[](https://www.r-pkg.org/pkg/viewscape) [](https://opensource.org/licenses/) [](https://github.com/land-info-lab/viewscape/actions/workflows/R-CMD-check.yaml) [](https://CRAN.R-project.org/package=viewscape)  > [!NOTE] > ⚠️ This repo has retired. Please check out the [repo](https://github.com/billbillbilly/viewscape) # viewscape

## Introduction The goal of viewscape package is to provide an accessible method of carrying out landscape spatial analysis based on the viewshed within the R environment. The viewscape R pacakge can currently be installed via github. ## Intallation ``` r # Install the package from CRAN install.packages("viewscape") # Install the package from GitHub devtools::install_github("land-info-lab/viewscape", dependencies=TRUE) # Load the package library(viewscape) ``` ## Functionality and metrics The basic viewshed analysis can be accessed through calling the `compute_viewshed`. The two needed objects are a digital surface model (DSM) and a viewpoint. It provides flexibility for single or multi-viewpoint analyses and allows options for parallel processing, raster output, and plotting. In addition, a `fov_mask` function is designed to subset a viewshed based on its viewpoint and the field of view. Based on the viewshed, visual magnitude, a gradient ofvisibility across the range, can be calculated using the `visual_magnitude` function. Also, a set of configuration metrics can be calculated using `calculate_viewmetrics`, `calculate_diversity`, and `calculate_feature`. The metrics are including: - Number of patches: Visible fragmentation measured by total visible patches with the viewscape. - Mean shape index: Visible patchiness based on average perimeter-to-area ratio for all viewscape patches (vegetation and building). - Edge density: A measure of visible complexity based on the length of patch edges per unit area. - Patch size: Total average size of a patches over the entire viewscape area. - Patch density: Visible landscape granularity based on measuring patch density. - Extent: The total area of the viewshed, calculated as the number of visible grid cells multiplied by the grid resolution. - Depth: The furthest visible distance within the viewshed from the viewpoint. - Vdepth: The standard deviation of distances to visible points, providing a measure of the variation in visible distances. - Horizontal: The total visible horizontal or terrestrial area within the viewshed. - Relief: The standard deviation of elevations of the visible ground surface. - Skyline: Variation of (Standard deviation) of the vertical viewscape (visible canopy and buildings). - Shannon diversity index: Based on the number of land use/cover classes and the proportion of distribution. - Proportion of other object: Building, trees, or paved surface. ``` r #Load in DSM test_dsm <- terra::rast(system.file("test_dsm.tif", package ="viewscape")) #Load in the viewpoint test_viewpoint <- sf::read_sf(system.file("test_viewpoint.shp", package = "viewscape")) #Compute viewshed output <- viewscape::compute_viewshed(dsm = test_dsm, viewpoints = test_viewpoint, offset_viewpoint = 6, plot=TRUE) # Load DTM test_dtm <- terra::rast(system.file("test_dtm.tif", package ="viewscape")) # load landuse raster test_landuse <- terra::rast(system.file("test_landuse.tif", package ="viewscape")) # Load canopy raster test_canopy <- terra::rast(system.file("test_canopy.tif", package ="viewscape")) # Load building footprints raster test_building <- terra::rast(system.file("test_building.tif", package ="viewscape")) # calculate metrics given the viewshed test_metrics <- viewscape::calculate_viewmetrics(output, test_dsm, test_dtm, list(test_canopy, test_building)) # the Shannon Diversity Index (SDI) test_diversity <- calculate_diversity(test_landuse, output, proportion = TRUE) ``` From viewshed analysis, the visible area of a viewpoint is presented by visible points. There are several viewshed metrics such as can be calculated based on the visible points. For further information on these metrics and the rest of the functions available in this package please refer to the [package website](https://land-info-lab.github.io/viewscape/). For more information and examples of the functions check out the [package vignette](https://github.com/land-info-lab/viewscape/blob/master/viewscape.md). ## Note The package currently does not support multi-core processing on Windows system. The function 'compute_viewshed' with 'parallel = TRUE' will automatically work on single worker. ## Reference Franklin, W. R., & Ray, C. (1994, May). Higher isn’t necessarily better: Visibility algorithms and experiments. In Advances in GIS research: sixth international symposium on spatial data handling (Vol. 2, pp. 751-770). Edinburgh: Taylor & Francis. Wang, J., Robinson, G. J., & White, K. (2000). Generating viewsheds without using sightlines. Photogrammetric engineering and remote sensing, 66(1), 87-90. Chamberlain, B. C., & Meitner, M. J. (2013). A route-based visibility analysis for landscape management. Landscape and Urban Planning, 111, 13-24. Tabrizian, P., Baran, P. K., Van Berkel, D., Mitasova, H., & Meentemeyer, R. (2020). Modeling restorative potential of urban environments by coupling viewscape analysis of lidar data with experiments in immersive virtual environments. Landscape and Urban planning, 195, 103704. ## Issues and bugs This package may take a long time to run if using spatially large or high resolution digital elevation models. If you discover a bug not associated with connection to the API that is not already a [reported issue](https://github.com/land-info-lab/viewscape/issues), please [open a new issue](https://github.com/land-info-lab/viewscape/issues/new) providing a reproducible example.