Designing Mobile Applications for Citizen Participation in Urban Planning

As a result of the increasing requirements for urban planning, a paradigm shift towards citizen participation has evolved to collaboratively address enhancing urban challenges and social conflicts. Past projects have examined urban citizen participation processes and methods to support citizen participation. However, the challenges in the domain of informing, encouraging, and enabling participation at any time are not sufficiently examined and less attention was devoted to urban participation through mobile applications, even if required devices are widely used and can enable permanent communication channels between citizens and planning authorities. Therefore, a design science research project was initiated to examine how to design mobile applications to support citizen participation in urban planning projects. In this paper, the findings of the first cycle are presented including issues, meta-requirements, design principles, the development of a mock-up, and its evaluation to provide insight into the design of mobile applications for citizen participation

Exploring AI supported Citizen Argumentation on Urban Participation Platforms

The paradigm shift in urban planning toward citizen participation originates from the Smart City concept, as politicians and scientists argue that citizens should be included in the design of their environment. This led to the development of urban participation platforms and was enhanced by the COVID-19 pandemic as on-site participation was unavailable. Past projects showed that urban participation platforms can reach thousands of citizens, but it became apparent that citizens' contributions vary widely and are sometimes not understandable and comprehensible which limits their value for urban projects. Therefore, we examined how an AI-based feedback system can increase citizens’ argumentation on urban platforms. For this, an explorative comparison of two prototypes was conducted by applying Argumentation Theory and Mayring's qualitative content analysis to empirically analyze collected data. The findings highlight that the developed AI-based feedback system supports citizens and leads to more argumentative and comprehensible argumentations on urban participation platforms

Illuminating Smart City Solutions – A Taxonomy and Clusters

With urban problems intensifying, Smart City solutions are recognized by researchers and practitioners as one of the most promising solutions to make urban areas economically, environmentally, and socially sustainable. While many elements of Smart City solutions have been explored, existing works either treat Smart City solutions as technical black boxes or focus exclusively on Smart City solutions’ technical or non-technical characteristics. Therefore, to conceptualize the unique characteristics of Smart City solutions currently available, we developed a multi-layer taxonomy based on Smart City solution literature and a sample of 106 Smart City solutions. Moreover, we identified three clusters, each covering a typical combination of characteristics of Smart City solutions. We evaluated our findings by applying the Q-sort method. The results contribute to the descriptive knowledge of Smart City solutions as a first step for a theory for analyzing and enable researchers and practitioners to understand Smart City solutions more holistically