Grant: Collaborative Research and Development Grants
University of Regina
$132,002.00
Predictive analytics for social innovation and public safety
FY 2019-20
Summary
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.
Program
Collaborative Research and Development Grants
Recipient
Location
Saskatchewan, Canada
Recipient Type
Academia
Type
Grant
Source
NSERC's Awards Data
As published
Application id
514906-2017
Name nom
Hamilton, Howard
Department département
Computer Science
Organization id
17
Institution établissement
University of Regina
Province en
Saskatchewan
Province fr
Saskatchewan
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2019
Competition year année de concours
2017
Award amount
132002
Program id
CRDPJ
Program name en
Collaborative Research and Development Grants
Program name fr
Subventions de recherche et développement coopérative
Group en
RESEARCH PARTNERSHIPS
Group fr
PARTENARIATS DE RECHERCHE
Committee code
96
Committee name en
RPP Internal Decision Cttee
Committee name fr
Cté de décision interne - PPR
Area of application code
802
Area of application group en
Information and communication services
Area of application group fr
Services d'information et de télécommunications
Area of application en
Information systems and technology
Area of application fr
Systèmes d'information et technologie de l'information
Research subject code
2700
Research subject group en
Information technology
Research subject group fr
Technologie de l'information
Research subject en
Information technology
Research subject fr
Technologie de l'information
Installment
inst:2 - 3
Partie
2
Nb partie
3
Application title
Predictive analytics for social innovation and public safety
Keyword
NSERC
Application summary
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.