Jun Xu’s research while affiliated with Changchun University of Science and Technology and other places

The data set used by machine learning usually contains missing value and text type data, and sometimes, it is necessary to combine the attributes in the data set. The data set must be cleaned and converted before the machine learning model can be generated. This is frequently a chain of events. The entire processing procedure will be time-consuming and inconvenient. This article examines the data pipeline and recommends that it be used to process all data. We carry out automation and use k-fold cross-validation to evaluate the performance of the model. Experiments demonstrate that it can lower the regression prediction model’s root mean square error and enhance prediction accuracy.

Chinese cities have been undergoing extraordinary changes in many respects during the process of urbanization, which has caused crime patterns to evolve accordingly. This research applies a Bayesian spatiotemporal model to explore and understand the spatiotemporal patterns of crime risk from 2008 to 2017 in Changchun, China. The overall temporal trend of crime risk, the effects of land use covariates, spatial random effects, and area-specific differential trends are estimated through a Bayesian spatiotemporal model fitted using the Integrated Nested Laplace Approximation (INLA). The analytical results show that the regression coefficient for the overall temporal trend of crime risk changed from significantly positive to negative after the land use variables are incorporated into the Bayesian spatiotemporal model. The covariates of road density, commercial and recreational land per capita, residential land per capita, and industrial land per capita are found to be significantly associated with crime risk, which relates to classic theories in environmental criminology. In addition, some areas still exhibit significantly increasing crime risks compared with the general trend even after controlling for the land use covariates and the spatial random effects, which may provide insights for law enforcement and researchers regarding where more attention is required since there may be some unmeasured factors causing higher crime trend in these areas.