Developing a volunteered geographic information-based system for rapidly estimating damage from natural disasters
- SPRINGER HEIDELBERG
- Publication Type:
- Journal Article
- Arabian Journal of Geosciences, 2021, 14, (17)
- Issue Date:
|Tables only_AJGS_2-19- 2021_revBP.docx||Supporting information||13.16 kB|
|Texts only_AJGS_2-19-2021_Sympletic.docx||Submitted version||4.15 MB|
|Figures only_AJGS_6-14-2021_revBP_final.docx||Supporting information||5.06 MB|
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When it comes to crisis management in times of earthquake, a large volume of damage information is required in the first hours in order to reduce its damages. Damage estimation based on pre-determined models and field visits has many limitations as it is both time-consuming and expensive. Thus, the present study tries to develop a web-based system to collect volunteered geographic information (VGI) provided by users who, when an earthquake occurs, immediately share the information about their or others’ economic losses and casualties, providing the crisis management team with a large volume of information to analyze. The system developed in the present study not only collects and stores VGI, but also performs heatmap spatial analyzes on the VGI. This study uses heatmaps to create classified damage maps based on the damage rate reported by people. In order to assess the results, the system was implemented in Masjed Soleyman (Iran) where an earthquake recently occurred. One hundred thirty two people cooperated in information collection. Heatmaps have been used to assess the accuracy of the data, which resulted in a significantly increased speed and accuracy of the validation, with 76.65% of information considered correct. The results suggest that, compared to the existing methods, VGI increases the damage estimation speed by 83% and reduces the costs by 75%. The results also show that implementing these systems in any organization not only significantly increases the information collection speed, but by using of spatial analysis we can reduce the time and increase the accuracy of data analysis.
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