Penerapan Amazon Quicksight dalam Menganalisis Data Kecelakaan di Kota New York - Annisha Maharany; David Setiadi (Pusat Riset dan Inovasi Nasional, 2025)
This research emphasizes the use of Amazon QuickSight as a data visualization tool to analyze and generate recommendations based on traffic accident data. The case study was conducted on crash data in New York City for 2022, with a focus on identifying crash patterns and key causal factors. This research uses a methodology consisting of understanding business problems, data cleaning and processing, data analysis & insight gathering, and data visualization. The dataset analyzed includes 100,280 accident records, with variables such as ID, borough, street, contributing factors, vehicle type, date, person injured, person killed, pedestrians injured, pedestrians killed, cyclists injured, cyclists killed, motorists injured, motorists killed. Analysis results using Amazon QuickSight revealed significant accident patterns, such as a concentration of incidents in certain areas during peak hours. Recommendations include optimizing road design, improving traffic monitoring, and driver education to improve safety. This research shows the potential of Amazon QuickSight in supporting data-driven decision making for traffic policy and crash prevention measures in urban environments.