Package 'flexurba'

Description

The functions applies the majority rule to smooth edges of clusters of cells. The function supports two different version of the majority rule algorithm: the version of GHSL Data Package 2022 and GHSL Data Package 2023:

• version="R2022A": If a cell has at least five of the eight surrounding cells belonging to a unique cluster of cells, then the cell is added to that cluster. The process is iteratively repeated until no more cells are added.

• version="R2023A": A cell is added to a cluster if the majority of the surrounding cells belongs to one unique cluster, with majority only computed among populated (pop > 0) or land cells (land > 0.5). Cells with permanent water (permanent_water not NA) can never be added to a cluster of cells. The process is iteratively repeated until no more cells are added.

Usage

apply_majority_rule(
  x,
  version = "R2022A",
  permanent_water = NULL,
  land = NULL,
  pop = NULL
)

Arguments

Value

SpatRaster with clusters of cells with smoothed edges

Examples

nr <- nc <- 8
r <- terra::rast(nrows = nr, ncols = nc, ext = c(0, nc, 0, nr), vals = c(
  NA, NA, 1, 1, 1, NA, NA, NA,
  NA, NA, NA, 1, 1, NA, NA, NA,
  NA, NA, 2, NA, NA, NA, NA, NA,
  NA, NA, 2, NA, NA, 2, NA, NA,
  NA, NA, 2, NA, 2, 2, NA, NA,
  2, 2, 2, 2, 2, 2, NA, NA,
  NA, NA, 2, 2, NA, NA, NA, NA,
  NA, NA, NA, 2, NA, NA, NA, NA
))
terra::plot(r)
smoothed <- apply_majority_rule(r)
terra::plot(smoothed)

Description

The function identifies urban areas by applying a threshold to individual grid cells. Two key decisions must be made regarding the thresholding approach:

1. How is the threshold value determined?

   • The threshold can be predefined by the user (type="predefined") or derived from the data (type="data-driven").

2. How and where is the threshold enforced?

   • The threshold can be enforced consistently across the study area (= absolute approach, regions=NULL) or tailored within specific regions (= relative approach, regions not NULL).

For more details on these thresholding approaches, including their advantages and limitations, see the vignette("vig8-apply-thresholds") The table below outlines the appropriate combination of function arguments for each approach:

Usage

apply_threshold(
  grid,
  type = "predefined",
  threshold_value = NULL,
  fun = NULL,
  ...,
  regions = NULL,
  operator = "greater_than",
  smoothing = TRUE
)

Arguments

Value

named list with the following elements:

• rboundaries: SpatRaster in which cells that are part of an urban area have a value of '1'

• vboundaries: sf object with the urban areas as separate polygons

• threshold: dataframe with per region the threshold value that is applied

• regions: SpatRaster with the gridded version of the regions that is employed

Examples

proxies <- load_proxies_belgium()

# option 1: predefined - absolute threshold
predefined_absolute <- apply_threshold(proxies$pop,
  type = "predefined",
  threshold_value = 1500
)
terra::plot(predefined_absolute$rboundaries)

# option 2: data-driven - absolute threshold
datadriven_absolute <- apply_threshold(proxies$pop,
  type = "data-driven",
  fun = "p90"
)
terra::plot(datadriven_absolute$rboundaries)

# in the examples below we will use 'Bruxelles', 'Vlaanderen' and 'Wallonie' as separate regions
regions <- convert_regions_to_grid(flexurba::units_belgium, proxies$pop, "NAME_1")
terra::plot(regions)

# option 3: predefined - relative threshold
# note that the threshold values are linked to the regions in alphabetical
# order based on their IDs. So, the threshold of 1500 is applied to
# 'Bruxelles', # 1200 to 'Vlaanderen', and 1000 to 'Wallonie'.
predefined_relative <- apply_threshold(proxies$pop,
  type = "predefined",
  threshold_value = c(1500, 1200, 1000),
  regions = regions
)
terra::plot(predefined_relative$rboundaries)

# option 4: data-driven - relative threshold
datadriven_relative <- apply_threshold(proxies$pop,
  type = "data-driven",
  fun = "p95",
  regions = regions
)
terra::plot(datadriven_relative$rboundaries)

Description

Convert the value of regions associated with a vector layer to a grid layer.

Usage

convert_regions_to_grid(regions, referencegrid, id = NULL)

Arguments

Value

SpatRaster with the regions

Examples

# load data for urban proxies and regions
proxies <- load_proxies_belgium()
regions <- flexurba::units_belgium

# convert Belgian provinces ('GID_2') to a zonal grid
gridded_regions <- convert_regions_to_grid(regions, proxies$pop, "GID_2")
terra::plot(gridded_regions)

Description

The grid cell classification of the Degree of Urbanisation requires three different inputs: a built-up area grid, a population grid, and a land grid. The grids can be downloaded from the GHSL website with download_GHSLdata().

This function reads the downloaded grids from the global_directory and crops them to the provided extent. The newly cropped grids will be saved in the output_directory, together with their respective metadata (in JSON format).

Usage

crop_GHSLdata(
  extent,
  output_directory,
  global_directory,
  output_filenames,
  global_filenames,
  buffer = 5
)

Arguments

Value

path to the created files.

Description

The function reconstructs the grid cell classification of the Degree of Urbanisation. The arguments of the function allow to adapt the standard specifications in the Degree of Urbanisation in order to construct an alternative version (see section "Custom specifications" below).

For more information about the Degree of Urbanisation methodology, see the methodological manual, GHSL Data Package 2022 and GHSL Data Package 2023.

Usage

DoU_classify_grid(
  data,
  level1 = TRUE,
  parameters = NULL,
  values = NULL,
  regions = FALSE,
  filename = NULL
)

Arguments

Value

SpatRaster with the grid cell classification

Classification rules

The Degree of Urbanisation consists of two hierarchical levels. In level 1, the cells of a 1 km² grid are classified in urban centres, urban clusters and rural cells (and water cells). In level 2, urban cluster are further divided in dense urban clusters, semi-dense urban clusters and suburbs or peri-urban cells. Rural cells are further divided in rural clusters, low density rural cells and very low density rural cells.

The detailed classification rules are as follows:

LEVEL 1:

• Urban centres are identified as clusters of continuous grid cells (based on rook contiguity) with a minimum density of 1500 inhabitants per km² (or with a minimum built-up area; see section "Built-up area criterium" below), and a minimum total population of 50 000 inhabitants. Gaps smaller than 15 km² in the urban centres are filled and edges are smoothed by a 3x3-majority rule (see section "Edge smoothing" below).

• Urban clusters are identified as clusters of continuous grid cells (based on queen contiguity) with a minimum density of 300 inhabitants per km², and a minimum total population of 5000 inhabitants.

• Water cells contain no built-up area, no population, and less than 50% permanent land. All other cells not belonging to an urban centre or urban cluster are considered rural cells.

LEVEL 2:

• Urban centres are identified as clusters of continuous grid cells (based on rook contiguity) with a minimum density of 1500 inhabitants per km² (or with a minimum built-up area; see section "Built-up area criterium" below), and a minimum total population of 50 000 inhabitants. Gaps smaller than 15 km² in the urban centres are filled and edges are smoothed by a 3x3-majority rule (see section "Edge smoothing" below).

• Dense urban clusters are identified as clusters of continuous grid cells (based on rook contiguity) with a minimum density of 1500 inhabitants per km² (or with a minimum built-up area; see section "Built-up area criterium" below), and a minimum total population of 5000 inhabitants.

• Semi-dense urban clusters are identified as clusters of continuous grid cells (based on rook contiguity) with a minimum density of 900 inhabitants per km², and a minimum total population of 2500 inhabitants, that are not within 2 km away from urban centres and dense urban clusters. Clusters that are within 2 km away are classified as suburban and peri-urban cells.

• Rural clusters are clusters of continuous grid cells (based on queen contiguity) with a minimum density of 300 inhabitants per km², and a minimum total population of 500 inhabitants.

• Low density rural cells are remaining cells with a population density less than 50 inhabitants per km².

• Water cells contain no built-up area, no population, and less than 50% permanent land. All cells not belonging to an other class are considered very low density rural cells.

For more information about the Degree of Urbanisation methodology, see the methodological manual, GHSL Data Package 2022 and GHSL Data Package 2023.

Custom specifications

The function allows to change the standard specifications of the Degree of Urbanisation in order to construct an alternative version of the grid classification. Custom specifications can be passed in a named list by the argument parameters. The supported parameters with their default values are returned by the function DoU_get_grid_parameters() and are as follows:

LEVEL 1

• UC_density_threshold numeric (default: 1500).

  Minimum population density per permanent land of a cell required to belong to an urban centre

• UC_size_threshold numeric (default: 50000).

  Minimum total population size required for an urban centre

• UC_contiguity_rule integer (default: 4).

  Which cells are considered adjacent in urban centres: 4 for rooks case (horizontal and vertical neighbours) or 8 for queens case (horizontal, vertical and diagonal neighbours)

• UC_built_criterium logical (default: TRUE).

  Whether to use the additional built-up area criterium (see section "Built-up area criterium" below). If TRUE, not only cells that meet the population density requirement will be considered when delineating urban centres, but also cells with a built-up area per permanent land above the UC_built_threshold

• UC_built_threshold numeric or character (default: 0.2).

  Additional built-up area threshold. Can be a value between 0 and 1, representing the minimum built-up area per permanent land, or "optimal" (see section "Built-up area criterium" below). Ignored when UC_built_criterium is FALSE.

• built_optimal_data character / list (default: NULL).

  Path to the directory with the data, or named list with the data as returned by function DoU_preprocess_grid() used to determine the optimal built threshold (see section "Built-up area criterium" below). Ignored when UC_built_criterium is FALSE or when UC_built_threshold is not "optimal".

• UC_smooth_pop logical (default: FALSE).

  Whether to smooth the population grid before delineating urban centres. If TRUE, the population grid will be smoothed with a moving average of window size UC_smooth_pop_window.

• UC_smooth_pop_window integer (default: 5).

  Size of the moving window used to smooth the population grid before delineating urban centres. Ignored when UC_smooth_pop is FALSE.

• UC_gap_fill logical (default: TRUE).

  Whether to perform gap filling. If TRUE, gaps in urban centres smaller than UC_max_gap are filled.

• UC_max_gap integer (default: 15).

  Gaps with an area smaller than this threshold in urban centres will be filled (unit is km²). Ignored when UC_gap_fill is FALSE.

• UC_smooth_edge logical (default: TRUE).

  Whether to perform edge smoothing. If TRUE, edges of urban centres are smoothed with the function UC_smooth_edge_fun.

• UC_smooth_edge_fun character / function (default: "majority_rule_R2023A").

  Function used to smooth the edges of urban centres. Ignored when UC_smooth_edge is FALSE. Possible values are:

  • "majority_rule_R2022A" to use the edge smoothing algorithm in GHSL Data Package 2022 (see section "Edge smoothing" below)

  • "majority_rule_R2023A" to use the edge smoothing algorithm in GHSL Data Package 2023 (see section "Edge smoothing" below)

  • a custom function with a signature similar as apply_majority_rule().

• UCL_density_threshold numeric (default: 300).

  Minimum population density per permanent land of a cell required to belong to an urban cluster

• UCL_size_threshold numeric (default: 5000).

  Minimum total population size required for an urban cluster

• UCL_contiguity_rule integer (default: 8).

  Which cells are considered adjacent in urban clusters: 4 for rooks case (horizontal and vertical neighbours) or 8 for queens case (horizontal, vertical and diagonal neighbours)

• UCL_smooth_pop logical (default: FALSE).

  Whether to smooth the population grid before delineating urban clusters. If TRUE, the population grid will be smoothed with a moving average of window size UCL_smooth_pop_window.

• UCL_smooth_pop_window integer (default: 5).

  Size of the moving window used to smooth the population grid before delineating urban clusters. Ignored when UCL_smooth_pop is FALSE.

• water_land_threshold numeric (default: 0.5).

  Maximum proportion of permanent land allowed in a water cell

• water_pop_threshold numeric (default: 0).

  Maximum population size allowed in a water cell

• water_built_threshold numeric (default: 0).

  Maximum built-up area allowed in a water cell

LEVEL 2

• UC_density_threshold numeric (default: 1500).

  Minimum population density per permanent land of a cell required to belong to an urban centre

• UC_size_threshold numeric (default: 50000).

  Minimum total population size required for an urban centre

• UC_contiguity_rule integer (default: 4).

  Which cells are considered adjacent in urban centres: 4 for rooks case (horizontal and vertical neighbours) or 8 for queens case (horizontal, vertical and diagonal neighbours)

• UC_built_criterium logical (default: TRUE).

  Whether to use the additional built-up area criterium (see section "Built-up area criterium" below). If TRUE, not only cells that meet the population density requirement will be considered when delineating urban centres, but also cells with a built-up area per permanent land above the UC_built_threshold

• UC_built_threshold numeric or character (default: 0.2).

  Additional built-up area threshold. Can be a value between 0 and 1, representing the minimum built-up area per permanent land, or "optimal" (see section "Built-up area criterium" below). Ignored when UC_built_criterium is FALSE.

• built_optimal_data character / list (default: NULL).

  Path to the directory with the data, or named list with the data as returned by function DoU_preprocess_grid() used to determine the optimal built threshold (see section "Built-up area criterium" below). Ignored when UC_built_criterium is FALSE or when UC_built_threshold is not "optimal".

• UC_smooth_pop logical (default: FALSE).

  Whether to smooth the population grid before delineating urban centres. If TRUE, the population grid will be smoothed with a moving average of window size UC_smooth_pop_window.

• UC_smooth_pop_window integer (default: 5).

  Size of the moving window used to smooth the population grid before delineating urban centres. Ignored when UC_smooth_pop is FALSE.

• UC_gap_fill logical (default: TRUE).

  Whether to perform gap filling. If TRUE, gaps in urban centres smaller than UC_max_gap are filled.

• UC_max_gap integer (default: 15).

  Gaps with an area smaller than this threshold in urban centres will be filled (unit is km²). Ignored when UC_gap_fill is FALSE.

• UC_smooth_edge logical (default: TRUE).

  Whether to perform edge smoothing. If TRUE, edges of urban centres are smoothed with the function UC_smooth_edge_fun.

• UC_smooth_edge_fun character / function (default: "majority_rule_R2023A").

  Function used to smooth the edges of urban centres. Ignored when UC_smooth_edge is FALSE. Possible values are:

  • "majority_rule_R2022A" to use the edge smoothing algorithm in GHSL Data Package 2022 (see section "Edge smoothing" below)

  • "majority_rule_R2023A" to use the edge smoothing algorithm in GHSL Data Package 2023 (see section "Edge smoothing" below)

  • a custom function with a signature similar as apply_majority_rule().

• DUC_density_threshold numeric (default: 1500).

  Minimum population density required for a dense urban cluster

• DUC_size_threshold numeric (default: 5000).

  Minimum total population size required for a dense urban cluster

• DUC_built_criterium logical (default: TRUE).

  Whether to use the additional built-up area criterium (see section "Built-up area criterium" below). If TRUE, not only cells that meet the population density requirement will be considered when delineating dense urban clusters, but also cells with a built-up area per permanent land above the DUC_built_threshold

• DUC_built_threshold numeric or character (default: 0.2).

  Additional built-up area threshold. Can be a value between 0 and 1, representing the minimum built-up area per permanent land, or "optimal" (see section "Built-up area criterium" below). Ignored when DUC_built_criterium is FALSE.

• DUC_contiguity_rule integer (default: 4).

  Which cells are considered adjacent in dense urban clusters: 4 for rooks case (horizontal and vertical neighbours) or 8 for queens case (horizontal, vertical and diagonal neighbours)

• SDUC_density_threshold numeric (default: 900).

  Minimum population density per permanent land of a cell required to belong to a semi-dense urban cluster

• SDUC_size_threshold numeric (default: 2500).

  Minimum total population size required for a semi-dense urban cluster

• SDUC_contiguity_rule integer (default: 4).

  Which cells are considered adjacent in semi-dense urban clusters: 4 for rooks case (horizontal and vertical neighbours) or 8 for queens case (horizontal, vertical and diagonal neighbours)

• SDUC_buffer_size integer (default: 2).

  The distance to urban centres and dense urban clusters required for a semi-dense urban cluster

• SUrb_density_threshold numeric (default: 300).

  Minimum population density per permanent land of a cell required to belong to a suburban or peri-urban area

• SUrb_size_threshold numeric (default: 5000).

  Minimum total population size required for a suburban or peri-urban area

• SUrb_contiguity_rule integer (default: 8).

  Which cells are considered adjacent in suburban or peri-urban area: 4 for rooks case (horizontal and vertical neighbours) or 8 for queens case (horizontal, vertical and diagonal neighbours)

• RC_density_threshold numeric (default: 300).

  Minimum population density per permanent land of a cell required to belong to a rural cluster

• RC_size_threshold numeric (default: 500).

  Minimum total population size required for a rural cluster

• RC_contiguity_rule integer (default: 8).

  Which cells are considered adjacent in rural clusters: 4 for rooks case (horizontal and vertical neighbours) or 8 for queens case (horizontal, vertical and diagonal neighbours)

• LDR_density_threshold numeric (default: 50).

  Minimum population density per permanent land of a low density rural grid cell

• water_land_threshold numeric (default: 0.5).

  Maximum proportion of permanent land allowed in a water cell

• water_pop_threshold numeric (default: 0).

  Maximum population size allowed in a water cell

• water_built_threshold numeric (default: 0).

  Maximum built-up area allowed in a water cell

Built-up area criterium

In Data Package 2022, the Degree of Urbanisation includes an optional built-up area criterium to account for the presence of office parks, shopping malls, factories and transport infrastructure. When the setting is enabled, urban centres (and dense urban clusters) are created using both cells with a population density of at least 1500 inhabitants per km² and cells that have at least 50% built-up area on permanent land. For more information: see GHSL Data Package 2022, footnote 25. The parameter settings UC_built_criterium=TRUE and UC_built_threshold=0.5 (level 1 & 2) and DUC_built_criterium=TRUE and DUC_built_threshold=0.5 (level 2) reproduce this built-up area criterium in urban centres and dense urban clusters respectively.

In Data Package 2023, the built-up area criterium is slightly adapted and renamed to the "Reduce Fragmentation Option". Instead of using a fixed threshold of built-up area per permanent land of 50%, an "optimal" threshold is employed. The optimal threshold is dynamically identified as the global average built-up area proportion in clusters with a density of at least 1500 inhabitants per permanent land with a minimum population of 5000 people. We determined empirically that this optimal threshold is 20% for the data of 2020. For more information: see GHSL Data Package 2023, footnote 30. The "Reduce Fragmentation Option" can be reproduced with the parameter settings UC_built_criterium=TRUE and UC_built_threshold="optimal" (level 1 & 2) and DUC_built_criterium=TRUE and DUC_built_threshold="optimal" (level 2). In addition, the parameter built_optimal_data must contain the path to the directory with the (global) data to compute the optimal built-up area threshold.

Edge smoothing

In Data Package 2022, edges of urban centres are smoothed by an iterative majority rule. The majority rule works as follows: if a cell has at least five of the eight surrounding cells belonging to an unique urban centre, then the cell is added to that urban centre. The process is iteratively repeated until no more cells are added. The parameter setting UC_smooth_edge=TRUE and UC_smooth_edge_fun="majority_rule_R2022A" reproduces this edge smoothing rule.

In Data Package 2023, the majority rule is slightly adapted. A cell is added to an urban centre if the majority of the surrounding cells belongs to an unique urban centre, with majority only computed among populated or land cells (proportion of permanent land > 0.5). In addition, cells with permanent water are never added to urban centres. The process is iteratively repeated until no more cells are added. For more information: see GHSL Data Package 2023, footnote 29. The parameter setting UC_smooth_edge=TRUE and UC_smooth_edge_fun="majority_rule_R2023A" reproduces this edge smoothing rule.

Regions

Because of the large amount of data at a global scale, the grid classification procedure is quite memory-consuming. To optimise the procedure, we divided the world in 9 pre-defined regions. These regions are the smallest grouping of GHSL tiles while ensuring that no continuous land mass is split into two different regions (for more information, see the figure below and GHSL_tiles_per_region).

If regions=TRUE, a global grid classification is created by (1) executing the grid classification procedure separately in the 9 pre-defined regions, and (2) afterwards merging these classifications together. The argument data should contain the path to a directory with the data of all pre-defined regions (for example as created by ⁠download_GHSLdata(... extent="regions"⁠). Note that although the grid classification is optimised, it still takes approx. 145 minutes and requires 116 GB RAM to execute the grid classification with the standard parameters (performed on a Kubernetes server with 32 cores and 256 GB RAM). For a concrete example on how to construct the grid classification on a global scale, see vignette("vig3-DoU-global-scale").

Examples

# load the data
data_belgium <- DoU_load_grid_data_belgium()

# classify with standard parameters:
classification1 <- DoU_classify_grid(data = data_belgium)


# classify with custom parameters:
classification2 <- DoU_classify_grid(
  data = data_belgium,
  parameters = list(
    UC_density_threshold = 3000,
    UC_size_threshold = 75000,
    UC_gap_fill = FALSE,
    UC_smooth_edge = FALSE,
    UCL_contiguity_rule = 4
  )
)

Description

The Degree of Urbanisation identifies rural cells as all cells not belonging to an urban centre or urban cluster.

For more information about the Degree of Urbanisation methodology, see the methodological manual, GHSL Data Package 2022 and GHSL Data Package 2023.

Usage

DoU_classify_grid_rural(data, classification, value = 1)

Arguments

Value

SpatRaster with the grid cell classification of rural cells

Examples

data_belgium <- DoU_load_grid_data_belgium()
classification <- DoU_classify_grid_urban_centres(data_belgium)
classification <- DoU_classify_grid_urban_clusters(data_belgium, classification = classification)
classification <- DoU_classify_grid_rural(data_belgium, classification = classification)
DoU_plot_grid(classification)

Description

The Degree of Urbanisation identifies urban centres as clusters of continuous grid cells (based on rook contiguity) with a minimum density of 1500 inhabitants per km² (or with a minimum built-up area; see details), and a minimum total population of 50 000 inhabitants. Gaps smaller than 15 km² in the urban centres are filled and edges are smoothed by a 3x3-majority rule (see details).

For more information about the Degree of Urbanisation methodology, see the methodological manual, GHSL Data Package 2022 and GHSL Data Package 2023.

The arguments of the function allow to adapt the standard specifications in the Degree of Urbanisation in order to construct an alternative version of the grid classification.

Usage

DoU_classify_grid_urban_centres(
  data,
  density_threshold = 1500,
  size_threshold = 50000,
  contiguity_rule = 4,
  built_criterium = TRUE,
  built_threshold = 0.2,
  smooth_pop = FALSE,
  smooth_pop_window = 5,
  gap_fill = TRUE,
  max_gap = 15,
  smooth_edge = TRUE,
  smooth_edge_fun = "majority_rule_R2023A",
  value = 3
)

Arguments

Details

• Optional built-up area criterium

In Data Package 2022, the Degree of Urbanisation includes an optional built-up area criterium to account for the presence of office parks, shopping malls, factories and transport infrastructure. When the setting is enabled, urban centres are created using both cells with a population density of at least 1500 inhabitants per km² and cells that have at least 50% built-up area on permanent land. For more information: see GHSL Data Package 2022, footnote 25. The parameter setting built_criterium=TRUE and built_threshold=0.5 reproduces this built-up area criterium.

In Data Package 2023, the built-up area criterium is slightly adapted and renamed to the "Reduce Fragmentation Option". Instead of using a fixed threshold of built-up area per permanent land of 50%, an "optimal" threshold is employed. The optimal threshold is dynamically identified as the global average built-up area proportion in clusters with a density of at least 1500 inhabitants per permanent land with a minimum population of 5000 people. For more information: see GHSL Data Package 2023, footnote 30. The optimal built-up threshold can be computed with the function DoU_get_optimal_builtup(). We determined empirically that this optimal threshold is 20% for the data of 2020.

• Edge smoothing: majority rule algorithm

In Data Package 2022, edges of urban centres are smoothed by an iterative majority rule. The majority rule works as follows: if a cell has at least five of the eight surrounding cells belonging to an unique urban centre, then the cell is added to that urban centre. The process is iteratively repeated until no more cells are added. The parameter setting smooth_edge=TRUE and smooth_edge_fun="majority_rule_R2022A" reproduces this edge smoothing rule.

In Data Package 2023, the majority rule is slightly adapted. A cell is added to an urban centre if the majority of the surrounding cells belongs to an unique urban centre, with majority only computed among populated or land cells (proportion of permanent land > 0.5). In addition, cells with permanent water are never added to urban centres. The process is iteratively repeated until no more cells are added. For more information: see GHSL Data Package 2023, footnote 29. The parameter setting smooth_edge=TRUE and smooth_edge_fun="majority_rule_R2023A" reproduces this edge smoothing rule.

Value

SpatRaster with the grid cell classification of urban centres

Examples

data_belgium <- DoU_load_grid_data_belgium()

# standard parameters of the Degree of Urbanisation:
classification1 <- DoU_classify_grid_urban_centres(data_belgium)
DoU_plot_grid(classification1)

# with custom parameters:
classification2 <- DoU_classify_grid_urban_centres(data_belgium,
  density_threshold = 1000,
  gap_fill = FALSE,
  smooth_edge = FALSE
)
DoU_plot_grid(classification2)

Description

The Degree of Urbanisation identifies urban clusters as clusters of continuous grid cells (based on queen contiguity) with a minimum density of 300 inhabitants per km², and a minimum total population of 5000 inhabitants.

For more information about the Degree of Urbanisation methodology, see the methodological manual, GHSL Data Package 2022 and GHSL Data Package 2023.

The arguments of the function allow to adapt the standard specifications in the Degree of Urbanisation in order to construct an alternative version of the grid classification.

Usage

DoU_classify_grid_urban_clusters(
  data,
  classification,
  density_threshold = 300,
  size_threshold = 5000,
  contiguity_rule = 8,
  smooth_pop = FALSE,
  smooth_pop_window = 5,
  value = 2
)

Arguments

Value

SpatRaster with the grid cell classification of urban clusters

Examples

data_belgium <- DoU_load_grid_data_belgium()
classification <- DoU_classify_grid_urban_centres(data_belgium)
classification <- DoU_classify_grid_urban_clusters(data_belgium, classification = classification)
DoU_plot_grid(classification)

Description

The Degree of Urbanisation identifies water cells as cells with no built-up area, no population, and less than 50% permanent land.

For more information about the Degree of Urbanisation methodology, see the methodological manual, GHSL Data Package 2022 and GHSL Data Package 2023.

The arguments of the function allow to adapt the standard specifications in the Degree of Urbanisation in order to construct an alternative version of the grid classification.

Usage

DoU_classify_grid_water(
  data,
  classification = NULL,
  water_land_threshold = 0.5,
  water_pop_threshold = 0,
  water_built_threshold = 0,
  value = 0,
  allow_overwrite = c(1)
)

Arguments

Value

SpatRaster with the grid cell classification of water cells

Examples

data_belgium <- DoU_load_grid_data_belgium()
classification <- DoU_classify_grid_urban_centres(data_belgium)
classification <- DoU_classify_grid_urban_clusters(data_belgium, classification = classification)
classification <- DoU_classify_grid_rural(data_belgium, classification = classification)
classification <- DoU_classify_grid_water(data_belgium, classification = classification)
DoU_plot_grid(classification)

Description

The function reconstructs the spatial units classification of the Degree of Urbanisation based on the grid cell classification.

Usage

DoU_classify_units(
  data,
  id = "UID",
  level1 = TRUE,
  values = NULL,
  official_workflow = TRUE,
  rules_from_2021 = FALSE,
  filename = NULL
)

Arguments

Value

dataframe with for each spatial unit the classification and the share of population per grid class

Classification rules

The Degree of Urbanisation consists of two hierarchical levels. In level 1, the spatial units are classified in cities, towns and semi-dense areas, and rural areas. In level 2, towns and semi-dense areas are further divided in dense towns, semi-dense towns and suburban or peri-urban areas. Rural areas are further divided in villages, dispersed rural areas and mostly uninhabited areas.

The detailed classification rules are as follows:

LEVEL 1:

• Cities: units that have at least 50% of their population in urban centres

• Towns and semi-dense areas: units that have less than 50% of their population in urban centres and no more than 50% of their population in rural grid cells

• Rural areas: units that have more then 50% of their population in rural grid cells

LEVEL 2:

• Cities: units that have at least 50% of their population in urban centres

• Dense towns: units that have at least 50% of their population in the combination of urban centres and dense urban clusters

• Semi-dense towns: units that have less than 50% of their population in the combination of urban centres and dense urban clusters, or have less than 50% of their population in the combination of suburban and peri-urban cells and rural grid cells

• Suburbs or peri-urban areas: units that have at least 50% of their population in the combination of suburban and peri-urban cells and rural grid cells

• Villages: units that have at least 50% of their population in the combination of urban centres, urban clusters and rural clusters

• Dispersed rural areas: units that have less than 50% of their population in the combination of urban centres, urban clusters and rural clusters, or have less than 50% of their population in very low-density rural grid cells

• Mostly uninhabited areas: units that have at least 50% of their population in very low-density rural grid cells

Workflow

The classification of small spatial units requires a vector layer with the small spatial units, a raster layer with the grid cell classification, and a raster layer with the population grid. Standard, a population grid of 100 m resolution is used by the Degree of Urbanisation.

The function includes two different workflows to establish the spatial units classification based on these three data sources.

Official workflow according to the GHSL:

For the official workflow, the three layers should be pre-processed by DoU_preprocess_units(). In this function, the classification grid and population grid are resampled to a user-defined resample_resolution with the nearest neighbour algorithm (the Degree of Urbanisation uses standard a resample resolution of 50 m). In doing this, the values of the population grid are divided by the oversampling ratio (for example: going from a resolution of 100 m to a resolution of 50 m, the values of the grid are divided by 4).

Afterwards, the spatial units classification is constructed with DoU_classify_units() as follows. The vector layer with small spatial units is rasterised to match the population and classification grid. Based on the overlap of the three grids, the share of population per flexurba grid class is computed per spatial unit with a zonal statistics procedure. The units are subsequently classified according to the classification rules (see above).

Apart from this, there are two special cases. First, if a unit has no population, it is classified according to the share of land area in each of the flexurba grid classes (computed with a zonal statistics procedure). Second, if a unit initially could not be rasterised (can occur if the area of the unit < resample_resolution), then it is processed separately as follows. The unit is individually rasterised by all touching cells. The unit is classified according to the share of population in the flexurba grid classes in these touching cells. However, to avoid double counting of population, no population is assigned to the unit in the result.

For more information about the official workflow to construct the units classification, see GHSL Data Package 2023 (Section 2.7.2.3).

Alternative workflow:

Besides the official workflow of the GHSL, the function also includes an alternative workflow to construct the spatial units classification. The alternative workflow does not require rasterising the spatial units layer, but relies on the overlap between the spatial units layer and the grid layers.

The three layers should again be pre-processed by the function DoU_preprocess_units(), but this time without resampling_resolution. For the classification in DoU_classify_units(), the function exactextractr::exact_extract() is used to (1) overlay the grids with the spatial units layer, and (2) summarise the values of the population grid and classification grid per unit. The units are subsequently classified according to the classification rules (see above). As an exception, if a unit has no population, it is classified according to the share of land area in each of the flexurba grid classes. The alternative workflow is slightly more efficient as it does not require resampling the population and classification grids and rasterising the spatial units layer.

Modification of the unit classification rules

The unit classification rules of Level 2 of DEGURBA were updated in July 2024. By default, the function DoU_classify_units() applies the latest classification rules, as described in the online version of the methodological manual. However, you can also use the original 2021 classification rules if desired, by setting the argument rules_from_2021 to TRUE. In that case, the rules to classify units are as follows:

• Cities: units that have at least 50% of their population in urban centres

• Dense towns: units that have a larger share of the population in dense urban clusters than in semi-dense urban clusters, and that have a larger share of the population in dense + semi-dense urban clusters than in suburban or peri-urban cells

• Semi-dense towns: units that have a larger share of the population in semi-dense urban clusters than in semi-dense urban clusters, and that have a larger share of the population in dense + semi-dense urban clusters than in suburban or peri-urban cells

• Suburbs or peri-urban areas: units that have a larger share in suburban or peri-urban cells than in dense + semi-dense urban clusters

• Villages: units that have the largest share of their rural grid cell population living in rural clusters

• Dispersed rural areas: units that have the largest share of their rural grid cell population living in low density rural grid cells

• Mostly uninhabited areas: units that have the largest share of their rural grid cell population living in very low density rural grid cells

Examples

# load the grid data
data_belgium <- flexurba::DoU_load_grid_data_belgium()
# load the units and filter for West-Flanders
units_data <- flexurba::units_belgium %>%
  dplyr::filter(GID_2 == "30000")
# classify the grid
classification <- DoU_classify_grid(data = data_belgium)


# official workflow
data1 <- DoU_preprocess_units(
  units = units_data,
  classification = classification,
  pop = data_belgium$pop,
  resample_resolution = 50
)
units_classification1 <- DoU_classify_units(data1)


# alternative workflow
data2 <- DoU_preprocess_units(
  units = units_data,
  classification = classification,
  pop = data_belgium$pop
)
units_classification2 <- DoU_classify_units(data2, official_workflow = FALSE)

# spatial units classification, dissolved at level 3 (Belgian districts)
data3 <- DoU_preprocess_units(
  units = units_data,
  classification = classification,
  pop = data_belgium$pop,
  dissolve_units_by = "GID_3"
)
units_classification3 <- DoU_classify_units(data3, id = "GID_3")

Description

The argument parameter of the function DoU_classify_grid() allows to adapt the standard specifications in the Degree of Urbanisation in order to construct an alternative version of the grid classification. This function returns a named list with the standard parameters.

Usage

DoU_get_grid_parameters(level1 = TRUE)

Arguments

Value

named list with the standard parameters

Examples

# example on how to employ the function to construct
# an alternative version of the grid classification:
# get the standard parameters
parameters <- DoU_get_grid_parameters()

# adapt the standard parameters
parameters$UCL_density_threshold <- 500
parameters$UCL_size_threshold <- 6000
parameters$UCL_smooth_pop <- TRUE
parameters$UCL_smooth_pop_window <- 7

# load the data
grid_data <- DoU_load_grid_data_belgium()

# use the adapted parameters to construct a grid cell classification
classification <- DoU_classify_grid(
  data = grid_data,
  parameters = parameters
)

Description

In Data Package 2023, the Degree of Urbanisation includes a "Reduce Fragmentation Option" to account for the presence of office parks, shopping malls, factories and transport infrastructure. When the setting is enabled, urban centres are created using both cells with a population density of at least 1500 inhabitants per km² and cells that have an "optimal" built-up area on permanent land. This function can be used to determine this "optimal" threshold value.

The optimal threshold is dynamically identified as the global average built-up area proportion in clusters with a density of at least 1500 inhabitants per permanent land with a minimum population of 5000 people. We empirically discovered that the Degree of Urbanisation uses the rounded up (ceiled) optimal threshold to two decimal places.

For more information: see GHSL Data Package 2023, footnote 30. The arguments of the function allow to adapt the standard specifications in order to determine an alternative "optimal" threshold.

Usage

DoU_get_optimal_builtup(
  data,
  density_threshold = 1500,
  size_threshold = 5000,
  directions = 4
)

Arguments

Value

optimal built-up area threshold

Examples

data_belgium <- DoU_load_grid_data_belgium()

# determine the optimal built-up threshold with standard specifications
DoU_get_optimal_builtup(data_belgium)

# determine the optimal built-up threshold with custom specification
DoU_get_optimal_builtup(data_belgium,
  density_threshold = 1000,
  size_threshold = 3500,
  directions = 8
)

Description

The function loads the data required to execute the grid classification of the Degree of Urbanisation for Belgium with the function DoU_preprocess_grid().

The data was constructed with the code below:

# download the GHSL data on a global scale
download_GHSLdata(output_directory = "inst/extdata/global")

# crop the global grid to Belgium
crop_GHSLdata(extent = terra::ext(192000, 485000, 5821000, 6030000),
              global_directory = "inst/extdata/global",
              output_directory = "inst/extdata/belgium")

Usage

DoU_load_grid_data_belgium()

Value

named list with the data of Belgium required for the grid classification of the Degree of Urbanisation.

Examples

DoU_load_grid_data_belgium()

Description

The function can be used to plot the results of the grid cell classification of the Degree of Urbanisation. The implementation relies upon the function tidyterra::geom_spatraster(). By default, the standard color scheme of the Global Human Settlement Layer (GHSL) is used (see GHSL Data Package 2023), but this can be altered by the palette argument.

Note that the function is computational quite heavy for large spatial extents (regional or global scale). It is advised to use the extent argument to plot only a selection of the grid classification.

Usage

DoU_plot_grid(
  classification,
  extent = NULL,
  level1 = TRUE,
  palette = NULL,
  labels = NULL,
  title = NULL,
  scalebar = FALSE,
  filename = NULL
)

Arguments

Value

ggplot object

Examples

classification <- DoU_classify_grid(DoU_load_grid_data_belgium())

# plot with standard color scheme
DoU_plot_grid(classification)

# use custom palette and labels
DoU_plot_grid(classification,
  palette = c("3" = "#e16c72", "2" = "#fac66c", "1" = "#97c197", "0" = "#acd3df"),
  labels = c("UC", "UCL", "RUR", "WAT")
)

Description

The function can be used to plot the results of the spatial units classification of the Degree of Urbanisation. The implementation relies upon the function ggplot2::geom_sf(). By default, the standard color scheme of the Global Human Settlement Layer (GHSL) is used (see GHSL Data Package 2023), but this can be altered by the palette argument.

Note that the function is computational quite heavy for large spatial extents (regional or global scale). It is advised to use the extent argument to plot only a selection of the spatial units classification.

Usage

DoU_plot_units(
  units,
  classification = NULL,
  level1 = TRUE,
  extent = NULL,
  column = NULL,
  palette = NULL,
  labels = NULL,
  title = NULL,
  scalebar = FALSE,
  filename = NULL
)

Arguments

Value

ggplot object

Examples

# get spatial units classification
data_belgium <- DoU_load_grid_data_belgium()
grid_classification <- DoU_classify_grid(data_belgium)
data1 <- DoU_preprocess_units(
  units = flexurba::units_belgium,
  classification = grid_classification,
  pop = data_belgium$pop
)
units_classification <- DoU_classify_units(data1)

# plot using the standard color palette
DoU_plot_units(data1$units, units_classification)

# plot using custom palette and labels
DoU_plot_units(data1$units, units_classification,
  palette = c("3" = "#e16c72", "2" = "#fac66c", "1" = "#97c197"),
  labels = c("C", "T", "R")
)

Description

The grid cell classification of the Degree of Urbanisation requires three different inputs:

• a built-up area grid

• a population grid

• a land grid

The function rescales the built-up area and land grid if necessary (see details) and computes the population and built-up area per permanent land by dividing the respective grids by the land layer.

Usage

DoU_preprocess_grid(
  directory,
  filenames = c("BUILT_S.tif", "POP.tif", "LAND.tif"),
  rescale_land = TRUE,
  rescale_built = TRUE
)

Arguments

Details

The values of the land grid and built-up area grid should range from 0 to 1, representing the proportion of permanent land and built-up area respectively. However, the grid values of the GHSL are standard in m². With a cell size of 1 km², the values thus range from 0 to ⁠1 000 000⁠. The two grids can be rescaled by setting rescale_land=TRUE and/or rescale_built=TRUE respectively.

Value

named listed with the required data to execute the grid cell classification procedure. The list contains following elements:

• built: built-up area grid

• pop: population grid

• land: land grid

• pop_per_land: population per area of permanent land

• built_per_land: built-up area per permanent land

• metadata_BUILT_S: the metadata of the built-up area grid

• metadata_POP: the metadata of the population grid

• metadata_LAND: the metadata of the land grid.

Description

The spatial units classification of the Degree of Urbanisation requires three different inputs (all input sources should be in the Mollweide coordinate system):

• a vector layer with the small spatial units

• a raster layer with the grid cell classification of the Degree of Urbanisation

• a raster layer with the population grid

The three input layers are pre-processed as follows. The classification grid and population grid are resampled to the resample_resolution with the nearest neighbour algorithm. In doing this, the values of the population grid are divided by the oversampling ratio (for example: going from a resolution of 100 m to a resolution of 50 m, the values of the grid are divided by 4).

In addition, the function makes sure the extents of the three input layers match. If the bounding box of the units layer is smaller than the extent of the grids, then the grids are cropped to the bounding box of the units layer. Alternatively, if the units layer covers a larger area than the grids, then the units that do not intersect with the grids are discarded (and a warning message is printed). This ensures that the classification algorithm runs efficiently and does not generate any incorrect classifications due to missing data.

More information about the pre-processing workflow, see GHSL Data Package 2023 (Section 2.7.2.3).

Usage

DoU_preprocess_units(
  units,
  classification,
  pop,
  resample_resolution = NULL,
  dissolve_units_by = NULL
)

Arguments

Value

named list with the required data to execute the spatial units classification procedure, and their metadata. The list contains the following elements:

• classification: the (resampled and cropped) grid cell classification layer

• pop: the (resampled and cropped) population grid

• units: the (dissolved and filtered) spatial units (object of class sf)

• metadata: named list with the metadata of the input files. It contains the elements units, classification and pop (with paths to the respective data sources), resample_resolution and dissolve_units_by if not NULL. (Note that when the input sources are passed by object , the metadata might be empty).

Examples

# load the grid data
grid_data <- flexurba::DoU_load_grid_data_belgium()
# load the units and filter for West-Flanders
units_data <- flexurba::units_belgium %>%
  dplyr::filter(GID_2 == "30000")
# classify the grid
classification <- DoU_classify_grid(data = grid_data)

# preprocess the data for units classification
data1 <- DoU_preprocess_units(
  units = units_data,
  classification = classification,
  pop = grid_data$pop,
  resample_resolution = 50
)

# preprocess the data for units classification at level 3 (Belgian districts)
data2 <- DoU_preprocess_units(
  units = units_data,
  classification = classification,
  pop = grid_data$pop,
  resample_resolution = 50,
  dissolve_units_by = "GID_3"
)

Description

The function will download data products with certain specifications from the Global Human Settlement Layer (GHSL) website. The following data products are supported:

• BUILT_S: the built-up area grid

• POP: the population grid

• LAND: the land grid (with the proportion of permanent land)

These are also the three data products that are required for the grid cell classification of the Degree of Urbanisation. For more information about the data products and their available specifications, see GHSL Download page. The downloaded data will be saved in the output_directory together with a JSON metadata-file. This function downloads large volumes of data, make sure the timeout parameter is sufficiently high (for example: options(timeout=500)).

Note that the land grid is only available for epoch 2018 and release R2022A on the GHSL website. The land grid will consequently always be downloaded with these specifications, regardless of the epoch and release specified in the arguments (a warning message is printed).

Usage

download_GHSLdata(
  output_directory,
  filenames,
  products = c("BUILT_S", "POP", "LAND"),
  epoch = 2020,
  release = "R2023A",
  crs = 54009,
  resolution = 1000,
  version = c("V1", "0"),
  extent = "global"
)

Arguments

Value

path to the created files.

Examples

# Download the population grid for epoch 2000 for specific tiles
download_GHSLdata(
  output_directory = tempdir(),
  filename = "POP_2000.tif",
  products = "POP",
  extent = c("R3_C19", "R4_C19"),
  epoch = 2000,
)

Description

The function fills gaps with an area smaller than max_gap. A gap is considered a patch of NA cells that lies within a cluster of cells with the same value. The implementation of the function relies on the function nngeo::st_remove_holes().

Usage

fill_gaps(x, max_gap = 15)

Arguments

Details

max_gap has the same unit as the resolution of x. For example, with a SpatRaster in Mollweide (EPSG:54009) and a resolution of 1 km², max_gap is interpreted in km².

Value

SpatRaster

Examples

nr <- nc <- 8
r <- terra::rast(nrows = nr, ncols = nc, ext = c(0, nc, 0, nr), crs = "epsg:25831")
terra::values(r) <- c(
  NA, NA, NA, NA, 1, 1, 1, NA,
  NA, 2, 2, 2, NA, NA, 1, NA,
  NA, 2, NA, 2, 2, NA, 1, 1,
  NA, 2, NA, NA, 2, 2, NA, NA,
  NA, 2, 2, 2, 2, NA, NA, NA,
  NA, NA, NA, NA, NA, NA, NA, NA,
  NA, NA, NA, NA, NA, NA, NA, NA,
  NA, NA, NA, NA, NA, NA, NA, NA
)
terra::plot(r)
gaps_filled <- fill_gaps(r)
terra::plot(gaps_filled)

Description

The function identifies all cells that are adjacent to non-NA cells in x. The implementation of the function relies on the function terra::adjacent().

Usage

get_adjacent(
  x,
  cells = "all",
  adjacent_value = 1,
  include = TRUE,
  directions = 8
)

Arguments

Value

SpatRaster with adjacent cells

Examples

set.seed(10)
nr <- nc <- 10
r <- terra::rast(
  ncols = nc, nrows = nr,
  ext = c(0, nc, 0, nr),
  vals = sample(c(NA, 1, 2), nr * nc, replace = TRUE, prob = c(0.8, 0.1, 0.1))
)
terra::plot(r)
adj1 <- get_adjacent(r)
terra::plot(adj1)
adj2 <- get_adjacent(r, cells = 1, include = FALSE)
terra::plot(adj2)

Description

Identify clusters of cells that meet the minimum density criterium/citeria, the minimum size criterium and the contiguity criterium.

The function can be executed with one density criterium, or with two density criteria:

• With one density criterium: Cells are selected if their value is above or equal to the density threshold (xden >= minden). Selected cells are afterwards grouped together based on the contiguity rule. Groups of cells are valid clusters if they also meet the total size criterium (sum of xsiz per group above or equal tominsiz).

• With two density criteria: Cells are selected if their value is above or equal to one of two density thresholds (xden >= minden, or xden2 >= minden2). Selected cells are afterwards grouped together based on the contiguity rule. Groups of cells are valid clusters if they also meet the total size criterium (sum of xsiz per group above or equal tominsiz).

Usage

get_clusters(
  xden,
  minden,
  xden2 = NULL,
  minden2 = NULL,
  xsiz = NULL,
  minsiz,
  directions
)

Arguments

Value

SpatRaster with cluster of cells. The value of the cells represent the id of the clusters.

Examples

# load data
grid_data_belgium <- flexurba::DoU_load_grid_data_belgium()

# get clusters of cells (4-cell connectivity) with at least 1500 inhabitants
# per km² of permanent land and a minimum total population of 50 000
# inhabitants:
terra::plot(get_clusters(
  xden = grid_data_belgium$pop_per_land,
  minden = 1500,
  xsiz = grid_data_belgium$pop,
  minsiz = 50000,
  directions = 4
))

# get clusters of cells (4-cell connectivity) with at least 1500 inhabitants
# per km² of permanent land or at least 20% built-up area per permanent
# land, and a minimum total population of 50 000 inhabitants:
terra::plot(get_clusters(
  xden = grid_data_belgium$pop_per_land,
  minden = 1500,
  xden2 = grid_data_belgium$built_per_land,
  minden2 = 0.2,
  xsiz = grid_data_belgium$pop,
  minsiz = 50000,
  directions = 4
))

# get clusters of cells (8-cell connectivity) with at least 300 inhabitants
# per km² of permanent land, and a minimum total population of 5000
# inhabitants:
terra::plot(get_clusters(
  xden = grid_data_belgium$pop_per_land,
  minden = 300,
  xsiz = grid_data_belgium$pop,
  minsiz = 5000,
  directions = 8
))

Description

The function detects patches of cells based on rook contiguity (horizontal and vertical neighbours) or queen contiguity (horizontal, vertical and diagonal neighbours).

Patches of cells are groups of cells that are surrounded by NA of NaN values. The function identifies patches by converting the SpatRaster into polygons with the functions geos::as_geos_geometry() and geos::geos_unnest(). During this conversion polygons are automatically created as cells with rook contiguity. Touching polygons are afterwards dissolved to create patches with queen contiguity.

The function is similar as terra::patches(), but is faster for large SpatRasters (see details).

Usage

get_patches(x, directions, cells = "all")

Arguments

Details

The function is similar as terra::patches(), but is faster for large SpatRasters.

Rook contiguity

Queen contiguity

Value

SpatRaster with patches of cells. The value of the cells represent the id of the patches.

Examples

r <- terra::rast(nrows = 8, ncols = 8, vals = c(
  2, 2, NA, NA, NA, NA, NA, NA,
  NA, NA, NA, NA, NA, NA, NA, NA,
  NA, NA, 1, NA, NA, NA, NA, NA,
  NA, NA, NA, 1, NA, NA, NA, NA,
  NA, NA, NA, NA, 1, 1, 1, 1,
  NA, NA, NA, NA, NA, 1, 1, 1,
  2, NA, NA, NA, NA, NA, NA, NA,
  NA, 2, NA, NA, NA, NA, NA, NA
))
terra::plot(r)
patches_rook1 <- get_patches(r, directions = 4)
terra::plot(patches_rook1)
patches_rook2 <- get_patches(r, directions = 4, cells = 1)
terra::plot(patches_rook2)
patches_queen1 <- get_patches(r, directions = 8)
terra::plot(patches_queen1)
patches_queen2 <- get_patches(r, directions = 8, cells = 1)
terra::plot(patches_queen2)

Description

The Global Human Settlement Layer (GHSL) divides the world into different rectangular areas called "tiles". These tiles can be used to download the GHSL products from the website (see GHSL Download page).

The dataset GHSL_tiles contains the geometry and metadata of all valid tiles.

Usage

GHSL_tiles

Format

GHSL_tiles

A dataframe with 375 rows and 6 columns

tile_id

id of the tile

left, top, right, bottom

The coordinates of the bounding box of the tile (in Mollweide, EPSG: 54009)

geometry

sfc_POLYGON geometry of the tile

Details

Spatial distribution of the GHSL tiles:

Source

https://ghsl.jrc.ec.europa.eu/download/GHSL_data_54009_shapefile.zip

Description

The Global Human Settlement Layer (GHSL) divides the world into different rectangular tiles. To execute the Degree of Urbanisation in a memory-efficient manner, we grouped these tiles into 9 different regions. These regions are the smallest possible grouping of GHSL tiles, ensuring that no continuous land mass is split across two regions. By splitting the world into different parts, the RAM required to execute the Degree of Urbanisation is optimised. For a concrete example on how to use the regions to construct the grid classification on a global scale, see vignette("vig3-DoU-global-scale").

The 9 regions cover approximately the following areas:

• W_AEA: Asia - Europe - Africa - Oceania (eastern hemisphere)

• W_AME: North and South America (+ Greenland and Iceland)

• W_ISL1: Hawaii

• W_ISL2: Oceanic Islands (western hemisphere)

• W_ISL3: Chatham Islands

• W_ISL4: Scott Island

• W_ISL5: Saint-Helena, Ascension and Tristan da Cunha

• W_ISL6: French Southern and Antarctic Lands

• W_ANT: Antarctica

For more information about the GHSL tiles and their extent see GHSL Download page.

Usage

GHSL_tiles_per_region

Format

GHSL_tiles_per_region

A named list of length 9:

• the names represent the 9 different regions (W_AEA, W_AME, W_ISL1, W_ISL2, W_ISL3, W_ISL4, W_ISL5, W_ISL6, W_ANT)

• the elements are vectors with the GHSL tile ids that make up the regions

Source

https://ghsl.jrc.ec.europa.eu/download/GHSL_data_54009_shapefile.zip

Description

The function loads example population, built-up area and night-time light data for Belgium. It is based on the global datasets provided in the accompanying flexurbaData package.

# POPULATION DATA
terra::rast(system.file("proxies/processed-ghs-pop.tif", package = "flexurbaData")) %>%
   terra::crop(terra::ext(187000, 490000, 5816000, 6035000)) %>%
   terra::writeRaster('inst/extdata/belgium/processed-ghs-pop-belgium.tif')

# BUILT-UP AREA DATA
terra::rast(system.file("proxies/processed-ghs-built-s.tif", package = "flexurbaData")) %>%
   terra::crop(terra::ext(187000, 490000, 5816000, 6035000)) %>%
   terra::writeRaster('inst/extdata/belgium/processed-ghs-built-s-belgium.tif')

# LIGHT DATA
terra::rast(system.file("proxies/processed-viirs-light.tif", package = "flexurbaData")) %>%
   terra::crop(terra::ext(187000, 490000, 5816000, 6035000)) %>%
   terra::writeRaster('inst/extdata/belgium/processed-viirs-light-belgium.tif')

The data are processed versions of the population and built-up grid from the Global Human Settlement Layer and the night-time light grid from the Earth Observation Group. See the flexurbaData package for more information of how these raw data produced were processed.

Usage

load_proxies_belgium()

Value

named list with gridded population, built-up area and night-time light data for Belgium.

Examples

load_proxies_belgium()

Description

An object of class sf with the small spatial units of Belgium (based on data from the Algemene Directie Statistiek - Statistics Belgium). The data is aggregated to municipal level and converted to the Mollweide projection with the following code:

# read the data
statsec <- st_read('sh_statbel_statistical_sectors_31370_20240101.geojson')

# aggregate units to munipal level and rename variables
units_belgium <- statsec %>%
  group_by(cd_munty_refnis) %>%
  summarise(UID = first(as.integer(cd_munty_refnis)),
            GID_0 = first(cd_country),
            NAME_0 = 'Belgium',
            GID_1 = first(cd_rgn_refnis),
            NAME_1 = first(tx_rgn_descr_nl),
            GID_2 = first(cd_prov_refnis),
            NAME_2 = first(tx_prov_descr_nl),
            GID_3 = first(cd_dstr_refnis),
            NAME_3 = first(tx_adm_dstr_descr_nl),
            GID_4 = first(cd_munty_refnis),
            NAME_4 = first(tx_munty_descr_nl)
  )

# simplify geometries and convert to Mollweide
units_belgium <- units_belgium %>% select(-cd_munty_refnis) %>%
  rmapshaper::ms_simplify() %>%
  st_transform('ESRI:54009') %>%
  rename(geom = geometry)

Usage

units_belgium

Format

units_belgium

A object of class sf with spatial units for Belgium

Source

https://statbel.fgov.be/nl/open-data/statistische-sectoren-2024