Monika Hinduja
Engineering
December 2024
This paper discusses the development of a risk early warning model for spatial economic networks in urban agglomeration areas by employing multi-source heterogeneous data fusion techniques. It studies the effects of urbanization, economic linkages, infrastructure, and labor mobility on the resilience and risk factors associated with spatial economic networks. The empirical analysis and prediction of risk patterns within these networks are done by utilizing the Random Forest algorithm. Against various data sources, the model highlights the interdependence that exists between city nodes in making different risk possibilities that challenge those economic systems. The report postulates that robust infrastructure combined with labor mobility and sound economic integration are critical toward ensuring that urban agglomeration stays resilient. This study contributes to the progress of the risk prediction models and gives useful tools for the policymakers and urban planners to address the complexities of urbanization and the spatial economic network management.
183- 190
© Airo Journals 2021
