greenR is an open-source R package designed to quantify,
analyze, and visualize urban greenness using data from OpenStreetMap
(OSM). This package is essential for urban planners, environmental
researchers, and policy-makers interested in urban sustainability and
green infrastructure.
This vignette will guide you through the installation and basic usage
of greenR, providing examples of its main features.
You can install the development version of greenR from
GitHub:
The first step is to acquire data. This provides a systematic approach to collecting the requisite geospatial data from OSM, thereby serving as the foundation for all subsequent analyses. The users can simply specify any city or neighborhood (that has data available in OSM database). This function looks in the database and finds any city and downloads OSM data for the specified spatial area with regard to three key environmental features: highways, green areas, and trees. Here green areas include all the areas with the following tags: “forest”, “vineyard”, “plant_nursery”, “orchard”, “greenfield”, “recreation_ground”, “allotments”, “meadow”,“village_green”,“flowerbed”, “grass”, “farmland”, “garden”, “dog_park”,“nature_reserve”, and “park”.
The ‘visualize_green_spaces()’ function is designed to aid in the visual assessment of green space data. Utilizing an integrated leaflet map, users can explore the distribution and mapping quality of green spaces within a specified area. By plotting this data on an interactive leaflet map, users gain insights into the extent and accuracy of green space representation. After visualizing the green spaces within the desired area, users may wish to contribute to the OpenStreetMap project to enhance the data quality or add unrepresented areas. Additionally, the green_space_clustering() function initially transforms the green spaces into an equal-area projection to calculate the areas accurately. Post-transformation, the K-means algorithm is applied to these areas, clustering the green spaces based on the number of clusters specified.
The accessibility_greenspace function creates an interactive map displaying accessible green spaces within a specified walking time from a provided location. It utilizes isochrones to visualize the areas reachable by walking. The function relies on pedestrian routing information and green space data to accurately delineate accessible areas. There are two functions to do this. One with Mapbox and the other with leaflet. For Mapbox, first remember to add the token that you can get from the website.
Mapbox Version (Dynamic): The accessibility_mapbox function creates an accessibility map using Mapbox GL JS, showing green areas and allowing users to generate isochrones for walking times. This produces an HTML that has the inbuilt feature of changing the walking time and also moving the location marker.
#> Writing layer `file1174d13accc4b' to data source
#> `/var/folders/ds/b7pvhdhs7bnd7sqmv2gvqpl80000gs/T//Rtmp2CpTbq/file1174d13accc4b.geojson' using driver `GeoJSON'
#> Writing 347 features with 61 fields and geometry type Polygon.Leaflet version: This on euses Leaflet to create the map. In this case, the latitude and longitude has to be given by the user. The default maximum walking time is set to 15 minutes but can be adjusted using the ‘max_walk_time’ parameter. The ‘nearest_greenspace’ function calculates and visualizes the shortest walking route to the nearest green space of a specified type from a given location. You can also select the type of green space (c(“park”, “forest”)). By default it looks at all the green space.
#> Warning: st_centroid assumes attributes are constant over geometriesThe ‘hexGreenSpace’ function offers an innovative approach to visualizing the distribution and density of green spaces within a specified urban area using hexagonal binning. This method provides a clear, quantifiable view of green coverage, integrating both green areas and tree data to present a comprehensive spatial analysis.
#> Warning: attribute variables are assumed to be spatially constant throughout
#> all geometries
#> Warning: attribute variables are assumed to be spatially constant throughout
#> all geometriesThis function takes as input the OSM data, a Coordinate Reference System (CRS) code, and parameter D for the distance decay functions. The algorithm extracts the highways, green areas, and trees data from the input list and transforms the data into the given CRS. The CRS affects how distances, areas, and other measurements are calculated. Different CRSs may represent the Earth’s surface in ways that either exaggerate or minimize certain dimensions. So, using the wrong CRS can lead to incorrect calculations and analyses. If you’re focusing on a city or other localized area, you’ll likely want to use a CRS that is tailored to that specific location. This could be a local city grid system or other local CRS that has been designed to minimize distortions in that area. This function then defines distance decay functions for green areas and trees using the parameter D. For each edge in the highway data, the function calculates the green index using the decay functions and returns a data frame with the green index for each edge. By default, D is specified to 100 (distance decay parameter in meters) but it can be changed by the user. Similarly, the users must specify the CRS (https://epsg.io/). The green index ranges from 0 to 1 and it represents the relative greenness of each section, factoring in proximity to green spaces and tree density.
This function visualizes the green index on a map, with options for both static and interactive display. Interactive maps are rendered using Leaflet, allowing users to zoom, pan, and interact with the map to explore the green index in more detail.
# Create a static plot
map <- plot_green_index(green_index)
# Customize static plot
map <- plot_green_index(green_index, colors = c("#FF0000", "#00FF00"), line_width = 1, line_type = "dashed")
# Create an interactive plot using Leaflet
map <- plot_green_index(green_index, interactive = TRUE, base_map = "CartoDB.DarkMatter")
# Use a light-themed base map
map <- plot_green_index(green_index, interactive = TRUE, base_map = "CartoDB.Positron")The create_linestring_3D function generates a 3D linestring map using Mapbox GL JS. This map is designed to visualize linear features such as roads, trails, or any other types of linestring data. This can be particularly useful for visualizing connectivity, transportation networks, or other linear spatial patterns.The function supports interactive controls for adjusting the line width and toggling building visibility on the map. It accepts linestring data and automatically processes it to create a visually appealing 3D map.
#> Writing layer `file1174da49b733' to data source
#> `/var/folders/ds/b7pvhdhs7bnd7sqmv2gvqpl80000gs/T//Rtmp2CpTbq/file1174da49b733.geojson' using driver `GeoJSON'
#> Writing 9125 features with 4 fields and geometry type Line String.The ‘create_hexmap_3D’ function generates a 3D hexagon map using H3 hexagons and Mapbox GL JS. This map can visualize various types of geographical data, such as points, linestrings, polygons, and multipolygons. It is particularly useful for visualizing density or green indices over an area. It automatically processes these geometries, converting them to points for visualization. Users can dynamically change the radius of the hexagons and their heights to better represent the data. The resulting map includes controls for adjusting hexagon height and H3 resolution, and selecting different Mapbox styles.
This function groups the edges by their respective green index and calculates the percentage of edges for each green index. For easier interpretation, we categorize the index into three tiers: Low ( < 0.4), Medium (0.4-0.7), and High (> 0.7).
These functions allow the user to download the green index values as a GeoJson file as well as a Leaflet map. The GeoJSON file retains the geographical properties of the data and can be readily employed in a broad range of GIS applications. The Leaflet map, saved as an HTML file, provides an interactive user experience, facilitating dynamic exploration of the data. The users should specify the file path to save these files.
You can make your own greenness analysis without having to code using
an R Shiny implementation of the package. It is easily accessible from
within R by calling the function run_app().
This function allows the users to quantify urban greenness through image analysis. Utilizing the SuperpixelImageSegmentation library, it reads an image of an urban landscape and segments it into superpixels. The Green View Index (GVI) is then calculated by identifying green pixels within these segments. The GVI provides an objective measure of the proportion of visible vegetation in an image and is an important indicator for understanding urban greenness and its impact on ecological and human health.
result <- calculate_and_visualize_GVI("image.png") #specify the image path
OpenImageR::imageShow(result$segmented_image) #To visualize the segmented image
green_pixels_raster <- as.raster(result$green_pixels_image) #To visualize green pixels
plot(green_pixels_raster)
# Save the segmented image
OpenImageR::writeImage(result$segmented_image, "segmented_image.png")
# Save the green pixels image
OpenImageR::writeImage(result$green_pixels_image, "green_pixels_image.png")The gssi() function calculates the Green Space
Similarity Index (GSSI), a composite metric for evaluating and comparing
urban green spaces across different regions by analyzing their size and
spatial connectivity. This function transforms spatial data into an
equal-area projection for accurate area measurements, computes the total
area of green spaces, and assesses their spatial connectivity using the
Average Nearest Neighbor Distance (ANND). This dual approach provides a
comprehensive view of the distribution and accessibility of green
spaces.
The GSSI is calculated by inversely weighting the coefficient of variation in area sizes with the ANND, offering a score that reflects the abundance and accessibility of green spaces. Scores are normalized against the highest scoring city in the dataset for relative comparisons.
d1 <- get_osm_data("New Delhi, India")
dsf <- d1$green_areas$osm_polygons
d2 <- get_osm_data("Basel, Switzerland")
bsf <- d2$green_areas$osm_polygons
d3 <- get_osm_data("Medellin, Colombia")
msf <- d3$green_areas$osm_polygons
cities_data <- list(dsf, bsf, msf)
gssi_values <- gssi(cities_data, "ESRI:54009")