Crime risk prediction incorporating geographical spatiotemporal dependency into machine learning models-Reference-Cited by-同舟云学术

Crime risk prediction incorporating geographical spatiotemporal dependency into machine learning models

Published:2023-10 Issue: Volume:646 Page:119414
ISSN:0020-0255
Container-title:Information Sciences
language:en
Short-container-title:Information Sciences

Author:

Deng Yue^ORCID,He Rixing,Liu Yang^ORCID

Publisher

Elsevier BV

Subject

Artificial Intelligence,Information Systems and Management,Computer Science Applications,Theoretical Computer Science,Control and Systems Engineering,Software

Reference44 articles.

1. Mapping the Risk Terrain for Crime Using Machine Learning;Wheeler;J. Quant. Criminol.,2021

2. The Effects of Hot Spots Policing on Crime: An Updated Systematic Review and Meta-Analysis;Braga;Justice Q.,2014

3. Environmental criminology: Evolution, theory and practice;Reyns;Secur. J.,2016

4. Discovering spatial interaction patterns of near repeat crime by spatial association rules mining;He;Sci. Rep.,2020

5. Geographical random forests: a spatial extension of the random forest algorithm to address spatial heterogeneity in remote sensing and population modelling;Georganos;Geocarto Int.,2021

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