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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.
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Camera-First Form Filling: Reducing the Friction in Climate Hazard Reporting
The effective reporting of climate hazards, such as flash floods, hurricanes, and earthquakes, is critical. To quickly and correctly assess the situation and deploy resou rces, emergency services often rely on citizen reports that must be timely, comprehensive, and accurate. The pervasive availability and use of smartphone cameras allow the transmission of dynamic incident information from citizens in near-real-time. While high-quality reporting is beneficial, generating such reports can place an additional burden on citizens who are already suffering from the stress of a climate-related disaster. Furthermore, reporting methods are often challenging to use, due to their length and complexity. In this paper, we explore reducing the friction of climate hazard reporting by automating parts of the form-filling process. By building on existing computer vision and natural language models, we demonstrate the automated generation of a full-form hazard impact assessment report from a single photograph. Our proposed data pipeline can be integrated with existing systems and used with geospatial data solutions, such as flood hazard maps.
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- Award ID(s):
- 1910356
- PAR ID:
- 10464587
- Date Published:
- Journal Name:
- HILDA '23: Proceedings of the Workshop on Human-In-the-Loop Data Analytics
- Page Range / eLocation ID:
- 1 to 7
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3597465.3605218
