Research and Analysis of Crime Prediction in China and Abroad Based on Knowledge Graph

DOI

10.2991/978-94-6463-064-0_81How to use a DOI?

Keywords

Crime prediction; CiteSpace; Visualization; Co-occurrence Map

Abstract

The era of big data is changing the mode of police work. Crime prediction based on big data analysis has become a research hotspot in crime prevention and combat. In order to fully reveal and summarize the current development status and characteristics of the crime prediction field in China and abroad, and to provide reference for the next development and research direction of the crime prediction field. This article will use the documents in the CNKI and Web of Science databases as the research samples, using the knowledge graph visualization software Citespace, and focus on analyzing the research hotspots and research progress in the field of crime prediction from the perspectives of the number of articles, research institutions, and keywords. Through research, it is concluded that the research hotspots in the field of crime prediction in China and abroad focus on prediction methods, prediction objects, and factors that affect the prediction results.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Yixin Liu
AU  - Juan Wang
AU  - Peng Zhang
PY  - 2022
DA  - 2022/12/27
TI  - Research and Analysis of Crime Prediction in China and Abroad Based on Knowledge Graph
BT  - Proceedings of the 2022 3rd International Conference on Big Data and Social Sciences (ICBDSS 2022)
PB  - Atlantis Press
SP  - 784
EP  - 793
SN  - 2589-4900
UR  - https://doi.org/10.2991/978-94-6463-064-0_81
DO  - 10.2991/978-94-6463-064-0_81
ID  - Liu2022
ER  -