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<br /> 48 | P a g e <br />City of San Leandro, CA <br />Fiber Master Plan <br />• Reduced fear <br /> <br />In addition, time and money can be saved, while staffing resources are utilized in the most <br />production and beneficial manner to both the police department and the community. <br /> <br />However, while the San Leandro Police Department is receptive and supportive of the <br />concept, there are significant hurdles to implementing such a program. It requires <br />partnerships with the academic and research community and a paradigm shift toward <br />applying research in practice. To address these challenges, a variety of factors are <br />recommended by experts, including new methods of officer training33. <br />In addition to partnerships with the academic community and a willingness to implement <br />a paradigm-shift, evidenced-based policing will require large amounts of data and the <br />infrastructure to layer sophisticated analytics on top of those data to uncover the research <br />on which evidenced-based policing policies will be designed and applied. <br /> <br />To apply the philosophy of evidence-based policing, the San Leandro Police Department <br />will need the ability to compare disparate data sets to look for possible correlations. The <br />intended goal of an evidence-based policing philosophy is to reduce and/or prevent crime <br />and disorder, but a common mistake in this endeavor is to limit analysis to data related to <br />crime. This may reveal crime trends and aid in predicting what future numbers will be, <br />but it does little to understand the causalities associated with crime and disorder. To <br />unlock the full potential of an evidence-based philosophy, the data must be very broad <br />and include data collected by other city departments and outside organizations. <br /> <br />Public Policy meets Data Analytics <br /> <br />SLPD is making strides in using data to inform public policy decisions. Currently, the <br />department manually aggregates multiple data sources to provide crucial insights around <br />a number of subjects, like its service delivery model, workforce recruitment, and crime- <br />fighting efforts. The next step in the evolution for this data-driven policymaking (i.e. a <br />“public policy hub”) concept will be deploying business intelligence (“BI”) software. <br />Integrating BI software removes time-consuming manual aggregation of disparate data <br />sets while minimizing the demands of visualizing any insights through manually crafted <br />dashboards or infographics. Deploying new business intelligence software technology <br />will streamline the processes of data collection, aggregation, and visualization. By <br />streamlining these processes, key operational insights can be delivered at a fraction of <br />the current cost. Ultimately, these insights deliver policymakers with better information— <br />a more comprehensive look—and a list of policy alternatives for making decisions that <br />could have far-reaching impacts. The department intends to use this policy hub platform <br /> <br />33 Huey, Laura and Mitchell, Renee J. (2016). Unearthing Hidden Keys: Why Pracademics Are an Invaluable (If <br />Underutilized) Resource in Policing Research. Policing, Volume 10, Number 3, pp. 300–307. 28 July 2016. Oxford <br />University Press <br />