Predictive analytics for social innovation and public safety

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This research project will address the problem of using data analytics to enable social innovation and increase public safety. Principally, we will create techniques for mining knowledge from government data accumulated by multiple ministries. The industrial partner for this proposal is ISM Canada, a leading market provider of innovative IT business solutions in Western Canada. ISM offers innovative services related to mobile solutions, cloud computing, big data, and analytics. The research team, which consists of U. of Regina and ISM personnel, will develop novel techniques for collecting, storing, mining, and visualizing government data, often with spatio-temporal features. We will apply these techniques to multiple government databases to gain more insight into how the government can change the way services are delivered to the public to increase their effectiveness and reduce their costs. In our research, we will focus on five main problems. We will design and evaluate algorithms for finding and analyzing user patterns to identify the highest-cost users of government services; finding and analyzing service patterns to quantify the costs and benefits of using individual government programs on the overall costs and benefits to the government; and recommending services to users of government programs after finding patterns in both the services that are recommended to groups of users by particular social workers and the benefits obtained by those users. As well, we will design and evaluate new approaches to predicting and visualizing the occurrence and spread of wildfires that can be utilized to increase public safety, and identify a new methodology for preserving the privacy of public data to permit information to be mined from public data in a way that protects the privacy of the people described by that data.