Zurich Like New

Abstract

Citizen-driven platforms for enhancing local public services have been adopted in several countries like the UK and Switzerland. Local governments use data collected from these platforms to solve reported issues. Data can also be used by governments for data-driven decision-making and to improve the operation of the platforms themselves. In particular, as citizen reports become increasingly popular, there is a need to handle them more efficiently. In this paper, we present an analysis of ZüriWieNeu, a map-based website helping people in Zurich, Switzerland to report urban issues related to waste, broken streetlamps, or graffiti, among others. Our contributions are two-fold. First, we analyze what machine-extracted textual, visual, spatial and temporal features reveal about the dynamics of reporting and the content of each report category. This analysis provides a snapshot of the common patterns of urban issues in the Zurich area. Second, we perform classification to automatically infer the category of reports, achieving promising performance. Our work contributes towards developing machine learning-based systems to classify report categories, with the ultimate goal of supporting both users and platform operation.