Abstract
Vector based shortest path analysis in geographic information system (GIS) is well established for road networks. Even though these network algorithms can be applied to river layers, they do not generally consider the direction of flow. This paper presents a Python 3.7 program (upstream_downstream_shortests_path_dijkstra.py) that was specifically developed for river networks. It implements multiple single-source (one to one) weighted Dijkstra shortest path calculations, on a list of provided source and target nodes, and returns the route geometry, the total distance between each source and target node, and the total upstream and downstream distances for each shortest path. The end result is similar to what would be obtained by an “all-pairs” weighted Dijkstra shortest path algorithm. Contrary to an “all-pairs” Dijkstra, the algorithm only operates on the source and target nodes that were specified by the user and not on all of the nodes contained within the graph. For efficiency, only the upper distance matrix is returned (e.g., distance from node A to node B), while the lower distance matrix (e.g., distance from nodes B to A) is not. The program is intended to be used in a multiprocessor environment and relies on Python’s multiprocessing package.
Highlights
River networks around the world are vital for the transportation of goods and people, and spatial accessibility via rivers is an important driver of human settlement, market formation, land use patterns, and resource exploitation
Several online mapping systems (e.g., Google Maps), portable GPS devices, and algorithms implemented within a geographic information system (GIS) [12,13,14] have been developed to help users to find the shortest path to a desired destination, while optimizing for parameters, such as road direction, transportation mode, travel time, traffic, or the presence of tolls [15,16,17]
We developed a Python-based software for river networks to find the shortest path between a source and a destination and calculate the total upstream and downstream distance navigated on each part of the route
Summary
River networks around the world are vital for the transportation of goods and people, and spatial accessibility via rivers is an important driver of human settlement, market formation, land use patterns, and resource exploitation. Several online mapping systems (e.g., Google Maps), portable GPS devices, and algorithms implemented within a geographic information system (GIS) [12,13,14] have been developed to help users to find the shortest path to a desired destination, while optimizing for parameters, such as road direction, transportation mode, travel time, traffic, or the presence of tolls [15,16,17]. We need to accommodate for travel in both up or downstream directions, with the potential for multiple changes in current flow direction during a single trip This flexibility is a main aspect that differentiates algorithms that are designed for street and rivers networks. Centroids from grid polygons intersecting with the river lines were selected for the study (n = 16,419 for the largest basin) These new source and target nodes needed to be connected (or tied) to the graph. The most appropriate algorithm to implement was determined to be the one-to-one weighted Dijkstra algorithm [20,24]
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