Robert Power, Bella Robinson, David Alexander, & Mahesh Prakash. (2018). Predicting Demand for Government Services during Disaster Events. In Kristin Stock, & Deborah Bunker (Eds.), Proceedings of ISCRAM Asia Pacific 2018: Innovating for Resilience – 1st International Conference on Information Systems for Crisis Response and Management Asia Pacific. (pp. 89–96). Albany, Auckland, New Zealand: Massey Univeristy.
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.
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Robert Power, Bella Robinson, John Colton, & Mark Cameron. (2015). A Case Study for Monitoring Fires with Twitter. In L. Palen, M. Buscher, T. Comes, & A. Hughes (Eds.), ISCRAM 2015 Conference Proceedings ? 12th International Conference on Information Systems for Crisis Response and Management. Kristiansand, Norway: University of Agder (UiA).
Abstract: This paper presents a user configurable monitoring system to track in near-real-time tweets describing fire events. The system targets fire related words in a user defined region of interest published on Twitter which are further processed by a text classifier to determine if they describe a known fire event of interest. The system was motivated from a case study that examined a corpus of tweets posted during active bushfires. This demonstrated that useful information is available on Twitter about fire events from people who are in the vicinity.
We present an overview of the system describing how it is initially configured by a user to focus on specific fire events in Australia, the development of a text classifier to identify tweets of interest, especially those with accompanying photos, and the monitoring system that can track multiple events at once.
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Robert Power, Bella Robinson, & Mark Cameron. (2023). Insights from a Decade of Twitter Monitoring for Emergency Management. In V. L. Thomas J. Huggins (Ed.), Proceedings of the ISCRAM Asia Pacific Conference 2022 (pp. 247–257). Palmerston North, New Zealand: Massey Unversity.
Abstract: The Emergency Situation Awareness (ESA) tool began as a research study into automated web text mining to support emergency management use cases. It started in late 2009 by investigating how people respond on Twitter to specific emergency events and we quickly realized that every emergency situation is different and preemptively defining keywords to search for content on Twitter beforehand would likely miss important information. So, in late September 2011 we established location-based searches with the aim of collecting all the tweets published in Australia and New Zealand. This was the beginning of over a decade of collecting and processing tweets to help emergency response agencies and crisis coordination centres use social media content as a new channel of information to support their work practices and to engage with the community impacted by emergency events. This journey has seen numerous challenges overcome to continuously maintain a tweet stream for an operational system. This experience allows us to derive insights into the changing use of Twitter over this time. In this paper we present some of the lessons we’ve learned from maintaining a Twitter monitoring system for emergency management use cases and we provide some insights into the changing nature of Twitter usage by users over this period.
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Robert Power, Mahesh Prakash, Bella Robinson, Nikhil Garg, Maria Wikstrom, & Martijn Mooij. (2023). A Climate Resilience Platform for Agriculture. In V. L. Thomas J. Huggins (Ed.), Proceedings of the ISCRAM Asia Pacific Conference 2022 (pp. 164–172). Palmerston North, New Zealand: Massey Unversity.
Abstract: The changing climate will see an increase in the frequency, scale, and intensity of future natural disasters. While communities and governments need to work together to mitigate the impact of these emergency events, the business community will also need to adapt to ensure the ongoing sustainability of their enterprises. This is especially true of the agricultural sector which is exposed to climate variability. The Climate Services for Agriculture (CSA) tool is an online interactive digital platform bringing together a variety of climate information specifically for farmers and the agricultural sector. It will enable agricultural businesses, planners, and communities to explore various climate related datasets to better understand how the expected future climate may impact different regions and commodities. This will help people to anticipate and plan for the impacts of a variable and changing climate. We present the CSA tool, available at https://climateservicesforag.indraweb.io/, outlining how it is being developed in collaboration with key stakeholders in the Australian farming community, the climate data available and usage scenarios.
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