ValueCountFrequency (%)
541
74.2%
1 40
 
5.5%
2 27
 
3.7%
3 18
 
2.5%
0 17
 
2.3%
5 16
 
2.2%
9 13
 
1.8%
- 10
 
1.4%
4 10
 
1.4%
6 8
 
1.1%
Other values (7) 29
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2944
78.6%
ASCII 794
 
21.2%
None 8
 
0.2%
Geometric Shapes 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
541
68.1%
1 40
 
5.0%
2 27
 
3.4%
3 18
 
2.3%
0 17
 
2.1%
5 16
 
2.0%
9 13
 
1.6%
M 12
 
1.5%
- 10
 
1.3%
4 10
 
1.3%
Other values (26) 90
 
11.3%
Hangul
ValueCountFrequency (%)
앞 156
 
5.3%
마 109
 
3.7%
을 100
 
3.4%
관 91
 
3.1%
회 90
 
3.1%
대 53
 
1.8%
리 48
 
1.6%
구 45
 
1.5%
지 44
 
1.5%
동 43
 
1.5%
Other values (325) 2165
73.5%
None
ValueCountFrequency (%)
㈜ 8
100.0%
Geometric Shapes
ValueCountFrequency (%)
○ 1
100.0%

수리형식
Categorical

IMBALANCE 

Distinct4
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
지상
375 
비상소화장치
 
29
일반지하
 
19
저수조
 
10

Length

Max length6
Median length2
Mean length2.3787529
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
지상 375
86.6%
비상소화장치 29
 
6.7%
일반지하 19
 
4.4%
저수조 10
 
2.3%

Length

2023-12-11T08:44:42.603009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:44:42.737341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
지상 375
86.6%
비상소화장치 29
 
6.7%
일반지하 19
 
4.4%
저수조 10
 
2.3%

Interactions

2023-12-11T08:44:38.194512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T08:44:42.832883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번센터명지역대수리형식
순번1.0000.9690.3110.508
센터명0.9691.000NaN0.141
지역대0.311NaN1.0000.133
수리형식0.5080.1410.1331.000
2023-12-11T08:44:42.981589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
센터명지역대수리형식
센터명1.0001.0000.056
지역대1.0001.0000.219
수리형식0.0560.2191.000
2023-12-11T08:44:43.125705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번센터명지역대수리형식
순번1.0000.9040.3750.326
센터명0.9041.0001.0000.056
지역대0.3751.0001.0000.219
수리형식0.3260.0560.2191.000

Missing values

2023-12-11T08:44:38.304656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T08:44:38.464155image/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.
2023-12-11T08:44:38.636011image/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

순번관서명센터명지역대관리번호수리위치주변 대상물수리형식
01창녕창녕대합1-A-01창녕군 대합면 십이리길 29십이리길 29 건물 앞 도로변지상
12창녕창녕대합1-A-02창녕군 대합면 둔지길 31둔지교 입구지상
23창녕창녕<NA>1-A-03창녕군 창녕읍 남창녕로 52창녕도서관 앞지상
34창녕창녕<NA>1-A-04창녕군 창녕읍 명덕로 121송현사거리 고암방향 신호점멸기 앞지상
45창녕창녕<NA>1-A-05창녕군 창녕읍 종로 20미미분식 앞 전봇대지상
56창녕창녕<NA>1-A-06창녕군 창녕읍 창녕공단길 41동호산업 앞지상
67창녕창녕<NA>1-A-07창녕군 창녕읍 남창녕로 26창녕등기소 앞지상
78창녕창녕<NA>1-A-08창녕군 창녕읍 화왕산1로 35창녕씽크공장 앞지상
89창녕창녕<NA>1-A-09창녕군 창녕읍 화왕산1로 21올레 KT 창녕점지상
910창녕창녕<NA>1-A-10창녕군 창녕읍 명덕로 36아디다스 건물 앞지상
순번관서명센터명지역대관리번호수리위치주변 대상물수리형식
423424창녕영산<NA>4-B-7창녕군 영산면 영산중앙길 54만물슈퍼일반지하
424425창녕영산<NA>4-B-9창녕군 영산면 골룡길 26-1약수빌라일반지하
425426창녕영산<NA>4-B-10창녕군 영산면 골룡길 3밀양목공소일반지하
426427창녕영산<NA>4-C-1창녕군 영산면 시장길 5영산의소대 사무실저수조
427428창녕영산<NA>4-C-2창녕군 계성면 영산계성로 256계성면사무소저수조
428429창녕남지<NA>4-C-3창녕군 도천면 도천중앙로 51농협도천지점저수조
429430창녕영산<NA>4-D-1창녕군 영산면 사라1길 66-28죽사2구 마을회관비상소화장치
430431창녕영산<NA>4-D-2창녕군 계성면 북암길 147삼성암비상소화장치
431432창녕영산<NA>4-D-3창녕군 영산면 구계로 229집앞비상소화장치
432433창녕영산<NA>4-D-4창녕군 도천면 마을안길 1일리마을회관비상소화장치