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Author (up) Robert Power; Bella Robinson; David Alexander; Mahesh Prakash pdf  openurl
  Title Predicting Demand for Government Services during Disaster Events Type Conference Article
  Year 2018 Publication Proceedings of ISCRAM Asia Pacific 2018: Innovating for Resilience – 1st International Conference on Information Systems for Crisis Response and Management Asia Pacific. Abbreviated Journal Iscram Ap 2018  
  Volume Issue Pages 89-96  
  Keywords Situation Awareness, Data Integration, Disaster Management, Crisis Coordination  
  Abstract Smart Service Queensland (SSQ) is the 'front door' to the Queensland State Government in Australia, providing services and information for citizens and businesses. Services are delivered through online channels, call centres and face-to-face service centres. Rostering to adequately staff the call centres during business as usual demand is well supported using existing workforce planning tools and content management systems alongside real-time telephonic monitoring. However, during times of large scale emergency events, such as floods and tropical cyclones, effective workforce planning heavily relies upon experienced SSQ personnel constantly monitoring and updating call centre staffing levels leading up to and during the disaster event to ensure customer demands are met. Achieving the right balance of call centre servicing without over provisioning is a challenging task. We present a prototype analytics tool that supports the SSQ Forecast Analyst responsible for workforce planning during disaster events and provides initial results of modelling caller behavior during two recent tropical cyclones. The tool provides a single point of reference to a wide collection of relevant datasets, including population demographics and details of the natural and built environment, data feeds describing the emergency event under investigation, relevant social media posts and call centre operations metrics. The tool is an early proof of concept demonstrator highlighting the utility of data integration, web mapping, real-time event monitoring, and predictive modelling.  
  Address Csiro; Csiro; Csiro; Csiro  
  Corporate Author Thesis  
  Publisher Massey Univeristy Place of Publication Albany, Auckland, New Zealand Editor Kristin Stock; Deborah Bunker  
  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Track Monitoring and Alerting Systems supporting Business as Usual and Emergency Warnings Expedition Conference  
  Notes Approved no  
  Call Number Serial 1668  
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