Dynamic Choropleth Maps – Using Amalgamation to Increase Area Perceivability
Liam McNabb, Robert S. Laramee, Richard J. Fry · 2018
Choropleths are a common and useful way of depicting area-coupled data on a geo-spatial map. One advantage they provide is combining area-based data accurately with geo-space. However perceptual problems arise when areas are too small, i.e when they only cover a few pixels or less. This is a very common occurrence when zooming or in densely populated areas like capital cities. We present a novel algorithm that ensures the user is able to observe area-based data coupled to geo-space based on their interactive level of zoom without distorting the original geo-spatial map. This is resolved by building a hierarchical data structure in which each area and its data is merged with one of its smallest neighbor recursively until only one polygon covers each contiguous region. The benefits are that the viewer can always view area-based data contained in the map regardless of how small any individual area becomes during interactive zooming. We break down each step of the algorithm and provide pseudo-code to enable reproducibility. We also discuss unique test cases that challenge the robustness of the algorithm with 30,000 polygons and 4,652,800 vertices as well as the performance.