Applying Mobile Location Data to Improve Hurricane Evacuation Plans

Cedric Harper, Brigitte Hogan, Briana K. Wright
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Abstract

Can private location data be used for the public good? During an emergency, cities and municipalities must disperse limited resources to the areas of greatest need. The data which can best inform these decisions may be hidden within the mobile apps that city residents use on an everyday basis. Given the ethical concerns surrounding location tracking, we address this question using data from X-Mode Social, Inc., a start-up company with open and transparent data sharing policies. X-Mode’s high-quality location data are compliant with both regulations in the European Union (GDPR) and the United States (CCPA). We narrowed our focus to the City of Jacksonville, Florida, which issued mandatory evacuations prior to Hurricane Dorian’s approach in early September 2019. After validating that X-Mode’s data correlates with local population densities, we visualized locations pre- and post-hurricane in order to establish whether mobile app users were able to heed government warnings. Next, we used a combination of both spatial analysis and generalized linear modeling methods to characterize patterns of movement during the evacuation. Finally, we built an interactive web-based app to reveal areas where the evacuation process could potentially be improved. Our results work to fill current knowledge gaps and provide a process with which city and municipal managers might utilize to more effectively allocate resources during a crisis.
应用移动定位数据改进飓风疏散计划
私人位置数据可以用于公共利益吗?在紧急情况下,城市和市政当局必须将有限的资源分配给最需要的地区。最能为这些决策提供信息的数据可能隐藏在城市居民每天使用的移动应用程序中。考虑到围绕位置跟踪的道德问题,我们使用X-Mode Social, Inc.的数据来解决这个问题,X-Mode Social, Inc.是一家拥有公开透明数据共享政策的初创公司。X-Mode的高质量位置数据符合欧盟(GDPR)和美国(CCPA)的规定。我们将重点缩小到佛罗里达州杰克逊维尔市,该市在2019年9月初飓风多里安接近之前发布了强制疏散。在验证了X-Mode的数据与当地人口密度相关后,我们将飓风前后的位置可视化,以确定移动应用程序用户是否能够注意到政府的警告。接下来,我们结合空间分析和广义线性建模方法来描述疏散过程中的运动模式。最后,我们建立了一个交互式的基于网络的应用程序,以揭示疏散过程可能得到改进的领域。我们的研究结果填补了目前的知识空白,并提供了一个流程,城市和市政管理者可以利用该流程在危机期间更有效地分配资源。
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