Abstract. Social media (SM) has become a principal information source and the vast amounts of generated data are increasingly used to inform various disciplines. Most platforms, such as Instagram, Twitter or Flickr, offer the option to tag a post with a location or a precise coordinate, which also fosters applications of data in the geospatial fields. Notwithstanding the many ways in which these data could be analyzed and applied, scandals such as Cambridge Analytica have also shown the risks to user privacy that seem inherently part of the data.Is it possible to mitigate these risks, while maintaining the collective usability of this data for society questions? We identify urban planning as a key field for socio-spatial justice and propose an open source map-based cross-platform dashboard, fueled by geospatial SM, as a supporting tool for municipal decision-makers and citizens alike. As a core part of this tool, we implement a novel privacy-aware data structure that allows for both, a more transparent, encompassing data ground for municipalities, and a reduced data collection footprint, preventing the misuse of data or compromising user privacy.

Abstract. The use of georeferenced social media data (GSMD) for informing municipal policy-making has significant potential, particularly in addressing pressing socio-environmental challenges. Geospatial dashboards have emerged as a powerful tool for knowledge communication and supporting urban sustainability. However, there has been little emphasis on how to display and make GSMD more accessible, partly due to their complex nature. Existing visualization tools lack sophisticated methods, especially for complex urban contexts, and the methodological choice can significantly impact the interpretation of results. In this study, we propose the use of hexagonal binning as an interactive visualization method and assess three different on-the-fly binning metrics for mapping GSMD. We expand the use of the signed chi metric for spatial purposes and apply it in a case study in Bonn, Germany. We evaluate the advantages and disadvantages of the proposed metrics as well as visualizations and highlight the challenges of visualizing GSMD particularly in the context of Instagram. Our findings highlight the importance of using appropriate context-dependent visualization methods when analyzing data at the municipal level.