Crime Data Mapping Using HDFS for Data Visualization

Crime data analysis is an important aspect of law enforcement and requires sophisticated techniques to uncover patterns and enhance predictive capabilities. This paper focuses on the mapping stage of crime data analysis, offering a thorough method for integrating Hadoop Distributed File System (HDFS) with the pre-clustering data using Haversine k-means algorithm for efficient crime data visualization. The ability to accurately identify and visually display spatial patterns and trends in criminal activity is of particular importance. Leveraging Haversine k-means clustering geo-location based grouping of crime data sets the foundation for subsequent mapping using HDFS. This integrated approach, which is an essential step towards improving decision making in the enforcement of legislation and urban planning, not only increases the effectiveness of crime data analysis but also facilitates accurate and efficient visualization.