Digital neighborhood frameworks in community-centric urban infrastructure planning
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https://doi.org/10.5281/zenodo.17073266##article.subject##:
digital neighborhoods, smart urbanization, algorithmic planning, institutional inertia, urban analytics, civic technology, infrastructure governance##article.abstract##
The rise of the digital neighborhood paradigm presents complex challenges for urban planners and policymakers
entering data-informed infrastructure ecosystems and deciding resource allocation priorities – a crucial topic in urban
development research. This study investigates the structure and behavioral dynamics of digitally-mediated communities
and aims to contribute to the literature on the “smart urbanization” problem. This paper addresses this knowledge gap
with empirical evidence on how community-centric digital frameworks are used for planning infrastructure investments
by different types of municipal entities at different stages of neighborhood development, in medium-sized urban areas. It
explains how digital engagement platforms and institutional structures, rules, decision-making protocols, and participatory
mechanisms interconnect to influence infrastructure planning outcomes using the conceptual lens and method of
correlational urban analytics, the digital infrastructure mapping concept, and a regression-based diagnostic method. Our
findings show that due to the particular governance dynamics of digitally-networked communities, local actors do not
welcome the uncertainties related to automated planning algorithms, and the benefit of predictive insights from digital
infrastructure modeling is lower than the social cost of abandoning traditional consultative planning. They reveal that
the “stability bias” (maintaining a familiar governance routine) plays a role in resistance to digital planning models and
delayed policy adaptation. In addition, it is believed that these behavioral tendencies may have long-term implications to
data-driven decision-making adoption trajectories
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