CAMELON: A System for Crime Metadata Extraction and Spatiotemporal Visualization From Online News Articles
Siripen Pongpaichet, Boonyapat Sukosit, Chitchaya Duangtanawat, Jiramed Jamjongdamrongkit, Chancheep Mahacharoensuk, Kantapong Matangkarat, Pattadon Singhajan, Thanapon Noraset, Suppawong Tuarob
Abstract
Abstract
Online news articles serve as a primary source of information for understanding social phenomena, including criminal activities. However, the unstructured nature of news text makes it difficult to systematically analyze and visualize crime patterns across time and space. This paper presents CAMELON, an end-to-end system designed for the automated extraction of crime metadata and spatiotemporal visualization from online news articles. The system utilizes advanced Natural Language Processing (NLP) techniques to identify key crime attributes such as crime types, locations, and timestamps from Thai news reports. Furthermore, CAMELON provides an interactive dashboard that enables users to explore and visualize crime trends through heatmaps and temporal charts. The effectiveness of the system was evaluated using real-world news data, demonstrating its utility for law enforcement agencies, urban planners, and the general public in gaining actionable insights into public safety.
Cite this work
@article{ camelon,
title={ CAMELON: A System for Crime Metadata Extraction and Spatiotemporal Visualization From Online News Articles },
author={ Siripen Pongpaichet and Boonyapat Sukosit and Chitchaya Duangtanawat and Jiramed Jamjongdamrongkit and Chancheep Mahacharoensuk and Kantapong Matangkarat and Pattadon Singhajan and Thanapon Noraset and Suppawong Tuarob },
journal={ IEEE Access },
year={ 2024 },
doi={ 10.1109/ACCESS.2024.3363879 },
url={ https://prayat-pu.github.io/mike-lab/publications/camelon-a-system-for-crime-metadata-extraction-and-spatiotemporal-visualization-from-online-news-articles/ }
}