Risk Hotspot Conceptual Space Characterized by Hexagonal Data Binning Technique: An Application in Albay, Philippines

  • Ana Marie R. Abante BUCENG, Bicol University, Philippines

Abstract

Purpose  – This study sought to develop a model to locate and visualize the risk hotspots and coldspots in Albay using the binning technique.


Method – Hexagonal binning technique leads to complexities in quantifying risk hotspots and coldspots. It is regarded as storing weighted values ranging from 1 which is the lowest to 5 which pertains to the highest value. The weights may be influenced by hazard return periods or proximity to the critical condition of the landscapes or seascapes that are highly prone or regularly impacted by hazards. The Binning Parameters following data binning parameters were created and applied to study variations of the six elements of risk reality.


Results –The hexagonal bin reveals that about 29,400 hectares in Albay are significant risk hotspots, with 99% confidence. Also, at least 7,100 hectares of land are significant risk hotspots, with 95% confidence, and 3,100 hectares are significant risk hotspots with 90% confidence.


Conclusion – The researcher concluded that disaster risk reduction entails interdisciplinary thinking to apply hexagonal to determine where the natural and man-made hazards, landscape vulnerable, and passive and active exposure hotspots or coldspots exist.


Recommendations – The researcher proposes applying hexagonal data mining techniques to dig deep into the risk realms to avoid unwanted effects in natural and built environments.

Published
2020-08-03
How to Cite
ABANTE, Ana Marie R.. Risk Hotspot Conceptual Space Characterized by Hexagonal Data Binning Technique: An Application in Albay, Philippines. International Journal of Computing Sciences Research, [S.l.], v. 5, n. 1, p. 550-567, aug. 2020. ISSN 2546-115X. Available at: <//stepacademic.net/ijcsr/article/view/208>. Date accessed: 18 june 2021.
Section
Articles