An accessibility spatial search algorithm to optimize defibrillator deployment in indoor space

ABSTRACT

Indoor space, where humans spend 80% of their lives, is subject to frequent out-of-hospital cardiac arrest (OHCA). Optimizing the spatial deployment of automated external defibrillators (AEDs) has the potential to improve OHCA survival rate. Complex indoor space is typically divided by hard barriers into multiple discrete subspaces across floors. Commonly used distance measurements, such as Euclidean distance and network distance, are unsuitable for indoor AED deployment. Instead, we propose an accessibility spatial search algorithm (ASSA) to generate accessible areas of candidate facilities, i.e. AEDs, based on an indoor space model, and optimizes the facility deployment subject to three objectives: maximizing the survival rate, maximizing total spatial coverage and maximizing backup coverage. Additionally, improved artificial bee colony (ABC) algorithm is used to solve the optimization problem. We design experiments with simulated and real-world scenarios for AED placements and evaluate the ASSA results. The experiments show that the ASSA can provide helpful guidance in optimizing AED placement in indoor space.

KEYWORDS:

• Automated external defibrillator
• deployment
• indoor space
• spatial coverage
• spatial optimization

Acknowledgments

We thank the editor and anonymous reviewers who provided helpful suggestions on ways to improve the paper.

Disclosure statement

No potential conflicts of interest are reported by the authors.

Data and codes availability statement

The data and codes that support the findings of this study are available at figshare.com with the identifier [https://doi.org/10.6084/m9.figshare.12623858.v1]. The original data used in the real-world data experiment of this study are provided by the Smart City Development Bureau, China-Singapore Tianjin Eco-city, and cannot be made publicly available to protect data confidentiality.

Correction Statement

This article has been republished with minor changes. These changes do not impact the academic content of the article.

Additional information

Notes on contributors

Lina Yang

Lina Yang received the B.S. degree in remote sensing and geographic information systems from Wuhan University, Wuhan, China, in 2006, and the Ph.D. degree in cartography and geographical information systems from the Graduate University of the Chinese Academy of Sciences, Beijing, China, in 2011. Currently, she is an Associate Professor with the Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China. Her research interests include data-driven smart city information analysis and intelligent spatial optimization.

Lei Xiong

Lei Xiong received the B.S. degree in computer science and technology from Shandong University of science and technology, Qingdao, China, in 2008, Currently, he is in charge of the smart city application in Smart City Development Bureau of China-Singapore Tianjin Eco-city, Tianjin, China. His research interests include big data analysis and smart city applications.

Wujun Yang

Wujun Yang received the B.M. degree in clinical medicine from Hubei Medical University, Wuhan, China, in 1999, and M.M. degree in clinical medicine from Wuhan University, Wuhan, China, in 2015. Currently, he is the director and chief physician in Neurosurgery Department, Center Hospital of Enshi Tujia and Miao Autonomous Prefecture, Enshi, Hubei, China. His research interests include medical data analysis, surgical treatment of cerebrovascular disease and comprehensive treatment for critical care medicine.