Embedding vertex/edge information

The focal point of the provided code snippet revolves around the process of constructing a spatial graph from raster data. This spatial graph serves as a powerful tool for visualizing and analyzing spatial relationships and patterns present within the underlying geographic data.

In addition to forming the basic structure of vertices and edges, the code snippet emphasizes the integration of supplementary information. By utilizing custom structures, developers can enrich vertices and edges with additional details specific to their application domain. For instance, vertices can be decorated with labels or population data, while edges can incorporate distance metrics or other spatial attributes.

This approach enables the creation of spatial graphs that not only capture the topological relationships between locations but also incorporate contextual information essential for comprehensive spatial analysis and modeling.

Input

#include "quetzal/geography.hpp"
 
namespace geo = quetzal::geography;
 
// User-defined data structure to store arbitrary vertex information
struct MyVertexInfo {
    std::string population_label = "NA";
    std::vector<double> population_data_chunk;
    // ... anything else required by the user context
};
 
// User-defined data structure to store arbitrary edge information
struct MyEdgeInfo {
    mp_units::quantity<mp_units::si::metre> distance;
    // needs to be default-constructible
    MyEdgeInfo() = default;
    // constructor required by from_grid method: takes a source and a target vertex descriptor, and a spatial grid
    MyEdgeInfo(auto s, auto t, auto const& raster){ 
        distance = raster.to_lonlat(s).great_circle_distance_to( raster.to_lonlat(t) );
    }
};
 
int main()
{
    auto file = std::filesystem::current_path() / "data/bio1.tif";
 
    // The raster have 10 bands that we will assign to 2001 ... 2010.
    std::vector<int> times(10);
    std::iota(times.begin(), times.end(), 2001);
 
    // Landscape construction
    using landscape_type = geo::raster<>;
    auto land = geo::raster<>::from_file(file, times);
 
    // Graph construction with vertex and edge info
    auto graph = geo::from_grid(land, MyVertexInfo(), MyEdgeInfo(), geo::connect_fully(), geo::isotropy(), geo::mirror());
 
    std::cout << "Graph has " << graph.num_vertices() << " vertices, " << graph.num_edges() << " edges." << std::endl;
 
    for( auto const& e : graph.edges() ){
        std::cout << graph.source(e) << " <-> " << graph.target(e) << " : " << graph[e].distance << std::endl;
    }
 
}

Output

Graph has 9 vertices, 36 edges.
1 <-> 0 : 0 m
2 <-> 0 : 0 m
2 <-> 1 : 0 m
3 <-> 0 : 0 m
3 <-> 1 : 0 m
3 <-> 2 : 0 m
4 <-> 0 : 0 m
4 <-> 1 : 0 m
4 <-> 2 : 0 m
4 <-> 3 : 0 m
5 <-> 0 : 0 m
5 <-> 1 : 0 m
5 <-> 2 : 0 m
5 <-> 3 : 0 m
5 <-> 4 : 0 m
6 <-> 0 : 0 m
6 <-> 1 : 0 m
6 <-> 2 : 0 m
6 <-> 3 : 0 m
6 <-> 4 : 0 m
6 <-> 5 : 0 m
7 <-> 0 : 0 m
7 <-> 1 : 0 m
7 <-> 2 : 0 m
7 <-> 3 : 0 m
7 <-> 4 : 0 m
7 <-> 5 : 0 m
7 <-> 6 : 0 m
8 <-> 0 : 0 m
8 <-> 1 : 0 m
8 <-> 2 : 0 m
8 <-> 3 : 0 m
8 <-> 4 : 0 m
8 <-> 5 : 0 m
8 <-> 6 : 0 m
8 <-> 7 : 0 m

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