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Policing decisions, allocations and outcomes are determined by mapping historical crime data geospatially using popular algorithms. In this extended abstract, we present early results from a mixedmethods study of the practices, policies, and perceptions of algorithmic crime mapping in the city of Milwaukee, Wisconsin. We investigate this differential by visualizing potential demographic biases from publicly available crime data over 12 years (2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016) and conducting semi-structured interviews of 19 city stakeholders and provide future research directions from this study.

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