Overview

Dataset statistics

Number of variables8
Number of observations42
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.0 KiB
Average record size in memory74.0 B

Variable types

Numeric7
Categorical1

Dataset

Description대전지역 시간대별 강력범, 절도범, 폭력범, 지능범, 풍속범, 기타형법범, 특별법범 범죄발생 현황으로 어느 시간대에 범죄발생이 많고 적은지 파악할 수 있는 자료 활용
Author경찰청 대전광역시경찰청
URLhttps://www.data.go.kr/data/15094058/fileData.do

Alerts

심 야(0시-4시) is highly overall correlated with 새 벽(4시-7시) and 5 other fieldsHigh correlation
새 벽(4시-7시) is highly overall correlated with 심 야(0시-4시) and 5 other fieldsHigh correlation
오 전(7시-12시) is highly overall correlated with 심 야(0시-4시) and 5 other fieldsHigh correlation
오 후(12시-18시) is highly overall correlated with 심 야(0시-4시) and 5 other fieldsHigh correlation
초 저 녁(18시-20시) is highly overall correlated with 심 야(0시-4시) and 5 other fieldsHigh correlation
밤(20시-24시) is highly overall correlated with 심 야(0시-4시) and 4 other fieldsHigh correlation
항목 is highly overall correlated with 심 야(0시-4시) and 4 other fieldsHigh correlation
심 야(0시-4시) has unique valuesUnique
새 벽(4시-7시) has unique valuesUnique
초 저 녁(18시-20시) has unique valuesUnique

Reproduction

Analysis started2024-03-14 21:23:49.693940
Analysis finished2024-03-14 21:24:03.569511
Duration13.88 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연도
Real number (ℝ)

Distinct6
Distinct (%)14.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2019.5
Minimum2017
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size506.0 B
2024-03-15T06:24:03.679533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2017
5-th percentile2017
Q12018
median2019.5
Q32021
95-th percentile2022
Maximum2022
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.7285268
Coefficient of variation (CV)0.00085591819
Kurtosis-1.275956
Mean2019.5
Median Absolute Deviation (MAD)1.5
Skewness0
Sum84819
Variance2.9878049
MonotonicityIncreasing
2024-03-15T06:24:03.992544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2017 7
16.7%
2018 7
16.7%
2019 7
16.7%
2020 7
16.7%
2021 7
16.7%
2022 7
16.7%
ValueCountFrequency (%)
2017 7
16.7%
2018 7
16.7%
2019 7
16.7%
2020 7
16.7%
2021 7
16.7%
2022 7
16.7%
ValueCountFrequency (%)
2022 7
16.7%
2021 7
16.7%
2020 7
16.7%
2019 7
16.7%
2018 7
16.7%
2017 7
16.7%

항목
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Memory size464.0 B
강력범
절도범
폭력범
지능범
풍속범
Other values (2)
12 

Length

Max length5
Median length3
Mean length3.4285714
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row강력범
2nd row절도범
3rd row폭력범
4th row지능범
5th row풍속범

Common Values

ValueCountFrequency (%)
강력범 6
14.3%
절도범 6
14.3%
폭력범 6
14.3%
지능범 6
14.3%
풍속범 6
14.3%
기타형법범 6
14.3%
특별법범 6
14.3%

Length

2024-03-15T06:24:04.395856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T06:24:04.659201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
강력범 6
14.3%
절도범 6
14.3%
폭력범 6
14.3%
지능범 6
14.3%
풍속범 6
14.3%
기타형법범 6
14.3%
특별법범 6
14.3%

심 야(0시-4시)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct42
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2246.6667
Minimum40
Maximum8325
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size506.0 B
2024-03-15T06:24:05.083588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum40
5-th percentile50.25
Q1315.5
median1306.5
Q33608.25
95-th percentile6850.1
Maximum8325
Range8285
Interquartile range (IQR)3292.75

Descriptive statistics

Standard deviation2265.6859
Coefficient of variation (CV)1.0084655
Kurtosis0.57984824
Mean2246.6667
Median Absolute Deviation (MAD)1095.5
Skewness1.1923691
Sum94360
Variance5133332.4
MonotonicityNot monotonic
2024-03-15T06:24:05.584058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
279 1
 
2.4%
77 1
 
2.4%
7671 1
 
2.4%
50 1
 
2.4%
1263 1
 
2.4%
3697 1
 
2.4%
264 1
 
2.4%
997 1
 
2.4%
1757 1
 
2.4%
6491 1
 
2.4%
Other values (32) 32
76.2%
ValueCountFrequency (%)
40 1
2.4%
47 1
2.4%
50 1
2.4%
55 1
2.4%
77 1
2.4%
98 1
2.4%
264 1
2.4%
271 1
2.4%
279 1
2.4%
292 1
2.4%
ValueCountFrequency (%)
8325 1
2.4%
7671 1
2.4%
6869 1
2.4%
6491 1
2.4%
5969 1
2.4%
4721 1
2.4%
4709 1
2.4%
4308 1
2.4%
3954 1
2.4%
3866 1
2.4%

새 벽(4시-7시)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct42
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean290.92857
Minimum3
Maximum867
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size506.0 B
2024-03-15T06:24:06.020496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile8.05
Q181.5
median132
Q3485.75
95-th percentile750.5
Maximum867
Range864
Interquartile range (IQR)404.25

Descriptive statistics

Standard deviation270.96147
Coefficient of variation (CV)0.93136768
Kurtosis-1.018652
Mean290.92857
Median Absolute Deviation (MAD)123.5
Skewness0.66204497
Sum12219
Variance73420.117
MonotonicityNot monotonic
2024-03-15T06:24:06.528825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
94 1
 
2.4%
8 1
 
2.4%
123 1
 
2.4%
5 1
 
2.4%
112 1
 
2.4%
694 1
 
2.4%
48 1
 
2.4%
464 1
 
2.4%
493 1
 
2.4%
163 1
 
2.4%
Other values (32) 32
76.2%
ValueCountFrequency (%)
3 1
2.4%
5 1
2.4%
8 1
2.4%
9 1
2.4%
10 1
2.4%
12 1
2.4%
48 1
2.4%
55 1
2.4%
71 1
2.4%
75 1
2.4%
ValueCountFrequency (%)
867 1
2.4%
797 1
2.4%
751 1
2.4%
741 1
2.4%
719 1
2.4%
694 1
2.4%
631 1
2.4%
577 1
2.4%
576 1
2.4%
555 1
2.4%

오 전(7시-12시)
Real number (ℝ)

HIGH CORRELATION 

Distinct41
Distinct (%)97.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean825.04762
Minimum10
Maximum2727
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size506.0 B
2024-03-15T06:24:07.118327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile16.3
Q181
median864.5
Q31172.75
95-th percentile2519.65
Maximum2727
Range2717
Interquartile range (IQR)1091.75

Descriptive statistics

Standard deviation756.70325
Coefficient of variation (CV)0.91716312
Kurtosis0.51186314
Mean825.04762
Median Absolute Deviation (MAD)519
Skewness0.97192196
Sum34652
Variance572599.8
MonotonicityNot monotonic
2024-03-15T06:24:08.141623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
361 2
 
4.8%
79 1
 
2.4%
260 1
 
2.4%
1297 1
 
2.4%
16 1
 
2.4%
2342 1
 
2.4%
69 1
 
2.4%
758 1
 
2.4%
767 1
 
2.4%
1001 1
 
2.4%
Other values (31) 31
73.8%
ValueCountFrequency (%)
10 1
2.4%
14 1
2.4%
16 1
2.4%
22 1
2.4%
24 1
2.4%
25 1
2.4%
64 1
2.4%
69 1
2.4%
76 1
2.4%
77 1
2.4%
ValueCountFrequency (%)
2727 1
2.4%
2630 1
2.4%
2529 1
2.4%
2342 1
2.4%
1794 1
2.4%
1445 1
2.4%
1349 1
2.4%
1297 1
2.4%
1244 1
2.4%
1228 1
2.4%

오 후(12시-18시)
Real number (ℝ)

HIGH CORRELATION 

Distinct41
Distinct (%)97.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1315.7619
Minimum34
Maximum4067
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size506.0 B
2024-03-15T06:24:08.931211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum34
5-th percentile48.05
Q1140
median1426.5
Q31768
95-th percentile3586.15
Maximum4067
Range4033
Interquartile range (IQR)1628

Descriptive statistics

Standard deviation1168.4572
Coefficient of variation (CV)0.8880461
Kurtosis-0.19712227
Mean1315.7619
Median Absolute Deviation (MAD)953.5
Skewness0.75945391
Sum55262
Variance1365292.3
MonotonicityNot monotonic
2024-03-15T06:24:09.657728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
140 2
 
4.8%
122 1
 
2.4%
2132 1
 
2.4%
1577 1
 
2.4%
2033 1
 
2.4%
48 1
 
2.4%
532 1
 
2.4%
3551 1
 
2.4%
1560 1
 
2.4%
1405 1
 
2.4%
Other values (31) 31
73.8%
ValueCountFrequency (%)
34 1
2.4%
45 1
2.4%
48 1
2.4%
49 1
2.4%
51 1
2.4%
59 1
2.4%
122 1
2.4%
124 1
2.4%
127 1
2.4%
129 1
2.4%
ValueCountFrequency (%)
4067 1
2.4%
3810 1
2.4%
3588 1
2.4%
3551 1
2.4%
3302 1
2.4%
2770 1
2.4%
2427 1
2.4%
2132 1
2.4%
2033 1
2.4%
1921 1
2.4%

초 저 녁(18시-20시)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct42
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean527.71429
Minimum19
Maximum2030
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size506.0 B
2024-03-15T06:24:10.198833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19
5-th percentile22.1
Q168.25
median413
Q3745.75
95-th percentile1614.4
Maximum2030
Range2011
Interquartile range (IQR)677.5

Descriptive statistics

Standard deviation520.34591
Coefficient of variation (CV)0.98603719
Kurtosis0.9047262
Mean527.71429
Median Absolute Deviation (MAD)340.5
Skewness1.1961282
Sum22164
Variance270759.87
MonotonicityNot monotonic
2024-03-15T06:24:10.647806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
64 1
 
2.4%
38 1
 
2.4%
438 1
 
2.4%
22 1
 
2.4%
173 1
 
2.4%
1484 1
 
2.4%
78 1
 
2.4%
727 1
 
2.4%
1099 1
 
2.4%
656 1
 
2.4%
Other values (32) 32
76.2%
ValueCountFrequency (%)
19 1
2.4%
21 1
2.4%
22 1
2.4%
24 1
2.4%
30 1
2.4%
38 1
2.4%
47 1
2.4%
60 1
2.4%
62 1
2.4%
64 1
2.4%
ValueCountFrequency (%)
2030 1
2.4%
1643 1
2.4%
1620 1
2.4%
1508 1
2.4%
1484 1
2.4%
1099 1
2.4%
949 1
2.4%
785 1
2.4%
760 1
2.4%
755 1
2.4%

밤(20시-24시)
Real number (ℝ)

HIGH CORRELATION 

Distinct41
Distinct (%)97.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1127.5952
Minimum32
Maximum5804
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size506.0 B
2024-03-15T06:24:11.089020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum32
5-th percentile45.05
Q1177
median595.5
Q31939.75
95-th percentile3698.4
Maximum5804
Range5772
Interquartile range (IQR)1762.75

Descriptive statistics

Standard deviation1316.6254
Coefficient of variation (CV)1.1676401
Kurtosis3.0542544
Mean1127.5952
Median Absolute Deviation (MAD)450
Skewness1.7381406
Sum47359
Variance1733502.5
MonotonicityNot monotonic
2024-03-15T06:24:11.333980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
177 2
 
4.8%
163 1
 
2.4%
32 1
 
2.4%
735 1
 
2.4%
45 1
 
2.4%
427 1
 
2.4%
3250 1
 
2.4%
117 1
 
2.4%
538 1
 
2.4%
1609 1
 
2.4%
Other values (31) 31
73.8%
ValueCountFrequency (%)
32 1
2.4%
33 1
2.4%
45 1
2.4%
46 1
2.4%
48 1
2.4%
50 1
2.4%
117 1
2.4%
144 1
2.4%
147 1
2.4%
163 1
2.4%
ValueCountFrequency (%)
5804 1
2.4%
4226 1
2.4%
3722 1
2.4%
3250 1
2.4%
2785 1
2.4%
2373 1
2.4%
2364 1
2.4%
2302 1
2.4%
2144 1
2.4%
2055 1
2.4%

Interactions

2024-03-15T06:24:01.555450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:23:50.006512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:23:51.758447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:23:53.295531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:23:55.171218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:23:57.113603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:23:59.725773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:24:01.806072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:23:50.254771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:23:51.960699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:23:53.536719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:23:55.446215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:23:57.533758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:23:59.981362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:24:01.950640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:23:50.488979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:23:52.190877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:23:53.802438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:23:55.748825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:23:57.898308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:24:00.229697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:24:02.088303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:23:50.724724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:23:52.422809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:23:54.099464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:23:55.977625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:23:58.313618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:24:00.485332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:24:02.237051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:23:50.976053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:23:52.658179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:23:54.363429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:23:56.232376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:23:58.616966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:24:00.742167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:24:02.401497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:23:51.237198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:23:52.873136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:23:54.648642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:23:56.521866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:23:58.961985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:24:01.017413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:24:02.570437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:23:51.504090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:23:53.029629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:23:54.914531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:23:56.842903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:23:59.435410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:24:01.285138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T06:24:11.502922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도항목심 야(0시-4시)새 벽(4시-7시)오 전(7시-12시)오 후(12시-18시)초 저 녁(18시-20시)밤(20시-24시)
연도1.0000.0000.0000.0000.0000.0000.0000.000
항목0.0001.0000.8670.7920.8510.8380.7820.684
심 야(0시-4시)0.0000.8671.0000.7950.9030.7890.8830.899
새 벽(4시-7시)0.0000.7920.7951.0000.8370.8220.8180.853
오 전(7시-12시)0.0000.8510.9030.8371.0000.8610.9720.938
오 후(12시-18시)0.0000.8380.7890.8220.8611.0000.8070.766
초 저 녁(18시-20시)0.0000.7820.8830.8180.9720.8071.0000.965
밤(20시-24시)0.0000.6840.8990.8530.9380.7660.9651.000
2024-03-15T06:24:11.719405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도심 야(0시-4시)새 벽(4시-7시)오 전(7시-12시)오 후(12시-18시)초 저 녁(18시-20시)밤(20시-24시)항목
연도1.000-0.037-0.097-0.1070.0530.071-0.0560.000
심 야(0시-4시)-0.0371.0000.6960.8890.8190.7440.7620.682
새 벽(4시-7시)-0.0970.6961.0000.8580.8490.9690.9600.570
오 전(7시-12시)-0.1070.8890.8581.0000.9390.8640.9030.653
오 후(12시-18시)0.0530.8190.8490.9391.0000.8790.8720.638
초 저 녁(18시-20시)0.0710.7440.9690.8640.8791.0000.9560.549
밤(20시-24시)-0.0560.7620.9600.9030.8720.9561.0000.433
항목0.0000.6820.5700.6530.6380.5490.4331.000

Missing values

2024-03-15T06:24:02.911560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T06:24:03.404384image/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

연도항목심 야(0시-4시)새 벽(4시-7시)오 전(7시-12시)오 후(12시-18시)초 저 녁(18시-20시)밤(20시-24시)
02017강력범279947912264163
12017절도범1301429101916976551113
22017폭력범2546631107714187552373
32017지능범472111413491367302639
42017풍속범401225512146
52017기타형법범116288361476140438
62017특별법범47098672727406716435804
72018강력범315927712462177
82018절도범12474118761569582855
92018폭력범2736741107015067852364
연도항목심 야(0시-4시)새 벽(4시-7시)오 전(7시-12시)오 후(12시-18시)초 저 녁(18시-20시)밤(20시-24시)
322021풍속범77814593832
332021기타형법범1135103260470241272
342021특별법범33427191794330220302302
352022강력범292558714065177
362022절도범101141911121921615955
372022폭력범207343690914447522144
382022지능범8325133122827706491019
392022풍속범98310453050
402022기타형법범131286309422164361
412022특별법범3954555144524279492785