In this work, we present a study that traces the airport arrival delay and the change in its performance to help users understand the trend and make informed decisions. We have conducted a comparative analysis of the airports during the years 2011 to 2016. This exploration consists of several distinct set of delays like security, carrier, late aircraft, weather delay and their contribution to the total. The work also performs spatial analysis by utilizing a bee-swarm visualization and a geographical map, to investigate the correlation between the delay and the airport location, which is hard to get using any conventional data science methodologies. K-means clustering algorithm is used to find airports with a similar delay trend. Together we provide interconnected visualizations to support the usefulness of visual analytics in analyzing spatial datasets and finding meaningful patterns.