Topics Learnt Today:
1: Clustering algorithms are employed to group similar incidents, such as fatal police shootings, based on their spatial proximity.
- Clustering refers to the process of identifying groups of data points that are located close to each other in space.
- The purpose of clustering in this scenario is to detect patterns or trends in the spatial distribution of these incidents.
- It shows us that there are specific geographic areas with a higher incidence of fatal police shootings compared to others.
- Clustering can provide valuable insights into the spatial relationships between incidents, potentially aiding law enforcement agencies, policymakers, and researchers in understanding the underlying factors contributing to the occurrences.
2: The DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm is mentioned as a specific method for clustering the data related to fatal police shootings.
- DBSCAN is a popular clustering algorithm that can identify clusters of data points in a dataset while also recognizing and labeling noise points. By applying DBSCAN to the data with carefully chosen parameters, it will group data points into clusters based on their spatial proximity.
- Data points that do not belong to any cluster are considered noise points. This process assists in discerning geographic regions with a higher density of incidents and isolating areas with fewer occurrences.
- DBSCAN is known for its ability to handle clusters of various shapes and sizes, making it suitable for spatial analysis of incidents like fatal police shootings. The algorithm is valuable for identifying patterns and trends in the data and can provide a deeper understanding of the spatial distribution of these incidents.