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Table 2 Number of features per crime instance for each crime type in a 12 month period (07/2018 to 06/2019)

From: Built environment attributes and crime: an automated machine learning approach

Crime type Occurrence Building Door Fence Streetlight
Min Avg Max Min Avg Max Min Avg Max Min Avg Max
Anti-social behaviour 728 0 6.8 18 0 1.8 16 0 1.8 26 0 0.5 6
Bicycle theft 25 0 7.1 16 0 2.1 13 0 1.3 13 0 0.3 2
Burglary 354 0 7.1 20 0 2 13 0 2 28 0 0.4 5
Criminal damage and arson 478 0 6.7 19 0 1.8 13 0 2 28 0 0.4 6
Drugs 228 0 7.2 16 0 1.9 10 0 1.8 21 0 0.5 6
Possession of weapons 65 0 6.8 17 0 2 11 0 1.4 11 0 0.4 3
Public order 545 0 6.1 17 0 1.5 11 0 1.4 15 0 0.3 6
Robbery 71 0 5.5 13 0 1.6 11 0 0.9 9 0 0.3 3
Shoplifting 326 0 5.7 16 0 1.3 11 0 1.2 16 0 0.4 5
Theft from the person 75 0 4.7 18 0 1.2 7 0 1.1 11 0 0.3 5
Vehicle Crime 360 0 1 9 0 1 15 0 0.3 3 0 1.5 16
Violence and sexual offences 2033 0 6.6 22 0 1.8 18 0 1.6 28 0 0.4 7
Average 440.7 0.0 5.9 16.8 0.0 1.7 12.4 0.0 1.4 17.4 0.0 0.5 5.8
Crime type Occurrence Tree Window Hedge Garage
Min Avg Max Min Avg Max Min Avg Max Min Avg Max
Anti-social behaviour 728 0 1.8 16 0 24.1 83 0 0.3 6 0 0.4 11
Bicycle theft 25 0 1.7 13 0 23.7 75 0 0.6 6 0 0.5 5
Burglary 354 0 2 20 0 23 93 0 0.4 7 0 0.4 11
Criminal damage and arson 478 0 1.9 19 0 22.3 80 0 0.3 7 0 0.4 6
Drugs 228 0 1.2 14 0 25.3 80 0 0.3 6 0 0.4 5
Possession of weapons 65 0 1.7 17 0 18.8 66 0 0.4 4 0 0.4 6
Public order 545 0 1.7 18 0 20.1 83 0 0.3 7 0 0.3 6
Robbery 71 0 2 12 0 16.5 62 0 0.3 4 0 0.2 5
Shoplifting 326 0 1.5 20 0 17.5 85 0 0.3 7 0 0.3 7
Theft from the person 75 0 1.4 13 0 15 77 0 0.2 3 0 0.2 3
Vehicle Crime 360 0 15.5 63 0 0.2 5 0 0.2 5 0 0 0
Violence and sexual offences 2033 0 1.7 25 0 21.4 93 0 0.3 7 0 0.4 11
Average 440.7 0.0 2.8 20.8 0.0 19.0 73.5 0.0 0.3 5.8 0.0 0.3 6.3