CommuniWave:A Machine Learning Model for Quantifying the Degree of Temporary Informal Behavior in Urban Communities
arXiv:2607.08554v1 Announce Type: new Abstract: For urban managers and designers, improving the functional attributes of urban communities to enhance territorial resilience in the face of complexity and uncertainty is crucial. Currently, community planning often follows a top-down approach and lacks effective metrics to quantify informal behaviors of residents, leading to frequent conflicts with original plans. This study introduces CommuniWave, a machine learning model designed to efficiently detect and quantify the Degree of Informal Behavior (DIB) in urban communities. The model integrates
The increasing availability of urban data and advancements in machine learning are making it possible to quantify previously unmeasurable social dynamics, addressing a long-standing challenge in urban planning.
This model introduces a data-driven approach to understanding informal urban behaviors, which could significantly improve the efficacy and adaptability of community planning by moving beyond traditional top-down methods.
Urban planning and management can now be informed by quantifiable metrics of resident behavior, allowing for more responsive and resilient designs that better align with actual community dynamics.
- · Urban planners
- · Smart city technology providers
- · Community organizers
- · Local governments
- · Traditional urban planning methodologies
- · Purely top-down urban development models
Urban managers gain a new tool for data-driven decision-making regarding community functionality and resilience.
Improved urban designs could lead to more harmonious and functional communities, reducing conflicts between official plans and resident behaviors.
The application of similar AI models could extend to other social systems, enabling more dynamic and adaptive governance across various public sectors.
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Read at arXiv cs.AI