Overview

Dataset statistics

Number of variables9
Number of observations49
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.7 KiB
Average record size in memory77.6 B

Variable types

Categorical2
Text4
Numeric3

Dataset

Description광주광역시 자치구별 5대 범죄현황에 대한 데이터로 (살인/강도/강간 및 강제추행/절도/폭력) 관련 데이터를 제공합니다
Author경찰청 광주광역시경찰청
URLhttps://www.data.go.kr/data/15086190/fileData.do

Alerts

살인 is highly overall correlated with 강도High correlation
강도 is highly overall correlated with 살인 and 1 other fieldsHigh correlation
강간·강제추행 is highly overall correlated with 강도High correlation
폭력 has unique valuesUnique
살인 has 25 (51.0%) zerosZeros
강도 has 14 (28.6%) zerosZeros
강간·강제추행 has 1 (2.0%) zerosZeros

Reproduction

Analysis started2024-04-20 13:48:25.931622
Analysis finished2024-04-20 13:48:29.244229
Duration3.31 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

관서명
Categorical

Distinct7
Distinct (%)14.3%
Missing0
Missing (%)0.0%
Memory size520.0 B
광주경찰청계
7 
광주광역시경찰청
7 
광주동부경찰서
7 
광주서부경찰서
7 
광주북부경찰서
7 
Other values (2)
14 

Length

Max length8
Median length7
Mean length7
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
광주경찰청계 7
14.3%
광주광역시경찰청 7
14.3%
광주동부경찰서 7
14.3%
광주서부경찰서 7
14.3%
광주북부경찰서 7
14.3%
광주광산경찰서 7
14.3%
광주남부경찰서 7
14.3%

Length

2024-04-20T22:48:29.380407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-20T22:48:29.596427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
광주경찰청계 7
14.3%
광주광역시경찰청 7
14.3%
광주동부경찰서 7
14.3%
광주서부경찰서 7
14.3%
광주북부경찰서 7
14.3%
광주광산경찰서 7
14.3%
광주남부경찰서 7
14.3%

구분
Categorical

Distinct7
Distinct (%)14.3%
Missing0
Missing (%)0.0%
Memory size520.0 B
발생건수
7 
검거건수
7 
검거인원
7 
구속
7 
불구속
7 
Other values (2)
14 

Length

Max length4
Median length3
Mean length3.1428571
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row발생건수
2nd row검거건수
3rd row검거인원
4th row구속
5th row불구속

Common Values

ValueCountFrequency (%)
발생건수 7
14.3%
검거건수 7
14.3%
검거인원 7
14.3%
구속 7
14.3%
불구속 7
14.3%
기타 7
14.3%
미검거 7
14.3%

Length

2024-04-20T22:48:29.834816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-20T22:48:30.044623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
발생건수 7
14.3%
검거건수 7
14.3%
검거인원 7
14.3%
구속 7
14.3%
불구속 7
14.3%
기타 7
14.3%
미검거 7
14.3%

총범죄
Text

Distinct48
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Memory size520.0 B
2024-04-20T22:48:30.830172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length4.6326531
Min length1

Characters and Unicode

Total characters227
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique47 ?
Unique (%)95.9%

Sample

1st row43,517
2nd row37,472
3rd row48,975
4th row674
5th row26,389
ValueCountFrequency (%)
674 2
 
4.1%
876 2
 
4.1%
124 1
 
2.0%
11,572 1
 
2.0%
11,706 1
 
2.0%
9,874 1
 
2.0%
13,092 1
 
2.0%
174 1
 
2.0%
6,863 1
 
2.0%
6,055 1
 
2.0%
Other values (37) 37
75.5%
2024-04-20T22:48:31.923218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 36
15.9%
1 27
11.9%
7 25
11.0%
2 21
9.3%
9 20
8.8%
6 19
8.4%
3 18
7.9%
0 18
7.9%
4 17
7.5%
5 13
 
5.7%
Other values (2) 13
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 190
83.7%
Other Punctuation 36
 
15.9%
Dash Punctuation 1
 
0.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 27
14.2%
7 25
13.2%
2 21
11.1%
9 20
10.5%
6 19
10.0%
3 18
9.5%
0 18
9.5%
4 17
8.9%
5 13
6.8%
8 12
6.3%
Other Punctuation
ValueCountFrequency (%)
, 36
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 227
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
, 36
15.9%
1 27
11.9%
7 25
11.0%
2 21
9.3%
9 20
8.8%
6 19
8.4%
3 18
7.9%
0 18
7.9%
4 17
7.5%
5 13
 
5.7%
Other values (2) 13
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 227
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 36
15.9%
1 27
11.9%
7 25
11.0%
2 21
9.3%
9 20
8.8%
6 19
8.4%
3 18
7.9%
0 18
7.9%
4 17
7.5%
5 13
 
5.7%
Other values (2) 13
 
5.7%

소계
Text

Distinct48
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Memory size520.0 B
2024-04-20T22:48:32.632724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length3.9387755
Min length1

Characters and Unicode

Total characters193
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique47 ?
Unique (%)95.9%

Sample

1st row12,921
2nd row10,589
3rd row13,238
4th row248
5th row7,775
ValueCountFrequency (%)
1,469 2
 
4.1%
97 2
 
4.1%
2,640 1
 
2.0%
1,984 1
 
2.0%
560 1
 
2.0%
3,521 1
 
2.0%
2,874 1
 
2.0%
3,621 1
 
2.0%
82 1
 
2.0%
2,194 1
 
2.0%
Other values (37) 37
75.5%
2024-04-20T22:48:33.779539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 27
14.0%
1 25
13.0%
2 25
13.0%
5 18
9.3%
9 17
8.8%
4 16
8.3%
3 16
8.3%
6 15
7.8%
7 13
6.7%
8 10
 
5.2%
Other values (2) 11
5.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 165
85.5%
Other Punctuation 27
 
14.0%
Dash Punctuation 1
 
0.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 25
15.2%
2 25
15.2%
5 18
10.9%
9 17
10.3%
4 16
9.7%
3 16
9.7%
6 15
9.1%
7 13
7.9%
8 10
 
6.1%
0 10
 
6.1%
Other Punctuation
ValueCountFrequency (%)
, 27
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 193
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
, 27
14.0%
1 25
13.0%
2 25
13.0%
5 18
9.3%
9 17
8.8%
4 16
8.3%
3 16
8.3%
6 15
7.8%
7 13
6.7%
8 10
 
5.2%
Other values (2) 11
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 193
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 27
14.0%
1 25
13.0%
2 25
13.0%
5 18
9.3%
9 17
8.8%
4 16
8.3%
3 16
8.3%
6 15
7.8%
7 13
6.7%
8 10
 
5.2%
Other values (2) 11
5.7%

살인
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)16.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7959184
Minimum0
Maximum12
Zeros25
Zeros (%)51.0%
Negative0
Negative (%)0.0%
Memory size569.0 B
2024-04-20T22:48:33.982136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33
95-th percentile9.2
Maximum12
Range12
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.8575893
Coefficient of variation (CV)1.5911577
Kurtosis4.5056367
Mean1.7959184
Median Absolute Deviation (MAD)0
Skewness2.15238
Sum88
Variance8.1658163
MonotonicityNot monotonic
2024-04-20T22:48:34.177662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 25
51.0%
1 7
 
14.3%
3 7
 
14.3%
4 4
 
8.2%
10 2
 
4.1%
2 2
 
4.1%
12 1
 
2.0%
8 1
 
2.0%
ValueCountFrequency (%)
0 25
51.0%
1 7
 
14.3%
2 2
 
4.1%
3 7
 
14.3%
4 4
 
8.2%
8 1
 
2.0%
10 2
 
4.1%
12 1
 
2.0%
ValueCountFrequency (%)
12 1
 
2.0%
10 2
 
4.1%
8 1
 
2.0%
4 4
 
8.2%
3 7
 
14.3%
2 2
 
4.1%
1 7
 
14.3%
0 25
51.0%

강도
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct13
Distinct (%)26.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.244898
Minimum0
Maximum28
Zeros14
Zeros (%)28.6%
Negative0
Negative (%)0.0%
Memory size569.0 B
2024-04-20T22:48:34.468603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q35
95-th percentile19.6
Maximum28
Range28
Interquartile range (IQR)5

Descriptive statistics

Standard deviation6.1492907
Coefficient of variation (CV)1.448631
Kurtosis6.6345515
Mean4.244898
Median Absolute Deviation (MAD)2
Skewness2.535806
Sum208
Variance37.813776
MonotonicityNot monotonic
2024-04-20T22:48:34.800461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0 14
28.6%
5 9
18.4%
1 8
16.3%
6 4
 
8.2%
3 4
 
8.2%
2 2
 
4.1%
4 2
 
4.1%
24 1
 
2.0%
22 1
 
2.0%
28 1
 
2.0%
Other values (3) 3
 
6.1%
ValueCountFrequency (%)
0 14
28.6%
1 8
16.3%
2 2
 
4.1%
3 4
 
8.2%
4 2
 
4.1%
5 9
18.4%
6 4
 
8.2%
8 1
 
2.0%
9 1
 
2.0%
16 1
 
2.0%
ValueCountFrequency (%)
28 1
 
2.0%
24 1
 
2.0%
22 1
 
2.0%
16 1
 
2.0%
9 1
 
2.0%
8 1
 
2.0%
6 4
8.2%
5 9
18.4%
4 2
 
4.1%
3 4
8.2%

강간·강제추행
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct47
Distinct (%)95.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean104.97959
Minimum-63
Maximum704
Zeros1
Zeros (%)2.0%
Negative2
Negative (%)4.1%
Memory size569.0 B
2024-04-20T22:48:35.169436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-63
5-th percentile0.4
Q113
median56
Q3132
95-th percentile517.2
Maximum704
Range767
Interquartile range (IQR)119

Descriptive statistics

Standard deviation156.16451
Coefficient of variation (CV)1.4875702
Kurtosis6.6125837
Mean104.97959
Median Absolute Deviation (MAD)45
Skewness2.5664838
Sum5144
Variance24387.354
MonotonicityNot monotonic
2024-04-20T22:48:35.605492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
13 2
 
4.1%
132 2
 
4.1%
582 1
 
2.0%
587 1
 
2.0%
64 1
 
2.0%
153 1
 
2.0%
161 1
 
2.0%
11 1
 
2.0%
95 1
 
2.0%
55 1
 
2.0%
Other values (37) 37
75.5%
ValueCountFrequency (%)
-63 1
2.0%
-5 1
2.0%
0 1
2.0%
1 1
2.0%
2 1
2.0%
3 1
2.0%
4 1
2.0%
6 1
2.0%
8 1
2.0%
11 1
2.0%
ValueCountFrequency (%)
704 1
2.0%
587 1
2.0%
582 1
2.0%
420 1
2.0%
248 1
2.0%
173 1
2.0%
164 1
2.0%
161 1
2.0%
155 1
2.0%
153 1
2.0%

절도
Text

Distinct42
Distinct (%)85.7%
Missing0
Missing (%)0.0%
Memory size520.0 B
2024-04-20T22:48:36.227379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length2.8367347
Min length1

Characters and Unicode

Total characters139
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

Unique38 ?
Unique (%)77.6%

Sample

1st row4,939
2nd row3,307
3rd row3,013
4th row89
5th row2,431
ValueCountFrequency (%)
0 5
 
10.2%
27 2
 
4.1%
2 2
 
4.1%
89 2
 
4.1%
1,151 1
 
2.0%
701 1
 
2.0%
4,939 1
 
2.0%
15 1
 
2.0%
823 1
 
2.0%
673 1
 
2.0%
Other values (32) 32
65.3%
2024-04-20T22:48:37.279451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 25
18.0%
2 17
12.2%
3 17
12.2%
4 13
9.4%
6 13
9.4%
0 12
8.6%
9 11
7.9%
8 9
 
6.5%
7 8
 
5.8%
, 8
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 131
94.2%
Other Punctuation 8
 
5.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 25
19.1%
2 17
13.0%
3 17
13.0%
4 13
9.9%
6 13
9.9%
0 12
9.2%
9 11
8.4%
8 9
 
6.9%
7 8
 
6.1%
5 6
 
4.6%
Other Punctuation
ValueCountFrequency (%)
, 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 139
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 25
18.0%
2 17
12.2%
3 17
12.2%
4 13
9.4%
6 13
9.4%
0 12
8.6%
9 11
7.9%
8 9
 
6.5%
7 8
 
5.8%
, 8
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 139
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 25
18.0%
2 17
12.2%
3 17
12.2%
4 13
9.4%
6 13
9.4%
0 12
8.6%
9 11
7.9%
8 9
 
6.5%
7 8
 
5.8%
, 8
 
5.8%

폭력
Text

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size520.0 B
2024-04-20T22:48:38.061753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length3.5306122
Min length1

Characters and Unicode

Total characters173
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique49 ?
Unique (%)100.0%

Sample

1st row7,366
2nd row6,663
3rd row9,481
4th row99
5th row4,917
ValueCountFrequency (%)
34 2
 
4.1%
7,366 1
 
2.0%
2,495 1
 
2.0%
2,042 1
 
2.0%
1,823 1
 
2.0%
2,627 1
 
2.0%
37 1
 
2.0%
1,423 1
 
2.0%
1,167 1
 
2.0%
219 1
 
2.0%
Other values (38) 38
77.6%
2024-04-20T22:48:39.386012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 28
16.2%
, 21
12.1%
7 17
9.8%
2 17
9.8%
3 15
8.7%
6 15
8.7%
4 15
8.7%
9 14
8.1%
8 14
8.1%
0 9
 
5.2%
Other values (2) 8
 
4.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 151
87.3%
Other Punctuation 21
 
12.1%
Dash Punctuation 1
 
0.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 28
18.5%
7 17
11.3%
2 17
11.3%
3 15
9.9%
6 15
9.9%
4 15
9.9%
9 14
9.3%
8 14
9.3%
0 9
 
6.0%
5 7
 
4.6%
Other Punctuation
ValueCountFrequency (%)
, 21
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 173
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 28
16.2%
, 21
12.1%
7 17
9.8%
2 17
9.8%
3 15
8.7%
6 15
8.7%
4 15
8.7%
9 14
8.1%
8 14
8.1%
0 9
 
5.2%
Other values (2) 8
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 173
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 28
16.2%
, 21
12.1%
7 17
9.8%
2 17
9.8%
3 15
8.7%
6 15
8.7%
4 15
8.7%
9 14
8.1%
8 14
8.1%
0 9
 
5.2%
Other values (2) 8
 
4.6%

Interactions

2024-04-20T22:48:28.185699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-20T22:48:26.680568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-20T22:48:27.437280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-20T22:48:28.390240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-20T22:48:26.926121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-20T22:48:27.688170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-20T22:48:28.540271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-20T22:48:27.173035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-20T22:48:27.920396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-20T22:48:39.653265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관서명구분총범죄소계살인강도강간·강제추행절도폭력
관서명1.0000.0000.9050.9050.7300.2950.4650.9851.000
구분0.0001.0000.9050.9050.1590.3220.5520.7081.000
총범죄0.9050.9051.0000.9960.0000.0001.0000.9981.000
소계0.9050.9050.9961.0000.9090.9671.0000.9821.000
살인0.7300.1590.0000.9091.0000.8570.7920.8861.000
강도0.2950.3220.0000.9670.8571.0000.9190.9171.000
강간·강제추행0.4650.5521.0001.0000.7920.9191.0000.9911.000
절도0.9850.7080.9980.9820.8860.9170.9911.0001.000
폭력1.0001.0001.0001.0001.0001.0001.0001.0001.000
2024-04-20T22:48:39.949857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분관서명
구분1.0000.000
관서명0.0001.000
2024-04-20T22:48:40.193478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
살인강도강간·강제추행관서명구분
살인1.0000.6050.4960.3280.016
강도0.6051.0000.6480.1500.167
강간·강제추행0.4960.6481.0000.2620.327
관서명0.3280.1500.2621.0000.000
구분0.0160.1670.3270.0001.000

Missing values

2024-04-20T22:48:28.878111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-20T22:48:29.132825image/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

관서명구분총범죄소계살인강도강간·강제추행절도폭력
0광주경찰청계발생건수43,51712,92110245824,9397,366
1광주경찰청계검거건수37,47210,58910225873,3076,663
2광주경찰청계검거인원48,97513,23812287043,0139,481
3광주경찰청계구속674248816368999
4광주경찰청계불구속26,3897,775164202,4314,917
5광주경찰청계기타21,9125,215362484934,465
6광주경찰청계미검거6,0452,33202-51,632703
7광주광역시경찰청발생건수0000000
8광주광역시경찰청검거건수876970063034
9광주광역시경찰청검거인원2,2961810082297
관서명구분총범죄소계살인강도강간·강제추행절도폭력
39광주광산경찰서불구속6,0781,79501925821,120
40광주광산경찰서기타5,3701,4983050891,356
41광주광산경찰서미검거1,9996450012450183
42광주남부경찰서발생건수5,1331,5773356615900
43광주남부경찰서검거건수4,3071,2643352424782
44광주남부경찰서검거인원5,3931,46941603261,078
45광주남부경찰서구속271631291
46광주남부경찰서불구속2,7177271031222473
47광주남부경찰서기타2,649726002795604
48광주남부경찰서미검거826313004191118