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ANALYSIS OF LOST SALES OPPORTUNITIES DUE TO FLOOD DAMAGE USING PURCHASING BIG DATA
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1  Kanazawa University
Academic Editor: Wataru Takeuchi

Abstract:

In recent years, the risk of urban flood in Japan has increased due to the intensification of heavy rainfall associated with climate change and the deterioration of drainage functions associated with urbanization. During flood disaster, buildings are inundated for a long time, causing various facilities to become unusable, leading to chronic malfunctions. Stores that purchase foodstuffs and other items become difficult to operate when their premises are inundated, resulting in lost sales opportunities and economic losses due to lost profits. However, the amount of damage associated with loss of sales opportunities is not considered in the calculation of the amount of inundation damage in inundation damage. In addition, the effect of the reduction in the duration of inundation by flood control projects on the reduction of damage to stores has not been fully clarified. In this study, we used purchasing big data accumulated by past consumers to estimate the amount of inundation damage associated with loss of sales opportunities. Kakehashi river in Komatsu City, Ishikawa Prefecture, Japan, was targeted for study. We estimated the loss of sales opportunities based on past purchase histories during the assumed duration of inundation of the Kakehashi River and found that the loss of sales opportunities in inundated areas including stores would be approximately 50,000 yen per day. Damage estimation by pattern with varying break points revealed that the amount of damage in each region varied depending on the break point. By considering break points in the estimation of flood damage, it was shown that the benefits of levee improvement can be evaluated not only in terms of protection of existing assets, but also in terms of toughening urban functions to minimize loss of opportunities.

Keywords: big data of purchasing, flood damage, opportunity loss, levee break simulation, GIS
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