Keywords
urban systems
Abstract
Urban systems and financial risk governance increasingly depend on computational infrastructure capable of handling large-scale graph, spatial, and transactional data. This topic connects sparse matrix computation, urban systems science, urban sub-center analysis, and AI-assisted financial fraud risk assessment. IA-SpGEMM provides an example of input-aware auto-tuning for sparse matrix-matrix multiplication, which is relevant to graph analytics, spatial interaction modeling, and large-scale risk networks. Urban systems science frames cities as cross-scale dynamic systems, while urban sub-center analysis captures spatial heterogeneity in economic agglomeration. Financial fraud risk assessment contributes explainable AI methods for identifying abnormal risk patterns in complex data environments. Together, these references support a computationally grounded approach to urban and financial risk intelligence.
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