IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i16p10247-d891072.html
   My bibliography  Save this article

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

Author

Listed:
  • Ranon Jientrakul

    (Department of Industrial Engineering, School of Engineering, King Mongkut’s Institute of Technology Ladkrabang, Bangkok 10520, Thailand)

  • Chumpol Yuangyai

    (Department of Industrial Engineering, School of Engineering, King Mongkut’s Institute of Technology Ladkrabang, Bangkok 10520, Thailand)

  • Klongkwan Boonkul

    (Department of Industrial Engineering, School of Engineering, King Mongkut’s Institute of Technology Ladkrabang, Bangkok 10520, Thailand)

  • Pakinai Chaicharoenwut

    (Department of Industrial Engineering, School of Engineering, King Mongkut’s Institute of Technology Ladkrabang, Bangkok 10520, Thailand)

  • Suriyaphong Nilsang

    (Department of Production Engineering, Faculty of Engineering, King Mongkut’s University of Technology Thonburi, Bangkok 10140, Thailand)

  • Sittiporn Pimsakul

    (Department of Industrial Engineering, School of Engineering, King Mongkut’s Institute of Technology Ladkrabang, Bangkok 10520, Thailand)

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.

Suggested Citation

  • Ranon Jientrakul & Chumpol Yuangyai & Klongkwan Boonkul & Pakinai Chaicharoenwut & Suriyaphong Nilsang & Sittiporn Pimsakul, 2022. "Integrating Spatial Risk Factors with Social Media Data Analysis for an Ambulance Allocation Strategy: A Case Study in Bangkok," Sustainability, MDPI, vol. 14(16), pages 1-15, August.
  • Handle: RePEc:gam:jsusta:v:14:y:2022:i:16:p:10247-:d:891072
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/16/10247/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/14/16/10247/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Erdemir, Elif Tokar & Batta, Rajan & Rogerson, Peter A. & Blatt, Alan & Flanigan, Marie, 2010. "Joint ground and air emergency medical services coverage models: A greedy heuristic solution approach," European Journal of Operational Research, Elsevier, vol. 207(2), pages 736-749, December.
    2. David, Guy & Harrington, Scott E., 2010. "Population density and racial differences in the performance of emergency medical services," Journal of Health Economics, Elsevier, vol. 29(4), pages 603-615, July.
    3. Chang, Mei-Shiang & Tseng, Ya-Ling & Chen, Jing-Wen, 2007. "A scenario planning approach for the flood emergency logistics preparation problem under uncertainty," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 43(6), pages 737-754, November.
    4. Constantine Toregas & Ralph Swain & Charles ReVelle & Lawrence Bergman, 1971. "The Location of Emergency Service Facilities," Operations Research, INFORMS, vol. 19(6), pages 1363-1373, October.
    5. Kvalseth, T.O. & Deems, J.M., 1979. "Statistical models of the demand for emergency medical services in an urban area," American Journal of Public Health, American Public Health Association, vol. 69(3), pages 250-255.
    6. van den Berg, Pieter L. & Aardal, Karen, 2015. "Time-dependent MEXCLP with start-up and relocation cost," European Journal of Operational Research, Elsevier, vol. 242(2), pages 383-389.
    7. Xueping Li & Zhaoxia Zhao & Xiaoyan Zhu & Tami Wyatt, 2011. "Covering models and optimization techniques for emergency response facility location and planning: a review," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 74(3), pages 281-310, December.
    8. Richard Church & Charles R. Velle, 1974. "The Maximal Covering Location Problem," Papers in Regional Science, Wiley Blackwell, vol. 32(1), pages 101-118, January.
    9. Mark S. Daskin & Kayse Lee Maass, 2015. "The p-Median Problem," Springer Books, in: Gilbert Laporte & Stefan Nickel & Francisco Saldanha da Gama (ed.), Location Science, edition 127, chapter 0, pages 21-45, Springer.
    10. Martínez-Rojas, María & Pardo-Ferreira, María del Carmen & Rubio-Romero, Juan Carlos, 2018. "Twitter as a tool for the management and analysis of emergency situations: A systematic literature review," International Journal of Information Management, Elsevier, vol. 43(C), pages 196-208.
    11. Armann Ingolfsson & Susan Budge & Erhan Erkut, 2008. "Optimal ambulance location with random delays and travel times," Health Care Management Science, Springer, vol. 11(3), pages 262-274, September.
    12. Schmid, Verena, 2012. "Solving the dynamic ambulance relocation and dispatching problem using approximate dynamic programming," European Journal of Operational Research, Elsevier, vol. 219(3), pages 611-621.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Suriyaphong Nilsang & Chumpol Yuangyai & Chen-Yang Cheng & Udom Janjarassuk, 2019. "Locating an ambulance base by using social media: a case study in Bangkok," Annals of Operations Research, Springer, vol. 283(1), pages 497-516, December.
    2. Bélanger, V. & Lanzarone, E. & Nicoletta, V. & Ruiz, A. & Soriano, P., 2020. "A recursive simulation-optimization framework for the ambulance location and dispatching problem," European Journal of Operational Research, Elsevier, vol. 286(2), pages 713-725.
    3. Dmitrii Usanov & G.A. Guido Legemaate & Peter M. van de Ven & Rob D. van der Mei, 2019. "Fire truck relocation during major incidents," Naval Research Logistics (NRL), John Wiley & Sons, vol. 66(2), pages 105-122, March.
    4. Nelas, José & Dias, Joana, 2020. "Optimal Emergency Vehicles Location: An approach considering the hierarchy and substitutability of resources," European Journal of Operational Research, Elsevier, vol. 287(2), pages 583-599.
    5. Wajid, Shayesta & Nezamuddin, N., 2023. "Capturing delays in response of emergency services in Delhi," Socio-Economic Planning Sciences, Elsevier, vol. 87(PA).
    6. Carvalho, A.S. & Captivo, M.E. & Marques, I., 2020. "Integrating the ambulance dispatching and relocation problems to maximize system’s preparedness," European Journal of Operational Research, Elsevier, vol. 283(3), pages 1064-1080.
    7. McCormack, Richard & Coates, Graham, 2015. "A simulation model to enable the optimization of ambulance fleet allocation and base station location for increased patient survival," European Journal of Operational Research, Elsevier, vol. 247(1), pages 294-309.
    8. Bélanger, V. & Ruiz, A. & Soriano, P., 2019. "Recent optimization models and trends in location, relocation, and dispatching of emergency medical vehicles," European Journal of Operational Research, Elsevier, vol. 272(1), pages 1-23.
    9. Dirk Degel & Lara Wiesche & Sebastian Rachuba & Brigitte Werners, 2015. "Time-dependent ambulance allocation considering data-driven empirically required coverage," Health Care Management Science, Springer, vol. 18(4), pages 444-458, December.
    10. Wang, Wei & Wang, Shuaian & Zhen, Lu & Qu, Xiaobo, 2022. "EMS location-allocation problem under uncertainties," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 168(C).
    11. Farahani, Reza Zanjirani & Fallah, Samira & Ruiz, Rubén & Hosseini, Sara & Asgari, Nasrin, 2019. "OR models in urban service facility location: A critical review of applications and future developments," European Journal of Operational Research, Elsevier, vol. 276(1), pages 1-27.
    12. Thije van Barneveld, 2016. "The Minimum Expected Penalty Relocation Problem for the Computation of Compliance Tables for Ambulance Vehicles," INFORMS Journal on Computing, INFORMS, vol. 28(2), pages 370-384, May.
    13. Wang, Wei & Wu, Shining & Wang, Shuaian & Zhen, Lu & Qu, Xiaobo, 2021. "Emergency facility location problems in logistics: Status and perspectives," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 154(C).
    14. Hamid Mousavi & Soroush Avakh Darestani & Parham Azimi, 2021. "An artificial neural network based mathematical model for a stochastic health care facility location problem," Health Care Management Science, Springer, vol. 24(3), pages 499-514, September.
    15. Martin van Buuren & Caroline Jagtenberg & Thije van Barneveld & Rob van der Mei & Sandjai Bhulai, 2018. "Ambulance Dispatch Center Pilots Proactive Relocation Policies to Enhance Effectiveness," Interfaces, INFORMS, vol. 48(3), pages 235-246, June.
    16. Sondes Hammami & Aida Jebali, 2021. "Designing modular capacitated emergency medical service using information on ambulance trip," Operational Research, Springer, vol. 21(3), pages 1723-1742, September.
    17. Mohri, Seyed Sina & Akbarzadeh, Meisam & Sayed Matin, Seyed Hamed, 2020. "A Hybrid model for locating new emergency facilities to improve the coverage of the road crashes," Socio-Economic Planning Sciences, Elsevier, vol. 69(C).
    18. Yoon, Soovin & Albert, Laura A., 2021. "Dynamic dispatch policies for emergency response with multiple types of vehicles," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 152(C).
    19. Areej Alhothali & Budoor Alwated & Kamil Faisal & Sultanah Alshammari & Reem Alotaibi & Nusaybah Alghanmi & Omaimah Bamasag & Manal Bin Yamin, 2022. "Location-Allocation Model to Improve the Distribution of COVID-19 Vaccine Centers in Jeddah City, Saudi Arabia," IJERPH, MDPI, vol. 19(14), pages 1-21, July.
    20. Lee, Yu-Ching & Chen, Yu-Shih & Chen, Albert Y., 2022. "Lagrangian dual decomposition for the ambulance relocation and routing considering stochastic demand with the truncated Poisson," Transportation Research Part B: Methodological, Elsevier, vol. 157(C), pages 1-23.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:14:y:2022:i:16:p:10247-:d:891072. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.