Overview

Dataset statistics

Number of variables13
Number of observations614
Missing cells1178
Missing cells (%)14.8%
Duplicate rows2
Duplicate rows (%)0.3%
Total size in memory65.5 KiB
Average record size in memory109.2 B

Variable types

Categorical6
Text2
Unsupported1
DateTime2
Numeric2

Dataset

Description파일 다운로드
Author도봉구
URLhttps://data.seoul.go.kr/dataList/OA-13463/F/1/datasetView.do

Alerts

관리기관명 has constant value ""Constant
카메라대수 has constant value ""Constant
보관일수 has constant value ""Constant
관리기관전화번호 has constant value ""Constant
데이터기준일자 has constant value ""Constant
Dataset has 2 (0.3%) duplicate rowsDuplicates
촬영방면정보 is highly imbalanced (59.3%)Imbalance
소재지지번주소 has 564 (91.9%) missing valuesMissing
카메라화소수 has 614 (100.0%) missing valuesMissing
카메라화소수 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-12-11 04:04:36.000836
Analysis finished2023-12-11 04:04:37.646138
Duration1.65 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

관리기관명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.9 KiB
서울특별시 도봉구청
614 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row서울특별시 도봉구청
2nd row서울특별시 도봉구청
3rd row서울특별시 도봉구청
4th row서울특별시 도봉구청
5th row서울특별시 도봉구청

Common Values

ValueCountFrequency (%)
서울특별시 도봉구청 614
100.0%

Length

2023-12-11T13:04:37.726636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T13:04:37.829062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서울특별시 614
50.0%
도봉구청 614
50.0%

소재지도로명주소
Text

Distinct576
Distinct (%)93.8%
Missing0
Missing (%)0.0%
Memory size4.9 KiB
2023-12-11T13:04:38.200863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length57
Median length43
Mean length27.76873
Min length16

Characters and Unicode

Total characters17050
Distinct characters377
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique539 ?
Unique (%)87.8%

Sample

1st row서울특별시 도봉구 삼양로146길 16앞
2nd row서울특별시 도봉구 도봉로113길 34
3rd row서울특별시 도봉구 도봉로115길 23
4th row서울특별시 도봉구 도봉로109길 46-18
5th row서울특별시 도봉구 도봉로 489
ValueCountFrequency (%)
도봉구 625
 
18.2%
서울특별시 614
 
17.9%
앞 121
 
3.5%
도봉로 28
 
0.8%
공원 28
 
0.8%
창1동 23
 
0.7%
어린이 22
 
0.6%
마들로 21
 
0.6%
노해로 20
 
0.6%
시루봉로 19
 
0.6%
Other values (1132) 1914
55.7%
2023-12-11T13:04:38.710617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2821
 
16.5%
봉 883
 
5.2%
도 868
 
5.1%
시 684
 
4.0%
1 683
 
4.0%
구 673
 
3.9%
서 625
 
3.7%
울 622
 
3.6%
별 617
 
3.6%
특 614
 
3.6%
Other values (367) 7960
46.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 10978
64.4%
Space Separator 2821
 
16.5%
Decimal Number 2801
 
16.4%
Dash Punctuation 168
 
1.0%
Close Punctuation 107
 
0.6%
Open Punctuation 107
 
0.6%
Uppercase Letter 42
 
0.2%
Other Punctuation 20
 
0.1%
Connector Punctuation 6
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
봉 883
 
8.0%
도 868
 
7.9%
시 684
 
6.2%
구 673
 
6.1%
서 625
 
5.7%
울 622
 
5.7%
별 617
 
5.6%
특 614
 
5.6%
로 525
 
4.8%
길 434
 
4.0%
Other values (341) 4433
40.4%
Decimal Number
ValueCountFrequency (%)
1 683
24.4%
3 336
12.0%
2 325
11.6%
4 273
 
9.7%
6 254
 
9.1%
5 237
 
8.5%
7 182
 
6.5%
0 180
 
6.4%
8 166
 
5.9%
9 165
 
5.9%
Uppercase Letter
ValueCountFrequency (%)
A 12
28.6%
C 7
16.7%
T 6
14.3%
U 5
11.9%
S 4
 
9.5%
K 3
 
7.1%
P 3
 
7.1%
G 1
 
2.4%
B 1
 
2.4%
Other Punctuation
ValueCountFrequency (%)
, 14
70.0%
. 6
30.0%
Space Separator
ValueCountFrequency (%)
2821
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 168
100.0%
Close Punctuation
ValueCountFrequency (%)
) 107
100.0%
Open Punctuation
ValueCountFrequency (%)
( 107
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 10978
64.4%
Common 6030
35.4%
Latin 42
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
봉 883
 
8.0%
도 868
 
7.9%
시 684
 
6.2%
구 673
 
6.1%
서 625
 
5.7%
울 622
 
5.7%
별 617
 
5.6%
특 614
 
5.6%
로 525
 
4.8%
길 434
 
4.0%
Other values (341) 4433
40.4%
Common
ValueCountFrequency (%)
2821
46.8%
1 683
 
11.3%
3 336
 
5.6%
2 325
 
5.4%
4 273
 
4.5%
6 254
 
4.2%
5 237
 
3.9%
7 182
 
3.0%
0 180
 
3.0%
- 168
 
2.8%
Other values (7) 571
 
9.5%
Latin
ValueCountFrequency (%)
A 12
28.6%
C 7
16.7%
T 6
14.3%
U 5
11.9%
S 4
 
9.5%
K 3
 
7.1%
P 3
 
7.1%
G 1
 
2.4%
B 1
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 10978
64.4%
ASCII 6072
35.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2821
46.5%
1 683
 
11.2%
3 336
 
5.5%
2 325
 
5.4%
4 273
 
4.5%
6 254
 
4.2%
5 237
 
3.9%
7 182
 
3.0%
0 180
 
3.0%
- 168
 
2.8%
Other values (16) 613
 
10.1%
Hangul
ValueCountFrequency (%)
봉 883
 
8.0%
도 868
 
7.9%
시 684
 
6.2%
구 673
 
6.1%
서 625
 
5.7%
울 622
 
5.7%
별 617
 
5.6%
특 614
 
5.6%
로 525
 
4.8%
길 434
 
4.0%
Other values (341) 4433
40.4%

소재지지번주소
Text

MISSING 

Distinct50
Distinct (%)100.0%
Missing564
Missing (%)91.9%
Memory size4.9 KiB
2023-12-11T13:04:38.979676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length20
Mean length19.18
Min length16

Characters and Unicode

Total characters959
Distinct characters31
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

Unique50 ?
Unique (%)100.0%

Sample

1st row서울특별시 도봉구 쌍문동 422-76
2nd row서울특별시 도봉구 쌍문동 120-10
3rd row서울특별시 도봉구 쌍문동 94-39
4th row서울특별시 도봉구 쌍문동 123-24
5th row서울특별시 도봉구 쌍문동 88-21
ValueCountFrequency (%)
서울특별시 50
25.1%
도봉구 50
25.1%
창동 12
 
6.0%
쌍문동 10
 
5.0%
도봉동 7
 
3.5%
도봉2동 5
 
2.5%
방학동 4
 
2.0%
도봉1동 4
 
2.0%
쌍문1동 2
 
1.0%
697-4 1
 
0.5%
Other values (54) 54
27.1%
2023-12-11T13:04:39.377720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
149
15.5%
도 66
 
6.9%
봉 66
 
6.9%
서 50
 
5.2%
특 50
 
5.2%
별 50
 
5.2%
시 50
 
5.2%
구 50
 
5.2%
울 50
 
5.2%
동 49
 
5.1%
Other values (21) 329
34.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 538
56.1%
Decimal Number 230
24.0%
Space Separator 149
 
15.5%
Dash Punctuation 42
 
4.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 66
12.3%
봉 66
12.3%
서 50
9.3%
특 50
9.3%
별 50
9.3%
시 50
9.3%
구 50
9.3%
울 50
9.3%
동 49
9.1%
창 14
 
2.6%
Other values (9) 43
8.0%
Decimal Number
ValueCountFrequency (%)
2 36
15.7%
1 35
15.2%
6 33
14.3%
5 31
13.5%
4 28
12.2%
3 17
7.4%
9 15
6.5%
8 14
 
6.1%
7 13
 
5.7%
0 8
 
3.5%
Space Separator
ValueCountFrequency (%)
149
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 42
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 538
56.1%
Common 421
43.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 66
12.3%
봉 66
12.3%
서 50
9.3%
특 50
9.3%
별 50
9.3%
시 50
9.3%
구 50
9.3%
울 50
9.3%
동 49
9.1%
창 14
 
2.6%
Other values (9) 43
8.0%
Common
ValueCountFrequency (%)
149
35.4%
- 42
 
10.0%
2 36
 
8.6%
1 35
 
8.3%
6 33
 
7.8%
5 31
 
7.4%
4 28
 
6.7%
3 17
 
4.0%
9 15
 
3.6%
8 14
 
3.3%
Other values (2) 21
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 538
56.1%
ASCII 421
43.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
149
35.4%
- 42
 
10.0%
2 36
 
8.6%
1 35
 
8.3%
6 33
 
7.8%
5 31
 
7.4%
4 28
 
6.7%
3 17
 
4.0%
9 15
 
3.6%
8 14
 
3.3%
Other values (2) 21
 
5.0%
Hangul
ValueCountFrequency (%)
도 66
12.3%
봉 66
12.3%
서 50
9.3%
특 50
9.3%
별 50
9.3%
시 50
9.3%
구 50
9.3%
울 50
9.3%
동 49
9.1%
창 14
 
2.6%
Other values (9) 43
8.0%

설치목적구분
Categorical

Distinct5
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size4.9 KiB
생활방범
428 
어린이보호
93 
교통단속
71 
재난재해
 
13
차량방범
 
9

Length

Max length5
Median length4
Mean length4.1514658
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
생활방범 428
69.7%
어린이보호 93
 
15.1%
교통단속 71
 
11.6%
재난재해 13
 
2.1%
차량방범 9
 
1.5%

Length

2023-12-11T13:04:39.523904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T13:04:39.657677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
생활방범 428
69.7%
어린이보호 93
 
15.1%
교통단속 71
 
11.6%
재난재해 13
 
2.1%
차량방범 9
 
1.5%

카메라대수
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.9 KiB
1
614 

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 (%)
1 614
100.0%

Length

2023-12-11T13:04:39.813444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T13:04:39.918453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 614
100.0%

카메라화소수
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing614
Missing (%)100.0%
Memory size5.5 KiB

촬영방면정보
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size4.9 KiB
360도 전방면
564 
360도전방면
 
50

Length

Max length8
Median length8
Mean length7.9185668
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row360도전방면
2nd row360도전방면
3rd row360도전방면
4th row360도전방면
5th row360도전방면

Common Values

ValueCountFrequency (%)
360도 전방면 564
91.9%
360도전방면 50
 
8.1%

Length

2023-12-11T13:04:40.055878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T13:04:40.179512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
360도 564
47.9%
전방면 564
47.9%
360도전방면 50
 
4.2%

보관일수
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.9 KiB
30
614 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row30
2nd row30
3rd row30
4th row30
5th row30

Common Values

ValueCountFrequency (%)
30 614
100.0%

Length

2023-12-11T13:04:40.323385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T13:04:40.466397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
30 614
100.0%

설치년월
Date

Distinct22
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Memory size4.9 KiB
Minimum1905-07-01 00:00:00
Maximum2018-09-01 00:00:00
2023-12-11T13:04:40.587361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T13:04:40.711947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)

관리기관전화번호
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.9 KiB
02-2091-4273
614 

Length

Max length12
Median length12
Mean length12
Min length12

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row02-2091-4273
2nd row02-2091-4273
3rd row02-2091-4273
4th row02-2091-4273
5th row02-2091-4273

Common Values

ValueCountFrequency (%)
02-2091-4273 614
100.0%

Length

2023-12-11T13:04:40.856892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T13:04:40.983057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
02-2091-4273 614
100.0%

위도
Real number (ℝ)

Distinct563
Distinct (%)91.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.658186
Minimum37.631981
Maximum37.693706
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.5 KiB
2023-12-11T13:04:41.121693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.631981
5-th percentile37.637967
Q137.648362
median37.655489
Q337.667408
95-th percentile37.685774
Maximum37.693706
Range0.061725
Interquartile range (IQR)0.019046475

Descriptive statistics

Standard deviation0.013591158
Coefficient of variation (CV)0.00036090847
Kurtosis-0.29481234
Mean37.658186
Median Absolute Deviation (MAD)0.00847285
Skewness0.52424313
Sum23122.126
Variance0.00018471958
MonotonicityNot monotonic
2023-12-11T13:04:41.274198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.652892 3
 
0.5%
37.6379829 2
 
0.3%
37.6515453 2
 
0.3%
37.6555212 2
 
0.3%
37.6544878 2
 
0.3%
37.6594869 2
 
0.3%
37.6612379 2
 
0.3%
37.6411246 2
 
0.3%
37.6319809 2
 
0.3%
37.633966 2
 
0.3%
Other values (553) 593
96.6%
ValueCountFrequency (%)
37.6319809 2
0.3%
37.6328613 1
0.2%
37.6332235 1
0.2%
37.6332397 1
0.2%
37.6337085 1
0.2%
37.633966 2
0.3%
37.6339712 1
0.2%
37.6340739 1
0.2%
37.634118 1
0.2%
37.6346442 1
0.2%
ValueCountFrequency (%)
37.6937059 1
0.2%
37.6909989 1
0.2%
37.6908949 1
0.2%
37.690469 1
0.2%
37.690346 1
0.2%
37.690032 1
0.2%
37.689646 1
0.2%
37.6895609 1
0.2%
37.6892727 1
0.2%
37.689225 1
0.2%

경도
Real number (ℝ)

Distinct563
Distinct (%)91.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.03731
Minimum127.01357
Maximum127.05392
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.5 KiB
2023-12-11T13:04:41.425621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum127.01357
5-th percentile127.02024
Q1127.03242
median127.039
Q3127.04331
95-th percentile127.04888
Maximum127.05392
Range0.0403469
Interquartile range (IQR)0.0108991

Descriptive statistics

Standard deviation0.0084520106
Coefficient of variation (CV)6.6531717 × 10-5
Kurtosis0.32822749
Mean127.03731
Median Absolute Deviation (MAD)0.00533345
Skewness-0.75924866
Sum78000.911
Variance7.1436483 × 10-5
MonotonicityNot monotonic
2023-12-11T13:04:41.584336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.040863 2
 
0.3%
127.03771 2
 
0.3%
127.0459555 2
 
0.3%
127.044966 2
 
0.3%
127.0473281 2
 
0.3%
127.0407103 2
 
0.3%
127.0405959 2
 
0.3%
127.037985 2
 
0.3%
127.0412259 2
 
0.3%
127.038769 2
 
0.3%
Other values (553) 594
96.7%
ValueCountFrequency (%)
127.013572 1
0.2%
127.013576 1
0.2%
127.0136154 2
0.3%
127.0136677 1
0.2%
127.0137654 2
0.3%
127.014242 1
0.2%
127.0142955 1
0.2%
127.0144595 1
0.2%
127.0145919 1
0.2%
127.0146526 2
0.3%
ValueCountFrequency (%)
127.0539189 2
0.3%
127.0530493 2
0.3%
127.0529587 1
0.2%
127.0527079 1
0.2%
127.0523369 1
0.2%
127.0519884 1
0.2%
127.051811 1
0.2%
127.0516617 1
0.2%
127.050991 1
0.2%
127.0509018 1
0.2%

데이터기준일자
Date

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.9 KiB
Minimum2018-10-06 00:00:00
Maximum2018-10-06 00:00:00
2023-12-11T13:04:41.705932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T13:04:41.799905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-11T13:04:37.038949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T13:04:36.530526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T13:04:37.165823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T13:04:36.627691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T13:04:41.897436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
소재지지번주소설치목적구분촬영방면정보설치년월위도경도
소재지지번주소1.000NaNNaN1.0001.0001.000
설치목적구분NaN1.0000.1470.4040.3340.313
촬영방면정보NaN0.1471.0000.3480.2010.128
설치년월1.0000.4040.3481.0000.7060.360
위도1.0000.3340.2010.7061.0000.632
경도1.0000.3130.1280.3600.6321.000
2023-12-11T13:04:42.030726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
촬영방면정보설치목적구분
촬영방면정보1.0000.179
설치목적구분0.1791.000
2023-12-11T13:04:42.120999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위도경도설치목적구분촬영방면정보
위도1.0000.1530.1440.153
경도0.1531.0000.1340.098
설치목적구분0.1440.1341.0000.179
촬영방면정보0.1530.0980.1791.000

Missing values

2023-12-11T13:04:37.365519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T13:04:37.570819image/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

관리기관명소재지도로명주소소재지지번주소설치목적구분카메라대수카메라화소수촬영방면정보보관일수설치년월관리기관전화번호위도경도데이터기준일자
0서울특별시 도봉구청서울특별시 도봉구 삼양로146길 16앞서울특별시 도봉구 쌍문동 422-76생활방범1<NA>360도전방면302018-0902-2091-427337.652315127.0135722018-10-06
1서울특별시 도봉구청서울특별시 도봉구 도봉로113길 34서울특별시 도봉구 쌍문동 120-10생활방범1<NA>360도전방면302018-0902-2091-427337.648521127.0322812018-10-06
2서울특별시 도봉구청서울특별시 도봉구 도봉로115길 23서울특별시 도봉구 쌍문동 94-39생활방범1<NA>360도전방면302018-0902-2091-427337.648733127.0331512018-10-06
3서울특별시 도봉구청서울특별시 도봉구 도봉로109길 46-18서울특별시 도봉구 쌍문동 123-24생활방범1<NA>360도전방면302011-0602-2091-427337.647705127.0309452018-10-06
4서울특별시 도봉구청서울특별시 도봉구 도봉로 489서울특별시 도봉구 쌍문동 88-21생활방범1<NA>360도전방면302011-0602-2091-427337.649362127.0347962018-10-06
5서울특별시 도봉구청서울특별시 도봉구 방학로6길 26서울특별시 도봉구 방학동 697-4생활방범1<NA>360도전방면302014-1202-2091-427337.664056127.0371092018-10-06
6서울특별시 도봉구청서울특별시 도봉구 방학로 130-30서울특별시 도봉구 방학동 690-16생활방범1<NA>360도전방면302014-1202-2091-427337.664277127.037822018-10-06
7서울특별시 도봉구청서울특별시 도봉구 방학로12가길 5-16서울특별시 도봉구 방학동 306생활방범1<NA>360도전방면302014-1202-2091-427337.66341127.030632018-10-06
8서울특별시 도봉구청서울특별시 도봉구 도당로9길 3서울특별시 도봉구 방학동 665-5생활방범1<NA>360도전방면302014-1202-2091-427337.662932127.0343652018-10-06
9서울특별시 도봉구청서울특별시 도봉구 도봉로110나길 42서울특별시 도봉구 창동 657-45생활방범1<NA>360도전방면301905-0702-2091-427337.647811127.0354642018-10-06
관리기관명소재지도로명주소소재지지번주소설치목적구분카메라대수카메라화소수촬영방면정보보관일수설치년월관리기관전화번호위도경도데이터기준일자
604서울특별시 도봉구청서울특별시 도봉구 도봉구 도봉로164길 34서울특별시 도봉구 도봉2동 625-11생활방범1<NA>360도전방면302014-1202-2091-427337.675432127.0465092018-10-06
605서울특별시 도봉구청서울특별시 도봉구 도봉구 우이천로 34나길 1-3서울특별시 도봉구 쌍문1동 298-1생활방범1<NA>360도전방면302017-0802-2091-427337.649538127.0240492018-10-06
606서울특별시 도봉구청서울특별시 도봉구 도봉구 시루봉로13길 13서울특별시 도봉구 방학2동 614-5생활방범1<NA>360도전방면302014-1202-2091-427337.666907127.0315252018-10-06
607서울특별시 도봉구청서울특별시 도봉구 도봉구 해등로16길 52서울특별시 도봉구 창5동 286-17생활방범1<NA>360도전방면302015-0202-2091-427337.655062127.0426562018-10-06
608서울특별시 도봉구청서울특별시 도봉구 도봉구 시루봉로23가길 11-2서울특별시 도봉구 도봉1동 453-1생활방범1<NA>360도전방면302014-1202-2091-427337.672192127.0392312018-10-06
609서울특별시 도봉구청서울특별시 도봉구 도봉구 도봉로 851서울특별시 도봉구 도봉1동 595생활방범1<NA>360도전방면302014-1202-2091-427337.679404127.0444982018-10-06
610서울특별시 도봉구청서울특별시 도봉구 도봉구 마들로 916서울특별시 도봉구 도봉2동 4-1생활방범1<NA>360도전방면302017-1202-2091-427337.689273127.0475812018-10-06
611서울특별시 도봉구청서울특별시 도봉구 도봉구 해등로 337-20서울특별시 도봉구 쌍문동 486-18생활방범1<NA>360도전방면302018-0902-2091-427337.656277127.018652018-10-06
612서울특별시 도봉구청서울특별시 도봉구 도봉구 우이천로42길서울특별시 도봉구 쌍문동 481-1생활방범1<NA>360도전방면302018-0902-2091-427337.654281127.0183162018-10-06
613서울특별시 도봉구청서울특별시 도봉구 도봉구 도봉로160길 5서울특별시 도봉구 도봉동 625-141생활방범1<NA>360도전방면302014-1202-2091-427337.674296127.0449232018-10-06

Duplicate rows

Most frequently occurring

관리기관명소재지도로명주소소재지지번주소설치목적구분카메라대수촬영방면정보보관일수설치년월관리기관전화번호위도경도데이터기준일자# duplicates
0서울특별시 도봉구청서울특별시 도봉구 노해로63길 75 대림아파트 앞(창동역 2번출구)<NA>교통단속1360도 전방면302012-1202-2091-427337.652892127.0460322018-10-062
1서울특별시 도봉구청서울특별시 도봉구 도봉산길 43 도봉산 입구<NA>교통단속1360도 전방면302016-0902-2091-427337.686991127.0400662018-10-062