The algorithm is very similar to the random forest, but compared with random forest, all the samples used in this algorithm are only randomly selected for their features. Because the splitting is random, the results obtained are better than those obtained by random forest to some extent.
Published in Chapter:
Crime Hotspot Prediction Using Big Data in China
Chunfa Xu (Tianjin University, China), Xiaoyang Hu (Tianjin University, China), Anqi Yang (Tianjin University, China), Yimin Zhang (Tianjin University, China), Cailing Zhang (Tianjin University, China), Yufei Xia (Tianjin University, China), and Yanan Cao (Tianjin University, China)
Copyright: © 2020
|Pages: 21
DOI: 10.4018/978-1-7998-0357-7.ch019
Abstract
This chapter proves that utilizing big data and machine learning to predict crime is feasible in China. Researchers introduce five new machine learning algorithms into the field of crime prediction and compare them with four methods widely used in previous research. Using a weekly dataset in 213 street-level cells of Shanghai from April 2017 to March 2018, the researchers find new methods work better in predicting whether a specific cell will be a crime hotspot in next week. Five among nine methods can predict crime with more than 90 percent accuracy. These findings provide a scientific reference for urban safety protection. The research adds some significant evidence to a theoretical literature emphasizing that big data can predict crime.