International Journal of Computing Sciences Research (Jun 2018)
Resource Location-Intelligence Model Conceptualized for Mayon Volcano Danger Zones in Albay, Philippines
Abstract
Purpose–This paper presents a cloud-based GIS that aims to store, retrieve, manipulate and analyze Disaster Risk Reduction and Management (DRRM)-database human resource data for students, professional volunteers, emergency responders, social and health workers. The location-intelligence is significant inmaintaining public safety and peace and order during disaster and post-disaster phases. Locatingtrained personnel during emergency response stage is critical in DRRM given that responders can alsobe exposed to disaster risks together withthe evacuees during crisis. Method–The researchers triedto put intothe picture how spatial integration could enhance existing Local Government Unit (LGU)information systems. Enhancing existing information systems with humanresource or household locations (x, y) is critical in analyzing and validating incoming real-time data or emergency incident reports. Reliable reports and location can be quickly collected, processed and manipulated to produce crisis maps from the DRRM databases stored at the cloud GIS at real-time. Crisis maps should be produced timely for complex emergencies and responseoperations.The sampling coveredgeographic locations intersecting the outer boundary of the Mayon 6-kilometer danger zone stretched up to the 10-kilometer buffering atleast 2,898 families or 15,049 residents distributed in 25 barangays of 2 cities and 6municipalities of Albay Province. The researcherstried to highlight how spatial integration could enhance existing LGU information systems by considering human resource locations (x, y) as fundamental in integrating incoming numerous real-time information from emergency incidents. The data are quickly collected, manipulated then processed with other data retrieved from the readily-availableDRRM database, to simultaneously alert the concerned government agencies and responders, thru a Human Resource Location Intelligence Systemstored at the cloud GIS. Results–The proposed system cangenerate tailored maps (combining both official and latest unofficial information from the ground during emergencies),which indicates visualization of the extent of ashfallalong with lahar prone map layersduring disaster phase or post disaster phase. The layers are processed through a model-builder toolto quickly disclose real-time people and critical infrastructures atrisk. Conclusion –This study providesreal-time exposure critical to responding to clearing operations, emergency response, retrieval of cadaver, and so on, and better understanding of how to enhance DRRM. Practical Implication–The proposed systemslocation-intelligence is significant in maintaining public safety and peace and order during disaster and post-disaster phases.
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