An application-oriented implementation of hexagonal on-the-fly binning metrics for city-scale georeferenced social media data This presentation focuses on utilizing georeferenced social media (SM) data for informed municipal policy-making. It emphasizes the need for customized visualization techniques for SM data, addressing challenges beyond traditional cartographic methods. The study explores various statistical metrics and visualization approaches, particularly the signed chi metric and hexagonal binning, for frontend applications like dashboards. The problem statement identifies challenges related to SM data, including limited access, noise from super users, and a lack of research for municipal-level data visualization. The research interest lies in proposing a system of metrics for data processing and visualization, catering to different user needs. Three key metrics are evaluated: absolute values, relative values, and the signed chi metric, with the latter showing promising results in dealing with the complexities of SM data. The presentation provides insights into the application of these metrics and their practical use in understanding geospatial SM data for decision-making.