6th Nov 2023

Geographic clustering, also known as spatial clustering, is a data analysis technique that focuses on identifying patterns and groupings in spatial data or geographical locations. It aims to discover areas on a map where similar or related data points are concentrated. This method is particularly valuable in various fields, such as urban planning, epidemiology, and marketing, where understanding the spatial distribution of data can provide valuable insights.

Geographic clustering can be accomplished using various algorithms, such as K-Means with geographic coordinates, DBSCAN, or methods specifically designed for spatial data. The resulting clusters can reveal geographic regions with similar characteristics or behaviors, allowing businesses, researchers, and policymakers to make informed decisions, such as targeting marketing campaigns, allocating resources, or identifying areas with specific health concerns. In essence, geographic clustering helps unveil hidden patterns and relationships within spatial data, contributing to more effective decision-making in various applications.