Density-based multi-scale flow mapping and generalization.

Mapping large volume of origin-destination flow data (or spatial interactions) has long been a challenging problem because of the conflict between massive location-to-location connections and the limited map space. Current approaches for flow mapping only work with a small dataset or have to use data aggregation, which not only cause a significant loss of information but may also produce misleading maps. In this paper, we present a density-based flow map generalization approach that can extract flow patterns and facilitate the analysis and visualization of big origin-destination flow data at multiple scales. Unlike existing methods that generalize flow data by spatial unit-based aggregation, our new flow map generalization algorithm is based on flow density distribution. To demonstrate the approach and assess its effectiveness, a case study is carried out to map 829,039 taxi trips within the New York City. With parameter settings, the proposed method can discover inherent and abstract flow patterns at different map scales and generalization levels, which naturally supports interactive and multi-scale flow mapping.