CausalPOI: Spatio-Temporal Graph-Based Causal Modeling for Cold-Start POI Check-in Forecasting
arXiv:2606.05413v1 Announce Type: new Abstract: As urban environments continue to evolve rapidly, accurately modeling the dynamic behaviour of Points of Interest is essential for supporting data-driven urban planning and commercial decision-making. While recent advancements in spatio-temporal graph learning have improved POI forecasting, most methods rely on proximity-based graphs and correlation-driven modeling, which overlook the functional dependencies between POIs and fail to capture the causal effects of urban interventions. In this paper, we introduce a novel research problem -- cold-sta
The rapid evolution of urban environments and the increasing availability of spatio-temporal data are driving the need for more sophisticated AI models to understand complex urban dynamics.
Improved forecasting of Points of Interest (POIs) can significantly enhance data-driven urban planning, optimize commercial decision-making, and lead to more efficient infrastructure development.
The shift from correlation-driven to causal modeling in spatio-temporal AI introduces the capacity to understand the 'why' behind urban interventions, enabling more effective and predictable outcomes.
- · Urban Planners
- · Real Estate Developers
- · Retail Businesses
- · Smart City Technology Providers
- · Traditional Urban Planning Firms
- · Businesses reliant on static market analysis
- · Inefficient Commercial Developers
More accurate demand prediction for various urban services and commercial establishments becomes possible.
This leads to optimized resource allocation, reduced urban waste, and enhanced city resilience to unexpected events.
The ability to model causal effects could foster the development of 'self-optimizing cities' where interventions are automatically determined by AI based on real-time causal insights.
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Read at arXiv cs.LG