Spatial Data Analysis on On-Demand Cab Services using Spark

Spatial data analysis is a technique used to analyze large amounts of spatial data generated by on-demand cab services such as Uber, Lyft, and Grab. This type of data includes information on the pickup and drop-off locations of riders, the trajectories of drivers, and the time when demand for cabs is highest. By analyzing this data, the on-demand cab services can gain insights on the customer demand, optimize driver allocation, improve pricing strategies, and enhance overall system efficiency. Spark is an open-source framework used for processing large-scale spatial data. It provides a set of APIs for processing and analyzing spatial data in distributed computing environments by using Apache Spark as its computing engine. This research explores the utilization of Spark, an open-source framework tailored for large-scale spatial data processing. Spark offers a comprehensive set of APIs designed for distributed computing environments, leveraging the power of Apache Spark as its underlying computational engine. By using Spark, on on-demand cab services can efficiently analyze large amounts of spatial data generated by their systems, enabling them to make data-driven decisions and improve the efficiency of their services. There by, we did analyze and visualize, handle the spatial data through spark with the help of graphs.