Prediction of excavation-type heritage crimes based on random forests

Hongyu Lv; Ning Ding; Shancheng Lin; Huaice Liu; Xinyan Liu

doi:10.1117/12.2656770

13 January 2023 Prediction of excavation-type heritage crimes based on random forests

Hongyu Lv, Ning Ding, Shancheng Lin, Huaice Liu, Xinyan Liu

Proceedings Volume 12510, International Conference on Statistics, Data Science, and Computational Intelligence (CSDSCI 2022); 1251008 (2023) https://doi.org/10.1117/12.2656770
Event: International Conference on Statistics, Data Science, and Computational Intelligence (CSDSCI 2022), 2022, Qingdao, China

Abstract

In recent years, driven by the huge profits in the illegal cultural relics circulation market, smuggling and excavation of cultural relics have been repeated, and the situation of heritage crimes has become more and more serious. It is important to understand the occurrence pattern of excavation-type heritage crimes and construct a time-series prediction model of excavation-type heritage crimes to prevent them. This paper uses the random forests algorithm to construct a time-series prediction model of heritage crimes, which effectively solves the problem of poor timeliness of traditional prevention methods and is an attempt in the field of heritage crimes prediction. This paper constructs a time-series data of heritage crimes at several time scales and finds that the model has the best prediction effect when the time step is set to 30. It suggests that there may be a certain pattern of occurrence of excavation-type heritage crimes at the monthly scale. The findings of this paper are expected to provide decision support for the deployment of prevention and control resources for protected heritage units.

Citation Download Citation

Hongyu Lv, Ning Ding, Shancheng Lin, Huaice Liu, and Xinyan Liu "Prediction of excavation-type heritage crimes based on random forests", Proc. SPIE 12510, International Conference on Statistics, Data Science, and Computational Intelligence (CSDSCI 2022), 1251008 (13 January 2023); https://doi.org/10.1117/12.2656770

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available