An accessibility spatial search algorithm to optimize defibrillator deployment in indoor space
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...
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| Published in | International journal of geographical information science : IJGIS Vol. 35; no. 6; pp. 1251 - 1272 |
|---|---|
| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Abingdon
Taylor & Francis
03.06.2021
Taylor & Francis LLC |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1365-8816 1362-3087 1365-8824 |
| DOI | 10.1080/13658816.2020.1844890 |
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| 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. |
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| AbstractList | 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. |
| Author | Yang, Lina Yang, Wujun Xiong, Lei |
| Author_xml | – sequence: 1 givenname: Lina surname: Yang fullname: Yang, Lina organization: Chinese Academy of Sciences – sequence: 2 givenname: Lei surname: Xiong fullname: Xiong, Lei organization: China-Singapore Tianjin Eco-city – sequence: 3 givenname: Wujun surname: Yang fullname: Yang, Wujun email: 201630034@hbmy.edu.cn organization: Hubei University for Nationalities |
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| SubjectTerms | Accessibility Algorithms Automated external defibrillator Defibrillators deployment Design optimization Distance measurement Euclidean geometry Indoor environments indoor space Maximization Search algorithms spatial coverage spatial optimization Subspaces Survival Swarm intelligence |
| Title | An accessibility spatial search algorithm to optimize defibrillator deployment in indoor space |
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