Package: grainscape 1.0.0

Overview | Finding the Minimum Planar Graph | Technical reference to the MPG engine written in C++ | Terminology | Data Structures | Type Definitions | The Engine Class | Fields/Properties | Methods/Functions | Public Functions | Patch Finding Functions | Linking Functions | Common Functions | Static Functions | How to Use the Engine | Debugging the package C++ code | References

Visual table of contents | Introduction | Modelling with grainscape | Model 1: The minimum planar graph | Step 1: Preparing the resistance surface | Step 2: Extracting the MPG | Step 3: Quick visualization of the MPG | Step 4: Reporting on the MPG | Step 5: Thresholding the MPG | Step 6: Visualizing a thresholded graph | Step 7: Next steps | Model 2: Patch grains of connectivity | Step 1: Begin with a MPG | Step 2: Exploring the Voronoi tessellation | Step 3: Building GOC models | Step 4: Visualizing a GOC model | Landscape networks with 1D and 2D nodes | Modelling | Planar network with one-dimensional nodes on a Euclidean surface | Make a new resistance raster of 400 by 400 cells | with a coordinate system that corresponds to cells | Assign all values to 1 | Create 20 "random" points representing nodes, | (i.e. the loci of a process of interest) | Represent these on a patch raster | by duplicating the resistance raster and | setting the relevant cells to 1 | Extract the MPG | Plot the result using the quick 'network' visualization | setting and add labels (dodging them by 3 to the upper-right) | Planar network with one-dimensional nodes on a non-Euclidean resistance surface | Add some cost values to the resistance | surface we used in the last step | Here we use random integers >= 2 | Extract the minimum planar graph using the | raster made previously which represents the points only | Plot the result using the quick 'mpgPlot' visualization | This demonstrates the non-linear paths. | Planar network with two-dimensional nodes on a non-Euclidean resistance surface (minimum planar graph) | Load a land cover raster distributed with grainscape | Convert land cover to resistance units | Use an "is-becomes" reclassification | Extract a network using cells = 1 on original raster | as the focal patches or nodes | Plot the minimum planar graph, labelling node 7 and the patches directly | linked to it (its neighbours in the MPG), matching the fragNeighbours | table below. | Visualization | Centroid representation of nodes | Perimeter representation of links | Spatially-explicit representation of links | Characteristics of nodes (weights) | Characteristics of links (weights) | Link thresholding by plotting | Link thresholding to show components | Analysis | Network metrics to assess node importance | Shortest-path distance between nodes | Declare the start and end patchIds | These were identified by plotting the patchIds (see earlier examples) | Find the shortest path between these nodes using | the shortest path through the resistance surface | (i.e. weighted by 'lcpPerimWeight') | Extract the nodes and links of this shortest path | Produce shortest path tables for plotting | Find the distance of the shortest path | Plot shortest path | Scaling landscape networks | Modeling | Scaling resistance surfaces (lattice grains of connectivity) | Scaling networks with two-dimensional nodes (patch grains of connectivity) | Characteristics of grains of connectivity | Corridor analyses at multiple scales | Set coordinates for the start and end of the corridor | Use the default plotting functionality for corridor objects | References