REA-FM: automated generation of natural-looking flow maps through river extraction algorithm

ABSTRACT

A flow map is a type of thematic visualization that depicts the movement of objects across a geographical space using a tree layout resembling a natural river system. In this paper, we introduce an innovative and automated approach called REA-FM, which leverages the power of the maze-solving algorithm to extract rivers from digital elevation models (DEMs). This enables the creation of flow maps that originate from a single source and extend to multiple destinations. Initially, REA-FM represents the mapping space of a flow map using a DEM. Subsequently, a maze-solving algorithm is adapted to extract flow paths from the destinations to the origin within the DEM data, with constraints on search directions, direction weights, and search ranges based on quality criteria specific to flow maps. To obtain comprehensive flow maps, the maze-solving algorithm is employed iteratively, considering the importance of each flow path, as determined by their respective lengths. These obtained paths are finally rendered smoothly with varying widths using Bézier curves, thereby enhancing the visual aesthetics of the flow map. A comparative evaluation with existing approaches demonstrates that REA-FM can generate natural-looking flow maps with reduced total length and improved node distribution, eliminating node overlaps and edge crossings. Furthermore, the effectiveness of REA-FM is validated through three extension experiments involving heterogeneous mapping spaces and areas with obstacles. Parameter analysis confirms that REA-FM offers intuitive control over the layout of flow maps. Project website: https://github.com/TrentonWei/FlowMap

KEYWORDS:

• Flow map
• automatic cartography
• hydrology model
• maze solving algorithm
• digital elevation model

Acknowledgments

The authors wish to thank Dr. Tingzhong Huang for his help in data collection and Professor Yalong Yang for sharing his JavaScript code.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data and code availability statement and data deposition

The data and code that support the findings of this study are all openly available, website is: https://github.com/TrentonWei/FlowMap.

Supplementary material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/15230406.2024.2311259.

Additional information

Funding

The work was supported by the National Natural Science Foundation of China [62301063]; National Key Laboratory of Microwave Imaging Technology Grant [No number].