Comparison of compression estimations under the penalty functions of different violent crimes on campus through deep learning and linear spatial autoregressive models
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Fig. 1
Model structure of BPNN. BPNN, backpropagation neural network.
Fig. 2
Structure of DBM. RBM, restricted Boltzmann machine.
Fig. 3
Statistics of campus crime in a determinate area.
Fig. 4
Simulation results when N = 200, R = 10 and ρ = 0.1. (a) The result of ALASSO penalty function; (b) the result of LASSO penalty function; and (c) the result of SCAD penalty function. ALASSO, adaptive least absolute shrinkage and selection operator; LASSO, least absolute shrinkage and selection operator; SCAD, smoothly clipped absolute deviation.
Fig. 5
Simulation results when N = 200, R = 10 and ρ = 0.5. (a) The result of ALASSO penalty function; (b) the result of LASSO penalty function; and (c) the result of SCAD penalty function. ALASSO, adaptive least absolute shrinkage and selection operator; LASSO, least absolute shrinkage and selection operator; SCAD, smoothly clipped absolute deviation.
Fig. 6
Simulation results when N = 400, R = 30 and ρ = 0.1. (a) The result of ALASSO penalty function; (b) the result of LASSO penalty function; and (c) the result of SCAD penalty function. ALASSO, adaptive least absolute shrinkage and selection operator; LASSO, least absolute shrinkage and selection operator; SCAD, smoothly clipped absolute deviation.
Fig. 7
Simulation results when N = 400, R = 30 and ρ = 0.5. (a) The result of ALASSO penalty function; (b) the result of LASSO penalty function; and (c) the result of SCAD penalty function. ALASSO, adaptive least absolute shrinkage and selection operator; LASSO, least absolute shrinkage and selection operator; SCAD, smoothly clipped absolute deviation.
Data parameter estimation of crime influencing factors
