Overview

Dataset statistics

Number of variables9
Number of observations123
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.3 KiB
Average record size in memory77.1 B

Variable types

Numeric3
Categorical3
Text2
DateTime1

Dataset

Description부산광역시 서구에 배치된 모래주머니 위치에 대한 정보(관리번호, 행정동명, 설치장소, 소재지 도로명주소, 수량, 관리기관, 데이터기준일자, 위도, 경도)
Author부산광역시 서구
URLhttps://www.data.go.kr/data/15113387/fileData.do

Alerts

수량 has constant value ""Constant
관리기관(부서) has constant value ""Constant
데이터기준일자 has constant value ""Constant
관리번호 is highly overall correlated with 위도 and 1 other fieldsHigh correlation
위도 is highly overall correlated with 관리번호 and 1 other fieldsHigh correlation
행정동명 is highly overall correlated with 관리번호 and 1 other fieldsHigh correlation
관리번호 has unique valuesUnique

Reproduction

Analysis started2024-04-21 02:19:47.402450
Analysis finished2024-04-21 02:19:50.276396
Duration2.87 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

관리번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct123
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62
Minimum1
Maximum123
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-04-21T11:19:50.365730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile7.1
Q131.5
median62
Q392.5
95-th percentile116.9
Maximum123
Range122
Interquartile range (IQR)61

Descriptive statistics

Standard deviation35.651087
Coefficient of variation (CV)0.57501753
Kurtosis-1.2
Mean62
Median Absolute Deviation (MAD)31
Skewness0
Sum7626
Variance1271
MonotonicityStrictly increasing
2024-04-21T11:19:50.507914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.8%
79 1
 
0.8%
92 1
 
0.8%
91 1
 
0.8%
90 1
 
0.8%
89 1
 
0.8%
88 1
 
0.8%
87 1
 
0.8%
86 1
 
0.8%
85 1
 
0.8%
Other values (113) 113
91.9%
ValueCountFrequency (%)
1 1
0.8%
2 1
0.8%
3 1
0.8%
4 1
0.8%
5 1
0.8%
6 1
0.8%
7 1
0.8%
8 1
0.8%
9 1
0.8%
10 1
0.8%
ValueCountFrequency (%)
123 1
0.8%
122 1
0.8%
121 1
0.8%
120 1
0.8%
119 1
0.8%
118 1
0.8%
117 1
0.8%
116 1
0.8%
115 1
0.8%
114 1
0.8%

행정동명
Categorical

HIGH CORRELATION 

Distinct13
Distinct (%)10.6%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
암남동
30 
아미동
17 
서대신4동
15 
동대신3동
11 
남부민2동
10 
Other values (8)
40 

Length

Max length5
Median length5
Mean length4.0243902
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row동대신1동
2nd row동대신1동
3rd row동대신1동
4th row동대신1동
5th row동대신1동

Common Values

ValueCountFrequency (%)
암남동 30
24.4%
아미동 17
13.8%
서대신4동 15
12.2%
동대신3동 11
 
8.9%
남부민2동 10
 
8.1%
서대신3동 8
 
6.5%
서대신1동 7
 
5.7%
동대신1동 5
 
4.1%
초장동 5
 
4.1%
동대신2동 4
 
3.3%
Other values (3) 11
 
8.9%

Length

2024-04-21T11:19:50.668006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
암남동 30
24.4%
아미동 17
13.8%
서대신4동 15
12.2%
동대신3동 11
 
8.9%
남부민2동 10
 
8.1%
서대신3동 8
 
6.5%
서대신1동 7
 
5.7%
동대신1동 5
 
4.1%
초장동 5
 
4.1%
동대신2동 4
 
3.3%
Other values (3) 11
 
8.9%

설치장소
Text

Distinct122
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2024-04-21T11:19:50.952165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length22
Mean length13.886179
Min length5

Characters and Unicode

Total characters1708
Distinct characters253
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique121 ?
Unique (%)98.4%

Sample

1st row동대맨션 한양옷집 앞
2nd row보동길 178 맞은편
3rd row보동길 162-1 앞
4th row로또판매센터 맞은편
5th row아리랑빌라 앞
ValueCountFrequency (%)
앞 49
 
11.9%
맞은편 20
 
4.9%
옆 18
 
4.4%
입구 16
 
3.9%
밑 11
 
2.7%
화단 5
 
1.2%
게시판 5
 
1.2%
횡단보도 4
 
1.0%
아래 4
 
1.0%
구덕기상관측소 4
 
1.0%
Other values (227) 275
66.9%
2024-04-21T11:19:51.355601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
288
 
16.9%
앞 51
 
3.0%
로 43
 
2.5%
1 34
 
2.0%
구 33
 
1.9%
편 23
 
1.3%
은 23
 
1.3%
동 23
 
1.3%
입 22
 
1.3%
맞 22
 
1.3%
Other values (243) 1146
67.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1212
71.0%
Space Separator 288
 
16.9%
Decimal Number 137
 
8.0%
Uppercase Letter 19
 
1.1%
Dash Punctuation 18
 
1.1%
Open Punctuation 15
 
0.9%
Close Punctuation 15
 
0.9%
Math Symbol 2
 
0.1%
Other Symbol 1
 
0.1%
Lowercase Letter 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
앞 51
 
4.2%
로 43
 
3.5%
구 33
 
2.7%
편 23
 
1.9%
은 23
 
1.9%
동 23
 
1.9%
입 22
 
1.8%
맞 22
 
1.8%
대 22
 
1.8%
장 21
 
1.7%
Other values (218) 929
76.7%
Decimal Number
ValueCountFrequency (%)
1 34
24.8%
2 22
16.1%
3 17
12.4%
8 14
10.2%
9 12
 
8.8%
0 12
 
8.8%
5 10
 
7.3%
7 6
 
4.4%
6 5
 
3.6%
4 5
 
3.6%
Uppercase Letter
ValueCountFrequency (%)
A 5
26.3%
P 4
21.1%
T 4
21.1%
C 2
 
10.5%
U 1
 
5.3%
G 1
 
5.3%
S 1
 
5.3%
B 1
 
5.3%
Space Separator
ValueCountFrequency (%)
288
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 18
100.0%
Open Punctuation
ValueCountFrequency (%)
( 15
100.0%
Close Punctuation
ValueCountFrequency (%)
) 15
100.0%
Math Symbol
ValueCountFrequency (%)
~ 2
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 1
100.0%
Lowercase Letter
ValueCountFrequency (%)
m 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1213
71.0%
Common 475
 
27.8%
Latin 20
 
1.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
앞 51
 
4.2%
로 43
 
3.5%
구 33
 
2.7%
편 23
 
1.9%
은 23
 
1.9%
동 23
 
1.9%
입 22
 
1.8%
맞 22
 
1.8%
대 22
 
1.8%
장 21
 
1.7%
Other values (219) 930
76.7%
Common
ValueCountFrequency (%)
288
60.6%
1 34
 
7.2%
2 22
 
4.6%
- 18
 
3.8%
3 17
 
3.6%
( 15
 
3.2%
) 15
 
3.2%
8 14
 
2.9%
9 12
 
2.5%
0 12
 
2.5%
Other values (5) 28
 
5.9%
Latin
ValueCountFrequency (%)
A 5
25.0%
P 4
20.0%
T 4
20.0%
C 2
 
10.0%
U 1
 
5.0%
G 1
 
5.0%
S 1
 
5.0%
B 1
 
5.0%
m 1
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1212
71.0%
ASCII 495
29.0%
None 1
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
288
58.2%
1 34
 
6.9%
2 22
 
4.4%
- 18
 
3.6%
3 17
 
3.4%
( 15
 
3.0%
) 15
 
3.0%
8 14
 
2.8%
9 12
 
2.4%
0 12
 
2.4%
Other values (14) 48
 
9.7%
Hangul
ValueCountFrequency (%)
앞 51
 
4.2%
로 43
 
3.5%
구 33
 
2.7%
편 23
 
1.9%
은 23
 
1.9%
동 23
 
1.9%
입 22
 
1.8%
맞 22
 
1.8%
대 22
 
1.8%
장 21
 
1.7%
Other values (218) 929
76.7%
None
ValueCountFrequency (%)
㈜ 1
100.0%

소재지도로명주소
Text

Distinct107
Distinct (%)87.0%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2024-04-21T11:19:51.655958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length22
Mean length17.186992
Min length13

Characters and Unicode

Total characters2114
Distinct characters69
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

Unique96 ?
Unique (%)78.0%

Sample

1st row부산광역시 서구 대영로 78
2nd row부산광역시 서구 보동길 178
3rd row부산광역시 서구 보동길 162-1
4th row부산광역시 서구 대영로 94-1
5th row부산광역시 서구 보동길 191
ValueCountFrequency (%)
부산광역시 123
25.5%
서구 123
25.5%
해돋이로 13
 
2.7%
감천로 7
 
1.4%
꽃마을로 7
 
1.4%
까치고개로 6
 
1.2%
망양로 6
 
1.2%
암남공원로 6
 
1.2%
충무대로 5
 
1.0%
대티로 5
 
1.0%
Other values (136) 182
37.7%
2024-04-21T11:19:52.087168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
360
17.0%
산 129
 
6.1%
부 127
 
6.0%
시 127
 
6.0%
구 125
 
5.9%
광 124
 
5.9%
역 123
 
5.8%
서 123
 
5.8%
로 114
 
5.4%
1 89
 
4.2%
Other values (59) 673
31.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1374
65.0%
Space Separator 360
 
17.0%
Decimal Number 359
 
17.0%
Dash Punctuation 21
 
1.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 129
 
9.4%
부 127
 
9.2%
시 127
 
9.2%
구 125
 
9.1%
광 124
 
9.0%
역 123
 
9.0%
서 123
 
9.0%
로 114
 
8.3%
길 31
 
2.3%
번 27
 
2.0%
Other values (47) 324
23.6%
Decimal Number
ValueCountFrequency (%)
1 89
24.8%
2 51
14.2%
3 43
12.0%
8 30
 
8.4%
6 30
 
8.4%
7 26
 
7.2%
5 25
 
7.0%
9 25
 
7.0%
4 20
 
5.6%
0 20
 
5.6%
Space Separator
ValueCountFrequency (%)
360
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 21
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1374
65.0%
Common 740
35.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 129
 
9.4%
부 127
 
9.2%
시 127
 
9.2%
구 125
 
9.1%
광 124
 
9.0%
역 123
 
9.0%
서 123
 
9.0%
로 114
 
8.3%
길 31
 
2.3%
번 27
 
2.0%
Other values (47) 324
23.6%
Common
ValueCountFrequency (%)
360
48.6%
1 89
 
12.0%
2 51
 
6.9%
3 43
 
5.8%
8 30
 
4.1%
6 30
 
4.1%
7 26
 
3.5%
5 25
 
3.4%
9 25
 
3.4%
- 21
 
2.8%
Other values (2) 40
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1374
65.0%
ASCII 740
35.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
360
48.6%
1 89
 
12.0%
2 51
 
6.9%
3 43
 
5.8%
8 30
 
4.1%
6 30
 
4.1%
7 26
 
3.5%
5 25
 
3.4%
9 25
 
3.4%
- 21
 
2.8%
Other values (2) 40
 
5.4%
Hangul
ValueCountFrequency (%)
산 129
 
9.4%
부 127
 
9.2%
시 127
 
9.2%
구 125
 
9.1%
광 124
 
9.0%
역 123
 
9.0%
서 123
 
9.0%
로 114
 
8.3%
길 31
 
2.3%
번 27
 
2.0%
Other values (47) 324
23.6%

수량
Categorical

CONSTANT 

Distinct1
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
1
123 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1 123
100.0%

Length

2024-04-21T11:19:52.221282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T11:19:52.342631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 123
100.0%

관리기관(부서)
Categorical

CONSTANT 

Distinct1
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
부산광역시 서구청(구민안전과)
123 

Length

Max length16
Median length16
Mean length16
Min length16

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row부산광역시 서구청(구민안전과)
2nd row부산광역시 서구청(구민안전과)
3rd row부산광역시 서구청(구민안전과)
4th row부산광역시 서구청(구민안전과)
5th row부산광역시 서구청(구민안전과)

Common Values

ValueCountFrequency (%)
부산광역시 서구청(구민안전과) 123
100.0%

Length

2024-04-21T11:19:52.448160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T11:19:52.567392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
부산광역시 123
50.0%
서구청(구민안전과 123
50.0%

데이터기준일자
Date

CONSTANT 

Distinct1
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
Minimum2024-04-15 00:00:00
Maximum2024-04-15 00:00:00
2024-04-21T11:19:52.654097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:19:52.776007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

위도
Real number (ℝ)

HIGH CORRELATION 

Distinct118
Distinct (%)95.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.098172
Minimum35.062505
Maximum35.128837
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-04-21T11:19:52.943830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.062505
5-th percentile35.06296
Q135.082633
median35.0993
Q335.116101
95-th percentile35.122293
Maximum35.128837
Range0.0663323
Interquartile range (IQR)0.03346755

Descriptive statistics

Standard deviation0.018401713
Coefficient of variation (CV)0.00052429264
Kurtosis-1.0473025
Mean35.098172
Median Absolute Deviation (MAD)0.0167249
Skewness-0.20568447
Sum4317.0752
Variance0.00033862305
MonotonicityNot monotonic
2024-04-21T11:19:53.185457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.0807796 2
 
1.6%
35.0627028 2
 
1.6%
35.0625049 2
 
1.6%
35.0629 2
 
1.6%
35.1278372 2
 
1.6%
35.0842811 1
 
0.8%
35.0824061 1
 
0.8%
35.083093 1
 
0.8%
35.0855421 1
 
0.8%
35.0976903 1
 
0.8%
Other values (108) 108
87.8%
ValueCountFrequency (%)
35.0625049 2
1.6%
35.0627028 2
1.6%
35.062759 1
0.8%
35.0629 2
1.6%
35.0635 1
0.8%
35.071 1
0.8%
35.071676 1
0.8%
35.0723995 1
0.8%
35.0756964 1
0.8%
35.0779059 1
0.8%
ValueCountFrequency (%)
35.1288372 1
0.8%
35.1286303 1
0.8%
35.1278372 2
1.6%
35.126161 1
0.8%
35.1253387 1
0.8%
35.1223372 1
0.8%
35.1218906 1
0.8%
35.1218706 1
0.8%
35.1217706 1
0.8%
35.121715 1
0.8%

경도
Real number (ℝ)

Distinct115
Distinct (%)93.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean129.01673
Minimum129.00488
Maximum129.0933
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-04-21T11:19:53.367547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum129.00488
5-th percentile129.00901
Q1129.01219
median129.01595
Q3129.01984
95-th percentile129.02379
Maximum129.0933
Range0.0884234
Interquartile range (IQR)0.007646

Descriptive statistics

Standard deviation0.0085693757
Coefficient of variation (CV)6.6420655 × 10-5
Kurtosis52.167648
Mean129.01673
Median Absolute Deviation (MAD)0.003831
Skewness5.8981839
Sum15869.058
Variance7.3434199 × 10-5
MonotonicityNot monotonic
2024-04-21T11:19:53.519952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
129.0152358 4
 
3.3%
129.0058161 3
 
2.4%
129.018635 2
 
1.6%
129.014416 2
 
1.6%
129.0196969 2
 
1.6%
129.0197194 1
 
0.8%
129.01448 1
 
0.8%
129.019752 1
 
0.8%
129.0232161 1
 
0.8%
129.0174891 1
 
0.8%
Other values (105) 105
85.4%
ValueCountFrequency (%)
129.0048766 1
 
0.8%
129.0058161 3
2.4%
129.0068161 1
 
0.8%
129.0085938 1
 
0.8%
129.009 1
 
0.8%
129.0091 1
 
0.8%
129.0092 1
 
0.8%
129.0093067 1
 
0.8%
129.009397 1
 
0.8%
129.0096575 1
 
0.8%
ValueCountFrequency (%)
129.0933 1
0.8%
129.0329 1
0.8%
129.0267 1
0.8%
129.0241526 1
0.8%
129.0241 1
0.8%
129.024 1
0.8%
129.0238 1
0.8%
129.023656 1
0.8%
129.0235 1
0.8%
129.0232161 1
0.8%

Interactions

2024-04-21T11:19:49.733784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:19:49.148203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:19:49.458447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:19:49.835046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:19:49.288177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:19:49.549661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:19:49.932242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:19:49.380984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:19:49.646536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-21T11:19:53.621285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관리번호행정동명위도경도
관리번호1.0000.9230.9140.721
행정동명0.9231.0000.8170.710
위도0.9140.8171.0000.603
경도0.7210.7100.6031.000
2024-04-21T11:19:53.728048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관리번호위도경도행정동명
관리번호1.000-0.8650.1080.719
위도-0.8651.000-0.3370.511
경도0.108-0.3371.0000.470
행정동명0.7190.5110.4701.000

Missing values

2024-04-21T11:19:50.045938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-21T11:19:50.213819image/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

관리번호행정동명설치장소소재지도로명주소수량관리기관(부서)데이터기준일자위도경도
01동대신1동동대맨션 한양옷집 앞부산광역시 서구 대영로 781부산광역시 서구청(구민안전과)2024-04-1535.11033129.019719
12동대신1동보동길 178 맞은편부산광역시 서구 보동길 1781부산광역시 서구청(구민안전과)2024-04-1535.10999129.0329
23동대신1동보동길 162-1 앞부산광역시 서구 보동길 162-11부산광역시 서구청(구민안전과)2024-04-1535.10944129.0238
34동대신1동로또판매센터 맞은편부산광역시 서구 대영로 94-11부산광역시 서구청(구민안전과)2024-04-1535.11122129.0214
45동대신1동아리랑빌라 앞부산광역시 서구 보동길 1911부산광역시 서구청(구민안전과)2024-04-1535.11072129.0241
56동대신2동전원빌라 맞은편 북산경로당 앞부산광역시 서구 중앙공원로 241부산광역시 서구청(구민안전과)2024-04-1535.115978129.024153
67동대신2동신익빌라 맞은편부산광역시 서구 대영로 112번길 401부산광역시 서구청(구민안전과)2024-04-1535.112217129.023656
78동대신2동버드나무슈퍼 앞부산광역시 서구 보동길 289-61부산광역시 서구청(구민안전과)2024-04-1535.11415129.0229
89동대신2동대청맨션 1C 동 앞부산광역시 서구 망양로193번길 101부산광역시 서구청(구민안전과)2024-04-1535.11386129.0267
910동대신3동깔끄미세탁 맞은편부산광역시 서구 망양로 1381부산광역시 서구청(구민안전과)2024-04-1535.116224129.02149
관리번호행정동명설치장소소재지도로명주소수량관리기관(부서)데이터기준일자위도경도
113114암남동예비군 교육장 중간(가로등 81-9)부산광역시 서구 장군산로1부산광역시 서구청(구민안전과)2024-04-1535.071676129.015249
114115암남동송도태경시티빌 앞부산광역시 서구 충무대로 761부산광역시 서구청(구민안전과)2024-04-1535.079055129.020744
115116암남동삼경빌라 앞부산광역시 서구 감천로 2771부산광역시 서구청(구민안전과)2024-04-1535.080015129.018194
116117암남동암남동주민센터 앞 버스정류장부산광역시 서구 충무대로831부산광역시 서구청(구민안전과)2024-04-1535.079716129.021204
117118암남동고신대병원 교직원주차장 앞부산광역시 서구 감천로 2621부산광역시 서구청(구민안전과)2024-04-1535.080417129.013737
118119암남동암남로78번길 31 사거리 반사경 밑부산광역시 서구 암남로78번길 311부산광역시 서구청(구민안전과)2024-04-1535.078493129.012415
119120암남동삼정하이빌 맞은편부산광역시 서구 암남로78번길 461부산광역시 서구청(구민안전과)2024-04-1535.078985129.012066
120121암남동암남로58번길 20-1 맞은편 전신주 아래부산광역시 서구 암남로58번길 20-11부산광역시 서구청(구민안전과)2024-04-1535.078656129.012782
121122암남동충무대로83번길 59 골목길 모퉁이부산광역시 서구 충무대로83번길 591부산광역시 서구청(구민안전과)2024-04-1535.082421129.022908
122123암남동현대아파트 앞 도로부산광역시 서구 암남공원로 721부산광역시 서구청(구민안전과)2024-04-1535.071129.0171