@article{ref1,
title="Crime exposure along my way home: estimating crime risk along personal trajectory by visual analytics",
journal="Geographical analysis",
year="2020",
author="Xiao, Jia and Zhou, Xiaolu",
volume="52",
number="1",
pages="49-68",
abstract="Crime has been one of the notorious public threats in cities. Fortunately, the increasing digital crime data provide great opportunities to analyze and control crime incidents. However, studies that predict the risk of crime exposure for an individual's spatiotemporal paths based on historical crime big data are still limited. In this study, we have proposed the crime risk index (CRI) for spatiotemporal trajectory and built a model to estimate the CRI. Furthermore, an online crime risk analysis platform has been developed based on the model. First, we proposed a multi-scale tile system and a novel algorithm to estimate trajectory-based CRI using big historical crime data and entropy-based weighting. Second, we created a web-based platform that allows users to provide a spatiotemporal trajectory and estimate the crime risk for such trajectory. We conducted several experiments based on the crime data in Detroit. <br><br>RESULTS demonstrate the practicability and generalizability of our platform. The proposed model and platform can be applied to multiple cities, providing useful references for crime information and public safety.<p /> <p>Language: en</p>",
language="en",
issn="0016-7363",
doi="10.1111/gean.12187",
url="http://dx.doi.org/10.1111/gean.12187"
}