Overview

Dataset statistics

Number of variables19
Number of observations2391
Missing cells1
Missing cells (%)< 0.1%
Duplicate rows3
Duplicate rows (%)0.1%
Total size in memory369.1 KiB
Average record size in memory158.1 B

Variable types

Numeric4
Categorical9
Text5
DateTime1

Alerts

데이터기준 has constant value ""Constant
자료출처 has constant value ""Constant
공개여부 has constant value ""Constant
작성일 has constant value ""Constant
갱신주기 has constant value ""Constant
Dataset has 3 (0.1%) duplicate rowsDuplicates
관리기관명 is highly overall correlated with 순번 and 5 other fieldsHigh correlation
시군명 is highly overall correlated with 순번 and 5 other fieldsHigh correlation
순번 is highly overall correlated with 시군명 and 2 other fieldsHigh correlation
경도 is highly overall correlated with 시군명 and 1 other fieldsHigh correlation
위도 is highly overall correlated with 시군명 and 2 other fieldsHigh correlation
설치목적 is highly overall correlated with 순번 and 4 other fieldsHigh correlation
카메라화소 is highly overall correlated with 보관일수High correlation
보관일수 is highly overall correlated with 시군명 and 3 other fieldsHigh correlation
보관일수 is highly imbalanced (50.1%)Imbalance

Reproduction

Analysis started2024-03-14 01:16:19.012594
Analysis finished2024-03-14 01:16:21.999922
Duration2.99 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순번
Real number (ℝ)

HIGH CORRELATION 

Distinct2386
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1309.8398
Minimum1
Maximum2622
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.1 KiB
2024-03-14T10:16:22.055463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile133.5
Q1667.5
median1306
Q31940.5
95-th percentile2496.5
Maximum2622
Range2621
Interquartile range (IQR)1273

Descriptive statistics

Standard deviation754.01738
Coefficient of variation (CV)0.57565617
Kurtosis-1.177354
Mean1309.8398
Median Absolute Deviation (MAD)637
Skewness0.021206613
Sum3131827
Variance568542.2
MonotonicityIncreasing
2024-03-14T10:16:22.163708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1812 2
 
0.1%
2442 2
 
0.1%
1820 2
 
0.1%
955 2
 
0.1%
1984 2
 
0.1%
1733 1
 
< 0.1%
1729 1
 
< 0.1%
1730 1
 
< 0.1%
1731 1
 
< 0.1%
1732 1
 
< 0.1%
Other values (2376) 2376
99.4%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
2622 1
< 0.1%
2621 1
< 0.1%
2620 1
< 0.1%
2619 1
< 0.1%
2618 1
< 0.1%
2617 1
< 0.1%
2616 1
< 0.1%
2615 1
< 0.1%
2614 1
< 0.1%
2613 1
< 0.1%

시군명
Categorical

HIGH CORRELATION 

Distinct13
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size18.8 KiB
전주시
459 
군산시
455 
익산시
320 
정읍시
233 
김제시
170 
Other values (8)
754 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
전주시 459
19.2%
군산시 455
19.0%
익산시 320
13.4%
정읍시 233
9.7%
김제시 170
 
7.1%
고창군 159
 
6.6%
완주군 133
 
5.6%
남원시 124
 
5.2%
부안군 97
 
4.1%
임실군 85
 
3.6%
Other values (3) 156
 
6.5%

Length

2024-03-14T10:16:22.269066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
전주시 459
19.2%
군산시 455
19.0%
익산시 320
13.4%
정읍시 233
9.7%
김제시 170
 
7.1%
고창군 159
 
6.6%
완주군 133
 
5.6%
남원시 124
 
5.2%
부안군 97
 
4.1%
임실군 85
 
3.6%
Other values (3) 156
 
6.5%

관리기관명
Categorical

HIGH CORRELATION 

Distinct25
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size18.8 KiB
전주시청
459 
군산시청
455 
익산시청
320 
정읍시청
233 
김제시청
170 
Other values (20)
754 

Length

Max length11
Median length4
Mean length4.1764952
Min length4

Unique

Unique4 ?
Unique (%)0.2%

Sample

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

Common Values

ValueCountFrequency (%)
전주시청 459
19.2%
군산시청 455
19.0%
익산시청 320
13.4%
정읍시청 233
9.7%
김제시청 170
 
7.1%
고창군청 159
 
6.6%
완주군청 133
 
5.6%
남원시청 124
 
5.2%
부안군청 97
 
4.1%
장수군청 76
 
3.2%
Other values (15) 165
 
6.9%

Length

2024-03-14T10:16:22.371964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
전주시청 459
18.6%
군산시청 455
18.4%
익산시청 320
13.0%
정읍시청 233
9.4%
김제시청 170
 
6.9%
고창군청 159
 
6.4%
완주군청 133
 
5.4%
남원시청 124
 
5.0%
부안군청 97
 
3.9%
임실군 77
 
3.1%
Other values (16) 241
9.8%

도로명주소
Text

Distinct1132
Distinct (%)47.3%
Missing0
Missing (%)0.0%
Memory size18.8 KiB
2024-03-14T10:16:22.681141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length19
Mean length8.6787955
Min length1

Characters and Unicode

Total characters20751
Distinct characters303
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

Unique971 ?
Unique (%)40.6%

Sample

1st row전주시 완산구 서학3길 34
2nd row전주시 완산구 거마평로 24
3rd row전주시 완산구 풍남문5길 24-2
4th row전주시 완산구 소대배기로 18-20
5th row전주시 완산구 전라감영4길 14
ValueCountFrequency (%)
967
 
16.2%
전주시 347
 
5.8%
군산시 227
 
3.8%
익산시 182
 
3.0%
완산구 182
 
3.0%
정읍시 170
 
2.8%
덕진구 164
 
2.7%
고창군 108
 
1.8%
완주군 91
 
1.5%
남원시 80
 
1.3%
Other values (1531) 3456
57.9%
2024-03-14T10:16:23.107199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3583
 
17.3%
- 1289
 
6.2%
시 1063
 
5.1%
1 1028
 
5.0%
산 775
 
3.7%
로 761
 
3.7%
길 754
 
3.6%
2 639
 
3.1%
군 600
 
2.9%
3 536
 
2.6%
Other values (293) 9723
46.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 11505
55.4%
Decimal Number 4374
 
21.1%
Space Separator 3583
 
17.3%
Dash Punctuation 1289
 
6.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 1063
 
9.2%
산 775
 
6.7%
로 761
 
6.6%
길 754
 
6.6%
군 600
 
5.2%
주 496
 
4.3%
구 386
 
3.4%
전 368
 
3.2%
면 294
 
2.6%
읍 289
 
2.5%
Other values (281) 5719
49.7%
Decimal Number
ValueCountFrequency (%)
1 1028
23.5%
2 639
14.6%
3 536
12.3%
4 410
 
9.4%
5 351
 
8.0%
6 341
 
7.8%
7 276
 
6.3%
0 272
 
6.2%
9 270
 
6.2%
8 251
 
5.7%
Space Separator
ValueCountFrequency (%)
3583
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1289
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 11505
55.4%
Common 9246
44.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 1063
 
9.2%
산 775
 
6.7%
로 761
 
6.6%
길 754
 
6.6%
군 600
 
5.2%
주 496
 
4.3%
구 386
 
3.4%
전 368
 
3.2%
면 294
 
2.6%
읍 289
 
2.5%
Other values (281) 5719
49.7%
Common
ValueCountFrequency (%)
3583
38.8%
- 1289
 
13.9%
1 1028
 
11.1%
2 639
 
6.9%
3 536
 
5.8%
4 410
 
4.4%
5 351
 
3.8%
6 341
 
3.7%
7 276
 
3.0%
0 272
 
2.9%
Other values (2) 521
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 11505
55.4%
ASCII 9246
44.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3583
38.8%
- 1289
 
13.9%
1 1028
 
11.1%
2 639
 
6.9%
3 536
 
5.8%
4 410
 
4.4%
5 351
 
3.8%
6 341
 
3.7%
7 276
 
3.0%
0 272
 
2.9%
Other values (2) 521
 
5.6%
Hangul
ValueCountFrequency (%)
시 1063
 
9.2%
산 775
 
6.7%
로 761
 
6.6%
길 754
 
6.6%
군 600
 
5.2%
주 496
 
4.3%
구 386
 
3.4%
전 368
 
3.2%
면 294
 
2.6%
읍 289
 
2.5%
Other values (281) 5719
49.7%

지번주소
Text

Distinct2033
Distinct (%)85.0%
Missing0
Missing (%)0.0%
Memory size18.8 KiB
2024-03-14T10:16:23.410285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length18
Mean length15.530322
Min length9

Characters and Unicode

Total characters37133
Distinct characters233
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

Unique1818 ?
Unique (%)76.0%

Sample

1st row전주시 완산구 동서학동 236-1
2nd row전주시 완산구 삼천동1가 660-1
3rd row전주시 완산구 다가동1가 73-1
4th row전주시 완산구 평화동2가 891-5
5th row전주시 완산구 중앙동3가 102-5
ValueCountFrequency (%)
전주시 459
 
5.3%
군산시 453
 
5.2%
익산시 318
 
3.7%
완산구 243
 
2.8%
정읍시 235
 
2.7%
덕진구 216
 
2.5%
김제시 173
 
2.0%
고창군 159
 
1.8%
완주군 136
 
1.6%
남원시 124
 
1.4%
Other values (2637) 6186
71.1%
2024-03-14T10:16:23.817682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6311
 
17.0%
1 1927
 
5.2%
시 1795
 
4.8%
- 1764
 
4.8%
산 1512
 
4.1%
동 1440
 
3.9%
2 1290
 
3.5%
군 1091
 
2.9%
리 1074
 
2.9%
3 984
 
2.6%
Other values (223) 17945
48.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 19436
52.3%
Decimal Number 9622
25.9%
Space Separator 6311
 
17.0%
Dash Punctuation 1764
 
4.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 1795
 
9.2%
산 1512
 
7.8%
동 1440
 
7.4%
군 1091
 
5.6%
리 1074
 
5.5%
면 789
 
4.1%
주 667
 
3.4%
읍 592
 
3.0%
구 533
 
2.7%
전 488
 
2.5%
Other values (211) 9455
48.6%
Decimal Number
ValueCountFrequency (%)
1 1927
20.0%
2 1290
13.4%
3 984
10.2%
5 931
9.7%
4 923
9.6%
6 829
8.6%
8 774
8.0%
7 740
 
7.7%
9 620
 
6.4%
0 604
 
6.3%
Space Separator
ValueCountFrequency (%)
6311
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1764
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 19436
52.3%
Common 17697
47.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 1795
 
9.2%
산 1512
 
7.8%
동 1440
 
7.4%
군 1091
 
5.6%
리 1074
 
5.5%
면 789
 
4.1%
주 667
 
3.4%
읍 592
 
3.0%
구 533
 
2.7%
전 488
 
2.5%
Other values (211) 9455
48.6%
Common
ValueCountFrequency (%)
6311
35.7%
1 1927
 
10.9%
- 1764
 
10.0%
2 1290
 
7.3%
3 984
 
5.6%
5 931
 
5.3%
4 923
 
5.2%
6 829
 
4.7%
8 774
 
4.4%
7 740
 
4.2%
Other values (2) 1224
 
6.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 19436
52.3%
ASCII 17697
47.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6311
35.7%
1 1927
 
10.9%
- 1764
 
10.0%
2 1290
 
7.3%
3 984
 
5.6%
5 931
 
5.3%
4 923
 
5.2%
6 829
 
4.7%
8 774
 
4.4%
7 740
 
4.2%
Other values (2) 1224
 
6.9%
Hangul
ValueCountFrequency (%)
시 1795
 
9.2%
산 1512
 
7.8%
동 1440
 
7.4%
군 1091
 
5.6%
리 1074
 
5.5%
면 789
 
4.1%
주 667
 
3.4%
읍 592
 
3.0%
구 533
 
2.7%
전 488
 
2.5%
Other values (211) 9455
48.6%

설치목적
Categorical

HIGH CORRELATION 

Distinct48
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size18.8 KiB
어린이보호
434 
생활방범
338 
<NA>
282 
방범CCTV
174 
방범용
141 
Other values (43)
1022 

Length

Max length17
Median length15
Mean length5.0250941
Min length1

Unique

Unique2 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
어린이보호 434
18.2%
생활방범 338
14.1%
<NA> 282
11.8%
방범CCTV 174
 
7.3%
방범용 141
 
5.9%
어린이보호구역 133
 
5.6%
시설물관리 107
 
4.5%
차량방범 101
 
4.2%
교통정보수집 85
 
3.6%
도심공원．놀이터 62
 
2.6%
Other values (38) 534
22.3%

Length

2024-03-14T10:16:23.972752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
어린이보호 434
16.7%
생활방범 338
13.0%
na 282
 
10.9%
방범cctv 174
 
6.7%
방범용 141
 
5.4%
어린이보호구역 133
 
5.1%
시설물관리 111
 
4.3%
차량방범 101
 
3.9%
교통정보수집 85
 
3.3%
도심공원．놀이터 62
 
2.4%
Other values (48) 732
28.2%

카메라대수
Real number (ℝ)

Distinct25
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.427018
Minimum0
Maximum65
Zeros5
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size21.1 KiB
2024-03-14T10:16:24.081659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q32
95-th percentile6
Maximum65
Range65
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.8267265
Coefficient of variation (CV)1.1646912
Kurtosis200.16636
Mean2.427018
Median Absolute Deviation (MAD)0
Skewness11.458477
Sum5803
Variance7.9903827
MonotonicityNot monotonic
2024-03-14T10:16:24.186296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
2 1220
51.0%
1 612
25.6%
3 310
 
13.0%
4 90
 
3.8%
6 34
 
1.4%
8 27
 
1.1%
7 21
 
0.9%
5 19
 
0.8%
10 14
 
0.6%
9 13
 
0.5%
Other values (15) 31
 
1.3%
ValueCountFrequency (%)
0 5
 
0.2%
1 612
25.6%
2 1220
51.0%
3 310
 
13.0%
4 90
 
3.8%
5 19
 
0.8%
6 34
 
1.4%
7 21
 
0.9%
8 27
 
1.1%
9 13
 
0.5%
ValueCountFrequency (%)
65 1
 
< 0.1%
57 1
 
< 0.1%
48 1
 
< 0.1%
36 1
 
< 0.1%
25 1
 
< 0.1%
24 1
 
< 0.1%
20 1
 
< 0.1%
19 2
0.1%
16 4
0.2%
15 3
0.1%

카메라화소
Categorical

HIGH CORRELATION 

Distinct28
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size18.8 KiB
200
1317 
41
231 
-
215 
130
148 
300
 
106
Other values (23)
374 

Length

Max length10
Median length3
Mean length2.7281472
Min length1

Unique

Unique4 ?
Unique (%)0.2%

Sample

1st row200
2nd row200
3rd row200
4th row200
5th row200

Common Values

ValueCountFrequency (%)
200 1317
55.1%
41 231
 
9.7%
- 215
 
9.0%
130 148
 
6.2%
300 106
 
4.4%
35 84
 
3.5%
140 81
 
3.4%
100 67
 
2.8%
30 29
 
1.2%
40 22
 
0.9%
Other values (18) 91
 
3.8%

Length

2024-03-14T10:16:24.286624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
200 1346
55.3%
41 261
 
10.7%
215
 
8.8%
130 169
 
6.9%
300 106
 
4.4%
35 84
 
3.5%
140 81
 
3.3%
100 67
 
2.8%
30 29
 
1.2%
40 22
 
0.9%
Other values (15) 54
 
2.2%

촬영방면
Text

Distinct220
Distinct (%)9.2%
Missing1
Missing (%)< 0.1%
Memory size18.8 KiB
2024-03-14T10:16:24.483929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length45
Median length1
Mean length4.0297071
Min length1

Characters and Unicode

Total characters9631
Distinct characters244
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

Unique184 ?
Unique (%)7.7%

Sample

1st row반경100m이내
2nd row반경100m이내
3rd row반경100m이내
4th row반경100m이내
5th row반경100m이내
ValueCountFrequency (%)
1387
48.7%
반경100m이내 428
 
15.0%
360도전방면 101
 
3.5%
주변 83
 
2.9%
초교 70
 
2.5%
장수군 40
 
1.4%
360도 36
 
1.3%
전방면 36
 
1.3%
쓰레기배출장소 28
 
1.0%
도로변 24
 
0.8%
Other values (316) 615
21.6%
2024-03-14T10:16:24.839752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 1413
 
14.7%
0 1038
 
10.8%
내 483
 
5.0%
1 472
 
4.9%
459
 
4.8%
이 436
 
4.5%
경 429
 
4.5%
반 428
 
4.4%
m 428
 
4.4%
도 222
 
2.3%
Other values (234) 3823
39.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5215
54.1%
Decimal Number 1969
 
20.4%
Dash Punctuation 1413
 
14.7%
Space Separator 459
 
4.8%
Lowercase Letter 428
 
4.4%
Other Punctuation 79
 
0.8%
Close Punctuation 25
 
0.3%
Open Punctuation 25
 
0.3%
Uppercase Letter 10
 
0.1%
Math Symbol 8
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
내 483
 
9.3%
이 436
 
8.4%
경 429
 
8.2%
반 428
 
8.2%
도 222
 
4.3%
면 218
 
4.2%
방 199
 
3.8%
전 181
 
3.5%
장 179
 
3.4%
주 150
 
2.9%
Other values (210) 2290
43.9%
Decimal Number
ValueCountFrequency (%)
0 1038
52.7%
1 472
24.0%
3 184
 
9.3%
6 178
 
9.0%
4 20
 
1.0%
2 19
 
1.0%
8 16
 
0.8%
5 16
 
0.8%
9 14
 
0.7%
7 12
 
0.6%
Uppercase Letter
ValueCountFrequency (%)
C 4
40.0%
I 2
20.0%
A 2
20.0%
M 2
20.0%
Other Punctuation
ValueCountFrequency (%)
, 51
64.6%
/ 26
32.9%
: 2
 
2.5%
Math Symbol
ValueCountFrequency (%)
↔ 7
87.5%
~ 1
 
12.5%
Dash Punctuation
ValueCountFrequency (%)
- 1413
100.0%
Space Separator
ValueCountFrequency (%)
459
100.0%
Lowercase Letter
ValueCountFrequency (%)
m 428
100.0%
Close Punctuation
ValueCountFrequency (%)
) 25
100.0%
Open Punctuation
ValueCountFrequency (%)
( 25
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5215
54.1%
Common 3978
41.3%
Latin 438
 
4.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
내 483
 
9.3%
이 436
 
8.4%
경 429
 
8.2%
반 428
 
8.2%
도 222
 
4.3%
면 218
 
4.2%
방 199
 
3.8%
전 181
 
3.5%
장 179
 
3.4%
주 150
 
2.9%
Other values (210) 2290
43.9%
Common
ValueCountFrequency (%)
- 1413
35.5%
0 1038
26.1%
1 472
 
11.9%
459
 
11.5%
3 184
 
4.6%
6 178
 
4.5%
, 51
 
1.3%
/ 26
 
0.7%
) 25
 
0.6%
( 25
 
0.6%
Other values (9) 107
 
2.7%
Latin
ValueCountFrequency (%)
m 428
97.7%
C 4
 
0.9%
I 2
 
0.5%
A 2
 
0.5%
M 2
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5215
54.1%
ASCII 4409
45.8%
Arrows 7
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 1413
32.0%
0 1038
23.5%
1 472
 
10.7%
459
 
10.4%
m 428
 
9.7%
3 184
 
4.2%
6 178
 
4.0%
, 51
 
1.2%
/ 26
 
0.6%
) 25
 
0.6%
Other values (13) 135
 
3.1%
Hangul
ValueCountFrequency (%)
내 483
 
9.3%
이 436
 
8.4%
경 429
 
8.2%
반 428
 
8.2%
도 222
 
4.3%
면 218
 
4.2%
방 199
 
3.8%
전 181
 
3.5%
장 179
 
3.4%
주 150
 
2.9%
Other values (210) 2290
43.9%
Arrows
ValueCountFrequency (%)
↔ 7
100.0%

보관일수
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size18.8 KiB
30
1181 
0
1160 
60
 
20
90
 
17
210
 
13

Length

Max length3
Median length2
Mean length1.5202844
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
30 1181
49.4%
0 1160
48.5%
60 20
 
0.8%
90 17
 
0.7%
210 13
 
0.5%

Length

2024-03-14T10:16:24.963171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T10:16:25.051323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
30 1181
49.4%
0 1160
48.5%
60 20
 
0.8%
90 17
 
0.7%
210 13
 
0.5%

설치년월
Text

Distinct96
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size18.8 KiB
2024-03-14T10:16:25.191291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length4
Mean length3.8485989
Min length1

Characters and Unicode

Total characters9202
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique39 ?
Unique (%)1.6%

Sample

1st row2011
2nd row2005
3rd row2010
4th row2006
5th row2012
ValueCountFrequency (%)
676
28.2%
2013 309
12.9%
2014 253
 
10.6%
2011 206
 
8.6%
2012 185
 
7.7%
2010 114
 
4.8%
2015 105
 
4.4%
2009 48
 
2.0%
2013-01 41
 
1.7%
2014-01 33
 
1.4%
Other values (84) 425
17.7%
2024-03-14T10:16:25.460936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 2494
27.1%
1 2139
23.2%
2 2070
22.5%
- 1225
13.3%
3 431
 
4.7%
4 335
 
3.6%
5 217
 
2.4%
9 94
 
1.0%
6 78
 
0.8%
8 57
 
0.6%
Other values (3) 62
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7969
86.6%
Dash Punctuation 1225
 
13.3%
Other Punctuation 4
 
< 0.1%
Space Separator 4
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2494
31.3%
1 2139
26.8%
2 2070
26.0%
3 431
 
5.4%
4 335
 
4.2%
5 217
 
2.7%
9 94
 
1.2%
6 78
 
1.0%
8 57
 
0.7%
7 54
 
0.7%
Dash Punctuation
ValueCountFrequency (%)
- 1225
100.0%
Other Punctuation
ValueCountFrequency (%)
, 4
100.0%
Space Separator
ValueCountFrequency (%)
4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 9202
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2494
27.1%
1 2139
23.2%
2 2070
22.5%
- 1225
13.3%
3 431
 
4.7%
4 335
 
3.6%
5 217
 
2.4%
9 94
 
1.0%
6 78
 
0.8%
8 57
 
0.6%
Other values (3) 62
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9202
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2494
27.1%
1 2139
23.2%
2 2070
22.5%
- 1225
13.3%
3 431
 
4.7%
4 335
 
3.6%
5 217
 
2.4%
9 94
 
1.0%
6 78
 
0.8%
8 57
 
0.6%
Other values (3) 62
 
0.7%

전화번호
Text

Distinct75
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size18.8 KiB
2024-03-14T10:16:25.615804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length7.8732748
Min length1

Characters and Unicode

Total characters18825
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique40 ?
Unique (%)1.7%

Sample

1st row063-281-2072
2nd row063-281-2072
3rd row063-281-2072
4th row063-281-2072
5th row063-281-2072
ValueCountFrequency (%)
897
37.5%
063-281-2072 428
17.9%
063-539-5682 233
 
9.7%
063-540-2913 170
 
7.1%
063-560-2334 159
 
6.6%
063-290-2305 113
 
4.7%
063-580-4862 97
 
4.1%
063-281-2728 31
 
1.3%
063-430-2358 25
 
1.0%
063-320-2493 20
 
0.8%
Other values (65) 218
 
9.1%
2024-03-14T10:16:25.893336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 3885
20.6%
0 2875
15.3%
3 2635
14.0%
2 2591
13.8%
6 2110
11.2%
5 1203
 
6.4%
8 992
 
5.3%
1 694
 
3.7%
4 677
 
3.6%
9 612
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14940
79.4%
Dash Punctuation 3885
 
20.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2875
19.2%
3 2635
17.6%
2 2591
17.3%
6 2110
14.1%
5 1203
8.1%
8 992
 
6.6%
1 694
 
4.6%
4 677
 
4.5%
9 612
 
4.1%
7 551
 
3.7%
Dash Punctuation
ValueCountFrequency (%)
- 3885
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 18825
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 3885
20.6%
0 2875
15.3%
3 2635
14.0%
2 2591
13.8%
6 2110
11.2%
5 1203
 
6.4%
8 992
 
5.3%
1 694
 
3.7%
4 677
 
3.6%
9 612
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 18825
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 3885
20.6%
0 2875
15.3%
3 2635
14.0%
2 2591
13.8%
6 2110
11.2%
5 1203
 
6.4%
8 992
 
5.3%
1 694
 
3.7%
4 677
 
3.6%
9 612
 
3.3%

경도
Real number (ℝ)

HIGH CORRELATION 

Distinct2047
Distinct (%)85.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.76328
Minimum126.28223
Maximum127.5146
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.1 KiB
2024-03-14T10:16:26.038834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.28223
5-th percentile126.3916
Q1126.45203
median126.57386
Q3127.08122
95-th percentile127.31061
Maximum127.5146
Range1.2323709
Interquartile range (IQR)0.62918554

Descriptive statistics

Standard deviation0.33712267
Coefficient of variation (CV)0.0026594664
Kurtosis-1.5401689
Mean126.76328
Median Absolute Deviation (MAD)0.16912666
Skewness0.33864875
Sum303090.99
Variance0.11365169
MonotonicityNot monotonic
2024-03-14T10:16:26.156603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.49438112 22
 
0.9%
126.51450845 15
 
0.6%
126.58469205 10
 
0.4%
127.07426278 8
 
0.3%
126.31456278 7
 
0.3%
126.53479844 6
 
0.3%
126.58268225 6
 
0.3%
126.50584358 5
 
0.2%
126.56229998 5
 
0.2%
126.51217069 5
 
0.2%
Other values (2037) 2302
96.3%
ValueCountFrequency (%)
126.28222842 1
< 0.1%
126.28361696 1
< 0.1%
126.28393256 1
< 0.1%
126.2904922 1
< 0.1%
126.29147501 1
< 0.1%
126.29209587 1
< 0.1%
126.29246185 1
< 0.1%
126.29397195 1
< 0.1%
126.29407809 1
< 0.1%
126.2951683 1
< 0.1%
ValueCountFrequency (%)
127.51459933 1
< 0.1%
127.50522274 1
< 0.1%
127.5042864 1
< 0.1%
127.50203716 1
< 0.1%
127.47430843 1
< 0.1%
127.47292852 1
< 0.1%
127.47282503 1
< 0.1%
127.47262043 1
< 0.1%
127.47200897 1
< 0.1%
127.46568297 1
< 0.1%

위도
Real number (ℝ)

HIGH CORRELATION 

Distinct2047
Distinct (%)85.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.485713
Minimum35.184237
Maximum36.084874
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.1 KiB
2024-03-14T10:16:26.274571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.184237
5-th percentile35.255066
Q135.385253
median35.494129
Q335.571617
95-th percentile35.591871
Maximum36.084874
Range0.90063674
Interquartile range (IQR)0.18636435

Descriptive statistics

Standard deviation0.15174213
Coefficient of variation (CV)0.0042761471
Kurtosis3.9281523
Mean35.485713
Median Absolute Deviation (MAD)0.079218087
Skewness1.256126
Sum84846.34
Variance0.023025674
MonotonicityNot monotonic
2024-03-14T10:16:26.591650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.585032629 22
 
0.9%
35.590627295 15
 
0.6%
35.571617362 10
 
0.4%
35.574577489 8
 
0.3%
35.434704687 7
 
0.3%
35.481760016 6
 
0.3%
35.574608707 6
 
0.3%
35.344663615 5
 
0.2%
35.564419663 5
 
0.2%
35.341153404 5
 
0.2%
Other values (2037) 2302
96.3%
ValueCountFrequency (%)
35.184237234 1
 
< 0.1%
35.185646676 1
 
< 0.1%
35.194361363 1
 
< 0.1%
35.201340195 1
 
< 0.1%
35.202620192 3
0.1%
35.204260648 1
 
< 0.1%
35.204754377 1
 
< 0.1%
35.205010647 1
 
< 0.1%
35.210259054 1
 
< 0.1%
35.21228257 1
 
< 0.1%
ValueCountFrequency (%)
36.084873975 1
< 0.1%
36.080429259 1
< 0.1%
36.074598847 1
< 0.1%
36.071958545 1
< 0.1%
36.07154902 1
< 0.1%
36.071373048 1
< 0.1%
36.071275562 1
< 0.1%
36.06332775 1
< 0.1%
36.061844324 1
< 0.1%
36.061639664 1
< 0.1%

데이터기준
Date

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size18.8 KiB
Minimum2015-09-30 00:00:00
Maximum2015-09-30 00:00:00
2024-03-14T10:16:26.687083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T10:16:26.774735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

자료출처
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size18.8 KiB
정보화총괄과
2391 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
정보화총괄과 2391
100.0%

Length

2024-03-14T10:16:26.888021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T10:16:26.966178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
정보화총괄과 2391
100.0%

공개여부
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size18.8 KiB
공개
2391 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
공개 2391
100.0%

Length

2024-03-14T10:16:27.044633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T10:16:27.119146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
공개 2391
100.0%

작성일
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size18.8 KiB
2015.1
2391 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2015.1
2nd row2015.1
3rd row2015.1
4th row2015.1
5th row2015.1

Common Values

ValueCountFrequency (%)
2015.1 2391
100.0%

Length

2024-03-14T10:16:27.192726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T10:16:27.261405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2015.1 2391
100.0%

갱신주기
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size18.8 KiB
1년
2391 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1년
2nd row1년
3rd row1년
4th row1년
5th row1년

Common Values

ValueCountFrequency (%)
1년 2391
100.0%

Length

2024-03-14T10:16:27.329668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T10:16:27.398443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1년 2391
100.0%

Interactions

2024-03-14T10:16:21.350645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T10:16:20.398169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T10:16:20.705616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T10:16:21.046532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T10:16:21.421609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T10:16:20.477912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T10:16:20.774994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T10:16:21.128320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T10:16:21.509457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T10:16:20.563707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T10:16:20.856134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T10:16:21.200333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T10:16:21.583401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T10:16:20.638016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T10:16:20.943662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T10:16:21.276219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T10:16:27.466153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번시군명관리기관명설치목적카메라대수카메라화소보관일수설치년월전화번호경도위도
순번1.0000.9500.9710.9530.2010.7750.8020.8320.9550.7940.815
시군명0.9501.0001.0000.9650.2570.8510.8350.8880.9940.9140.906
관리기관명0.9711.0001.0000.9840.3070.8230.9110.8611.0000.9390.928
설치목적0.9530.9650.9841.0000.6230.8530.8600.8580.9880.8180.851
카메라대수0.2010.2570.3070.6231.0000.7540.0360.8100.6560.2590.136
카메라화소0.7750.8510.8230.8530.7541.0000.7720.9150.9150.6820.662
보관일수0.8020.8350.9110.8600.0360.7721.0000.7810.9850.6040.517
설치년월0.8320.8880.8610.8580.8100.9150.7811.0000.9830.7620.688
전화번호0.9550.9941.0000.9880.6560.9150.9850.9831.0000.9130.921
경도0.7940.9140.9390.8180.2590.6820.6040.7620.9131.0000.619
위도0.8150.9060.9280.8510.1360.6620.5170.6880.9210.6191.000
2024-03-14T10:16:27.611271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관리기관명카메라화소시군명보관일수설치목적
관리기관명1.0000.3430.9970.6460.740
카메라화소0.3431.0000.4690.5030.335
시군명0.9970.4691.0000.6500.738
보관일수0.6460.5030.6501.0000.599
설치목적0.7400.3350.7380.5991.000
2024-03-14T10:16:27.712408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번카메라대수경도위도시군명관리기관명설치목적카메라화소보관일수
순번1.0000.035-0.040-0.4840.8100.8070.7250.4080.459
카메라대수0.0351.0000.122-0.0880.1200.1280.2870.4080.021
경도-0.0400.1221.000-0.1600.7380.7370.4740.3400.428
위도-0.484-0.088-0.1601.0000.7400.7390.5610.3660.383
시군명0.8100.1200.7380.7401.0000.9970.7380.4690.650
관리기관명0.8070.1280.7370.7390.9971.0000.7400.3430.646
설치목적0.7250.2870.4740.5610.7380.7401.0000.3350.599
카메라화소0.4080.4080.3400.3660.4690.3430.3351.0000.503
보관일수0.4590.0210.4280.3830.6500.6460.5990.5031.000

Missing values

2024-03-14T10:16:21.699813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T10:16:21.922086image/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

순번시군명관리기관명도로명주소지번주소설치목적카메라대수카메라화소촬영방면보관일수설치년월전화번호경도위도데이터기준자료출처공개여부작성일갱신주기
01전주시전주시청전주시 완산구 서학3길 34전주시 완산구 동서학동 236-1생활방범1200반경100m이내302011063-281-2072127.09063535.4825112015-09-30정보화총괄과공개2015.11년
12전주시전주시청전주시 완산구 거마평로 24전주시 완산구 삼천동1가 660-1생활방범2200반경100m이내302005063-281-2072127.07014535.4740172015-09-30정보화총괄과공개2015.11년
23전주시전주시청전주시 완산구 풍남문5길 24-2전주시 완산구 다가동1가 73-1생활방범1200반경100m이내302010063-281-2072127.08305735.4851162015-09-30정보화총괄과공개2015.11년
34전주시전주시청전주시 완산구 소대배기로 18-20전주시 완산구 평화동2가 891-5생활방범2200반경100m이내302006063-281-2072127.07552135.4711892015-09-30정보화총괄과공개2015.11년
45전주시전주시청전주시 완산구 전라감영4길 14전주시 완산구 중앙동3가 102-5생활방범2200반경100m이내302012063-281-2072127.08412135.4900252015-09-30정보화총괄과공개2015.11년
56전주시전주시청전주시 완산구 속거길 2전주시 완산구 효자동3가 1273-5생활방범2200반경100m이내302007063-281-2072127.05161735.4858772015-09-30정보화총괄과공개2015.11년
67전주시전주시청전주시 완산구 서신천변9길 5전주시 완산구 서신동 825-1생활방범1200반경100m이내302007063-281-2072127.06491835.4946912015-09-30정보화총괄과공개2015.11년
78전주시전주시청전주시 완산구 송정로 19-15전주시 완산구 효자동1가 575-15생활방범2200반경100m이내302005063-281-2072127.07074335.4812392015-09-30정보화총괄과공개2015.11년
89전주시전주시청전주시 완산구 서곡로 78전주시 완산구 효자동3가 1436-1생활방범2200반경100m이내302012063-281-2072127.06064835.5007992015-09-30정보화총괄과공개2015.11년
910전주시전주시청전주시 완산구 성지산2길 19전주시 완산구 효자동1가 558-11생활방범2200반경100m이내302012063-281-2072127.07180535.4815772015-09-30정보화총괄과공개2015.11년
순번시군명관리기관명도로명주소지번주소설치목적카메라대수카메라화소촬영방면보관일수설치년월전화번호경도위도데이터기준자료출처공개여부작성일갱신주기
23812613부안군부안군청-부안군 백산면 대수리 1교통정보수집2200-30-063-580-4862126.50202635.4157722015-09-30정보화총괄과공개2015.11년
23822614부안군부안군청-부안군 변산면 도청리 490-3교통정보수집2200-30-063-580-4862126.29049235.3607072015-09-30정보화총괄과공개2015.11년
23832615부안군부안군청부안군 번영로 96부안군 부안읍 서외리 58-3생활방범1200-302015-07-15063-580-4862126.43514435.4334932015-09-30정보화총괄과공개2015.11년
23842616부안군부안군청부안군 오리정로 89부안군 부안읍 서외리 536-15생활방범1200-302015-07-15063-580-4862126.43544335.4324222015-09-30정보화총괄과공개2015.11년
23852617부안군부안군청부안군 번영로 173부안군 부안읍 동중리 130-5생활방범1200-302015-07-15063-580-4862126.44212835.4337852015-09-30정보화총괄과공개2015.11년
23862618부안군부안군청부안군 석정로 203부안군 부안읍 봉덕리 574-105생활방범1200-302015-07-15063-580-4862126.44147435.4338842015-09-30정보화총괄과공개2015.11년
23872619부안군부안군청부안군 석정로 179부안군 부안읍 선은리 260-6생활방범1200-302015-07-15063-580-4862126.44154635.4347392015-09-30정보화총괄과공개2015.11년
23882620부안군부안군청부안군 청자로 959-1부안군 진서면 곰소리 226생활방범1200-302015-07-15063-580-4862126.36327835.3516022015-09-30정보화총괄과공개2015.11년
23892621부안군부안군청부안군 줄포중앙로 52부안군 줄포면 줄포리 411-7생활방범1200-302015-07-15063-580-4862126.40443935.3525752015-09-30정보화총괄과공개2015.11년
23902622부안군부안군청부안군 당산로 27-1부안군 부안읍 서외리 332-5생활방범1200-302015-07-15063-580-4862126.43394135.4342052015-09-30정보화총괄과공개2015.11년

Duplicate rows

Most frequently occurring

순번시군명관리기관명도로명주소지번주소설치목적카메라대수카메라화소촬영방면보관일수설치년월전화번호경도위도데이터기준자료출처공개여부작성일갱신주기# duplicates
01812김제시김제시청김제시 동서16길 23김제시 검산동 1031방범2--30-063-540-2913126.53479835.481762015-09-30정보화총괄과공개2015.11년2
11820김제시김제시청김제시 동서16길 23김제시 검산동 1031방범3--30-063-540-2913126.53479835.481762015-09-30정보화총괄과공개2015.11년2
21984완주군완주군청완주군 이서면 양동길 15완주군 이서면 갈산리 642-2방범용2200-02014063-290-2305127.03103435.5020932015-09-30정보화총괄과공개2015.11년2