Tracing Economic Vibrancy: AI-Driven Analysis of Geographic Clustering in Legal Businesses

Geographic clustering of businesses holds significant importance in understanding local economic dynamics, identifying areas of commercial activity, and assisting in spatial analysis for economic development. Artificial intelligence (AI) driven analysis is employed in this paper to investigate patterns of geographic clustering, particularly focusing on legal businesses within a given area. Data extraction techniques help preprocess business directories and classification codes to aggregate business addresses and visualize their spatial distribution. Clustering algorithms are used in conjunction with Geographic Information System (GIS) tools for data visualization and precise mapping, with respect to economic indicators. Expected outcomes include generating geographical distribution maps, comparing clustering algorithm results, and insight into urban business clustering patterns. This research considers potential external factors influencing business agglomeration and data currency. Recommendations focus on integrating AI-driven analysis with GIS tools and future research domains. Overall, this paper highlights the intersection of AI and geospatial analysis, providing stakeholders with valuable insights into the spatial distribution of economic activities within a target area.