AOI-shapes: An Efficient Footprint Algorithm to Support Visualization of User-defined Urban Areas of Interest

Abstract

Understanding urban areas of interest (AOIs) is essential in many real-life scenarios, and such AOIs can be computed based on the geographic points that satisfy user queries. In this article, we study the problem of efficient and effective visualization of user-defined urban AOIs in an interactive manner. In particular, we first define the problem of user-defined AOI visualization based on a real estate data visualization scenario, and we illustrate why a novel footprint method is needed to support the visualization. After extensively reviewing existing “footprint” methods, we propose a parameter-free footprint method, named AOI-shapes, to capture the boundary information of a user-defined urban AOI. Next, to allow interactive query refinements by the user, we propose two efficient and scalable algorithms to incrementally generate urban AOIs by reusing existing visualization results. Finally, we conduct extensive experiments with both synthetic and real-world datasets to demonstrate the quality and efficiency of the proposed methods.