Historically business enterprises have been gathering data as part of their “business as usual” operations. The evolution of the digital era has both enhanced this capability and increased the rate at which data is collected at unprecedented levels. The parallel evolution of spatially enabled data, data analytics and the visualisation of data presents opportunities to analyse spatial-temporal databases to a degree never before available. This ability provides the opportunity to incorporate the results of this analysis into corporate planning processes, policy and strategy development and risk identification and mitigation.  However, this new capability may also identify deficiencies in historically utilised databases which have led to poor decision making and setting of policy and strategy that has unknowingly limited business performance, misdirected capital investment and impacted resource utilisation.

This paper will address these issues by understanding of the concept of “concurrency” in database visualisation via a spatially enabled decision support tool developed by the Centre for Disaster Management and Public Safety (CDMPS), the University of Melbourne. A specific case study is performed to analyse historic incidents and explore response capacities across Victoria. A snapshot of emergency management data has been subjected to data cleaning, aggregation and harmonisation processes to support our proposed spatial-temporal concurrency analysis methodology. The output identifies key components such as demands and supplies. Each of these components can be investigated at various temporal granularity levels such as daily, monthly and yearly. Besides statistics, the developed tool can also interactively manipulate the results on a 4D visualisation engine by using dynamic demand-supply heat maps and spider webs that precisely describe the concurrent characteristics. The developed system helps decision makers better understand when and where demands are trigged and how supplies are distributed in busy seasons and eventually identify research priority needs to enhance their workforce planning capability.