Overview

Dataset statistics

Number of variables14
Number of observations10000
Missing cells6810
Missing cells (%)4.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.2 MiB
Average record size in memory124.0 B

Variable types

Categorical1
Text7
Numeric4
Boolean1
DateTime1

Dataset

Description공공데이터 제공 표준데이터 속성정보(허용값, 표현형식/단위 등)는 [공공데이터 제공 표준] 전문을 참고하시기 바랍니다.(공공데이터포털>정보공유>자료실) 각 기관에서 등록한 표준데이터를 취합하여 제공하기 때문에 갱신주기는 개별 파일마다 다릅니다.(기관에서 등록한 데이터를 취합한 것으로 개별 파일별 갱신시점이 다름)
Author지방자치단체
URLhttps://www.data.go.kr/data/15012891/standard.do

Alerts

소재지도로명주소 has 174 (1.7%) missing valuesMissing
소재지지번주소 has 1699 (17.0%) missing valuesMissing
CCTV설치대수 has 2104 (21.0%) missing valuesMissing
보호구역도로폭 has 2833 (28.3%) missing valuesMissing
CCTV설치대수 has 1085 (10.8%) zerosZeros

Reproduction

Analysis started2024-05-11 07:47:39.246066
Analysis finished2024-05-11 07:47:49.074579
Duration9.83 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시설종류
Categorical

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
초등학교
4802 
유치원
2940 
어린이집
2082 
특수학교
 
117
학원
 
47
Other values (3)
 
12

Length

Max length5
Median length4
Mean length3.6971
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row어린이집
2nd row유치원
3rd row초등학교
4th row초등학교
5th row유치원

Common Values

ValueCountFrequency (%)
초등학교 4802
48.0%
유치원 2940
29.4%
어린이집 2082
20.8%
특수학교 117
 
1.2%
학원 47
 
0.5%
외국인학교 5
 
0.1%
국제학교 4
 
< 0.1%
대안학교 3
 
< 0.1%

Length

2024-05-11T16:47:49.239094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T16:47:49.494555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
초등학교 4802
48.0%
유치원 2940
29.4%
어린이집 2082
20.8%
특수학교 117
 
1.2%
학원 47
 
0.5%
외국인학교 5
 
< 0.1%
국제학교 4
 
< 0.1%
대안학교 3
 
< 0.1%

대상시설명
Text

Distinct7808
Distinct (%)78.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-11T16:47:50.280953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length24
Mean length6.9697
Min length2

Characters and Unicode

Total characters69697
Distinct characters653
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

Unique6425 ?
Unique (%)64.2%

Sample

1st row코레일대전어린이집
2nd row양운초교 병설유치원
3rd row동부초등학교
4th row녹산초등학교
5th row반월 초등학교 병설유치원
ValueCountFrequency (%)
병설유치원 429
 
3.9%
어린이집 127
 
1.2%
유치원 90
 
0.8%
포함 70
 
0.6%
초등학교 68
 
0.6%
병설 28
 
0.3%
중앙초등학교 27
 
0.2%
양평군 14
 
0.1%
경기도 14
 
0.1%
성모유치원 14
 
0.1%
Other values (7598) 10082
92.0%
2024-05-11T16:47:51.435897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
교 5892
 
8.5%
초 5854
 
8.4%
학 4674
 
6.7%
등 4487
 
6.4%
원 3328
 
4.8%
유 2981
 
4.3%
치 2923
 
4.2%
이 2478
 
3.6%
린 2126
 
3.1%
어 2126
 
3.1%
Other values (643) 32828
47.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 67197
96.4%
Space Separator 963
 
1.4%
Close Punctuation 387
 
0.6%
Open Punctuation 387
 
0.6%
Decimal Number 301
 
0.4%
Dash Punctuation 250
 
0.4%
Uppercase Letter 111
 
0.2%
Math Symbol 54
 
0.1%
Other Punctuation 36
 
0.1%
Lowercase Letter 11
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
교 5892
 
8.8%
초 5854
 
8.7%
학 4674
 
7.0%
등 4487
 
6.7%
원 3328
 
5.0%
유 2981
 
4.4%
치 2923
 
4.3%
이 2478
 
3.7%
린 2126
 
3.2%
어 2126
 
3.2%
Other values (598) 30328
45.1%
Uppercase Letter
ValueCountFrequency (%)
C 12
10.8%
S 11
9.9%
A 11
9.9%
Y 10
 
9.0%
L 9
 
8.1%
G 8
 
7.2%
K 7
 
6.3%
M 6
 
5.4%
B 6
 
5.4%
I 5
 
4.5%
Other values (12) 26
23.4%
Lowercase Letter
ValueCountFrequency (%)
e 3
27.3%
s 2
18.2%
i 2
18.2%
d 1
 
9.1%
k 1
 
9.1%
b 1
 
9.1%
u 1
 
9.1%
Decimal Number
ValueCountFrequency (%)
1 141
46.8%
2 77
25.6%
3 43
 
14.3%
4 20
 
6.6%
5 12
 
4.0%
6 8
 
2.7%
Other Punctuation
ValueCountFrequency (%)
/ 17
47.2%
, 13
36.1%
. 3
 
8.3%
& 2
 
5.6%
· 1
 
2.8%
Space Separator
ValueCountFrequency (%)
963
100.0%
Close Punctuation
ValueCountFrequency (%)
) 387
100.0%
Open Punctuation
ValueCountFrequency (%)
( 387
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 250
100.0%
Math Symbol
ValueCountFrequency (%)
+ 54
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 67197
96.4%
Common 2378
 
3.4%
Latin 122
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
교 5892
 
8.8%
초 5854
 
8.7%
학 4674
 
7.0%
등 4487
 
6.7%
원 3328
 
5.0%
유 2981
 
4.4%
치 2923
 
4.3%
이 2478
 
3.7%
린 2126
 
3.2%
어 2126
 
3.2%
Other values (598) 30328
45.1%
Latin
ValueCountFrequency (%)
C 12
 
9.8%
S 11
 
9.0%
A 11
 
9.0%
Y 10
 
8.2%
L 9
 
7.4%
G 8
 
6.6%
K 7
 
5.7%
M 6
 
4.9%
B 6
 
4.9%
I 5
 
4.1%
Other values (19) 37
30.3%
Common
ValueCountFrequency (%)
963
40.5%
) 387
16.3%
( 387
16.3%
- 250
 
10.5%
1 141
 
5.9%
2 77
 
3.2%
+ 54
 
2.3%
3 43
 
1.8%
4 20
 
0.8%
/ 17
 
0.7%
Other values (6) 39
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 67194
96.4%
ASCII 2499
 
3.6%
Compat Jamo 3
 
< 0.1%
None 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
교 5892
 
8.8%
초 5854
 
8.7%
학 4674
 
7.0%
등 4487
 
6.7%
원 3328
 
5.0%
유 2981
 
4.4%
치 2923
 
4.4%
이 2478
 
3.7%
린 2126
 
3.2%
어 2126
 
3.2%
Other values (597) 30325
45.1%
ASCII
ValueCountFrequency (%)
963
38.5%
) 387
15.5%
( 387
15.5%
- 250
 
10.0%
1 141
 
5.6%
2 77
 
3.1%
+ 54
 
2.2%
3 43
 
1.7%
4 20
 
0.8%
/ 17
 
0.7%
Other values (34) 160
 
6.4%
Compat Jamo
ValueCountFrequency (%)
ㆍ 3
100.0%
None
ValueCountFrequency (%)
· 1
100.0%

소재지도로명주소
Text

MISSING 

Distinct8659
Distinct (%)88.1%
Missing174
Missing (%)1.7%
Memory size156.2 KiB
2024-05-11T16:47:52.099430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length47
Median length42
Mean length21.509974
Min length13

Characters and Unicode

Total characters211357
Distinct characters574
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

Unique7585 ?
Unique (%)77.2%

Sample

1st row대전광역시 대덕구 벚꽃길 148
2nd row부산광역시 해운대구 대천로67번길 36
3rd row경상남도 거제시 동부면 동부로 27
4th row부산광역시 강서구 과학산단2로20번길 55 (지사동) 녹산초등학교
5th row경기도 안산시 상록구 건건로 39
ValueCountFrequency (%)
경기도 1740
 
3.8%
경상남도 1023
 
2.2%
서울특별시 941
 
2.0%
경상북도 592
 
1.3%
전라북도 586
 
1.3%
부산광역시 546
 
1.2%
강원도 532
 
1.1%
충청남도 510
 
1.1%
전라남도 502
 
1.1%
대구광역시 407
 
0.9%
Other values (11603) 38994
84.1%
2024-05-11T16:47:53.319294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
36548
 
17.3%
시 8666
 
4.1%
로 7688
 
3.6%
1 7080
 
3.3%
도 7007
 
3.3%
구 5490
 
2.6%
길 5432
 
2.6%
2 4853
 
2.3%
3 3795
 
1.8%
동 3621
 
1.7%
Other values (564) 121177
57.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 135729
64.2%
Space Separator 36548
 
17.3%
Decimal Number 33383
 
15.8%
Close Punctuation 1875
 
0.9%
Open Punctuation 1875
 
0.9%
Dash Punctuation 1709
 
0.8%
Other Punctuation 219
 
0.1%
Uppercase Letter 18
 
< 0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 8666
 
6.4%
로 7688
 
5.7%
도 7007
 
5.2%
구 5490
 
4.0%
길 5432
 
4.0%
동 3621
 
2.7%
경 3615
 
2.7%
남 3288
 
2.4%
산 3236
 
2.4%
광 2930
 
2.2%
Other values (535) 84756
62.4%
Uppercase Letter
ValueCountFrequency (%)
K 4
22.2%
S 3
16.7%
D 2
11.1%
W 1
 
5.6%
E 1
 
5.6%
I 1
 
5.6%
V 1
 
5.6%
R 1
 
5.6%
J 1
 
5.6%
G 1
 
5.6%
Other values (2) 2
11.1%
Decimal Number
ValueCountFrequency (%)
1 7080
21.2%
2 4853
14.5%
3 3795
11.4%
5 3043
9.1%
4 3023
9.1%
6 2589
 
7.8%
7 2501
 
7.5%
0 2275
 
6.8%
9 2134
 
6.4%
8 2090
 
6.3%
Other Punctuation
ValueCountFrequency (%)
, 218
99.5%
. 1
 
0.5%
Space Separator
ValueCountFrequency (%)
36548
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1875
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1875
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1709
100.0%
Math Symbol
ValueCountFrequency (%)
+ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 135729
64.2%
Common 75610
35.8%
Latin 18
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 8666
 
6.4%
로 7688
 
5.7%
도 7007
 
5.2%
구 5490
 
4.0%
길 5432
 
4.0%
동 3621
 
2.7%
경 3615
 
2.7%
남 3288
 
2.4%
산 3236
 
2.4%
광 2930
 
2.2%
Other values (535) 84756
62.4%
Common
ValueCountFrequency (%)
36548
48.3%
1 7080
 
9.4%
2 4853
 
6.4%
3 3795
 
5.0%
5 3043
 
4.0%
4 3023
 
4.0%
6 2589
 
3.4%
7 2501
 
3.3%
0 2275
 
3.0%
9 2134
 
2.8%
Other values (7) 7769
 
10.3%
Latin
ValueCountFrequency (%)
K 4
22.2%
S 3
16.7%
D 2
11.1%
W 1
 
5.6%
E 1
 
5.6%
I 1
 
5.6%
V 1
 
5.6%
R 1
 
5.6%
J 1
 
5.6%
G 1
 
5.6%
Other values (2) 2
11.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 135729
64.2%
ASCII 75628
35.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
36548
48.3%
1 7080
 
9.4%
2 4853
 
6.4%
3 3795
 
5.0%
5 3043
 
4.0%
4 3023
 
4.0%
6 2589
 
3.4%
7 2501
 
3.3%
0 2275
 
3.0%
9 2134
 
2.8%
Other values (19) 7787
 
10.3%
Hangul
ValueCountFrequency (%)
시 8666
 
6.4%
로 7688
 
5.7%
도 7007
 
5.2%
구 5490
 
4.0%
길 5432
 
4.0%
동 3621
 
2.7%
경 3615
 
2.7%
남 3288
 
2.4%
산 3236
 
2.4%
광 2930
 
2.2%
Other values (535) 84756
62.4%

소재지지번주소
Text

MISSING 

Distinct7315
Distinct (%)88.1%
Missing1699
Missing (%)17.0%
Memory size156.2 KiB
2024-05-11T16:47:54.011224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length44
Median length40
Mean length19.889893
Min length13

Characters and Unicode

Total characters165106
Distinct characters384
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6431 ?
Unique (%)77.5%

Sample

1st row부산광역시 해운대구 좌동 1372
2nd row부산광역시 강서구 지사동 1184-2
3rd row경기도 안산시 상록구 건건동 629
4th row충청북도 청주시 청원구 주중동428
5th row인천광역시 부평구 부개동 155-1
ValueCountFrequency (%)
경기도 1666
 
4.4%
서울특별시 802
 
2.1%
경상남도 783
 
2.1%
경상북도 561
 
1.5%
강원도 532
 
1.4%
전라북도 485
 
1.3%
전라남도 426
 
1.1%
충청남도 419
 
1.1%
부산광역시 381
 
1.0%
인천광역시 359
 
1.0%
Other values (9088) 31093
82.9%
2024-05-11T16:47:55.227776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
29209
 
17.7%
시 7255
 
4.4%
1 6484
 
3.9%
동 6428
 
3.9%
도 6110
 
3.7%
- 4793
 
2.9%
구 4559
 
2.8%
2 4059
 
2.5%
3 3460
 
2.1%
경 3158
 
1.9%
Other values (374) 89591
54.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 99671
60.4%
Decimal Number 31397
 
19.0%
Space Separator 29209
 
17.7%
Dash Punctuation 4793
 
2.9%
Close Punctuation 16
 
< 0.1%
Open Punctuation 16
 
< 0.1%
Other Punctuation 3
 
< 0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 7255
 
7.3%
동 6428
 
6.4%
도 6110
 
6.1%
구 4559
 
4.6%
경 3158
 
3.2%
리 2888
 
2.9%
남 2552
 
2.6%
산 2393
 
2.4%
광 2241
 
2.2%
북 2154
 
2.2%
Other values (357) 59933
60.1%
Decimal Number
ValueCountFrequency (%)
1 6484
20.7%
2 4059
12.9%
3 3460
11.0%
5 2865
9.1%
4 2830
9.0%
6 2642
8.4%
7 2471
 
7.9%
8 2303
 
7.3%
0 2234
 
7.1%
9 2049
 
6.5%
Other Punctuation
ValueCountFrequency (%)
@ 2
66.7%
, 1
33.3%
Space Separator
ValueCountFrequency (%)
29209
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4793
100.0%
Close Punctuation
ValueCountFrequency (%)
) 16
100.0%
Open Punctuation
ValueCountFrequency (%)
( 16
100.0%
Math Symbol
ValueCountFrequency (%)
+ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 99671
60.4%
Common 65435
39.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 7255
 
7.3%
동 6428
 
6.4%
도 6110
 
6.1%
구 4559
 
4.6%
경 3158
 
3.2%
리 2888
 
2.9%
남 2552
 
2.6%
산 2393
 
2.4%
광 2241
 
2.2%
북 2154
 
2.2%
Other values (357) 59933
60.1%
Common
ValueCountFrequency (%)
29209
44.6%
1 6484
 
9.9%
- 4793
 
7.3%
2 4059
 
6.2%
3 3460
 
5.3%
5 2865
 
4.4%
4 2830
 
4.3%
6 2642
 
4.0%
7 2471
 
3.8%
8 2303
 
3.5%
Other values (7) 4319
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 99671
60.4%
ASCII 65435
39.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
29209
44.6%
1 6484
 
9.9%
- 4793
 
7.3%
2 4059
 
6.2%
3 3460
 
5.3%
5 2865
 
4.4%
4 2830
 
4.3%
6 2642
 
4.0%
7 2471
 
3.8%
8 2303
 
3.5%
Other values (7) 4319
 
6.6%
Hangul
ValueCountFrequency (%)
시 7255
 
7.3%
동 6428
 
6.4%
도 6110
 
6.1%
구 4559
 
4.6%
경 3158
 
3.2%
리 2888
 
2.9%
남 2552
 
2.6%
산 2393
 
2.4%
광 2241
 
2.2%
북 2154
 
2.2%
Other values (357) 59933
60.1%

위도
Real number (ℝ)

Distinct8712
Distinct (%)87.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.400044
Minimum33.169167
Maximum38.542101
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T16:47:55.627667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33.169167
5-th percentile34.838088
Q135.449509
median36.367979
Q337.44983
95-th percentile37.772501
Maximum38.542101
Range5.3729343
Interquartile range (IQR)2.000321

Descriptive statistics

Standard deviation1.0880468
Coefficient of variation (CV)0.029891359
Kurtosis-0.6497694
Mean36.400044
Median Absolute Deviation (MAD)1.0348483
Skewness-0.3913017
Sum364000.44
Variance1.1838458
MonotonicityNot monotonic
2024-05-11T16:47:56.055148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.65082981 9
 
0.1%
38.14775096 4
 
< 0.1%
37.3354300185 4
 
< 0.1%
35.80084303 4
 
< 0.1%
35.95523492 4
 
< 0.1%
35.47790985 4
 
< 0.1%
37.68735318 4
 
< 0.1%
37.3463726754 4
 
< 0.1%
35.39009713 4
 
< 0.1%
37.6094088 4
 
< 0.1%
Other values (8702) 9955
99.6%
ValueCountFrequency (%)
33.16916683 1
< 0.1%
33.2205253 1
< 0.1%
33.22559249 1
< 0.1%
33.22651574 2
< 0.1%
33.23207825 1
< 0.1%
33.23373681 2
< 0.1%
33.23500897 2
< 0.1%
33.23773943 2
< 0.1%
33.23908982 1
< 0.1%
33.24107413 1
< 0.1%
ValueCountFrequency (%)
38.54210112 1
 
< 0.1%
38.49694799 1
 
< 0.1%
38.4525810779 1
 
< 0.1%
38.43847289 1
 
< 0.1%
38.37730554 3
< 0.1%
38.37526383 2
< 0.1%
38.33033639 1
 
< 0.1%
38.31375296 1
 
< 0.1%
38.3137529594 1
 
< 0.1%
38.289723 1
 
< 0.1%

경도
Real number (ℝ)

Distinct8690
Distinct (%)86.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.60075
Minimum124.71533
Maximum130.9028
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T16:47:56.348463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum124.71533
5-th percentile126.62449
Q1126.93561
median127.22979
Q3128.42216
95-th percentile129.15285
Maximum130.9028
Range6.1874654
Interquartile range (IQR)1.4865511

Descriptive statistics

Standard deviation0.86186155
Coefficient of variation (CV)0.0067543612
Kurtosis-0.90014734
Mean127.60075
Median Absolute Deviation (MAD)0.46700936
Skewness0.64058329
Sum1276007.5
Variance0.74280533
MonotonicityNot monotonic
2024-05-11T16:47:56.666166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.0778147 9
 
0.1%
126.9563936 5
 
0.1%
127.0217658 5
 
0.1%
126.9553744 5
 
0.1%
126.9695872 4
 
< 0.1%
127.1236915 4
 
< 0.1%
128.5085808 4
 
< 0.1%
126.9923556 4
 
< 0.1%
127.9216593234 4
 
< 0.1%
129.0347767 4
 
< 0.1%
Other values (8680) 9952
99.5%
ValueCountFrequency (%)
124.7153333 1
< 0.1%
125.427644 1
< 0.1%
125.7010236 1
< 0.1%
125.9211577 2
< 0.1%
125.9626266 1
< 0.1%
125.9798266 1
< 0.1%
126.0468919 2
< 0.1%
126.0492442 1
< 0.1%
126.0770212 1
< 0.1%
126.0958194 1
< 0.1%
ValueCountFrequency (%)
130.9027987 1
< 0.1%
130.9027334 1
< 0.1%
129.557283 2
< 0.1%
129.5517505 2
< 0.1%
129.5483756 1
< 0.1%
129.5131629 2
< 0.1%
129.5013972 1
< 0.1%
129.4943247 2
< 0.1%
129.4699798 1
< 0.1%
129.458399 1
< 0.1%

관리기관명
Text

Distinct271
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-11T16:47:57.380637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length19
Mean length9.404
Min length3

Characters and Unicode

Total characters94040
Distinct characters158
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row대전광역시
2nd row부산광역시 해운대구청
3rd row경상남도 거제시
4th row부산광역시 강서구청
5th row경기도 안산시 상록구 도로교통과
ValueCountFrequency (%)
경기도 1817
 
8.8%
경상남도 1005
 
4.9%
서울특별시 953
 
4.6%
전라북도 586
 
2.8%
경상북도 562
 
2.7%
부산광역시 545
 
2.6%
강원도 524
 
2.5%
충청남도 519
 
2.5%
전라남도 494
 
2.4%
대전광역시 417
 
2.0%
Other values (253) 13274
64.1%
2024-05-11T16:47:58.411429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
10721
 
11.4%
시 8434
 
9.0%
청 7868
 
8.4%
도 6627
 
7.0%
구 4017
 
4.3%
경 3687
 
3.9%
광 2787
 
3.0%
남 2605
 
2.8%
역 2267
 
2.4%
전 2037
 
2.2%
Other values (148) 42990
45.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 83319
88.6%
Space Separator 10721
 
11.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 8434
 
10.1%
청 7868
 
9.4%
도 6627
 
8.0%
구 4017
 
4.8%
경 3687
 
4.4%
광 2787
 
3.3%
남 2605
 
3.1%
역 2267
 
2.7%
전 2037
 
2.4%
북 2022
 
2.4%
Other values (147) 40968
49.2%
Space Separator
ValueCountFrequency (%)
10721
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 83319
88.6%
Common 10721
 
11.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 8434
 
10.1%
청 7868
 
9.4%
도 6627
 
8.0%
구 4017
 
4.8%
경 3687
 
4.4%
광 2787
 
3.3%
남 2605
 
3.1%
역 2267
 
2.7%
전 2037
 
2.4%
북 2022
 
2.4%
Other values (147) 40968
49.2%
Common
ValueCountFrequency (%)
10721
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 83319
88.6%
ASCII 10721
 
11.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
10721
100.0%
Hangul
ValueCountFrequency (%)
시 8434
 
10.1%
청 7868
 
9.4%
도 6627
 
8.0%
구 4017
 
4.8%
경 3687
 
4.4%
광 2787
 
3.3%
남 2605
 
3.1%
역 2267
 
2.7%
전 2037
 
2.4%
북 2022
 
2.4%
Other values (147) 40968
49.2%

관할경찰서명
Text

Distinct270
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-11T16:47:58.983511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length5
Mean length6.264
Min length2

Characters and Unicode

Total characters62640
Distinct characters150
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st row대전대덕경찰서
2nd row부산광역시 해운대경찰서
3rd row거제경찰서
4th row부산강서경찰서
5th row안산상록경찰서
ValueCountFrequency (%)
경기도 284
 
2.5%
수원시 250
 
2.2%
남부경찰서 213
 
1.9%
익산경찰서 174
 
1.5%
원주경찰서 173
 
1.5%
서부경찰서 153
 
1.3%
중부경찰서 144
 
1.3%
군산경찰서 138
 
1.2%
전주완산경찰서 138
 
1.2%
서울특별시 137
 
1.2%
Other values (265) 9670
84.3%
2024-05-11T16:47:59.878816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
서 11303
18.0%
경 10124
16.2%
찰 9647
15.4%
부 2745
 
4.4%
산 1777
 
2.8%
주 1477
 
2.4%
1474
 
2.4%
천 1244
 
2.0%
시 926
 
1.5%
남 904
 
1.4%
Other values (140) 21019
33.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 61166
97.6%
Space Separator 1474
 
2.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 11303
18.5%
경 10124
16.6%
찰 9647
15.8%
부 2745
 
4.5%
산 1777
 
2.9%
주 1477
 
2.4%
천 1244
 
2.0%
시 926
 
1.5%
남 904
 
1.5%
동 881
 
1.4%
Other values (139) 20138
32.9%
Space Separator
ValueCountFrequency (%)
1474
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 61166
97.6%
Common 1474
 
2.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 11303
18.5%
경 10124
16.6%
찰 9647
15.8%
부 2745
 
4.5%
산 1777
 
2.9%
주 1477
 
2.4%
천 1244
 
2.0%
시 926
 
1.5%
남 904
 
1.5%
동 881
 
1.4%
Other values (139) 20138
32.9%
Common
ValueCountFrequency (%)
1474
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 61166
97.6%
ASCII 1474
 
2.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
서 11303
18.5%
경 10124
16.6%
찰 9647
15.8%
부 2745
 
4.5%
산 1777
 
2.9%
주 1477
 
2.4%
천 1244
 
2.0%
시 926
 
1.5%
남 904
 
1.5%
동 881
 
1.4%
Other values (139) 20138
32.9%
ASCII
ValueCountFrequency (%)
1474
100.0%

CCTV설치여부
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size87.9 KiB
True
7875 
False
2125 
ValueCountFrequency (%)
True 7875
78.8%
False 2125
 
21.2%
2024-05-11T16:48:00.143162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

CCTV설치대수
Real number (ℝ)

MISSING  ZEROS 

Distinct29
Distinct (%)0.4%
Missing2104
Missing (%)21.0%
Infinite0
Infinite (%)0.0%
Mean2.6761651
Minimum0
Maximum46
Zeros1085
Zeros (%)10.8%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T16:48:00.368284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile9
Maximum46
Range46
Interquartile range (IQR)2

Descriptive statistics

Standard deviation3.0377184
Coefficient of variation (CV)1.1351013
Kurtosis17.361434
Mean2.6761651
Median Absolute Deviation (MAD)1
Skewness3.1300154
Sum21131
Variance9.2277333
MonotonicityNot monotonic
2024-05-11T16:48:00.640886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
1 2323
23.2%
2 1702
17.0%
0 1085
10.8%
3 873
 
8.7%
4 673
 
6.7%
5 325
 
3.2%
6 259
 
2.6%
7 143
 
1.4%
8 109
 
1.1%
9 102
 
1.0%
Other values (19) 302
 
3.0%
(Missing) 2104
21.0%
ValueCountFrequency (%)
0 1085
10.8%
1 2323
23.2%
2 1702
17.0%
3 873
 
8.7%
4 673
 
6.7%
5 325
 
3.2%
6 259
 
2.6%
7 143
 
1.4%
8 109
 
1.1%
9 102
 
1.0%
ValueCountFrequency (%)
46 1
 
< 0.1%
35 1
 
< 0.1%
34 1
 
< 0.1%
29 1
 
< 0.1%
28 1
 
< 0.1%
24 4
< 0.1%
23 5
0.1%
22 5
0.1%
20 4
< 0.1%
19 9
0.1%

보호구역도로폭
Text

MISSING 

Distinct449
Distinct (%)6.3%
Missing2833
Missing (%)28.3%
Memory size156.2 KiB
2024-05-11T16:48:01.288570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length2.0632064
Min length1

Characters and Unicode

Total characters14787
Distinct characters16
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique216 ?
Unique (%)3.0%

Sample

1st row7~20
2nd row8
3rd row20
4th row3~6
5th row4~8
ValueCountFrequency (%)
6 1084
 
15.1%
8 891
 
12.4%
7 611
 
8.5%
10 362
 
5.1%
12 338
 
4.7%
5 270
 
3.8%
20 218
 
3.0%
9 215
 
3.0%
4~8 207
 
2.9%
15 187
 
2.6%
Other values (439) 2784
38.8%
2024-05-11T16:48:02.259833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 2320
15.7%
6 1853
12.5%
~ 1820
12.3%
8 1652
11.2%
2 1470
9.9%
0 1224
8.3%
5 1204
8.1%
7 867
 
5.9%
4 789
 
5.3%
3 778
 
5.3%
Other values (6) 810
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 12585
85.1%
Math Symbol 1820
 
12.3%
Other Punctuation 369
 
2.5%
Lowercase Letter 11
 
0.1%
Dash Punctuation 1
 
< 0.1%
Modifier Symbol 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2320
18.4%
6 1853
14.7%
8 1652
13.1%
2 1470
11.7%
0 1224
9.7%
5 1204
9.6%
7 867
 
6.9%
4 789
 
6.3%
3 778
 
6.2%
9 428
 
3.4%
Other Punctuation
ValueCountFrequency (%)
. 367
99.5%
, 2
 
0.5%
Math Symbol
ValueCountFrequency (%)
~ 1820
100.0%
Lowercase Letter
ValueCountFrequency (%)
m 11
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 14776
99.9%
Latin 11
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
1 2320
15.7%
6 1853
12.5%
~ 1820
12.3%
8 1652
11.2%
2 1470
9.9%
0 1224
8.3%
5 1204
8.1%
7 867
 
5.9%
4 789
 
5.3%
3 778
 
5.3%
Other values (5) 799
 
5.4%
Latin
ValueCountFrequency (%)
m 11
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14787
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 2320
15.7%
6 1853
12.5%
~ 1820
12.3%
8 1652
11.2%
2 1470
9.9%
0 1224
8.3%
5 1204
8.1%
7 867
 
5.9%
4 789
 
5.3%
3 778
 
5.3%
Other values (6) 810
 
5.5%

데이터기준일자
Date

Distinct170
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2018-08-30 00:00:00
Maximum2024-04-30 00:00:00
2024-05-11T16:48:02.601724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:48:02.990946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

제공기관코드
Real number (ℝ)

Distinct259
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4406041
Minimum3000000
Maximum6500000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T16:48:03.354713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3000000
5-th percentile3130000
Q13620000
median4201000
Q35020000
95-th percentile6480000
Maximum6500000
Range3500000
Interquartile range (IQR)1400000

Descriptive statistics

Standard deviation965123.58
Coefficient of variation (CV)0.21904553
Kurtosis-0.47010899
Mean4406041
Median Absolute Deviation (MAD)651000
Skewness0.6246877
Sum4.406041 × 1010
Variance9.3146352 × 1011
MonotonicityNot monotonic
2024-05-11T16:48:04.191434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6480000 504
 
5.0%
3740000 250
 
2.5%
6500000 210
 
2.1%
5710000 178
 
1.8%
6300000 163
 
1.6%
4050000 136
 
1.4%
5020000 130
 
1.3%
4641000 126
 
1.3%
4490000 126
 
1.3%
4640000 124
 
1.2%
Other values (249) 8053
80.5%
ValueCountFrequency (%)
3000000 24
0.2%
3010000 18
 
0.2%
3020000 20
 
0.2%
3030000 35
0.4%
3040000 44
0.4%
3050000 45
0.4%
3060000 24
0.2%
3070000 55
0.5%
3080000 23
0.2%
3090000 46
0.5%
ValueCountFrequency (%)
6500000 210
2.1%
6480000 504
5.0%
6300000 163
 
1.6%
5710000 178
 
1.8%
5700000 21
 
0.2%
5690000 52
 
0.5%
5680000 42
 
0.4%
5670000 118
 
1.2%
5600000 58
 
0.6%
5590000 57
 
0.6%

제공기관명
Text

Distinct259
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-11T16:48:05.131635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length10
Mean length8.0077
Min length4

Characters and Unicode

Total characters80077
Distinct characters138
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row대전광역시
2nd row부산광역시 해운대구
3rd row경상남도
4th row부산광역시 강서구
5th row경기도 안산시
ValueCountFrequency (%)
경기도 1817
 
9.5%
경상남도 1023
 
5.4%
서울특별시 999
 
5.2%
경상북도 598
 
3.1%
부산광역시 546
 
2.9%
충청남도 519
 
2.7%
전라남도 505
 
2.6%
전라북도 432
 
2.3%
전북특별자치도 426
 
2.2%
대전광역시 417
 
2.2%
Other values (210) 11789
61.8%
2024-05-11T16:48:06.392409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9071
 
11.3%
시 8116
 
10.1%
도 6783
 
8.5%
경 3531
 
4.4%
구 3486
 
4.4%
광 2784
 
3.5%
남 2585
 
3.2%
역 2263
 
2.8%
북 2163
 
2.7%
특 2095
 
2.6%
Other values (128) 37200
46.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 71006
88.7%
Space Separator 9071
 
11.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 8116
 
11.4%
도 6783
 
9.6%
경 3531
 
5.0%
구 3486
 
4.9%
광 2784
 
3.9%
남 2585
 
3.6%
역 2263
 
3.2%
북 2163
 
3.0%
특 2095
 
3.0%
별 2095
 
3.0%
Other values (127) 35105
49.4%
Space Separator
ValueCountFrequency (%)
9071
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 71006
88.7%
Common 9071
 
11.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 8116
 
11.4%
도 6783
 
9.6%
경 3531
 
5.0%
구 3486
 
4.9%
광 2784
 
3.9%
남 2585
 
3.6%
역 2263
 
3.2%
북 2163
 
3.0%
특 2095
 
3.0%
별 2095
 
3.0%
Other values (127) 35105
49.4%
Common
ValueCountFrequency (%)
9071
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 71006
88.7%
ASCII 9071
 
11.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9071
100.0%
Hangul
ValueCountFrequency (%)
시 8116
 
11.4%
도 6783
 
9.6%
경 3531
 
5.0%
구 3486
 
4.9%
광 2784
 
3.9%
남 2585
 
3.6%
역 2263
 
3.2%
북 2163
 
3.0%
특 2095
 
3.0%
별 2095
 
3.0%
Other values (127) 35105
49.4%

Interactions

2024-05-11T16:47:46.729529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:47:43.442629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:47:44.370102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:47:45.826519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:47:47.000858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:47:43.668886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:47:44.594943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:47:46.061305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:47:47.372130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:47:43.884018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:47:44.876445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:47:46.292910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:47:47.619614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:47:44.109870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:47:45.103330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:47:46.496551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-11T16:48:06.618017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시설종류위도경도CCTV설치여부CCTV설치대수제공기관코드
시설종류1.0000.1320.1150.3460.1710.153
위도0.1321.0000.5530.2270.1180.728
경도0.1150.5531.0000.1310.0510.712
CCTV설치여부0.3460.2270.1311.0000.1690.180
CCTV설치대수0.1710.1180.0510.1691.0000.197
제공기관코드0.1530.7280.7120.1800.1971.000
2024-05-11T16:48:06.848953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시설종류CCTV설치여부
시설종류1.0000.260
CCTV설치여부0.2601.000
2024-05-11T16:48:07.037103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위도경도CCTV설치대수제공기관코드시설종류CCTV설치여부
위도1.000-0.278-0.081-0.3920.0630.174
경도-0.2781.0000.0970.1300.0560.130
CCTV설치대수-0.0810.0971.0000.1780.0840.168
제공기관코드-0.3920.1300.1781.0000.0760.181
시설종류0.0630.0560.0840.0761.0000.260
CCTV설치여부0.1740.1300.1680.1810.2601.000

Missing values

2024-05-11T16:47:47.986828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-11T16:47:48.544713image/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.
2024-05-11T16:47:48.893716image/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

시설종류대상시설명소재지도로명주소소재지지번주소위도경도관리기관명관할경찰서명CCTV설치여부CCTV설치대수보호구역도로폭데이터기준일자제공기관코드제공기관명
2991어린이집코레일대전어린이집대전광역시 대덕구 벚꽃길 148<NA>36.425354127.426941대전광역시대전대덕경찰서Y1<NA>2023-01-056300000대전광역시
1060유치원양운초교 병설유치원부산광역시 해운대구 대천로67번길 36부산광역시 해운대구 좌동 137235.174719129.166707부산광역시 해운대구청부산광역시 해운대경찰서Y<NA>7~202024-04-083330000부산광역시 해운대구
11612초등학교동부초등학교경상남도 거제시 동부면 동부로 27<NA>34.820022128.610386경상남도 거제시거제경찰서Y482023-12-186480000경상남도
7951초등학교녹산초등학교부산광역시 강서구 과학산단2로20번길 55 (지사동) 녹산초등학교부산광역시 강서구 지사동 1184-235.151475128.837122부산광역시 강서구청부산강서경찰서Y2202024-03-113360000부산광역시 강서구
11361유치원반월 초등학교 병설유치원경기도 안산시 상록구 건건로 39경기도 안산시 상록구 건건동 62937.305203126.900609경기도 안산시 상록구 도로교통과안산상록경찰서Y53~62023-12-263930000경기도 안산시
16029유치원북일초교 병설유치원충청북도 청주시 청원구 주중로 13-19 (주중동)충청북도 청주시 청원구 주중동42836.689649127.485398청주시청원Y<NA>4~82020-07-175710000충청북도 청주시
11422어린이집남천어린이집부산광역시 수영구 남천동로22번길 28<NA>35.141208129.10976부산광역시 수영구청남부경찰서Y<NA>6~82023-12-213380000부산광역시 수영구
14424초등학교부개서초등학교인천광역시 부평구 부일로 39인천광역시 부평구 부개동 155-137.48973126.734397인천광역시 부평구삼산경찰서Y610~132024-02-073540000인천광역시 부평구
13669유치원한천초등학교 병설유치원제주특별자치도 제주시 남성로11길 22제주특별자치도 제주시 용담일동 254-133.507334126.512797제주특별자치도 자치경찰단 교통정보센터제주서부경찰서Y1<NA>2023-10-026500000제주특별자치도
67초등학교영남초교경상북도 안동시 영남길 15경상북도 안동시 안기동 195-1036.573457128.720834경상북도 안동시청안동경찰서Y4252023-10-275070000경상북도 안동시
시설종류대상시설명소재지도로명주소소재지지번주소위도경도관리기관명관할경찰서명CCTV설치여부CCTV설치대수보호구역도로폭데이터기준일자제공기관코드제공기관명
1023유치원펀키즈 유치원경기도 오산시 큰말25번길 18 (세교동)경기도 오산시 세교동 40137.187942127.034634경기도 오산시청오산경찰서N042024-02-054000000경기도 오산시
13008초등학교문수초등학교울산광역시 울주군 청량읍 청송길 8울산광역시 울주군 청량읍 율리 39035.518877129.24063울산광역시 울주군청울주경찰서N0<NA>2023-11-023730000울산광역시 울주군
14066초등학교정릉초등학교서울특별시 성북구 솔샘로25가길 8서울특별시 성북구 정릉동 79837.619795127.005221서울특별시 성북구청서울특별시 성북경찰서Y1<NA>2024-04-123070000서울특별시 성북구
4526초등학교오봉초등학교전라북도 군산시 회현면 오봉길 55전라북도 군산시 회현면 월연리 491-135.888356126.725575전라북도 군산시청군산경찰서Y2<NA>2023-11-234670000전라북도 군산시
6636초등학교강당초등학교충청남도 서산시 부석면 인정로 183<NA>36.662214126.402058충청남도 서산시청서산경찰서Y<NA><NA>2022-07-294530000충청남도 서산시
6859초등학교산성초교충청북도 청주시 상당구 호미로 306 (용담동)충청북도 청주시 상당구 용담동43836.633848127.511082청주시상당Y54~82020-07-175710000충청북도 청주시
8330유치원구정 초등학교 병설유치원경상북도 포항시 남구 오천읍 구정길 11경상북도 포항시 남구 오천읍 구정리 441-135.98089129.408577경상북도 포항시청포항남부 경찰서N0<NA>2023-07-135020000경상북도 포항시
16012초등학교흥덕초교충청북도 청주시 흥덕구 흥덕로 121 (운천동)충청북도 청주시 흥덕구 운천동86536.645634127.473756청주시흥덕Y104~82020-07-175710000충청북도 청주시
1185유치원왕조초등학교 병설유치원전라남도 순천시 봉화1길 105<NA>34.960683127.513865전라남도 순천시청순천경찰서Y292024-02-144820000전라남도 순천시
5790어린이집큰별어린이집경기도 시흥시 장곡로54번길 43-3경기도 시흥시 장곡동 794-537.382215126.782008경기도 시흥시청시흥경찰서Y152023-11-304010000경기도 시흥시