Integrating Spatial-Temporal Risk Factors for an Ambulance Allocation Strategy: A Case Study in Bangkok

Chaicharoenwut, Pakinai; Koiwanit, Jarotwan; Changpetch, Pannapa; Buatongkue, Sirisuda; Yuangyai, Chumpol

doi:10.1051/matecconf/201819201038

Cited by 2 publications

(2 citation statements)

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“…The study developed a mathematical model utilizing geographical data to support the decision. This is in accordance with [28], who utilized social media and spatial data to develop a risk assessment model for medical service requests. They also constructed an EMS base allocation model based on multiple factors, i.e., the number of accidents, type of accident, population, number of elderly people, and number and size of public events.…”

Section: A Utilization Of Spatial and Social Media Datasupporting

confidence: 72%

“…[34,35] used kernel estimator to estimate the chance of crime in an area of interest by using criminal history to construct a heatmap that describes frequency of crime. Likewise, [28,36] constructed a heatmap to investigate unusual events represented by the color. The author used color temperatures to describe the risk of receiving EMS.…”

Section: Multivariate Density Estimationmentioning

confidence: 99%

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Integrating Spatial Risk Factors with Social Media Data Analysis for an Ambulance Allocation Strategy: A Case Study in Bangkok

et al. 2022

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Emergency medical service (EMS) base allocation plays a critical role in emergency medical service systems. Fast arrival of an EMS unit to an incident scene increases the chance of survival and reduces the chance of victim disability. However, recently, the allocation strategy has been performed by experts using past data and experiences. This may lead to ineffective planning due to a lack of consideration of a recent and relevant data, such as disaster events, population density, public transportation stations, and public events. Therefore, we propose an approach of the integration of using spatial risk factors and social media factors to identify EMS bases. These factors are combined into a single domain by using the kernel density estimation technique, resulting in a heatmap. Then, the heatmap is used in a modified maximizing covering location problem with a heatmap (MCLP-Heatmap) to allocate ambulance base. To acquire recent data, social media is then used for collecting road accidents, traffic, flood, and fire incidents. Additionally, another data source, spatial risk information, is collected from Bangkok GIS. These data are analyzed using the kernel density estimation method to construct a heatmap before being sent to the MCLP-heatmap to identify EMS bases in the area of interest. In addition, the proposed integrated approach is applied to the Bangkok area with a smaller number of EMS bases than that of the existing approach. The simulated results indicated that the number of covered EMS requests was increased by 3.6% and the number of ambulance bases in action was reduced by approximately 26%. Additionally, the bases defined by the proposed approach covered more area than those of the existing approach.

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Section: A Utilization Of Spatial and Social Media Datasupporting

confidence: 72%

Section: Multivariate Density Estimationmentioning

confidence: 99%