SRITNE is leading the research to develop a predictive Service Analytics data model to study the impact of Pune City Police’s online interventions on citizen satisfaction and on their policing efficacy.
The research aims to develop a Service Analytics data model that will use crime reporting data generated by S.E.V.A. (Service Excellence and Victim Assistance) app of Pune Police. Using other open-source data (e.g. rainfall / weather / land-use / population) and specially-sourced data (e.g. mobility and traffic data), the effectiveness of S.E.V.A. will be measured by the study.
With data being made available by Centre for Police Research, Pune (CPR), this research would aid the national organisation to effectively utilize statistical tools and machine learning techniques to analyze trends, diversity in crime reporting, complainant satisfaction and on other parameters of crime reporting to resolution.
Studying the impact of economic development on Pune’s crime rate is also a subject of this study. Data collected from various open and specially-sourced datasets such as nightlights, mobility, population density, land-use patterns, etc., will act as effective measures for economic development in a given region. CPR would use the data analytics model to map these attributes on crime rates in different regions in Pune across a host of parameters and cross mapping them to predict crime patterns.
Lastly, the study aims to model false registrations, deduce crime rates and uncover biases in criminal judgements related to cases in Pune.