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The relationship between local governments and the general public is being redefined by the increasing use of online platforms that enable participatory reporting of non-emergency urban issues, such as potholes and illegal graffiti by concerned citizens to their local authorities. In this work, we study, for the first time, participatory reporting data together with neighborhood-level demographics, socioeconomic indicators, and pedestrian friendliness and transit and bike scores, across multiple neighborhoods in the Capital District of the New York State. Our data-driven approach offers a large-scale, low-cost alternative to traditional survey methods, and provides insights on citizen participation and satisfaction, and public value creation on such platforms. Our findings can be used to guide government service departments to work more closely with each neighborhood to improve the offline and online communication channels through which citizens can report urban issues. 

Civic engagement platforms such as SeeClickFix and FixMyStreet have revolutionized the way citizens interact with local governments to report and resolve urban issues. However, recognizing which urban issues are important to the community in an accurate and timely manner is essential for authorities to prioritize important issues, allocate resources and maintain citizens' satisfaction with local governments. To this end, a novel formulation based on optimal stopping theory is devised to infer urban issues importance from ambiguous textual, time and location information. The goal is to optimize recognition accuracy, while minimizing the time to reach a decision. The optimal classification and stopping rules are derived. Furthermore, a near-real-time urban issue reports processing method to infer the importance of incoming issues is proposed. The effectiveness of the proposed method is illustrated on a real-word dataset from SeeClick-Fix, where significant reduction in time-to-decision without sacrificing accuracy is observed. 

Participatory civil issue monitoring has emerged as an easy way for concerned citizens to report problems to their local government. For reported issues to be timely processed and addressed however, accurate, online and real-time processing methods to infer issue types are necessary. To address this challenge, we propose a computational, near-real-time civil issue reports processing method to estimate the actual issue from ambiguous and/or complementary information accurately and efficiently. We demonstrate the effectiveness of the proposed approach using a real-world dataset from SeeClickFix. We show that our approach is both highly accurate and scalable. 

Participatory civil issue monitoring has emerged as an easy way for concerned citizens to report problems to their local government. For reported issues to be timely processed and addressed however, accurate, online and real–time processing methods to infer issue types are necessary. To address this challenge, we propose a computational, near–real–time civil issue reports processing method to estimate the actual issue from ambiguous and/or complementary information accurately and efficiently. We demonstrate the effectiveness of the proposed approach using a real-world dataset from SeeClickFix. We show that our approach is both highly accurate and scalable.