A GIS enhanced data analytics approach for predicting nursing home hurricane evacuation response
A GIS enhanced data analytics approach for predicting nursing home hurricane evacuation response
复制标题
用于预测疗养院飓风疏散响应的 GIS 增强数据分析方法
DOI：
10.1007/s13755-022-00190-y
复制
发表时间：
2022
影响因子：
6
通讯作者：
Li, Mingyang
中科院分区：
文献类型：
--
作者：
Sakib, Nazmus;Hyer, Kathryn;Dobbs, Debra;Peterson, Lindsay;Jester, Dylan J.;Kong, Nan;Li, Mingyang
养老院(NHS)负责照顾体弱多病的老年人，他们非常容易受到飓风等自然灾害的影响。由于高度不确定的环境条件和不同的NH特征(例如地理位置、人员配备、居民的健康状况)的影响，NH疏散反应，即疏散或就地避难，具有高度的不确定性。准确预测NH疏散响应对于应急管理机构在主动规划医疗资源的情况下准确预测NH疏散需求激增具有重要意义。现有的飓风疏散研究主要集中在普通人群。对于NH疏散，现有的研究主要集中在使用单一数据来源的概念性研究和/或定性分析，例如调查或居民健康数据。通过融合丰富的环境数据和NH数据来开发基于分析的方法来提高预测精度，目前还缺乏研究。本文提出了一种地理信息系统(GIS)数据增强的预测分析方法，通过融合与风暴条件、地理信息、NH组织特征以及每个NH的人员和居民特征相关的多源数据来预测NH疏散响应。特别是，从丰富的地理信息中提取到风暴轨迹的距离、预计风速、潜在风暴潮和NH高程等多种地理信息，并结合这些特征来提高预测性能。对2017年伊尔玛飓风期间NH疏散的真实世界案例进行了研究，以证明所提出的工作比大量没有地理信息的预测分析方法具有更好的预测性能。
Nursing homes (NHs) are responsible for caring for frail, older adults, who are highly vulnerable to natural disasters, such as hurricanes. Due to the influence of highly uncertain environmental conditions and varied NH characteristics (e.g., geo-location, staffing, residents’ health conditions), the NH evacuation response, namely evacuating or sheltering-in-place, is highly uncertain. Accurate prediction of NH evacuation response is important for emergency management agencies to accurately anticipate the NH evacuation demand surge with healthcare resources proactively planned. Existing hurricane evacuation research mainly focuses on the general population. For NH evacuation, existing studies mainly focus on conceptual studies and/or qualitative analysis using a single source of data, such as surveys or resident health data. There is a lack of research to develop analytics-based method by fusing rich environmental data with NH data to improve the prediction accuracy. In this paper, we propose a Geographic Information System (GIS) data enhanced predictive analytics approach for forecasting NH evacuation response by fusing multi-source data related to storm conditions, geographical information, NH organizational characteristics as well as staffing and residents characteristics of each NH. In particular, multiple GIS features, such as distance to storm trajectory, projected wind speed, potential storm surge and NH elevation, are extracted from rich GIS information and incorporated to improve the prediction performance. A real-world case study of NH evacuation during Hurricane Irma in 2017 is examined to demonstrate superior prediction performance of the proposed work over a large number of predictive analytics methods without GIS information.
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作为灾害受害者的体弱老年人：应急管理策略
影响因子：
2.2
作者：
L. Fernández;Deana Byard;Chien‐Chih Lin;S. Benson;J. Barbera
通讯作者：
J. Barbera
疗养院患者和接受家庭医疗保健的患者的需求增加。
DOI：
10.1056/nejm199001043220105
发表时间：
1990
期刊：
The New England journal of medicine
影响因子：
--
作者：
P. Shaughnessy;Andrew M. Kramer
通讯作者：
Andrew M. Kramer
影响因子：
3.4
作者：
Konetzka, R. Tamara;Stearns, Sally C.;Park, Jeongyoung
通讯作者：
Park, Jeongyoung
影响因子：
5.7
作者：
Hyer, Kathryn;Thomas, Kali S.;Weech-Maldonado, Robert
通讯作者：
Weech-Maldonado, Robert
影响因子：
4
作者：
Claver, Maria;Dobalian, Aram;Mallers, Melanie Horn
通讯作者：
Mallers, Melanie Horn