Citation: Jientrakul, R.; Yuangyai, C.;
Boonkul, K.; Chaicharoenwut, P.;
Nilsang, S.; Pimsakul, S. Integrating
Spatial Risk Factors with Social
Media Data Analysis for an
Ambulance Allocation Strategy: A
Case Study in Bangkok. Sustainability
2022, 14, 10247. https://doi.org/
10.3390/su141610247
Academic Editors: João Carlos de
Oliveira Matias and Paolo Renna
Received: 21 May 2022
Accepted: 2 August 2022
Published: 18 August 2022
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Article
Integrating Spatial Risk Factors with Social Media Data
Analysis for an Ambulance Allocation Strategy: A Case Study
in Bangkok
Ranon Jientrakul
1
, Chumpol Yuangyai
1,
*, Klongkwan Boonkul
1
, Pakinai Chaicharoenwut
1
,
Suriyaphong Nilsang
2
and Sittiporn Pimsakul
1
1
Department of Industrial Engineering, School of Engineering, King Mongkut’s Institute of Technology
Ladkrabang, Bangkok 10520, Thailand
2
Department of Production Engineering, Faculty of Engineering, King Mongkut’s University of Technology
Thonburi, Bangkok 10140, Thailand
* Correspondence: chumpol.yu@kmitl.ac.th; Tel.: +66-98-742-5453
Abstract:
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.
Keywords:
emergency medical service base allocation; covering model; kernel density estimation;
social media information
1. Introduction
At present, rescue squads in many countries are encountering the problem of coping
with incidents that cost people lives and properties, e.g., natural disasters, terrorism,
political protests, chemical storage explosions or diseases. In a rescue squad, an emergency
medical service (EMS) is vital for victims [
1
–
3
]. Recent research reveals that survival rate
was increased and severe injury chance was reduced for the victim when an EMS unit
reached the scene of the incident within 8 min [
4
,
5
]. Therefore, it is necessary that the
EMS unit must always be ready and on stand-by close to the incident scene so that the
service can reach the scene as soon as possible. Generally, an EMS base is defined by the
experienced officer. Their decision primarily relies only on the demand factor. The decision
for the allocation of EMS bases is irrespective of other factors that may not be suitable for
an ever-changing environment.
Sustainability 2022, 14, 10247. https://doi.org/10.3390/su141610247 https://www.mdpi.com/journal/sustainability
