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This paper analyzes the relationship between property crime and socioeconomic factors by choosing section data of American states from 2011 to 2015. The research shows that: 1) The factors-unemployment rate, median household income and education level do not strong affect the change of property crime; the covered people of unemployment insurance policy and law enforcement are significant for variance to property crime. 2) Economic determinants should be included in the model of criminal activity, but the economic model does not explain the overall change in crime rates.

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Introduction: Hepatocellular carcinoma (HCC) is a liver cancer. In contrast, ferroptosis is a novel iron-dependent and ROS reliant type of cell death that is observed under various disease conditions.Methods and analysis: RNA sequencing data from HCC patients were downloaded from The Cancer Genome Atlas (TCGA) database. Ferroptosis-related long non-coding RNAs (lncRNAs) were screened by Pearson correlation analysis. Patients were randomized into training or testing sets in a 1:1 ratio. They were constructed in the training set using univariate-Lasso and multivariate Cox regression analysis and further tested for prognostic values in the testing set. Four lncRNAs were identified. Kaplan-Meier analysis showed that patients in the high-risk group had a worse prognosis than those in the low-risk group. Following differentially expressed genes analysis of these two groups. Functional analysis showed association with oxidative stress response. Cox regression analyses showed that risk score was an independent prognostic indicator. Receiver operating characteristic curve (ROC) and decision curve analysis demonstrated the accuracy of prediction. Four ferroptosis-related lncRNAs based on differential expression of HCC were screened by bioinformatic methods to construct a prognostic risk model and accurately predict the prognosis of HCC patients. Four lncRNAs may have a potential role in the anti-tumor immune process and serve as therapeutic targets for HCC. To lay the foundation for subsequent studies.Abbreviations: CC = cellulose component, DCA = decision curve analysis, DEG = differentially expressed gene, FC = fold change, FDR = false discovery rate, lncRNA = long non-coding RNA, GO = gene ontology, HCC = hepatocellular carcinoma, KEGG = Kyoto Encyclopedia of Genes and Genomes, Lasso = least absolute shrinkage and selection operator, ROC = receiver operating characteristic curve, TCGA = The Cancer Genome Atlas.