Using Geographic Information Systems (GIS) to develop prioritization maps in urban search and rescue operations, after a natural disaster. Case study: the municipality of Agia Paraskevi, Athens, Greece.

Nefros, Constantinos; Kitsara, Gianna; Photis, Yorgos N.

doi:10.1016/j.ifacol.2018.11.332

Cited by 9 publications

(3 citation statements)

References 13 publications

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“…The popular technology such as GIS is widely used in spatial data surveys. Risk maps and simulations were often developed based on GIS technology for many objectives such as understanding flood areas, rescue operations after disasters and epidemics [6,83,93]. Effectiveness and usefulness are important issues that users concerned when using GIS.…”

Section: Perceived Usefulness (Pu)mentioning

confidence: 99%

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The Acceptance of Using Information Technology for Disaster Risk Management: A Systematic Review

Meechang¹,

Leelawat²,

Tang³

et al. 2020

EngJ

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The numbers of natural disaster events are continuously affecting human and the world economics. For coping with disaster, several sectors try to develop the frameworks, systems, technologies and so on. However, there are little researches focusing on the usage behavior of Information Technology (IT) for disaster risk management (DRM). Therefore, this study investigates the affecting factors on the intention to use IT for mitigating disaster's impacts. This study conducted a systematic review with the academic researches during 2011-2018. Two important factors from the Technology Acceptance Model (TAM) and others are used in describing individual behavior. In order to investigate the potential factors, the technology platforms are divided into nine types. According to the findings, computer software such as GIS applications are frequently used for simulation and spatial data analysis. Social media is preferred among the first choices during disaster events in order to communicate about situations and damages. Finally, we found five major potential factors which are Perceived Usefulness (PU), Perceived Ease of Use (PEOU), information accessibility, social influence, and disaster knowledge. Among them, the most essential one of using IT for disaster management is PU, while PEOU and information accessibility are more important in the web platforms.

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Section: Perceived Usefulness (Pu)mentioning

confidence: 99%

“…In computer software and SNS research articles, PU was frequently mentioned as 67%. For example, GIS software for flood areas and heat map represented the effectiveness of using software [6,93]. The developed model of seismic vulnerability roads based on GIS was also useful for assessment damages during earthquake [94].…”

Section: Perceived Usefulness (Pu)mentioning

confidence: 99%

The Acceptance of Using Information Technology for Disaster Risk Management: A Systematic Review

Meechang¹,

Leelawat²,

Tang³

et al. 2020

EngJ

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“…The colors in the map represent the density of risk factor in such an area, ranging from a red color, representing a high density of risk factors to a dark green color, representing a low density of risk factors. In this work, five levels of density are defined with five different colors, as used in [47], which identify and prioritize the most critical regions in an area prior to the occurrence of the natural disaster. The meaning of each level is shown in Table 1.…”

Section: Data Preparation and Analysismentioning

confidence: 99%

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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Overview of Spatial Decision Support System and Multiple Criteria Analysis in District Heating Network Maintenance

Triapitcin

Dahanayake

Thalheim

2021

Lecture Notes in Networks and Systems

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Using Geographic Information Systems (GIS) to develop prioritization maps in urban search and rescue operations, after a natural disaster. Case study: the municipality of Agia Paraskevi, Athens, Greece.

Cited by 9 publications

References 13 publications

The Acceptance of Using Information Technology for Disaster Risk Management: A Systematic Review

The Acceptance of Using Information Technology for Disaster Risk Management: A Systematic Review

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

Overview of Spatial Decision Support System and Multiple Criteria Analysis in District Heating Network Maintenance

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