Overview

Dataset statistics

Number of variables9
Number of observations97
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.3 KiB
Average record size in memory77.4 B

Variable types

Numeric3
Categorical3
Text2
DateTime1

Dataset

Description부산광역시 서구 제설함 배치 정보(행정동명, 설치장소, 소재지도로), 수량, 관리기관, 담당자 연락처에 대한 데이터
Author부산광역시 서구
URLhttps://www.data.go.kr/data/15035253/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
설치장소 has unique valuesUnique

Reproduction

Analysis started2023-12-12 19:14:49.630478
Analysis finished2023-12-12 19:14:51.334639
Duration1.7 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

관리번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct97
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49
Minimum1
Maximum97
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1005.0 B
2023-12-13T04:14:51.440940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5.8
Q125
median49
Q373
95-th percentile92.2
Maximum97
Range96
Interquartile range (IQR)48

Descriptive statistics

Standard deviation28.145456
Coefficient of variation (CV)0.57439705
Kurtosis-1.2
Mean49
Median Absolute Deviation (MAD)24
Skewness0
Sum4753
Variance792.16667
MonotonicityStrictly increasing
2023-12-13T04:14:51.612269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.0%
74 1
 
1.0%
72 1
 
1.0%
71 1
 
1.0%
70 1
 
1.0%
69 1
 
1.0%
68 1
 
1.0%
67 1
 
1.0%
66 1
 
1.0%
65 1
 
1.0%
Other values (87) 87
89.7%
ValueCountFrequency (%)
1 1
1.0%
2 1
1.0%
3 1
1.0%
4 1
1.0%
5 1
1.0%
6 1
1.0%
7 1
1.0%
8 1
1.0%
9 1
1.0%
10 1
1.0%
ValueCountFrequency (%)
97 1
1.0%
96 1
1.0%
95 1
1.0%
94 1
1.0%
93 1
1.0%
92 1
1.0%
91 1
1.0%
90 1
1.0%
89 1
1.0%
88 1
1.0%

행정동명
Categorical

HIGH CORRELATION 

Distinct13
Distinct (%)13.4%
Missing0
Missing (%)0.0%
Memory size908.0 B
암남동
23 
서대신4동
19 
아미동
13 
남부민2동
8 
동대신3동
7 
Other values (8)
27 

Length

Max length5
Median length5
Mean length4.1134021
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
암남동 23
23.7%
서대신4동 19
19.6%
아미동 13
13.4%
남부민2동 8
 
8.2%
동대신3동 7
 
7.2%
서대신1동 7
 
7.2%
동대신1동 6
 
6.2%
서대신3동 3
 
3.1%
초장동 3
 
3.1%
동대신2동 2
 
2.1%
Other values (3) 6
 
6.2%

Length

2023-12-13T04:14:51.806032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
암남동 23
23.7%
서대신4동 19
19.6%
아미동 13
13.4%
남부민2동 8
 
8.2%
동대신3동 7
 
7.2%
서대신1동 7
 
7.2%
동대신1동 6
 
6.2%
서대신3동 3
 
3.1%
초장동 3
 
3.1%
동대신2동 2
 
2.1%
Other values (3) 6
 
6.2%

설치장소
Text

UNIQUE 

Distinct97
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size908.0 B
2023-12-13T04:14:52.131505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length21
Mean length13.886598
Min length4

Characters and Unicode

Total characters1347
Distinct characters237
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

Unique97 ?
Unique (%)100.0%

Sample

1st row동대맨션 한양옷집 앞
2nd row보동길 178 맞은편
3rd row보동길 162-1 앞
4th row로또판매센터 맞은편
5th row옹벽 앞
ValueCountFrequency (%)
앞 44
 
13.7%
입구 17
 
5.3%
맞은편 17
 
5.3%
옆 14
 
4.4%
밑 9
 
2.8%
게시판 5
 
1.6%
횡단보도 4
 
1.2%
화단 4
 
1.2%
구덕기상관측소 4
 
1.2%
암남공원 3
 
0.9%
Other values (175) 200
62.3%
2023-12-13T04:14:52.605923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
224
 
16.6%
앞 46
 
3.4%
로 33
 
2.4%
구 32
 
2.4%
1 24
 
1.8%
입 23
 
1.7%
장 20
 
1.5%
편 19
 
1.4%
대 18
 
1.3%
은 18
 
1.3%
Other values (227) 890
66.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 977
72.5%
Space Separator 224
 
16.6%
Decimal Number 90
 
6.7%
Close Punctuation 15
 
1.1%
Open Punctuation 15
 
1.1%
Dash Punctuation 14
 
1.0%
Uppercase Letter 9
 
0.7%
Math Symbol 1
 
0.1%
Lowercase Letter 1
 
0.1%
Other Symbol 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
앞 46
 
4.7%
로 33
 
3.4%
구 32
 
3.3%
입 23
 
2.4%
장 20
 
2.0%
편 19
 
1.9%
대 18
 
1.8%
은 18
 
1.8%
맞 18
 
1.8%
동 16
 
1.6%
Other values (202) 734
75.1%
Decimal Number
ValueCountFrequency (%)
1 24
26.7%
2 14
15.6%
8 10
11.1%
3 9
 
10.0%
5 7
 
7.8%
9 7
 
7.8%
0 6
 
6.7%
7 5
 
5.6%
4 4
 
4.4%
6 4
 
4.4%
Uppercase Letter
ValueCountFrequency (%)
A 2
22.2%
U 1
11.1%
C 1
11.1%
T 1
11.1%
S 1
11.1%
G 1
11.1%
B 1
11.1%
P 1
11.1%
Space Separator
ValueCountFrequency (%)
224
100.0%
Close Punctuation
ValueCountFrequency (%)
) 15
100.0%
Open Punctuation
ValueCountFrequency (%)
( 15
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 14
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%
Lowercase Letter
ValueCountFrequency (%)
m 1
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 978
72.6%
Common 359
 
26.7%
Latin 10
 
0.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
앞 46
 
4.7%
로 33
 
3.4%
구 32
 
3.3%
입 23
 
2.4%
장 20
 
2.0%
편 19
 
1.9%
대 18
 
1.8%
은 18
 
1.8%
맞 18
 
1.8%
동 16
 
1.6%
Other values (203) 735
75.2%
Common
ValueCountFrequency (%)
224
62.4%
1 24
 
6.7%
) 15
 
4.2%
( 15
 
4.2%
2 14
 
3.9%
- 14
 
3.9%
8 10
 
2.8%
3 9
 
2.5%
5 7
 
1.9%
9 7
 
1.9%
Other values (5) 20
 
5.6%
Latin
ValueCountFrequency (%)
A 2
20.0%
U 1
10.0%
C 1
10.0%
T 1
10.0%
S 1
10.0%
G 1
10.0%
m 1
10.0%
B 1
10.0%
P 1
10.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 977
72.5%
ASCII 369
 
27.4%
None 1
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
224
60.7%
1 24
 
6.5%
) 15
 
4.1%
( 15
 
4.1%
2 14
 
3.8%
- 14
 
3.8%
8 10
 
2.7%
3 9
 
2.4%
5 7
 
1.9%
9 7
 
1.9%
Other values (14) 30
 
8.1%
Hangul
ValueCountFrequency (%)
앞 46
 
4.7%
로 33
 
3.4%
구 32
 
3.3%
입 23
 
2.4%
장 20
 
2.0%
편 19
 
1.9%
대 18
 
1.8%
은 18
 
1.8%
맞 18
 
1.8%
동 16
 
1.6%
Other values (202) 734
75.1%
None
ValueCountFrequency (%)
㈜ 1
100.0%

소재지도로명주소
Text

Distinct84
Distinct (%)86.6%
Missing0
Missing (%)0.0%
Memory size908.0 B
2023-12-13T04:14:52.917712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length23
Mean length16.659794
Min length13

Characters and Unicode

Total characters1616
Distinct characters67
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

Unique76 ?
Unique (%)78.4%

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 236
ValueCountFrequency (%)
부산광역시 97
25.9%
서구 97
25.9%
해돋이로 11
 
2.9%
꽃마을로 8
 
2.1%
감천로 6
 
1.6%
꽃마을로163번길 5
 
1.3%
충무대로 5
 
1.3%
까치고개로 5
 
1.3%
암남공원로 5
 
1.3%
보동길 4
 
1.1%
Other values (102) 131
35.0%
2023-12-13T04:14:53.314139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
277
17.1%
산 104
 
6.4%
시 101
 
6.2%
부 99
 
6.1%
광 99
 
6.1%
구 98
 
6.1%
역 97
 
6.0%
서 97
 
6.0%
로 93
 
5.8%
1 63
 
3.9%
Other values (57) 488
30.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1072
66.3%
Space Separator 277
 
17.1%
Decimal Number 252
 
15.6%
Dash Punctuation 15
 
0.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 104
9.7%
시 101
9.4%
부 99
 
9.2%
광 99
 
9.2%
구 98
 
9.1%
역 97
 
9.0%
서 97
 
9.0%
로 93
 
8.7%
마 17
 
1.6%
길 17
 
1.6%
Other values (45) 250
23.3%
Decimal Number
ValueCountFrequency (%)
1 63
25.0%
2 35
13.9%
3 27
10.7%
7 24
 
9.5%
8 21
 
8.3%
6 20
 
7.9%
5 18
 
7.1%
0 15
 
6.0%
4 15
 
6.0%
9 14
 
5.6%
Space Separator
ValueCountFrequency (%)
277
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 15
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1072
66.3%
Common 544
33.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 104
9.7%
시 101
9.4%
부 99
 
9.2%
광 99
 
9.2%
구 98
 
9.1%
역 97
 
9.0%
서 97
 
9.0%
로 93
 
8.7%
마 17
 
1.6%
길 17
 
1.6%
Other values (45) 250
23.3%
Common
ValueCountFrequency (%)
277
50.9%
1 63
 
11.6%
2 35
 
6.4%
3 27
 
5.0%
7 24
 
4.4%
8 21
 
3.9%
6 20
 
3.7%
5 18
 
3.3%
0 15
 
2.8%
4 15
 
2.8%
Other values (2) 29
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1072
66.3%
ASCII 544
33.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
277
50.9%
1 63
 
11.6%
2 35
 
6.4%
3 27
 
5.0%
7 24
 
4.4%
8 21
 
3.9%
6 20
 
3.7%
5 18
 
3.3%
0 15
 
2.8%
4 15
 
2.8%
Other values (2) 29
 
5.3%
Hangul
ValueCountFrequency (%)
산 104
9.7%
시 101
9.4%
부 99
 
9.2%
광 99
 
9.2%
구 98
 
9.1%
역 97
 
9.0%
서 97
 
9.0%
로 93
 
8.7%
마 17
 
1.6%
길 17
 
1.6%
Other values (45) 250
23.3%

수량
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size908.0 B
1
97 

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 97
100.0%

Length

2023-12-13T04:14:53.436578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:14:53.529981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 97
100.0%

관리기관(부서)
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size908.0 B
서구청(구민안전과)
97 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
서구청(구민안전과) 97
100.0%

Length

2023-12-13T04:14:53.615338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:14:53.703685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서구청(구민안전과 97
100.0%

데이터기준일자
Date

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size908.0 B
Minimum2022-04-13 00:00:00
Maximum2022-04-13 00:00:00
2023-12-13T04:14:53.774446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:14:53.871222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

위도
Real number (ℝ)

HIGH CORRELATION 

Distinct91
Distinct (%)93.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.101407
Minimum35.062505
Maximum35.193908
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1005.0 B
2023-12-13T04:14:54.004006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.062505
5-th percentile35.0629
Q135.083955
median35.10175
Q335.116783
95-th percentile35.127996
Maximum35.193908
Range0.1314031
Interquartile range (IQR)0.0328281

Descriptive statistics

Standard deviation0.021010214
Coefficient of variation (CV)0.00059855759
Kurtosis2.5725811
Mean35.101407
Median Absolute Deviation (MAD)0.0165562
Skewness0.52973378
Sum3404.8365
Variance0.00044142909
MonotonicityNot monotonic
2023-12-13T04:14:54.168834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.0629 2
 
2.1%
35.10175 2
 
2.1%
35.0807796 2
 
2.1%
35.1278372 2
 
2.1%
35.0627028 2
 
2.1%
35.0625049 2
 
2.1%
35.1103299 1
 
1.0%
35.0839546 1
 
1.0%
35.093331 1
 
1.0%
35.090853 1
 
1.0%
Other values (81) 81
83.5%
ValueCountFrequency (%)
35.0625049 2
2.1%
35.0627028 2
2.1%
35.0629 2
2.1%
35.0635 1
1.0%
35.0723995 1
1.0%
35.0756964 1
1.0%
35.0779059 1
1.0%
35.0788688 1
1.0%
35.0789267 1
1.0%
35.0789699 1
1.0%
ValueCountFrequency (%)
35.193908 1
1.0%
35.131002 1
1.0%
35.1298372 1
1.0%
35.1288372 1
1.0%
35.1286303 1
1.0%
35.1278372 2
2.1%
35.126161 1
1.0%
35.1253387 1
1.0%
35.1223372 1
1.0%
35.1219026 1
1.0%

경도
Real number (ℝ)

Distinct89
Distinct (%)91.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean129.01624
Minimum129.00488
Maximum129.0933
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1005.0 B
2023-12-13T04:14:54.329870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum129.00488
5-th percentile129.00797
Q1129.01166
median129.01524
Q3129.0197
95-th percentile129.02386
Maximum129.0933
Range0.0884234
Interquartile range (IQR)0.0080372

Descriptive statistics

Standard deviation0.0094017003
Coefficient of variation (CV)7.2872223 × 10-5
Kurtosis47.229289
Mean129.01624
Median Absolute Deviation (MAD)0.0038804
Skewness5.8289235
Sum12514.575
Variance8.8391969 × 10-5
MonotonicityNot monotonic
2023-12-13T04:14:54.475144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
129.0152358 4
 
4.1%
129.0058161 3
 
3.1%
129.0111538 2
 
2.1%
129.014416 2
 
2.1%
129.0196969 2
 
2.1%
129.0197194 1
 
1.0%
129.0186405 1
 
1.0%
129.0189031 1
 
1.0%
129.022025 1
 
1.0%
129.021671 1
 
1.0%
Other values (79) 79
81.4%
ValueCountFrequency (%)
129.0048766 1
 
1.0%
129.0058161 3
3.1%
129.0068161 1
 
1.0%
129.00826 1
 
1.0%
129.0085938 1
 
1.0%
129.0091847 1
 
1.0%
129.0093067 1
 
1.0%
129.009397 1
 
1.0%
129.0094225 1
 
1.0%
129.0096575 1
 
1.0%
ValueCountFrequency (%)
129.0933 1
1.0%
129.0329 1
1.0%
129.0251 1
1.0%
129.0241526 1
1.0%
129.0241 1
1.0%
129.0238 1
1.0%
129.0232161 1
1.0%
129.022032 1
1.0%
129.022025 1
1.0%
129.021805 1
1.0%

Interactions

2023-12-13T04:14:50.744770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:14:50.108520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:14:50.411028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:14:50.832787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:14:50.213465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:14:50.525178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:14:50.940809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:14:50.324811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:14:50.644847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T04:14:54.565613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관리번호행정동명설치장소소재지도로명주소위도경도
관리번호1.0000.9071.0000.9590.8090.781
행정동명0.9071.0001.0001.0000.8940.697
설치장소1.0001.0001.0001.0001.0001.000
소재지도로명주소0.9591.0001.0001.0000.9771.000
위도0.8090.8941.0000.9771.0000.370
경도0.7810.6971.0001.0000.3701.000
2023-12-13T04:14:54.687910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관리번호위도경도행정동명
관리번호1.000-0.8060.1560.676
위도-0.8061.000-0.4080.671
경도0.156-0.4081.0000.451
행정동명0.6760.6710.4511.000

Missing values

2023-12-13T04:14:51.080197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T04:14:51.269122image/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 781서구청(구민안전과)2022-04-1335.11033129.019719
12동대신1동보동길 178 맞은편부산광역시 서구 보동길 1781서구청(구민안전과)2022-04-1335.10999129.0329
23동대신1동보동길 162-1 앞부산광역시 서구 보동길 162-11서구청(구민안전과)2022-04-1335.10944129.0238
34동대신1동로또판매센터 맞은편부산광역시 서구 대영로 94-11서구청(구민안전과)2022-04-1335.11122129.0214
45동대신1동옹벽 앞부산광역시 서구 보동길 2361서구청(구민안전과)2022-04-1335.11247129.0251
56동대신1동아리랑빌라 앞부산광역시 서구 보동길 1911서구청(구민안전과)2022-04-1335.11072129.0241
67동대신2동전원빌라 맞은편 북산경로당 앞부산광역시 서구 중앙공원로 241서구청(구민안전과)2022-04-1335.115978129.024153
78동대신2동버드나무슈퍼 맞은편부산광역시 서구 보수대로 201번길 411서구청(구민안전과)2022-04-1335.11306129.0201
89동대신3동망양로 138 깔끄미세탁 맞은편부산광역시 서구 망양로 1381서구청(구민안전과)2022-04-1335.116224129.02149
910동대신3동중앙빌라 앞 화단부산광역시 서구 망양로 1271서구청(구민안전과)2022-04-1335.117069129.020605
관리번호행정동명설치장소소재지도로명주소수량관리기관(부서)데이터기준일자위도경도
8788암남동곡각지 탑스빌 앞(충무대로 56)부산광역시 서구 충무대로 561서구청(구민안전과)2022-04-1335.077906129.01851
8889암남동약수탕 옆 횡단보도 앞부산광역시 서구 감천로 2841서구청(구민안전과)2022-04-1335.079408129.017541
8990암남동진성석유 옆 암남동 곡각지 화단부산광역시 서구 충무대로 671서구청(구민안전과)2022-04-1335.07897129.019966
9091암남동수국마을 맞은편 전신주 앞부산광역시 서구 천해로45번길 71서구청(구민안전과)2022-04-1335.084127129.014578
9192암남동암남동주민센터 앞 버스정류소 밑부산광역시 서구 충무대로 851서구청(구민안전과)2022-04-1335.079973129.02136
9293암남동알로이시오 기념관 옆부산광역시 서구 감천로 2291서구청(구민안전과)2022-04-1335.082575129.013138
9394암남동암남공원 진입로 시계탑 옆부산광역시 서구 암남공원로1서구청(구민안전과)2022-04-1335.062703129.019697
9495암남동예비군 교육장 중간(가로등 81-9)부산광역시 서구 장군산로1서구청(구민안전과)2022-04-1335.0629129.015236
9596암남동송도태경시티빌 앞부산광역시 서구 충무대로 761서구청(구민안전과)2022-04-1335.078927129.02082
9697암남동삼경빌라 앞부산광역시 서구 감천로 2771서구청(구민안전과)2022-04-1335.080032129.018201