In today’s analysis, I ventured into the realm of geospatial calculations, specifically calculating the geodesic distance between two geographic coordinates: Seattle, WA, and Miami, FL. To accomplish this, I harnessed the geodesic function from the geopy library, a reliable tool for accurately computing distances on the Earth’s curved surface. The outcome provided the distance between these two locations in both miles and kilometers, delivering valuable information for geospatial analysis.
For my next analytical step, I’m gearing up to execute clustering based on geographic locations within the state of California. In pursuit of this objective, I explored two clustering algorithms: K-Means and DBSCAN.
K-Means:
K-Means is a widely adopted clustering algorithm renowned for partitioning a dataset into ‘K’ distinct clusters. Its operation involves iteratively assigning data points to the nearest cluster center (centroid) and recalculating these centroids until the algorithm converges. K-Means is prized for its simplicity in implementation and computational efficiency, making it a go-to choice for a range of applications, including image segmentation and customer segmentation.
DBSCAN (Density-Based Spatial Clustering of Applications with Noise:
DBSCAN takes a different approach, relying on density-based criteria to cluster data points. Unlike K-Means, it doesn’t require a predetermined number of clusters. DBSCAN identifies core points, which have a sufficient number of data points in their neighborhood, and border points, which are in proximity to core points but lack a critical mass of neighbors to qualify as core points. Noise points, on the other hand, don’t align with any cluster. DBSCAN’s resilience to noise and its ability to unveil clusters of various sizes and shapes make it particularly well-suited for datasets with intricate structures.
These clustering techniques have the potential to reveal valuable patterns and structures within the geographic data, advancing our understanding of the distribution of police shootings in the state of California.