Predictive analytics for social innovation and public safety

该研究项目将解决使用数据分析实现社会创新和提高公共安全的问题。主要是，我们将创造从多个部委积累的政府数据中挖掘知识的技术。该提案的工业合作伙伴是ISM Canada，它是加拿大西部创新IT业务解决方案的领先市场提供商。ISM提供与移动的解决方案、云计算、大数据和分析相关的创新服务。该研究小组由美国科学院院士、里贾纳和ISM人员，将开发用于收集，存储，挖掘和可视化政府数据的新技术，通常具有时空特征。我们将把这些技术应用到多个政府数据库中，以更深入地了解政府如何改变向公众提供服务的方式，从而提高服务效率并降低成本。在我们的研究中，我们将重点关注五个主要问题。我们将设计和评估算法，以发现和分析用户模式，以确定政府服务的最高成本用户;发现和分析服务模式，以量化使用单个政府计划的成本和收益，以及政府的整体成本和收益;以及在找到由特定用户推荐给用户组的两种服务中的模式之后向政府计划的用户推荐服务社会工作者和这些使用者获得的福利。此外，我们将设计和评估新的方法来预测和可视化野火的发生和蔓延，可用于提高公共安全，并确定一种新的方法来保护公共数据的隐私，以允许从公共数据中挖掘信息，以保护该数据所描述的人的隐私。

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.