基于转移概率矩阵自学习的犯罪分布预测

针对犯罪分布预测准确率低，历史犯罪数据缺失严重的问题，提出了基于历史犯罪数据，融合所研究地区的社会环境因素的转移概率矩阵自学习的犯罪分布预测算法——TWcS.将包括距离信息、面积信息、人口信息在内的社会环境因素作为权重值引入到梯度下降策略中，利用梯度下降实现TWcS算法的转移概率矩阵自学习.实验结果证明，TWcS算法的性能明显优于包括当前最优基线算法（TPML-WMA）在内的其他预测算法（如LR、AR、Lasso回归算法、贝叶斯算法、决策树算法等），TWcS算法的MAE值是其他算法MAE平均值的33%. 

Predicting Crime Distribution Based on Transition Probability Matrix Self-Learning Algorithm

Aiming at the problem of low accuracy of crime distribution prediction and serious lack of historical crime data, a crime distribution prediction algorithm, TWcS, was proposed based on a transition probability matrix model, the historical crime data and integrating social environmental factors in the studied area. In this paper, the social environment factors including distance information, area information and population information were introduced as weights into the gradient descent strategy, and the transition probability matrix self-learning of TWcS algorithm was realized by gradient descent. The experimental results show that the performance of TWcS algorithm is superior to other prediction algorithms including TPML-WMA, LR, AR, Lasso regression algorithm, Bayesian algorithm, decision tree algorithm, etc.The MAE value of TWcS algorithm is only 33% of the average MAE value of the other algorithms.